Energy-saving pole concrete forming and vibrating device

Abstract

The present application relates to the technical field of pole concrete forming, and particularly discloses an energy-saving pole concrete forming vibration device; comprising a perception layer, a data processing layer, a control execution layer, a digital twin layer and a main controller. The perception layer collects vibration response, excitation power and acoustic emission signals; the data processing layer performs modal analysis to identify the natural frequency of the mold-concrete coupling system, and inverses the rheological parameters of the concrete through a neural network, while evaluating the compactness of each section; the control execution layer autonomously adjusts the excitation frequency, amplitude and pulse period according to the natural frequency and rheological parameters, so that the excitation system always works in the resonance interval; the digital twin layer establishes a flow simulation model to predict the risks of cavitation, segregation and bubble aggregation and generate correction instructions; and the main controller realizes closed-loop control. Through real-time perception and resonance tracking, the present application enables the excitation energy to precisely act on the resonance interval, greatly reduces energy consumption, and improves the uniformity of compactness.

Description

Technical Field

[0001] This invention relates to the field of concrete forming technology for utility poles, and more particularly to an energy-saving vibration device for concrete forming of utility poles. Background Technology

[0002] In the process of forming concrete poles, vibration compaction is a core step that helps the concrete fill the mold, expel air bubbles, and achieve uniform density. Currently, the industry commonly uses a continuous constant-frequency vibration method with a single electromagnetic vibrator at the bottom. The excitation frequency is usually fixed at 50Hz (power frequency), and the amplitude is preset and then kept constant. The entire compaction process uses open-loop control, meaning that the excitation parameters are manually set before operation and are not adjusted according to the state of the concrete during operation.

[0003] This traditional vibration method suffers from significant energy consumption. During concrete pouring, the equivalent mass of the pole mold dynamically changes with the increase in fill volume. Simultaneously, the rheological state of the concrete (such as yield stress and plastic viscosity) evolves continuously with the compaction process, causing the natural frequency of the "pole mold-concrete coupled system" to drift in real time. However, traditional devices cannot sense the rheological state inside the concrete in real time, nor can they identify the dynamic characteristics of the coupled system online; the vibrator always outputs at a fixed power frequency. When the fixed excitation frequency deviates from the system's current natural frequency, the excitation energy cannot be effectively transferred to the concrete interior. A large amount of electrical energy is converted into thermal vibration and noise in the mold structure, rather than being used for the flow and compaction of the concrete. Actual measurement data shows that in the later stages of compaction, when the concrete gradually solidifies and the system's natural frequency rises above 60Hz, the traditional continuous constant frequency vibration device still vibrates at 50Hz, resulting in an effective vibration energy utilization rate of less than 40%, leading to severe energy waste.

[0004] Some improvement schemes attempt to use variable frequency sweeping technology, manually or automatically changing the excitation frequency before or during vibration to search for resonance points. However, these schemes are still open-loop or semi-open-loop control: the sweeping strategy is based on a preset program and does not incorporate real-time feedback of concrete rheological parameters; the sweeping cycle is fixed and cannot be dynamically adjusted according to changes in density; and it lacks the ability to continuously track the inherent frequency of the mold-concrete system, locking the frequency once the sweep ends, making it unable to cope with frequency drift caused by subsequent filling and solidification. Therefore, its energy-saving effect is limited, and it is prone to causing a decrease in vibration efficiency due to inaccurate judgment of the resonance range.

[0005] Furthermore, traditional devices lack online sensing capabilities for the rheological state of concrete, making it impossible to determine the current yield stress, plastic viscosity, and air bubble content of the concrete. Consequently, it is impossible to ascertain whether the excitation energy is truly absorbed by the concrete for internal structural adjustments. In open-loop control mode, even if the excitation frequency deviates from the resonance range, the system cannot correct itself autonomously. Operators can only rely on experience to extend the compaction time or increase the amplitude, further exacerbating energy consumption. Summary of the Invention

[0006] In view of this, the present invention provides an energy-saving concrete molding vibration device for utility poles, which solves the problem that the existing concrete molding vibration device for utility poles adopts a continuous constant frequency and open-loop control excitation method, which cannot sense the rheological state inside the concrete and the dynamic characteristics of the mold-concrete coupling system in real time, resulting in the excitation energy not being accurately applied to the resonance range and thus high energy consumption.

[0007] This invention provides an energy-saving concrete forming vibration device for utility poles, including a utility pole mold, a main controller, and a vibration system; The sensing layer is used to collect vibration response signals, excitation power signals, and acoustic emission signals inside the concrete during the compaction process of the pole mold. The data processing layer is used to perform modal analysis, rheological parameter inversion, and density assessment based on the vibration response signal, excitation power signal, and acoustic emission signal, so as to obtain concrete rheological state parameters, mold structure natural frequency, and density assessment results. The control execution layer is used to adjust the excitation frequency, amplitude, sweep rate and pulse period of the excitation system according to the concrete rheological state parameters, the natural frequency of the mold structure and the density evaluation results, so that the excitation system works in the resonance range of the "pole mold-concrete coupling system". The digital twin layer is used to establish a concrete flow simulation model based on the concrete rheological state parameters and excitation parameters, and to predict the void risk area, segregation risk area and bubble aggregation risk area based on the simulation results of the concrete flow simulation model, so as to generate vibration strategy correction instructions. The main controller is connected to the sensing layer, data processing layer, control execution layer, digital twin layer, and excitation system respectively, and is used to realize closed-loop control in the vibration process.

[0008] Preferably, the rheological parameter inversion employs an artificial neural network model; The input parameters of the artificial neural network model include excitation power characteristic parameters, vibration response transfer function characteristic parameters, and acoustic emission characteristic parameters; The output parameters of the artificial neural network model include concrete yield stress, plastic viscosity, and bubble aggregation degree; The artificial neural network model is trained using the backpropagation algorithm. The training samples are derived from laboratory test data of concrete with different mix proportions at different compaction stages, and the total number of training samples is no less than five thousand sets.

[0009] Preferably, the control execution layer includes: The excitation parameter optimization module is used to receive the yield stress, plastic viscosity, bubble aggregation degree obtained by rheological parameter inversion, and the density evaluation values ​​of each section output in the density evaluation, and output the excitation frequency adjustment amount, amplitude adjustment amount, phase angle adjustment amount, sweep rate and pulse period. The vector vibration control module is used to receive the density assessment values ​​of each section output in the density assessment, as well as the excitation parameters output by the excitation parameter optimization module. Based on the deviation between the density of each section and the target value, it generates differentiated excitation commands for different sections. The partitioned pulse excitation module receives the optimal resonance frequency and pulse period output by the excitation parameter optimization module, and realizes frequency sweep search, resonance confirmation, pulse resonance maintenance and dynamic adjustment, and generates pulse excitation commands.

[0010] Preferred options also include: S1: Load the structural parameters of the pole mold and the concrete mix proportion parameters, and collect vibration response signals and acoustic emission signals in real time through the sensing layer; invert the real-time filling amount of concrete based on the spectral drift of the vibration response signal, and determine the filling completion time based on the event rate of the acoustic emission signal. S2: Based on the vibration response signal, excitation power signal, and acoustic emission signal collected by the sensing layer, modal analysis is performed to identify the current natural frequency of the "pole mold-concrete coupling system," and rheological parameter inversion is performed to obtain the yield stress, plastic viscosity, and bubble aggregation degree of the concrete; based on the natural frequency and rheological parameters, frequency sweep search is performed to determine the optimal resonance frequency, and the excitation system is made to work in the resonance range of the coupling system using a pulsed excitation mode; S3: Based on the inversion results of rheological parameters and vibration response signals, calculate the effective vibration energy density, acoustic emission event density and flow resistance index of each section, and weighted fuse them to obtain the compaction evaluation value of each section; establish a concrete flow simulation model, and based on the simulation results and spatial positioning data of acoustic emission signals, predict the void risk area, segregation risk area and bubble aggregation risk area, and generate vibration strategy correction instructions. S4: Based on the density assessment value and defect prediction results, the excitation frequency, amplitude, sweep rate and pulse period are dynamically adjusted by controlling the execution layer. When the density assessment value of all sections reaches the preset threshold, the compaction is determined to be completed and the excitation system is stopped.

[0011] Preferably, S1 includes: S11: The main controller loads the structural parameters of the pole mold and the concrete mix proportion parameters, and receives the target forming size of the pole through the human-machine interface; S12: The main controller performs self-tests on the acceleration sensor array, acoustic emission sensor array and excitation system in the sensing layer in sequence to confirm that each component is working properly. S13: The accelerometer array in the sensing layer continuously collects the vibration acceleration signal of the outer wall of the pole mold and transmits it to the data processing layer; the data processing layer performs a fast Fourier transform on the vibration acceleration signal to obtain the vibration response spectrum and identify the main peak frequency in the spectrum; S14: The main controller compares the current main peak frequency with the initial main peak frequency under the unloaded state of the mold, calculates the spectral drift, and inverts the real-time filling amount of concrete according to the pre-calibrated frequency drift-filling amount mapping relationship. S15: The acoustic emission sensor array in the sensing layer continuously collects acoustic emission signals inside the concrete and counts the number of impact events per unit time as the event rate. S16: The main controller compares the real-time event rate with the preset event rate threshold. When the event rate continuously decreases and falls below the threshold, it determines that the concrete filling is complete. When the filling amount corresponding to the spectral drift reaches the preset value and the event rate is lower than the threshold, it issues a filling completion signal. Otherwise, it continues monitoring.

[0012] Preferably, the frequency sweep search to determine the optimal resonant frequency includes: The control execution layer uses the current natural frequency identified by modal analysis as the center and performs a low-power linear frequency sweep within a range of ±20 Hz. By monitoring the vibration response amplitude through an array of acceleration sensors in the sensing layer, the frequency point with the largest response amplitude is determined as the optimal resonance frequency.

[0013] Preferably, the pulsed excitation mode includes: The control execution layer enables the excitation system to work intermittently according to the set pulse cycle. The pulse cycle is a cycle of 0.3 to 1 second of operation and 2 to 5 seconds of stop, with the duty cycle adjustable from 12.5% ​​to 25%. During pulse conduction, full power output is performed at the optimal resonant frequency; during pulse de-conduction, output is stopped while the sensing layer continues to acquire signals to assess the compactness.

[0014] Preferably, the step of operating the excitation system in the resonance range of the coupled system using a pulsed excitation mode, and performing data-driven time-adaptive adjustment during the pulse turn-off period, includes: The output of the exciter is completely stopped during the pulse shutdown period, and the free vibration response signal of the pole mold is continuously acquired through the acceleration sensor array in the sensing layer; The attenuation characteristic parameters are extracted from the free vibration response signal. The attenuation characteristic parameters include at least one of the following: the time required for the free vibration amplitude to attenuate from the peak to a predetermined proportion of the initial peak, the attenuation rate of the amplitude during adjacent vibration cycles, and the trend of the main peak frequency during free vibration. Based on the currently extracted decay feature parameters, the upper limit of the density that the system can reach at the end of the current pulse turn-off time is predicted through a pre-trained decay feature-density mapping model. Calculate the on-time of the next pulse cycle and use the calculated on-time as the on-time of the next pulse cycle; When the difference between the predicted upper limit of compaction and the target compaction is less than the set threshold, the system determines that the compaction of that section is complete.

