Integrated forming supervision method for industrial building frame floor carborundum terrace

Abstract

An industrial building frame floor carborundum terrace integral forming supervision method belongs to the construction monitoring technology field, and comprises the following steps: constructing a wireless topology network in a building monitoring area to collect global monitoring data in the building monitoring area; and a high-density monitoring module is arranged at the local part of the building monitoring area so as to acquire local monitoring data of the building monitoring area. A vibration feedback acquisition unit is additionally arranged on a beam-column node of a building monitoring area, and vibration energy distribution of the beam-column node is monitored in real time so as to output node vibration data. And preprocessing and fusing the global monitoring data, the local monitoring data and the node vibration data to generate correction feedback data of the building monitoring area. Adjusting the carborundum process parameters of the building monitoring area based on the correction feedback data; wherein the wireless topology network is constructed by a plurality of sensors, and both the global monitoring data and the local monitoring data comprise the vibration value, the temperature and the initial setting state of the concrete.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction monitoring, and more specifically, relates to a method for monitoring the integrated molding of diamond abrasive flooring on industrial building frame floors. Background Art

[0002] Deficiencies in the local overlap and compaction detection and monitoring system: While the overall solution utilizes a wireless sensor network to monitor the entire floor, it lacks a system for detecting overlap and compaction in specific areas (such as panel boundaries and uneven areas). Due to the continuous construction of large-scale integrated corundum molding, localized areas often experience insufficient sensing or data coverage blind spots, potentially leading to discrepancies in the embedding and compaction of corundum aggregate.

[0003] Insufficient local vibration feedback at beam-column joints: In frame structures, beam-column joints are prone to local resonance or uneven vibration energy due to large variations in structural stiffness. While the current solution uses a 300kg±10% deadweight grinding machine and vibrating rods to achieve overall vibration compaction, it lacks a dedicated closed-loop control system for vibration monitoring and control at local nodes (such as beam-column intersections). Summary of the Invention

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to solve the above-mentioned defects and further propose an integrated molding and supervision method for the diamond abrasive floor of the industrial building frame floor.

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

[0006] The first aspect of the present invention discloses a method for supervising the integrated molding of diamond abrasive flooring on the frame floor of an industrial building, the method comprising:

[0007] Constructing a wireless topology network in the building monitoring area to collect global monitoring data in the building monitoring area;

[0008] A high-density monitoring module is provided in a part of the building monitoring area to collect local monitoring data of the building monitoring area;

[0009] Adding a vibration feedback acquisition unit to the beam-column nodes in the building monitoring area to monitor the vibration energy distribution of the beam-column nodes in real time and output node vibration data;

[0010] Preprocessing and fusing the global monitoring data, local monitoring data, and node vibration data to generate correction feedback data for the building monitoring area;

[0011] Adjusting the diamond grinding process parameters of the building monitoring area based on the correction feedback data;

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

[0013] Furthermore, the wireless topology network is constructed in the building monitoring area to collect global monitoring data in the building monitoring area, including:

[0014] Obtaining the floor geometry layout and concrete pouring design parameters of the building monitoring area, and determining the types and installation locations of the plurality of sensors based on the design parameters to generate a sensor arrangement matrix;

[0015] Building a wireless sensor network, and wirelessly connecting a plurality of sensors in the sensor arrangement matrix through the wireless sensor network to build the wireless topology network;

[0016] Based on the wireless topology network, a data acquisition module of each sensor is set, and clock correction is performed on each data acquisition module;

[0017] Noise correction and data fusion are performed on the sensor data output by the data acquisition module to obtain global monitoring data of the building monitoring area.

[0018] Furthermore, the high-density monitoring module is set in a part of the building monitoring area to collect local monitoring data of the building monitoring area, including:

[0019] Calculating a deviation between sensor data corresponding to a key monitoring area and the global monitoring data, and determining that the key monitoring area is an abnormal area when the deviation exceeds a first threshold;

[0020] Planning and deploying high-precision vibration sensors and image acquisition equipment in the abnormal area to construct the high-density monitoring module;

[0021] The key monitoring area is the board surface boundary and uneven area within the building monitoring area.

