An intelligent monitoring system and method for manufacturing prefabricated building components

Abstract

This invention relates to the field of intelligent monitoring technology, specifically an intelligent monitoring system and method for the manufacturing of precast building components. The system includes: constructing a particle migration coherence model and a slurry rheological stress model based on a velocity vector field; calculating a first monitoring effectiveness; when the first monitoring effectiveness exceeds a preset threshold, determining that the ceramsite group is about to enter a collective migration mode; calculating a first optimized excitation frequency; plotting the slurry steric hindrance coefficient evolution characteristic curve for the entire molding cycle and identifying it as a critical precursor point where the internal stress field of the slurry is about to undergo a sudden change; calculating a second monitoring effectiveness and a second optimized excitation frequency; and outputting the second optimized excitation frequency as the monitoring result to guide the current molding process of the precast component. This invention solves the problem of insufficient monitoring depth in the molding process of precast building components in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and is an intelligent monitoring system and method for the manufacturing of prefabricated building components. Background Technology

[0002] Currently, high-performance expanded clay foam concrete (HPCFC) is widely used in the field of precast building components due to its lightweight, high strength, and excellent thermal insulation properties. It is particularly important in projects with strict requirements for weight control and energy conservation, such as subway superstructures and high-rise buildings, where HPCFC precast wall panels have become a key choice. However, during the industrial-scale mass production process, HPCFC slurry, containing low-density (approximately 500-800 kg / m³) expanded clay and a high proportion of foam, exhibits extremely complex multiphase rheological characteristics. The vibration compaction process commonly presents problems with the dynamic migration and floating of expanded clay, leading to quality defects such as density stratification, poor homogeneity, and even drying shrinkage cracking after the components have hardened. To address this issue, existing technologies mainly focus on empirical methods such as optimizing the basic mix ratio, adjusting vibration time, or fixing the frequency. Their control objective is limited to ensuring the immediate process index of molding density. It is assumed that as long as there are no obvious defects in the appearance when the product is demolded, it is considered a qualified product. However, after HPCFC wall panels are installed in specific service environments (such as areas near subway tracks) and undergo a certain period of environmental temperature and humidity cycles and micro-vibration coupling, non-random periodic micro-cracks will appear on their surface along a specific direction, accompanied by the appearance of acoustic emission signal characteristic peaks. This phenomenon is often simply attributed to material aging or improper construction in traditional understanding. Summary of the Invention

[0003] It should be noted that the root cause of the technical problems pointed out in the background art lies in the fact that the information in the manufacturing stage is completely ignored. During the vibration molding process, the non-uniform propagation of the vibration spectrum in the mold space will induce the ceramsite to form a specific topological distribution pattern in three-dimensional space. This distribution pattern is solidified into the micro-stress gradient field inside the component after the slurry hardens, forming micro-mechanical memory. When the component encounters an external excitation that matches its own natural frequency during its service life, it will trigger a resonance amplification effect, causing the damage to develop in an orderly manner along the initial stress gradient direction.

[0004] The technical problem to be solved by the present invention is that the monitoring depth of the forming process of prefabricated building components is insufficient in the prior art, and an intelligent monitoring system and method for the manufacturing of prefabricated building components is proposed.

[0005] In a first aspect, the technical solution of the intelligent monitoring method for manufacturing prefabricated building components according to the present invention includes the following steps:

[0006] S1: Extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data using a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in the three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field.

[0007] S2: Extract the set of dynamic feature parameters from the particle migration coherent model, and calculate the first monitoring effectiveness based on the set of dynamic feature parameters. When the first monitoring effectiveness exceeds the preset threshold, it is determined that the ceramsite group is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency.

[0008] S3: Extract the set of rheological characteristic parameters from the slurry rheological stress model, calculate the transient slurry steric retardation coefficient in the precast component forming process in real time, and plot the evolution characteristic curve of slurry steric retardation coefficient throughout the forming cycle with the excitation duration as the abscissa and the transient slurry steric retardation coefficient as the ordinate.

