A traffic indication pole wind-induced vibration intelligent vibration reduction control method, device and equipment

Abstract

The application relates to a traffic indication pole wind-induced vibration intelligent vibration reduction control method, device and equipment, and belongs to the technical field of road traffic signs. The device is arranged on a traffic indication pole and comprises a sensor for spatiotemporal synchronous acquisition of multi-source signals, a controller for receiving sensor acquisition data, performing pretreatment, respectively performing Kalman filtering responsible for reconstructing a complete dynamic state vector of the traffic indication pole and Kalman filtering responsible for online identification of equivalent wind loads acting on the traffic indication pole, and performing a composite control decision containing feedforward, feedback and self-adaption based on filtering estimation results to generate a total target control instruction, and a vibration reduction adjustment mechanism for changing the circumferential and axial position mass distribution of the vibration reduction adjustment mechanism according to the total target control instruction, thereby changing the mass distribution of the overall structure of the traffic indication pole, so as to achieve the purpose of vibration reduction and vibration reduction. The application can guarantee the safety of the traffic pole structure and the normal operation of the pole-mounted intelligent equipment.

Description

Technical Field

[0001] This application relates to the field of road traffic sign technology, and in particular to a method, device and equipment for intelligent vibration damping control of wind-induced vibrations of traffic signs. Background Technology

[0002] With the accelerated advancement of intelligent highway networks, more and more smart devices are being installed on traffic cantilever poles. The addition of traffic lights, countdown timers, high-definition cameras, LED supplementary lights, radar-visual integrated machines, 5G antennas, and other equipment significantly increases the load on the cantilever poles. Traffic cantilever poles are typically 6–7 meters high, with the cantilever length usually 4–6 times its height. The pole has a tapered structure with a 2–2.5 times difference in diameter between the top and bottom ends, and its slender, low-damping characteristics make it extremely sensitive to wind loads. Long-term wind-induced high-frequency vibrations of the cantilever poles can induce resonance, leading to breakage at the connection between the pole and the cantilever, causing safety accidents. Furthermore, vibrations can easily interfere with the target acquisition accuracy of intelligent equipment such as radar and cameras, affecting the reliability of road information collection.

[0003] Currently, vibration reduction in engineering projects often employs passive tuned mass dampers (TMDs) or increasing the wall thickness of the poles. However, passive TMDs have a narrow vibration reduction frequency bandwidth, and even after optimization for a single wind direction and speed, they are difficult to adapt to the rapid changes in wind direction and speed in actual windy and rainy weather. Therefore, there is an urgent need for an intelligent vibration reduction solution for traffic signs that can actively sense environmental weather conditions and adjust its damping characteristics in real time to cope with different wind conditions. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, and equipment for intelligent vibration damping control of traffic sign poles caused by wind, in order to address the aforementioned technical problems.

[0005] A smart vibration damping control device for wind-induced vibration of traffic poles, the device being installed on the traffic poles, comprising: Sensors are used to collect real-time data on environmental wind speed, wind direction, vibration response of traffic signs, and stress-strain status. The controller receives data collected by sensors and performs preprocessing including low-pass filtering and adaptive moving average. For the preprocessed data, it performs one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign and another Kalman filter to identify the equivalent wind load acting on the traffic sign online. Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, it performs a composite control decision including feedforward, feedback and adaptation to generate the overall target control command for flutter reduction. The vibration damping adjustment mechanism is used to change its circumferential and axial positional mass distribution according to the overall target control command issued by the controller, thereby changing the mass distribution of the traffic sign pole's overall structure, thus changing the mode shape of the traffic sign pole and reducing its amplitude.

[0006] In one embodiment, the sensors include a wind speed and direction sensor, a vibration acceleration sensor, and a stress and strain sensor, and all sensors are hardware-level synchronized triggered by the same high-precision clock source to achieve spatiotemporal synchronous acquisition of multi-source signals. Wind speed and direction sensors are installed on the top of traffic sign poles to collect ambient wind speed and direction in real time. A total of 6 vibration acceleration sensors are arranged in 3 locations: near the column end, in the middle section, and on both the front and rear sides of the far end of the traffic sign. These sensors are used to collect real-time displacement, velocity, and acceleration response data of the traffic sign under wind vibration. The data collected by the wind speed and direction sensors and the vibration acceleration sensors constitute the total observation vector, expressed as: ; in, Indicates the first k At that moment, This is the total observation vector. This indicates the real-time wind speed output by the wind speed and direction sensor. The average wind speed, This represents the wind speed fluctuation component; This indicates the real-time wind direction output by the wind speed and direction sensor. Average wind direction, This represents the wind direction pulsation component. This represents the vibration response data of the traffic sign pole, where , and These represent the sensor position displacement, sensor position velocity, and sensor position acceleration output by the vibration acceleration sensor, respectively; the superscript T indicates matrix transpose. There are a total of two sets of stress and strain sensors. One set is arranged on the top surface of the vertical section through the axis of the traffic pole, and the other set is arranged on the side surface within the same cross section but at an orientation 90° different from the top surface. These sensors are used to collect the stress and strain status of the traffic pole in real time.

[0007] In one embodiment, the controller is sealed inside the pole of the traffic sign and includes a wireless communication module, a data processing module, and a control decision module. The wireless communication module communicates with the sensor, the vibration damping adjustment mechanism, and the remote traffic management control terminal. It is used to transmit the environmental wind speed, wind direction, and vibration response data collected by the sensor to the data processing module, send the overall target control command generated by the control decision module to the vibration damping adjustment mechanism, and send a warning signal to the remote traffic management control terminal when the stress and strain state collected by the sensor exceeds the preset safety threshold. The data processing module includes a preprocessing unit and a filtering state estimation unit. The preprocessing unit performs preprocessing, including low-pass filtering and adaptive moving average, on the total observation vector composed of environmental wind speed, wind direction, and vibration response data to obtain preprocessed data. The filtering state estimation unit performs one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign and another Kalman filter to identify the equivalent wind load acting on the traffic sign online, outputting the reconstructed complete dynamic state vector and the online identified equivalent wind load. The control decision module is used to execute composite control decisions, including feedforward, feedback and adaptation, based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, and to generate the overall target control command for flutter reduction.

[0008] In one embodiment, the preprocessing unit is configured as follows: First, a hardware-level anti-aliasing low-pass filter is used to suppress high-frequency noise in the total observation vector. The filter transfer function... Represented as: ; in, The cutoff angular frequency is determined based on the fundamental frequency of the traffic sign's structure. set up ; For the Laplace operator; Then, a software-level adaptive window moving average is performed on the low-pass filter output, and the preprocessed data is output to the filter state estimation unit; wherein, the adaptive window moving average is expressed as: ; in, Indicates the first k At that moment, This is the preprocessed data output after adaptive window moving average. For the first i The original total observation vector at each moment; To adapt to window length, The value is dynamically adjusted based on the intensity of wind speed fluctuations. When wind speed fluctuations are large, the window is shortened to retain high-frequency information; when wind speed fluctuations are small, the window is extended to enhance the smoothing effect. The expression is: ; in, Based on the window length, The standard deviation of wind speed fluctuation. This is an adjustable coefficient. This represents the average wind speed.