[0022] Furthermore, the high-density monitoring module is set in a part of the building monitoring area to collect local monitoring data of the building monitoring area, and further includes:

[0023] Obtaining a data vector output by each high-precision vibration sensor, wherein the data vector is composed of a local vibration value measured by each high-precision vibration sensor, a sensor noise standard deviation, and a 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 nodes of the building monitoring area to monitor the vibration energy distribution of the beam-column nodes in real time to output node vibration data, including:

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

[0027] Disposing a plurality of micro vibration sensors in each beam-column node region and laying out the plurality of micro vibration sensors to generate a micro vibration sensor arrangement matrix in each beam-column node region;

[0028] Installing the plurality of micro-vibration sensors according to the micro-vibration sensor arrangement matrix, and configuring a corresponding data acquisition submodule for each micro-vibration sensor to integrate outputs of the data acquisition submodule to obtain a micro-vibration sensor data set;

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

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

[0031] Performing preprocessing of denoising and low-pass filtering on the global monitoring data, local monitoring data, and node vibration data to obtain a preprocessed data set;

[0032] Matching the local monitoring data and the node vibration data in the preprocessed data set to time-align the local monitoring data and the node vibration data to obtain a corrected data set;

[0033] The data fusion formula is called to integrate the local monitoring data and the node vibration data in the correction data set to correct the local deviation and output the correction feedback data.

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

[0035] Establishing a closed-loop control module, and calling 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 parameters to be adjusted;

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

[0037] Furthermore, adjusting the corundum process parameters of the building monitoring area based on the correction feedback data includes:

[0038] Extracting initial process parameters of diamond abrasives from the correction feedback data based on the parameters to be adjusted, and comparing the initial process parameters with set target process parameters to calculate an error signal between the initial process parameters and the target process parameters;

[0039] Based on the error signal, proportional regulation and PID control algorithm are used to adjust the grinding machine vibration parameters, the diamond aggregate throwing level and throwing frequency in the diamond grinding process parameters to set the control gain parameters;

[0040] Converting the control gain parameter into a control instruction, and performing parameter correction on the grinding machine vibration parameter, diamond aggregate scattering level and scattering frequency in response to the control instruction to output optimized process parameters;

[0041] Adjusting the diamond grinding process parameters of the building monitoring area based on the optimized process parameters;

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

[0043] A third aspect of the present invention discloses a terminal, comprising 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 execute the steps of the method of the first aspect.

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

[0047] The beneficial effects of the present invention are that, compared with the prior art, the present invention has the following advantages:

[0048] (1) The present invention adds local high-density monitoring modules at the board surface boundary and uneven areas, and uses auxiliary high-precision vibration sensors and camera systems to perform real-time monitoring of key areas. By comparing the global data, data coverage blind spots can be discovered in a timely manner.

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

[0050] (3) The present invention establishes data fusion and intelligent closed-loop control, matches, filters, corrects and fuses global monitoring data, local monitoring data and node vibration data, and forms global correction data and deviation warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention provides a flow chart of the integrated molding and supervision method of the diamond abrasive floor on the frame floor of an industrial building. DETAILED DESCRIPTION

[0052] The present application will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.

[0053] like Figure 1 As shown, in one embodiment, a method for supervising the integrated molding of diamond abrasive flooring on an industrial building frame floor comprises the following steps:

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

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

[0056] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S110 specifically includes the following steps:

[0057] Step S111 : obtaining the geometric layout of the floor of the building monitoring area and the design parameters of the concrete pouring, and determining the types and installation positions of multiple sensors based on the design parameters to generate a sensor arrangement matrix.

[0058] Step S112: constructing a wireless sensor network, and wirelessly connecting multiple sensors in the sensor arrangement matrix through the wireless sensor network to construct a wireless topology network.