[0009] S4: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the steric retardation coefficient evolution characteristic curve, track the local geometric changes of the evolution characteristic curve in real time, and identify the critical precursor point of the internal stress field of the slurry about to undergo a sudden change when the curve curvature is detected. Evaluate the second monitoring effectiveness under the current excitation parameters at the critical precursor point, and calculate the second dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness to obtain the second optimized excitation frequency.

[0010] S5: Output the second optimized excitation frequency as the monitoring result to guide the current molding process of the precast component.

[0011] Preferably, in S1, the parameters describing the orderly motion of the ceramsite swarm in the particle migration coherence model include: the component values ​​of the average velocity vector of the ceramsite swarm in three-dimensional space within each spatial analysis window, and the spatial coherence coefficient calculated based on the consistency of the velocity vector directions of adjacent windows.

[0012] The slurry rheological stress model includes parameters describing the stress response of the slurry matrix, such as shear stress value, shear rate value, instantaneous apparent viscosity of the slurry, and local volume fraction of ceramsite.

[0013] Preferably, in S2, the first monitoring effectiveness The specific calculation strategy is as follows:

[0014] ;

[0015] The monitoring area covered by the particle swarm migration coherence model is divided into M continuous strip-shaped analysis intervals along the direction perpendicular to the excitation, where m is the subscript of the strip interval. Let be the spatial average of the vertical projection components of the velocity vectors of all ceramsite particles within the m-th strip interval. The mean of the vertical velocity components of the M strip intervals; N is the number of grid rows after the monitoring area is gridded. Let be the velocity vector of the ceramic particles at grid point (i,j).

[0016] Preferably, in S2, the first optimized excitation frequency The specific calculation strategy is as follows: ;

[0017] in, The reference excitation frequency of the variable frequency vibration table is preset based on the initial rheological properties of the slurry and the natural frequency of the mold. The maximum permissible threshold for the first monitoring effectiveness, calibrated based on historical statistics or offline experiments; This is the preset intervention intensity coefficient.

[0018] Preferably, S3 includes the following specific steps:

[0019] S31: Extract the shear stress values ​​at each spatial location at the current moment from the slurry rheological stress model. Shear rate value and local volume fraction of ceramsite ;

[0020] S32: Real-time calculation of transient slurry steric hindrance coefficient during the precast component molding process. The specific strategy for calculating the transient steric hindrance coefficient of the slurry is as follows:

[0021] ;

[0022] in, The spatial weighted average of the shear stress at all wall surfaces of the mold at time t is obtained by integrating the calculated values ​​at the wall boundaries using the rheological stress model.

[0023] The spatial average value of the apparent viscosity of the slurry in the monitoring area at time t;

[0024] The spatial average value of the shear rate in the monitored region at time t; The spatial average value of the volume fraction of ceramsite in the monitored area at time t.

[0025] is the inherent density constant of the cement-based paste; g is the acceleration due to gravity. These are the contribution weights for wall friction and viscous dissipation, respectively. ;

[0026] S33: The transient slurry steric retardation coefficient calculated with excitation duration as the abscissa. Using the vertical axis as the ordinate, a continuous and smooth evolution characteristic curve of the steric stagnation coefficient of the slurry throughout the entire forming cycle is plotted using the cubic spline interpolation method.

