Vibration quality online monitoring method and system based on multivariate perception

Abstract

The invention relates to the technical field of concrete vibration, in particular to a vibration quality online monitoring method and a vibration quality online monitoring system based on multi-element perception. Comprehensive real-time sensing of a vibration operation space track, a working posture and an internal excitation state is realized; performing time sequence alignment and fusion processing on the multi-dimensional sensing data to effectively extract characteristic parameter vectors capable of stably representing the interaction state of the vibrating rod and the concrete; the characteristic parameter vectors are input into a vibration quality discrimination model, and after intelligent analysis and calculation, quality discrimination results including under-vibration, over-vibration, vibration leakage and non-compliance of angles are automatically output in real time, so that subjective experience judgment depending on artificial sense is replaced; and finally, according to a quality discrimination result, automatic matching is carried out and specific operation guidance information is generated so that real-time and accurate action guidance is provided for field personnel.

Description

Technical Field

[0001] This invention relates to the field of concrete vibration technology, specifically to an online monitoring method and system for vibration quality based on multi-sensor perception. Background Technology

[0002] Concrete vibration is a crucial process in building construction, as its density directly affects structural strength, durability, and waterproofing performance. Currently, vibration quality control primarily relies on the operator's experience and judgment, assessing quality through methods such as observing the surface slurry, listening to the vibration sound characteristics, and controlling vibration time. Some projects use simple timing devices or marking methods to assist the operation, but none have established an objective, quantitative monitoring system for the entire vibration process.

[0003] However, the above methods have the following drawbacks: due to the lack of real-time monitoring and analysis of the working parameters, spatial position and action trajectory of the vibrator, it is impossible to accurately identify key quality defects such as under-vibration, over-vibration, missed vibration and non-compliant vibration angle; differences in the skill level of operators lead to inconsistent judgment standards and make it difficult to adapt to the process characteristics of concrete with different mix proportions; in noisy and high-intensity construction environments, personnel fatigue further reduces the reliability of quality judgment, resulting in hidden quality hazards inside the concrete.

[0004] The aforementioned deficiencies in existing technologies make it impossible to establish a unified and traceable vibration quality standard system in engineering practice. This results in the quality control of concrete structures relying excessively on individual experience, making it difficult to achieve process data management and precise definition of quality responsibilities. Ultimately, this affects the long-term safety performance and service life of building structures, becoming a key technical bottleneck restricting the improvement of building engineering quality. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for online monitoring of vibration quality based on multi-dimensional perception, in order to solve the technical problem in the prior art that the lack of real-time monitoring and intelligent analysis capabilities of multi-dimensional parameters of the vibration process makes it impossible to objectively and accurately identify quality defects, resulting in excessive reliance on the subjective experience judgment of operators for concrete vibration quality control.

[0006] To achieve the above objectives, the present invention provides a method for online monitoring of vibration quality based on multi-source sensing, comprising the following steps: S1. Real-time acquisition of multi-dimensional sensing data of the vibrator during concrete vibration construction; wherein, the multi-dimensional sensing data includes the vibrator's posture angle data, vibration frequency, vibration amplitude, and the acquired real-time three-dimensional spatial position data of the vibrator in the pouring area. S2. Perform time-series alignment and fusion processing on the multi-dimensional sensing data to extract the feature parameter vector representing the current vibration state; S3. Obtain the pre-constructed vibration quality discrimination model, input the feature parameter vector into the vibration quality discrimination model, calculate and output the quality discrimination result of the current vibration point in real time; the quality discrimination result includes any one of the vibration quality categories of under-vibration, over-vibration, missed vibration, and non-compliant vibration angle; S4. Obtain a standardized operation knowledge base, match the corresponding standardized operation actions according to the quality judgment results, thereby generating operation guidance information and providing operators with standardized operation action guidance in real time; wherein, the standardized operation knowledge base contains the mapping relationship between vibration quality categories and standardized operation actions.

[0007] Furthermore, the step S1 of obtaining the real-time three-dimensional spatial position data of the vibrator within the pouring area specifically includes the following steps: S11. Control the vibrator to periodically broadcast wireless positioning signals; S12. Set up at least three non-collinearly distributed signal receiving stations to receive the wireless positioning signal, and each signal receiving station generates a corresponding original observation data packet; wherein, the original observation data packet includes at least the received signal strength, the signal transmission timestamp, and the local station's reception timestamp; S13. Obtain the original observation data packet. For each signal receiving station, calculate the first distance estimate based on the received signal strength and calculate the second distance estimate based on the time difference between the signal transmission timestamp and the station's reception timestamp. S14. For each of the signal receiving stations, the multiple first distance estimates and multiple second distance estimates obtained in succession are filtered to obtain the corresponding first optimized distance and second optimized distance respectively. S15. Obtain the coordinates of each signal receiving station; based on the coordinates of each signal receiving station and the first optimized distance corresponding to each signal receiving station, obtain the first candidate positioning coordinates through spatial intersection calculation; based on the coordinates of each signal receiving station and the second optimized distance corresponding to each signal receiving station, obtain the second candidate positioning coordinates through spatial intersection calculation. S16. Calculate the distance deviation between the first candidate positioning coordinates and the second candidate positioning coordinates. When the distance deviation is less than a preset fusion threshold, perform weighted fusion on the first candidate positioning coordinates and the second candidate positioning coordinates, and output the fused coordinates as the real-time three-dimensional spatial position data. When the distance deviation is greater than or equal to the fusion threshold, output the second candidate positioning coordinates as the real-time three-dimensional spatial position data.