[0009] In one embodiment, the filter state estimation unit includes a first filter and a second filter; The filtered state estimation unit is configured to construct the structural dynamic differential equation of the traffic sign based on the preprocessed data, as follows: ; in, Indicates time, , , These are the mass matrix, damping matrix, and stiffness matrix of the traffic sign pole. , , These are the displacement vector, velocity vector, and acceleration vector of the traffic indicator structure, respectively. This is the load location matrix, used to map wind loads to the degrees of freedom of the traffic sign structure; The equivalent wind load, in physical terms, is the random wind excitation acting on the traffic sign pole; Define state vector for: ; Wherein, the superscript T denotes matrix transpose; The discretized state-space equations are defined as follows: ; ; in, and The first k The moment and the k +1 time-state vectors Here is the state transition matrix. For discrete control matrices, For the first k The equivalent wind load at a given moment For process noise, The process noise covariance matrix; For the first k The observation vector at each time step. For the observation matrix, To observe the noise, To observe the noise covariance matrix; The first filter is configured to reconstruct the complete dynamic state vector of the traffic sign through Kalman filtering state estimation, including: Prediction Step: ; ; in, For the first k The prior state vector estimate at time t, For the first k The optimal estimate of the posterior state vector at time -1 For the first k Estimated equivalent wind load at time -1; For the first k The prior estimate of the covariance matrix at time -1. For the first k -1 time point posterior estimated covariance matrix; Update steps: ; ; ; in, Here is the Kalman gain matrix. For the first k The optimal estimate of the posterior state vector at time n. For the first k The posterior estimated covariance matrix at time n. It is the identity matrix; The second filter is configured to identify the equivalent wind load acting on the traffic sign pole online using recursive least squares and Kalman filtering, including: The equivalent wind load is treated as an unmeasurable unknown input, and the observation equation is constructed using a projection matrix, as follows: ; in, For the first k The observation vector constructed at each time point, For traffic signs at the first k The state vector at each time step To expand the observation matrix, For direct matrix transmission; Real-time estimation using the least squares method: ; in, For the first k Estimated equivalent wind load at each moment; final, and The reconstructed complete dynamic state vector and the online identified equivalent wind load are respectively output to the control decision module.

[0010] In one embodiment, the control decision module is configured to input the reconstructed complete dynamic state vector and the online identified equivalent wind load into a three-channel composite control architecture consisting of a feedforward control path, a feedback control path, and an adaptive gain scheduling layer to generate a total target control command. The feedforward control path is configured based on the online identified equivalent wind load. Calculate the feedforward compensation component , represented as: ; ; in, Indicates feedforward gain; The feedback control path is configured to employ an adaptive neural-fuzzy inference system to adjust the fuzzy membership function parameters online to adapt to the drift of the traffic sign's structural parameters caused by changes in ambient temperature and humidity. The adaptive neural-fuzzy inference system includes: The fuzzing layer is used to fuzz the displacements in the reconstructed complete dynamic state vector. ,speed and acceleration The membership degrees are converted into fuzzy membership degrees, which are expressed as follows: ; in, , and Displacement ,speed and acceleration Their respective fuzzy membership degrees , , Displacement ,speed and acceleration Each corresponding Gaussian membership function; The rule layer, used to calculate the activation strength of each fuzzy rule, is represented as: ; in, For the first l The activation strength of a fuzzy rule This is the output of the rule layer; The normalization layer, used to perform normalization calculations, is represented as: ; in, For the output of the normalization layer, To normalize the rule strength, For the first m The activation strength of a fuzzy rule This represents the total number of fuzzy rules. The consequent layer, used to calculate rule outputs, is represented as: ; in, For the output of the consequent layer, For the first l The linear output function of the fuzzy rules, Indicates the first l The consequent parameters to be learned for each fuzzy rule; The output layer is used for deblurring to obtain the precise feedback adjustment components. , represented as: ; in, For the output of the output layer; The adaptive gain scheduling layer is configured to dynamically adjust the weighting coefficients of the feedforward compensation component and the feedback regulation component based on the wind speed fluctuation intensity and vibration amplitude, generating a total target control command to achieve optimal control across the entire wind speed domain; the total target control command... The expression is: ; in, For adaptive weighting coefficients, This represents the variance of wind speed fluctuations. To determine the variance of the vibration response, the feedforward effect is enhanced when the wind speed pulsation is large, and the feedback effect is enhanced when the vibration response is severe.

[0011] In one embodiment, the controller is further configured to, after the amplitude of the traffic pole decreases, re-receive the vibration response data of the traffic pole after the action of the damping adjustment mechanism collected by the sensor, and calculate and compare the current measured vibration amplitude with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller drives the internal data processing module and control decision module to perform online iterative adjustment of parameters and re-execute the generation of the overall target control command. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the controller enters the steady-state monitoring mode.

