Rural house assembly type light steel frame splicing quality detection method and system

Abstract

The invention provides a rural house assembly type light steel frame splicing quality detection method and system, and relates to the technical field of house quality detection, and the method comprises the steps: obtaining house use environment data; acquiring node detection data, house design information and column detection data; acquiring historical house use environment data, historical node detection data and historical column detection data in a historical detection period; determining a node quality coefficient according to the node detection data and the house design information; determining a frame quality coefficient according to the column detection data; determining a house quality change speed function according to the historical house use environment data, the historical node detection data and the historical column detection data; according to the house quality change speed function, the house use environment data, the node quality coefficient and the frame quality coefficient, the predicted house maintenance time is determined; and generating a detection report. According to the invention, the accuracy of the splicing quality detection of the house fabricated light steel frame can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of house quality detection, and particularly relates to a rural house fabricated light steel frame splicing quality detection method and system. BACKGROUND

[0002] In the related art, the splicing quality of a fabricated light steel frame of a house can be detected by a professional technical personnel using a detection instrument for manual detection, that is, mainly relying on human factors, and the workload of the detection work is very large, therefore, excessive reliance on human factors can not guarantee the accuracy of the detection result, and it is difficult to predict the time when the house needs to be maintained in combination with the use environment of the house.

[0003] The information disclosed in the background section of this application is only intended to deepen the understanding of the general background of the application, and should not be regarded as acknowledging or implying in any form that the information constitutes prior art known to those skilled in the art. SUMMARY

[0004] The present application provides a rural house fabricated light steel frame splicing quality detection method and system, which can solve the technical problem that the related art cannot guarantee the accuracy of the detection result, and it is difficult to predict the time when the house needs to be maintained in combination with the use environment of the house.

[0005] According to a first aspect of the present application, a rural house fabricated light steel frame splicing quality detection method is provided, comprising: acquiring house use environment data; acquiring node detection data, house design information and column detection data; acquiring historical house use environment data, historical node detection data and historical column detection data of a historical detection period; determining a node quality coefficient according to the node detection data and the house design information; determining a frame quality coefficient according to the column detection data; determining a house quality change speed function according to the historical house use environment data, the historical node detection data and the historical column detection data; determining a predicted house maintenance time according to the house quality change speed function, the house use environment data, the node quality coefficient and the frame quality coefficient; and generating a detection report according to the node quality coefficient, the frame quality coefficient and the predicted house maintenance time.

[0006] According to the application, the node quality coefficient is determined according to the node detection data and the house design information, including: determining the node coating thickness, the node torque and the node image according to the node detection data; determining the node screw angle and the actual node screw quantity according to the node image; determining the design torque, the design node screw quantity and the standard connecting piece image according to the house design information; determining the node structure similarity according to the standard connecting piece image and the node image; determining the node type of each node according to the house design information; determining the preset weight coefficient according to the node type, wherein the preset weight coefficient includes: the first preset weight, the second preset weight, the third preset weight and the fourth preset weight; determining the node quality coefficient according to the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual node screw quantity, the design torque, the design node screw quantity and the node structure similarity.

[0007] According to the application, the preset weight coefficient is determined according to the node type, including: if the node type is a high-corrosion-environment node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.6, 0.15, 0.05 and 0.2 respectively; if the node type is a standard indoor environment node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.65, 0.2, 0.1 and 0.05 respectively; if the node type is a main load-bearing node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.7, 0.19, 0.09 and 0.02 respectively; if the node type is a lateral force resisting node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.25, 0.15, 0.55 and 0.05 respectively.

[0008] According to the application, the node quality coefficient is determined according to the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual node screw quantity, the design torque, the design node screw quantity and the node structure similarity, including: according to the formula: the node quality coefficient of the i th node of the house is determined , wherein if is a conditional function, or is a logical operator of "or", max is a maximum value function, is a preset coefficient, is the first preset weight, is the second preset weight, is the third preset weight, is the fourth preset weight, is the node torque of the i th node, is the design torque of the i th node, a number of actual node screws of the i th node, a number of designed node screws of the i th node, a node coating thickness of the i th node, a preset node coating thickness threshold, a node structure similarity of the i th node, a preset node structure similarity threshold, a node screw angle of the j th screw of the i th node, m is a number of screws of the node, j≤m, j and m are positive integers.