[0008] Furthermore, step S2 specifically includes the following steps: S21. Receive the multidimensional sensing data from different information sources, wherein the multidimensional sensing data constitutes at least an inertial measurement data stream, a vibration sensing data stream, and a spatial positioning data stream; perform timestamp unification and interpolation synchronization processing on each data stream to generate synchronized data frames with time axis alignment. S22. For each type of sensor data in the synchronized data frame, calculate the data quality evaluation index for each type of sensor data in real time; wherein, the data quality evaluation index includes signal-to-noise ratio, data continuity, and reasonableness of instantaneous changes; S23. Dynamically assign fusion weights to each type of sensor data in the synchronized data frame according to the data quality assessment index; wherein, the higher the data quality assessment index, the greater the assigned fusion weight; S24. According to the assigned fusion weights, the data from different sources but representing the same physical dimension in the synchronized data frame are weighted and fused to generate a fused data vector; S25. Extract time-domain statistical features, frequency-domain transformation features, and cross-dimensional correlation features from the fused data vector, and concatenate and normalize all the extracted features to generate the feature parameter vector representing the current vibration state.

[0009] Furthermore, the vibration quality discrimination model described in step S3 is obtained through the following steps: S31. Perform vibration operation under various working conditions simulating actual construction, and simultaneously collect multi-dimensional training samples composed of the multi-dimensional sensing data to obtain the concrete site appearance state and construction specifications. Assign vibration quality category labels to the concrete site appearance state according to the construction specifications. The vibration quality category labels include under-vibration, missed vibration, over-vibration, non-compliant vibration angle, and compliant vibration. S32. Associate each group of multidimensional training samples with its corresponding vibration quality category label to construct an initial training dataset; S33. Construct a multi-layer feedforward neural network model containing an input layer, at least one hidden layer, and an output layer; wherein the number of nodes in the input layer is consistent with the dimension of the multi-dimensional training samples; the number of nodes in the output layer is consistent with the number of categories of the vibration quality category label, and each node outputs a confidence probability representing belonging to the corresponding vibration quality category; S34. Using the initial training dataset, iteratively train the multilayer feedforward neural network model, optimize the connection weights and bias parameters between each layer in the multilayer feedforward neural network model, until the model loss function converges to a preset threshold, thereby obtaining the vibration quality discrimination model.

[0010] More preferably, step S4 specifically includes the following steps: S41. Obtain a standardized operation knowledge base, wherein the standardized operation knowledge base stores a standard operation guidance text template that uniquely corresponds to each of the vibration quality categories; wherein the standard operation guidance text template includes at least targeted adjustment actions, target parameters and key operation points; S42. After receiving the quality judgment result, the corresponding standard operation guidance text template is retrieved from the standardized operation guidance knowledge base based on the vibration quality category in the quality judgment result as an index. S43. Combining the real-time three-dimensional spatial location data of the current vibration point associated with the quality judgment result, instantiate and fill the retrieved standard operation guidance text template to generate personalized operation guidance information containing specific spatial location indications. S44. Send the personalized operation guidance information to the interactive terminal deployed at the construction site, and drive the interactive terminal to present it to the operator in a preset human-computer interaction form, thereby providing the operator with standardized operation action guidance in real time.

[0011] More preferably, step S44, which involves driving the interactive terminal to present itself to the operator in a preset human-computer interaction format, specifically includes the following steps: S441. The interactive terminal includes a voice broadcast module and a graphic display terminal deployed at the construction site, and sends the personalized operation guidance information to the voice broadcast module and the graphic display terminal. S442. Control the voice broadcast module to convert the personalized operation guidance information into a playable audio stream and broadcast it in real time; S443. On the graphic display terminal, based on the quality discrimination result and the real-time three-dimensional spatial location data of the associated vibration points, the vibration quality distribution heat map is dynamically updated and rendered; wherein, the vibration quality distribution heat map is displayed in different regions with different visual codes to distinguish the different vibration quality categories corresponding to the quality discrimination result; S444. In the heat map of the vibration mass distribution, the spatial areas directly related to the currently broadcast personalized operation guidance information are visually highlighted.

[0012] Furthermore, the following steps are included after step S4: The real-time operations of the operators are acquired and stored as operation records according to the time sequence. Based on a unified time reference, the multi-dimensional sensing data, quality judgment results and operation records are time-aligned to generate a time-synchronized vibration process dataset. Based on the real-time three-dimensional spatial location data, each data point in the time-synchronized vibration process dataset is mapped to its corresponding spatial location coordinates to generate a spatialized vibration quality dataset. Obtain the confidence level corresponding to the quality discrimination result, and configure confidence labels according to the confidence level for each quality discrimination result in the spatialized vibration quality dataset to generate a vibration quality dataset with confidence labels; Based on the vibration quality dataset with confidence labels, a three-dimensional data index structure is constructed using timestamps, spatial coordinates, and quality discrimination results as indexes to generate structured vibration quality index data. Obtain a preset file format template, and encapsulate the structured vibration quality index data into a vibration quality file according to the preset file format template.

[0013] The present invention also provides a multi-sensor-based online monitoring system for vibration quality, which applies the multi-sensor-based online monitoring method for vibration quality as described above, including a data acquisition module, at least three signal receiving stations, and a task execution system; The data acquisition module is installed on the vibrating rod to collect the attitude angle data, vibration frequency and vibration amplitude of the vibrating rod, and send the attitude angle data, vibration frequency and vibration amplitude to the task execution system. The signal receiving station, deployed around the periphery of the pouring area, includes a wireless positioning signal receiving module and a second wireless communication module. The wireless positioning signal receiving module is used to receive the wireless positioning signal and record the corresponding received signal strength and the local station receiving timestamp. The second wireless communication module is used to send the received signal strength, the signal transmission timestamp contained in the wireless positioning signal, and the local station receiving timestamp to the task execution system. The task execution system is connected to the signal receiving station and the data acquisition module. It is used to perform time-series alignment and fusion processing on all received data to extract feature parameter vectors, input the feature parameter vectors into the vibration quality discrimination model to obtain quality discrimination results, and generate operation guidance information based on the quality discrimination results to provide operators with standardized operation action guidance in real time.