[0027] Preferably, S4 includes the following specific steps:

[0028] S41: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the slurry steric retardation coefficient evolution characteristic curve, and obtain the monitoring time node set. Where p is a subscript, representing the p-th frequency adjustment event, and P is the total number of adjustment events that have occurred up to the current time; Indicates the time when the p-th adjustment event occurs; express The steric susceptibility coefficient of the slurry at that moment; This represents the excitation frequency after the p-th adjustment event is implemented;

[0029] S42: Track the local geometric changes of the evolutionary characteristic curve in real time and evaluate the effectiveness of the second monitoring under the current excitation parameters. The specific strategy for calculating the effectiveness of the second monitoring is as follows:

[0030] ;

[0031] Specifically, on the evolutionary characteristic curve, a time window of length T is extracted forward from the current time, and L consecutive time nodes are uniformly selected within the time window. , The time of the most recent frequency adjustment event;

[0032] These represent the evolutionary characteristic curves at time nodes. The first and second derivatives at the point are calculated using the five-point numerical differential formula;

[0033] It is the midpoint between two adjacent time points; This represents the first derivative of the evolutionary characteristic curve at the midpoint.

[0034] S43: Calculate the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness, and obtain the second optimized excitation frequency. The second optimized excitation frequency acquisition strategy is as follows: ;

[0035] in, The first optimized excitation frequency being executed at the current moment; The second monitoring effectiveness reference value is set based on the statistical average value during the historical molding process of good-quality components. This is the preset fine-tuning step size factor;

[0036] when Higher than When the curve shows a drastic change in local curvature, the internal stress field of the slurry is about to undergo a sudden change. Increasing the excitation frequency enhances the intervention response speed, and the magnitude of the increase is related to... The degree to which it exceeds the reference value is directly proportional;

[0037] when Below When the curve is flat, the slurry state is stable, and the frequency of the oscillation is adjusted to avoid excessive intervention. The magnitude of the adjustment is related to... The degree to which it falls below the reference value is directly proportional.

[0038] Secondly, the present invention provides an intelligent monitoring system for the manufacturing of prefabricated building components, comprising the following modules:

[0039] The system includes a multi-field coupling modeling module, an initial optimization module, a feature generation module, a secondary correction module, a monitoring result output module, and a vibration table collaborative control module.

[0040] The multi-field coupling modeling module is used to extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data with a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in the three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field.

[0041] The initial optimization module extracts a set of dynamic feature parameters from the particle swarm migration coherent model and calculates the first monitoring effectiveness based on the set of dynamic feature parameters. When the first monitoring effectiveness exceeds a preset threshold, it is determined that the ceramsite swarm is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency.

[0042] The feature generation module extracts a set of rheological feature parameters from the slurry rheological stress model, calculates the transient slurry steric retardation coefficient in real time during the precast component forming process, and plots the slurry steric retardation coefficient evolution feature curve for the entire forming cycle with the excitation duration as the abscissa and the transient slurry steric retardation coefficient as the ordinate.

[0043] The secondary correction module marks the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the evolution characteristic curve of the slurry steric hindrance coefficient. It tracks the local geometric changes of the evolution characteristic curve in real time. When an increase in the curvature of the curve is detected, it is identified as a critical precursor point where the internal stress field of the slurry is about to undergo a sudden change. At the critical precursor point, the second monitoring effectiveness under the current excitation parameters is evaluated, and the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table is calculated based on the second monitoring effectiveness to obtain the second optimized excitation frequency.

[0044] The monitoring result output module is used to output the second optimized excitation frequency as the monitoring result to guide the current forming process of the precast component;

[0045] The vibration table collaborative control module is used to collaboratively control the control console.

[0046] Thirdly, a storage medium storing instructions that, when read by a computer, cause the computer to execute the aforementioned intelligent monitoring method for the manufacture of prefabricated building components.

[0047] Fourthly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned intelligent monitoring method for the manufacture of prefabricated building components.

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

[0049] 1. This invention achieves a deep correlation between the particle motion state during the manufacturing stage and the dynamic response characteristics during service life, transforming vibration parameters from physical compaction methods into information carriers of component service behavior. This solves the technical problem of difficulty in tracing delayed interface debonding caused by missing manufacturing information. While maintaining the inherent advantages of HPCFC materials such as lightweight, high strength, and excellent thermal insulation, it significantly extends the service life and reliability of precast components, providing technical support for the large-scale application of high-performance precast components in complex environments.