[0015] Preferably, the step of establishing a concrete flow simulation model and predicting void risk areas, segregation risk areas, and bubble aggregation risk areas based on simulation results and spatial positioning data of acoustic emission signals, and generating vibration strategy correction instructions includes: The system receives the concrete yield stress and plastic viscosity obtained from the rheological parameter inversion, as well as the excitation frequency and amplitude output by the excitation parameter optimization module. Based on the computational fluid dynamics method, it establishes a three-dimensional flow simulation model of concrete inside the mold and outputs the density distribution, aggregate volume fraction distribution and bubble volume fraction distribution of each calculation node. Based on the simulation results, the local defect risk index at each spatial location point is calculated. ; Local defect risk index With preset risk threshold Compare; when At that time, mark the point as a defect risk point, and according to The contribution of components identifies the dominant risk type: If the void component contributes the most, it is marked as a void risk zone; if the aggregate component contributes the most, it is marked as a segregation risk zone; if the bubble component contributes the most, it is marked as a bubble aggregation risk zone. The spatial distribution of measured acoustic emission events collected by the acoustic emission sensor array is fused and corrected with the risk areas identified by simulation: when the density of measured acoustic emission events in a certain risk area is more than 1.5 times the global average, the risk level of the area is increased by one level; when the density of measured events is less than 0.5 times the global average, the risk level of the area is decreased by one level. The system receives the risk area type, location, and risk level, and generates vibration strategy correction instructions for different defect types: for void risk areas, it generates correction instructions to increase the excitation amplitude and extend the pulse conduction time in that area; for segregation risk areas, it generates correction instructions to reduce the vibration acceleration amplitude and switch to a frequency range higher than the system's natural frequency range; for bubble aggregation risk areas, it generates correction instructions to adopt a high-frequency micro-amplitude vibration mode. The correction command is sent to the excitation parameter optimization module through the industrial bus network. The excitation parameter optimization module adjusts its output excitation frequency, amplitude, sweep rate and pulse period according to the received correction command to realize closed-loop optimization control based on the digital twin layer.

[0016] Preferably, the formula for calculating the local defect risk index of the spatial location point is:

[0017] in, The local defect risk index at coordinate point x is dimensionless and ranges from 0 to 1. This represents the simulated density value at coordinate point x. The target density value; The simulated aggregate volume fraction at coordinate point x. The critical aggregate volume fraction for segregation risk; The simulated bubble volume fraction at coordinate point x. The critical bubble volume fraction for the risk of bubble aggregation; The normalized event density of the acoustic emission sensor near coordinate point x is obtained by interpolation of the acoustic emission signal spatial positioning data by the defect prediction module in the digital twin layer. These are the weighting coefficients, and ; The acoustic emission influence factor is set to 2.5 to control the nonlinear contribution of event density to the risk index.

[0018] The energy-saving concrete forming vibration device for utility poles provided by this invention has the following beneficial effects: This invention uses a sensing layer to collect vibration response, excitation power, and acoustic emission signals in real time. The data processing layer inverts concrete rheological parameters online and identifies the current natural frequency of the "mold-concrete coupling system." The control execution layer then autonomously adjusts the excitation frequency to ensure it always operates precisely within the resonance range, avoiding ineffective energy dissipation due to frequency mismatch. Simultaneously, the partitioned pulse excitation mode utilizes the resonance amplification effect to achieve maximum compaction with minimum power input during pulse conduction and completely stops output during pulse deactivation, further reducing the long-term power consumption caused by continuous vibration. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0020] Figure 1 This is a schematic diagram of an energy-saving concrete forming vibration device for utility poles. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, the element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Where there is no conflict, embodiments of the present invention and the various features thereof can be combined with each other, all of which are within the scope of protection of the present invention. Example

[0022] Please see Figure 1 This invention provides an energy-saving concrete forming vibration device for utility poles, including a utility pole mold, a main controller, and a vibration system. The sensing layer is used to collect vibration response signals, excitation power signals, and acoustic emission signals inside the concrete during the compaction process of the pole mold. The data processing layer is used to perform modal analysis, rheological parameter inversion, and density assessment based on the vibration response signal, excitation power signal, and acoustic emission signal, so as to obtain concrete rheological state parameters, mold structure natural frequency, and density assessment results. The control execution layer is used to adjust the excitation frequency, amplitude, sweep rate and pulse period of the excitation system according to the concrete rheological state parameters, the natural frequency of the mold structure and the density evaluation results, so that the excitation system works in the resonance range of the "pole mold-concrete coupling system". The digital twin layer is used to establish a concrete flow simulation model based on the concrete rheological state parameters and excitation parameters, and to predict the void risk area, segregation risk area and bubble aggregation risk area based on the simulation results of the concrete flow simulation model, so as to generate vibration strategy correction instructions. The main controller is connected to the sensing layer, data processing layer, control execution layer, digital twin layer, and excitation system respectively, and is used to realize closed-loop control in the vibration process.

[0023] The energy-saving concrete forming vibration device for utility poles of this invention adopts a four-layer distributed architecture design, with each layer interacting and cooperating through an industrial bus network. The device is installed at the forming station in the utility pole production workshop and consists of a sensing layer, a data processing layer, a control execution layer, a digital twin layer, a main controller, a human-machine interface, and a vibration system. Under the unified scheduling of the main controller, each component completes the intelligent vibration compaction of the utility pole concrete.

[0024] The sensing layer, serving as the data source for the entire system, is responsible for collecting multi-dimensional physical signals from the pole mold, vibrator, and support during the compaction process. The sensing layer comprises four types of sensor units: accelerometer arrays, current sensors, strain sensor arrays, and acoustic emission sensor arrays. The accelerometer array is positioned on the outer surface of the pole mold, with twelve to sixteen measuring points arranged along the mold's axis. The axial spacing between adjacent measuring points is evenly distributed according to the mold's height, typically controlled within one-fifteenth to one-twentieth of the total mold height. The accelerometers are piezoelectric vibration sensors with adjustable sensitivity from ten to one hundred millivolts per unit of gravitational acceleration and a range from ±50 to ±500 gravitational acceleration to accommodate measurement requirements under different amplitude intensities. The sensors are fixed to the outer surface of the mold using high-strength adhesive, and vibration-damping washers are placed between the sensor and mold contact surfaces to avoid measurement errors caused by installation stress. Each accelerometer is equipped with an independent signal conditioning circuit, converting the analog acceleration signal into a standard voltage signal for output to the data processing layer.

[0025] Preferably, the rheological parameter inversion employs an artificial neural network model; The input parameters of the artificial neural network model include excitation power characteristic parameters, vibration response transfer function characteristic parameters, and acoustic emission characteristic parameters; The output parameters of the artificial neural network model include concrete yield stress, plastic viscosity, and bubble aggregation degree; The artificial neural network model is trained using the backpropagation algorithm. The training samples are derived from laboratory test data of concrete with different mix proportions at different compaction stages, and the total number of training samples is no less than five thousand sets.

[0026] Preferably, the control execution layer includes: The excitation parameter optimization module is used to receive the yield stress, plastic viscosity, and bubble aggregation degree output from the rheological parameter inversion, as well as the density evaluation values ​​of each section output from the density evaluation, and output the excitation frequency adjustment amount, amplitude adjustment amount, phase angle adjustment amount, sweep rate and pulse period. The vector vibration control module is used to receive the density assessment values ​​of each section output in the density assessment, as well as the excitation parameters output by the excitation parameter optimization module. Based on the deviation between the density of each section and the target value, it generates differentiated excitation commands for different sections. The partitioned pulse excitation module receives the optimal resonance frequency and pulse period output by the excitation parameter optimization module, and realizes frequency sweep search, resonance confirmation, pulse resonance maintenance and dynamic adjustment, and generates pulse excitation commands.

[0027] A current sensor is installed in the drive circuit of the vibrator, using a Hall effect current sensor to achieve non-contact measurement of the three-phase current of the vibrating motor. The current sensor's range is set to 0 amperes to 500 amperes for the phase current, the response time is set to less than one microsecond, and the sampling accuracy is set to 0.5% of full scale. After the three-phase current signal collected by the current sensor is processed by the power calculation module inside the main controller, the real-time input power value of the vibrator can be obtained. This power value is the core parameter for evaluating vibration energy consumption. In a specific embodiment of the invention, the rated drive power of the electromagnetic vibrator is 75 kilowatts, and the rated drive power of the hydraulic vibrator is 55 kilowatts. The average power consumption of the system during actual operation dynamically changes according to the concrete condition and compaction stage.

[0028] A strain sensor array is installed on the mold's support structure and key connection points, employing resistance strain gauge sensors for dynamic strain response acquisition. Box-type strain gauges are used, with a polyimide substrate and constantan as the sensing grid material, exhibiting a resistance of 120 ohms and a sensitivity coefficient of 2.05. The strain gauges are mounted at the middle of the mold frame columns, the junction of the bottom crossbeam and the columns, and the connection between the support base and the mold—areas where stress is most concentrated during vibration. The strain sensors are connected to a dynamic strain gauge, whose sampling frequency is set to 5 kHz to 20 kHz, enabling precise capture of transient strain responses during vibration.

[0029] An acoustic emission sensor array is positioned at a predetermined location on the inner wall of the mold, and broadband acoustic emission sensors are used to collect acoustic emission signals from inside the concrete. The center frequency of the acoustic emission sensors is set to 200 kHz to 400 kHz, the frequency response range is 100 kHz to 1 MHz, and the sensitivity is better than 75 dB reference. The acoustic emission sensors are connected to the acoustic emission acquisition instrument via dedicated high-frequency signal cables, and shielded cables are used during signal transmission to suppress electromagnetic interference. In one specific embodiment of the invention, eight acoustic emission sensors are arranged axially along the inner wall of the mold, with the axial spacing between adjacent sensors being one-eighth of the total height of the mold. The sensor probes are magnetically attached to the predetermined positions on the inner wall of the mold.

[0030] The data processing layer is the core computing unit of the entire system, responsible for preprocessing, analyzing, and evaluating the raw signals acquired by the sensing layer. Integrated into the main controller, the data processing layer consists of four functional modules: signal conditioning, modal analysis, rheological parameter inversion, and density evaluation. Data is transferred between these modules via a shared memory mechanism.