[0012] In one embodiment, the shock absorption adjustment mechanism includes a total of 3 sets, which are respectively installed at the end of the indicator rod near the column, the middle section of the indicator rod, and the far end of the indicator rod; Each vibration damping adjustment mechanism consists of a follower gear ring, mass position adjustment planetary gears, a fixed sun gear, drive planetary gears, auxiliary planetary gears, a load block displacement drive motor, a screw sleeve fixing cylinder, a screw sleeve, a load adjustment mass block, a fixed connecting cylinder, and a circumferential rotation drive motor. The fixed sun gear is centrally located and fixed to the traffic indicator pole. The follower gear ring is arranged around the outside of the fixed sun gear. The mass position adjustment planetary gears, drive planetary gears, and auxiliary planetary gears are all meshed and assembled between the fixed sun gear and the follower gear ring. The output end of the circumferential rotation drive motor is connected to the drive planetary gear transmission. The load block displacement drive motor is installed at the end of the screw sleeve fixing cylinder, and its output shaft is connected to the screw sleeve, driving the screw sleeve to rotate in both forward and reverse directions. The load adjustment mass block has an overall cylindrical structure. The internal casing contains a high-density liquid, and the outer wall has external threads that mate with the internal threads of the screw sleeve. The forward and reverse rotation of the screw sleeve drives the load adjustment mass block to slide axially, thereby changing its position along the traffic indicator pole's axial direction. The screw sleeve fixing cylinder and the fixed connecting cylinder are threaded together, and the fixed connecting cylinder is threadedly connected to the mass position adjustment planetary gear. The screw sleeve and the screw sleeve fixing cylinder have a clearance fit, allowing the screw sleeve to rotate freely within the fixing cylinder. The end face of the screw sleeve and the inner end face of the fixed connecting cylinder are separated and do not contact each other. Simultaneously, the load block displacement drive motor, the screw sleeve fixing cylinder, the screw sleeve, the load adjustment mass block, and the fixed connecting cylinder are integrated into a single modular component. This integrated modular component is screwed into the threaded hole of the mass position adjustment planetary gear to complete the assembly. The principle behind the vibration damping adjustment mechanism changing its circumferential and axial positional mass distribution according to the overall target control command issued by the controller, thereby altering the overall mass distribution of the traffic sign pole and thus changing its mode shape and reducing its amplitude, is as follows: The overall target control command is issued to the circumferential rotation drive motor, which drives the drive planetary gear to rotate. The drive planetary gear drives the follower gear ring to rotate, which in turn pushes the mass position adjustment planetary gear to rotate. The mass position adjustment planetary gear then rotates around the circumference of the traffic sign pole to reach a predetermined position, thus changing the circumferential positional mass distribution of the vibration damping adjustment mechanism on the traffic sign pole. Simultaneously, the load block displacement drive motor rotates in both forward and reverse directions, driving the screw sleeve to rotate in both forward and reverse directions, thereby causing the load adjustment mass block to move axially, changing the axial positional mass distribution of the load adjustment mass block on the traffic sign pole, and thus changing the overall mass distribution of the traffic sign pole structure, thereby changing the mode shape and reducing the amplitude of the traffic sign pole.

[0013] A method for intelligent vibration damping control of traffic poles based on the aforementioned intelligent vibration damping control device for traffic poles includes: Step 1: Sensors collect real-time data on ambient wind speed, wind direction, vibration response of traffic signs, and stress-strain status. Step 2: Receive sensor-acquired data and perform preprocessing including low-pass filtering and adaptive moving average; Step 3: For the preprocessed data, perform one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign pole and another Kalman filter to identify the equivalent wind load acting on the traffic sign pole online. Step 4: Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, execute a composite control decision including feedforward, feedback and adaptation to generate the overall target control command for flutter reduction; Step 5: According to the overall target control command, change the circumferential and axial positional mass distribution of the vibration damping adjustment mechanism, thereby changing the mass distribution of the overall structure of the traffic sign pole, thus changing the mode shape of the traffic sign pole and reducing the amplitude. Step 6: The sensor re-collects the vibration response data of the traffic pole after the action of the damping adjustment mechanism, and calculates the current measured vibration amplitude and compares it with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller is driven to perform online iterative adjustment of parameters and repeat steps 3 to 5. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the steady-state monitoring mode is entered.

[0014] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described intelligent vibration damping control method for traffic sign poles.

[0015] The aforementioned intelligent vibration damping control method, device, and equipment for traffic signs relies on sensors to accurately collect real-time data on wind speed and direction, vibration response, and stress-strain, comprehensively capturing the structural stress and vibration state and providing comprehensive and reliable data support for subsequent precise control. The controller performs low-pass filtering and adaptive moving average preprocessing on the sensor data to effectively eliminate data noise and improve data accuracy. A dual-path Kalman filter algorithm reconstructs the complete dynamic state vector of the sign and identifies the equivalent wind load online, accurately grasping the dynamic characteristics and wind disturbance forces of the traffic sign structure. Simultaneously, it integrates feedforward, feedback, and adaptive control strategies to form a composite control decision, offering stronger control accuracy and dynamic adaptability compared to a single control method. Finally, the damping adjustment mechanism optimizes its circumferential and axial mass distribution, changes the vibration mode, and suppresses vibration amplitude, enabling it to adapt to complex wind field environments, effectively reducing wind-induced vibration problems in traffic signs, improving the structural safety and service life of traffic signs, and ensuring the stable operation of road traffic facilities. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the intelligent vibration damping control device for wind-induced vibration of a traffic sign pole in one embodiment. Figure 2 This is a schematic diagram of the damping adjustment mechanism in one embodiment; Figure 3 This is a schematic diagram of the damping adjustment mechanism for resisting vertical vibration of the indicator rod in one embodiment; Figure 4 This is a schematic diagram of the anti-yaw vibration of the indicator rod by the damping adjustment mechanism in one embodiment; Figure 5 This is a schematic diagram of the vibration damping adjustment mechanism resisting vibrations caused by different wind directions in one embodiment; Figure 6 This is a flowchart illustrating a method for intelligent vibration damping control of traffic poles induced by wind, as described in one embodiment. Figure 7 This is a schematic diagram of the structure of a computer device in one embodiment; The attached diagram is labeled as follows: 1-Wind speed and direction sensor, 2-Controller (201-Data Processing Module (DAPM), 202-Control Decision Module (CDM), 203-Wireless Communication Module (WTM)), 3-Stress and strain sensor, 4-Frequency damping adjustment mechanism (401-Follower gear ring, 402-Mass position adjustment planetary gear, 403-Fixed sun gear, 404-Drive planetary gear, 405-Auxiliary planetary gear, 406-Load block displacement drive motor, 407-Screw sleeve fixing cylinder, 408-Screw sleeve, 409-Load adjustment mass block, 410-Fixed connecting cylinder, 411-Circumferential rotation drive motor), 5-Vibration acceleration sensor. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] In one embodiment, such as Figure 1As shown, a smart vibration damping control device for traffic poles is provided. This device is installed on the traffic pole and includes sensors, including a wind speed and direction sensor 1, a vibration acceleration sensor 5, and a stress and strain sensor 3, for real-time acquisition of environmental wind speed, wind direction, vibration response data of the traffic pole, and stress and strain state. A controller 2 receives the sensor data and performs preprocessing including low-pass filtering and adaptive moving average. For the preprocessed data, it performs two Kalman filters: one to reconstruct the complete dynamic state vector of the traffic pole, and another to identify the equivalent wind load acting on the traffic pole online. Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, it performs a composite control decision including feedforward, feedback, and adaptive mechanisms to generate a total target control command for vibration damping. A vibration damping adjustment mechanism 4 changes its circumferential and axial positional mass distribution according to the total target control command issued by the controller, thereby changing the mass distribution of the overall structure of the traffic pole, thus changing the mode shape and reducing the amplitude. The device also includes data lines connecting the sensors to the controller 2 and power lines supplying power to the components.

[0019] The aforementioned intelligent vibration damping control device for wind-induced vibration of traffic poles integrates three sources of information: wind speed and direction sensing, vibration response feedback of the pole structure, and online identification of equivalent wind load. It also introduces a feedforward-feedback composite control architecture and an online adaptive parameter iteration mechanism to achieve wideband adaptive suppression of wind-induced vibration of traffic poles, effectively ensuring the structural safety of traffic poles and the normal operation of pole-mounted intelligent equipment.

