Construction quality control method, system and medium for grid steel bar truss floor

Abstract

The present application relates to the technical field of construction quality control, and discloses a construction quality control method, system and medium for grid steel bar truss floor. The method comprises: obtaining the grid steel bar cage by binding connection and detecting the flatness, obtaining the structural parameters to determine the quality data; connecting the truss steel bars based on the quality data, calibrating the verticality and then detecting the nodes; arranging the supports and adjusting the elevations according to the node data, and formulating the support plan; pouring the concrete according to the plan, and controlling the pouring quality by density detection; formulating the maintenance plan and monitoring according to the quality data, and generating the control parameters; finally, inspecting the quality according to the parameters, and issuing a report after analysis. The present application realizes the quality control of the whole process of the grid steel bar truss floor construction, and ensures the reliability and traceability of the construction quality.

Description

Technical Field

[0001] The present application relates to the field of construction quality control, and in particular to a construction quality control method, system and medium for grid steel bar truss floors. Background Art

[0002] In modern construction projects, grid steel truss floors are widely used due to their excellent mechanical properties and construction efficiency. Traditional construction quality control methods mainly rely on manual inspection and empirical judgment, and use regular sampling to evaluate construction quality. This method measures key indicators such as concrete strength, steel bar position, and structural deformation by setting fixed inspection points, and compares the measurement results with design requirements. At the same time, during the construction process, manual inspections are used to monitor the formwork support, concrete pouring, maintenance and other construction links, and appropriate treatment measures are taken when problems are found.

[0003] However, traditional quality control methods have obvious limitations. First, manual inspection methods are easily affected by subjective factors, and the reliability and consistency of the inspection results are difficult to guarantee. Second, regular sampling cannot achieve continuous monitoring of construction quality, and it is easy to miss abnormal changes at key time points. Third, the collection, processing and analysis of quality control data lacks systematicity, making it difficult to establish a quality assessment system. The data correlation between various construction links is not strong, and it is impossible to achieve effective transmission and sharing of quality information. These problems have led to less than ideal construction quality control results and increased the risk of quality defects. Summary of the invention

[0004] This application provides a construction quality control method, system and medium for grid steel truss floor, which is used to achieve quality control of the entire construction process of grid steel truss floor, ensuring the reliability and traceability of construction quality. Through data collection, processing, analysis and feedback, accurate control of construction quality is achieved, effectively avoiding the occurrence of quality defects.

[0005] In a first aspect, the present application provides a construction quality control method for a grid steel bar truss floor, the construction quality control method for a grid steel bar truss floor comprising: obtaining a grid steel bar cage by tying and connecting the grid steel bars; obtaining grid structure parameters by flatness detection; converting the grid structure parameters into grid quality data; implementing connection between truss steel bars and grid steel bar cage based on the grid quality data to obtain a grid-truss combination structure; processing the grid-truss combination structure through verticality calibration to generate a calibrated grid-truss combination; obtaining node data of the grid-truss combination by node detection; and using the node data to obtain the grid-truss combination structure. Arrange formwork support according to data to obtain formwork structure; generate flatness data by adjusting formwork elevation; obtain support plan according to the flatness data; carry out concrete pouring operation according to the support plan to generate pouring layer; determine pouring parameters by density detection; generate pouring quality data through pouring parameter control; convert the pouring quality data into maintenance plan; obtain maintenance data through maintenance monitoring; generate maintenance control parameters by controlling the maintenance data; carry out quality inspection according to the maintenance control parameters to obtain test results; obtain quality characteristics through analysis of test results; generate quality report by quality characteristic evaluation.

[0006] In a second aspect, the present application provides a construction quality control system for a grid steel bar truss floor, the construction quality control system for a grid steel bar truss floor comprising:

[0007] The acquisition module is used to obtain the well grid reinforcement cage by binding and connecting the well grid reinforcement; obtain the well grid structure parameters by flatness detection; and convert the well grid structure parameters into well grid quality data;

[0008] A calibration module is used to connect the truss steel bars with the well grid steel cage based on the well grid quality data to obtain a well truss assembly structure; process the well truss assembly structure through verticality calibration to generate a calibrated well truss assembly; and obtain node data of the well truss assembly through node detection;

[0009] A generation module is used to arrange the template support by using the node data to obtain the template structure; generate flatness data by adjusting the template elevation; and obtain a support plan according to the flatness data;

[0010] An operation module is used to carry out concrete pouring operations according to the support scheme and generate a pouring layer; determine pouring parameters by density detection; and generate pouring quality data through pouring parameter control;

[0011] A monitoring module, used to convert the pouring quality data into a maintenance plan; obtain maintenance data through maintenance monitoring; and generate maintenance control parameters by regulating the maintenance data;

[0012] The inspection module is used to implement quality inspection according to the maintenance control parameters to obtain inspection results; obtain quality characteristics through analysis of the inspection results; and generate a quality report based on the quality characteristic evaluation.

[0013] A third aspect of the present application provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned construction quality control method for grid steel bar truss floors.

[0014] In the technical solution provided by the present application, the grid reinforcement cage is obtained by tying and connecting the grid reinforcements, and the grid structure parameters are obtained by using flatness detection, and then the grid structure parameters are converted into grid quality data, thereby ensuring the manufacturing quality of the basic components; secondly, the connection between the truss reinforcement and the grid reinforcement cage is implemented based on the grid quality data to obtain a grid truss composite structure, and then a calibrated grid truss composite body is formed through verticality calibration processing, and finally node data is obtained through node detection, thereby effectively ensuring the overall performance of the structure; subsequently, the formwork support is arranged using the node data to obtain the formwork structure, and the flatness data is generated after elevation adjustment, and the result is adjusted accordingly. The support scheme was adopted, which significantly improved the reliability of the support system. On this basis, the concrete pouring operation was carried out in accordance with the support scheme to generate the pouring layer. The pouring parameters were determined through density detection, and the pouring quality data was generated through parameter control to ensure the quality of concrete pouring. Then, the pouring quality data was converted into a maintenance scheme, and the maintenance data was obtained through maintenance monitoring, and the maintenance control parameters were generated by regulation to achieve precise control of the maintenance process. Quality inspection was carried out according to the maintenance control parameters to obtain the test results, and the quality characteristics were obtained through the analysis of the test results. The quality report was generated by the quality characteristics evaluation, and a quality evaluation system was established. Through the layer-by-layer transmission and processing of data, the controllability and traceability of construction quality were achieved, effectively improving the overall construction quality of the well grid steel truss floor. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 A schematic diagram of an embodiment of a construction quality control method for a grid steel bar truss floor in an embodiment of the present application;

[0017] Figure 2 It is a schematic diagram of an embodiment of a construction quality control system for a grid steel bar truss floor in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a construction quality control method, system and medium for a well grid steel bar truss floor. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the construction quality control method for the well grid steel bar truss floor comprises:

[0020] Step S101, obtaining a well grid reinforcement cage by binding and connecting well grid reinforcements; obtaining well grid structural parameters by flatness detection; and converting the well grid structural parameters into well grid quality data;

[0021] Step S102: Based on the well grid quality data, the truss steel bars are connected to the well grid steel bar cage to obtain a well truss assembly structure; the well truss assembly structure is processed by verticality calibration to generate a calibrated well truss assembly; and node data of the well truss assembly is obtained by node detection;

[0022] Step S103, using the node data to arrange the template support to obtain the template structure; generating flatness data by adjusting the template elevation; and obtaining the support plan according to the flatness data;

[0023] Step S104, carry out concrete pouring operation according to the support plan to generate a pouring layer; determine pouring parameters by density detection; generate pouring quality data through pouring parameter control;

[0024] Step S105, converting the pouring quality data into a maintenance plan; acquiring maintenance data through maintenance monitoring; and generating maintenance control parameters by regulating the maintenance data;

[0025] Step S106, perform quality inspection according to the maintenance control parameters to obtain the inspection results; obtain quality characteristics through analysis of the inspection results; and generate a quality report based on the quality characteristics evaluation.