[0009] According to the application, the frame quality coefficient is determined according to the column detection data, comprising: determining column verticality deviation of a plurality of columns according to the column detection data; determining column distance between the plurality of columns according to the column detection data; determining first deviation standard deviation and relative verticality deviation between any two columns according to the column verticality deviation of the plurality of columns; determining the frame quality coefficient according to the relative verticality deviation, the column verticality deviation, the column distance and the first deviation standard deviation.

[0010] According to the application, the frame quality coefficient is determined according to the relative verticality deviation, the column verticality deviation, the column distance and the first deviation standard deviation, comprising: determining the frame quality coefficient according to the formula: determining the frame quality coefficient wherein if is a conditional function, max is a maximum value function, a column verticality deviation of the k th column, a preset column verticality deviation threshold, a first deviation standard deviation, a relative verticality deviation between the k th column and the r th column, a column distance between the k th column and the r th column, a preset distance deviation ratio, K and R are numbers of columns, r≤R, k≤K, r, R, k and K are positive integers.

[0011] According to the application, the house quality change speed function is determined according to the historical house use environment data, the historical node detection data and the historical column detection data, comprising: determining a historical node quality coefficient according to the historical node detection data; determining a historical frame quality coefficient according to the historical column detection data; and determining the house quality change speed function according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, wherein the historical house use environment data comprises historical relative humidity data, historical rainfall PH value, historical average sunshine time, historical earthquake frequency and historical earthquake acceleration value.

[0012] According to the application, the house quality change speed function is determined according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, comprising: determining the house quality change speed function according to the formula: determining a first to-be-fitted equation and a second to-be-fitted equation of the house quality change speed function, wherein, , , , , , , , and is a first to-be-fitted coefficient, , , , , , , , and is a second to-be-fitted coefficient, is a length of the qth historical detection period, is the historical node quality coefficient at the start time of the qth historical detection period, is the historical node quality coefficient at the end time of the qth historical detection period, is the historical frame quality coefficient at the start time of the qth historical detection period, is the historical frame quality coefficient at the end time of the qth historical detection period, is the historical relative humidity data of the qth historical detection period, is a preset relative humidity threshold value, is the historical rainfall PH value of the qth historical detection period, is the historical earthquake acceleration value of the qth historical detection period, is a preset earthquake acceleration value threshold value, is the historical earthquake frequency of the qth historical detection period, a historical average sunshine time for a qth historical detection period, a preset sunshine time threshold; according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, the first to-be-fitted coefficient and the second to-be-fitted coefficient are solved to obtain a solving value of the first to-be-fitted coefficient and the second to-be-fitted coefficient; and according to the solving value of the first to-be-fitted coefficient and the second to-be-fitted coefficient, and the first to-be-fitted equation and the second to-be-fitted equation, a house quality change speed function is determined.

[0013] According to the second aspect of the present application, a rural house assembled light steel frame splicing quality detection system is provided, comprising: an environment data module for obtaining house use environment data; a detection data module for obtaining node detection data, house design information and column detection data; a historical data module for obtaining historical house use environment data, historical node detection data and historical column detection data of a historical detection period; a node quality module for determining a node quality coefficient according to the node detection data and the house design information; a frame quality module for determining a frame quality coefficient according to the column detection data; a change speed module for determining a house quality change speed function according to the historical house use environment data, the historical node detection data and the historical column detection data; a time prediction module for determining a predicted house maintenance time according to the house quality change speed function, the house use environment data, the node quality coefficient and the frame quality coefficient; and a detection report module for generating a detection report according to the node quality coefficient, the frame quality coefficient and the predicted house maintenance time.