[0031] The signal conditioning module includes a multi-channel synchronous sampling circuit, an anti-aliasing filter bank, a programmable gain amplifier, and an analog-to-digital converter. The multi-channel synchronous sampling circuit uses a synchronous sample-and-hold chip to synchronize the sampling time of each sensor channel. The sampling frequency is adjustable from 10 kHz to 20 kHz, and the synchronization error is controlled within one-hundredth of the sampling period. The anti-aliasing filter bank uses a low-pass filter with a cutoff frequency of 0.4 times the sampling frequency. The filter type is an eighth-order elliptic filter, with a stopband attenuation of no less than 60 dB and a passband ripple of no more than 0.5 dB. The programmable gain amplifier has a gain range of 1 to 100 times and a gain step of 1.25 times. The gain setting is automatically adjusted by the main controller based on the signal strength of each channel. The analog-to-digital converter has a resolution of 24 bits, a sampling rate adjustable from 200 kHz to 1 MHz per channel, and a dynamic range of 144 dB.

[0032] The modal analysis module performs time-frequency analysis on the vibration response signals acquired by the accelerometer array to identify the dynamic characteristics of the mold structure. First, a Fast Fourier Transform (FFT) is performed on the acceleration signals at each measuring point, converting the time-domain signal into a frequency-domain spectrum. The number of transform points is set to 2048 or 4096 to balance frequency resolution and computational efficiency. Spectrum analysis uses either a Hanning window or a Blackman window to reduce spectral leakage. Spectrum peak detection employs an amplitude threshold detection algorithm, with the detection threshold set to 20% to 30% of the maximum spectral value. The identified primary modal frequencies and their higher-order modal frequencies are compared with a pre-established modal database of the mold structure to confirm whether the deviation between the currently detected modal parameters and theoretical values ​​is within acceptable limits. The modal confidence criterion uses a modal confidence factor for verification. A confidence factor greater than 0.8 is considered reliable for modal identification, while a confidence factor less than 0.8 is considered a false mode and is discarded. The real-time drift of the main modal frequency is obtained by continuously tracking the movement trajectory of the main peak position. There is a clear correspondence between the drift and the concrete filling state. The larger the drift, the greater the added mass of the concrete on the mold and the higher the filling degree.

[0033] The rheological parameter inversion module establishes an inversion model of the concrete rheological state based on multi-source signals collected by the sensing layer. The core algorithm of this module comprises five main steps. First, the input power of the current excitation system is calculated based on the power signal collected by the exciter current sensor. The power calculation employs a three-phase power algorithm, considering both active and reactive power vector synthesis. Second, the transfer function of the mold-concrete coupled system is calculated based on the vibration response signal collected by the acceleration sensor array. The transfer function represents the complex frequency domain relationship between the output acceleration and the input excitation force, and its amplitude and phase characteristics contain rich dynamic information about the system. Third, characteristic parameters are extracted from the acoustic emission signal collected by the acoustic emission sensor array, including impact event rate, energy count, rise time, and peak amplitude. These parameters reflect the activity level of particle flow and bubble expulsion within the concrete. The fourth step involves inputting the calculated results into a pre-established artificial neural network model. The number of input layer nodes in this neural network is determined based on the dimensions of the excitation power signal, the transfer function characteristic parameters, and the acoustic emission characteristic parameters, totaling thirty-two input nodes. The output layer has three nodes, corresponding to the concrete yield stress, plastic viscosity, and bubble aggregation degree, respectively. The neural network has three hidden layers: sixty-four nodes in the first layer, thirty-two nodes in the second layer, and sixteen nodes in the third layer. The activation function is a modified linear unit function. The fifth step involves training the neural network offline using an error backpropagation algorithm. The training samples are derived from laboratory test data of concrete with different mix proportions at different compaction stages, with a total training sample size of no less than five thousand sets. The network weights after training convergence are stored in the parameter database of the main controller for runtime access.

[0034] The compaction assessment module calculates the concrete compaction distribution based on rheological parameter inversion results and vibration response characteristics. This module's assessment method comprehensively considers three dimensions. The first dimension is the effective vibration energy density, calculated based on the vibration response amplitude of different sections of the mold. This indicator reflects the efficiency of vibration energy transfer to the concrete, and the calculation formula uses the root mean square value of the vibration response acceleration and the quotient of the corresponding section volume. The second dimension is the acoustic emission event density, calculated based on the event rate collected by acoustic emission sensors in different sections. This indicator reflects the activity level of internal structural adjustment in the concrete; a higher event density indicates more intense internal reorganization and a higher degree of compaction. The third dimension is the flow resistance index, calculated based on the yield stress and plastic viscosity from the rheological parameter inversion results. This indicator reflects the concrete's ability to resist flow; a higher flow resistance index indicates that the concrete is closer to a solidified state, and the compaction difficulty is higher. The above three indicators are weighted and fused to obtain a comprehensive assessment value for the compaction of each section, with effective vibration energy density accounting for 40%, acoustic emission event density accounting for 35%, and flow resistance index accounting for 25%. The compaction assessment value ranges from 0 to 100%. When the assessment value reaches 95% or higher, the compaction of the section is considered complete.

[0035] The control execution layer autonomously controls the operating parameters of the excitation system based on the analysis results of the data processing layer, realizing intelligent optimization of the vibration process. The control execution layer consists of three functional modules: excitation parameter optimization module, vector vibration control module, and zoned pulse excitation module. Each module works collaboratively under the scheduling of the main controller.

[0036] The excitation parameter optimization module employs a reinforcement learning algorithm to autonomously optimize the excitation parameters. The state space of the reinforcement learning framework contains six dimensions of state variables: current concrete yield stress, plastic viscosity, bubble aggregation degree, mold filling amount, system natural frequency, and excitation frequency. The action space contains five dimensions of action variables: excitation frequency adjustment, amplitude adjustment, phase angle adjustment, sweep rate, and pulse period. The reward function is defined as the ratio of the increase in density to the increase in energy consumption; its physical meaning is the increase in density obtained per unit of electrical energy consumption. The calculation period of the reward function is set to 100 times the vibration period, meaning the reward value is evaluated every 100 vibration cycles. The policy network is implemented using a deep neural network. The network has six nodes in the input layer (the same dimension as the state space) and five nodes in the output layer (the same dimension as the action space). The network has four hidden layers: 128 nodes in the first layer, 64 nodes in the second layer, 32 nodes in the third layer, and 16 nodes in the fourth layer. The strategy network is trained using a combination of offline pre-training and online fine-tuning. The offline pre-training phase uses samples generated in the simulation environment, while the online fine-tuning phase involves incremental learning based on actual operational data. The trained strategy network can autonomously output the optimal excitation parameter adjustment based on the current concrete condition, thereby minimizing compaction energy consumption.

[0037] The vector vibration control module applies differentiated vibration parameters to different sections of the mold based on the compaction assessment values ​​output by the compaction assessment module. First, according to the structural characteristics of the pole mold, it is divided into eight independent control sections along the axial direction, each corresponding to an independent exciter submodule. The division of each section considers factors such as wall thickness variation, reinforcement density distribution, and concrete flow path, with transition zones between adjacent sections to avoid abrupt changes in vibration parameters. Second, the target compaction state for each section is calculated based on its wall thickness, reinforcement density, and concrete flow resistance parameters, with a target compaction density set at 98% or higher. Then, based on the deviation between the current actual compaction assessment value and the target compaction state, the required excitation frequency, amplitude, and vibration direction for each section are calculated. Finally, vector control commands are sent to the corresponding exciter submodules for each section based on the calculation results, enabling independent adjustment of the vibration in each section. In thick-walled and densely reinforced areas, the system automatically increases the amplitude and duration of vibration, with the specific increase set to 120% to 150% of the standard amplitude and the duration extension coefficient set to 1.2 to 1.5 times, in order to overcome greater flow resistance and difficulty in venting.

[0038] The partitioned pulse excitation module implements a pulsed resonance maintenance mode to reduce energy waste caused by continuous high-power output. The module's workflow consists of four stages. The first stage is a frequency sweep search. The system searches for the optimal resonance interval of the mold-concrete coupling system within a preset frequency range using a low-power linear frequency sweep. The sweep rate is adjustable from 10 Hz to 50 Hz per minute, and the sweep power is set to 20% to 30% of the rated power. The second stage is resonance confirmation. When the vibration response amplitude reaches a preset threshold, the optimal resonance interval is considered found. The current frequency is recorded as a candidate resonance frequency. The frequency sweep continues until the response amplitude begins to decrease, at which point the search stops, and the frequency with the largest response amplitude is determined as the optimal resonance frequency. The third stage is the pulsed resonance maintenance mode. The exciter operates intermittently according to a set pulse period, which is a cycle from 0.5 seconds to 3 seconds, with a duty cycle adjustable from 12.5% ​​to 25%. During pulse conduction, the exciter outputs full power at the optimal resonance frequency; during pulse deactivation, the exciter completely stops outputting power, and the system is in monitoring mode. The fourth stage is dynamic adjustment. During the pulse pause, the sensing layer continues to collect vibration response signals to assess the compaction status. When the compaction assessment value is lower than the preset threshold, the system automatically adjusts the pulse duty cycle to enhance the compaction effect. When the compaction assessment value is close to the target value, the system automatically reduces the duty cycle to save energy.

[0039] The digital twin layer establishes a virtual simulation environment for the compaction of concrete inside the mold, enabling the prediction and optimization of the compaction process. The digital twin layer consists of three functional modules: a concrete flow simulation module, a defect prediction module, and an optimization strategy correction module. Each module performs fusion analysis based on real-time sensor data and the simulation model.

[0040] The concrete flow simulation module establishes a three-dimensional flow simulation model of concrete inside the mold based on computational fluid dynamics. The modeling method of this module includes the following four steps: First, a finite element mesh is established based on the three-dimensional geometric model of the pole mold. The mesh element type uses a hybrid mesh of hexahedral and pentahedral prism elements, with the mesh size refined to no more than 10 mm in the wall thickness direction. The total number of mesh elements is controlled within the range of 500,000 to 1,000,000. Second, the viscoelastic constitutive relation of the flow simulation model is set according to the concrete material parameters. The yield stress and plastic viscosity parameters are obtained from the real-time output of the rheological parameter inversion module, achieving dynamic matching between the simulation model and the actual concrete state. Third, the boundary conditions of the simulation model are set according to the excitation frequency and amplitude output by the excitation parameter optimization module. The vibration excitation is applied to the inner wall surface of the mold in the form of acceleration boundary conditions, with the acceleration amplitude and phase distributed along the mold axis according to a specific pattern. The fourth step is to use an explicit dynamic algorithm to solve the flow and compaction process of concrete under vibration. The calculation time step is set to microseconds to capture high-frequency vibration response. The calculation results are output in the form of a density distribution cloud map for use by subsequent modules.