[0026] It is understandable that the execution subject of the present application may be a construction quality control system for a grid steel bar truss floor, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0027] Specifically, the grid steel cage is obtained by binding and connecting the grid steel bars. The binding connection of the grid steel bars adopts double spiral binding technology to ensure the connection strength of the node. Before binding, the main and secondary bars are arranged in a tic-tac-toe shape, and the orthogonal arrangement accuracy of the steel bars is ensured by the positioning plate, and then the connection point is fixed by the double spiral binding process. The double spiral binding process refers to the use of a bidirectional spiral wire winding method at the intersection of the steel bars, with at least three turns in each direction to ensure the firmness of the binding. After the binding is completed, positioning slots are set around for subsequent precise docking with the truss steel bars. After obtaining the grid steel cage, the grid structure parameters are obtained by flatness detection. The grid structure is dimensionally detected by three-dimensional laser scanning. The scanning data includes information such as the overall flatness of the grid steel cage, the spatial coordinates of the nodes, and the geometric dimensions of the components. By processing the point cloud data obtained by scanning, the flatness error value is extracted, and compared and analyzed with the design standards to generate a structural parameter data set.

[0028] After obtaining the structural parameters, these parameters are converted into grid quality data. This conversion process first establishes a spatial coordinate system for the steel bar position, integrates the flatness data and node position data obtained by detection, and forms a quality assessment system. The quality data contains information in multiple dimensions such as structural geometric parameters, node connection strength, and flatness error.

[0029] The connection between the truss reinforcement and the well grid reinforcement cage is implemented based on the well grid quality data. The truss reinforcement adopts high-strength cold-rolled ribbed steel bars, which are connected to the positioning slots of the well grid structure through quick connectors. During the connection process, a magnetic positioning device is used to ensure the verticality of the truss reinforcement and the well grid structure, and a digital level is used to monitor the elevation in real time. The verticality of the connected well truss combination structure is calibrated. The calibration process determines the deviation through multi-point measurement and makes dynamic adjustments to obtain the calibrated well truss combination. After the calibration is completed, the well truss combination is subjected to node detection, and the detection content includes parameters such as the stress state, deformation, and displacement of the connection node. The quality of the welding node is evaluated through non-destructive testing methods such as ultrasonic testing to generate detailed node data. The node data reflects the overall performance and local stress characteristics of the component.

[0030] Using node data to arrange formwork support is a key link to ensure construction quality. Determine the support point position according to the node data, and use a reusable modular formwork system. The surface of the formwork is treated with a nano-coating to improve the demoulding effect and the surface quality of the concrete. During the formwork installation process, a high-precision laser leveler is used for real-time elevation control, and the support position and support force are adjusted through data feedback. After the formwork elevation adjustment is completed, the flatness data is analyzed and processed. The data processing process includes flatness deviation calculation, support force analysis, etc. A support plan is formulated based on the flatness data to determine the position, spacing and support force of the support arrangement. The support plan also takes into account factors such as construction load and concrete deadweight.

[0031] When carrying out concrete pouring operations in accordance with the support plan, special attention should be paid to the concrete mix design and pouring process control. Fiber reinforcement and high-efficiency water reducer are added to the concrete, and the precise measurement and automatic mixing of raw materials are achieved through the intelligent mixing station. The pouring process uses variable frequency vibration technology to automatically adjust the vibration parameters according to the density of steel bars in different parts. The pouring parameters are determined by density detection, and the detection process uses acoustic wave detection technology to obtain the density distribution inside the concrete. Adjust the pouring process parameters according to the test data, including vibration time, vibration frequency, etc., and monitor the temperature field distribution of the concrete in real time to avoid temperature stress cracking.

[0032] The generation of pouring quality data is based on multi-dimensional test results, including strength development, surface flatness, density distribution and other information. After statistical analysis, these data are converted into a maintenance plan, which includes specific contents such as maintenance time, maintenance measures, and maintenance environment parameters. Maintenance monitoring is carried out through an intelligent maintenance system, which includes an automatic spraying device, a temperature and humidity adjustment device, and an automatic laying system for a curing film. The maintenance parameters are automatically adjusted according to the real-time data feedback from the embedded sensor. A new type of nano-curing agent is used to form a dense protective layer on the concrete surface to reduce water evaporation. The maintenance data obtained during the monitoring process includes the internal temperature, humidity, and strength development of the concrete.

[0033] Quality inspection is carried out according to maintenance control parameters, and multi-dimensional quality inspection methods are adopted, including ultrasonic testing, rebound testing and core sampling. Surface defects are identified through machine vision technology, and quality problems such as cracks and bubbles are automatically detected and located. Quality inspection data is analyzed and processed to extract quality features, including strength features, durability features, etc. Comprehensive evaluation is carried out based on quality features to generate quality reports. In the process of data processing, special attention is paid to the correlation analysis between data, and the reliability of the evaluation results is ensured through cross-validation of multi-source data.

[0034] In the embodiment of the present application, the grid reinforcement cage is obtained by tying and connecting the grid reinforcements, and the grid structure parameters are obtained by using flatness detection, and then the grid structure parameters are converted into grid quality data, thereby ensuring the manufacturing quality of the basic components; secondly, the truss reinforcement and the grid reinforcement cage are connected based on the grid quality data to obtain a grid truss assembly structure, and then a calibrated grid truss assembly is formed through verticality calibration, and finally node data is obtained through node detection, thereby effectively ensuring the overall performance of the structure; subsequently, the template support is arranged using the node data to obtain the template structure, and the flatness data is generated after elevation adjustment, and the support structure is obtained by regulation accordingly. The support scheme significantly improves the reliability of the support system; on this basis, the concrete pouring operation is carried out in accordance with the support scheme to generate the pouring layer, the pouring parameters are determined through density detection, and the pouring quality data is generated through parameter control to ensure the quality of concrete pouring; then, the pouring quality data is converted into a maintenance plan, and the maintenance data is obtained through maintenance monitoring, and the maintenance control parameters are generated by regulation to achieve precise control of the maintenance process; quality inspection is carried out according to the maintenance control parameters to obtain the test results, and the quality characteristics are obtained through the analysis of the test results, and the quality report is generated by the quality characteristics evaluation, and a quality evaluation system is established. Through the layer-by-layer transmission and processing of data, the controllability and traceability of construction quality are achieved, and the overall construction quality of the well grid steel bar truss floor is effectively improved.

[0035] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0036] (1) Generate a tic-tac-toe structure by orthogonally arranging the main reinforcement and the secondary reinforcement to obtain an initial reinforcement grid; tie the connection points of the initial reinforcement grid in a double-helix manner to obtain a tied reinforcement structure; set positioning slots around the tied reinforcement structure to generate a tic-tac-toe reinforcement cage;

[0037] (2) Scan and measure the grid reinforcement cage to collect structural dimension data; record the flatness error value by analyzing the deviation of the structural dimension data; compare the flatness error value with the preset standard to obtain the grid structure parameters;

[0038] (3) Establishing steel bar position coordinate data based on the grid structure parameters; matching the steel bar position coordinate data with the specification parameters to generate material information records; combining the material information records with the flatness error value to output the grid error data;

[0039] (4) Classify and sort the grid error data and determine the key detection points; perform sampling inspections on the key detection points and record the detection values; perform statistical analysis on the detection values ​​and generate quality control records;

[0040] (5) Set monitoring frequency for quality control records and establish data collection cycle; conduct dynamic tracking based on data collection cycle and record parameter changes; perform data screening based on parameter changes and generate monitoring data set;

[0041] (6) Classify and summarize the monitoring data sets and establish quality assessment indicators; compare the quality assessment indicators with standard specifications to generate well quality data.

[0042] Specifically, the main reinforcement and secondary reinforcement are arranged according to the design specifications, and the orthogonal arrangement is carried out to form a well-shaped structure. The main reinforcement refers to the steel bar that bears the main force, and the secondary reinforcement is used to distribute stress and control cracks. Orthogonal arrangement means that the main reinforcement and secondary reinforcement are arranged perpendicular to each other to ensure the formation of a regular well-shaped grid structure. During the arrangement, the spacing of the steel bars is controlled by the positioning tool. The spacing of the main reinforcement and the spacing of the secondary reinforcement are determined according to the design requirements, usually between 200-300mm. After the arrangement is completed, the initial steel grid is formed. The double spiral binding method is used for connection at the intersection of the steel bars. Double spiral binding is a special binding process. First, the steel bar is spirally wound for more than three turns in the clockwise direction, and then spirally wound for more than three turns in the counterclockwise direction to form a bidirectional spiral wrap. This binding method significantly improves the stability of the node connection. After the binding is completed, positioning slots are set around. The positioning slots use I-shaped slots. The slot width matches the cross-sectional size of the truss steel bars, providing a reference for the subsequent precise installation of the truss steel bars, thereby generating a standardized well grid steel cage.