[0014] Technical effects: According to the present application, the node detection data and the column detection data of the house can be accurately obtained, and the node quality and the frame quality are evaluated according to the node detection data and the column detection data, the node quality coefficient and the frame quality coefficient are determined, further, the change condition of the node quality and the frame quality under the current use environment condition can be accurately analyzed, the predicted house maintenance time is determined, and then the detection report is generated according to the predicted house maintenance time, the node quality coefficient and the frame quality coefficient, the comprehensiveness and accuracy of the rural house assembled light steel frame splicing quality detection are improved. When determining the node quality coefficient, the node quality coefficient can be determined according to the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual node screw quantity, the design torque, the design node screw quantity and the node structure similarity, in the calculation process, whether the node has obvious serious quality problems can be analyzed, further, when the node does not have obvious serious quality problems, the node quality coefficient is determined by weighted evaluation according to the node torque, the node screw angle, the node structure similarity and the node coating thickness, the comprehensiveness and accuracy of the node quality coefficient are improved. When determining the frame quality coefficient, the frame quality coefficient can be determined according to the relative perpendicularity deviation, the column perpendicularity deviation, the column distance and the first deviation standard deviation, in the calculation process, whether the column perpendicularity deviation of a single column has abnormal condition can be analyzed, in the case that the column perpendicularity deviation of a single column does not have abnormal condition, further analysis is carried out according to the overall deviation confusion degree of the column and the distortion degree between any local structure, the comprehensiveness and accuracy of the frame quality coefficient are improved. When determining the house quality change speed function, the house quality change speed function can be determined according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, the influence condition of the house use environment data on the node quality coefficient and the frame quality coefficient is accurately described, the objectivity of the house quality change speed function is improved.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application. Other features and aspects of the present application will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate examples of the application. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other embodiments can be obtained by those skilled in the art without creative labor on the basis of these drawings;

[0017] Figure 1An exemplary flowchart illustrates a method for inspecting the splicing quality of prefabricated light steel frames for rural houses according to an embodiment of the present invention.

[0018] Figure 2 An exemplary schematic diagram illustrating the determination of node quality coefficients according to an embodiment of the present invention is shown;

[0019] Figure 3 A schematic diagram illustrating the determination of the frame quality coefficient according to an embodiment of the present invention is shown;

[0020] Figure 4 An exemplary schematic diagram illustrating the determination of the rate of change function of house mass according to an embodiment of the present invention is shown;

[0021] Figure 5 A block diagram of a rural housing prefabricated light steel frame splicing quality inspection system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The technical solution of the present invention will be described in detail below with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0024] Figure 1An exemplary flowchart illustrates a method for inspecting the splicing quality of prefabricated light steel frames for rural houses according to an embodiment of the present invention. The method includes: Step S1, acquiring housing usage environment data; Step S2, acquiring node inspection data, housing design information, and column inspection data; Step S3, acquiring historical housing usage environment data, historical node inspection data, and historical column inspection data for historical inspection periods; Step S4, determining node quality coefficients based on the node inspection data and the housing design information; Step S5, determining frame quality coefficients based on the column inspection data; Step S6, determining a housing quality change rate function based on the historical housing usage environment data, the historical node inspection data, and the historical column inspection data; Step S7, determining a predicted housing maintenance time based on the housing quality change rate function, the housing usage environment data, the node quality coefficient, and the frame quality coefficient; and Step S8, generating an inspection report based on the node quality coefficient, the frame quality coefficient, and the predicted housing maintenance time.

[0025] The method for inspecting the splicing quality of prefabricated light steel frames for rural houses according to embodiments of the present invention can accurately acquire node inspection data and column inspection data of the house, and evaluate the node quality and frame quality based on the node inspection data and column inspection data to determine the node quality coefficient and frame quality coefficient. Furthermore, it can accurately analyze the changes in node quality and frame quality under the current usage environment, determine the predicted house maintenance time, and then generate an inspection report based on the predicted house maintenance time, node quality coefficient, and frame quality coefficient, thereby improving the comprehensiveness and accuracy of the quality inspection of prefabricated light steel frames for rural houses.

[0026] According to one embodiment of the present invention, in step S1, housing usage environment data is acquired.

[0027] For example, by using professional meteorological and environmental software, environmental data for the area where the building to be tested is located can be obtained for the past month. This data includes relative humidity, rainfall pH value, sunshine duration, number of earthquakes, and earthquake acceleration.

