Industrial building frame floor gold sand floor integrated molding supervision method

Abstract

A method for monitoring the integrated molding of corundum concrete flooring in industrial building frames, belonging to the field of construction monitoring technology, includes: constructing a wireless topology network within the building monitoring area to collect global monitoring data; setting up high-density monitoring modules in specific areas of the building monitoring area to collect local monitoring data; adding vibration feedback acquisition units to beam-column joints within the building monitoring area to monitor the vibration energy distribution of these joints in real time and output joint vibration data; preprocessing and fusing the global monitoring data, local monitoring data, and joint vibration data to generate correction feedback data for the building monitoring area; and adjusting the corundum process parameters within the building monitoring area based on the correction feedback data. The wireless topology network is constructed from multiple sensors, and both the global and local monitoring data include the concrete's vibration value, temperature, and initial setting state.

Description

Technical Field

[0001] This invention belongs to the field of construction monitoring technology, and more specifically, relates to a method for monitoring the integrated molding of corundum flooring on the frame floor of an industrial building. Background Technology

[0002] Deficiencies in the Local Overlap Compaction Detection and Monitoring System: While the overall solution uses a wireless sensor network to monitor the entire floor, it lacks a system for detecting overlap compaction in specific areas (such as slab boundaries or uneven areas). Due to the large-scale continuous construction of integrated diamond aggregate molding, insufficient sensing or data coverage blind spots often occur in certain areas, potentially leading to deviations in the embedding and compaction of diamond aggregate in specific locations.

[0003] Insufficient feedback on local vibration at beam-column joints: In frame structures, the beam-column joint area is prone to local resonance or uneven vibration energy due to significant variations in structural stiffness. Although the current solution achieves overall vibration compaction using a 300kg±10% self-weight grinder and vibratory rods, it has not yet established a dedicated closed-loop control system for monitoring and regulating the vibration of local joints (such as beam-column intersections). Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to overcome the aforementioned deficiencies and propose an integrated molding and supervision method for corundum flooring in industrial building frames.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention discloses a method for monitoring the integrated molding of corundum flooring on the frame floor of an industrial building, the method comprising:

[0007] A wireless topology network is constructed within the building monitoring area to collect global monitoring data within the building monitoring area;

[0008] High-density monitoring modules are installed in certain areas of the building monitoring area to collect local monitoring data of the building monitoring area;

[0009] A vibration feedback acquisition unit is added to the beam-column joints in the building monitoring area to monitor the vibration energy distribution of the beam-column joints in real time and output the joint vibration data.

[0010] The global monitoring data, local monitoring data, and nodal vibration data are preprocessed and fused to generate correction feedback data for the building monitoring area;

[0011] Based on the correction feedback data, the diamond abrasive process parameters of the building monitoring area are adjusted;

[0012] The wireless topology network is constructed from multiple sensors, and the global and local monitoring data include the vibration value, temperature, and initial setting state of the concrete.

[0013] Furthermore, the construction of a wireless topology network within the building monitoring area to collect global monitoring data within the building monitoring area includes:

[0014] The floor geometry layout and concrete pouring design parameters of the building monitoring area are obtained, and the types and installation positions of the multiple sensors are determined based on the design parameters to generate a sensor layout matrix.

[0015] A wireless sensor network is constructed, and multiple sensors in the sensor arrangement matrix are wirelessly connected through the wireless sensor network to construct the wireless topology network;

[0016] Based on the wireless topology network, data acquisition modules for each sensor are set up, and clock calibration is performed on each data acquisition module.

[0017] The sensor data output by the data acquisition module is subjected to noise correction and data fusion to obtain global monitoring data for the building monitoring area.

[0018] Furthermore, the step of setting up high-density monitoring modules in specific areas of the building monitoring area to collect local monitoring data of the building monitoring area includes:

[0019] Calculate the deviation between the sensor data corresponding to the key monitoring area and the global monitoring data, and determine the key monitoring area as an abnormal area when the deviation exceeds a first threshold;

[0020] High-precision vibration sensors and image acquisition devices are planned and deployed in the abnormal area to construct the high-density monitoring module;

[0021] The key monitoring area refers to the boundary and uneven areas of the slab surface within the building monitoring area.

[0022] Furthermore, the step of setting up high-density monitoring modules in specific areas of the building monitoring area to collect local monitoring data of the building monitoring area also includes:

[0023] Acquire the data vector output by each high-precision vibration sensor. The data vector consists of the local vibration value measured by each high-precision vibration sensor, the standard deviation of sensor noise, and the data acquisition timestamp.

[0024] The image data output by the image acquisition device is acquired, and the image data corresponding to each abnormal area is fused with the data vector to generate local monitoring data of the building monitoring area.

[0025] Furthermore, a vibration feedback acquisition unit is added to the beam-column joints in the building monitoring area to monitor the vibration energy distribution of the beam-column joints in real time, so as to output the joint vibration data, including:

[0026] Based on the local monitoring data, beam-column nodes in the concrete floor of the building monitoring area are extracted to output a list of beam-column node regions; each beam-column node region has corresponding location information and area size;

[0027] Multiple micro vibration sensors are set in each beam-column node area, and the multiple micro vibration sensors are arranged to generate a micro vibration sensor arrangement matrix in each beam-column node area.