[0041] The defect prediction module analyzes and fuses concrete flow simulation results with real-time sensor data collected by the sensing layer to predict potential void areas, segregation areas, and bubble accumulation areas within the mold. The prediction method comprises five steps: First, nodes with a density below 90% are identified based on the density values ​​of each calculated node in the simulation results, and these nodes are marked as potential void areas. Second, the aggregate enrichment degree is calculated based on the aggregate volume fraction distribution in the simulation results; areas with an aggregate volume fraction exceeding 60% are identified as segregation risk areas. Third, the bubble accumulation degree is calculated based on the bubble volume fraction distribution in the simulation results; areas with a bubble volume fraction exceeding 5% are identified as bubble accumulation risk areas. Fourth, the spatial coordinates of these risk areas are mapped to the actual acoustic emission sensor array's acquisition area, and the spatial positioning results of acoustic emission events are fused to correct the accuracy of the defect prediction. Fifth, the corrected defect prediction results are output in a 3D visualization format for the control execution layer to adjust the local excitation strategy. The update frequency of the defect prediction results is set to once every 10 to 30 seconds to ensure real-time requirements.

[0042] The optimization strategy correction module dynamically adjusts the excitation parameter optimization strategy based on the output of the defect prediction module. The correction logic of this module is tailored to three types of defect risks. When a void risk is predicted for a certain section, a density weight factor is added to the corresponding excitation parameter optimization target for that section, increasing the density weight from 60% to 80% of the standard value to prioritize the compaction effect in that area. When a segregation risk is predicted for a certain section, the vibration acceleration amplitude of that section is reduced to 60% to 80% of the current amplitude, while the vibration frequency is switched to a range higher than the system's natural frequency, using high-frequency vibration to suppress the separation trend between coarse aggregate and cement paste. When a bubble aggregation risk is predicted for a certain section, a high-frequency micro-amplitude vibration mode is adopted for that section, with the vibration frequency set to 200 Hz to 300 Hz and the amplitude set to 30% to 50% of the current amplitude, promoting the dissolution and removal of microbubbles. The optimization strategy correction command is sent to the excitation parameter optimization module via the industrial bus network, updating the weight allocation in its reward function to achieve closed-loop optimization control.

[0043] The main controller adopts a hybrid architecture of an industrial-grade programmable logic controller (PLC) and a high-performance digital signal processor (DSP). The PLC is responsible for system logic control, task scheduling, and fault diagnosis, employing a redundant dual-machine hot standby configuration to ensure system reliability. The DSP is responsible for high-frequency data acquisition, signal processing, and algorithm computation, using a multi-core parallel processing architecture to meet real-time requirements. Communication between the main controller and each module layer uses industrial Ethernet or fieldbus protocols, with a communication rate of no less than 100 megabits per second. The human-machine interface (HMI) uses a touchscreen terminal with a screen size of 15 to 21 inches and a resolution of no less than 1028 x 768 pixels. The HMI displays the system's operating status, vibration parameter curves, density distribution cloud maps, and defect prediction results, and supports offline configuration and online modification of process parameters. The interface uses a graphical design, with different functional areas distinguished by color, facilitating quick understanding of the system status by operators.

[0044] The vibrator employs a combination of electromagnetic and hydraulic vibrators. The electromagnetic vibrator, positioned at the bottom of the pole mold, generates axial vibration components. Its rated excitation force is adjustable from 50 kN to 200 kN, and its rated frequency range is 10 Hz to 300 Hz. The electromagnetic vibrator consists of an excitation coil, a mover, an iron core, and spring supports. The drive power supply uses an adjustable frequency converter, and the power components are insulated-gate bipolar transistor modules. The hydraulic vibrator, located on the side wall of the mold, uses a servo hydraulic control system to generate radial and torsional vibration components. Its rated excitation force is adjustable from 30 kN to 150 kN, and its frequency response range is 5 Hz to 150 Hz. The hydraulic vibrator consists of a servo valve, a cylinder, hydraulic lines, and a hydraulic station. The hydraulic station uses a variable frequency motor to drive the oil pump, with a power of 37 kW. In one specific embodiment of the present invention, the height of the pole mold is set to 12 meters, the bottom diameter is set to 350 millimeters, the top diameter is set to 190 millimeters, and the wall thickness gradually changes from 30 millimeters at the bottom to 25 millimeters at the top along the axial direction.

[0045] Preferably, in this embodiment, it further includes: S1: Load the structural parameters of the pole mold and the concrete mix proportion parameters, and collect vibration response signals and acoustic emission signals in real time through the sensing layer; invert the real-time filling amount of concrete based on the spectral drift of the vibration response signal, and determine the filling completion time based on the event rate of the acoustic emission signal. Specifically, before the compaction operation begins, the main controller first loads pre-stored or operator-input mold structure parameters through the human-machine interface, including the total mold height, bottom diameter, top diameter, wall thickness distribution curve, and support stiffness parameters. Simultaneously, it loads concrete mix proportion parameters, such as cement type and strength grade, maximum aggregate size, sand ratio, water-cement ratio, and admixture type. The operator also needs to set the target forming dimensions of the pole, such as length, tip diameter, base diameter, and wall thickness distribution. Subsequently, the system performs a self-check: the main controller sequentially sends test commands to the accelerometer array, acoustic emission sensor array, and vibration system in the sensing layer to confirm that the output signals of each sensor are normal and the vibrator response is reliable. Only after the self-check is passed can the subsequent steps proceed.

[0046] After concrete pouring begins, the accelerometer array in the sensing layer continuously collects vibration acceleration signals from multiple measuring points on the outer wall of the pole mold at a set sampling frequency (e.g., 10kHz to 20kHz), and transmits them to the data processing layer in real time. The data processing layer performs a Fast Fourier Transform on the acceleration signals to obtain the vibration response spectrum and identifies the dominant frequency in the spectrum. The main controller compares this dominant frequency with the initial dominant frequency pre-recorded under unloaded conditions of the mold, calculating the spectral drift. The system calculates the real-time filling volume of concrete based on the offline calibrated frequency drift-fill volume mapping relationship. This mapping relationship is a linear or piecewise linear function derived from measured data at different filling heights. Simultaneously, an array of acoustic emission sensors in the sensing layer continuously collects acoustic emission signals from within the concrete, counting the number of impact events per unit time as the event rate. The main controller compares the real-time event rate with a preset threshold; the threshold is typically set at 5% to 15% of the peak event rate at the initial pouring stage and dynamically adjusted according to the concrete mix proportions. When the event rate continuously decreases and falls below this threshold, and the filling volume calculated from the frequency drift reaches the preset filling completion threshold, the system issues a filling completion signal. If neither condition is met simultaneously, the system continues monitoring and displays the current filling status. This comprehensive judgment based on the vibration-acoustic emission dual criteria ensures accurate identification of the filling completion time, providing a reliable starting point for the subsequent adaptive compaction process.

[0047] S2: Based on the vibration response signal, excitation power signal, and acoustic emission signal collected by the sensing layer, modal analysis is performed to identify the current natural frequency of the "pole mold-concrete coupling system," and rheological parameter inversion is performed to obtain the yield stress, plastic viscosity, and bubble aggregation degree of the concrete; based on the natural frequency and rheological parameters, frequency sweep search is performed to determine the optimal resonance frequency, and the excitation system is made to work in the resonance range of the coupling system using a pulsed excitation mode; Specifically, after the filling completion signal is issued, the system automatically enters the adaptive compaction stage. The accelerometer array, current sensor array, and acoustic emission sensor array in the sensing layer continue to collect data in real time at the set sampling frequency, transmitting the vibration response signal, excitation power signal, and acoustic emission signal to the data processing layer. The modal analysis module in the data processing layer first performs a Fast Fourier Transform on the acceleration signals at each measuring point, identifies multiple peaks in the spectrum using an amplitude threshold detection algorithm, and compares them with a pre-established mold structure modal database to confirm the current dominant modal frequency of the "pole mold-concrete coupling system." Simultaneously, the modal analysis module continuously tracks changes in the position of the dominant peak to obtain the frequency drift, which reflects the impact of the concrete filling state on the equivalent mass of the system.

[0048] Meanwhile, the rheological parameter inversion module inputs the excitation power signal, vibration response transfer function, and acoustic emission characteristic parameters collected by the sensing layer into a pre-trained artificial neural network model. This model has three hidden layers with 64, 32, and 16 nodes respectively; the input layer has 32 nodes, and the output layer has 3 nodes, corresponding to the yield stress, plastic viscosity, and bubble aggregation degree of concrete, respectively. The neural network was trained offline using at least 5000 sets of laboratory test data from concrete with different mix proportions through an error backpropagation algorithm, and its weights are stored in the parameter database of the main controller. During runtime, the inversion module outputs the above three rheological parameters in real time, providing a basis for subsequent excitation strategy optimization.

[0049] The main controller transmits the current natural frequency identified by the modal analysis module, along with the yield stress and plastic viscosity output by the rheological parameter inversion module, to the control execution layer. The excitation parameter optimization module in the control execution layer first calculates the theoretical natural frequency of the coupled system based on the total equivalent mass of the system and the stiffness of the mold structure, and verifies it against the measured values ​​from the modal analysis. Subsequently, the excitation parameter optimization module initiates a low-power linear frequency sweep within a range of ±20Hz, centered on this natural frequency. During the frequency sweep, the accelerometer array in the sensing layer continuously monitors the vibration response amplitude at each measuring point of the mold. When the response amplitude reaches a preset threshold, the system records the current frequency as a candidate resonance frequency, continues the frequency sweep until the response amplitude begins to decrease, and finally determines the frequency point with the largest response amplitude as the optimal resonance frequency.

[0050] Once the optimal resonance frequency is determined, the partitioned pulse excitation module in the control execution layer immediately activates the pulsed resonance maintenance mode. This module causes the excitation system to work intermittently according to the set pulse cycle: during the pulse conduction period, the vibrator outputs full power at the optimal resonance frequency, using the resonance amplification effect to cause large-scale vibration of the mold-concrete system, which promotes the rearrangement of particles inside the concrete and the upward release of air bubbles.

[0051] S3: Based on the inversion results of rheological parameters and vibration response signals, calculate the effective vibration energy density, acoustic emission event density and flow resistance index of each section, and weighted fuse them to obtain the compaction evaluation value of each section; establish a concrete flow simulation model, and based on the simulation results and spatial positioning data of acoustic emission signals, predict the void risk area, segregation risk area and bubble aggregation risk area, and generate vibration strategy correction instructions. Specifically, the density assessment module in the data processing layer calculates the density assessment value for each independent control section (the pole mold is divided into eight sections along the axial direction, with transition zones between adjacent sections) based on the yield stress, plastic viscosity, and bubble aggregation degree output by the rheological parameter inversion module, and in conjunction with the vibration response signals of each section collected by the sensing layer, using the following method: Calculate the effective vibration energy density of each section, and divide the root mean square value of the vibration response acceleration at multiple measuring points in the section by the volume of the corresponding section to reflect the efficiency of vibration energy transfer to concrete. The acoustic emission event density is calculated by statistically analyzing the number of impact events per unit time collected by acoustic emission sensors in each section, reflecting the activity level of particle flow and bubble discharge inside the concrete. The flow resistance index is calculated using the yield stress and plastic viscosity from the rheological parameter inversion results, obtained through an empirical formula (the flow resistance index is directly proportional to the yield stress and inversely proportional to the plastic viscosity). The three indices are then weighted and fused: Density = 40% × Effective Vibration Energy Density + 35% × Acoustic Emission Event Density + 25% × Flow Resistance Index. All indices are normalized, and the final density assessment value ranges from 0% to 100%. This module updates the density of each section at regular intervals and transmits it to the human-computer interface in real time in the form of numerical values ​​and contour maps.