[0028] According to one embodiment of the present invention, in step S2, node detection data, building design information and column detection data are acquired.

[0029] For example, node inspection data (such as node coating thickness, node torque, and node images) can be obtained through digital torque sensors, high-definition cameras, and coating inspection instruments; building design information (such as design torque of each node, number of design node screws, and images of standard connectors) can be obtained through design documents; and column inspection data (such as column verticality deviation) can be obtained through high-precision laser levels.

[0030] According to one embodiment of the present invention, in step S3, historical building usage environment data, historical node detection data, and historical column detection data of historical detection cycles are acquired.

[0031] For example, acquire historical building usage environment data, historical node detection data, and historical column detection data of similar buildings with similar structures to the building that needs to be detected in the historical detection period. The historical detection period is the time period between two adjacent detections of similar buildings.

[0032] According to one embodiment of the present invention, in step S4, the node quality coefficient is determined based on the node detection data and the house design information.

[0033] Figure 2 A schematic diagram illustrating the determination of node quality coefficients according to an embodiment of the present invention is shown.

[0034] According to an embodiment of the present invention, step S4 includes: step S41, determining the node coating thickness, node torque, and node image based on the node detection data; step S42, determining the node screw angle and the actual number of node screws based on the node image; step S43, determining the design torque, the design number of node screws, and the standard connector image based on the house design information; step S44, determining the node structure similarity based on the standard connector image and the node image; step S45, determining the node type of each node based on the house design information; step S46, determining a preset weight coefficient based on the node type, wherein the preset weight coefficient includes: a first preset weight, a second preset weight, a third preset weight, and a fourth preset weight; step S47, determining a node quality coefficient based on the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual number of node screws, the design torque, the design number of node screws, and the node structure similarity.

[0035] For example, based on node detection data acquired through digital torque sensors, high-definition cameras, and coating inspection instruments, the node coating thickness, node torque, and node image are determined. Based on the node image, the node screw angle and the actual number of node screws are determined. For instance, computer vision algorithms are used to identify and calculate the number of screws (i.e., the actual number of node screws) and the angle between the screw axis and the normal to the component surface (i.e., the node screw angle). Ideally, the node screw angle is 0. Based on standard connector images and node images, the node structural similarity is determined. For instance, based on the node image, the connector image of the node is determined, and computer vision algorithms are used to compare the similarity between the connector image and the standard connector image, i.e., the node structural similarity. The closer the node structural similarity is to 1, the better the actual fit of the connector installation. Based on building design information... The node types of each node are determined, such as nodes in high-corrosion environments (e.g., bathrooms and kitchens), nodes in standard indoor environments (e.g., living rooms and bedrooms), main load-bearing nodes (e.g., main beam-column nodes), and nodes resisting lateral forces (e.g., support nodes). Based on the node type, preset weighting coefficients are determined, including: a first preset weight (weight corresponding to torque), a second preset weight (weight corresponding to node screw angle), a third preset weight (weight corresponding to node structural similarity), and a fourth preset weight (weight corresponding to coating thickness). Based on the preset weighting coefficients, node torque, node screw angle, node coating thickness, actual number of node screws, design torque, design number of node screws, and node structural similarity, the splicing quality of the nodes in the prefabricated light steel frame of the house is evaluated, and a node quality coefficient is determined.

[0036] According to an embodiment of the present invention, step S46 includes: step S461, if the node type is a high-corrosion environment node, then the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight are 0.6, 0.15, 0.05, and 0.2, respectively; step S462, if the node type is a standard indoor environment node, then the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight are 0.65, 0.2, 0.1, and 0.05, respectively; step S463, if the node type is a main load-bearing node, then the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight are 0.7, 0.19, 0.09, and 0.02, respectively; step S464, if the node type is a lateral force resisting node, then the first preset weight, the second preset weight, the third preset weight, and the fourth preset weight are 0.25, 0.15, 0.55, and 0.05, respectively.

