An ultrahigh-rise concrete pumping quality control intelligent algorithm and system based on acoustic-vibration visual coupling

Abstract

The application discloses a kind of based on acoustic vibration vision coupling's super high-rise concrete pumping quality control intelligent algorithm and system, the algorithm includes in super high-rise building simultaneously collecting the sound, vibration and visual signal in the process of concrete pumping and respectively carries out data preprocessing and parameter feature extraction, the dynamic analysis of the transport time lag feature of the running state in the process of concrete pumping, establishes coupling analysis model and calculates concrete pumping comprehensive state evaluation index, the change trend of analysis comprehensive abnormal index with time, and combined with the transport continuity change rate and the transport time growth rate establish pipe block risk prediction model, according to the pipe block risk index size predicted by model, the concrete pumping state is divided into normal state, attention state, early warning state and dangerous state four grades and intelligent output early warning information.The application can intelligently analyze and control concrete pumping quality, improve pumping state recognition accuracy and pumping efficiency, realize the early warning of pumping abnormal state.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring of building construction and quality control of concrete pumping, and in particular to an intelligent algorithm and system for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling. Background Technology

[0002] During the construction of super high-rise buildings, concrete is typically transported to the construction site via long-distance pump pipes using a high-pressure pumping system. As building height increases, the pumping distance and height also increase significantly, leading to more complex changes in pumping pressure, pipe resistance, and concrete state. This can easily result in problems such as pipe blockage, segregation, flow interruption, or unstable pumping. Pipe blockage can not only affect construction progress but also potentially cause equipment damage or even safety accidents. Therefore, real-time monitoring and quality control of the concrete pumping process are of paramount importance.

[0003] In existing technologies, monitoring the pumping status of concrete mainly relies on manual experience or single-parameter monitoring. For example, construction workers observe the continuity of discharge from the outlet, listen to the operating sound of the pumping equipment, or monitor changes in pumping pressure using pressure sensors to determine the pumping status. However, judging the risk of pipe blockage solely based on pressure changes may lead to misjudgments due to fluctuations in concrete slump or changes in pumping rhythm. Relying solely on video images is easily affected by lighting, obstructions, and interference from the construction environment. Some systems install pressure and flow sensors on the pumping pipeline or pump to analyze pumping conditions by collecting relevant data. However, these methods often rely on only a single information source and do not comprehensively reflect the actual delivery status of concrete. When the pumping environment is complex or the concrete state changes, inaccurate judgments or delayed responses are likely to occur. When concrete segregation, abnormal slump, flow interruption, or pipe blockage occurs, existing methods often fail to identify these issues promptly and accurately, easily leading to misjudgments or delayed warnings, thus affecting construction efficiency and project quality.

[0004] Therefore, existing technologies still fall short in terms of accuracy, real-time performance, and multi-source information collaborative analysis capabilities for pumping status identification, making it difficult to meet the high reliability and intelligent quality control requirements in concrete pumping construction of super high-rise buildings. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent algorithm and system for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling. This addresses the problem that traditional single-parameter monitoring methods for judging concrete changes during concrete pumping in super high-rise buildings are prone to inaccurate judgments or delayed responses, thus affecting construction efficiency and project quality. Furthermore, this invention addresses the shortcomings of existing technologies in terms of accuracy, real-time performance, and multi-source information collaborative analysis capabilities for concrete state identification during pumping in super high-rise buildings, making it difficult to meet the high reliability and intelligent quality control requirements of super high-rise concrete pumping construction.

[0006] To solve the above-mentioned technical problems, the technical solution provided by this invention is: an intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling, comprising:

[0007] Step S1, Multi-source data synchronous acquisition: Simultaneously acquire sound, vibration and visual signals during the concrete pumping process in super high-rise buildings;

[0008] Step S2, Data Preprocessing: Preprocess the collected sound, vibration and visual signals respectively;

[0009] Step S3, Feature Extraction: Extract parameter features from the sound, vibration, and visual data respectively;

[0010] Step S4, Delivery Status Time Delay Analysis: Dynamically analyze the delivery time delay characteristics of the operating status during the concrete pumping process;

[0011] Step S5, Establish a coupled analysis model: Establish a coupled analysis model between sound characteristics, vibration characteristics, visual characteristics, and delivery time delay characteristics, and calculate the comprehensive state evaluation index of concrete pumping:

[0012] (1);

[0013] In Equation (1), EI represents the comprehensive evaluation index of concrete pumping status, A represents the sound abnormality characteristic value, V represents the vibration abnormality characteristic value, I represents the visual abnormality characteristic value, T represents the delivery time delay abnormality characteristic value, and ω1, ω2, ω3, and ω4 are the corresponding weight coefficients.