[0014] Furthermore, the data acquisition module includes an inertial measurement unit, a vibration sensor, a wireless positioning signal transmitter, and a first wireless communication module; the inertial measurement unit is used to acquire the attitude angle data of the vibrating rod; the vibration sensor is used to acquire the vibration frequency and vibration amplitude of the vibrating rod; the wireless positioning signal transmitter is used to periodically broadcast wireless positioning signals; and the first wireless communication module is used to send the attitude angle data, vibration frequency, and vibration amplitude to the task execution system.

[0015] Furthermore, the task execution system includes a data receiving module, a central processing unit, a voice broadcasting module, and a graphics display terminal; wherein; The data receiving module is used to receive the attitude angle data, vibration frequency and vibration amplitude from the first wireless communication module, as well as the received signal strength, signal transmission timestamp and local station reception timestamp from each of the second wireless communication modules. The central processing unit, connected to the data receiving module, is used to perform time-series alignment and fusion processing on all received data to extract feature parameter vectors, input the feature parameter vectors into the vibration quality discrimination model to obtain quality discrimination results, and generate corresponding personalized operation guidance information based on the quality discrimination results; the quality discrimination results include any one of under-vibration, over-vibration, missed vibration, and non-compliant vibration angle. The voice broadcast module is connected to the central processing unit and is used to receive and broadcast the operation guidance information; The graphic display terminal is connected to the central processing unit and is used to receive the quality judgment result and the associated vibratory rod position information, and to display a heat map of the vibration quality distribution.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves comprehensive real-time perception of the vibration operation's spatial trajectory, working posture, and internal excitation state by synchronously acquiring multi-dimensional sensing data such as the three-dimensional spatial position, posture angle, vibration frequency, and amplitude of the vibrator. Through temporal alignment and fusion processing of this multi-dimensional sensing data, a feature parameter vector that stably characterizes the interaction state between the vibrator and concrete is effectively extracted. This feature parameter vector is then input into a vibration quality discrimination model. After intelligent analysis and calculation, the model automatically and in real-time outputs quality discrimination results, including under-vibration, over-vibration, missed vibration, and non-compliant angles, replacing subjective judgments based on human senses. Finally, based on the quality discrimination results, specific operational guidance information is automatically matched and generated, providing real-time and precise action guidance for on-site personnel. This forms a real-time closed-loop operation process from multi-dimensional data real-time monitoring to intelligent analysis to real-time guidance, significantly improving the objectivity, standardization, and process controllability of vibration quality control. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the online monitoring method for vibration quality based on multi-sensoring according to the present invention. Figure 2 This is a schematic diagram of the model architecture of a vibration quality discrimination model according to an embodiment of the present invention; wherein, A represents the input layer, B represents the hidden layer, and C represents the output layer; Figure 3 This is a schematic diagram of the vibration position positioning in one embodiment of the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0024] As those skilled in the art will understand, the current mainstream methods for monitoring the quality of vibration compaction in the industry are based on human experience and rely on construction workers to assess the vibration effect by listening to the sound and observing the appearance of the concrete. This method lacks objective quantitative indicators, is highly subjective and inconsistent, and its arbitrariness can easily lead to potential quality problems.

[0025] New monitoring technologies developed in recent years still have significant shortcomings. For example, while UWB-TDOA-based positioning monitoring systems can track the vibration location, they cannot automatically issue alarms when parameters are abnormal, resulting in insufficient economic efficiency and practicality. Vibration quality monitoring technologies based on RFID and wireless sensor network technologies require dedicated nodes to be installed inside the vibrator or concrete, significantly increasing construction complexity and cost. Furthermore, sensor signal transmission is affected by concrete shielding, making it difficult to promote and apply on a large scale in practice. Non-destructive testing technologies based on ultrasonic methods are easily affected by aggregate particle size and internal defects, leading to erroneous signals and hindering quantitative assessment and real-time feedback. In addition, these new systems also suffer from high implementation costs, cumbersome usage processes, low intelligence levels, and weak engineering guidance capabilities, making them difficult to integrate into intelligent construction management systems.

[0026] Please see Figures 1 to 3 This embodiment provides a method for online monitoring of vibration quality based on multi-dimensional sensing, including the following steps: S1. Real-time acquisition of multi-dimensional sensing data of the vibrator during concrete vibration construction; wherein, the multi-dimensional sensing data includes the vibrator's posture angle data, vibration frequency, vibration amplitude, and the acquired real-time three-dimensional spatial position data of the vibrator in the pouring area. S2. Perform time-series alignment and fusion processing on the multi-dimensional sensing data to extract the feature parameter vector representing the current vibration state; S3. Obtain the pre-constructed vibration quality discrimination model, input the feature parameter vector into the vibration quality discrimination model, calculate and output the quality discrimination result of the current vibration point in real time; the quality discrimination result includes any one of the vibration quality categories of under-vibration, over-vibration, missed vibration, and non-compliant vibration angle; it is understood that in other embodiments, multiple vibration quality categories may coexist. S4. Obtain a standardized operation knowledge base, match the corresponding standardized operation actions according to the quality judgment results, thereby generating operation guidance information and providing operators with standardized operation action guidance in real time; wherein, the standardized operation knowledge base contains the mapping relationship between vibration quality categories and standardized operation actions.

[0027] Understandably, this embodiment achieves comprehensive real-time perception of the vibration operation's spatial trajectory, working posture, and internal excitation state by synchronously collecting multi-dimensional sensing data such as the three-dimensional spatial position, posture angle, vibration frequency, and amplitude of the vibrator. By performing time-series alignment and fusion processing on these multi-dimensional sensing data, feature parameter vectors that can stably characterize the interaction state between the vibrator and concrete are effectively extracted. These feature parameter vectors are then input into the vibration quality discrimination model. After intelligent analysis and calculation, the model automatically and in real-time outputs quality discrimination results, including under-vibration, over-vibration, missed vibration, and non-compliant angles, replacing subjective experience judgments based on human senses. Finally, based on the quality discrimination results, specific operation guidance information is automatically matched and generated, providing real-time and accurate action guidance for on-site personnel. This forms a real-time closed-loop operation process from real-time monitoring of multi-dimensional data to intelligent analysis to real-time guidance, significantly improving the objectivity, standardization, and process controllability of vibration quality control.