[0059] Step S113: Based on the wireless topology network, a data acquisition module of each sensor is set, and clock calibration is performed on each data acquisition module.

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

[0061] In a specific embodiment, the method for supervising the integrated molding of diamond abrasive flooring on the frame floor of an industrial building provided by the present invention includes steps 1 to 5:

[0062] Step 1: Build a full-coverage wireless sensor network.

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

[0064] Specifically, the following steps are included:

[0065] Step 1.1: Sensor selection and layout 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 blind-spot monitoring of the entire floor.

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

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

[0069] Where x(i,j) represents the coordinate of the jth sensor in the i-th row along the length of the floor (unit: m); y(i,j) represents the coordinate of the jth sensor in the i-th row along the width of the floor (unit: m); z(i,j) represents the sensor installation height, a fixed value usually ranging from 0.05 to 0.10 m; i = 1, 2, …, M represents the row number; j = 1, 2, …, N represents the column number.

[0070] To ensure uniform layout, the distance d between sensors must meet the following requirements:

[0071]

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

[0073] Step 1.2: Design wireless network topology.

[0074] Specifically, a wireless sensor network is constructed, each sensor is connected to the 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 ), corresponding to the sensor installation position in S; R(k) represents the wireless signal receiving range of node k (unit: m), which is generally in the range of 30 to 50 m.

[0078] To ensure that any two adjacent nodes can communicate, it is required that:

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

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

[0081] Step 1.3, data acquisition module configuration and clock synchronization.

[0082] Specifically, a data acquisition module is set for each sensor, and clock correction is performed to ensure that the concrete vibration, temperature and initial setting state data are collected synchronously at the same time.

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

[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); I(i) is the initial setting state index output by sensor i (dimensionless, ranging from 0 to 1); t is the vibration value measured by sensor i (unit: m / s 2 ); (i) is the data acquisition time (unit: second), provided by the built-in clock.

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

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

[0088] Specifically, all sensor data are 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 all sensor data:

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

[0091] in, Indicates the average value of all sensor vibration values; Indicates the average temperature value of all sensors; Represents the average value of all initial setting state indicators; t ref To unify the reference time, we can take the average of all t(i) or select a certain moment.

[0092] Step S120: Setting a high-density monitoring module in a part of the building monitoring area to collect local monitoring data of the building monitoring area.

[0093] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S120 specifically includes the following steps:

[0094] Step S121 : calculating the deviation between the sensor data corresponding to the key monitoring area and the global monitoring data, and determining that the key monitoring area is an abnormal area when the deviation exceeds a first threshold.

[0095] Among them, the key monitoring areas are the board surface boundaries and uneven areas within the building monitoring area.

[0096] Step S122 : planning and deploying high-precision vibration sensors and image acquisition equipment in the abnormal area to construct a high-density monitoring module.

[0097] In some embodiments, the method for supervising the integrated molding of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S120 specifically further includes the following steps:

[0098] Step S123 : obtaining a data vector output by each high-precision vibration sensor, where the data vector is composed 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 , acquiring image data output by the image acquisition device, and fusing 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 present invention provides a method for monitoring the integrated formation of diamond abrasive flooring on the frame floor of an industrial building, step 2, adding local high-density monitoring modules at the board surface boundary and uneven areas.

[0101] Use auxiliary installation of high-precision vibration sensors and camera systems to collect real-time data in key areas, compare global data, and promptly identify data coverage blind spots.

[0102] Specifically, the following steps are included:

[0103] Step 2.1: Division of local abnormal regions and calculation of deviation index.

[0104] Specifically, the entire floor monitoring data D1 is used to locate the board surface boundary and uneven areas, calculate the deviation between local data and global data, and identify key areas that require enhanced inspection.

[0105] It should be noted that when a plate surface boundary or an uneven area appears, the sensor vibration value, the average value, the average value of the sensor temperature value, and the average value of the initial setting state index will have a large gap compared with the corresponding average values ​​of other complete areas. The gap threshold range is set based on historical experience. When each average value exceeds the corresponding gap threshold range, the area is preliminarily judged to be a plate surface boundary or an uneven area, and the area is further determined by monitoring equipment to finally complete the positioning of the plate surface boundary and the uneven area.