[0028] The multiple micro vibration sensors are installed according to the micro vibration sensor arrangement matrix, and a corresponding data acquisition submodule is configured for each micro vibration sensor to integrate the output of the data acquisition submodule to obtain a micro vibration sensor data set.

[0029] A weighted average is performed on all the micro-vibration sensor data sets in the beam-column node region list to generate and output the node vibration data that characterizes the vibration energy distribution of the beam-column node.

[0030] Furthermore, the preprocessing and fusion of the global monitoring data, local monitoring data, and nodal vibration data to generate correction feedback data for the building monitoring area includes:

[0031] The global monitoring data, local monitoring data, and node vibration data are preprocessed by denoising and low-pass filtering to obtain a preprocessed dataset;

[0032] The local monitoring data and node vibration data in the preprocessed dataset are matched to perform time alignment of the local monitoring data and node vibration data to obtain the corrected dataset;

[0033] The local monitoring data and node vibration data in the correction dataset are integrated by calling the data fusion formula to correct local deviations and output the correction feedback data.

[0034] Furthermore, the preprocessing and fusion of the global monitoring data, local monitoring data, and nodal vibration data to generate correction feedback data for the building monitoring area also includes:

[0035] A closed-loop control module is established, and the closed-loop control module is called to compare the correction feedback data with the local monitoring data and the global monitoring data to trigger early warning feedback and output the parameters to be adjusted;

[0036] The parameters to be adjusted include the vibration frequency of the vibrating equipment and the spraying rate of the corundum.

[0037] Furthermore, adjusting the diamond abrasive process parameters in the building monitoring area based on the correction feedback data includes:

[0038] Based on the parameters to be adjusted, the initial process parameters of the diamond are extracted from the correction feedback data, and the initial process parameters are compared with the set target process parameters to calculate the error signal between the initial process parameters and the target process parameters.

[0039] Based on the error signal, proportional regulation and PID control algorithms are used to adjust the grinding mill vibration parameters, the amount of diamond aggregate being thrown, and the throwing frequency in the diamond abrasive process parameters, in order to set the control gain parameters.

[0040] The control gain parameters are converted into control commands, and the vibration parameters of the grinding mill, the amount and frequency of the diamond aggregate being thrown are corrected in response to the control commands, so as to output optimized process parameters.

[0041] The diamond abrasive process parameters for the building monitoring area are adjusted based on the optimized process parameters.

[0042] The control gain parameters include the adjustment amount of the grinding machine vibration parameters, the adjustment amount of the diamond aggregate scattering, and the adjustment amount of the scattering frequency.

[0043] A third aspect of the present invention discloses a terminal, including a processor and a storage medium; characterized in that:

[0044] The storage medium is used to store instructions;

[0045] The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0046] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0047] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages:

[0048] (1) The present invention adds a local high-density monitoring module to the plate boundary and uneven areas, and uses an auxiliary high-precision vibration sensor and camera system to monitor key areas in real time. By comparing global data, it can promptly discover data coverage blind spots.

[0049] (2) The present invention adds a dedicated vibration feedback acquisition unit to the beam-column joint area. By focusing on the arrangement of micro vibration sensors, the vibration energy distribution in the joint area is monitored in real time to form vibration signal data, thereby effectively preventing local resonance.

[0050] (3) This invention establishes data fusion and intelligent closed-loop control, which matches, filters, corrects and fuses global monitoring data, local monitoring data and node vibration data to form global correction data and deviation early warning information. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the integrated molding and supervision method for industrial building frame floor slabs with corundum aggregate, provided by the present invention. Detailed Implementation

[0052] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0053] like Figure 1 As shown in one embodiment, a method for monitoring the integrated molding of emery aggregate flooring on an industrial building frame includes the following steps:

[0054] Step S110: Construct a wireless topology network in the building monitoring area to collect global monitoring data within the building monitoring area.

[0055] The wireless topology network is constructed from multiple sensors, and both global and local monitoring data include the vibration value, temperature, and initial setting state of the concrete.

[0056] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention includes the following steps in step S110:

[0057] Step S111: Obtain the floor geometry layout and concrete pouring design parameters of the building monitoring area, and determine the types and installation locations of multiple sensors based on the design parameters to generate a sensor layout matrix.

[0058] Step S112: Construct a wireless sensor network and wirelessly connect multiple sensors in the sensor array matrix to build a wireless topology network.

[0059] Step S113: Based on the wireless topology network, set up the data acquisition modules for each sensor and perform clock calibration on each data acquisition module.

[0060] Step S114: Perform noise correction and data fusion on the sensor data output by the data acquisition module to obtain global monitoring data for the building monitoring area.

[0061] In a specific embodiment, the integrated molding and supervision method for industrial building frame floor slab with corundum abrasive flooring provided by the present invention includes steps 1 to 5:

[0062] Step 1: Build a fully covered wireless sensor network.

[0063] Sensors and monitoring modules are deployed throughout the entire floor area to collect data such as concrete vibration, temperature, and initial setting status.

[0064] Specifically, it includes the following steps:

[0065] Step 1.1, Sensor selection and deployment planning.

[0066] Specifically, based on the floor geometry and concrete pouring design parameters, the types and distribution locations of vibration, temperature, and initial setting state detection sensors are determined to ensure that the entire floor is monitored without blind spots.