[0020] In this embodiment, the wind speed and direction sensor 1, vibration acceleration sensor 5, and stress-strain sensor 3 are synchronously triggered at the hardware level through the same high-precision clock source, achieving spatiotemporal synchronous acquisition of multi-source signals. This ensures the accurate phase relationship between wind load excitation and structural response. The placement of each sensor is as follows: Figure 1 As shown.

[0021] The wind speed and direction sensor 1 is installed on the top of the traffic sign pole to collect ambient wind speed and direction in real time. A total of 6 vibration acceleration sensors 5 are arranged in 3 locations: near the column end, in the middle section, and on both the front and rear sides of the indicator pole. These sensors are used to collect real-time displacement, velocity, and acceleration response data of the traffic indicator pole under wind vibration. The data collected by the wind speed and direction sensors 1 and the vibration acceleration sensors 5 constitute the total observation vector, expressed as: ; in, Indicates the first k At that moment, This is the total observation vector. This indicates the real-time wind speed output by wind speed and direction sensor 1. The average wind speed, This represents the wind speed fluctuation component; This indicates the real-time wind direction output by wind speed and direction sensor 1. Average wind direction, This represents the wind direction pulsation component. This represents the vibration response data of the traffic sign pole, where , and These represent the sensor position displacement, sensor position velocity, and sensor position acceleration output by vibration acceleration sensor 5, respectively; the superscript T indicates matrix transpose.

[0022] There are a total of 2 sets of stress and strain sensors 3. One set is arranged on the top surface of the vertical section through the axis of the traffic sign, and the other set is arranged on the side surface within the same cross section but at an orientation 90° different from the top surface. These sensors are used to collect the stress and strain status of the traffic sign in real time.

[0023] In this embodiment, as Figure 1 As shown, the controller 2 is sealed and installed inside the pole of the traffic sign. It includes a wireless communication module 203, a data processing module 201 and a control decision module 202. In addition, the controller 2 also has an embedded microprocessor and a storage unit for storing wind condition identification models and adaptive control algorithms.

[0024] The wireless communication module 203 communicates and interconnects with the sensor, the vibration damping adjustment mechanism 4, and the remote traffic management control terminal. It transmits environmental wind speed, wind direction, and vibration response data collected by the sensor to the data processing module 201, sends the overall target control command generated by the control decision module 202 to the vibration damping adjustment mechanism 4, and sends a warning signal to the remote traffic management control terminal when the stress-strain state collected by the sensor exceeds a preset safety threshold. It can also receive instructions from the remote traffic management control terminal to adjust control parameters, forming a collaborative remote communication architecture between the terminal, edge, and cloud.

[0025] The data processing module 201 includes a preprocessing unit and a filtering state estimation unit.

[0026] The preprocessing unit performs preprocessing, including low-pass filtering and adaptive moving average, on the total observation vector composed of environmental wind speed, wind direction, and vibration response data to obtain preprocessed data. Specifically, the preprocessing unit is configured as follows: First, a hardware-level anti-aliasing low-pass filter is used to suppress high-frequency noise in the total observation vector. The filter transfer function... Represented as: ; in, The cutoff angular frequency is determined based on the fundamental frequency of the traffic sign's structure. set up ; For the Laplace operator.

[0027] Then, a software-level adaptive window moving average is performed on the low-pass filter output, and the preprocessed data is output to the filter state estimation unit; wherein, the adaptive window moving average is expressed as: ; in, Indicates the first k At that moment, This is the preprocessed data output after adaptive window moving average. For the first i The original total observation vector at each moment; To adapt to window length, The value is dynamically adjusted based on the intensity of wind speed fluctuations. When wind speed fluctuations are large, the window is shortened to retain high-frequency information; when wind speed fluctuations are small, the window is extended to enhance the smoothing effect. The expression is: ; in, Based on the window length, The standard deviation of wind speed fluctuation. This is an adjustable coefficient. This represents the average wind speed.

[0028] It should be understood that this preprocessing unit can effectively filter out high-frequency noise and random interference in wind speed, wind direction, and structural vibration observation data, retain effective low-frequency operating condition information through low-pass filtering, and smooth fluctuations and correct data anomalies by combining an adaptive moving average algorithm. This significantly improves the data purity, stability, and reliability of the total observation vector, avoiding the jitter and distortion problems of the original sensor data. It provides a high-precision, high-stability raw data foundation for subsequent Kalman filter state reconstruction, equivalent wind load identification, and precise control decisions, ensuring the accuracy and stability of vibration regulation from the source.

[0029] The filtering state estimation unit performs two Kalman filters: one to reconstruct the complete dynamic state vector of the traffic sign and the other to identify the equivalent wind load acting on the traffic sign. The outputs the reconstructed complete dynamic state vector and the identified equivalent wind load. Specifically, the filtering state estimation unit includes a first filter and a second filter.

[0030] The filtered state estimation unit is configured to construct the structural dynamic differential equation of the traffic sign based on the preprocessed data, as follows: ; in, Indicates time, , , These are the mass matrix, damping matrix, and stiffness matrix of the traffic sign pole. , , These are the displacement vector, velocity vector, and acceleration vector of the traffic indicator structure, respectively. This is the load location matrix, used to map wind loads to the degrees of freedom of the traffic sign structure; The equivalent wind load, in physical terms, is the random wind excitation acting on the traffic sign pole.

[0031] Define state vector for: ; The superscript T indicates matrix transpose.

[0032] The discretized state-space equations are defined as follows: ; ; in, and The first k The moment and the k +1 time-state vectors Here is the state transition matrix. For discrete control matrices, For the first k The equivalent wind load at a given moment For process noise, The process noise covariance matrix; For the first k The observation vector at each time step. For the observation matrix, To observe the noise, To observe the noise covariance matrix.

[0033] The first filter is configured to reconstruct the complete dynamic state vector of the traffic sign through Kalman filtering state estimation, including: Prediction Step: ; ; in, For the first k The prior state vector estimate at time t, For the first kThe optimal estimate of the posterior state vector at time -1 For the first k Estimated equivalent wind load at time -1; For the first k The prior estimate of the covariance matrix at time -1. For the first k -1 time point posterior estimated covariance matrix; Update steps: ; ; ; in, Here is the Kalman gain matrix. For the first k The optimal estimate of the posterior state vector at time n. For the first k The posterior estimated covariance matrix at time n. It is an identity matrix.