[0014] The changing trends of comprehensive abnormal indicators in concrete pumping over time were analyzed, and a pipe blockage risk prediction model was established by combining the rate of change of continuous conveying and the rate of increase of conveying time.

[0015] (7);

[0016] In equation (7), R represents the pipe blockage risk index; EI represents the comprehensive evaluation index of concrete pumping status. This indicates the rate of change of comprehensive abnormal indicators; This indicates the growth rate of delivery time; C represents the continuity index of concrete discharge.

[0017] Step S6, Intelligent Early Warning:

[0018] Based on the magnitude of the pipe blockage risk index, the concrete pumping status is divided into four levels: normal, attention, warning, and danger, and corresponding warning information is sent to the construction site.

[0019] Furthermore, in the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention, the data preprocessing method in step S2 includes:

[0020] For audio signals, filtering and noise reduction methods are used to eliminate mechanical noise and environmental noise interference at the construction site, and spectrum transformation and signal enhancement are performed.

[0021] For vibration signals, denoising, zero-drift correction, and frequency decomposition are performed to improve the stability of vibration feature extraction.

[0022] For visual signals, image enhancement, brightness compensation, target region extraction, and continuous frame calibration are performed sequentially to reduce the impact of dust, lighting changes, and camera shake on the recognition results.

[0023] At the same time, time synchronization between sound data, vibration data and visual data is completed based on a unified timestamp, so that data from different sources can correspond to the operating status of the same pumping process.

[0024] Furthermore, in the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention, the feature extraction method in step S3 includes:

[0025] Sound parameter features are extracted from the sound data to reflect the changes in material state and conveying stability during concrete flow. The sound parameter features include sound pressure change rate, dominant frequency change, spectral energy distribution, and high-frequency energy proportion.

[0026] Vibration parameter features are extracted from vibration data to reflect the stability of equipment operation and changes in conveying resistance. The vibration features include the root mean square value, peak value, dominant frequency, and pulse fluctuation index.

[0027] Visual parameter features are extracted from visual data to evaluate the continuous flow capability during concrete conveying. These visual parameter features include parameters such as the continuity of concrete feeding, the continuity of discharge, the duration of flow interruption, the rate of change of flow area, and the flow velocity.

[0028] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention includes, in step S4, the method for time delay analysis of the conveying state as follows:

[0029] During continuous concrete pumping, tracer markers with distinct color or shape characteristics are placed into the concrete at fixed time intervals. Visual acquisition devices at the inlet and outlet positions are used to identify the time when the tracer markers enter and leave the conveying system, calculate the actual concrete conveying time, and dynamically analyze the conveying time lag characteristics of the concrete pumping process.

[0030] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention calculates the sound anomaly feature value according to formula (2) in step S5:

[0031] (2);

[0032] In equation (2), A represents the sound abnormality characteristic value; X i X represents the i-th real-time sound feature parameter; io X represents i The corresponding normal reference value; m represents the number of sound feature parameters; Let be the weight coefficients of each feature parameter, and satisfy . .

[0033] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention calculates the vibration anomaly characteristic value according to formula (3) in step S5:

[0034] (3);

[0035] In equation (3), V represents the vibration anomaly characteristic value; RMS represents the root mean square value of vibration; P represents the vibration peak value; K is the kurtosis; β1, β2, and β3 are the weighting coefficients, respectively.

[0036] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention calculates the visual anomaly feature value according to formula (4) in step S5:

[0037] (4);

[0038] In equation (4), I represents the visual anomaly feature value; Z i Z represents the i-th real-time visual feature parameter; io Z represents i The corresponding normal reference value; γ i Let be the weighting coefficients for each vibration parameter, and satisfy . k represents the number of visual feature parameters.

[0039] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention calculates the abnormal characteristic value of delivery time delay according to formula (5) in step S5:

[0040] (5);

[0041] In equation (5), T represents the abnormal characteristic value of the transport time delay; t represents the current transport time; t o Reference conveying time under normal operating conditions.

[0042] Furthermore, the intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided by the present invention calculates the abnormal characteristic value of the delivery time delay according to formula (6) in step S5:

[0043] (6);

[0044] In equation (6), T represents the abnormal characteristic value of the delivery time delay; t i The time of the i-th tracer transmission; N is the statistical period.