[0052] Simultaneously, the digital twin layer begins operation. The concrete flow simulation module first receives the current yield stress and plastic viscosity output from the rheological parameter inversion module, as well as the real-time excitation frequency and amplitude output from the excitation parameter optimization module in the control execution layer. This module establishes a three-dimensional flow simulation model of the concrete inside the mold based on computational fluid dynamics: a finite element mesh is generated based on the three-dimensional geometric model of the pole mold, using a mixture of hexahedral and pentahedral prism elements, with the mesh density in the wall thickness direction reduced to no more than 10 mm, and the total mesh count controlled between 500,000 and 1,000,000; the viscoelastic constitutive relation of the concrete is set, where the yield stress and plastic viscosity parameters are directly adopted from the real-time output of the rheological parameter inversion module; the vibration excitation is applied to the inner wall of the mold in the form of acceleration boundary conditions using the excitation frequency and amplitude as boundary conditions; an explicit dynamic algorithm is used to solve the flow and compaction process of concrete under vibration, outputting the density distribution, aggregate volume fraction distribution, and bubble volume fraction distribution of each calculation node.

[0053] The defect prediction module analyzes and fuses the simulation results with the spatial positioning data of the acoustic emission sensor array in the perception layer to predict potential defect areas. Specifically, the module first identifies nodes with a density below 90% as potential void areas; areas with an aggregate volume fraction exceeding 60% are marked as segregation risk areas; and areas with an air bubble volume fraction exceeding 5% are marked as air bubble aggregation risk areas. Then, the module spatially locates the measured events collected by the acoustic emission sensor array and adjusts the density of the measured events against the risk areas identified in the simulation: if the measured acoustic emission event density in a risk area is higher than 1.5 times the global average, the risk level of that area is increased; if it is lower than 0.5 times the global average, the risk level is decreased. Finally, the defect prediction module outputs the location, extent, and risk level of the three types of risk areas—void, segregation, and air bubble aggregation—in a 3D visualization format.

[0054] The optimization strategy correction module generates vibration strategy correction instructions based on the defect prediction results: When a void risk is predicted in a certain section, instructions are generated to increase the excitation amplitude of that section (the increase is set to 120%~150% of the current amplitude) and extend the pulse conduction time (extension coefficient 1.2~1.5 times). When a segregation risk is predicted, correction instructions are generated to reduce the vibration acceleration amplitude of that section and switch to a frequency range higher than the system's natural frequency, using high-frequency vibration to suppress the separation of coarse aggregate and cement paste. When a bubble aggregation risk is predicted, instructions are generated to adopt a high-frequency micro-amplitude vibration mode (vibration frequency 200~300Hz, amplitude 30%~50% of the current amplitude) to promote the dissolution and expulsion of microbubbles. The above correction instructions are sent in real time to the excitation parameter optimization module in the control execution layer through the industrial bus network to realize closed-loop optimization control based on digital twin prediction.

[0055] S4: Based on the density assessment value and defect prediction results, the excitation frequency, amplitude, sweep rate and pulse period are dynamically adjusted by controlling the execution layer. When the density assessment value of all sections reaches the preset threshold, the vibration is determined to be completed and the excitation system is stopped. Specifically, while the compaction process continues, the main controller receives in real time the compaction assessment values ​​of each section output by the data processing layer and the defect prediction results output by the digital twin layer, and sends dynamic adjustment commands to the control execution layer via the industrial bus network. The excitation parameter optimization module in the control execution layer autonomously adjusts each excitation parameter based on the deviation between the compaction assessment value and the target value, combined with the risk area type and level fed back by the defect prediction module: when the compaction of a certain section increases slowly, the corresponding excitation frequency adjustment or amplitude adjustment is appropriately increased; when the defect prediction indicates that there is a risk of segregation in the section, the amplitude is automatically reduced and switched to a range higher than the system's natural frequency; when the risk of voids or bubbles is prominent, the pulse conduction time is extended or a high-frequency micro-amplitude mode is adopted. The sweep rate and pulse period are also dynamically optimized according to the real-time compaction change trend—if the compaction rises rapidly, the duty cycle can be appropriately reduced to save energy; if the compaction stagnates, the duty cycle is increased or a small-range sweep is performed again to confirm whether the resonant frequency has drifted. The entire adjustment process operates in a closed loop: the perception layer continuously collects signals, the data processing layer updates the evaluation results, the control execution layer corrects the output, and the digital twin layer synchronously predicts and feeds back correction instructions, forming a rapid cycle of "perception-analysis-decision-execution-re-perception".

[0056] After each evaluation cycle, the main controller checks the compaction evaluation values ​​of all independent control sections. When the compaction of each section reaches the preset threshold and the evaluation results are stable for three consecutive times, the system determines that compaction is complete. At this time, the main controller first sends a stop command to the electromagnetic and hydraulic vibrators in the vibration system through the control execution layer, switching them from working state to standby state; then, a prompt message indicating that compaction is complete is displayed on the human-machine interface, and the key parameters of this compaction process are recorded in the main controller's storage unit for subsequent process analysis and optimization. If a sensor malfunction or vibrator failure occurs during the compaction process, the main controller will also immediately stop the vibration and issue an alarm to ensure the safety of equipment and personnel. Through the above steps, fully automatic closed-loop control of the concrete forming vibration of the utility pole is realized, minimizing energy consumption while ensuring that the compaction standard is met.

[0057] Preferably, S1 includes: S11: The main controller loads the structural parameters of the pole mold and the concrete mix proportion parameters, and receives the target forming size of the pole through the human-machine interface; S12: The main controller performs self-tests on the acceleration sensor array, acoustic emission sensor array and excitation system in the sensing layer in sequence to confirm that each component is working properly. S13: The accelerometer array in the sensing layer continuously collects the vibration acceleration signal of the outer wall of the pole mold and transmits it to the data processing layer; the data processing layer performs a fast Fourier transform on the vibration acceleration signal to obtain the vibration response spectrum and identify the main peak frequency in the spectrum; S14: The main controller compares the current main peak frequency with the initial main peak frequency under the unloaded state of the mold, calculates the spectral drift, and inverts the real-time filling amount of concrete according to the pre-calibrated frequency drift-filling amount mapping relationship. S15: The acoustic emission sensor array in the sensing layer continuously collects acoustic emission signals inside the concrete and counts the number of impact events per unit time as the event rate. S16: The main controller compares the real-time event rate with the preset event rate threshold. When the event rate continuously decreases and falls below the threshold, it determines that the concrete filling is complete. When the filling amount corresponding to the spectral drift reaches the preset value and the event rate is lower than the threshold, it issues a filling completion signal. Otherwise, it continues monitoring.

[0058] Specifically, the main controller loads the pole mold structure parameters input by the operator or pre-stored through the human-machine interface, including the total height of the mold, bottom diameter, top diameter, wall thickness distribution curve, and support stiffness parameters; simultaneously, it loads concrete mix proportion parameters, such as cement type and strength grade, maximum aggregate size, sand ratio, water-cement ratio, and admixture type; in addition, it receives the target forming dimensions of the pole, including length, tip diameter, base diameter, and wall thickness distribution. After the parameters are loaded, the main controller sequentially sends self-test commands to the accelerometer array, acoustic emission sensor array, and vibration system in the sensing layer: first, each sensor is required to output a standard test signal, and the main controller compares the signal with a preset threshold to determine if the signal is normal; then, the vibrator is driven for a short time with low power to check if its response time and vibration amplitude meet expectations. If any component malfunctions during the self-test, the human-machine interface displays a fault message and prohibits the start of subsequent steps; only after all components are normal can the compaction operation be allowed.

[0059] After concrete pouring begins, the accelerometer array in the sensing layer continuously collects vibration acceleration signals from multiple axial measuring points on the outer wall of the pole mold at a set sampling frequency, and transmits them to the data processing layer in real time. The data processing layer performs a Fast Fourier Transform on the acceleration signal at each measuring point to obtain the vibration response spectrum, and identifies the dominant frequency in the spectrum using an amplitude threshold detection algorithm. The main controller compares the current dominant frequency with the pre-calibrated initial dominant frequency under unloaded conditions of the mold, and calculates the spectral drift. Based on the correspondence between frequency drift and filling volume measured at different filling heights in offline experiments, a linear or piecewise linear mapping function is established, and the main controller converts the real-time drift into the real-time filling volume of concrete accordingly.

[0060] Meanwhile, the acoustic emission sensor array in the sensing layer continuously acquires acoustic emission signals from inside the concrete at a center frequency of 200kHz~400kHz and a sampling rate of no less than 1MHz. The main controller filters and thresholds the raw signals, and counts the number of impact events exceeding the threshold in each unit of time, which is recorded as the event rate. The preset event rate threshold is dynamically set according to the concrete mix proportion: the average event rate of the first 10 seconds of initial pouring is measured, and 5%~15% of it is taken as the filling completion threshold.

[0061] When the real-time event rate continuously decreases and falls below the threshold, the main controller initially determines that filling is nearing completion. To ensure reliability, the system uses a comprehensive spectral drift criterion: when the filling amount derived from the drift reaches a preset value and the event rate is simultaneously below the threshold, the main controller issues a definitive filling completion signal; if the two conditions are not simultaneously met, the system continues monitoring and displays the current filling progress on the human-machine interface until both conditions are met. Through this dual vibration-acoustic emission verification, falsely high event rates caused by sudden releases of local bubble clusters due to a single acoustic emission criterion, or drift jumps caused by slight impacts to the mold due to a single spectral criterion, are effectively avoided, ensuring the accuracy and robustness of the filling completion judgment.

[0062] Preferably, the frequency sweep search to determine the optimal resonant frequency includes: The control execution layer uses the current natural frequency identified by modal analysis as the center and performs a low-power linear frequency sweep within a range of ±20 Hz. By monitoring the vibration response amplitude through an array of acceleration sensors in the sensing layer, the frequency point with the largest response amplitude is determined as the optimal resonance frequency.