[0050] 2. This invention constructs a particle migration coherence model and a slurry rheological stress model, and defines a first monitoring effectiveness and transient slurry steric hindrance coefficient, thereby realizing the quantitative characterization and real-time tracking of the synchronicity of ceramsite movement and the stress accumulation process inside the slurry. When it is detected that ceramsite is about to form a harmful collective migration mode or that the slurry steric hindrance coefficient curve shows a critical abrupt change, the system can adaptively calculate and output an optimized excitation frequency. By actively intervening to disrupt the synchronous settling of particles and releasing the risk of stress concentration in advance, the traditional passive remedial mode of post-event detection is transformed into active prevention in the manufacturing process. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 these drawings without creative effort. Wherein:

[0052] Figure 1 This is a flowchart illustrating an intelligent monitoring method for the manufacturing of prefabricated building components according to the present invention.

[0053] Figure 2 This is a structural schematic diagram of an intelligent monitoring system for the manufacture of prefabricated building components according to the present invention. Detailed Implementation

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0057] Example 1:

[0058] like Figure 1 As shown in the figure, an intelligent monitoring method for the manufacturing of prefabricated building components according to an embodiment of the present invention includes the following specific steps:

[0059] S1: Extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data using a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in the three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field.

[0060] It should be noted that the particle swarm migration coherence model is used to describe the orderliness of the movement of ceramsite swarms; the slurry rheological stress model is used to describe the stress response of the slurry matrix.

[0061] In S1, the particle group migration coherence model includes the following parameters describing the orderly motion of the ceramsite group: the component values ​​of the average motion velocity vector of the ceramsite group in three-dimensional space within each spatial analysis window, and the spatial coherence coefficient calculated based on the consistency of the velocity vector directions of adjacent windows.

[0062] The slurry rheological stress model includes parameters describing the stress response of the slurry matrix, such as: shear stress value at each spatial location, shear rate value, instantaneous apparent viscosity of the slurry based on the Cross model fitting, and local ceramsite volume fraction.

[0063] S2: Extract the set of dynamic characteristic parameters characterizing the synchronicity and spatial coherence of the ceramsite group movement from the particle group migration coherence model, and calculate the first monitoring effectiveness based on the set of dynamic characteristic parameters. When the first monitoring effectiveness exceeds the preset threshold, it is determined that the ceramsite group is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency.

[0064] It should be noted that this is done to disrupt the synchronicity of the movement of the expanded clay pellets;

[0065] In S2, the first monitoring effectiveness The specific calculation strategy is as follows:

[0066] ;

[0067] The monitoring area covered by the particle swarm migration coherence model is divided into M continuous strip-shaped analysis intervals along the direction perpendicular to the excitation, where m is the subscript of the strip interval. Let be the spatial average of the vertical projection components of the velocity vectors of all ceramsite particles within the m-th strip interval. The mean of the vertical velocity components of the M strip intervals; N is the number of grid rows (or columns) after the monitoring area is gridded. Let be the velocity vector of the ceramic particle at grid point (i,j);

[0068] It should be noted that when all the ceramsite particles move in the same direction, the effectiveness of the first monitoring approaches its maximum value, indicating that the ceramsite particles are forming an integral migration flow.

[0069] In S2, the first optimized excitation frequency The specific calculation strategy is as follows: ;

[0070] in, The reference excitation frequency of the variable frequency vibration table is preset based on the initial rheological properties of the slurry and the natural frequency of the mold. The maximum permissible threshold for the first monitoring effectiveness, calibrated based on historical statistics or offline experiments; The preset intervention intensity coefficient ranges from 0.2 to 0.5; it should be noted that when near When the excitation frequency is significantly reduced, it indicates that the movement of the ceramsite is too orderly and is about to form a collective migration. At this time, the excitation frequency is significantly reduced to disrupt its motion synchronization.