[0045] To address the aforementioned problems, this invention also provides an intelligent quality control system for ultra-high-rise concrete pumping based on acoustic-vibration-visual coupling, employing the aforementioned intelligent quality control algorithm for ultra-high-rise concrete pumping based on acoustic-vibration-visual coupling, including:

[0046] The data acquisition module is used to simultaneously collect sound, vibration, and visual signals during the concrete pumping process in super high-rise buildings.

[0047] The data processing module is used to preprocess and extract parameter features from the collected sound, vibration, and visual signals, and to dynamically analyze the delivery time lag characteristics of the concrete pumping process. A coupled analysis model is established using sound, vibration, visual, and delivery time lag characteristics to calculate the comprehensive evaluation index of the concrete pumping status. Based on the comprehensive evaluation index and combined with the rate of change of delivery continuity and the rate of increase of delivery time, a blockage risk prediction model is established. The blockage risk prediction model is used to calculate the blockage risk index. Based on the magnitude of the blockage risk index, the concrete pumping status is divided into four levels: normal, watch-out, warning, and dangerous. Corresponding warning information is then output to the construction site, enabling intelligent early warning of the concrete pumping status in super high-rise buildings.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] This invention provides an intelligent algorithm and system for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling. It synchronously collects sound, vibration, and visual signals during the concrete pumping process in super high-rise buildings, performs data preprocessing and parameter feature extraction, and constructs a multi-source coupling analysis model using sound, vibration, visual, and delivery time delay characteristics. This model, combined with the rate of change of delivery continuity and the rate of increase of delivery time, establishes a blockage risk prediction model, enabling intelligent monitoring, status identification, and blockage risk prediction throughout the entire concrete pumping process. This allows for intelligent analysis of concrete pumping quality in super high-rise buildings, i.e., quality control analysis, which helps to promptly detect abnormal concrete conditions and equipment malfunctions, improving the stability and reliability of pumping operations. Compared to traditional methods that rely solely on pressure, flow rate, or single signal monitoring, this approach provides a more comprehensive reflection of the pumping system's operating status, significantly improving the accuracy of identifying abnormal pumping conditions. By analyzing the continuity of concrete discharge, changes in pumping sound, and equipment vibration characteristics in real time, it can promptly identify abnormal situations such as concrete flow interruption, pumping instability, and blockage risks, and issue early warnings. Intelligent monitoring and early warning mechanisms can reduce downtime and equipment maintenance time caused by abnormal situations such as pipe blockage and flow interruption, thereby improving the efficiency of concrete pumping construction and reducing construction risks. Attached Figure Description

[0050] Figure 1 This is a flowchart of the steps of an intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling.

[0051] Figure 2 This is a flowchart illustrating the intelligent monitoring of pumped concrete in super high-rise buildings using an intelligent system for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling. Detailed Implementation

[0052] The present invention will now be described in detail with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0053] Please refer to Figure 1 This invention provides an intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling, comprising the following steps:

[0054] Step S1, Multi-source Data Synchronous Acquisition: This involves synchronously acquiring sound, vibration, and video signals during the concrete pumping process in high-rise buildings. Specifically, during construction, sound acquisition devices, vibration acquisition devices, and industrial vision acquisition devices are deployed along the concrete pumping path. These devices can be placed at key locations on the concrete pump truck, conveying pipeline, and at the concrete inlet and outlet to synchronously monitor the entire concrete pumping process. The sound acquisition devices collect the flow sounds generated during concrete transport and the operating sounds of the equipment, recording the sound changes over different time periods to reflect the flow state of the concrete within the pipeline. The vibration acquisition devices are installed on the outer walls of the pumping equipment and conveying pipeline to acquire vibration signals generated during equipment operation in real time, reflecting changes in equipment load and pipeline transport status. The vision acquisition devices are deployed at the concrete inlet and outlet locations to collect video of concrete transport and record continuous changes, flow velocity changes, and discharge status changes during the concrete flow process. All acquisition devices complete synchronous data acquisition according to a unified time reference, providing a unified data foundation for subsequent multi-source fusion analysis.

[0055] Step S2, Multi-source Data Preprocessing: The acquired sound, vibration, and video signals are preprocessed separately. For sound signals, filtering and noise reduction methods are used to eliminate mechanical noise and environmental noise interference from the construction site, and spectrum transformation and signal enhancement are performed. For vibration signals, denoising, zero-drift correction, and frequency decomposition are performed to improve the stability of vibration feature extraction. For visual signals, image enhancement, brightness compensation, target region extraction, and continuous frame calibration are performed sequentially to reduce the impact of dust, lighting changes, and camera shake on the recognition results. Simultaneously, time synchronization is achieved between sound, vibration, and visual data using a unified timestamp, ensuring that data from different sources correspond to the same operating state during the pumping process.