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

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

[0108] Among them, v local represents the local average vibration value in area L; T local represents the local average temperature in area L; I local represents the local average initial setting state index in the region L; t local Indicates the data collection time, and t ref As close as possible.

[0109] Next, define the coefficient of deviation δ as:

[0110] δ=|v local -v bar | / v bar

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

[0112] Step 2.2: High-density monitoring module deployment planning.

[0113] Specifically, within the abnormal area L identified in step 2.1, high-density monitoring modules are 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] Among them, x H(i,j) represents the x-direction coordinate of the j-th high-density sensor in the i-th row in the local area L (unit: m); H(i,j) represents the Y-direction coordinate of the j-th high-density sensor in the i-th row within the local area L (unit: m); z H(i,j) Indicates the sensor installation height (unit: m), generally 0.05 to 0.10 m; type(i,j) indicates the sensor type, such as "high-precision vibration" or "camera module".

[0117] At the same time, in order to ensure high-density coverage of local areas, the number of sensors per unit area is required to be n H satisfy:

[0118] n H ≥K H / A L

[0119] Among them, K H Indicates the total number of high-density sensors in the local area (total of all types); A L Indicates the area of ​​the local abnormal region L (unit: m 2 ), in this case, 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] Each high-precision vibration sensor output data vector 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 the data accuracy; t H (i) represents the data collection time (unit: second), which must be the same as t ref Stay consistent.

[0125] The camera system output image data G(j) is expressed 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) collected by the jth camera; Q(j) represents the image quality index (dimensionless, ranging from 0 to 1, with 1 being the best); t G (j) represents 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] Recommended SNR in this example threshold 30dB to 40dB.

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

[0132] Specifically, the high-precision vibration data collected by the high-density monitoring module is integrated with the camera system data, and the global data is referenced to correct and fuse the local area data to generate local high-precision monitoring data.

[0133] Assume that the local high-precision monitoring data D2 is the fused data vector, which is recorded as:

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

[0135] Among them, v D2Indicates the vibration value after local area fusion (unit: m / s 2 );T D2 Indicates the temperature value of the local area after fusion (unit: °C). If temperature is collected, it will be fused; I D2 represents the initial setting index after local area fusion (dimensionless); t D2 Indicates that the data corresponds to the same time; Img D2 Represents the rectified image data after image processing and quality assessment.

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

[0137]

[0138] Among them, α represents the high-precision data weight factor, with a value range of 0.6 to 0.8, determined according to the actual calibration situation; K H Represents the total number of high-precision vibration sensors in the local high-density monitoring module; v local Represents the average value of global data initially measured in a local area.

[0139] Step S130 , adding a vibration feedback acquisition unit to the beam-column nodes in the building monitoring area to monitor the vibration energy distribution of the beam-column nodes in real time to output node vibration data.

[0140] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S130 specifically includes the following steps:

[0141] Step S131 , extracting the beam-column nodes in the concrete floor within the building monitoring area based on the local monitoring data to output a list of beam-column node areas; each beam-column node area has corresponding location information and area size.

[0142] Step S132 : setting a plurality of micro vibration sensors in each beam-column node region and laying out the plurality of micro vibration sensors to generate a micro vibration sensor arrangement matrix in each beam-column node region.

[0143] Step S133 , 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.

[0144] Step S134 , performing weighted averaging on all micro-vibration sensor data sets in the beam-column node area list, generating and outputting node vibration data for characterizing the vibration energy distribution of the beam-column node.

[0145] In a specific embodiment, the present invention provides a method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors. In step 3, a dedicated vibration feedback collection unit is added to the beam-column node area.

[0146] Focus on arranging micro vibration sensors to monitor the vibration energy distribution in the node area in real time, generate vibration signal data, and prevent local resonance.

[0147] Specifically, the following steps are included:

[0148] Step 3.1, identification of beam-column node areas and division of key distribution points.