[0043] The quality control of the well grid steel cage is carried out by measuring the structural dimensions through three-dimensional laser scanning. The scanning process adopts a multi-station scanning method, and multiple scanning points are set around the well grid steel cage. The point cloud data collected at each scanning point contains spatial coordinate information. The structural dimension data is obtained by splicing and registering the point cloud data. When the deviation analysis of the structural dimension data is performed, a theoretical reference plane is first established, and the measured data is compared with the theoretical reference to calculate the deviation value of each measuring point. The deviation analysis uses the least squares method to fit the plane and record the flatness error value of each point. The flatness error value is compared with the preset construction specification standard. The comparison result determines whether the geometric parameters of the well grid steel cage meet the requirements, so as to obtain the well grid structure parameters. Based on the well grid structure parameters, a three-dimensional coordinate system is established to map the steel bar position data to the coordinate system. The steel bar position coordinate data includes the spatial position information of the main and secondary bars, as well as the coordinate data of the node connection. These coordinate data are matched with the design specification parameters, which include steel bar model, diameter, strength grade and other information. The matching process is realized by the data association algorithm to generate information records containing geometric position and material properties. The material information record is fused with the aforementioned flatness error value to output the grid error data.

[0044] The grid error data is systematically analyzed, and first classified and sorted according to the error type and distribution characteristics. The weights of various errors are determined by the hierarchical analysis method, and key detection points are screened out. Key detection points refer to characteristic points that have a greater impact on structural performance, including stress concentration areas, deformation sensitive areas, etc. These key detection points are sampled and checked, and the detection values ​​are recorded using precision measuring instruments. The detection values ​​are analyzed through mathematical statistics, including variance analysis and correlation analysis, to form quality control records.

[0045] Set a reasonable monitoring frequency for quality control records and establish a hierarchical data collection cycle. The determination of the monitoring frequency takes into account factors such as structural characteristics and construction progress, and adopts differentiated collection frequencies for different parts. Track and monitor according to the set data collection cycle, and record the dynamic changes of various parameters. The parameter change data is filtered to eliminate random interference factors, screen out effective monitoring data, and generate a structured monitoring data set. The monitoring data set is classified and summarized in multiple dimensions, and a quality assessment index system including geometric dimensions, material properties, structural properties, etc. is established. The quality assessment indicators are compared with the engineering construction standards and specifications, and the quality grade is determined by a comprehensive scoring method to generate well quality data.

[0046] For example, in the construction of a grid steel truss floor, the grid-shaped steel grid is arranged, the main reinforcement is HRB400 grade steel with a spacing of 250mm, the secondary reinforcement is HRB335 grade steel with a spacing of 200mm, and the node connection is completed by double spiral binding. The point cloud data collected by 3D laser scanning shows that more than 2,000 valid measurement points are collected within the range of 5m×6m grid steel cage. The flatness analysis of these measurement point data shows that the maximum deviation occurs in the center area of ​​the grid, and the flatness error distribution is calculated by the least squares method. The error data is correlated with the steel bar position coordinates to determine 15 key detection points. After statistical analysis of the dynamic monitoring data of these detection points, evaluation indicators including geometric accuracy, node connection strength, overall stability and other dimensions are established. The generated grid quality data intuitively reflects the construction quality status.

[0047] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) Convert the grid quality data into connection point identification to generate positioning data; mark the connection position on the grid reinforcement cage according to the positioning data to obtain a connection point distribution map; determine the truss reinforcement installation path through the connection point distribution map to generate a grid truss composite structure;

[0049] (2) Using the positioning slot of the shaft beam assembly structure as the reference point, collect the verticality value; compare the verticality value with the standard value and record the deviation data; perform correction processing on the deviation data to generate a calibrated shaft beam assembly;

[0050] (3) Setting measurement reference points for the calibrated shaft-truss assembly and establishing a detection coordinate system; recording node position information in the detection coordinate system to obtain node distribution data; spatially mapping the node distribution data to obtain node spacing values;

[0051] (4) Using the node spacing value as a reference, calculate the stress distribution and generate force data; select key nodes through the force data to obtain a monitoring position map; perform welding quality inspection based on the monitoring position map and record the node strength value;

[0052] (5) Perform statistical analysis on the node strength values ​​to generate a strength distribution diagram; compare the strength distribution diagram with the design requirements and output a qualified judgment result; classify the data based on the qualified judgment result to generate a node feature set;

[0053] (6) Comprehensively evaluate the node feature set and establish a quality scoring system; score the nodes using the quality scoring system to obtain the node quality level; and generate node data based on the distribution of node quality levels.

[0054] Specifically, the quality data of the manhole grid needs to be converted into connection point identification. The connection point identification refers to the connection position mark between the truss steel bar and the manhole grid steel cage. Each connection point has a unique spatial coordinate and identification code. During the conversion process, data standardization is used to integrate the spatial parameters, material properties and other information in the quality data to generate a positioning data set. The positioning data includes the three-dimensional coordinates of the connection point, the connection strength requirements, the installation process parameters, etc. According to the positioning data, physical marking is performed on the manhole grid steel cage. The marking method uses a special marking piece, which is engraved with the connection point number and direction indication. The marking process is carried out strictly in accordance with the spatial positioning data to ensure that the position of each connection point is accurate. After the marking is completed, the marking point position information is collected through spatial scanning, and a connection point distribution map is drawn. The distribution map clearly shows the spatial distribution relationship of all connection points, and marks the relative position and connection requirements between each point.

[0055] According to the connection point distribution map, the installation path of the truss reinforcement is formulated. The installation path is determined by considering the construction convenience and the structural stress characteristics, and the optimal path algorithm is used for calculation. When planning the path, factors such as steel bar avoidance and connection operation space are fully considered to ensure the feasibility of the installation process. The truss reinforcement is installed according to the planned path, and fixed with a special connector to form a well-truss combination structure.

[0056] The verticality of the formed well-beam combination structure is detected, and the positioning slot is used as the reference point for measurement. The verticality detection uses a high-precision digital rangefinder to collect data at multiple measuring points. The collected verticality values ​​are compared with the specifications and standards to calculate the deviation value. The deviation data includes two dimensions: spatial position deviation and angle deviation. According to the detected deviation, a precision adjustment device is used for calibration. The calibration process adopts the stepwise approximation method until the deviation value meets the requirements and the calibrated well-beam combination is obtained. On the calibrated well-beam combination, a network of measurement reference points is laid out. The layout of the reference points follows the principle of uniform distribution, and the layout is denser at key nodes. A three-dimensional detection coordinate system is established with these reference points. The establishment of the coordinate system adopts the principle of least squares adjustment to ensure measurement accuracy. In the established coordinate system, the location information of each node is recorded in detail, including the spatial coordinates and relative position relationship. The node distribution data is spatially mapped, and the distance values ​​between adjacent nodes are calculated to obtain the node spacing data set.

[0057] In the stress distribution calculation, the following formula is used:

[0058]

[0059] in: represents the stress value of node ij; Indicates the load correction factor; represents the weight of the kth influencing factor; Indicates the node spacing; represents the spatial displacement of the kth node; Represents the distance decay exponent; represents the displacement influence index; represents the time correction factor; n represents the total number of affected nodes.

[0060] According to the stress calculation results, the key nodes with stress concentration are screened out and the monitoring location map is drawn. The monitoring location map is marked with the node locations and monitoring requirements that need to be focused on. The welding quality of these nodes is inspected by ultrasonic detection method and the strength values ​​of the nodes are recorded. The obtained node strength values ​​are statistically analyzed and the strength distribution map is generated by the kernel density estimation method. The distribution map intuitively shows the spatial distribution characteristics and change patterns of the node strength. The strength distribution is quantitatively compared with the design requirements to obtain the qualified judgment results of each node. According to the judgment results, the node data is systematically classified to establish a node feature set containing multi-dimensional information such as strength characteristics, position characteristics, and deformation characteristics.

[0061] The node feature set is input into the scoring algorithm to establish a multi-level quality scoring system. The scoring system covers multiple dimensions such as geometric accuracy, connection strength, and overall stability, and each dimension is set with a corresponding weight coefficient. Each node is quantitatively scored through the quality scoring system to determine the quality level of the node. The quality level distribution of all nodes is comprehensively analyzed to generate a node data report.