[0037] For example, for nodes in highly corrosive environments, corrosion is the primary risk, so a larger weight is assigned to the fourth preset weight, i.e., the fourth preset weight is set to 0.2. The bolt torque of the node remains the most important inspection point, so the first preset weight is set to 0.6. The third preset weight corresponding to the torque is reduced to the minimum, i.e., the third preset weight is set to 0.05. Furthermore, the second preset weight corresponding to the bolt angle is set to 0.15. Therefore, for nodes in highly corrosive environments, the first, second, third, and fourth preset weights are 0.6, 0.15, 0.05, and 0.2, respectively. For nodes in standard indoor environments, the corrosion risk is lower, so the fourth preset weight can be set to 0.05 as a basic inspection item, while the first... The first, second, and third preset weights are set to 0.65, 0.2, and 0.1 respectively, with torque and node screw angles as key inspection items. For major load-bearing nodes, the fourth preset weight can be set to the lowest possible value, i.e., 0.02, with a focus on torque, node screw angles, and structural similarity. The first, second, and third preset weights are set to 0.7, 0.19, and 0.09 respectively. For lateral force resisting nodes, the fit of the connecting plates is more important to ensure smooth force flow, and the coating thickness weight can also be set lower. Therefore, the first, second, third, and fourth preset weights can be set to 0.25, 0.15, 0.55, and 0.05 respectively.

[0038] According to an embodiment of the present invention, step S47 includes: determining the node quality coefficient of the i-th node of the house according to formula (1). , (1)

[0039] In this context, `if` is a conditional function, `or` is the logical operator for "or", and `max` is the function to find the maximum value. For preset coefficients, As the first preset weight, The second preset weight, The third preset weight, The fourth preset weight, Let be the node torque of the i-th node. Let the design torque be the torque of the i-th node. Let be the actual number of screws at the i-th node. Let i be the number of design node screws for the i-th node. Let be the node coating thickness of the i-th node. To preset the node coating thickness threshold, Let be the node structure similarity of the i-th node. To set a preset node structure similarity threshold, Let be the node screw angle of the j-th screw of the i-th node, m be the number of screws in the node, j≤m, and j and m are both positive integers.

[0040] According to one embodiment of the present invention, This indicates that the torque of the i-th node has not reached the design torque, and the torque of this node is unqualified. This indicates that the actual number of screws at the i-th node does not match the designed number of screws, indicating a serious quality problem at this node (too few screws result in insufficient node stiffness and strength, while too many screws may alter the structural failure mode). This indicates that the coating thickness of the i-th node is less than the preset node coating thickness threshold, meaning that the anti-corrosion coating thickness of that node does not meet the standard. This indicates that the node structure similarity of the i-th node is less than the preset node structure similarity threshold (which can be set to 0.95), and the installation fit of the connector of the i-th node does not meet the standard. In formula (1), the condition function... The value includes the following two cases, when the following conditions are met: When the condition is met, it indicates that the i-th node has an unqualified torque, a discrepancy between the actual number of screws and the designed number, an anti-corrosion coating thickness that does not meet the standard, or a non-compliant fit of the connector. This indicates a significant and serious quality problem at the node, directly determining that the node's quality inspection does not meet the standard, and the value of the condition function is 0. When the condition is met, it indicates that the node does not have any obvious serious quality problems, and further testing is needed based on the node type. The value of the condition function is... .

[0041] According to one embodiment of the present invention, This is the ratio of the difference between the node torque and the design torque at the i-th node to the design torque. The larger this ratio, the greater the node torque compared to the design torque. This indicates that the node may be over-tightened, potentially leading to thread damage or excessive stress on the component, resulting in relatively low node quality. This indicates that the maximum value of the node screw angles of the m screws at the i-th node is taken. The above process of taking the maximum value can be used to determine the case where the installation tilt of the node screw angle at the i-th node is most severe. The larger the maximum value of the node screw angle of the m screws at the i-th node, the more inclined the screw, and the force applied to the screw head will generate a bending moment, causing the screw shank to bear bending stress. This makes the screw more prone to bending or even breakage, especially under dynamic loads (such as wind and earthquakes). The lower the node mass of this node, the more likely it is to break. The change in is exponential, indicating that as the angle of the node screw increases, the impact on the node quality increases rapidly, and not at a uniform rate. For preset coefficients, For a constant greater than 0, it can be set to 3. Let be the node structure similarity of the i-th node. The larger the value, the better the fit of the connector at the i-th node, and thus the higher the node quality. This is the ratio of the difference between the node coating thickness of the i-th node and the preset node coating thickness threshold to the preset node coating thickness threshold. The larger this ratio, the greater the node coating thickness relative to the preset node coating thickness threshold, resulting in greater internal stress accumulation, which leads to a more brittle coating, significantly reduced adhesion to the substrate, and a greater likelihood of complete peeling, loss of protective function, and lower node quality. This indicates that the node quality coefficient is determined by a weighted evaluation based on four aspects: node torque, node screw angle, node structural similarity, and node coating thickness.