[0067] First, define the sensor arrangement matrix S as follows:

[0068] S={s(i,j)|s(i,j)=(x(i,j),y(i,j),z(i,j))}

[0069] Where x(i,j) represents the coordinates of the j-th sensor in the i-th row along the length of the floor (in meters); y(i,j) represents the coordinates of the j-th sensor in the i-th row along the width of the floor (in meters); z(i,j) represents the sensor installation height, a fixed value, usually between 0.05 and 0.10 m; i = 1, 2, ..., M represents the row number; j = 1, 2, ..., N represents the column number.

[0070] To ensure uniform distribution, the spacing d between each sensor satisfies:

[0071]

[0072] Where A represents the total floor area (unit: m²) 2 K represents the total number of sensors (K = M × N).

[0073] Step 1.2, Wireless Network Topology Design.

[0074] Specifically, a wireless sensor network is constructed, connecting each sensor and data acquisition module wirelessly, and the communication topology between nodes is designed to ensure real-time data transmission.

[0075] First, define the network topology matrix T as:

[0076] T={t(k)|t(k)=(ID(k),P(k),R(k))}

[0077] Where ID(k) represents the unique identifier of node k; P(k) represents the position vector of node k, P(k) = (x k ,y k ,z k ), which corresponds to the sensor installation location in S; R(k) represents the wireless signal receiving range of node k (unit: m), which is generally 30 to 50 m.

[0078] To ensure communication between any two adjacent nodes, the following requirements must be met:

[0079] distance(P(k),P(l))≤min(R(k),R(l))

[0080] For all connected nodes (k,l), distance(P(k),P(l)) can be calculated using Euclidean distance.

[0081] Step 1.3: Configure the data acquisition module to synchronize with the clock.

[0082] Specifically, data acquisition modules for each sensor are set up and clock calibration is performed to ensure that data on concrete vibration, temperature, and initial setting status are collected synchronously at the same time.

[0083] First, define the data output vector D(i) for each sensor as follows:

[0084] D(i)=(v(i),T(i),I(i),t(i))

[0085] Where v(i) is the temperature value measured by sensor i (unit: °C); T(i) is the initial condensation state index output by sensor i (dimensionless, ranging from 0 to 1); and t is the vibration value measured by sensor i (unit: m / s). 2 (i) represents the data acquisition time (in seconds), provided by the built-in clock.

[0086] Synchronization requires that the time difference between any two sensor acquisitions satisfies: |t(i)-t(j)|≤Δt, and it is recommended that Δt≤1s.

[0087] Step 1.4: Summarize and correct the monitoring data of the entire building.

[0088] Specifically, all sensor data is integrated, and after noise correction and data fusion processing, full-floor monitoring data D1 representing the status of the entire floor is generated.

[0089] First, define the full-floor monitoring data D1 as the average vector obtained by fusing data from all sensors:

[0090] D1=(v bar ,T bar ,I bar ,t ref )

[0091] in, This represents the average vibration values ​​from all sensors. This represents the average temperature values ​​from all sensors. t represents the average value of all initial setting state indices; ref To unify the reference time, the average of all t(i) can be taken or a certain time can be selected.

[0092] Step S120: Set up high-density monitoring modules in a localized area of ​​the building monitoring area to collect localized monitoring data of the building monitoring area.

[0093] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention includes the following steps in step S120:

[0094] Step S121: Calculate the deviation between the sensor data corresponding to the key monitoring area and the global monitoring data, and determine the key monitoring area as an abnormal area when the deviation exceeds the first threshold.

[0095] The key monitoring areas are the boundaries of the slab surfaces and the uneven areas within the building monitoring area.

[0096] Step S122: Plan and deploy high-precision vibration sensors and image acquisition devices in the abnormal area to build a high-density monitoring module.

[0097] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention further includes the following steps in step S120:

[0098] Step S123: Obtain the data vector output by each high-precision vibration sensor. The data vector consists of the local vibration value measured by each high-precision vibration sensor, the sensor noise standard deviation, and the data acquisition timestamp.

[0099] Step S124: Acquire image data output by the image acquisition device, and fuse the image data corresponding to each abnormal area with the data vector to generate local monitoring data of the building monitoring area.

[0100] In a specific embodiment, the integrated molding and monitoring method for industrial building frame floor slabs with corundum abrasive flooring provided by the present invention includes step 2, which involves adding local high-density monitoring modules to the slab boundaries and uneven areas.

[0101] By using auxiliary installation of high-precision vibration sensors and camera systems, key areas are collected in real time, and global data is compared to promptly identify blind spots in data coverage.

[0102] Specifically, it includes the following steps:

[0103] Step 2.1, Local abnormal area division and deviation index calculation.

[0104] Specifically, the full-floor monitoring data D1 is used to locate the boundaries and uneven areas of the slab surface, calculate the deviation between local and global data, and identify key areas that need to be monitored more closely.

[0105] It should be noted that when a plate boundary or uneven area appears, the average values ​​of sensor vibration, sensor temperature, and initial condensation state indicators will differ significantly from the corresponding average values ​​of other intact areas. Based on historical experience, a threshold range for the difference is set. When each average value exceeds the corresponding threshold range, the area is initially determined to be a plate boundary or uneven area. Further monitoring equipment is then used to further confirm the area, ultimately completing the location of the plate boundary and uneven area.

[0106] First, for a local area L on the board surface, the collected data is recorded as follows:

[0107] D local =(v local ,T local ,I local ,t local )

[0108] Among them, v local T represents the local average vibration value within region L; local I represents the local average temperature within region L; local This represents the local average initial setting index within region L; t local Indicates the data acquisition time, relative to t ref As close as possible.