[0028] In one embodiment, the step S1 of obtaining the real-time three-dimensional spatial position data of the vibrator within the pouring area specifically includes the following steps: S11. Control the vibrator to periodically broadcast wireless positioning signals; S12. Set up at least three non-collinearly distributed signal receiving stations to receive the wireless positioning signal, and each signal receiving station generates a corresponding original observation data packet; wherein, the original observation data packet includes at least the received signal strength, the signal transmission timestamp, and the local station's reception timestamp; S13. Obtain the original observation data packet. For each signal receiving station, calculate the first distance estimate based on the received signal strength and calculate the second distance estimate based on the time difference between the signal transmission timestamp and the station's reception timestamp. S14. For each of the signal receiving stations, the multiple first distance estimates and multiple second distance estimates obtained in succession are filtered to obtain the corresponding first optimized distance and second optimized distance respectively. S15. Obtain the coordinates of each signal receiving station; based on the coordinates of each signal receiving station and the first optimized distance corresponding to each signal receiving station, obtain the first candidate positioning coordinates through spatial intersection calculation; based on the coordinates of each signal receiving station and the second optimized distance corresponding to each signal receiving station, obtain the second candidate positioning coordinates through spatial intersection calculation. S16. Calculate the distance deviation between the first candidate positioning coordinates and the second candidate positioning coordinates. When the distance deviation is less than a preset fusion threshold, perform weighted fusion on the first candidate positioning coordinates and the second candidate positioning coordinates, and output the fused coordinates as the real-time three-dimensional spatial position data. When the distance deviation is greater than or equal to the fusion threshold, output the second candidate positioning coordinates as the real-time three-dimensional spatial position data.

[0029] Specifically, the formula for calculating the first distance estimate is as follows: d: First distance estimate; RSSI: Received signal strength (negative); A: Signal strength when the vibrator and the signal receiving station are 1 meter apart; n: Environmental attenuation factor, which is fitted by the d value corresponding to multiple radio values ​​tested on site.

[0030] The formula for calculating the second distance estimate is as follows: D: Second distance estimate; v: Speed ​​of radio waves in the air; Δt: Time difference between the signal transmission timestamp and the local station reception timestamp.

[0031] Understandably, to obtain accurate vibratory rod positions, at least three signal receiving stations are needed to acquire positioning contour lines formed by the possible location distribution of the vibratory rods. The intersection of the three positioning contour lines indicates the position of the vibratory rod. Note that to eliminate data deviations caused by obstructions from personnel, equipment, and other objects on site, data is collected 10 times per second. After removing the maximum and minimum values, the average value is calculated, filtered, and used as the first or second optimized distance.

[0032] More specifically, such as Figure 3 As shown in the figure, the black box represents the concrete pouring area, the positioning point is the current vibration point, and the upper left, upper right, and lower right are three signal receiving stations. The signal receiving stations obtain absolute coordinates through RTK or other positioning methods. The three positioning contour lines passing through the positioning point represent the possible location distribution of the vibrator calculated by each signal receiving station based on the received signal strength or the signal transmission and reception time difference. The intersection of the three positioning contour lines is the position of the vibrator, i.e., the first candidate positioning coordinate or the second candidate positioning coordinate. The positioning contour lines are generated as an arc with the signal receiving station as the center and the first optimized distance or the second optimized distance as the radius.

[0033] By controlling the vibrating rod to periodically broadcast wireless positioning signals, which are received by at least three non-collinearly distributed signal receiving stations, this method ensures spatial coverage and data redundancy of the positioning signals, providing a geometric basis for the solution. For each signal receiving station, a first distance estimate and a second distance estimate are calculated in parallel based on the received signal strength attenuation model and the signal transmission and reception time difference. This dual-mode ranging mechanism comprehensively utilizes different physical characteristics of signal propagation to overcome the limitations of single methods that are susceptible to interference from specific environments. By filtering the multiple continuously acquired distance estimates, instantaneous signal fluctuations and random noise are effectively suppressed, resulting in more stable and reliable first and second optimized distances. Based on the known coordinates and corresponding optimized distances of each signal receiving station, spatial intersection calculations are performed to obtain independent first and second candidate positioning coordinates. Intelligent decision-making is achieved by calculating the distance deviation between two candidate coordinates and comparing it with a preset fusion threshold: when the deviation is small, the two are weighted and fused to combine the advantages of different modes and improve positioning accuracy; when the deviation is large, the second candidate positioning coordinate calculated based on the time difference is selected first, because this mode is usually not sensitive to environmental factors such as occlusion, thus ensuring that the final output of real-time three-dimensional spatial position data in complex construction environments has both high accuracy and high reliability.

[0034] In one embodiment, step S2 specifically includes the following steps: S21. Receive the multidimensional sensing data from different information sources, wherein the multidimensional sensing data constitutes at least an inertial measurement data stream, a vibration sensing data stream, and a spatial positioning data stream; perform timestamp unification and interpolation synchronization processing on each data stream to generate synchronized data frames with time axis alignment. S22. For each type of sensor data in the synchronized data frame, calculate the data quality evaluation index for each type of sensor data in real time; wherein, the data quality evaluation index includes signal-to-noise ratio, data continuity, and reasonableness of instantaneous changes; S23. Dynamically assign fusion weights to each type of sensor data in the synchronized data frame according to the data quality assessment index; wherein, the higher the data quality assessment index, the greater the assigned fusion weight; S24. According to the assigned fusion weights, the data from different sources but representing the same physical dimension in the synchronized data frame are weighted and fused to generate a fused data vector; S25. Extract time-domain statistical features, frequency-domain transformation features, and cross-dimensional correlation features from the fused data vector, and concatenate and normalize all the extracted features to generate the feature parameter vector representing the current vibration state.