[0062] For example: In an actual construction project, the positions of 32 connection points are determined based on the grid quality data, and each connection point is marked with a unique spatial coordinate and identification code. When installing truss reinforcement, the installation sequence calculated by the optimal path algorithm significantly improves construction efficiency. The verticality test found that the deviations of 3 connection points exceeded the standard, and they were calibrated by a precision adjustment device. In the stress distribution calculation, multiple influencing factors such as the diameter of the steel bar and the connection method are considered. The calculation results show that the node stress value at the mid-span position of the floor slab is large. These key nodes are tested in key areas, and the welding strength data is obtained through ultrasonic testing. Data analysis shows that the strength values ​​of all nodes meet the design requirements, but nodes in stress concentration areas need to be monitored more closely. The node quality scoring system quantitatively evaluates the various indicators of each node and generates a quality control data set.

[0063] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0064] (1) Convert node data into support point information to obtain a support layout diagram; determine the support spacing through the support layout diagram to obtain support distribution data; mark the template support positions based on the support distribution data to obtain the template structure;

[0065] (2) Measure the elevation of the template structure and record the elevation value; compare the elevation value with the benchmark elevation and output the deviation value; perform data statistics based on the deviation value to obtain an elevation distribution map;

[0066] (3) Set the measuring point grid according to the elevation distribution map to obtain the measuring point array; re-measure the levelness of the measuring point array and output the height difference data; calculate the flatness parameters through the height difference data to obtain the flatness data;

[0067] (4) Draw the flatness data into a contour map to screen the deformation area; calculate the support force of the deformation area and output the force value; determine the support adjustment amount based on the force value to obtain the adjustment parameter;

[0068] (5) Summarize the adjustment parameters by partition and output the partition adjustment table; sort the data in the partition adjustment table by priority to obtain the adjustment sequence; adjust the support force based on the adjustment sequence and output the support reaction force value;

[0069] (6) Compare the support reaction force value with the load requirement to obtain the verification result; conduct a comprehensive analysis of the verification result and output the support effect evaluation; formulate a support plan based on the support effect evaluation.

[0070] Specifically, the node data contains information such as the key node positions and stress characteristics of the grid steel truss floor, which are mapped into the spatial layout requirements of the support points through the data conversion algorithm. During the conversion process, the reference position of the support point is first determined, and then the distribution of the support points is optimized according to the structural stress characteristics. The support layout diagram is drawn using CAD, with the precise coordinates and numbers of each support point marked.

[0071] When determining the support spacing based on the support layout diagram, factors such as the floor span, load size, and formwork stiffness need to be considered. The support spacing is determined by a combination of force analysis and empirical data. Generally, the main support spacing does not exceed 1 meter, and the secondary support spacing does not exceed 1.5 meters. In the support distribution data, in addition to recording the spatial coordinates of the support points, it also includes information such as support strength grade and estimated load. Based on these data, the specific support positions are marked on site, and a laser marking instrument is used to assist in positioning to ensure the accuracy of the support positions and form a formwork structure. When measuring the elevation of the formwork structure, a high-precision level is used to measure the elevation at the preset measuring points. The elevation measurement adopts the closed measurement method. Each measuring point is measured at least three times, and the average value is taken as the elevation value. The elevation value is compared with the design benchmark elevation to calculate the elevation deviation value. The deviation value is calculated using the following formula:

[0072]

[0073] in: represents the flatness parameter; Indicates the height difference of the i-th measuring point; Indicates the time correction factor; Indicates the distance between measuring points; represents the spatial impact factor; represents the standard deviation correction factor; Indicates a single point measurement value; represents the average value; Indicates the number of measurement points.

[0074] The support force calculation based on the deformation area adopts the following formula:

[0075]

[0076] in: Indicates the size of the supporting force; It represents the power amplification factor; represents the quality of the jth region; It represents the volume of newly poured concrete; represents the elastic modulus; represents the stress influence index; represents the temperature influence factor; k represents the number of deformation areas.

[0077] The support force adjustment is calculated using the following formula:

[0078]

[0079] in: Indicates the support reaction value; represents the bearing capacity coefficient; represents the displacement of the lth support point; represents the support stiffness; Indicates the axial deformation of the support; represents the nonlinear correction index; represents the environmental impact factor; p represents the number of supporting points.

[0080] According to the support distribution data, the measurement point array is set on the template. The measurement point spacing is generally 500mm×500mm, and it is appropriately encrypted in the deformation sensitive area. The horizontality of the measurement point array is re-measured, and the height difference data is obtained by using a precision level. The height difference data is drawn into a contour map through the contour interpolation algorithm, which clearly shows the undulation of the template surface. According to the contour map, the deformation exceeding the standard area is screened out. These areas often appear in the mid-span or load concentration. When the support adjustment parameters are partitioned and summarized, the entire area is divided into several control partitions according to the deformation degree and force characteristics. The control parameters of each partition include information such as the support force size, adjustment amount, and adjustment sequence. The data in the control table are prioritized according to the construction process and structural safety requirements to determine the control sequence. The support is mechanically adjusted according to the control sequence. The adjustment process adopts a graded loading method, and the support reaction value is measured after each level of adjustment.

[0081] Compare the support reaction force value with the design load requirements to analyze the rationality and effectiveness of the support arrangement. The support effect evaluation conducts a comprehensive analysis from multiple aspects such as support stability, flatness control effect, construction safety, etc., to provide a basis for the optimization of the support scheme.

[0082] For example: In a grid steel truss floor project, the support layout scheme with a main support spacing of 900mm and a secondary support spacing of 1200mm was determined based on the node data. The elevation measurement found that there was a 20mm elevation deviation at the mid-span position of the floor slab. Through statistical analysis of the deviation data, an elevation distribution cloud map was drawn. 16 measuring points were arranged in the deformation area to form a 4×4 measurement grid, and the elevation difference data was obtained through leveling. The flatness parameters were calculated based on the elevation difference data, and the results showed that the flatness deviation in the mid-span area was large. After the support force calculation, it was determined that the support force needed to be increased in the deformation area, and the support adjustment was carried out in an order from the outside to the inside. After the adjustment was completed, the support reaction force value was evenly distributed, and the flatness index met the design requirements. Through data collection, analysis and feedback, precise control of the formwork support was achieved.

[0083] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0084] (1) Divide the pouring area by the support plan to obtain the pouring sequence table; convert the pouring sequence table into time nodes to obtain the pouring plan; prepare concrete according to the pouring plan and output the pouring layer;

[0085] (2) Setting a detection grid for the casting layer to obtain a measurement point layout diagram; performing vibration detection on each point on the measurement point layout diagram to obtain vibration data; calculating the compaction degree based on the vibration data and outputting a density distribution diagram;

[0086] (3) Evaluate the density distribution map by zoning to obtain weak areas; perform additional vibration on the weak areas to obtain processing records; calculate the density value based on the processing records and output the pouring parameters;

[0087] (4) Perform layered calculations through pouring parameters to obtain inter-layer indicators; compare inter-layer indicators with standard requirements to obtain deviation data; adjust the pouring process based on the deviation data and output the adjustment plan;

[0088] (5) Refine the adjustment plan into control points and obtain a monitoring list; collect data on the monitoring list to obtain real-time data; perform trend analysis with real-time data and output a change curve;

[0089] (6) Evaluate the quality through the change curve to obtain the evaluation results; summarize and analyze the evaluation results to obtain the quality status; summarize and organize according to the quality status and output the casting quality data.

[0090] Specifically, the pouring area is divided according to the support plan. The following factors are considered in the pouring area division: structural stress characteristics, construction joint setting, concrete supply capacity, construction equipment layout, etc. After the pouring area division is completed, a detailed pouring sequence table is formulated. The pouring sequence table contains information such as the specific location, pouring direction, and pouring time of each area. When converting the pouring sequence table into a construction time node, factors such as the setting time of concrete, the capacity of construction equipment, and climatic conditions are considered. The arrangement of the time nodes adopts the reverse method, which is calculated from the completion time to determine the specific pouring time of each area and form a pouring plan. The concrete preparation work is arranged according to the pouring plan. The concrete mix ratio needs to meet the requirements of strength, workability, crack resistance, etc. The mix ratio is determined through trial mixing, and the concrete is mixed on site according to the mix ratio requirements. A detection grid should be established for the quality control of the pouring layer. The layout of the detection grid adopts the equal spacing point method. Generally, the side length of each grid is 1-2 meters, and the points are appropriately increased in the weak parts of the structure or stress concentration areas. Each measuring point is marked with a unique number and its spatial coordinates are recorded to form a measuring point layout diagram. Each measuring point is vibrated and tested using the acoustic wave method or the resistance method to measure the vibration density of the concrete and record parameters such as vibration time and vibration depth. After the collected vibration data is processed, a density distribution diagram is drawn.