[0109] Secondly, the deviation coefficient δ is defined as:

[0110] δ=|v local -v bar | / v bar

[0111] It should be noted that T local ,I local Similarly.

[0112] Step 2.2, Deployment planning of high-density monitoring modules.

[0113] Specifically, within the abnormal area L identified in step 2.1, a high-density monitoring module is planned and deployed, and high-precision vibration sensors and auxiliary camera systems are installed.

[0114] First, define the high-density monitoring module layout matrix S. H for:

[0115] S H ={s H(i,j) |s H(i,j) =(x H(i,j) ,y H(i,j) ,z H(i,j) ,type(i,j))}

[0116] Where, x H(i,j) This represents the coordinates (in meters) of the j-th high-density sensor in the i-th row along the X-direction within the local region L; y H(i,j) This represents the coordinates (in meters) of the j-th high-density sensor in the local region L along the Y direction; z H(i,j) The sensor installation height (in meters) is typically 0.05 to 0.10 meters; type(i,j) indicates the sensor type, such as "high-precision vibration" or "camera module".

[0117] Meanwhile, to ensure high-density coverage in local areas, the number of sensors per unit area (n) is required. H satisfy:

[0118] n H ≥K H / A L

[0119] Among them, K H This indicates the total number of high-density sensors (all types combined) within a local area; A L L represents the area of ​​the local anomaly region (unit: m). 2 In this example, n is recommended. H The value range is 0.5 to 1 per m 2 .

[0120] Step 2.3: Installation and calibration of high-precision vibration sensor and camera system.

[0121] Specifically, in S H High-precision vibration sensors and camera systems are installed at designated locations and preliminary calibration is performed to ensure data accuracy and real-time performance.

[0122] The output data vector of each high-precision vibration sensor is denoted as D. H (i):

[0123] D H (i)=(v H (i),σH (i),t H (i))

[0124] Among them, v H (i) represents the local vibration value measured by the i-th high-precision sensor (unit: m / s). 2 );σ H (i) represents the standard deviation of sensor noise (unit: m / s) 2 ), reflecting data accuracy; t H (i) represents the data acquisition time (in seconds), which needs to be compared with t. ref Maintain consistency.

[0125] The image data G(j) output by the camera system is represented as:

[0126] G(j)=(img(j),Q(j),t G (j))

[0127] Where img(j) represents the real-time image data (image file or video frame sequence) acquired by the j-th camera; Q(j) represents the image quality index (dimensionless, ranging from 0 to 1, with 1 being optimal); t G (j) indicates the image acquisition time (unit: seconds).

[0128] For high-precision data, the signal-to-noise ratio (SNR) must meet the following requirements:

[0129] SNR=20×log10(v H (i) / σ H (i))≥SNR threshold

[0130] This example recommends SNR. threshold It ranges from 30dB to 40dB.

[0131] Step 2.4, data fusion and generation of local high-precision monitoring data.

[0132] Specifically, the system integrates high-precision vibration data collected by the high-density monitoring module with data from the camera system, while also referencing global data to correct and fuse local area data, generating local high-precision monitoring data.

[0133] Let the local high-precision monitoring data D2 be the fused data vector, denoted as:

[0134] D2=(v D2 ,T D2 ,I D2 ,t D2 1mg D2 )

[0135] Among them, v D2The vibration value after local area fusion (unit: m / s) 2 );T D2 This represents the temperature value (unit: °C) of the merged local area; merging is performed if temperature data is collected. D2 t represents the initial coagulation index (dimensionless) after local region fusion; D2 Indicates that the data corresponds to a unified time; Img D2 This indicates the corrected image data, after image processing and quality assessment.

[0136] The weighted average formula used in data fusion is:

[0137]

[0138] Where α represents the high-precision data weighting factor, with a value ranging from 0.6 to 0.8, determined based on the actual correction situation; K H This indicates the total number of high-precision vibration sensors in the local high-density monitoring module; v local This represents the average value of global data initially measured in a local area.

[0139] Step S130: Add a vibration feedback acquisition unit to the beam-column joint in the building monitoring area to monitor the vibration energy distribution of the beam-column joint in real time and output the joint vibration data.

[0140] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention includes the following steps in step S130:

[0141] Step S131: Based on the local monitoring data, extract the beam-column nodes in the concrete floor of the building monitoring area to output a list of beam-column node regions; each beam-column node region has corresponding location information and area size.

[0142] Step S132: Set multiple micro vibration sensors in each beam-column node area and arrange the multiple micro vibration sensors to generate a micro vibration sensor arrangement matrix in each beam-column node area.

[0143] Step S133: Install multiple micro vibration sensors according to the micro vibration sensor arrangement matrix, and configure a corresponding data acquisition submodule for each micro vibration sensor to integrate the output of the data acquisition submodule to obtain a micro vibration sensor data set.

[0144] Step S134: Perform a weighted average on all the micro vibration sensor data sets in the beam-column node region list to generate and output node vibration data that characterizes the vibration energy distribution of the beam-column node.

[0145] In a specific embodiment, the integrated molding and monitoring method for corundum flooring of industrial building frame slabs provided by the present invention includes step 3, which involves adding a dedicated vibration feedback acquisition unit to the beam-column joint area.