[0063] Specifically, after the modal analysis module identifies the current natural frequency of the "pole mold-concrete coupling system," the excitation parameter optimization module in the control execution layer initiates a frequency sweep search program. This module uses the identified current natural frequency as the center frequency and performs a linear frequency sweep within a frequency range of ±20 Hz. During the frequency sweep, the excitation system operates in low-power mode, with the sweep power set to 20% to 30% of the rated power, and the sweep rate set to an adjustable 10 Hz to 50 Hz per minute (the specific value is dynamically selected based on the rheological state of the concrete; for example, a slower sweep rate is used when the concrete has high plastic viscosity to ensure sufficient excitation). The sweep direction can be from low frequency to high frequency or from high frequency to low frequency. In this embodiment, a unidirectional sweep from 20 Hz below the center frequency to 20 Hz above the center frequency is preferred to avoid the time delay introduced by the return sweep.

[0064] At each frequency point during the frequency sweep, the accelerometer array in the sensing layer continuously acquires vibration acceleration signals from various measuring points on the outer wall of the pole mold at a high sampling rate, and calculates the vibration response amplitude in real time through the main controller. The control execution layer records the response amplitude corresponding to each frequency point, forming a frequency-response amplitude curve. After the frequency sweep completes the entire 40 Hz bandwidth, the system identifies the frequency point with the largest response amplitude from this curve and determines this frequency point as the optimal resonance frequency for this round of vibration compaction. To ensure the accuracy of the resonance frequency, the system can also use parabolic interpolation to fit three frequency points near the peak value, and use the fitted peak position as the final optimal resonance frequency to overcome the quantization error caused by the discrete frequency sweep step size.

[0065] After determining the optimal resonance frequency, the control execution layer immediately locks the output frequency of the excitation system to this resonance frequency and initiates the subsequent pulsed excitation mode. If, during subsequent compaction, the system's natural frequency drifts due to further compaction of the concrete or changes in the filling state, the modal analysis module will detect this change in real time and trigger a re-scanning frequency search, thereby ensuring that the excitation frequency always tracks the current optimal resonance range. Through the above-mentioned frequency scan search method, the device of this invention can quickly and accurately locate the resonance peak of the mold-concrete coupling system with extremely low energy consumption, providing an accurate frequency reference for the subsequent pulsed resonance maintenance mode, fundamentally solving the problem of ineffective energy dissipation caused by frequency mismatch in traditional devices.

[0066] Preferably, the pulsed excitation mode includes: The control execution layer enables the excitation system to work intermittently according to a set pulse cycle, which is a cycle of 0.3 seconds to 1 second of operation and 2 seconds to 5 seconds of stop, with a duty cycle adjustable from 12.5% ​​to 25%. During pulse conduction, full power output is performed at the optimal resonant frequency; during pulse de-conduction, output is stopped while the sensing layer continues to acquire signals to assess the compactness.

[0067] Specifically, after determining the optimal resonant frequency through frequency sweep search, the partitioned pulse excitation module in the control execution layer immediately initiates the pulsed resonance maintenance mode. This module operates intermittently according to a pre-set pulse period, dynamically adjusted by the main controller based on the rheological state of the concrete. Specifically, the pulse period consists of on-time and off-time: the on-time is set to 0.3 to 1 second, and the off-time to 2 to 5 seconds, corresponding to an adjustable duty cycle of 12.5% ​​to 25%. Initially, the system can automatically select the duty cycle based on the yield stress and plastic viscosity of the concrete. For example, when the concrete has high plastic viscosity, the initial on-time is selected as 0.8 seconds and the off-time as 2 seconds; when the concrete has good fluidity, the on-time is selected as 0.4 seconds and the off-time as 3 seconds. The duty cycle parameters can also be fine-tuned by the operator through the human-machine interface based on the actual vibration effect.

[0068] During pulse conduction, the excitation system outputs full power at the previously determined optimal resonant frequency. Since the excitation frequency matches the natural frequency of the "pole mold-concrete coupling system," the system is in a resonant state. Even with a small percentage of rated power output (e.g., 40%~60%), it can generate a large-amplitude vibration response in the mold and concrete, causing the concrete particles to rearrange and air bubbles to rise and be expelled rapidly. Simultaneously, the vector vibration control module can apply independent amplitude and phase control to the excitation submodules of different sections based on the density differences in each section, achieving differentiated compaction.

[0069] During the pulse shutdown period, the excitation system completely stops outputting, and the mold-concrete system enters a free vibration decay state. At this time, all sensors in the sensing layer continue to acquire signals at the same sampling rate. The accelerometer array records the free vibration decay curve of the mold, which reflects the concrete's ability to absorb vibration energy; the acoustic emission sensor array continues to monitor the weak acoustic emission signals generated inside the concrete due to bubble rupture, particle sliding, etc. The data processing layer uses the data acquired during the shutdown period to evaluate the change trend of density in real time: if the free vibration decay rate gradually slows down, the system can appropriately shorten the conduction time or extend the shutdown time in the next pulse cycle; if the decay rate is very fast, the system can increase the conduction time or shorten the shutdown time.

[0070] Compared to traditional continuous constant-frequency vibration modes, the pulsed excitation mode of this invention significantly reduces power consumption while ensuring effective compaction. Actual measurement data shows that, within the same compaction time, the total input energy of the pulsed mode is only 40% to 60% of that of the continuous mode. Simultaneously, since the exciter completely stops during the shutdown period, its heat generation is significantly reduced, extending the exciter's lifespan and lowering the energy consumption of the cooling system. Furthermore, the vibration-free environment during shutdown provides lower background noise for the acoustic emission sensor, which is beneficial for improving the accuracy of defect prediction.

[0071] Preferably, the step of operating the excitation system in the resonance range of the coupled system using a pulsed excitation mode, and performing data-driven time-adaptive adjustment during the pulse turn-off period, includes: The output of the exciter is completely stopped during the pulse shutdown period, and the free vibration response signal of the pole mold is continuously acquired through the acceleration sensor array in the sensing layer; The attenuation characteristic parameters are extracted from the free vibration response signal. The attenuation characteristic parameters include at least one of the following: the time required for the free vibration amplitude to attenuate from the peak to a predetermined proportion of the initial peak, the attenuation rate of the amplitude during adjacent vibration cycles, and the trend of the main peak frequency during free vibration. Based on the currently extracted decay feature parameters, the upper limit of the density that the system can reach at the end of the current pulse turn-off time is predicted through a pre-trained decay feature-density mapping model. Calculate the on-time of the next pulse cycle and use the calculated on-time as the on-time of the next pulse cycle; When the difference between the predicted upper limit of compaction and the target compaction is less than the set threshold, the system determines that the compaction of that section is complete.

[0072] Specifically, during the operation of the pulsed vibration mode, when the vibrator enters the off state, the system immediately initiates a data-driven time-adaptive adjustment process. The partitioned pulse vibration module in the control execution layer sends a complete stop command to the vibration system, and the electromagnetic vibrator and hydraulic vibrator immediately cut off their output, allowing the mold-concrete system to enter a free vibration state. At the same time, the accelerometer array in the sensing layer continuously collects the free vibration response signals of each measuring point on the outer wall of the pole mold at a high sampling rate (e.g., 10kHz to 20kHz) and transmits the signals to the data processing layer in real time.

[0073] The data processing layer extracts three types of attenuation characteristic parameters from the free vibration response signal: The first type is the time required for the free vibration amplitude to decay from its peak value to a predetermined proportion of its initial peak value. For example, it is the time it takes for the vibration amplitude to decay from its peak value at the start of the shutdown to 1 / e (approximately 36.8%) of that peak value, denoted as [missing information]. The second type is the attenuation rate of amplitude between adjacent oscillation cycles, which is the natural logarithm of the ratio of the amplitudes of two adjacent wave peaks. It reflects the degree of energy dissipation per cycle and is denoted as . The third category is the trend of the main peak frequency during free vibration, which is the slope of the frequency change over time tracked by short-time Fourier transform, denoted as... These characteristic parameters characterize the concrete's current ability to absorb vibration energy from different dimensions—the higher the density, the greater the damping of the concrete. The shorter The larger, The more significant the negative drift, the better.

[0074] The data processing layer pre-trains an attenuation feature-density mapping model. This model can be a multivariate nonlinear regression equation fitted based on measured data, or a lightweight neural network. During training, free vibration attenuation curves during the shut-off period at different compaction stages are collected, the three types of features mentioned above are extracted, and the actual density at that moment after compaction, obtained through detection or simulation, is used as the label. The mapping function is trained using the least squares method or error backpropagation algorithm. During runtime, the data processing layer will use the currently extracted features... , , Input the model and output a predicted value: the upper limit of density that the system can achieve at the end of the current pulse off-time. .

[0075] The main controller calculates the on-time of the next pulse cycle according to the following formula. :

[0076] in, For standard pulse on-time, The target density for the current section, The actual density at the current moment. The above-predicted upper limit of density is given by k, which is an adjustment coefficient ranging from 0.5 to 1.5. To prevent division by zero for small constants.

[0077] The logic of this formula is: if and A significant difference indicates that the system still has considerable compaction potential during the remaining off-time, in which case the conduction time of the next cycle can be appropriately shortened or even the next pulse can be skipped directly; conversely, if... It is close This indicates that relying solely on free vibration is insufficient to further increase density, necessitating an increase in conduction time to input more energy. After the calculation is complete, the main controller will... The signal is sent to the partition pulse excitation module as the conduction time for the next pulse cycle.

[0078] When predicting the upper limit of density Density with the target The difference is less than the set completion threshold (e.g.) When the pulse is applied to a section, the system determines that the vibration compaction is complete and no further pulses are needed. At this point, the main controller marks the section as complete and stops pulse output to that section; if all sections are marked as complete, the entire vibration compaction process ends.

[0079] In this embodiment, through the aforementioned data-driven time-adaptive adjustment, the device of the present invention transforms the free vibration attenuation signal, which was originally considered invalid during the pulse off-time, into a key input for control decisions, achieving intelligent energy-saving control with minimal energy input to reach the target density. Compared to a pulse mode with a fixed duty cycle, this adaptive method can dynamically adjust the conduction time of each pulse according to the actual state of the concrete, avoiding excessive vibration and energy waste, and further reducing total energy consumption by approximately 15% to 25%.

[0080] Preferably, the step of establishing a concrete flow simulation model and predicting void risk areas, segregation risk areas, and bubble aggregation risk areas based on simulation results and spatial positioning data of acoustic emission signals, and generating vibration strategy correction instructions includes: The system receives the concrete yield stress and plastic viscosity output from the rheological parameter inversion, as well as the excitation frequency and amplitude output from the excitation parameter optimization module. Based on the computational fluid dynamics method, a three-dimensional flow simulation model of concrete inside the mold is established, and the density distribution, aggregate volume fraction distribution, and bubble volume fraction distribution of each calculation node are output. Based on the simulation results, the local defect risk index at each spatial location point is calculated. ; Local defect risk index With preset risk threshold Compare; when At that time, mark the point as a defect risk point, and according to The contribution of components identifies the dominant risk type: The formula for calculating the local defect risk index of the spatial location point is as follows:

[0081] in, The local defect risk index at coordinate point x is dimensionless and ranges from 0 to 1. This represents the simulated compaction value at coordinate point x. The target density value is generally set at 95%. The simulated aggregate volume fraction at coordinate point x; The critical aggregate volume fraction for segregation risk is generally set at 60%. The simulated bubble volume fraction at coordinate point x; The critical bubble volume fraction for bubble aggregation risk is generally set at 5%; The normalized event density of the acoustic emission sensor near coordinate point x is obtained by the defect prediction module in the digital twin layer based on the spatial positioning data of the acoustic emission signal, and the value ranges from 0 to 1. These are the weighting coefficients, and The values ​​were set to 0.35, 0.25, 0.25, and 0.15 respectively. The acoustic emission influence factor is set to 2.5 to control the nonlinear contribution of event density to the risk index.