[0091] The density value is calculated using the following formula:

[0092]

[0093] in: Indicates the density value; represents the amplitude correction factor; represents the vibration energy of the i-th measuring point; Indicates the vibration strength index; represents the aggregate influence coefficient; represents the decay index; represents the density correction factor; q represents the total number of measuring points.

[0094] The inter-layer index is calculated using the following formula:

[0095]

[0096] in: Indicates the inter-layer index value; represents the bonding strength coefficient; represents the compaction degree of the jth layer;

[0097] Indicates the degree of interlayer bonding; represents the joint area; represents the interface impact index; represents the time impact factor; r represents the number of layers.

[0098] When evaluating the density distribution map by zoning, focus on areas where the density is lower than the standard value. These areas are marked as weak areas. Supplementary vibration treatment is carried out on the weak areas. When supplementing vibration, attention should be paid to controlling the vibration time and vibration spacing to avoid over-vibration or missed vibration. The processing record records the location, time, method and other information of the supplementary vibration in detail. Recalculate the density value according to the processing record and generate the pouring parameters. Perform layered calculations through pouring parameters, focusing on analyzing the bonding between different pouring layers. The interlayer indicators reflect the integrity and uniformity of the concrete. By comparing with the standard requirements, the problematic areas are found and deviation data is formed. Adjust the pouring process according to the deviation data, including adjusting the vibration method, changing the pouring sequence, etc., and formulate specific adjustment plans.

[0099] The adjustment plan is refined into specific control points, each of which has clear monitoring requirements and control standards. Continuous data collection is carried out on these monitoring points to record parameters such as temperature, humidity, and strength development of concrete. After the real-time data collected is processed, the change curves of various indicators are drawn to analyze the development trend of concrete performance. Quality evaluation is carried out based on the change curve, and the evaluation content includes multiple aspects such as density, uniformity, and surface flatness. The evaluation results are summarized and analyzed to determine the overall status of concrete pouring quality. According to the quality status, pouring quality data is sorted out to provide a basis for subsequent construction.

[0100] For example: In a well grid steel truss floor project, the pouring area is divided into three sections according to the support plan, numbered A, B, and C. The pouring order is ABC, the pouring time for each section is 2 hours, and 30 minutes of construction preparation time is reserved between sections. During the concrete pouring process, the detection points are arranged according to a grid of 1.5 meters × 1.5 meters, and 16 detection points are arranged in each section. The vibration effect was measured by an ultrasonic detector, and it was found that the density of the four measuring points in section B was low. These measuring points were supplemented with vibration, and the vibration time for each measuring point was 20 seconds, and the insertion depth of the vibrating rod was 400mm. After the supplementary vibration, the density value was re-tested and met the requirements. The layered calculation showed that there was a slight interlayer segregation phenomenon at the joint surface of section A and section B. The pouring process was then adjusted to reduce the pouring thickness and increase the number of vibration points. Continuous monitoring data showed that the pouring quality was significantly improved after adjustment, and the quality assessment results met the design requirements.

[0101] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0102] (1) Convert the pouring quality data into temperature control points to obtain a maintenance point map; divide the maintenance point map into time axes to obtain a maintenance schedule; formulate a maintenance plan based on the maintenance schedule;

[0103] (2) Use the maintenance plan to mark the on-site inspection points and output the monitoring coordinates; collect temperature and humidity at the monitoring coordinates to obtain an environmental parameter table; calculate the regional distribution based on the environmental parameter table and mark the maintenance data;

[0104] (3) Divide the maintenance data into time periods and obtain a trend chart; use the trend chart to determine the abnormal section and obtain an early warning list; perform supplementary maintenance based on the early warning list and output processing records;

[0105] (4) Extract key parameters based on processing records to obtain monitoring indicators; perform regional statistics on monitoring indicators to obtain regional analysis tables; adjust maintenance frequency based on regional analysis tables and output control values;

[0106] (5) Arrange the control values ​​by time period to obtain a data sequence; calculate the deviation of the data sequence to obtain a correction coefficient; adjust the maintenance factors according to the correction coefficient and mark the control data;

[0107] (6) Calculate the maintenance effect through the control data to obtain the index system; determine the control threshold through the index system to obtain the judgment standard; organize the maintenance parameters according to the judgment standard and output the maintenance control parameters.

[0108] Specifically, the pouring quality data is converted into temperature control points, and the hydration heat distribution characteristics after concrete pouring are analyzed. The layout of temperature control points needs to take into account factors such as structural thickness, reinforcement density, and environmental conditions. The key control positions are determined through data analysis to generate a maintenance point map. The maintenance point map marks the spatial coordinates and temperature monitoring requirements of each control point. The maintenance point map is divided on the time axis, and the law of concrete hydration heat release needs to be considered. According to the hydration heat development curve, the maintenance period is divided into three stages: initial maintenance, mid-term maintenance, and late maintenance. Corresponding maintenance requirements are formulated for each stage to form a maintenance schedule. The maintenance schedule records in detail the maintenance measures, maintenance frequency, and maintenance intensity for each time period. Based on the maintenance schedule, a specific maintenance plan is formulated in combination with on-site conditions.

[0109] In the actual maintenance process, detection points are arranged on site according to the maintenance plan. The detection points are arranged using the grid method to form a uniformly distributed monitoring network on the surface of the component. Each detection point is equipped with a temperature and humidity sensor to collect environmental parameters in real time. The collected temperature and humidity data are summarized to form an environmental parameter table, which contains information such as time, location, temperature, and humidity. According to the environmental parameter table, the spatial distribution law of temperature and humidity is calculated, and isothermal and isohumidity maps are drawn, and specific maintenance data are marked. The processing of maintenance data must be divided into time periods. According to the law of concrete strength development, the maintenance period is divided into several time periods, and the maintenance requirements for each time period are different. Through data statistical analysis, a temperature and humidity change trend chart is drawn, and the trend chart intuitively shows the dynamic change process of the maintenance environment. According to the change trend chart, sections with sudden temperature changes or abnormal humidity are identified. These sections are marked as maintenance warning areas and a warning list is generated.

[0110] For abnormal areas in the early warning list, supplementary maintenance measures are immediately initiated. Supplementary maintenance includes increasing the number of watering times, adjusting covering materials, adding shading facilities, etc. The treatment methods and effects are recorded in detail for each supplementary maintenance to form a treatment record. Key parameters such as maintenance frequency, maintenance time, and maintenance methods are extracted from the treatment records to establish a monitoring indicator system. The monitoring indicators are divided into statistical regions, and the differences in maintenance effects in different regions are analyzed to form a regional analysis table. According to the data in the regional analysis table, the maintenance frequency is dynamically adjusted. The frequency adjustment takes into account factors such as concrete age, ambient temperature, and wind speed, calculates the optimal maintenance interval time, and outputs specific control values. The control values ​​are systematically sorted by time period to form a data sequence. Statistical analysis is performed on the data sequence, the standard deviation and coefficient of variation are calculated, and the correction coefficient is determined. According to the correction coefficient, the maintenance elements are fine-tuned, including adjusting parameters such as watering volume and covering time.

[0111] The actual maintenance effect is calculated through maintenance control data. The evaluation of maintenance effect includes multiple aspects such as crack resistance, surface strength, and exposure degree. A quality control system containing multiple evaluation indicators is established, and the control thresholds of various parameters are determined through the indicator system. These thresholds are used as the standard basis for judging the maintenance quality and are organized into maintenance control parameters.