[0146] Miniature vibration sensors are deployed to monitor the vibration energy distribution in the node area in real time, generate vibration signal data, and prevent local resonance.

[0147] Specifically, it includes the following steps:

[0148] Step 3.1: Identification and key distribution of beam-column joint areas.

[0149] Specifically, based on local high-precision monitoring data, the beam-column joint area in the concrete floor is extracted to delineate key vibration monitoring zones.

[0150] Beam-column node region extraction uses the region partitioning function R_node, as follows:

[0151] R node ={L node (m)|m=1,2,…,M node}

[0152] Among them, L node (m) represents the m-th beam-column node region, and each L node (m) contains the location of region P node (m) and area A node (m); P node (m)=(x node (m),y node (m) represents the center coordinates of node m (unit: m); A node (m) represents the monitoring coverage area of ​​node m (unit: m). 2 The typical range is 4–16m. 2 .

[0153] Select Img from D2 D2 Image data is processed using image processing algorithms to detect structural boundaries, and a node discrimination function F is defined. node , so that: F node (Img D2 )≥Th node → Belongs to the beam-column joint area, where Th node For image processing discrimination thresholds, this example suggests a value range of 0.7 to 0.9.

[0154] Step 3.2, Selection of miniature vibration sensors and planning of node area layout.

[0155] Specifically, in each beam-column joint region L nodeIn (m), a miniature vibration sensor is selected for detailed layout to realize real-time acquisition of the vibration energy distribution of the nodes.

[0156] First, define the sensor layout matrix S within the node region. node for:

[0157] S node ={s node (i,j,m)|s node (i,j,m)

[0158] =(x node (i,j,m),y node (i,j,m),z node (i,j,m),type node (i,j,m))}

[0159] Where, x node (i,j,m) represents the coordinates (in meters) of the j-th sensor in the i-th row within the m-th node region along the X-direction; node (i,j,m) represents the Y-coordinate (in meters) of the j-th sensor in the i-th row within the m-th node region; z node (i,j,m) represents the installation height, with a fixed value of 0.05~0.10m; type node (i,j,m) represents the sensor type, all of which are "miniature vibration sensors".

[0160] Let A node (m) represents the area of ​​the m-th node region. Therefore, the required number of sensors per unit area is n. node satisfy:

[0161] n node ≥K node (m) / A node (m)

[0162] Among them, K node (m) represents the total number of sensors in the m-th node region; n is recommended. node The range is 1 to 2 per m 2 This ensures high-density data collection.

[0163] Step 3.3: Sensor installation, data acquisition configuration, and clock synchronization calibration.

[0164] Specifically, in S node At each of the designated installation locations, miniature vibration sensors are installed sequentially, and data acquisition modules are configured to ensure that vibration data in the node area is collected in real time and synchronously.

[0165] Define the data vector D of the node micro vibration sensor node(i,j,m) is:

[0166] D node (i,j,m)=(v node (i,j,m),σ node (i,j,m),t node (i,j,m))

[0167] Among them, v node (i,j,m) represents the vibration value (unit: m / s) measured by the j-th sensor in the i-th row within the m-th node region. 2 );σ node (i,j,m) represents the noise standard deviation of the vibration data measured by the sensor (unit: m / s). 2 ), reflecting data accuracy; t node (i,j,m) represents the acquisition time (unit: s), which needs to be synchronized with the global clock t. ref synchronous.

[0168] Synchronization requirements:

[0169] |t node (i,j,m)-t ref |≤Δt node It is recommended that Δt node ≤1s.

[0170] Step 3.4, Data fusion and generation of local vibration data at nodes.

[0171] Specifically, data collected by micro vibration sensors in all node areas are integrated, and local vibration data of the nodes is generated based on a weighted average method to reflect the vibration energy distribution in the node area and prevent resonance.

[0172] Define the nodal local vibration data V1 as:

[0173]

[0174] in, This represents the weighted average of vibration values ​​from all sensors within the m-th node region (unit: m / s). 2 ); This represents the uniform data collection time (unit: seconds) within the node region, and t can be selected. ref Or the average value at the time of sensor acquisition.

[0175] Using the weighted average formula:

[0176]

[0177] Where α represents the weighting factor, with a value ranging from 0.6 to 0.8, determined based on actual correction; K node(m) represents the total number of sensors in the m-th node region (from S). node get); This represents the pre-measured vibration value in the local high-precision monitoring data within the node area, which serves as a global reference (derived from D2, obtained within the corresponding node area after regional division). M represents the total number of rows within the m-th node area, and N represents the total number of columns within the m-th node area.

[0178] Step S140 involves preprocessing and fusing global monitoring data, local monitoring data, and nodal vibration data to generate correction feedback data for the building monitoring area.

[0179] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention includes the following steps in step S140:

[0180] Step S141: Denoising and low-pass filtering are performed on the global monitoring data, local monitoring data, and node vibration data to obtain a preprocessed dataset.

[0181] Step S142: Match the local monitoring data and node vibration data in the preprocessed dataset to perform time alignment between the local monitoring data and node vibration data, and obtain the corrected dataset.

[0182] Step S143: Invoke the data fusion formula to integrate the local monitoring data and node vibration data in the calibration dataset to correct local deviations and output calibration feedback data.

[0183] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention further includes the following steps in step S140:

[0184] Step S144: Establish a closed-loop control module and call the closed-loop control module to compare the correction feedback data with the local monitoring data and the global monitoring data to trigger early warning feedback and output the parameters to be adjusted.