[0034] The second filter is configured to identify the equivalent wind load acting on the traffic sign pole online using recursive least squares and Kalman filtering, including: The equivalent wind load is treated as an unmeasurable unknown input, and the observation equation is constructed using a projection matrix, as follows: ; in, For the first k The observation vector constructed at each time point, For traffic signs at the first k The state vector at each time step To expand the observation matrix, For direct matrix transmission; Real-time estimation using the least squares method: ; in, For the first k Estimated equivalent wind load at each moment.

[0035] final, and The reconstructed complete dynamic state vector and the online identified equivalent wind load are respectively output to the control decision module 202.

[0036] It should be understood that the aforementioned filtering state estimation unit employs a dual-path independent Kalman filter parallel computation mechanism, performing separate calculations for the structural dynamic characteristics and external wind disturbance loads. This effectively isolates the coupling interference between the structure's own vibration state and wind load excitation. One path of filtering accurately reconstructs the complete dynamic state vector of the traffic sign pole, comprehensively restoring the pole's true vibration dynamic characteristics. The other path of filtering enables online real-time identification of the equivalent wind load, accurately capturing wind speed, wind direction, and dynamic wind field excitation disturbance information. The two calculation steps are independent of each other and perform their respective functions, effectively improving the accuracy, real-time performance, and robustness of structural state perception and external load identification. This provides accurate, complete, and decoupled core inputs for subsequent composite adaptive control decisions, ensuring the targetedness and accuracy of vibration reduction control from an algorithmic perspective.

[0037] The control decision module 202 is used to execute a composite control decision including feedforward, feedback, and adaptation based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, and generate a total target control command for flutter reduction. Specifically, the control decision module 202 is configured to input the reconstructed complete dynamic state vector and the online identified equivalent wind load into a three-channel composite control architecture consisting of a feedforward control path, a feedback control path, and an adaptive gain scheduling layer to generate a total target control command.

[0038] The feedforward control path is configured based on the equivalent wind load identified online. Calculate the feedforward compensation component , is represented as: ; ; in, This represents the feedforward gain.

[0039] The feedback control path is configured to employ an adaptive neuro-fuzzy inference system (ANFIS) to adjust the fuzzy membership function parameters online to adapt to the drift of the traffic sign's structural parameters caused by changes in ambient temperature and humidity. The adaptive neuro-fuzzy inference system includes: First layer: Fuzzification layer, used to fuzzify the displacements in the reconstructed complete dynamic state vector. ,speed and acceleration The membership degrees are converted into fuzzy membership degrees, which are expressed as follows: ; in, , and Displacement ,speed and acceleration Their respective fuzzy membership degrees , , Displacement ,speed and acceleration The corresponding Gaussian membership functions, i.e. .in, , The antecedent parameters can be updated using gradient descent.

[0040] The second layer: the rule layer, used to calculate the activation strength of each fuzzy rule, represented as: ; in, For the first l The activation strength of a fuzzy rule This is the output of the rules layer.

[0041] The third layer: the normalization layer, used to perform normalization calculations, is represented as follows: ; in, For the output of the normalization layer, To normalize the rule strength, For the first m The activation strength of a fuzzy rule This represents the total number of fuzzy rules.

[0042] Fourth layer: Successor layer, used to calculate rule outputs, represented as: ; in, For the output of the consequent layer, For the first l The linear output function of the fuzzy rules, Indicates the first l The consequent parameters to be learned for a fuzzy rule can be updated using the recursive least squares method.

[0043] Fifth layer: Output layer, used for deblurring to obtain the precise feedback adjustment components. , is represented as: ; in, This is the output of the output layer.

[0044] The adaptive gain scheduling layer is configured to dynamically adjust the weighting coefficients of the feedforward compensation component and the feedback regulation component based on the wind speed fluctuation intensity and vibration amplitude, generating a total target control command to achieve optimal control across the entire wind speed domain; the total target control command... The expression is: ; in, For adaptive weighting coefficients, This represents the variance of wind speed fluctuations. To determine the variance of the vibration response, the feedforward effect is enhanced when the wind speed pulsation is large, and the feedback effect is enhanced when the vibration response is severe.

[0045] It should be understood that the control decision module 202 relies on a three-channel composite control architecture to fuse and regulate the dynamic state of the complete structure and the equivalent wind load information. It compensates for the impact of wind load disturbances in advance through a feedforward control path, achieving dynamic disturbance prediction and suppression. It corrects structural vibration state deviations through a feedback control path, ensuring the steady-state accuracy of the control output. Simultaneously, it dynamically adjusts control parameters in real time using an adaptive gain scheduling layer. This multi-channel collaborative approach overcomes the shortcomings of single control strategies in simultaneously addressing dynamic disturbance rejection and precise steady-state control. It can adapt to changes in operating conditions caused by wind loads and time-varying structural vibration characteristics, accurately outputting overall target control commands adapted to real-time operating conditions. This significantly improves the system's dynamic response capability, operating condition adaptability, and vibration reduction control accuracy, providing reliable control command support for subsequent vibration reduction adjustments.

[0046] In this embodiment, the controller 2 is also used to receive the vibration response data of the traffic pole after the vibration reduction mechanism 4 is activated by the sensor again after the amplitude of the traffic pole decreases, and calculate the current measured vibration amplitude and compare it with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller drives the internal data processing module 201 and control decision module 202 to perform online iterative adjustment of parameters and re-execute the generation of the overall target control command. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the controller enters the steady-state monitoring mode.

[0047] In this embodiment, as Figure 1 As shown, the vibration damping adjustment mechanism 4 includes a total of 3 sets, which are respectively installed at the end of the indicator rod near the column, the middle section of the indicator rod, and the far end of the indicator rod.

[0048] The structure of each group of shock-reducing adjustment mechanisms is as follows: Figure 2 As shown, it includes a follower gear ring 401, a mass position adjusting planetary gear 402, a fixed sun gear 403, a drive planetary gear 404, an auxiliary planetary gear 405, a load block shifting drive motor 406, a screw sleeve fixing cylinder 407, a screw sleeve 408, a load adjusting mass block 409, a fixed connecting cylinder 410, and a circumferential rotation drive motor 411.