[0042] In this way, the node quality coefficient can be determined based on preset weighting coefficients, node torque, node screw angle, node coating thickness, actual number of node screws, design torque, design number of node screws, and node structural similarity. During the calculation process, it can be analyzed whether there are obvious serious quality problems in the node. Furthermore, when there are no obvious serious quality problems in the node, the node quality coefficient is determined by weighted evaluation based on four aspects: node torque, node screw angle, node structural similarity, and node coating thickness, which improves the comprehensiveness and accuracy of the node quality coefficient.

[0043] According to one embodiment of the present invention, in step S5, the frame quality coefficient is determined based on the column detection data.

[0044] Figure 3 A schematic diagram illustrating the determination of the frame quality coefficient according to an embodiment of the present invention is shown.

[0045] According to an embodiment of the present invention, step S5 includes: step S51, determining the verticality deviation of multiple columns based on the column detection data; step S52, determining the column distance between multiple columns based on the column detection data; step S53, determining a first deviation standard deviation and a relative verticality deviation between any two columns based on the verticality deviation of the multiple columns; and step S54, determining a frame quality coefficient based on the relative verticality deviation, the verticality deviation of the columns, the column distance, and the first deviation standard deviation.

[0046] For example, based on the column inspection data, determine the horizontal offset (in mm) of the top of the column relative to the bottom at a specific height, i.e., the column verticality deviation; based on the column inspection data, determine the column distance between the centroids of the bottom of each column; calculate the standard deviation of the verticality deviation of multiple columns, i.e., the first deviation standard deviation. Since the direction of the verticality deviation of each column is different, for example, column A is offset 1 mm to the east and column B is offset 2 mm to the west, setting the east direction as the positive direction, then the relative verticality deviation of column A and column B is 3 mm; based on the relative verticality deviation, column verticality deviation, column distance, and the first deviation standard deviation, evaluate the quality of the frame and determine the frame quality coefficient.

[0047] According to an embodiment of the present invention, step S54 includes: determining the frame mass coefficient according to formula (2). , (2)

[0048] Where `if` is a conditional function and `max` is a function to find the maximum value. Let be the verticality deviation of the k-th column. To pre-set the column verticality deviation threshold, The first standard deviation is... Let be the relative verticality deviation between the k-th column and the r-th column. Let be the distance between the k-th column and the r-th column. The preset distance deviation ratio is defined as follows: K and R are the number of columns, r ≤ R, k ≤ K, and r, R, k, and K are all positive integers.

[0049] According to an embodiment of the present invention, in formula (2), the condition function The value includes the following two cases, when the following conditions are met: In the case where the maximum verticality deviation of the K columns is greater than or equal to the preset column verticality deviation threshold, excessive verticality deviation generates a significant additional bending moment on the column, increasing the risk of overall frame instability and causing the frame quality to seriously fail to meet standards, the value of the condition function is 0. The column verticality deviation threshold can be set to one two-hundred-fiftieth of the column height. In such cases, further quality checks of the framework are required, and the value of the condition function is... .