[0082] In this formula, the first term reflects the risk of voids caused by insufficient compaction, the second term reflects the risk of segregation caused by aggregate enrichment, the third term reflects the risk of excessive bubble aggregation, and the fourth term introduces the correction of the simulation results by the measured acoustic emission event density through a nonlinear function. When AE(x) is small, this term is close to 0, and when AE(x) is large, this term approaches δ, avoiding the overshoot of high event density by linear weighting.

[0083] If the void component contributes the most, it is marked as a void risk zone; if the aggregate component contributes the most, it is marked as a segregation risk zone; if the bubble component contributes the most, it is marked as a bubble aggregation risk zone. The spatial distribution of measured acoustic emission events collected by the acoustic emission sensor array is fused and corrected with the risk areas identified by simulation: when the density of measured acoustic emission events in a certain risk area is more than 1.5 times the global average, the risk level of the area is increased by one level; when the density of measured events is less than 0.5 times the global average, the risk level of the area is decreased by one level. The system receives the risk area type, location, and risk level, and generates vibration strategy correction instructions for different defect types: for void risk areas, it generates correction instructions to increase the excitation amplitude and extend the pulse conduction time in that area; for segregation risk areas, it generates correction instructions to reduce the vibration acceleration amplitude and switch to a frequency range higher than the system's natural frequency range; for bubble aggregation risk areas, it generates correction instructions to adopt a high-frequency micro-amplitude vibration mode. The correction command is sent to the excitation parameter optimization module in the control execution layer through the industrial bus network to update the weight allocation in the reinforcement learning reward function and realize closed-loop optimization control.

[0084] The correction command is sent to the excitation parameter optimization module via the industrial bus network. The excitation parameter optimization module adjusts its output excitation frequency, amplitude, sweep rate and pulse period according to the received correction command to realize closed-loop optimization control based on the digital twin layer.

[0085] Specifically, the concrete flow simulation module receives the current concrete yield stress and plastic viscosity from the rheological parameter inversion module, and the real-time excitation frequency and amplitude from the excitation parameter optimization module. This module establishes a three-dimensional flow simulation model of the concrete inside the pole mold based on computational fluid dynamics methods. A finite element mesh was generated based on the three-dimensional geometric model of the mold (height 12m, bottom diameter 350mm, top diameter 190mm, wall thickness gradually decreasing from 30mm at the bottom to 25mm at the top). A hybrid of hexahedral and pentahedral prism elements was used, with the wall thickness direction refined to no more than 10mm. The total mesh count was controlled between 500,000 and 1,000,000. The viscoelastic constitutive relation of the concrete was set, with the yield stress and plastic viscosity directly using the real-time output values ​​from the rheological parameter inversion module, achieving dynamic matching between the simulation model and the actual concrete state. Vibration excitation was applied to the inner wall of the mold as acceleration boundary conditions, using excitation frequency and amplitude as boundary conditions. The acceleration amplitude and phase along the mold axis were set according to the measured distribution. An explicit dynamic algorithm was used to solve the flow and compaction process of concrete under vibration. After the simulation, the module output the density distribution, aggregate volume fraction distribution, and bubble volume fraction distribution for each calculation node.

[0086] Based on the simulation results above, the defect prediction module calculates the local defect risk index R(x) for each spatial location point x using the following formula: Calculated With preset risk threshold (Set to 0.6) for comparison. If If so, mark the point as a defect risk point, and identify the dominant risk type based on the largest of the four contributions: if the void item... The area with the largest contribution is marked as a void risk zone; if the aggregate item The one that contributes the most is marked as the segregation risk zone; if the bubble item The one that contributes the most is marked as the bubble accumulation risk zone; if the acoustic emission correction term contributes the most, then the other three terms are considered together for a comprehensive judgment.

[0087] Subsequently, the spatial distribution of measured acoustic emission events collected by the acoustic emission sensor array is fused and corrected with the risk areas identified by simulation: when the density of measured acoustic emission events in a certain risk area is more than 1.5 times the global average, the risk level of that area is increased by one level; when the density of measured events is less than 0.5 times the global average, the risk level of that area is decreased by one level. The corrected risk area type, location, and risk level are output to the human-computer interaction interface in a three-dimensional visualization format.

[0088] The optimization strategy correction module generates vibration strategy correction instructions based on the received risk area information, targeting different defect types: for void risk areas, it generates instructions to increase the excitation amplitude and extend the pulse conduction time to enhance vibration energy input and promote concrete flow and filling; for segregation risk areas, it generates instructions to reduce the vibration acceleration amplitude and switch to a frequency range higher than the system's natural frequency range, using high-frequency vibration to suppress the separation trend of coarse aggregate and cement paste; for bubble aggregation risk areas, it generates instructions to adopt a high-frequency micro-amplitude vibration mode to promote the dissolution and expulsion of microbubbles. These correction instructions are sent in real-time to the excitation parameter optimization module in the control execution layer via the industrial bus network. Upon receiving the correction instructions, the excitation parameter optimization module immediately adjusts its output excitation frequency, amplitude, sweep rate, and pulse period. For example, it adjusts the amplitude of the void risk area from 3.2mm to 4.0mm and extends the conduction time from 0.5 seconds to 0.65 seconds, or switches the vibration frequency of the segregation risk area from 58Hz to 75Hz and simultaneously reduces the amplitude. The prediction results of the digital twin layer directly affect the decision-making of the control execution layer, realizing closed-loop optimization control of "simulation prediction - strategy correction - execution adjustment → re-simulation verification", effectively avoiding defects such as voids, segregation and bubble aggregation.

[0089] Example 1: The energy-saving concrete forming vibration device and forming method of the present invention were used for the compaction of the electric pole. A mold for a post-tensioned prestressed concrete electric pole was selected for the experiment. The mold was 12 meters high, with a bottom diameter of 350 mm and a top diameter of 190 mm. The concrete mix proportions were: ordinary Portland cement of grade 52.5, with a dosage of 450 kg / m³; graded crushed stone as coarse aggregate, with a maximum particle size of 25 mm; medium-coarse sand as fine aggregate, with a modulus of 2.8; a water-cement ratio of 0.35; and a 1% water-reducing agent. The compaction operation was carried out under ambient temperature of 25 degrees Celsius and relative humidity of 60%. After system initialization, the concrete pouring time was 45 minutes, and the filling volume monitoring and inversion error was controlled within 3%. The rheological parameter inversion module outputs a yield stress of 800 Pa in the initial stage of casting, which gradually increases to 3200 Pa as compaction progresses; the plastic viscosity increases from 180 Pa / s to 620 Pa / s; and the bubble aggregation degree decreases from 65% to 8%. The excitation parameter optimization module uses a reinforcement learning algorithm to find the optimal resonance frequency of 58 Hz and an amplitude of 3.2 mm. The zoned pulse excitation mode uses a cycle of 0.5 seconds working and 3 seconds stopping, with a duty cycle of 14.3%. The total compaction time is 25 minutes, and the average excitation power throughout the process is 28 kW, which is significantly more energy-efficient than the traditional continuous compaction mode. After compaction, the density assessment value of each section reaches over 97%, no void areas are detected inside the mold, the segregation risk area is effectively controlled through strategy correction, and the bubble aggregation area is eliminated through high-frequency micro-amplitude vibration. After demolding, the poles underwent standard curing for 28 days. The compressive strength of a single pole was tested at 58 MPa, and the flexural strength was tested at 8.2 MPa, both of which met the design requirements.

[0090] Example 2: Another batch of utility poles was compacted using the device and method of the present invention. This batch of concrete used a low-strength grade mix to verify the device's adaptability to different concrete conditions. The concrete mix was adjusted to use ordinary Portland cement of grade 42.5, with a dosage of 380 kg / m³ and a water-cement ratio of 0.45. During compaction, the system automatically adjusted the excitation strategy based on the rheological parameter inversion results. Due to the low yield stress of this batch of concrete, the system automatically reduced the amplitude setting to 2.8 mm to avoid segregation. The pulse duty cycle was adjusted to 18% to accommodate the longer compaction time. After compaction, the density assessment value also reached over 97%, indicating that the device of the present invention has good adaptability to different concrete mix proportions.

[0091] Comparative Example 1: A traditional continuous constant-frequency vibration device was used for pole compaction as a comparison. The traditional device employed a single electromagnetic vibrator positioned at the bottom of the mold, with a fixed excitation frequency of 50 Hz and a fixed amplitude of 4 mm, using continuous vibration without pulse interruptions. The concrete mix proportions were the same as in Example 1. During compaction, the excitation power remained at the rated power of 45 kW without any adaptive adjustment function. The compaction time was 32 minutes, 28% longer than in Example 1. The average energy consumption for the entire process was 45 kW multiplied by 0.533 hours, totaling 24 kWh. After compaction, the density of some sections was only 92% to 94%, failing to meet the target value. Inspection after pole demolding revealed slight segregation in the bottom area and air bubble aggregation in the top area. The compressive strength was 51 MPa, and the flexural strength was 7.4 MPa, both lower than in Example 1.

[0092] Comparative Example 2: A semi-intelligent vibration device without digital twin functionality was used for pole compaction. This device has a sensing layer and a data processing layer, capable of rheological parameter inversion and density assessment, but lacks a digital twin layer and zoned vector vibration control. Excitation parameter optimization employs a simple threshold adjustment strategy, lacking reinforcement learning optimization capabilities. During compaction, the system adjusts the overall amplitude based on the density assessment results, but cannot perform differentiated control for specific sections. The compaction took 28 minutes, with an average energy consumption of 35 kWh. After compaction, the density distribution was uneven, reaching 98% at the bottom but only 93% to 95% in the middle and upper parts. The defect prediction function was missing, failing to identify and prevent the risks of segregation and bubble aggregation in advance.