[0049] The fixed sun gear 403 is centrally positioned and fixed to the traffic indicator pole. The follower gear ring 401 is arranged around the outside of the fixed sun gear 403. The mass position adjustment planetary gear 402, the drive planetary gear 404, and the auxiliary planetary gear 405 are all meshed and assembled between the fixed sun gear 403 and the follower gear ring 401. The output end of the circumferential rotation drive motor 411 is connected to the drive planetary gear 404. The load block displacement drive motor 406 is installed at the end of the screw sleeve fixing cylinder 407, and the output shaft of the load block displacement drive motor 406 is connected to the screw sleeve 408, driving the screw sleeve 408 to rotate in the forward and reverse directions. The load adjustment mass block 409 has a cylindrical structure, is encapsulated with high-density liquid, and has external threads on its outer wall, which form a threaded pair with the internal threads of the screw sleeve 408. The forward and reverse rotation of the screw sleeve 408... Rotation drives the load adjustment mass block 409 to slide axially, thereby changing the position distribution of the load adjustment mass block 409 along the axial direction of the traffic indicator pole; the screw sleeve fixing cylinder 407 is threadedly assembled with the fixed connecting cylinder 410, and the fixed connecting cylinder 410 is threadedly connected with the mass position adjustment planetary gear 402; the screw sleeve 408 and the screw sleeve fixing cylinder 407 adopt a clearance fit, so that the screw sleeve 408 can rotate freely inside the screw sleeve fixing cylinder 407, and the end face of the screw sleeve 408 and the inner end face of the fixed connecting cylinder 410 are separated from each other and do not contact each other; at the same time, the load block displacement drive motor 406, the screw sleeve fixing cylinder 407, the screw sleeve 408, the load adjustment mass block 409 and the fixed connecting cylinder 410 are integrated into an integrated module component, and the integrated module component is screwed into the threaded hole of the mass position adjustment planetary gear 402 to complete the assembly.

[0050] The principle behind the vibration damping adjustment mechanism 4 changing its circumferential and axial positional mass distribution according to the overall target control command issued by the controller 2, thereby changing the overall mass distribution of the traffic pole structure and thus changing the mode shape and reducing the amplitude of the traffic pole, is as follows: The overall target control command is issued to the circumferential rotation drive motor 411, which drives the drive planetary gear 404 to rotate. The drive planetary gear 404 drives the follower gear ring 401 to rotate, which in turn pushes the mass position adjustment planetary gear 402 to rotate. The mass position adjustment planetary gear 402 then rotates around the circumference of the traffic pole to reach a predetermined position, thus changing the circumferential positional mass distribution of the vibration damping adjustment mechanism on the traffic pole. Meanwhile, the load block displacement drive motor 406 rotates forward and backward, driving the screw sleeve 408 to rotate forward and backward, thereby driving the axial movement of the load adjustment mass block 409, changing the axial positional mass distribution of the load adjustment mass block 409 on the traffic pole, and thus changing the overall mass distribution of the traffic pole structure, thereby changing the mode shape and reducing the amplitude of the traffic pole.

[0051] Furthermore, the structural dynamic differential equation of the traffic indicator pole after the action of the damping adjustment mechanism 4 is: ; in, The added mass, damping, and stiffness of the damping adjustment mechanism The control input matrix for the damping adjustment mechanism (actuator).

[0052] The equation of motion for the load adjustment mass 409 in the damping adjustment mechanism 4 is: .

[0053] The actual control force output of the flutter reduction adjustment mechanism is: ; in, This is the execution efficiency coefficient.

[0054] The damping adjustment mechanism 4 resists vertical vibration of the indicator rod, lateral vibration of the indicator rod, and vibration of the indicator rod caused by different wind directions, as follows: Figure 3 , Figure 4 and Figure 5 As shown.

[0055] In one embodiment, such as Figure 6 As shown, a method for intelligent vibration damping control of traffic poles based on the aforementioned intelligent vibration damping control device for traffic poles is provided, comprising the following steps: Step 1: Sensors collect real-time data on ambient wind speed, wind direction, vibration response of traffic signs, and stress-strain status.

[0056] Step 2: Receive sensor-acquired data and perform preprocessing including low-pass filtering and adaptive moving average.

[0057] Step 3: For the preprocessed data, perform one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign and another Kalman filter to identify the equivalent wind load acting on the traffic sign online.

[0058] Step 4: Based on the reconstructed complete dynamic state vector and the online-identified equivalent wind load, a composite control decision including feedforward, feedback, and adaptive mechanisms is executed to generate the overall target control command for vibration reduction. Specifically, the feedforward control path calculates the feedforward compensation component based on the online-identified equivalent wind load and real-time environmental wind speed and direction. The feedback control path uses an adaptive neural-fuzzy inference system to calculate the output feedback adjustment component. The adaptive gain scheduling layer dynamically adjusts the weighting coefficients of the feedforward compensation component and the feedback adjustment component according to the wind speed pulsation intensity and vibration amplitude, and then weighted and fused to obtain the final overall target control command.

[0059] Step 5: According to the overall target control command, change the circumferential and axial positional mass distribution of the vibration damping adjustment mechanism, thereby changing the mass distribution of the overall structure of the traffic sign pole, thus changing the mode shape of the traffic sign pole and reducing the amplitude.

[0060] Step 6: The sensor re-collects the vibration response data of the traffic pole after the action of the damping adjustment mechanism, and calculates the current measured vibration amplitude and compares it with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller is driven to perform online iterative adjustment of parameters and repeat steps 3 to 5. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the steady-state monitoring mode is entered.

[0061] It should be understood that, although Figure 6 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 6 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0062] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for intelligent vibration damping control of traffic sign poles caused by wind. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0063] Those skilled in the art will understand that Figure 7The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0064] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps: Step 1: Sensors collect real-time data on ambient wind speed, wind direction, vibration response of traffic signs, and stress-strain status. Step 2: Receive sensor-acquired data and perform preprocessing including low-pass filtering and adaptive moving average; Step 3: For the preprocessed data, perform one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign pole and another Kalman filter to identify the equivalent wind load acting on the traffic sign pole online. Step 4: Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, execute a composite control decision including feedforward, feedback and adaptation to generate the overall target control command for flutter reduction; Step 5: According to the overall target control command, change the circumferential and axial positional mass distribution of the vibration damping adjustment mechanism, thereby changing the mass distribution of the overall structure of the traffic sign pole, thus changing the mode shape of the traffic sign pole and reducing the amplitude. Step 6: The sensor re-collects the vibration response data of the traffic pole after the action of the damping adjustment mechanism, and calculates the current measured vibration amplitude and compares it with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller is driven to perform online iterative adjustment of parameters and repeat steps 3 to 5. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the steady-state monitoring mode is entered.