[0149] Specifically, based on local high-precision monitoring data, the beam-column node area in the concrete floor is extracted and divided into key vibration monitoring areas.

[0150] The beam-column node area extraction uses the area division function R_node as follows:

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

[0152] Among them, L node (m) represents the mth beam-column node area, and each L node (m) contains the area position P node (m) and area A node (m); P node (m)=(x node (m),y node (m)) represents the center coordinate of node m (unit: m); A node (m) represents the monitoring coverage area of ​​node m (unit: m 2 ), usually ranging from 4 to 16 meters 2 .

[0153] Select Img in D2 D2 Image data, detect structure boundaries through image processing algorithms, set node discrimination function F node , so that: F node (Img D2 )≥Th node → belongs to the beam-column joint area, where Th node is the image processing discrimination threshold. In this example, the recommended value range is 0.7 to 0.9.

[0154] Step 3.2, micro vibration sensor selection and node area layout planning.

[0155] Specifically, in each beam-column joint area L nodeIn (m), the design uses micro vibration sensors for detailed layout to achieve real-time acquisition of node vibration energy distribution.

[0156] First, define the sensor layout matrix S within the node area 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] Among them, x node (i, j, m) represents the X-direction coordinates of the j-th sensor in the i-th row of the m-th node area (unit: m); node (i, j, m) represents the Y-direction coordinates of the j-th sensor in the i-th row of the m-th node area (unit: m); z node (i, j, m) indicates the installation height, with a fixed value of 0.05 to 0.10 m; type node (i, j, m) represents the sensor type, all of which are “micro vibration sensors”.

[0160] Assume A node (m) is the area of ​​the mth node, and the number of sensors per unit area is required to be 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 mth node area; n is recommended node The range is 1 to 2 per m 2 , ensuring high-density collection.

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

[0164] Specifically, in S node Micro vibration sensors are installed in sequence at each determined installation location, and data acquisition modules are configured to ensure real-time and synchronous collection of vibration data in the node area.

[0165] Define the node micro vibration sensor data vector D 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 measured by the jth sensor in the i-th row of the m-th node area (unit: m / s 2 );σ node (i, j, m) represents the noise standard deviation of the vibration data measured by the sensor (unit: m / s 2 ), reflecting the data accuracy; t node (i, j, m) represents the acquisition time (unit: s), which must be consistent 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 node local vibration data generation.

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

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

[0173]

[0174] in, Represents the weighted average value of vibration values ​​of all sensors in the mth node area (unit: m / s 2 ); Indicates the unified collection time in the node area (unit: s), which can be selected as t ref Or the average value at the time of sensor collection.

[0175] Using the weighted average formula:

[0176]

[0177] Among them, α represents the weight factor, which ranges from 0.6 to 0.8 and is determined according to the actual calibration; K node(m) represents the total number of sensors in the mth node area (from S node get); represents the vibration value pre-measured in the local high-precision monitoring data within the node area, which serves as a global reference (derived from D2 and obtained in the corresponding node area after regional division). M represents the total number of rows in the mth node area, and N represents the total number of columns in the mth node area.

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

[0179] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S140 specifically includes the following steps:

[0180] Step S141 , performing denoising and low-pass filtering preprocessing on the global monitoring data, the local monitoring data, and the node vibration data to obtain a preprocessed data set.

[0181] Step S142 : matching the local monitoring data and the node vibration data in the pre-processed data set to time-align the local monitoring data and the node vibration data to obtain a corrected data set.

[0182] Step S143 , calling the data fusion formula to integrate the local monitoring data and the node vibration data in the correction data set to correct the local deviation and output the correction feedback data.

[0183] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S140 specifically further includes the following steps:

[0184] Step S144: establishing a closed-loop control module, and calling 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 parameters to be adjusted.

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

[0186] In a specific embodiment, the present invention provides a method for supervising the integrated molding of diamond abrasive flooring on industrial building frame floors, step 4, data fusion and intelligent closed-loop control establishment.