[0093] By comparing and analyzing the embodiments and comparative examples, the advantages of the technical solution of the present invention can be clearly seen. Compared with comparative example one, embodiment one reduces the compaction time by 28%, energy consumption by 37.8%, and significantly improves the uniformity of density. Compared with comparative example two, embodiment one exhibits a more uniform density distribution and stronger defect control capability. The following table summarizes the key performance indicators of the above embodiments and comparative examples:

[0094] The following conclusions can be drawn from the table data: The energy-saving concrete forming vibration device and forming method of the present invention are significantly superior to traditional technical solutions in terms of vibration efficiency, energy consumption level, density uniformity, and mechanical properties. The pulsed resonance maintenance mode significantly reduces energy consumption, reinforcement learning parameter optimization realizes adaptive optimization of the excitation strategy, the digital twin layer realizes defect prediction and prevention, and the partitioned vector vibration control realizes uniform improvement of density.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy-saving concrete forming vibration device for utility poles, comprising a pole mold, a main controller, and a vibration system; characterized in that: The sensing layer is used to collect vibration response signals, excitation power signals, and acoustic emission signals inside the concrete during the compaction process of the pole mold. The data processing layer is used to perform modal analysis, rheological parameter inversion, and density assessment based on the vibration response signal, excitation power signal, and acoustic emission signal, so as to obtain concrete rheological state parameters, mold structure natural frequency, and density assessment results. The control execution layer is used to adjust the excitation frequency, amplitude, sweep rate and pulse period of the excitation system according to the concrete rheological state parameters, the natural frequency of the mold structure and the density evaluation results, so that the excitation system works in the resonance range of the "pole mold-concrete coupling system". The digital twin layer is used to establish a concrete flow simulation model based on the concrete rheological state parameters and excitation parameters, and to predict the void risk area, segregation risk area and bubble aggregation risk area based on the simulation results of the concrete flow simulation model, so as to generate vibration strategy correction instructions. The main controller is connected to the sensing layer, data processing layer, control execution layer, digital twin layer, and excitation system respectively, and is used to realize closed-loop control in the vibration process.

2. The energy-saving concrete forming vibration device for utility poles according to claim 1, characterized in that, The rheological parameter inversion uses an artificial neural network model; The input parameters of the artificial neural network model include excitation power characteristic parameters, vibration response transfer function characteristic parameters, and acoustic emission characteristic parameters; The output parameters of the artificial neural network model include concrete yield stress, plastic viscosity, and bubble aggregation degree; The artificial neural network model is trained using the backpropagation algorithm. The training samples are derived from laboratory test data of concrete with different mix proportions at different compaction stages, and the total number of training samples is no less than five thousand sets.

3. The energy-saving concrete forming vibration device for utility poles according to claim 1, characterized in that, The control execution layer includes: The excitation parameter optimization module is used to receive the yield stress, plastic viscosity, bubble aggregation degree obtained by rheological parameter inversion, and the density evaluation values ​​of each section output in the density evaluation, and output the excitation frequency adjustment amount, amplitude adjustment amount, phase angle adjustment amount, sweep rate and pulse period. The vector vibration control module receives the density assessment values ​​of each section from the density assessment and the excitation parameters output by the excitation parameter optimization module. Based on the deviation between the density of each section and the target value, it generates differentiated excitation commands for different sections. The partitioned pulse excitation module receives the optimal resonance frequency and pulse period output by the excitation parameter optimization module, and realizes frequency sweep search, resonance confirmation, pulse resonance maintenance and dynamic adjustment, and generates pulse excitation commands.

4. The energy-saving concrete forming vibration device for utility poles according to any one of claims 1-3, characterized in that, Also includes: S1: Load the structural parameters of the pole mold and the concrete mix proportion parameters, and collect vibration response signals and acoustic emission signals in real time through the sensing layer; invert the real-time filling amount of concrete based on the spectral drift of the vibration response signal, and determine the filling completion time based on the event rate of the acoustic emission signal. S2: Based on the vibration response signal, excitation power signal and acoustic emission signal collected by the sensing layer, modal analysis is performed to identify the current natural frequency of the "pole mold-concrete coupling system", and rheological parameter inversion is performed to obtain the yield stress, plastic viscosity and bubble aggregation degree of the concrete; based on the natural frequency and rheological parameters, frequency sweep search is performed to determine the optimal resonance frequency, and the excitation system is made to work in the resonance range of the coupling system using pulsed excitation mode; S3: Based on the inversion results of rheological parameters and vibration response signals, calculate the effective vibration energy density, acoustic emission event density and flow resistance index of each section, and weighted fuse them to obtain the compaction evaluation value of each section; establish a concrete flow simulation model, and based on the simulation results and spatial positioning data of acoustic emission signals, predict the void risk area, segregation risk area and bubble aggregation risk area, and generate vibration strategy correction instructions. S4: Based on the density assessment value and defect prediction results, the excitation frequency, amplitude, sweep rate and pulse period are dynamically adjusted by controlling the execution layer. When the density assessment value of all sections reaches the preset threshold, the compaction is determined to be completed and the excitation system is stopped.

5. The energy-saving concrete forming vibration device for utility poles according to claim 4, characterized in that, S1 includes: S11: The main controller loads the structural parameters of the pole mold and the concrete mix proportion parameters, and receives the target forming size of the pole through the human-machine interface; S12: The main controller performs self-tests on the acceleration sensor array, acoustic emission sensor array and excitation system in the sensing layer in sequence to confirm that each component is working properly. S13: The accelerometer array in the sensing layer continuously collects the vibration acceleration signal of the outer wall of the pole mold and transmits it to the data processing layer; the data processing layer performs a fast Fourier transform on the vibration acceleration signal to obtain the vibration response spectrum and identify the main peak frequency in the spectrum; S14: The main controller compares the current main peak frequency with the initial main peak frequency under the unloaded state of the mold, calculates the spectral drift, and inverts the real-time filling amount of concrete according to the pre-calibrated frequency drift-filling amount mapping relationship. S15: The acoustic emission sensor array in the sensing layer continuously collects acoustic emission signals inside the concrete and counts the number of impact events per unit time as the event rate. S16: The main controller compares the real-time event rate with the preset event rate threshold. When the event rate continuously decreases and falls below the threshold, it determines that the concrete filling is complete. When the filling amount corresponding to the spectral drift reaches the preset value and the event rate is lower than the threshold, a filling completion signal is issued. Otherwise, monitoring continues.

6. The energy-saving concrete forming vibration device for utility poles according to claim 4, characterized in that, The frequency sweep search to determine the optimal resonant frequency includes: The control execution layer uses the current natural frequency identified by modal analysis as the center and performs a low-power linear frequency sweep within a range of ±20 Hz. By monitoring the vibration response amplitude through an array of acceleration sensors in the sensing layer, the frequency point with the largest response amplitude is determined as the optimal resonance frequency.

7. The energy-saving concrete forming vibration device for utility poles according to claim 6, characterized in that, The pulsed excitation mode includes: The control execution layer enables the excitation system to work intermittently according to a set pulse cycle. The pulse cycle is a cycle of 0.3 to 1 second of operation and 2 to 5 seconds of stop, with a duty cycle that is adjustable from 12.5% ​​to 25%. During pulse conduction, full power output is performed at the optimal resonant frequency; during pulse de-conduction, output is stopped while the sensing layer continues to acquire signals to assess the compactness.

8. The energy-saving concrete forming vibration device for utility poles according to claim 4, characterized in that, The method of operating the excitation system in the resonance range of the coupled system using a pulsed excitation mode, and performing data-driven time-adaptive adjustment during the pulse turn-off period, includes: The output of the exciter is completely stopped during the pulse shutdown period, and the free vibration response signal of the pole mold is continuously acquired through the acceleration sensor array in the sensing layer; The attenuation characteristic parameters are extracted from the free vibration response signal. The attenuation characteristic parameters include at least one of the following: the time required for the free vibration amplitude to attenuate from the peak to a predetermined proportion of the initial peak, the attenuation rate of the amplitude during adjacent vibration cycles, and the trend of the main peak frequency during free vibration. Based on the currently extracted decay feature parameters, the upper limit of the density that the system can reach at the end of the current pulse turn-off time is predicted through a pre-trained decay feature-density mapping model. Calculate the on-time of the next pulse cycle and use the calculated on-time as the on-time of the next pulse cycle; When the difference between the predicted upper limit of compaction and the target compaction is less than the set threshold, the system determines that the compaction of that section is complete.

9. The energy-saving concrete forming vibration device for utility poles according to claim 4, characterized in that, The process of establishing a concrete flow simulation model and predicting void risk areas, segregation risk areas, and bubble aggregation risk areas based on simulation results and spatial positioning data of acoustic emission signals, and generating vibration strategy correction instructions includes: The system receives the concrete yield stress and plastic viscosity obtained from the rheological parameter inversion, as well as the excitation frequency and amplitude output by the excitation parameter optimization module. Based on the computational fluid dynamics method, it establishes a three-dimensional flow simulation model of concrete inside the mold and outputs the density distribution, aggregate volume fraction distribution and bubble volume fraction distribution of each calculation node. Based on the simulation results, the local defect risk index at each spatial location point is calculated. ; Local defect risk index With preset risk threshold Compare; when At that time, mark the point as a defect risk point, and according to The contribution of components identifies the dominant risk type: If the void component contributes the most, it is marked as a void risk zone; if the aggregate component contributes the most, it is marked as a segregation risk zone; if the bubble component contributes the most, it is marked as a bubble aggregation risk zone. The spatial distribution of measured acoustic emission events collected by the acoustic emission sensor array is fused and corrected with the risk areas identified by simulation: when the density of measured acoustic emission events in a certain risk area is more than 1.5 times the global average, the risk level of the area is increased by one level; when the density of measured events is less than 0.5 times the global average, the risk level of the area is decreased by one level. The system receives the risk area type, location, and risk level, and generates vibration strategy correction instructions for different defect types: for void risk areas, it generates correction instructions to increase the excitation amplitude and extend the pulse conduction time in that area; for segregation risk areas, it generates correction instructions to reduce the vibration acceleration amplitude and switch to a frequency range higher than the system's natural frequency range; for bubble aggregation risk areas, it generates correction instructions to adopt a high-frequency micro-amplitude vibration mode. The correction command is sent to the excitation parameter optimization module through the industrial bus network. The excitation parameter optimization module adjusts its output excitation frequency, amplitude, sweep rate and pulse period according to the received correction command to realize closed-loop optimization control based on the digital twin layer.

10. The energy-saving concrete forming vibration device for utility poles according to claim 9, characterized in that, The formula for calculating the local defect risk index of the spatial location point is as follows: in, The local defect risk index at coordinate point x is dimensionless and ranges from 0 to 1. This represents the simulated density value at coordinate point x. The target density value; The simulated aggregate volume fraction at coordinate point x. The critical aggregate volume fraction for segregation risk; The simulated bubble volume fraction at coordinate point x. The critical bubble volume fraction for the risk of bubble aggregation; The normalized event density of the acoustic emission sensor near coordinate point x is obtained by interpolation of the acoustic emission signal spatial positioning data by the defect prediction module in the digital twin layer. These are the weighting coefficients, and ; The acoustic emission influence factor is set to 2.5 to control the nonlinear contribution of event density to the risk index.

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