[0071] S3: Extract the set of rheological characteristic parameters that characterize the ability of the slurry matrix to inhibit the migration of ceramsite from the slurry rheological stress model, calculate the transient slurry steric hindrance coefficient in the precast component forming process in real time, and plot the evolution characteristic curve of slurry steric hindrance coefficient throughout the forming cycle with the excitation duration as the abscissa and the transient slurry steric hindrance coefficient as the ordinate.

[0072] S3 includes the following specific steps:

[0073] S31: Extract the shear stress values ​​at each spatial location at the current moment from the slurry rheological stress model. Shear rate value and the local ceramic particle volume fraction obtained based on electrical resistance tomography data ;

[0074] S32: Real-time calculation of transient slurry steric hindrance coefficient during the precast component molding process. The specific strategy for calculating the transient steric hindrance coefficient of the slurry is as follows:

[0075] ;

[0076] in, The spatial weighted average of the shear stress at all wall surfaces of the mold at time t is obtained by integrating the calculated values ​​at the wall boundaries using the rheological stress model.

[0077] Let t be the spatial average value of the apparent viscosity of the slurry in the monitoring area, for example, S represents the total area of ​​the monitoring area;

[0078] The spatial average value of the shear rate in the monitored region at time t; The spatial average value of the volume fraction of ceramsite in the monitored area at time t.

[0079] For example, ;

[0080] is the inherent density constant of the cement-based paste; g is the acceleration due to gravity. These are the contribution weights for wall friction and viscous dissipation, respectively. For example, The value range is 0.3 to 0.7, preset according to the mold size and wall roughness;

[0081] S33: The transient slurry steric retardation coefficient calculated with excitation duration as the abscissa. Using the vertical axis as the ordinate, a continuous and smooth evolution characteristic curve of the steric stagnation coefficient of the slurry throughout the entire forming cycle is plotted using the cubic spline interpolation method.

[0082] S4: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the steric retardation coefficient evolution characteristic curve, track the local geometric changes of the evolution characteristic curve in real time, and identify the critical precursor point of the internal stress field of the slurry about to undergo a sudden change when the curve curvature is detected. Evaluate the second monitoring effectiveness under the current excitation parameters at the critical precursor point, and calculate the second dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness to obtain the second optimized excitation frequency.

[0083] It should be noted that this is done in order to proactively intervene before the large-scale collective migration of ceramsite occurs;

[0084] S5: Output the second optimized excitation frequency as the monitoring result to guide the current molding process of the precast component.

[0085] S4 includes the following specific steps:

[0086] S41: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the slurry steric retardation coefficient evolution characteristic curve, and obtain the monitoring time node set. Where p is a subscript, representing the p-th frequency adjustment event, and P is the total number of adjustment events that have occurred up to the current time; Indicates the time when the p-th adjustment event occurs; express The steric susceptibility coefficient of the slurry at that moment; This represents the excitation frequency after the p-th adjustment event is implemented;

[0087] S42: Track the local geometric changes of the evolutionary characteristic curve in real time and evaluate the effectiveness of the second monitoring under the current excitation parameters. The specific strategy for calculating the effectiveness of the second monitoring is as follows:

[0088] ;

[0089] Specifically, on the evolutionary characteristic curve, a time window of length T is extracted forward from the current time, and L consecutive time nodes are uniformly selected within the time window. , The time of the most recent frequency adjustment event;

[0090] These represent the evolutionary characteristic curves at time nodes. The first and second derivatives at the point are calculated using the five-point numerical differential formula;

[0091] It is the midpoint between two adjacent time points; This represents the first derivative of the evolutionary characteristic curve at the midpoint.

[0092] It should be noted that, As a time decay weighting factor, the curve features closer to the current time are more... The greater the contribution.