[0035] This method receives different data streams from various sensors and performs timestamp unification and interpolation synchronization on each data stream to generate synchronized data frames with precisely aligned timelines. This eliminates temporal misalignment between multi-source heterogeneous data, ensuring the correctness of subsequent fusion and analysis. Furthermore, it calculates data quality assessment indicators such as signal-to-noise ratio, continuity, and reasonableness of variation for each type of sensor data in real time, and dynamically assigns fusion weights to each type of sensor data accordingly. This allows the system to automatically reduce the contribution of data when it is momentarily disturbed or its quality degrades, improving the adaptability and robustness of data processing. Based on dynamic weights, multi-source data representing the same physical dimension are weighted and fused to generate a fused data vector with stronger anti-interference capabilities. By comprehensively extracting time-domain statistical features, frequency-domain transformation features, and cross-dimensional correlation features that reveal the intrinsic relationship between different physical quantities from the fused data vector, and after splicing and normalization, the generated feature parameter vector not only has high information density and low redundancy, but also can more comprehensively and stably characterize the complex state of the interaction between the vibrator and concrete, providing high-quality and highly discriminative input for subsequent intelligent discrimination models.

[0036] In one embodiment, the acquisition of the vibration quality discrimination model in step S3 is achieved by including the following steps: S31. Perform vibration operation under various working conditions simulating actual construction, and simultaneously collect multi-dimensional training samples composed of the multi-dimensional sensing data to obtain the concrete site appearance state and construction specifications. Assign vibration quality category labels to the concrete site appearance state according to the construction specifications. The vibration quality category labels include under-vibration, missed vibration, over-vibration, non-compliant vibration angle, and compliant vibration. S32. Associate each group of multidimensional training samples with its corresponding vibration quality category label to construct an initial training dataset; S33. Construct a multi-layer feedforward neural network model comprising an input layer, at least one hidden layer, and an output layer; wherein the number of nodes in the input layer is adapted to the dimension of the multi-dimensional training samples and is used to receive the multi-dimensional training samples; the number of nodes in the output layer is the same as the number of categories of the vibration quality category label, and each node outputs a confidence probability representing belonging to the corresponding vibration quality category; S34. Using the initial training dataset, iteratively train the multilayer feedforward neural network model, optimize the connection weights and bias parameters between each layer in the multilayer feedforward neural network model, until the model loss function converges to a preset threshold, thereby obtaining the vibration quality discrimination model.

[0037] Specifically, the multi-layer feedforward neural network model used in this embodiment has the following network structure: Figure 2 As shown: The input layers X1, X2…X nThe O1…O values ​​in the output layer represent the dimensions of the multi-dimensional training samples, including vibration amplitude, vibration frequency, and vibratory rod attitude angle. j This represents the confidence probability of the vibration quality category (under-vibration, missed vibration, over-vibration, non-compliant vibration angle, compliant), where the category with the largest output layer value is the quality judgment result. The hidden layer represents the mapping relationship from the input layer to the output layer.

[0038] This method ensures that the training data comprehensively covers key variables in real-world construction. By assigning vibration quality category labels to the data based on the concrete site appearance and construction specifications, it transforms the implicit experience of technical personnel and objective standards into learnable supervisory signals, constructing a high-quality, highly authoritative initial training dataset. The multilayer feedforward neural network model in this method provides a suitable function fitting architecture for the nonlinear mapping from complex multidimensional features to specific quality categories. In this method, the multilayer feedforward neural network model is iteratively trained using the initial training dataset, and the connection weights and bias parameters within the model are continuously adjusted and optimized. This process enables the model to automatically learn and solidify the deep and complex correspondence between multidimensional perceptual data and vibration quality categories, resulting in a final vibration quality discrimination model with the intelligent ability to classify real-time collected vibration state features into specific vibration quality categories with high accuracy and reliability.

[0039] In this embodiment, as a further preferred embodiment, step S4 specifically includes the following steps: S41. Obtain a standardized operation knowledge base, wherein the standardized operation knowledge base stores a standard operation guidance text template that uniquely corresponds to each of the vibration quality categories; wherein the standard operation guidance text template includes at least targeted adjustment actions, target parameters and key operation points; S42. After receiving the quality judgment result, the corresponding standard operation guidance text template is retrieved from the standardized operation guidance knowledge base based on the vibration quality category in the quality judgment result as an index. S43. Combining the real-time three-dimensional spatial location data of the current vibration point associated with the quality judgment result, instantiate and fill the retrieved standard operation guidance text template to generate personalized operation guidance information containing specific spatial location indications. S44. Send the personalized operation guidance information to the interactive terminal deployed at the construction site, and drive the interactive terminal to present it to the operator in a preset human-computer interaction form, thereby providing the operator with standardized operation action guidance in real time.

[0040] The standardized operation knowledge base provides an authoritative and structured instruction foundation. Upon receiving the quality assessment results, it quickly retrieves the corresponding standard operation guidance text template based on the category index, achieving accurate and efficient matching between diagnostic conclusions and standardized solutions. By instantiating and populating the text template with the real-time three-dimensional spatial location data of the current vibration point, personalized operation guidance information containing clear spatial location indications is generated, transforming the guidance content from general principles into specific action instructions for a specific location. Finally, this personalized information is sent to the on-site interactive terminal and presented in a preset format, ensuring that accurate and operable guidance is conveyed to the operators in real time and without errors, thus completing a complete closed loop from intelligent perception and analysis to on-site executable feedback.