[0056] Step S3, Feature Extraction: Parameter features are extracted from the sound, vibration, and visual data. Sound features include sound pressure change rate, dominant frequency change, spectral energy distribution, and high-frequency energy proportion, used to reflect changes in material state and conveying stability during concrete flow. Vibration features include root mean square value, peak value, dominant frequency, and impulse fluctuation index, used to reflect equipment operating stability and changes in conveying resistance. Visual features include parameters such as concrete feeding continuity, discharge continuity, flow interruption duration, flow area change rate, and flow velocity, used to evaluate the continuous flow capability of concrete during conveying. These feature parameters together constitute a multi-dimensional description of the concrete pumping state.

[0057] Step S4, Time Lag Analysis of Conveying Status: This step dynamically evaluates the operational status of concrete during the conveying process. During continuous concrete pumping, tracer markers with distinct color or shape characteristics are placed into the concrete at fixed time intervals. Visual acquisition devices at the inlet and outlet positions are used to identify the entry and exit times of these tracer markers, calculating the actual concrete conveying time and dynamically analyzing the time lag characteristics of the concrete's operational status during pumping. When the conveying time gradually increases over multiple consecutive conveying cycles, it indicates a continuous increase in the concrete's resistance within the conveying pipeline. Combined with the increased high-frequency energy in the sound signal and enhanced load fluctuations in the vibration signal, this further indicates potential abnormalities such as decreased workability, aggregate aggregation, slurry loss, or localized blockages. This allows for early identification of pipe blockage risks, rather than triggering an alarm only after blockage occurs.

[0058] Step S5, Establish a Coupled Analysis Model: Establish a coupled analysis model among sound features, vibration features, visual features, and delivery time delay features to improve the accuracy of concrete pumping anomaly identification. This model comprehensively considers the correlation and consistency between different data sources, performs unified normalization on various features, assigns corresponding weights according to the importance of different features, and calculates the comprehensive state evaluation index of concrete pumping. This model can be expressed as:

[0059] (1);

[0060] In equation (1), EI represents the comprehensive evaluation index of concrete pumping status, A represents the sound abnormality characteristic value, V represents the vibration abnormality characteristic value, I represents the visual abnormality characteristic value, T represents the delivery time delay abnormality characteristic value, and ω1, ω2, ω3, and ω4 are the corresponding weight coefficients, which satisfy: ω1+ω2+ω3+ω4=1.

[0061] Each weighting coefficient can be adaptively adjusted based on historical construction data, different pumping conditions, and model training results, so that the comprehensive evaluation index can more accurately reflect the actual pumping status.

[0062] The sonic anomaly feature value is used to characterize the degree of deviation of the flow sound during concrete pumping from the normal working state. The system first performs bandpass filtering and environmental noise suppression on the collected sound signal, and then segments the continuous sound data according to a fixed time window. Subsequently, characteristic parameters such as sound pressure level, dominant frequency, spectral energy distribution, high and low frequency energy ratio, and spectral centroid are extracted for each time window. Using the baseline characteristics established during the normal pumping stage as a reference, each characteristic parameter is normalized. Because concrete has a relatively stable flow rhythm during normal transportation, its sound characteristics change relatively smoothly over time. When the workability of the concrete decreases, coarse aggregate accumulates, slurry is lost, or local transportation resistance increases, the flow state changes, and the sound signal exhibits phenomena such as enhanced high-frequency energy, dominant frequency drift, and increased sound pressure fluctuations. Therefore, the sonic anomaly feature value is constructed by comprehensively considering the deviation of each sound characteristic parameter to quantitatively characterize the degree of sound anomaly during concrete transportation. Its calculation expression can be expressed as:

[0063] (2);

[0064] In equation (2), A represents the sound abnormality characteristic value; X i X represents the i-th real-time sound feature parameter; io X represents i The corresponding normal reference value; m represents the number of sound feature parameters; Let be the weight coefficients of each feature parameter, and satisfy . When the abnormal sound characteristic value continues to increase, it indicates that the flow state of the concrete during transportation is gradually deviating from the normal operating conditions, which can serve as an important basis for judging pumping abnormalities.