[0187] Input the data from step 2 and step 3 into the intelligent algorithm system, pre-process and fuse the local and node data to form global correction data and deviation warning information.

[0188] Specifically, the following steps are included:

[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 node vibration data V1 as:

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

[0193] in, Indicates the average vibration value of the mth beam-column node area (unit: m / s 2 ); Indicates the unified collection time of this node area (unit: seconds).

[0194] Perform low-pass filtering on the local monitoring data D2 and the node vibration data V1, which can be expressed as:

[0195] D2 filtered =F low (D2)

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

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

[0198] Step 4.2: Data matching and timing correction.

[0199] Specifically, D2 filtered With V1 filtered Match them and correct their acquisition time 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 allowed time difference threshold. In this example, Δt threshold ≤1s. If there exists Δt(m)>Δt threshold , then by linear interpolation I time Correct the corresponding sensor node data, and its expression is:

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

[0204] Among them, I time Represents a timestamp-based linear interpolation correction function to ensure 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 weighted fusion, and its expression is:

[0210]

[0211] Among them, v C1 Indicates the vibration value after global correction (unit: m / s 2 ); Indicates D2 corr The local vibration value after time series correction; Indicates the corrected vibration value of node m, belonging to V1 corr The amount in M node Represents the total number of node areas; β represents the weight factor, which ranges from 0.5 to 0.8 and is used to balance the influence of local data and node data, and is usually determined by field tests. At the same time, the temperature T and the initial setting index I can be weighted and fused in the same way. We will not go into details here. The weighted fusion of temperature T and initial setting index I is expressed 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 Indicates the deviation warning state, defined as:

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

[0216] Among them, δ total Represents the global deviation calculation result, δ threshold Indicates the deviation alarm threshold, "1" indicates 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 realize 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 Represents a set of control parameters (for example, vibration frequency adjustment Δf for vibration equipment, adjustment Δr for diamond abrasive throwing rate, etc.); C target Represents the preset target correction data set: C target =(v target ,T target ,I target );G control Indicates error feedback control, expressed as P adj =K p ×(C target -C1 meas ), K p Represents the proportional gain coefficient (constant, recommended range 0.5~1.5, determined according to the system debugging results); C1 meas Indicates the currently monitored global correction feedback data.

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

[0223] Step S150: adjusting the corundum process parameters in the building monitoring area based on the correction feedback data.

[0224] In some embodiments, the method for supervising the integrated formation of diamond abrasive flooring on industrial building frame floors provided by the present invention, step S150 specifically includes the following steps:

[0225] Step S151 : extracting initial process parameters of diamond abrasive from correction feedback data based on the parameters to be adjusted, and comparing the initial process parameters with the set target process parameters to calculate an 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 algorithm are used to adjust the grinding machine vibration parameters, the diamond aggregate throwing level and throwing frequency in the diamond grinding process parameters to set the control gain parameters.

[0227] 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 : converting the control gain parameter into a control instruction, and performing parameter correction on the grinding machine vibration parameter, the diamond aggregate scattering level and the scattering frequency in response to the control instruction, so as to output the optimized process parameters.

[0229] Step S154: adjusting the corundum process parameters of the building monitoring area based on the optimized process parameters.

[0230] In a specific embodiment, the method for supervising the integrated molding of diamond abrasive flooring on the frame floor of an industrial building provided by the present invention, step 5, implements real-time dynamic adjustment of process parameters.

[0231] Combined with closed-loop feedback data, the automatic control module is set to fine-tune the grinding machine vibration parameters, the amount of diamond aggregate thrown and the frequency to ensure that the local area density and node vibration balance meet the design indicators.

[0232] Specifically, the following steps are included:

[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, according to the global correction feedback data and the designed target data, that is, 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 scattering error E T and the throwing frequency error E I Same thing.