[0065] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A smart vibration damping control device for traffic sign poles, characterized in that, The device is installed on a traffic sign pole and includes: Sensors are used to collect real-time data on environmental wind speed, wind direction, vibration response of traffic signs, and stress-strain status. The controller receives data collected by sensors and performs preprocessing including low-pass filtering and adaptive moving average. For the preprocessed data, it performs one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign and another Kalman filter to identify the equivalent wind load acting on the traffic sign online. Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, it performs a composite control decision including feedforward, feedback and adaptation to generate the overall target control command for flutter reduction. The vibration damping adjustment mechanism is used to change its circumferential and axial positional mass distribution according to the overall target control command issued by the controller, thereby changing the mass distribution of the overall structure of the traffic pole, thus changing the mode shape of the traffic pole and reducing the amplitude; the vibration damping adjustment mechanism includes a total of 3 sets, which are respectively installed at the end of the pole near the column, the middle section of the pole, and the far end of the pole; Each shock-absorbing adjustment mechanism consists of a follower gear ring, mass position adjustment planetary gears, a fixed sun gear, drive planetary gears, auxiliary planetary gears, a load block displacement drive motor, a screw sleeve fixing cylinder, a screw sleeve, a load adjustment mass block, a fixed connecting cylinder, and a circumferential rotation drive motor. The fixed sun gear is centrally located and fixed to the traffic indicator pole. The follower gear ring surrounds the outside of the fixed sun gear. The mass position adjustment planetary gears, drive planetary gears, and auxiliary planetary gears are all meshed between the fixed sun gear and the follower gear ring. The output end of the circumferential rotation drive motor is connected to the drive planetary gears. The load block displacement drive motor is installed at the end of the screw sleeve fixing cylinder, and its output shaft is connected to the screw sleeve, driving the screw sleeve to rotate in both forward and reverse directions. The load adjustment mass block has a cylindrical structure with a high-density internal enclosure. The liquid has an external thread on its outer wall, which engages with the internal thread of the screw sleeve to form a threaded pair. The forward and reverse rotation of the screw sleeve drives the load adjustment mass block to slide axially, thereby changing the position distribution of the load adjustment mass block along the axial direction of the traffic indicator pole. The screw sleeve fixing cylinder is threadedly assembled with the fixed connecting cylinder, and the fixed connecting cylinder is threadedly connected to the mass position adjustment planetary gear. The screw sleeve and the screw sleeve fixing cylinder are clearance-fitted, allowing the screw sleeve to rotate freely inside the screw sleeve fixing cylinder. The end face of the screw sleeve and the inner end face of the fixed connecting cylinder are separated and do not contact each other. Simultaneously, the load block displacement drive motor, the screw sleeve fixing cylinder, the screw sleeve, the load adjustment mass block, and the fixed connecting cylinder are integrated into a single modular component. This integrated modular component is screwed into the threaded hole of the mass position adjustment planetary gear to complete the assembly. The principle behind the vibration damping adjustment mechanism changing its circumferential and axial positional mass distribution according to the overall target control command issued by the controller, thereby altering the overall mass distribution of the traffic sign pole and thus changing its mode shape and reducing its amplitude, is as follows: The overall target control command is issued to the circumferential rotation drive motor, which drives the drive planetary gear to rotate. The drive planetary gear drives the follower gear ring to rotate, which in turn pushes the mass position adjustment planetary gear to rotate. The mass position adjustment planetary gear then rotates around the traffic sign pole to a predetermined position, changing the circumferential positional mass distribution of the vibration damping adjustment mechanism on the traffic sign pole. Furthermore, the load block displacement drive motor rotates in both directions, driving the screw sleeve to rotate in both directions, thereby causing the load adjustment mass block to move axially, changing its axial positional mass distribution on the traffic sign pole, and thus changing the overall mass distribution of the traffic sign pole structure, thereby changing the mode shape and reducing its amplitude.

2. The intelligent vibration damping control device for traffic sign poles according to claim 1, characterized in that, The sensors include wind speed and direction sensors, vibration acceleration sensors, and stress and strain sensors. All sensors are synchronously triggered at the hardware level through the same high-precision clock source to achieve spatiotemporal synchronous acquisition of multi-source signals. The wind speed and direction sensor is installed on the top of the traffic sign pole to collect ambient wind speed and direction in real time. A total of 6 vibration acceleration sensors are arranged in 3 locations: near the column end, in the middle section, and on both the front and rear sides of the far end of the traffic sign. These sensors are used to collect real-time displacement, velocity, and acceleration response data of the traffic sign under wind vibration at the near-column end, middle section, and far end. The data collected by the wind speed and direction sensors and the vibration acceleration sensors constitute a total observation vector, expressed as: ； in, Indicates the first k At that moment, This is the total observation vector. This indicates the real-time wind speed output by the wind speed and direction sensor. The average wind speed, This represents the wind speed fluctuation component; This indicates the real-time wind direction output by the wind speed and direction sensor. Average wind direction This represents the wind direction pulsation component. This represents the vibration response data of the traffic sign pole, where , and These represent the sensor position displacement, sensor position velocity, and sensor position acceleration output by the vibration acceleration sensor, respectively; the superscript T indicates matrix transpose. The stress and strain sensors consist of two sets: one set is arranged on the top surface of the vertical section of the traffic sign pole, and the other set is arranged on the side surface within the same cross section but at an angle 90° away from the top surface. These sensors are used to collect the stress and strain status of the traffic sign pole in real time.

3. The intelligent vibration damping control device for traffic sign poles according to claim 1, characterized in that, The controller is sealed and installed inside the pole of the traffic sign, and includes a wireless communication module, a data processing module and a control decision module; The wireless communication module is interconnected with the sensor, the vibration damping adjustment mechanism, and the remote traffic management control terminal. It is used to transmit the environmental wind speed, wind direction, and vibration response data collected by the sensor to the data processing module, send the overall target control command generated by the control decision module to the vibration damping adjustment mechanism, and send an early warning signal to the remote traffic management control terminal when the stress and strain state collected by the sensor exceeds the preset safety threshold. The data processing module includes a preprocessing unit and a filtered state estimation unit; the preprocessing unit is used to perform preprocessing, including low-pass filtering and adaptive moving average, on the total observation vector composed of environmental wind speed, wind direction and vibration response data to obtain preprocessed data; The filtering state estimation unit is used to perform one Kalman filter responsible for reconstructing the complete dynamic state vector of the traffic sign and another Kalman filter responsible for online identification of the equivalent wind load acting on the traffic sign, and outputs the reconstructed complete dynamic state vector and the online identified equivalent wind load. The control decision module is used to execute a composite control decision including feedforward, feedback and adaptation based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, and generate a total target control command for flutter reduction.

4. The intelligent vibration damping control device for traffic sign poles according to claim 3, characterized in that, The preprocessing unit is configured as follows: First, a hardware-level anti-aliasing low-pass filter is used to suppress high-frequency noise in the total observation vector. The filter transfer function... Represented as: ； in, The cutoff angular frequency is determined based on the fundamental frequency of the traffic sign's structure. set up ; For the Laplace operator; Then, a software-level adaptive window moving average is performed on the low-pass filter output, and the preprocessed data is output to the filter state estimation unit; wherein, the adaptive window moving average is expressed as: ； in, Indicates the first k At that moment, This is the preprocessed data output after adaptive window moving average. For the first i The original total observation vector at each moment; To adapt to window length, The value is dynamically adjusted based on the intensity of wind speed fluctuations. When wind speed fluctuations are large, the window is shortened to retain high-frequency information; when wind speed fluctuations are small, the window is extended to enhance the smoothing effect. The expression is: ； in, Based on the window length, The standard deviation of wind speed fluctuation. This is an adjustable coefficient. This represents the average wind speed.