[0112] For example, in a grid steel truss floor project, 24 temperature control points were determined based on the pouring quality data, and these control points were distributed in a grid on the floor surface. The maintenance sequence is divided into three stages: 0-24 hours after pouring is the initial maintenance, mainly controlling the hydration heat; 24-72 hours is the mid-term maintenance, focusing on moisture retention; 72 hours later is the late maintenance, focusing on preventing cracking. In the initial maintenance stage, temperature monitoring showed that the temperature in the mid-span area was too high, so covering measures were immediately added and the temperature measurement frequency was increased. During the mid-term maintenance, it was found that the relative humidity in the edge area was lower than the standard, and the sprinkling frequency and covering method were adjusted in time. Through continuous monitoring and data analysis, the maintenance parameters were dynamically optimized, the maintenance effect met the design requirements, and there were no cracks, no pockmarks and other quality defects on the concrete surface. Through data collection, analysis and feedback, precise control of maintenance quality is achieved.

[0113] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0114] (1) Divide the inspection positions by maintenance control parameters to obtain the inspection results; perform error calibration on the inspection results to obtain basic data; perform statistical analysis on the basic data and mark the inspection results;

[0115] (2) Filter component features based on the test results to obtain quality features; classify the quality features to obtain a feature list; compare the feature list with the standard value and output the quality features;

[0116] (3) Extract key points according to quality characteristics to obtain quality data; verify and count the quality data to obtain verification records; compare the verification records with the construction files and output the verification data;

[0117] (4) Calculate the change value based on the verification data to obtain the analysis data; extract features from the analysis data to obtain key values; convert the key values ​​into state parameters and mark the analysis results;

[0118] (5) Use the analysis results to calculate the score value and obtain the quality grade; perform graded statistics on the quality grades to obtain the grade distribution; evaluate the grade distribution according to the standard and output the quality grade;

[0119] (6) Compare the acceptance criteria according to the quality grade to obtain the judgment results; summarize the judgment results to obtain the quality status; organize and analyze the quality status and output the quality report.

[0120] Specifically, the inspection location is determined according to the maintenance control parameters. The inspection location is divided by the zoning sampling method, and the entire floor area is divided into several inspection areas, and fixed inspection points are set in each area. The layout of the inspection points follows the principle of representativeness, and the key stress-bearing parts of the structure and the key construction parts are given priority. Multiple indicators are tested for each inspection point, including strength, flatness, density, etc. The test data is collected and sorted to form preliminary inspection results.

[0121] Error calibration of test results is a key step to ensure data reliability. The calibration process should identify and eliminate obvious outliers, and then analyze the systematic errors and random errors of the measured data. The measuring equipment is calibrated by standard instruments, and an error correction model is established to correct the original data to obtain basic data. After statistical analysis of the basic data, the mean value, standard deviation and coefficient of variation of each indicator are calculated, and these statistical results are marked on the test chart to form the test results. When selecting component features based on the test results, it is necessary to focus on the key indicators that affect the structural performance. These indicators include concrete strength, steel bar protective layer thickness, structural deformation, etc. These characteristic parameters are extracted through data analysis to obtain quality characteristics. The quality characteristics are classified according to different quality evaluation dimensions, such as bearing performance, durability, appearance quality, etc., to form a feature list. Compare the various indicators in the feature list with the design standard values ​​and specification requirements, evaluate the compliance of each indicator, and output a quality feature report.

[0122] When extracting key points according to quality characteristics, the importance evaluation method is used to identify the test data that has a greater impact on the overall performance of the structure. The data of these key points constitute the quality data set. The quality data is verified and counted, and the cross-validation method is used to ensure the accuracy and representativeness of the data, and generate verification records. The verification records are compared with archival materials such as construction logs and supervision records during the construction process to verify the compliance of the construction process and output verification data. The change values ​​of various indicators are calculated based on the verification data, and the change trends and spatial distribution laws of the indicators over time are analyzed in detail to obtain analysis data. Feature extraction is performed on the analysis data, and the principal component analysis method is used to extract the most representative eigenvalues, which are called key values. The key values ​​are converted into state parameters through data mapping. These parameters intuitively reflect the performance status of the structure, and are annotated to form analysis results.

[0123] When using the analysis results for quality scoring, a multi-dimensional scoring system is established. The scoring system takes into account multiple aspects such as structural safety, durability, and applicability, and obtains a comprehensive score through weighted calculation to determine the quality grade. Statistical analysis is performed on the quality grades to obtain the distribution of each grade, reflecting the overall level of quality control. The grade distribution data is compared with the acceptance criteria to comprehensively assess the structural quality grade. According to the comparison of the quality grade with the acceptance criteria, the qualification of each indicator is checked item by item to obtain detailed judgment results. The judgment results are systematically summarized, and the distribution characteristics and impact degree of various quality problems are statistically analyzed to form a quality status report. The various data of the quality status are deeply analyzed to find out the weak links in quality control, put forward improvement suggestions, and organize them into a quality report.

[0124] For example, in a well-grid steel truss floor project, inspection points were arranged on the floor surface according to the maintenance control parameters. The inspection points were arranged using the grid method, with a main grid spacing of 3 meters, and densely distributed points in key locations such as the mid-span of the floor, supports, and construction joints. Each inspection point was tested for concrete strength, flatness, and other items to obtain raw data. The data calibration process found that the strength values ​​of two measuring points were abnormal. After verification, it was caused by a failure of the test instrument. These data were removed and retested.

[0125] Statistical analysis shows that the concrete strength values ​​are normally distributed, and the mean and standard deviation are calculated. The quality characteristics are determined based on these data. Among the quality characteristics, it was found that the deflection value in the mid-span area was close to the design limit, and it was listed as a quality characteristic of focus. The area was tested more densely, and more deformation data was collected. By comparing with the construction archives, it was confirmed that it was caused by improper timing of early support removal. The verification data showed that the deformation of the floor slab showed a stable trend, but a long-term monitoring mechanism needed to be established. The analysis results show that, except for deformation control, other quality indicators meet the design requirements. According to the comprehensive score, the floor slab project reached the qualified level, but the mid-span area needs to be continuously observed. The quality report records in detail the problems found and treatment measures in the quality control process, providing an experience reference for subsequent similar projects.

[0126] In a specific embodiment, the process of calculating the change value according to the verification data to obtain the analysis data; extracting features from the analysis data to obtain key values; converting the key values ​​into state parameters, and marking the analysis results may specifically include the following steps:

[0127] (1) Restore the time series characteristics of the verification data through curve fitting and extract the mutation points; calibrate the warning threshold according to the numerical distribution law; randomly sample and calculate the corresponding interval to obtain the analysis data;

[0128] (2) Extract the operating parameters from the analysis data and map them with the standard library; create an abnormal data table for the areas with large deviations; define the boundary range based on the historical accumulated values ​​and mark the key values;

[0129] (3) Use the distribution density of key values ​​to deduce the change path and obtain the trend curve; determine the inflection point position and change rate for the trend curve; calibrate the parameter fluctuation range through data conversion and output the analysis results.

[0130] Specifically, the verification data is curve-fitted, and the least squares method is used to fit the discrete monitoring data points to restore the continuous time series change characteristics. The fitting curve reflects the change law of various quality indicators over time. The mutation points of data change are identified by analyzing the first-order derivative and second-order derivative of the curve. The mutation point refers to the moment when the data changes significantly in a short period of time. These points often reflect special events or quality abnormalities in the construction process. According to the distribution law of the values, the early warning threshold is calibrated by statistical analysis methods. The determination of the early warning threshold takes into account the mean value, standard deviation and engineering practice experience of the data. Usually, the interval exceeding the mean plus or minus three standard deviations is set as the early warning range. After determining the early warning threshold, the stratified random sampling method is used to extract sample data from different time periods and different locations, and the interval range to which these data belong is calculated to obtain the analysis data set.