[0093] S43: Calculate the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness, and obtain the second optimized excitation frequency. The second optimized excitation frequency acquisition strategy is as follows: ;

[0094] in, The first optimized excitation frequency being executed at the current moment; The second monitoring effectiveness reference value is set based on the statistical average value during the historical molding process of good-quality components. This is a preset fine-tuning step size factor, with a value range of 0.05 to 0.15;

[0095] It should be noted that, This represents the secondary dynamic correction amount for the instantaneous excitation frequency of the variable frequency vibration table;

[0096] when Higher than When the curve shows a drastic change in local curvature, the internal stress field of the slurry is about to undergo a sudden change. Increasing the excitation frequency enhances the intervention response speed, and the magnitude of the increase is related to... The degree to which it exceeds the reference value is directly proportional;

[0097] when Below When the curve is flat, the slurry state is stable, and the frequency of the oscillation is adjusted to avoid excessive intervention. The magnitude of the adjustment is related to... The degree to which it falls below the reference value is directly proportional.

[0098] Example 2:

[0099] like Figure 2 As shown in the figure, an intelligent monitoring system for the manufacturing of prefabricated building components according to an embodiment of the present invention includes the following modules:

[0100] The system includes a multi-field coupling modeling module, an initial optimization module, a feature generation module, a secondary correction module, a monitoring result output module, and a vibration table collaborative control module.

[0101] The multi-field coupling modeling module is used to extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data with a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in the three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field.

[0102] The initial optimization module extracts a set of dynamic feature parameters from the particle swarm migration coherent model and calculates the first monitoring effectiveness based on the set of dynamic feature parameters. When the first monitoring effectiveness exceeds a preset threshold, it is determined that the ceramsite swarm is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency.

[0103] The feature generation module extracts a set of rheological feature parameters from the slurry rheological stress model, calculates the transient slurry steric retardation coefficient in real time during the precast component forming process, and plots the slurry steric retardation coefficient evolution feature curve for the entire forming cycle with the excitation duration as the abscissa and the transient slurry steric retardation coefficient as the ordinate.

[0104] The secondary correction module marks the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the evolution characteristic curve of the slurry steric hindrance coefficient. It tracks the local geometric changes of the evolution characteristic curve in real time. When an increase in the curvature of the curve is detected, it is identified as a critical precursor point where the internal stress field of the slurry is about to undergo a sudden change. At the critical precursor point, the second monitoring effectiveness under the current excitation parameters is evaluated, and the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table is calculated based on the second monitoring effectiveness to obtain the second optimized excitation frequency.

[0105] The monitoring result output module is used to output the second optimized excitation frequency as the monitoring result to guide the current forming process of the precast component;

[0106] The vibration table collaborative control module is used to collaboratively control the control console.

[0107] Example 3:

[0108] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0109] The processor executes the aforementioned intelligent monitoring method for the manufacture of prefabricated building components by calling computer programs stored in memory.

[0110] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the intelligent monitoring method for the manufacture of prefabricated building components provided in the above-described embodiment. The electronic device may also include other components for realizing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted here.

[0111] Example 4:

[0112] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0113] When a computer program runs on a computer device, it causes the computer device to perform the aforementioned intelligent monitoring method for the manufacture of prefabricated building components.