[0041] More preferably, step S44, which involves driving the interactive terminal to present itself to the operator in a preset human-computer interaction format, specifically includes the following steps: S441. The interactive terminal includes a voice broadcast module and a graphic display terminal deployed at the construction site, and sends the personalized operation guidance information to the voice broadcast module and the graphic display terminal. S442. Control the voice broadcast module to convert the personalized operation guidance information into a playable audio stream and broadcast it in real time; S443. On the graphic display terminal, based on the quality discrimination result and the real-time three-dimensional spatial location data of the associated vibration points, the vibration quality distribution heat map is dynamically updated and rendered; wherein, the vibration quality distribution heat map is displayed in different regions with different visual codes to distinguish the different vibration quality categories corresponding to the quality discrimination result; S444. In the heat map of the vibration mass distribution, the spatial areas directly related to the currently broadcast personalized operation guidance information are visually highlighted.

[0042] This embodiment achieves simultaneous output of information through both auditory and visual channels by sending the personalized operation guidance information in parallel to the voice broadcast module and the graphic display terminal, ensuring redundancy and reliability of information transmission in complex construction environments. The voice broadcast module converts the guidance information into an audio stream and broadcasts it in real time, providing operators with clear and direct real-time auditory guidance. On the graphic display terminal, a heat map of the vibration quality distribution is dynamically rendered based on the quality judgment results and their location data, and various quality states are displayed in different areas using different visual codes, providing operators with a global and intuitive spatial visualization of the quality situation. Furthermore, spatial areas directly related to the current voice broadcast content are synchronously visually highlighted in the vibration quality distribution heat map, achieving precise spatiotemporal alignment and enhancement of voice commands and visual focus, thereby significantly improving the accuracy, comprehension speed, and execution efficiency of on-site guidance reception.

[0043] In one embodiment, the following steps are included after step S4: The real-time operations of the operators are acquired and stored as operation records according to the time sequence. Based on a unified time reference, the multi-dimensional sensing data, quality judgment results and operation records are time-aligned to generate a time-synchronized vibration process dataset. Based on the real-time three-dimensional spatial location data, each data point in the time-synchronized vibration process dataset is mapped to its corresponding spatial location coordinates to generate a spatialized vibration quality dataset. Obtain the confidence level corresponding to the quality discrimination result, and configure confidence labels according to the confidence level for each quality discrimination result in the spatialized vibration quality dataset to generate a vibration quality dataset with confidence labels; Based on the vibration quality dataset with confidence labels, a three-dimensional data index structure is constructed using timestamps, spatial coordinates, and quality discrimination results as indexes to generate structured vibration quality index data. Obtain a preset file format template, and encapsulate the structured vibration quality index data into a vibration quality file according to the preset file format template.

[0044] In this embodiment, real-time operation records, multi-dimensional sensing data, and quality judgment results are integrated into a time-synchronized dataset through time alignment processing, constructing a complete and orderly event chain of the vibration compaction process. Then, spatial mapping associates each data point with its corresponding coordinates, generating a spatialized quality dataset with clear geographical location information, enabling precise localization of quality issues. By attaching confidence labels derived from the intelligent discrimination model to each quality judgment result, the reliability dimension of the dataset is increased, providing a reliable reference for subsequent analysis and decision-making. A three-dimensional data index structure is constructed based on time, space, and vibration quality categories, enabling efficient and multi-dimensional retrieval of massive process data. The indexed data is encapsulated according to a preset archive format template to generate standardized vibration compaction quality archives, achieving systematic, standardized, and persistent storage of monitoring, judgment, guidance, and operation process data, providing a complete and reliable data foundation for engineering quality traceability, responsibility definition, and continuous optimization.

[0045] The present invention also provides a multi-sensor-based online monitoring system for vibration quality, which applies the multi-sensor-based online monitoring method for vibration quality as described above, including a data acquisition module, at least three signal receiving stations, and a task execution system; The data acquisition module is installed on the vibrating rod to collect the attitude angle data, vibration frequency and vibration amplitude of the vibrating rod, and send the attitude angle data, vibration frequency and vibration amplitude to the task execution system. The signal receiving station, deployed around the periphery of the pouring area, includes a wireless positioning signal receiving module and a second wireless communication module. The wireless positioning signal receiving module is used to receive the wireless positioning signal and record the corresponding received signal strength and the local station receiving timestamp. The second wireless communication module is used to send the received signal strength, the signal transmission timestamp contained in the wireless positioning signal, and the local station receiving timestamp to the task execution system. The task execution system is connected to the signal receiving station and the data acquisition module. It is used to perform time-series alignment and fusion processing on all received data to extract feature parameter vectors, input the feature parameter vectors into the vibration quality discrimination model to obtain quality discrimination results, and generate operation guidance information based on the quality discrimination results to provide operators with standardized operation action guidance in real time.

[0046] In this embodiment, the data acquisition module further includes an inertial measurement unit, a vibration sensor, a wireless positioning signal transmitter, and a first wireless communication module; the inertial measurement unit is used to acquire the attitude angle data of the vibrating rod; the vibration sensor is used to acquire the vibration frequency and vibration amplitude of the vibrating rod; the wireless positioning signal transmitter is used to periodically broadcast wireless positioning signals; and the first wireless communication module is used to send the attitude angle data, vibration frequency, and vibration amplitude to the task execution system.