[0065] The vibration anomaly characteristic values ​​are used to reflect the equipment operating status and changes in conveying resistance during concrete pumping. The system first filters and denoises the collected vibration signals, performs zero-drift correction, and performs frequency domain analysis to extract characteristic parameters such as the root mean square value of vibration, peak value of vibration, rate of change of dominant frequency, spectral energy, and pulse fluctuation index. Under normal operating conditions, the vibration of the pumping equipment and conveying pipeline exhibits good periodicity and stability; however, as the concrete conveying resistance increases, pumping pressure fluctuates, or local blockages form, the equipment load changes, and the corresponding vibration characteristics will also change significantly. To comprehensively evaluate the equipment operating status, this invention normalizes each vibration characteristic parameter and constructs vibration anomaly characteristic values, the calculation expression of which can be expressed as:

[0066] (3);

[0067] In equation (3), V represents the vibration anomaly characteristic value; RMS represents the root mean square value of vibration; P represents the vibration peak value; K is the kurtosis; β1, β2, and β3 are weighting coefficients, satisfying β1+β2+β3=1. The vibration anomaly characteristic value can comprehensively reflect the equipment's operational stability and the changes in conveying resistance, and complements the sound anomaly characteristic, improving the reliability of anomaly identification.

[0068] The visual anomaly feature values ​​are used to evaluate the flow continuity and discharge status changes during concrete conveying. First, image enhancement, target region extraction, and continuous frame motion analysis are performed on the acquired video images to segment and identify the concrete flow area, and feature parameters such as concrete flow area, flow velocity, flow interruption duration, and discharge continuity are calculated. Since concrete has a continuous and stable flow state during normal pumping, the visual feature parameters remain relatively stable. When concrete segregation, decreased fluidity, or local blockage occurs, phenomena such as reduced flow area, decreased flow velocity, increased flow interruption frequency, and decreased continuity will appear in the video images. Based on these changes, this invention establishes visual anomaly feature values, the calculation expression of which can be expressed as:

[0069] (4);

[0070] In equation (4), I represents the visual anomaly feature value; Z i Z represents the i-th real-time visual feature parameter; io Z represents i The corresponding normal reference value; γ i Let be the weighting coefficients for each vibration parameter, and satisfy . ;k represents the number of visual feature parameters. Visual anomaly feature values ​​mainly reflect the actual delivery state of concrete and can be cross-validated with sound and vibration analysis results to reduce misjudgments caused by environmental interference.

[0071] The abnormal transport time lag characteristic value is used to describe the change in the operating efficiency of concrete within the transport pipeline. In this invention, tracer tags with distinct identification features are placed into the concrete at set time intervals during continuous pumping. Visual acquisition devices at the inlet and outlet record the time the tracer tags enter and leave the transport system, respectively, to obtain the actual concrete transport time. The transport time for multiple transport cycles is continuously recorded, and the trend of transport time variation is analyzed. When the transport time continuously increases, it indicates that the operating resistance of the concrete during transport is gradually increasing, which may indicate aggregate accumulation, slurry loss, increased pipe wall friction, or local blockage. To quantify the change in transport efficiency, this invention constructs an abnormal transport time lag characteristic value, the calculation expression of which can be expressed as:

[0072] (5);

[0073] In equation (5), T represents the abnormal characteristic value of the transport time delay; t represents the current transport time; t o The reference conveying time under normal operating conditions. When the abnormal characteristic value of conveying time delay continues to rise for multiple consecutive cycles, the system further combines the abnormal characteristic values ​​of sound, vibration and vision to conduct a comprehensive analysis to determine whether the conveying resistance is showing a continuous increasing trend, and serves as an important basis for predicting the risk of pipe blockage.

[0074] The trend growth rate can also be used:

[0075] (6);

[0076] In equation (6), T represents the abnormal characteristic value of the delivery time delay; t i The time of the i-th tracer transmission; N is the statistical period.

[0077] Based on the comprehensive state evaluation index of concrete pumping, the changing trend of comprehensive abnormal indicators over time can be further analyzed, and a pipe blockage risk prediction model can be established by combining the rate of change of conveying continuity, the rate of increase of conveying time, and the consistency of multi-source information. For cases where the comprehensive abnormal indicators continuously increase within a continuous time window, accompanied by increased conveying time lag, decreased discharge continuity, and enhanced vibration anomalies, it is determined that pipe blockage risk is gradually forming, and the pipe blockage risk index can be calculated, its expression being:

[0078] (7);

[0079] In equation (7), R represents the pipe blockage risk index; EI represents the comprehensive evaluation index of concrete pumping status; This indicates the rate of change of comprehensive abnormal indicators; denoted by , which represents the growth rate of delivery time; C represents the continuity index of concrete output.