[0114] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0115] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0116] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0124] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring method for the manufacturing of prefabricated building components, characterized in that, The method includes: S1: Extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data using a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field. S2: Extract the set of dynamic feature parameters from the particle migration coherent model, and calculate the first monitoring effectiveness based on the set of dynamic feature parameters. When the first monitoring effectiveness exceeds the preset threshold, it is determined that the ceramsite group is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency. In S2, the first monitoring effectiveness The specific calculation strategy is as follows: ； The monitoring area covered by the particle swarm migration coherence model is divided into M continuous strip-shaped analysis intervals along the direction perpendicular to the excitation, where m is the subscript of the strip interval. Let be the spatial average of the vertical projection components of the velocity vectors of all ceramsite particles within the m-th strip interval. The mean of the vertical velocity components of the M strip intervals; N is the number of grid rows after the monitoring area is gridded. Let be the velocity vector of the ceramic particle at grid point (i,j); S3: Extract the set of rheological characteristic parameters from the slurry rheological stress model, calculate the transient slurry steric retardation coefficient in the precast component forming process in real time, and plot the evolution characteristic curve of slurry steric retardation coefficient throughout the forming cycle with the excitation duration as the abscissa and the transient slurry steric retardation coefficient as the ordinate. S3 includes the following specific steps: S31: Extract the shear stress values ​​at each spatial location at the current moment from the slurry rheological stress model. Shear rate value and local volume fraction of ceramsite ; S32: Real-time calculation of transient slurry steric hindrance coefficient during the precast component molding process. The specific strategy for calculating the transient steric stagnation coefficient is as follows: ； in, The spatial weighted average of the shear stress at all wall surfaces of the mold at time t is obtained by integrating the calculated values ​​at the wall boundaries using the rheological stress model. The spatial average value of the apparent viscosity of the slurry in the monitoring area at time t; The spatial average value of the shear rate in the monitored region at time t; The spatial average value of the volume fraction of ceramsite in the monitored area at time t. is the inherent density constant of the cement-based paste; g is the acceleration due to gravity. These are the contribution weights for wall friction and viscous dissipation, respectively. ; S33: The transient slurry steric retardation coefficient calculated with excitation duration as the abscissa. Using the vertical axis as the ordinate, a continuous and smooth evolution characteristic curve of the steric hindrance coefficient of the slurry throughout the entire forming cycle is plotted using the cubic spline interpolation method; S4: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the steric retardation coefficient evolution characteristic curve, track the local geometric changes of the evolution characteristic curve in real time, and identify the critical precursor point of the internal stress field of the slurry about to undergo a sudden change when the curve curvature is detected. Evaluate the second monitoring effectiveness under the current excitation parameters at the critical precursor point, and calculate the second dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness to obtain the second optimized excitation frequency. S4 includes the following specific steps: S41: Mark the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the slurry steric retardation coefficient evolution characteristic curve, and obtain the monitoring time node set. Where p is a subscript, representing the p-th frequency adjustment event, and P is the total number of adjustment events that have occurred up to the current time; Indicates the time when the p-th adjustment event occurs; express The steric susceptibility coefficient of the slurry at time t; This represents the excitation frequency after the p-th adjustment event is implemented; S42: Track the local geometric changes of the evolutionary characteristic curve in real time and evaluate the effectiveness of the second monitoring under the current excitation parameters. The specific strategy for calculating the effectiveness of the second monitoring is as follows: ； Specifically, on the evolutionary characteristic curve, a time window of length T is extracted forward from the current time, and L consecutive time nodes are uniformly selected within the time window. , The time of the most recent frequency adjustment event; These represent the evolutionary characteristic curves at time nodes. The first and second derivatives at the point are calculated using the five-point numerical differential formula; It is the midpoint between two adjacent time points; This represents the first derivative of the evolutionary characteristic curve at the midpoint. S5: Output the second optimized excitation frequency as the monitoring result to guide the current molding process of the precast component.

2. The intelligent monitoring method for manufacturing prefabricated building components according to claim 1, characterized in that, In S1, the particle group migration coherence model includes the following parameters describing the orderly motion of the ceramsite group: the component values ​​of the average motion velocity vector of the ceramsite group in three-dimensional space within each spatial analysis window, and the spatial coherence coefficient calculated based on the consistency of the velocity vector directions of adjacent windows. The slurry rheological stress model includes parameters describing the stress response of the slurry matrix, such as shear stress value, shear rate value, instantaneous apparent viscosity of the slurry, and local volume fraction of ceramsite.