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

[0237] Specifically, according to the vibration error signal E v Design control algorithm, use proportional regulation or PID control to make preliminary adjustments to the grinding machine vibration parameters, diamond aggregate throwing amount and frequency, and set the control gain coefficient

[0238] The control formula for adjusting the vibration parameters is expressed as:

[0239] ΔP v =K v ×E v

[0240] Where ΔP v Indicates the adjustment amount of vibration parameters (unit: m / s 2 or corresponding physical quantity); K v E represents the vibration control proportional gain (dimensionless), with a recommended range of 0.5 to 1.5, depending on the field test results; v The scattering amount and frequency of the corundum aggregate are similar and will not be described here.

[0241] Finally, the adjustment amount of the vibration parameter, the adjustment amount of the scattering amount of the corundum aggregate, and the adjustment amount of the scattering frequency are output.

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

[0243] Specifically, the vibration parameter adjustment amount, the corundum aggregate scattering amount adjustment amount, and the scattering frequency adjustment amount output in step 5.2 are converted into specific equipment control instructions, and the parameters of the grinding machine vibration, corundum aggregate scattering amount, and scattering frequency are corrected.

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

[0245]

[0246] in,( Indicates the current grinding machine vibration parameters (such as vibration acceleration, unit m / s 2);W current Indicates the current amount of diamond aggregate thrown (unit: kg / m 2 );f current Indicates the current aggregate scattering frequency (unit: times / s).

[0247] Optimized equipment parameters P opt Defined as:

[0248]

[0249] in, represents the optimized vibration parameters, W opt ,f opt Similarly, are the optimized scattering amount and scattering frequency of corundum aggregate.

[0250] The present invention performs weighted adjustments on the three key process parameters respectively to ensure that under closed-loop control, the equipment output is as consistent as possible with the design target, while taking into account local density and node vibration balance.

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

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

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

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

[0255] Among them, P measured is the actual measured process parameter (structural state feedback), the form is the same as P opt Then, recalculate the new adjustment error E new :

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

[0257] Similarly, new adjustment errors are calculated for vibration, spreading amount, and spreading frequency respectively until they meet the set allowable error range, and the adjustment is completed. After the adjustment is completed, the diamond vibration, spreading amount, and spreading frequency are adjusted based on the latest adjustment errors.

[0258] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0259] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

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

[0261] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state 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++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of 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., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0262] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in 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 so that a series of operational steps are 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 implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0265] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for supervising the integrated molding of diamond abrasive flooring on industrial building frame floors, characterized in that: The method comprises: Constructing a wireless topology network in the building monitoring area to collect global monitoring data in the building monitoring area; A high-density monitoring module is provided in a part of the building monitoring area to collect local monitoring data of the building monitoring area; Adding a vibration feedback acquisition unit to the beam-column nodes in the building monitoring area to monitor the vibration energy distribution of the beam-column nodes in real time and output node vibration data; Preprocessing and fusing the global monitoring data, local monitoring data, and node vibration data to generate correction feedback data for the building monitoring area; Adjusting the diamond grinding process parameters of the building monitoring area based on the correction feedback data; The wireless topology network is constructed by multiple sensors, and the global monitoring data and the local monitoring data both include the vibration value, temperature and initial setting state of the concrete.

2. The method for supervising the integrated molding of emery flooring for industrial building frame according to claim 1 is characterized in that: The step of constructing a wireless topology network in the building monitoring area to collect global monitoring data in the building monitoring area includes: Obtaining the floor geometry layout and concrete pouring design parameters of the building monitoring area, and determining the types and installation locations of the plurality of sensors based on the design parameters to generate a sensor arrangement matrix; Building a wireless sensor network, and wirelessly connecting a plurality of sensors in the sensor arrangement matrix through the wireless sensor network to build the wireless topology network; Based on the wireless topology network, a data acquisition module of each sensor is set, and clock correction is performed on each data acquisition module; Noise correction and data fusion are performed on the sensor data output by the data acquisition module to obtain global monitoring data of the building monitoring area.