[0131] The working condition parameters are extracted from the analysis data set. The working condition parameters include key indicators such as structural deformation, stress distribution, and temperature change. These parameters are mapped and compared with the standard specification library to find out the abnormal values ​​that exceed the standard range. For data points with large deviations, a special abnormal data table is established to record the time, location, degree and other information of the abnormality. At the same time, the cumulative values ​​in the historical construction data are collected. These data reflect the normal range of changes in similar projects. By comparing with the current data, a reasonable boundary range is delineated, and the key values ​​are marked on this basis. The distribution density analysis of key values ​​is an important basis for predicting the evolution of structural performance. The distribution probability of key values ​​in different intervals is calculated by the kernel density estimation method, and the probability density curve is drawn. According to the density distribution characteristics, the performance evolution path is derived by regression analysis method to obtain the trend curve. The trend curve clearly shows the changing trend of the quality indicators, and it is necessary to focus on determining the inflection point position on the curve. The inflection point is the position where the slope of the curve changes significantly, reflecting the turning point of the quality state. The inflection point position is determined by numerical differential calculation, and the change rate of each segment of the curve is calculated at the same time. The analysis of the change rate helps to predict the development trend of quality problems and provide a basis for taking preventive measures in a timely manner. After data conversion and calibration, the fluctuation range of each parameter is determined. The determination of the fluctuation range takes into account the discrete degree of data and the allowable error of the project, and outputs the analysis results containing multi-dimensional information.

[0132] For example, in a grid steel truss floor project, 30-day structural deformation monitoring data were collected. First, the data were fitted with a cubic spline curve to obtain a smooth deformation-time curve. Three mutation points were identified on the curve, corresponding to key moments such as support removal, load loading, and temperature drastic changes. The distribution characteristics of the data were determined by normal distribution test, and the warning threshold was set to the mean ± 2.5 times the standard deviation. Samples were extracted from the monitoring data by time period, and data from the morning, noon, and evening periods were extracted every day to calculate the change range of the deformation value. The extracted working condition parameters showed that the deformation value at the support was large, and after comparison with the standard specifications, it was found that it exceeded the allowable value. An abnormal data table was established to record the specific location and time of the deformation limit. The historical data of similar projects were consulted to determine the reasonable boundary range of deformation as ± 20% of the design value.

[0133] Kernel density estimation of deformation data revealed that the data showed a bimodal distribution, reflecting the impact of day and night temperature difference on structural deformation. The deformation development trend curve was obtained through polynomial regression analysis. Two inflection points were identified on the trend curve. The first inflection point appeared after the support was removed, showing a rapid increase in deformation; the second inflection point appeared after the concrete strength reached the design value, and the deformation tended to stabilize. The change rate analysis showed that the deformation rate was large in the early stage, and then gradually decreased and tended to zero. Through these analyses, the reasonable fluctuation range of deformation was determined and a reference benchmark for long-term monitoring was established.

[0134] The above describes the construction quality control method for the well grid steel bar truss floor in the embodiment of the present application. The following describes the construction quality control system for the well grid steel bar truss floor in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a construction quality control system for a well grid steel bar truss floor comprises:

[0135] The acquisition module 201 is used to obtain a well grid reinforcement cage by binding and connecting well grid reinforcements; obtain well grid structural parameters by flatness detection; and convert the well grid structural parameters into well grid quality data;

[0136] The calibration module 202 is used to connect the truss steel bars with the well grid steel cage based on the well grid quality data to obtain a well truss assembly structure; process the well truss assembly structure through verticality calibration to generate a calibrated well truss assembly; and obtain node data of the well truss assembly through node detection;

[0137] A generation module 203 is used to arrange the template support by using the node data to obtain the template structure; generate flatness data by adjusting the template elevation; and obtain a support plan according to the flatness data;

[0138] Operation module 204 is used to carry out concrete pouring operation according to the support scheme to generate a pouring layer; determine pouring parameters by density detection; and generate pouring quality data through pouring parameter control;

[0139] The monitoring module 205 is used to convert the pouring quality data into a maintenance plan; obtain maintenance data through maintenance monitoring; and generate maintenance control parameters by regulating the maintenance data;

[0140] The inspection module 206 is used to perform quality inspection according to the maintenance control parameters to obtain inspection results; obtain quality characteristics through analysis of the inspection results; and generate a quality report based on the quality characteristic evaluation.

[0141] Through the coordinated cooperation of the above-mentioned components, the grid reinforcement cage is obtained by tying and connecting the grid reinforcement, and the grid structure parameters are obtained by using flatness detection, and then the grid structure parameters are converted into grid quality data, thereby ensuring the manufacturing quality of the basic components; secondly, the connection between the truss reinforcement and the grid reinforcement cage is implemented based on the grid quality data to obtain the grid truss composite structure, and then the calibrated grid truss composite body is formed through verticality calibration, and finally the node data is obtained through node detection, which effectively ensures the overall performance of the structure; then, the formwork support is arranged using the node data to obtain the formwork structure, and the flatness data is generated after elevation adjustment, and the adjustment is made accordingly. The support scheme was obtained through control, which significantly improved the reliability of the support system. On this basis, the concrete pouring operation was carried out in accordance with the support scheme to generate the pouring layer. The pouring parameters were determined through density detection, and the pouring quality data was generated through parameter control to ensure the quality of concrete pouring. Then, the pouring quality data was converted into a maintenance scheme, and the maintenance data was obtained through maintenance monitoring, and the maintenance control parameters were generated by regulation to achieve precise control of the maintenance process. Quality inspection was carried out according to the maintenance control parameters to obtain the test results, and the quality characteristics were obtained through the analysis of the test results. The quality report was generated by the quality characteristics evaluation, and a quality evaluation system was established. Through the layer-by-layer transmission and processing of data, the controllability and traceability of construction quality were achieved, effectively improving the overall construction quality of the well grid steel truss floor.

[0142] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer executes the steps of the construction quality control method for grid steel bar truss floors.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0145] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application is described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A construction quality control method for a grid steel bar truss floor, characterized in that: The construction quality control method for the grid steel bar truss floor comprises: Obtain a well grid reinforcement cage by tying and connecting well grid reinforcements; obtain well grid structural parameters by flatness detection; and convert the well grid structural parameters into well grid quality data; Based on the well grid quality data, the truss steel bars are connected to the well grid steel bar cage to obtain a well truss assembly structure; the well truss assembly structure is processed by verticality calibration to generate a calibrated well truss assembly; and node data of the well truss assembly is obtained by node detection; Arranging the template support by using the node data to obtain the template structure; generating the flatness data by adjusting the template elevation; and obtaining the support scheme by adjusting according to the flatness data; Carry out concrete pouring operation according to the support scheme to generate a pouring layer; determine pouring parameters by density detection; generate pouring quality data through pouring parameter control; Convert the pouring quality data into a maintenance plan; obtain maintenance data through maintenance monitoring; and generate maintenance control parameters by regulating and controlling the maintenance data; Carry out quality inspection according to the maintenance control parameters to obtain test results; obtain quality characteristics through analysis of the test results; and generate a quality report based on the quality characteristic evaluation.

2. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: The grid reinforcement cage is obtained by tying and connecting the grid reinforcements; and the grid structure parameters are obtained by flatness detection; Converting the well grid structure parameters into well grid quality data includes: The orthogonal arrangement of the main reinforcement and the secondary reinforcement generates a tic-tac-toe structure to obtain an initial reinforcement grid; the connection points of the initial reinforcement grid are tied in a double-helix manner to obtain a tied reinforcement structure; positioning slots are set around the tied reinforcement structure to generate a tic-tac-toe reinforcement cage; Scan and measure the well grid reinforcement cage to collect structural dimension data; record the flatness error value by analyzing the deviation of the structural dimension data; compare the flatness error value with the preset standard to obtain the well grid structure parameters; Establishing steel bar position coordinate data based on the grid structure parameters; matching the steel bar position coordinate data with the specification parameters to generate material information records; combining the material information records with the flatness error value to output the grid error data; Classify and sort the grid error data to determine key detection points; perform sampling inspection on the key detection points and record the detection values; perform statistical analysis on the detection values ​​to generate quality control records; Setting a monitoring frequency for the quality control records and establishing a data collection cycle; performing dynamic tracking according to the data collection cycle and recording parameter changes; performing data screening in combination with the parameter changes and generating a monitoring data set; The monitoring data sets are classified and summarized to establish quality assessment indicators; the quality assessment indicators are compared with standard specifications to generate well grid quality data.

3. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: The connection between the truss steel bars and the well grid steel cage is implemented based on the well grid quality data to obtain a well truss combination structure; the well truss combination structure is processed by verticality calibration to generate a calibrated well truss assembly; Acquiring node data of the shaft-truss assembly by node detection includes: The grid quality data is converted into connection point identification to generate positioning data; the connection position is marked on the grid reinforcement cage according to the positioning data to obtain a connection point distribution map; the truss reinforcement installation path is determined by the connection point distribution map to generate a truss combined structure; Taking the positioning slot of the well-beam assembly structure as a reference point, collecting the verticality value; comparing the verticality value with the standard value, recording the deviation data; performing correction processing on the deviation data, and generating a calibrated well-beam assembly; Setting a measurement reference point for the calibrated well-truss assembly to establish a detection coordinate system; recording node position information in the detection coordinate system to obtain node distribution data; spatially mapping the node distribution data to obtain node spacing values; Using the node spacing value as a reference, perform stress distribution calculation to generate force data; select key nodes through the force data to obtain a monitoring position diagram; perform welding quality inspection based on the monitoring position diagram and record node strength values; Performing statistical analysis on the node strength values ​​to generate a strength distribution diagram; comparing the strength distribution diagram with the design requirements and outputting a qualified judgment result; classifying data based on the qualified judgment result to generate a node feature set; The node feature set is comprehensively evaluated to establish a quality scoring system; the nodes are scored using the quality scoring system to obtain node quality levels; and the distribution of the node quality levels is comprehensively considered to generate node data.

4. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: The node data is used to arrange the template support to obtain the template structure; The flatness data is generated by adjusting the template elevation; According to the flatness data, a support solution is obtained, including: Convert the node data into support point information to obtain a support layout diagram; determine the support spacing through the support layout diagram to obtain support distribution data; mark the template support position according to the support distribution data to obtain a template structure; Measuring the elevation of the template structure and recording the elevation value; comparing the elevation value with the reference elevation and outputting the deviation value; performing data statistics based on the deviation value to obtain an elevation distribution map; According to the elevation distribution map, a measuring point grid is set to obtain a measuring point matrix; the measuring point matrix is ​​re-measured for horizontality and height difference data is output; and flatness parameters are calculated through the height difference data to obtain flatness data; The flatness data is plotted into a contour map to screen the deformation area; the support force of the deformation area is calculated and the force value is output; the support adjustment amount is determined according to the force value to obtain the adjustment parameter; Summarize the adjustment parameters by partition and output a partition control table; prioritize the data in the partition control table to obtain a control sequence; adjust the support force based on the control sequence and output the support reaction force value; The support reaction force value is compared with the load requirement to obtain a verification result; a comprehensive analysis is performed on the verification result to output a support effect evaluation; and a support plan is formulated based on the support effect evaluation.

5. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: Carrying out concrete pouring operation according to the support scheme to generate a pouring layer; Determine pouring parameters by density testing; Generate pouring quality data through pouring parameter control, including: Divide the pouring area by the support scheme to obtain a pouring sequence table; convert the pouring sequence table into time nodes to obtain a pouring plan; prepare concrete according to the pouring plan and output the pouring layer; Setting a detection grid for the casting layer to obtain a measurement point arrangement diagram; performing a vibration test on each point on the measurement point arrangement diagram to obtain vibration data; using the vibration data to calculate the compaction degree and output a density distribution diagram; Performing a zoning evaluation on the density distribution map to obtain weak areas; performing additional vibration on the weak areas to obtain processing records; calculating the density value based on the processing records and outputting the pouring parameters; Perform layered calculations through the casting parameters to obtain interlayer indicators; compare the interlayer indicators with standard requirements to obtain deviation data; adjust the casting process based on the deviation data and output an adjustment plan; Refining the adjustment plan into control points to obtain a monitoring list; collecting data on the monitoring list to obtain real-time data; performing trend analysis on the real-time data to output a change curve; Quality evaluation is performed through the change curve to obtain an evaluation result; the evaluation results are summarized and analyzed to obtain a quality status; and the casting quality data is output based on the summary and arrangement of the quality status.

6. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: The step of converting the pouring quality data into a maintenance plan; acquiring maintenance data through maintenance monitoring; and generating maintenance control parameters by regulating the maintenance data includes: Convert the pouring quality data into temperature control points to obtain a maintenance point map; divide the maintenance point map into time axes to obtain a maintenance time sequence table; formulate a maintenance plan based on the maintenance time sequence table; Using the maintenance plan to mark the on-site detection points, output the monitoring coordinates; collect the temperature and humidity of the monitoring coordinates to obtain an environmental parameter table; calculate the regional distribution according to the environmental parameter table and mark the maintenance data; Divide the maintenance data into time periods to obtain a change trend graph; determine abnormal sections using the change trend graph to obtain an early warning list; perform supplementary maintenance based on the early warning list and output a processing record; Extract key parameters according to the processing records to obtain monitoring indicators; perform regional statistics on the monitoring indicators to obtain a regional analysis table; adjust the maintenance frequency according to the regional analysis table and output a control value; Arrange the control values ​​by time period to obtain a data sequence; perform deviation calculation on the data sequence to obtain a correction coefficient; adjust the maintenance elements according to the correction coefficient and mark the control data; The maintenance effect is calculated through the control data to obtain an index system; the control threshold is determined through the index system to obtain a judgment standard; the maintenance parameters are sorted according to the judgment standard, and the maintenance control parameters are output.

7. The construction quality control method for grid steel bar truss floor according to claim 1 is characterized in that: said performing quality inspection according to said maintenance control parameters to obtain inspection results; Obtaining quality characteristics through analysis of test results; Generate a quality report from the quality characteristics evaluation, including: Divide the inspection position by the maintenance control parameter to obtain the inspection result; perform error calibration on the inspection result to obtain basic data; perform statistical analysis on the basic data and mark the inspection result; Filter component features according to the test results to obtain quality features; classify the quality features to obtain a feature list; compare the feature list with standard values ​​to output quality features; Extract key points according to the quality characteristics to obtain quality data; verify and count the quality data to obtain verification records; compare the verification records with the construction files to output verification data; Calculate the change value according to the verification data to obtain analysis data; extract features from the analysis data to obtain key values; convert the key values ​​into state parameters and mark the analysis results; Calculate the score value using the analysis result to obtain the quality grade; perform graded statistics on the quality grade to obtain the grade distribution; evaluate the grade distribution according to the standard and output the quality grade; Compare the acceptance criteria according to the quality grade to obtain a determination result; summarize the determination results to obtain a quality status; organize and analyze the quality status and output a quality report.

8. The construction quality control method for grid steel bar truss floor according to claim 7 is characterized in that: The step of calculating the change value according to the verification data to obtain analysis data; extracting features from the analysis data to obtain key values; converting the key values ​​into state parameters and marking analysis results includes: The verification data is restored to time series characteristics through curve fitting to extract mutation points; the warning threshold is calibrated according to the numerical distribution law; the corresponding interval is randomly sampled to obtain analysis data; Extract the working condition parameters from the analysis data and map them with the standard library; establish an abnormal data table for places with large deviations; delineate the boundary range based on historical accumulated values ​​and mark key values; The distribution density of the key value is used to deduce the variation path to obtain a trend curve; the inflection point position and the variation rate are determined for the trend curve; the parameter fluctuation range is calibrated through data conversion, and the analysis result is output.

9. A construction quality control system for a grid steel bar truss floor, used to implement the construction quality control method for a grid steel bar truss floor as claimed in any one of claims 1 to 8, characterized in that: The construction quality control system for the well grid steel bar truss floor comprises: The acquisition module is used to obtain the well grid reinforcement cage by binding and connecting the well grid reinforcement; obtain the well grid structure parameters by flatness detection; and convert the well grid structure parameters into well grid quality data; A calibration module is used to connect the truss steel bars with the well grid steel cage based on the well grid quality data to obtain a well truss assembly structure; process the well truss assembly structure through verticality calibration to generate a calibrated well truss assembly; and obtain node data of the well truss assembly through node detection; A generation module is used to arrange the template support by using the node data to obtain the template structure; generate flatness data by adjusting the template elevation; and obtain a support plan according to the flatness data; An operation module is used to carry out concrete pouring operations according to the support scheme and generate a pouring layer; determine pouring parameters by density detection; and generate pouring quality data through pouring parameter control; A monitoring module, used to convert the pouring quality data into a maintenance plan; obtain maintenance data through maintenance monitoring; and generate maintenance control parameters by regulating the maintenance data; The inspection module is used to implement quality inspection according to the maintenance control parameters to obtain inspection results; obtain quality characteristics through analysis of the inspection results; and generate a quality report based on the quality characteristic evaluation.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, a construction quality control method for a grid steel bar truss floor as described in any one of claims 1 to 8 is implemented.

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