3. The intelligent monitoring method for manufacturing prefabricated building components according to claim 2, characterized in that, In S2, the first optimized excitation frequency The specific calculation strategy is as follows: ; in, The reference excitation frequency of the variable frequency vibration table is preset based on the initial rheological properties of the slurry and the natural frequency of the mold. The maximum permissible threshold for the first monitoring effectiveness, calibrated based on historical statistics or offline experiments; This is the preset intervention intensity coefficient.

4. The intelligent monitoring method for manufacturing prefabricated building components according to claim 3, characterized in that, S4 also includes the following specific steps: S43: Calculate the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table based on the second monitoring effectiveness, and obtain the second optimized excitation frequency. The second optimized excitation frequency acquisition strategy is as follows: ; in, The first optimized excitation frequency being executed at the current moment; The second monitoring effectiveness reference value is set based on the statistical average value during the historical molding process of good-quality components. This is the preset fine-tuning step size factor; when Higher than When the curve shows a drastic change in local curvature, the internal stress field of the slurry is about to undergo a sudden change. Increasing the excitation frequency enhances the intervention response speed, and the magnitude of the increase is related to... The degree to which it exceeds the reference value is directly proportional; when Below When the curve is flat, the slurry state is stable, and the frequency of the oscillation is adjusted to avoid excessive intervention. The magnitude of the adjustment is related to... The degree to which it falls below the reference value is directly proportional.

5. An intelligent monitoring system for the manufacture of precast building components, used to implement the intelligent monitoring method for the manufacture of precast building components as described in any one of claims 1-4, characterized in that, The system includes the following modules: The system includes a multi-field coupling modeling module, an initial optimization module, a feature generation module, a secondary correction module, a monitoring result output module, and a vibration table collaborative control module. The multi-field coupling modeling module is used to extract continuous multi-frame data during the operation of the variable frequency vibration table, process the multi-frame data with a particle image velocimetry algorithm, reconstruct the instantaneous velocity vector field of the ceramsite group in the three-dimensional space, and construct a particle group migration coherence model and a slurry rheological stress model based on the velocity vector field. The initial optimization module extracts a set of dynamic feature parameters from the particle swarm migration coherent model and calculates the first monitoring effectiveness based on the set of dynamic feature parameters. When the first monitoring effectiveness exceeds a preset threshold, it is determined that the ceramsite swarm is about to enter the collective migration mode. At this time, the initial adaptive adjustment value of the reference excitation frequency of the frequency conversion vibration table is calculated based on the first monitoring effectiveness to obtain the first optimized excitation frequency. The feature generation module extracts a set of rheological feature parameters from the slurry rheological stress model, calculates the transient slurry steric retardation coefficient in real time during the precast component forming process, and plots the slurry steric retardation coefficient evolution feature curve for the entire forming cycle with the excitation duration as the abscissa and the transient slurry steric retardation coefficient as the ordinate. The secondary correction module marks the monitoring time nodes corresponding to each frequency adjustment of the variable frequency vibration table on the evolution characteristic curve of the slurry steric hindrance coefficient. It tracks the local geometric changes of the evolution characteristic curve in real time. When an increase in the curvature of the curve is detected, it is identified as a critical precursor point where the internal stress field of the slurry is about to undergo a sudden change. At the critical precursor point, the second monitoring effectiveness under the current excitation parameters is evaluated, and the secondary dynamic correction amount of the instantaneous excitation frequency of the variable frequency vibration table is calculated based on the second monitoring effectiveness to obtain the second optimized excitation frequency. The monitoring result output module is used to output the second optimized excitation frequency as the monitoring result to guide the current forming process of the precast component; The vibration table collaborative control module is used to collaboratively control the control console.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements an intelligent monitoring method for the manufacture of prefabricated building components as described in any one of claims 1-4.

7. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the intelligent monitoring method for manufacturing prefabricated building components as described in any one of claims 1-4.

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