5. The intelligent vibration damping control device for traffic sign poles according to claim 4, characterized in that, The filtering state estimation unit includes a first filter and a second filter; The filtered state estimation unit is configured to construct the structural dynamic differential equation of the traffic sign based on the preprocessed data, expressed as: ； in, Indicates time, , , These are the mass matrix, damping matrix, and stiffness matrix of the traffic sign pole. , , These are the displacement vector, velocity vector, and acceleration vector of the traffic indicator structure, respectively. This is the load location matrix, used to map wind loads to the degrees of freedom of the traffic sign structure; The equivalent wind load, in physical terms, is the random wind excitation acting on the traffic sign pole; Define state vector for: ； Wherein, the superscript T denotes matrix transpose; The discretized state-space equations are defined as follows: ； ； in, and The first k The moment and the k +1 time-state vectors Here is the state transition matrix. For discrete control matrices, For the first k The equivalent wind load at a given moment For process noise, The process noise covariance matrix; For the first k The observation vector at each time step. For the observation matrix, To observe the noise, To observe the noise covariance matrix; The first filter is configured to reconstruct the complete dynamic state vector of the traffic sign through Kalman filtering state estimation, including: Prediction Step: ； ； in, For the first k The prior state vector estimate at time t, For the first k The optimal estimate of the posterior state vector at time -1 For the first k Estimated equivalent wind load at time -1; For the first k The prior estimate of the covariance matrix at time -1. For the first k -1 time point posterior estimated covariance matrix; Update steps: ； ； ； in, Here is the Kalman gain matrix. For the first k The optimal estimate of the posterior state vector at time n. For the first k The posterior estimated covariance matrix at time n. It is the identity matrix; The second filter is configured to identify the equivalent wind load acting on the traffic sign pole online using recursive least squares and Kalman filtering, including: The equivalent wind load is treated as an unmeasurable unknown input, and the observation equation is constructed using a projection matrix, as follows: ； in, For the first k The observation vector constructed at each time point, For traffic signs at the first k The state vector at each time step To expand the observation matrix, For direct matrix transmission; Real-time estimation using the least squares method: ； in, For the first k Estimated equivalent wind load at each moment; final, and The reconstructed complete dynamic state vector and the online identified equivalent wind load are respectively output to the control decision module.

6. The intelligent vibration damping control device for traffic sign poles according to claim 5, characterized in that, The control decision module is configured to input the reconstructed complete dynamic state vector and the online identified equivalent wind load into a three-channel composite control architecture consisting of a feedforward control path, a feedback control path, and an adaptive gain scheduling layer to generate a total target control command. The feedforward control path is configured based on the online identified equivalent wind load. Calculate the feedforward compensation component , is represented as: ； ； in, Indicates feedforward gain; The feedback control path is configured to employ an adaptive neural-fuzzy inference system to adjust the fuzzy membership function parameters online to adapt to the drift of the traffic sign's structural parameters caused by changes in ambient temperature and humidity. The adaptive neural-fuzzy inference system includes: The fuzzing layer is used to fuzz the displacements in the reconstructed complete dynamic state vector. ,speed and acceleration The membership degrees are converted into fuzzy membership degrees, which are expressed as follows: ； in, , and Displacement ,speed and acceleration Their respective fuzzy membership degrees , , Displacement ,speed and acceleration Each corresponding Gaussian membership function; The rule layer, used to calculate the activation strength of each fuzzy rule, is represented as: ； in, For the first l The activation strength of a fuzzy rule This is the output of the rule layer; The normalization layer, used to perform normalization calculations, is represented as: ； in, For the output of the normalization layer, To normalize the rule strength, For the first m The activation strength of a fuzzy rule This represents the total number of fuzzy rules. The consequent layer, used to calculate rule outputs, is represented as: ； in, For the output of the consequent layer, For the first l The linear output function of the fuzzy rules, Indicates the first l The consequent parameters to be learned for each fuzzy rule; The output layer is used for deblurring to obtain the precise feedback adjustment components. , is represented as: ； in, For the output of the output layer; The adaptive gain scheduling layer is configured to dynamically adjust the weighting coefficients of the feedforward compensation component and the feedback adjustment component based on the wind speed fluctuation intensity and vibration amplitude, generating a total target control command to achieve optimal control across the entire wind speed domain; the total target control command The expression is: ； in, For adaptive weighting coefficients, This represents the variance of wind speed fluctuations. To determine the variance of the vibration response, the feedforward effect is enhanced when the wind speed pulsation is large, and the feedback effect is enhanced when the vibration response is severe.

7. The intelligent vibration damping control device for traffic sign poles according to claim 3, characterized in that, The controller is also used to receive the vibration response data of the traffic pole after the vibration reduction mechanism is activated by the sensor after the amplitude of the traffic pole decreases, and calculate and compare the current measured vibration amplitude with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller drives the internal data processing module and control decision module to perform online iterative adjustment of parameters and re-execute the generation of the overall target control command. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the controller enters the steady-state monitoring mode.

8. A method for intelligent vibration damping control of traffic signs based on the intelligent vibration damping control device for wind-induced vibration of traffic signs according to any one of claims 1-7, characterized in that, The method includes: Step 1: Sensors collect real-time data on ambient wind speed, wind direction, vibration response of traffic signs, and stress-strain status. Step 2: Receive sensor-acquired data and perform preprocessing including low-pass filtering and adaptive moving average; Step 3: For the preprocessed data, perform one Kalman filter to reconstruct the complete dynamic state vector of the traffic sign pole and another Kalman filter to identify the equivalent wind load acting on the traffic sign pole online. Step 4: Based on the reconstructed complete dynamic state vector and the online identified equivalent wind load, execute a composite control decision including feedforward, feedback and adaptation to generate the overall target control command for flutter reduction; Step 5: According to the overall target control command, change the circumferential and axial positional mass distribution of the vibration damping adjustment mechanism, thereby changing the mass distribution of the overall structure of the traffic sign pole, thus changing the mode shape of the traffic sign pole and reducing the amplitude. Step 6: The sensor re-collects the vibration response data of the traffic pole after the action of the damping adjustment mechanism, and calculates the current measured vibration amplitude and compares it with the preset safety threshold. If the current measured vibration amplitude exceeds the preset vibration amplitude, the controller is driven to perform online iterative adjustment of parameters and repeat steps 3 to 5. If the current measured vibration amplitude does not exceed the preset vibration amplitude, the steady-state monitoring mode is entered.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 8.

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