[0080] Step S6, Intelligent Early Warning:

[0081] Based on the blockage risk index, the concrete pumping status is divided into four levels: normal, watch out, warning, and dangerous. Corresponding warning information is then sent to the construction site. Pumping quality evaluation results can be automatically generated based on the risk level, and model parameters are continuously updated using historical operating data to achieve adaptive analysis for different concrete mix proportions, different conveying heights, and different construction conditions.

[0082] Please refer to Figure 2 This invention also provides an intelligent quality control system for super high-rise concrete pumping based on acoustic-vibration-visual coupling, employing the aforementioned intelligent quality control algorithm for super high-rise concrete pumping based on acoustic-vibration-visual coupling, including:

[0083] The data acquisition module is used to simultaneously collect sound, vibration, and visual signals during the concrete pumping process in super high-rise buildings.

[0084] The data processing module is used to preprocess and extract parameter features from the collected sound, vibration, and visual signals, and to dynamically analyze the delivery time lag characteristics of the concrete pumping process. A coupled analysis model is established using sound, vibration, visual, and delivery time lag characteristics to calculate the comprehensive evaluation index of the concrete pumping status. Based on the comprehensive evaluation index and combined with the rate of change of delivery continuity and the rate of increase of delivery time, a blockage risk prediction model is established. The blockage risk prediction model is used to calculate the blockage risk index. Based on the magnitude of the blockage risk index, the concrete pumping status is divided into four levels: normal, watch-out, warning, and dangerous. Corresponding warning information is then output to the construction site, enabling intelligent early warning of the concrete pumping status in super high-rise buildings.

[0085] The intelligent algorithm and system for quality control of ultra-high-rise concrete pumping based on acoustic-vibration-visual coupling provided in this invention constructs a multi-source coupled analysis model using sound features, vibration features, visual features, and transport time delay features. This model, combined with the rate of change of transport continuity and the rate of increase of transport time, establishes a blockage risk prediction model, enabling intelligent monitoring, status identification, and blockage risk prediction throughout the entire concrete pumping process. The algorithm mainly comprises six stages: synchronous acquisition of multi-source data, data preprocessing, feature extraction, transport status time delay analysis, multi-source information fusion, and intelligent early warning. Each stage performs data synchronization and collaborative analysis according to a unified timeline, forming a complete intelligent analysis process for concrete pumping quality. Through collaborative analysis of sound, vibration, visual, and transport status information, it achieves a transformation from single-parameter monitoring of concrete pumping status to multi-source information fusion analysis, and from anomaly detection to trend prediction. This provides a quality control method with real-time monitoring, dynamic analysis, and intelligent early warning capabilities for ultra-high-rise concrete pumping construction.

[0086] The intelligent algorithm and system for quality control of ultra-high-rise concrete pumping based on acoustic-vibration-visual coupling provided in this invention synchronously collects and fuses sound signals, video signals, and equipment vibration signals during the concrete pumping process. This enables intelligent identification and early warning of concrete pumping status, discharge continuity, and abnormal working conditions, thereby improving the real-time monitoring capability and quality control level of the concrete pumping construction process, reducing the occurrence of abnormal situations such as pipe blockage, and improving the safety, stability, and construction efficiency of ultra-high-rise concrete pumping construction. Ultimately, this enhances the quality control level of ultra-high-rise concrete pumping construction.

[0087] Compared with existing concrete pumping monitoring technologies, the intelligent algorithm and system for quality control of super high-rise concrete pumping, which combines acoustic, vibration, and visual signals, provided in this invention, achieves intelligent identification and quality control of the concrete pumping status by performing multi-source fusion analysis of sound signals, equipment vibration signals, and visual signals during the pumping process. This has the following beneficial technical effects:

[0088] (1) Improve the accuracy of pumping status identification

[0089] This invention integrates visual, auditory, vibration, and transport time delay features to comprehensively assess the concrete pumping status. Compared to traditional methods that rely solely on pressure, flow rate, or a single signal for monitoring, this invention provides a more comprehensive reflection of the pumping system's operating status, thereby significantly improving the accuracy of identifying abnormal pumping conditions.

[0090] (2) Real-time early warning of abnormal pumping conditions

[0091] By analyzing the continuity of concrete discharge, changes in pumping sound, and equipment vibration characteristics in real time, abnormal situations such as concrete flow interruption, unstable pumping, and pipe blockage risk can be identified in a timely manner, and early warning information can be issued in advance. This provides a basis for construction personnel to take corresponding measures and reduces the probability of pipe blockage accidents.