[0047] In this embodiment, the task execution system further includes a data receiving module, a central processing unit, a voice broadcasting module, and a graphics display terminal; wherein; The data receiving module is used to receive the attitude angle data, vibration frequency and vibration amplitude from the first wireless communication module, as well as the received signal strength, signal transmission timestamp and local station reception timestamp from each of the second wireless communication modules. The central processing unit, connected to the data receiving module, is used to perform time-series alignment and fusion processing on all received data to extract feature parameter vectors, input the feature parameter vectors into the vibration quality discrimination model to obtain quality discrimination results, and generate corresponding personalized operation guidance information based on the quality discrimination results; the quality discrimination results include one of under-vibration, over-vibration, missed vibration, and non-compliant vibration angle. The voice broadcast module is connected to the central processing unit and is used to receive and broadcast the operation guidance information; The graphic display terminal, connected to the central processing unit, receives the quality judgment results and associated vibratory rod position information, and displays a heat map of the vibration quality distribution. In this embodiment, the graphic display terminal is equipped with a touchscreen. The touchscreen can display the acquired vibratory rod positions in real time, forming a vibration trajectory, and distinguishing the quality judgment results according to corresponding colors, ultimately forming a vibration quality heat map. After the vibration operation is completed, clicking the save button on the touchscreen stores the vibration trajectory and the vibration quality heat map. During the operation, the graphic display terminal can record evidence of the on-site operation at any time using the photo and video recording buttons.

[0048] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for online monitoring of vibrating quality based on multi-sensor perception, characterized in that, The method comprises the following steps: S1, collecting multi-dimensional sensing data of a vibrating rod in a concrete vibrating construction process in real time; wherein the multi-dimensional sensing data comprises vibrating rod attitude angle data, vibration frequency, vibration amplitude, and real-time three-dimensional spatial position data of the vibrating rod in a pouring area; S2, performing time sequence alignment and fusion processing on the multi-dimensional sensing data to extract a feature parameter vector representing a current vibrating state; S3, obtaining a pre-constructed vibrating quality discrimination model, inputting the feature parameter vector into the vibrating quality discrimination model, and receiving a quality discrimination result of a current vibrating point output by the vibrating quality discrimination model; the quality discrimination result comprises any one of vibrating quality categories, such as under-vibration, over-vibration, missed vibration, and non-compliant vibration angle; S4, obtaining a standardized operation knowledge base, matching a corresponding standardized operation action according to the quality discrimination result, thereby generating operation guidance information and providing standardized operation action guidance for an operator in real time; wherein the standardized operation knowledge base comprises a mapping relationship between vibrating quality categories and standardized operation actions. 2.The multi-sense-based online monitoring method for vibration quality according to claim 1, characterized in that, The real-time three-dimensional spatial position data of the vibrating rod in the pouring area in step S1 is obtained through the following steps: S11, controlling the vibrating rod to periodically broadcast a wireless positioning signal; S12, setting at least three non-collinear distributed signal receiving stations, receiving the wireless positioning signal through the signal receiving stations, and each signal receiving station generating a corresponding raw observation data packet; wherein the raw observation data packet at least contains a received signal strength, a signal transmission timestamp, and a station receiving timestamp; S13, obtaining the raw observation data packet, and for each signal receiving station, respectively calculating a first distance estimation value according to the received signal strength, and a second distance estimation value according to the time difference between the signal transmission timestamp and the station receiving timestamp; S14, for each signal receiving station, filtering a plurality of first distance estimation values and a plurality of second distance estimation values obtained continuously to obtain corresponding first optimized distances and second optimized distances respectively; S15, obtaining the coordinates of each signal receiving station, and obtaining a first candidate positioning coordinate through space intersection solution according to the coordinates of each signal receiving station and the first optimized distances corresponding to each signal receiving station; obtaining a second candidate positioning coordinate through space intersection solution according to the coordinates of each signal receiving station and the second optimized distances corresponding to each signal receiving station; S16, calculating the distance deviation between the first candidate positioning coordinate and the second candidate positioning coordinate, when the distance deviation is less than a preset fusion threshold, performing weighted fusion on the first candidate positioning coordinate and the second candidate positioning coordinate, outputting the fused coordinate as the real-time three-dimensional spatial position data; when the distance deviation is greater than or equal to the fusion threshold, outputting the second candidate positioning coordinate as the real-time three-dimensional spatial position data. 3.The multi-sense-based online monitoring method of vibration quality according to claim 1, characterized in that, Step S2 specifically comprises the following steps: S21, receiving the multi-dimensional perception data from different sources, the multi-dimensional perception data at least constituting an inertial measurement data stream, a vibration sensing data stream, and a spatial positioning data stream; performing time stamp unification and interpolation synchronization processing on each data stream to generate a time axis aligned synchronized data frame; S22, for each type of sensing data in the synchronized data frame, real-time calculating a data quality evaluation index of each type of sensing data; wherein the data quality evaluation index includes signal-to-noise ratio, data continuity, and instantaneous change rationality; S23, dynamically assigning a fusion weight to each type of sensing data in the synchronized data frame according to the data quality evaluation index; wherein the higher the data quality evaluation index, the greater the assigned fusion weight; S24, weighting and fusing the data from different sources but representing the same physical dimension in the synchronized data frame according to the assigned fusion weight to generate a fusion data vector; S25, extracting time domain statistical features, frequency domain transformation features, and cross-dimension correlation features from the fusion data vector, and concatenating and normalizing all the extracted features to generate a feature parameter vector representing the current vibration state. 4.The multi-sense-based online monitoring method of vibration quality according to claim 1, characterized in that, The vibration quality discrimination model in step S3 is obtained by the following steps: S31, performing vibration operation under multiple working conditions simulating actual construction, synchronously collecting multi-dimensional training samples composed of the multi-dimensional perception data, obtaining the concrete field apparent state and construction specification, and assigning a vibration quality category label to the concrete field apparent state according to the construction specification; the vibration quality category label includes any one of under-vibration, missed-vibration, over-vibration, non-compliant vibration angle, and compliant vibration angle; S32, associating each set of the multi-dimensional training samples with its corresponding vibration quality category label to form an initial training data set; S33, constructing a multi-layer feedforward neural network model including an input layer, at least one hidden layer, and an output layer; wherein the number of nodes of the input layer is consistent with the dimension of the multi-dimensional training samples; the number of nodes of the output layer is consistent with the number of categories of the vibration quality category label, and each node outputs a confidence probability representing belonging to the corresponding vibration quality category; S34, using the initial training data set to iteratively train the multi-layer feedforward neural network model, optimizing the connection weights and bias parameters between layers in the multi-layer feedforward neural network model until the model loss function converges to a preset threshold, thereby obtaining the vibration quality discrimination model.