[0185] The parameters to be adjusted include the vibration frequency of the vibrating equipment and the spraying rate of the corundum.

[0186] In a specific embodiment, the integrated molding and monitoring method for industrial building frame floor slabs with corundum abrasive flooring provided by the present invention includes step 4: data fusion and intelligent closed-loop control establishment.

[0187] The data from steps 2 and 3 are input into the intelligent algorithm system to preprocess and fuse local and node data, forming global correction data and deviation warning information.

[0188] Specifically, it includes the following steps:

[0189] Step 4.1, Data Preprocessing and Filtering.

[0190] Specifically, the input local high-precision monitoring data D2 and node vibration data V1 are preprocessed, denoised, and low-pass filtered to obtain smooth and accurate basic data.

[0191] First, define the nodal vibration data V1 as:

[0192] V1 = {V1(m)|m = 1, 2, ..., M} node}

[0193] in, This represents the average vibration value of the m-th beam-column joint region (unit: m / s). 2 ); This indicates the time of uniform data collection for this node area (unit: seconds).

[0194] The local monitoring data D2 and the nodal vibration data V1 are low-pass filtered and denoted as:

[0195] D2 filtered =F low (D2)

[0196] V1 filtered =F low (V1(m))

[0197] Among them, F low For the low-pass filter function, set the cutoff frequency F. c The frequency range is 0.5 to 2 Hz.

[0198] Step 4.2, data matching and timing correction.

[0199] Specifically, D2 filtered With V1 filtered The data collection time is then corrected to ensure that the two are aligned in the time dimension.

[0200] First, define the timing error Δt(m) as:

[0201]

[0202] Requirement: Δt(m) ≤ Δt threshold Where represents the permissible time difference threshold, and in this example, Δt is recommended. threshold ≤1s. If there exists Δt(m)>Δt threshold Then, through linear interpolation I time The corresponding sensor node data is corrected, and its expression is as follows:

[0203] V1 corr (m)=Itime (V1 filtered (m),t D2 )

[0204] Among them, I time This represents a timestamp-based linear interpolation correction function that ensures that node data is always consistent with global data.

[0205] Step 4.3, Data fusion and correction formula construction.

[0206] Specifically, a data fusion formula is designed and applied to integrate local data with node data, correct local deviations, and output global correction feedback data.

[0207] First, establish the fusion formula:

[0208] C1=f(D2 corr V1 corr )

[0209] The fusion function f is defined as a weighted fusion, and its expression is:

[0210]

[0211] Among them, v C1 The vibration value after global correction (unit: m / s) 2 ); D2 corr Local vibration values ​​after time-correction; This represents the corrected vibration value of node m, belonging to V1. corr The components in; M node β represents the total number of node regions; β represents the weighting factor, ranging from 0.5 to 0.8, used to balance the influence of local data and node data, and is usually determined by field experiments. Similarly, temperature T and initial freezing index I can be weighted and fused, which will not be elaborated here. The weighted fusion of temperature T and initial freezing index I is represented as T. C1 and I C1 .

[0212] Finally, the representation vector of the global correction feedback data is output:

[0213] C1=(v C1 ,T C1 ,I C1 ,t ref Warn flag )

[0214] Among them, Warn flag The deviation warning status is defined as follows:

[0215] Warn flag ={1,ifδtotal >δ threshold ;0,otherwise}

[0216] Where, δ total This represents the result of the global deviation calculation, δ threshold This indicates the deviation alarm threshold; "1" indicates an alarm, and "0" indicates no alarm.

[0217] Step 4.4, Intelligent closed-loop control module configuration and early warning feedback.

[0218] Specifically, an intelligent closed-loop control module is established to compare C1 with real-time monitoring parameters, automatically adjust construction parameters and equipment status, trigger early warning feedback, and achieve dynamic process control.

[0219] Define the closed-loop control adjustment function G control Output adjustment parameter P adj The formula is:

[0220] P adj =G control (C1,C target )

[0221] Among them, P adj This represents a set of control parameters (e.g., vibration frequency adjustment Δf for vibrating equipment, corundum spraying rate adjustment Δr, etc.); C target Represents the preset target correction data set: C target =(v target ,T target ,I target );G control This represents error feedback control, denoted as P. adj =K p ×(C target -C1 meas ), K p This represents the proportional gain coefficient (a constant, recommended range 0.5–1.5, determined based on system debugging results); C1 meas This represents the current global correction feedback data being monitored.

[0222] In this embodiment, when |C target -C1 meas s|>Δ threshold At that time, the system triggers an alarm, Δ threshold To allow for an acceptable error range, a value of 0.05 to 0.10 is recommended; otherwise, no alarm will be triggered.

[0223] Step S150: Based on the correction feedback data, adjust the diamond abrasive process parameters in the building monitoring area.

[0224] In some embodiments, the integrated molding and supervision method for industrial building frame floor slab concrete abrasive flooring provided by the present invention includes the following steps in step S150:

[0225] Step S151: Based on the parameters to be adjusted, extract the initial process parameters of the diamond from the calibration feedback data, and compare the initial process parameters with the set target process parameters to calculate the error signal between the initial process parameters and the target process parameters.

[0226] Step S152: Based on the error signal, proportional regulation and PID control algorithms are used to adjust the grinding mill vibration parameters, the amount and frequency of diamond aggregate scattering in the diamond abrasive process parameters, in order to set the control gain parameters.