[0092] (3) Improve the quality control level of super high-rise concrete pumping construction

[0093] This invention achieves dynamic quality control of the construction process by continuously monitoring and analyzing the data of the entire concrete pumping process. This helps to detect abnormalities in concrete condition and equipment operation in a timely manner, thereby improving the stability and reliability of pumping construction.

[0094] (4) Reduce construction risks and improve construction efficiency

[0095] Intelligent monitoring and early warning mechanisms can reduce downtime and equipment maintenance time caused by abnormal situations such as pipe blockage and flow interruption, thereby improving the efficiency of concrete pumping construction, reducing construction risks, and reducing the workload of manual monitoring.

[0096] (5) Possesses good engineering application value

[0097] By deploying visual, sound, and vibration acquisition devices at key locations in the pumping system and combining them with multi-source data fusion algorithms for analysis, it is possible not only to achieve intelligent monitoring of the concrete pumping status, but also to provide technical support for data-driven management and intelligent construction of the construction process, which has good engineering application prospects and promotion value.

[0098] The intelligent algorithm and system for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling provided in this invention, through steps S1 to S6, can solve the following technical problems:

[0099] (1) To solve the problem that the existing concrete pumping construction process mainly relies on manual experience to judge the pumping status and the monitoring methods are not intelligent enough, and to realize the automated identification and analysis of the pumping status.

[0100] (2) To solve the problem that existing technologies rely solely on single parameters such as pressure and flow rate for monitoring, which cannot fully reflect the concrete pumping status, by integrating multi-source data such as visual information, sound information and vibration information, the accuracy and reliability of monitoring results can be improved.

[0101] (3) Solve the problem of difficulty in timely identification of abnormal states such as flow interruption, segregation, and pipe blockage during the pumping of super high-rise concrete, and realize real-time detection and intelligent early warning of abnormal pumping conditions.

[0102] (4) Solve the problem of slow response and lack of continuous monitoring capability of existing pumping quality control methods, and realize real-time dynamic monitoring and data analysis of the entire concrete pumping process.

[0103] The present invention provides an intelligent algorithm and system for quality control of super high-rise concrete pumping, which combines acoustic, vibration, and visual coupling. It is mainly applied to real-time monitoring and quality management of the concrete pumping construction process in super high-rise buildings. It can be used to intelligently identify and warn of risks such as pipe blockage, segregation, and pumping stability during the pumping process, thereby improving the safety, stability, and construction quality of concrete pumping construction.

[0104] This invention is not limited to the specific embodiments described above. Obviously, the embodiments described above are only a part of the embodiments of this invention, not all of them. All other embodiments obtained by those skilled in the art based on the described embodiments of this invention are within the scope of protection of this invention. Those skilled in the art can make other modifications and variations to this invention. Therefore, if these modifications and variations of this invention fall within the scope of the claims of this invention, then this invention also intends to include these modifications and variations.

Claims

1. A smart algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling, characterized in that, include: Step S1, Multi-source data synchronous acquisition: Simultaneously acquire sound, vibration and visual signals during the concrete pumping process in super high-rise buildings; Step S2, Data Preprocessing: Preprocess the collected sound, vibration and visual signals respectively; Step S3, Feature Extraction: Extract parameter features from the sound, vibration, and visual data respectively; Step S4, Delivery Status Time Delay Analysis: Dynamically analyze the delivery time delay characteristics of the operating status during the concrete pumping process; Step S5, Establish a coupled analysis model: Establish a coupled analysis model between sound characteristics, vibration characteristics, visual characteristics, and delivery time delay characteristics, and calculate the comprehensive state evaluation index of concrete pumping: （1）； In Equation (1), EI represents the comprehensive evaluation index of concrete pumping status, A represents the sound abnormality characteristic value, V represents the vibration abnormality characteristic value, I represents the visual abnormality characteristic value, T represents the delivery time delay abnormality characteristic value, and ω1, ω2, ω3, and ω4 are the corresponding weight coefficients. The changing trends of comprehensive abnormal indicators in concrete pumping over time were analyzed, and a pipe blockage risk prediction model was established by combining the rate of change of continuous conveying and the rate of increase of conveying time. （7）； In equation (7), R represents the pipe blockage risk index; EI represents the comprehensive evaluation index of concrete pumping status. This indicates the rate of change of comprehensive abnormal indicators; This indicates the growth rate of delivery time; C represents the continuity index of concrete discharge. Step S6, Intelligent Early Warning: Based on the magnitude of the pipe blockage risk index, the concrete pumping status is divided into four levels: normal, attention, warning, and danger, and corresponding warning information is sent to the construction site.

2. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S2, the data preprocessing method includes: For audio signals, filtering and noise reduction methods are used to eliminate mechanical noise and environmental noise interference at the construction site, and spectrum transformation and signal enhancement are performed. For vibration signals, denoising, zero-drift correction, and frequency decomposition are performed to improve the stability of vibration feature extraction. For visual signals, image enhancement, brightness compensation, target region extraction, and continuous frame calibration are performed sequentially to reduce the impact of dust, lighting changes, and camera shake on the recognition results. At the same time, time synchronization between sound data, vibration data and visual data is completed based on a unified timestamp, so that data from different sources can correspond to the operating status of the same pumping process.

3. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S3, the feature extraction method includes: Sound parameter features are extracted from the sound data to reflect the changes in material state and conveying stability during concrete flow. The sound parameter features include sound pressure change rate, dominant frequency change, spectral energy distribution, and high-frequency energy proportion. Vibration parameter features are extracted from vibration data to reflect the stability of equipment operation and changes in conveying resistance. The vibration features include the root mean square value, peak value, dominant frequency, and pulse fluctuation index. Visual parameter features are extracted from visual data to evaluate the continuous flow capability during concrete conveying. These visual parameter features include the continuity of concrete feeding, the continuity of discharge, the duration of flow interruption, the rate of change of flow area, and the flow velocity.

4. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S4, the method for analyzing the time delay of the transport status includes: During continuous concrete pumping, tracer markers with distinct color or shape characteristics are placed into the concrete at fixed time intervals. Visual acquisition devices at the inlet and outlet positions are used to identify the time when the tracer markers enter and leave the conveying system, calculate the actual concrete conveying time, and dynamically analyze the conveying time lag characteristics of the concrete pumping process.

5. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S5, the sound anomaly characteristic value is calculated according to formula (2): （2）； In equation (2), A represents the sound abnormality characteristic value; X i X represents the i-th real-time sound feature parameter; io X represents i The corresponding normal reference value; m represents the number of sound feature parameters; Let be the weight coefficients of each feature parameter, and satisfy . .

6. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S5, the vibration anomaly characteristic value is calculated according to formula (3): （3）； In equation (3), V represents the vibration anomaly characteristic value; RMS represents the root mean square value of vibration; P represents the vibration peak value; K is the kurtosis; β1, β2, and β3 are the weighting coefficients, respectively.

7. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S5, the visual anomaly feature value is calculated according to formula (4): （4）； In equation (4), I represents the visual anomaly feature value; Z i This represents the i-th real-time visual feature parameter; Z io Z represents i The corresponding normal reference value; γ i Let be the weighting coefficients for each vibration parameter, and satisfy . k represents the number of visual feature parameters.

8. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling according to claim 1, characterized in that, In step S5, the abnormal characteristic value of the delivery time delay is calculated according to formula (5): （5）； In equation (5), T represents the abnormal characteristic value of the transport time delay; t represents the current transport time; t o Reference conveying time under normal operating conditions.

9. The intelligent algorithm for quality control of super high-rise concrete pumping based on acoustic-vibration-visual coupling as described in claim 1, characterized in that, In step S5, the abnormal characteristic value of the delivery time delay is calculated according to formula (6): （6）； In equation (6), T represents the abnormal characteristic value of the delivery time delay; t i The time of the i-th tracer transmission; N is the statistical period.

10. A smart quality control system for super high-rise concrete pumping based on acoustic-vibration-visual coupling, characterized in that, The intelligent quality control algorithm for super high-rise concrete pumping based on acoustic-vibration-visual coupling, as described in any one of claims 1-9, includes: The data acquisition module is used to simultaneously collect sound, vibration, and visual signals during the concrete pumping process in super high-rise buildings. The data processing module is used to preprocess and extract parameter features from the collected sound, vibration, and visual signals, and to dynamically analyze the delivery time lag characteristics of the concrete pumping process. A coupled analysis model is established using sound, vibration, visual, and delivery time lag characteristics to calculate the comprehensive evaluation index of the concrete pumping status. Based on the comprehensive evaluation index and combined with the rate of change of delivery continuity and the rate of increase of delivery time, a blockage risk prediction model is established. The blockage risk prediction model is used to calculate the blockage risk index. Based on the magnitude of the blockage risk index, the concrete pumping status is divided into four levels: normal, watch-out, warning, and dangerous. Corresponding warning information is then output to the construction site, enabling intelligent early warning of the concrete pumping status in super high-rise buildings.

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