5. The method of claim 4, wherein the method further comprises: The step S4 specifically includes the following steps: S41, obtaining a standardized operation knowledge base, the standardized operation knowledge base storing a standard operation guidance text template uniquely corresponding to each vibration quality category; wherein the standard operation guidance text template at least includes a targeted adjustment action, a target parameter, and an operation point; S42, after receiving the quality discrimination result, retrieving the corresponding standard operation guidance text template from the standardized operation guidance knowledge base according to the vibration quality category in the quality discrimination result as an index; S43, combine the real-time three-dimensional spatial position data of the current vibrating point associated in the quality discrimination result, instantiate and fill the retrieved standard operation guidance text template, and generate personalized operation guidance information containing specific spatial position indications; S44, send the personalized operation guidance information to an interactive terminal deployed on the construction site, drive the interactive terminal to present to the operator in a preset human-computer interaction form, thereby providing the operator with real-time standardized operation action guidance. 6.The multi-sense-based online monitoring method of vibration quality according to claim 5, characterized in that, The driving of the interactive terminal to present to the operator in a preset human-computer interaction form in step S44 specifically includes the following steps: S441, the interactive terminal includes a voice broadcast module and a graphical display terminal deployed on the construction site, and the personalized operation guidance information is sent to the voice broadcast module and the graphical display terminal; S442, control the voice broadcast module to convert the personalized operation guidance information into a playable audio stream and broadcast it in real time; S443, on the graphical display terminal, dynamically update and render a vibrating quality distribution heat map according to the quality discrimination result and the real-time three-dimensional spatial position data of the current vibrating point associated therewith; wherein the vibrating quality distribution heat map displays different vibrating quality categories corresponding to the quality discrimination result in different regions with different visual coding; S444, in the vibrating quality distribution heat map, the spatial region directly related to the currently broadcast personalized operation guidance information is synchronously visually highlighted. 7.The multi-sense-based online monitoring method of vibration quality according to claim 1, wherein, After step S4, the following steps are further included: Obtain real-time operations of the operator, store the real-time operations as operation records in time sequence, perform time alignment processing on the multi-dimensional perception data, quality discrimination result and operation record based on a unified time reference, and generate time-synchronized vibrating process data set; Map each data in the time-synchronized vibrating process data set to the spatial position coordinates corresponding thereto according to the real-time three-dimensional spatial position data, and generate spatialized vibrating quality data set; Obtain the confidence degree corresponding to the quality discrimination result, assign a confidence degree label to each quality discrimination result in the spatialized vibrating quality data set according to the confidence degree, and generate vibrating quality data set with confidence degree label; According to the vibrating quality data set with confidence degree label, construct a three-dimensional data index structure with time stamp, spatial coordinates and quality discrimination result as index, and generate structured vibrating quality index data; Obtain a preset archive format template, and encapsulate the structured vibrating quality index data into a vibrating quality archive according to the preset archive format template.

8. A multi-sensing based online monitoring system for quality of vibration, applying the multi-sensing based online monitoring method for quality of vibration according to any one of claims 1-7, characterized in that, The data acquisition module, at least three signal receiving stations, and a task execution system are included. The data acquisition module is installed on the vibrating rod and is used to collect attitude angle data of the vibrating rod, vibration frequency and vibration amplitude of the vibrating rod, and send the attitude angle data, vibration frequency and vibration amplitude to the task execution system. The signal receiving station is arranged at the periphery of the pouring area and comprises a wireless positioning signal receiving module and a second wireless communication module; the wireless positioning signal receiving module is configured to receive the wireless positioning signal and record corresponding received signal strength and a local station receiving timestamp; and the second wireless communication module is configured to send the received signal strength, a signal transmission timestamp contained in the wireless positioning signal and the local station receiving timestamp to the task execution system; The task execution system is connected with the signal receiving station and the data collection module, configured to perform time sequence alignment and fusion processing on all received data to extract a feature parameter vector, input the feature parameter vector into a vibration quality discrimination model to obtain a quality discrimination result, and generate operation guidance information according to the quality discrimination result and provide standardized operation action guidance for an operator in real time.

9. The multi-sensor based online monitoring system for quality of vibration according to claim 8, wherein, The data collection module comprises an inertial measurement unit, a vibration sensor, a wireless positioning signal transmitter and a first wireless communication module; the inertial measurement unit is configured to collect attitude angle data of the vibrating rod; the vibration sensor is configured to collect vibration frequency and vibration amplitude of the vibrating rod; the wireless positioning signal transmitter is configured to periodically broadcast wireless positioning signals; and the first wireless communication module is configured to send the attitude angle data, vibration frequency and vibration amplitude to the task execution system. 10.The multi-sense-based online monitoring system for vibration quality according to claim 9, wherein, The task execution system comprises a data receiving module, a central processing unit, a voice broadcast module and a graphic display terminal. The data receiving module is configured to receive the attitude angle data, vibration frequency and vibration amplitude from the first wireless communication module, and the received signal strength, signal transmission timestamp and local station receiving timestamp from each second wireless communication module; The central processing unit is connected with the data receiving module and configured to perform time sequence alignment and fusion processing on all received data to extract a feature parameter vector, input the feature parameter vector into a vibration quality discrimination model to obtain a quality discrimination result, and generate corresponding individualized operation guidance information according to the quality discrimination result; the quality discrimination result comprises any one of under-vibration, over-vibration, missed vibration and non-compliance of vibration angle; The voice broadcast module is connected with the central processing unit and configured to receive and broadcast the operation guidance information; The graphic display terminal is connected with the central processing unit and configured to receive the quality discrimination result and associated vibrating rod position information, and display a vibrating quality distribution heat map. ​

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