[0227] Among them, the control gain parameters include the adjustment amount of the grinding machine vibration parameters, the adjustment amount of the diamond aggregate scattering, and the adjustment amount of the scattering frequency.

[0228] Step S153: Convert the control gain parameters into control commands, and in response to the control commands, correct the vibration parameters of the grinding mill, the amount and frequency of diamond aggregate scattering, and output optimized process parameters.

[0229] Step S154: Adjust the diamond abrasive process parameters in the building monitoring area based on optimized process parameters.

[0230] In a specific embodiment, the integrated molding and monitoring method for industrial building frame floor slab with corundum abrasive flooring provided by the present invention includes step 5, which involves implementing real-time dynamic adjustment of process parameters.

[0231] By combining closed-loop feedback data, the automatic control module is set to finely adjust the vibration parameters of the grinding machine, the amount and frequency of diamond aggregate application, and ensure that the compaction of local areas and the vibration balance of nodes meet the design specifications.

[0232] Specifically, it includes the following steps:

[0233] Step 5.1: Closed-loop feedback data extraction and error signal calculation.

[0234] Specifically, key process data are extracted from the global correction feedback data C1 and compared with the design target data to calculate the error signals required for vibration, aggregate throwing amount and frequency adjustment.

[0235] First, based on the global correction feedback data and the design target data, i.e., C1 = (v C1 ,T C1 ,I C1 ,t ref Warn flag ) and C target =(v target ,Ttarget ,I target ), calculate the vibration error signal E v E v =(v target -v C1 ) / v target Aggregate spillage error E T and spraying frequency error E I Similarly.

[0236] Step 5.2, Dynamic control algorithm design and gain parameter determination.

[0237] Specifically, based on the vibration error signal E v A control algorithm was designed, employing proportional regulation or PID control to initially adjust the vibration parameters of the grinding machine, the amount of corundum aggregate sprinkled, and the frequency. The control gain coefficient was then set.

[0238] The control formula used to adjust vibration parameters is expressed as follows:

[0239] ΔP v =K v ×E v

[0240] Where, ΔP v The adjustment amount of the vibration parameters (unit: m / s) 2 (or corresponding physical quantity); K v This represents the vibration control proportional gain (dimensionless), with a recommended range of 0.5 to 1.5, depending on field test results; E v This represents the vibration error signal obtained from the aforementioned calculation. The same applies to the amount and frequency of scattering of corundum aggregate, which will not be repeated here.

[0241] Finally, the adjustment amounts for the output vibration parameters, the amount of corundum aggregate scattering, and the scattering frequency are adjusted.

[0242] Step 5.3: Convert the adjustment amount into specific process parameter control.

[0243] Specifically, the adjustment amounts of vibration parameters, the amount of corundum aggregate being sprinkled, and the frequency of sprinkling output in step 5.2 are converted into specific equipment control commands to correct the parameters of the grinding machine vibration, the amount of corundum aggregate being sprinkled, and the frequency of sprinkling.

[0244] Define the current equipment process parameter P current for:

[0245]

[0246] in,( This indicates the current vibration parameters of the grinding machine (such as vibration acceleration, in m / s²). 2);W current This indicates the current amount of corundum aggregate being applied (unit: kg / m³). 2 );f current This indicates the current aggregate scattering frequency (unit: times / s).

[0247] Optimized equipment parameters P opt Defined as:

[0248]

[0249] in, W represents the optimized vibration parameters. opt ,f opt Similarly, we can obtain the optimized amount and frequency of corundum aggregate application, respectively.

[0250] This invention performs weighted adjustments on three key process parameters to ensure that, under closed-loop control, the equipment output matches the design target as closely as possible, while also taking into account local compaction and node vibration balance.

[0251] Step 5.4: Closed-loop feedback verification and parameter adjustment confirmation.

[0252] Specifically, the optimized process parameters P opt Feedback is sent to the automatic control system, and a short trial run is conducted to verify whether the difference between the actual output parameters and the design target is within the allowable range; if the error still exists, the next cycle of fine-tuning begins.

[0253] Define the closed-loop feedback function F loop for:

[0254] P measured =F loop (P opt )

[0255] Among them, P measured These are the actual measured process parameters (structural state feedback), in the same form as P. opt Subsequently, the new adjustment error E is recalculated. new :

[0256] E new =(P target -P measured ) / P target

[0257] Similarly, calculate new adjustment errors for vibration, spraying volume, and spraying frequency until the set allowable error range is met, then the adjustment is complete. After adjustment, adjust the diamond abrasive vibration, spraying volume, and spraying frequency based on the latest adjustment errors.

[0258] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0259] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0260] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0261] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0262] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0263] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0264] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0265] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0266] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for supervising the integrated molding of corundum flooring for industrial building frames, characterized in that, The method includes: A wireless topology network is constructed within the building monitoring area to collect global monitoring data within the building monitoring area; High-density monitoring modules are installed in certain areas of the building monitoring area to collect local monitoring data of the building monitoring area; A vibration feedback acquisition unit is added to the beam-column joints in the building monitoring area to monitor the vibration energy distribution of the beam-column joints in real time and output the joint vibration data. The global monitoring data, local monitoring data, and nodal vibration data are preprocessed and fused to generate correction feedback data for the building monitoring area; Based on the correction feedback data, the diamond abrasive process parameters of the building monitoring area are adjusted; The wireless topology network is constructed from multiple sensors, and the global and local monitoring data include the vibration value, temperature, and initial setting state of the concrete.