3. The method for supervising the integrated molding of emery flooring for industrial building frame according to claim 2 is characterized in that: The high-density monitoring module is set in a part of the building monitoring area to collect local monitoring data of the building monitoring area, including: Calculating a deviation between sensor data corresponding to a key monitoring area and the global monitoring data, and determining that the key monitoring area is an abnormal area when the deviation exceeds a first threshold; Planning and deploying high-precision vibration sensors and image acquisition equipment in the abnormal area to construct the high-density monitoring module; The key monitoring area is the board surface boundary and uneven area within the building monitoring area.

4. The method for supervising the integrated molding of emery flooring for industrial building frame floors according to claim 3 is characterized in that: The high-density monitoring module is provided in a part of the building monitoring area to collect local monitoring data of the building monitoring area, and further includes: Obtaining a data vector output by each high-precision vibration sensor, wherein the data vector is composed of a local vibration value measured by each high-precision vibration sensor, a sensor noise standard deviation, and a 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 supervising the integrated molding of emery flooring for industrial building frame according to claim 4 is characterized in that: The step of adding a vibration feedback acquisition unit to the beam-column nodes in the building monitoring area to monitor the vibration energy distribution of the beam-column nodes in real time to output node vibration data includes: Extracting regions of beam-column nodes in the concrete floor within the building monitoring area based on the local monitoring data to output a list of beam-column node regions; each beam-column node region has corresponding location information and area size; Disposing a plurality of micro vibration sensors in each beam-column node region and laying out the plurality of micro vibration sensors to generate a micro vibration sensor arrangement matrix in each beam-column node region; Installing the plurality of micro-vibration sensors according to the micro-vibration sensor arrangement matrix, and configuring a corresponding data acquisition submodule for each micro-vibration sensor to integrate outputs of the data acquisition submodule to obtain a micro-vibration sensor data set; A weighted average is performed on all micro-vibration sensor data sets in the beam-column node area list to generate and output the node vibration data for characterizing the vibration energy distribution of the beam-column node.

6. The method for supervising the integrated molding of emery flooring for industrial building frames according to claim 5 is characterized in that: The preprocessing and fusing of the global monitoring data, the local monitoring data, and the node vibration data to generate correction feedback data for the building monitoring area includes: Performing preprocessing of denoising and low-pass filtering on the global monitoring data, local monitoring data, and node vibration data to obtain a preprocessed data set; Matching the local monitoring data and the node vibration data in the preprocessed data set to time-align the local monitoring data and the node vibration data to obtain a corrected data set; The data fusion formula is called to integrate the local monitoring data and the node vibration data in the correction data set to correct the local deviation and output the correction feedback data.

7. The method for supervising the integrated molding of emery flooring for industrial building frame according to claim 6 is characterized in that: The preprocessing and fusing of the global monitoring data, the local monitoring data and the node vibration data to generate correction feedback data for the building monitoring area further includes: Establishing a closed-loop control module, and calling 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 parameters to be adjusted; The parameters to be adjusted include the vibration frequency of the vibration equipment and the scattering rate of the corundum.

8. The method for supervising the integrated molding of emery flooring for industrial building frame according to claim 7 is characterized in that: The adjusting of the corundum process parameters of the building monitoring area based on the correction feedback data includes: Based on the parameters to be adjusted, extracting initial process parameters of diamond abrasive from the correction feedback data, and comparing the initial process parameters with set target process parameters to calculate an error signal between the initial process parameters and the target process parameters; Based on the error signal, proportional regulation and PID control algorithm are used to adjust the grinding machine vibration parameter, the diamond aggregate throwing level and throwing frequency in the diamond grinding process parameters to set the control gain parameter; Converting the control gain parameter into a control instruction, and performing parameter correction on the grinding machine vibration parameter, diamond aggregate scattering level and scattering frequency in response to the control instruction to output optimized process parameters; Adjusting the diamond grinding process parameters of the building monitoring area based on the optimized process parameters; The control gain parameters include a grinding machine vibration parameter adjustment amount, a diamond aggregate scattering adjustment amount, and a scattering frequency adjustment amount.

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