2. The method for integrated molding and supervision of industrial building frame floor slab with corundum aggregate as described in claim 1, characterized in that, The construction of a wireless topology network within the building monitoring area to collect global monitoring data within the building monitoring area includes: The floor geometry layout and concrete pouring design parameters of the building monitoring area are obtained, and the types and installation positions of the multiple sensors are determined based on the design parameters to generate a sensor layout matrix. A wireless sensor network is constructed, and multiple sensors in the sensor arrangement matrix are wirelessly connected through the wireless sensor network to construct the wireless topology network; Based on the wireless topology network, data acquisition modules for each sensor are set up, and clock calibration is performed on each data acquisition module. The sensor data output by the data acquisition module is subjected to noise correction and data fusion to obtain global monitoring data for the building monitoring area.

3. The method for integrated molding and supervision of industrial building frame floor slab with corundum aggregate as described in claim 2, characterized in that, The method of setting up high-density monitoring modules in specific areas of the building monitoring area to collect local monitoring data of the building monitoring area includes: Calculate the deviation between the sensor data corresponding to the key monitoring area and the global monitoring data, and determine the key monitoring area as an abnormal area when the deviation exceeds a first threshold; High-precision vibration sensors and image acquisition devices are planned and deployed in the abnormal area to construct the high-density monitoring module; The key monitoring area refers to the boundary and uneven areas of the slab surface within the building monitoring area.

4. The method for integrated molding and supervision of corundum flooring for industrial building frames according to claim 3, characterized in that, The method of setting up high-density monitoring modules in specific areas of the building monitoring area to collect local monitoring data of the building monitoring area also includes: Acquire the data vector output by each high-precision vibration sensor. The data vector consists of the local vibration value measured by each high-precision vibration sensor, the standard deviation of sensor noise, and the data acquisition timestamp. The image data output by the image acquisition device is acquired, and the image data corresponding to each abnormal area is fused with the data vector to generate local monitoring data of the building monitoring area.

5. The method for integrated molding and supervision of corundum flooring for industrial building frames according to claim 4, characterized in that, A vibration feedback acquisition unit is added to the beam-column joints in the building monitoring area to monitor the vibration energy distribution of the beam-column joints in real time and output the joint vibration data, including: Based on the local monitoring data, beam-column nodes in the concrete floor of the building monitoring area are extracted to output a list of beam-column node regions; each beam-column node region has corresponding location information and area size; Multiple micro vibration sensors are set in each beam-column node area, and the multiple micro vibration sensors are arranged to generate a micro vibration sensor arrangement matrix in each beam-column node area. The multiple micro vibration sensors are installed according to the micro vibration sensor arrangement matrix, and a corresponding data acquisition submodule is configured for each micro vibration sensor to integrate the output of the data acquisition submodule to obtain a micro vibration sensor data set. A weighted average is performed on all the micro-vibration sensor data sets in the beam-column node region list to generate and output the node vibration data that characterizes the vibration energy distribution of the beam-column node.

6. The method for integrated molding and supervision of corundum flooring for industrial building frames according to claim 5, characterized in that, The preprocessing and fusion of the global monitoring data, local monitoring data, and nodal vibration data to generate correction feedback data for the building monitoring area includes: The global monitoring data, local monitoring data, and node vibration data are preprocessed by denoising and low-pass filtering to obtain a preprocessed dataset; The local monitoring data and node vibration data in the preprocessed dataset are matched to perform time alignment of the local monitoring data and node vibration data to obtain the corrected dataset; The local monitoring data and node vibration data in the correction dataset are integrated by calling the data fusion formula to correct local deviations and output the correction feedback data.

7. The method for integrated molding and supervision of corundum flooring for industrial building frames according to claim 6, characterized in that, The preprocessing and fusion of the global monitoring data, local monitoring data, and nodal vibration data to generate correction feedback data for the building monitoring area also includes: A closed-loop control module is established, and the closed-loop control module is called to compare the correction feedback data with the local monitoring data and the global monitoring data to trigger early warning feedback and output the parameters to be adjusted; The parameters to be adjusted include the vibration frequency of the vibrating equipment and the spraying rate of the corundum.

8. The method for integrated molding and supervision of corundum flooring for industrial building frames according to claim 7, characterized in that, The adjustment of the diamond abrasive process parameters in the building monitoring area based on the correction feedback data includes: Based on the parameters to be adjusted, the initial process parameters of the diamond are extracted from the correction feedback data, and the initial process parameters are compared with the set target process parameters to calculate the error signal between the initial process parameters and the target process parameters. Based on the error signal, proportional regulation and PID control algorithms are used to adjust the grinding mill vibration parameters, the amount of diamond aggregate being thrown, and the throwing frequency in the diamond abrasive process parameters, in order to set the control gain parameters. The control gain parameters are converted into control commands, and the vibration parameters of the grinding mill, the amount and frequency of the diamond aggregate being thrown are corrected in response to the control commands, so as to output optimized process parameters. The diamond abrasive process parameters for the building monitoring area are adjusted based on the optimized process parameters. The control gain parameters include the adjustment amount of the grinding machine vibration parameters, the adjustment amount of the diamond aggregate scattering, and the adjustment amount of the scattering frequency.

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