[0050] According to one embodiment of the present invention, This is the ratio of the standard deviation (i.e., the first standard deviation) of the verticality deviation of the K columns to the average of the absolute values ​​of the verticality deviations of the K columns. The more chaotic the direction of the verticality deviations of the K columns, the larger the first standard deviation will be, leading to... The larger the value, the greater the risk of potential uneven stiffness in the structure, even without any single column exceeding the deviation threshold, due to the chaotic direction of the deviations. A lower frame quality coefficient indicates a lower risk of uneven stiffness. This represents the ratio of the relative verticality deviation between the k-th and r-th columns to the distance between the columns, indicating the greater the degree of torsion in the local structure formed by the k-th and r-th columns. This represents the maximum value of the ratio of the relative verticality deviation between any two columns to the distance between the columns. This maximum value method can be used to determine the most severe degree of distortion in any local part of the building's structure. This is the ratio of the maximum value of the ratio of the relative verticality deviation between any two columns to the distance between the columns, to a preset distance deviation ratio. The larger this ratio, the more severe the twisting and the lower the frame quality coefficient. The preset distance deviation ratio can be set to 0.005. This indicates that the frame quality coefficient is determined based on the overall degree of deviation and disorder of the columns and the degree of twisting between any local structures.

[0051] In this way, the frame quality coefficient can be determined based on the relative verticality deviation, column verticality deviation, column distance, and the first deviation standard deviation. During the calculation process, it is possible to analyze whether there are any abnormalities in the verticality deviation of individual columns. If there are no abnormalities in the verticality deviation of individual columns, further analysis can be conducted based on the overall degree of deviation disorder of the columns and the degree of distortion between any local structures, thereby improving the comprehensiveness and accuracy of the frame quality coefficient.

[0052] According to an embodiment of the present invention, in step S6, a rate function for the change of building quality is determined based on the historical building usage environment data, the historical node detection data, and the historical column detection data.

[0053] Figure 4 A schematic diagram illustrating the determination of the rate of change function of house mass according to an embodiment of the present invention is shown.

[0054] According to an embodiment of the present invention, step S6 includes: step S61, determining the historical node quality coefficient based on the historical node detection data; step S62, determining the historical frame quality coefficient based on the historical column detection data; step S63, determining the building quality change rate function based on the historical building usage environment data, the historical node quality coefficient, and the historical frame quality coefficient, wherein the historical building usage environment data includes: historical relative humidity data, historical rainfall pH value, historical average sunshine duration, historical earthquake frequency, and historical earthquake acceleration value.

[0055] For example, based on historical node detection data, the historical node quality coefficient is determined. The calculation method for the historical node quality coefficient is similar to that for the node quality coefficient, and will not be repeated here. Based on historical column detection data, the historical frame quality coefficient is determined. The calculation method for the historical frame quality coefficient is similar to that for the frame quality coefficient, and will not be repeated here. The building's usage environment will affect the safety status of the building's frame and nodes to a certain extent. For example, the duration of sunlight exposure will accelerate the aging of the frame. Based on the correlation of the above data, the rate of change function of building quality between historical building usage environment data, historical node quality coefficient, and historical frame quality coefficient can be determined. Among them, historical relative humidity data, historical rainfall pH value, and historical earthquake acceleration value are average values ​​in the historical detection period.

[0056] According to an embodiment of the present invention, step S63 includes: determining a first and a second equation to be fitted for the rate function of change of house mass according to formula (3). (3)

[0057] in, , , , , , , , and The first coefficient to be fitted, , , , , , , , and The second coefficient to be fitted. Let q be the duration of the q-th historical detection period. Let be the historical node quality coefficient at the start time of the q-th historical detection period. Let be the historical node quality coefficient at the end of the q-th historical detection period. Let be the historical frame quality coefficient at the start of the q-th historical detection period. Let be the historical frame quality coefficient at the end of the q-th historical detection period. This represents the historical relative humidity data for the q-th historical detection period. To preset the relative humidity threshold, The historical rainfall pH value for the qth historical monitoring period. The historical seismic acceleration value for the q-th historical detection period. To preset the threshold value of seismic acceleration, This represents the number of historical earthquakes in the q-th historical detection period. The historical average sunshine duration for the q-th historical monitoring period. A preset sunshine duration threshold is set; based on the historical building usage environment data, the historical node quality coefficient, and the historical frame quality coefficient, the first and second unfit coefficients are solved to obtain the solution values ​​of the first and second unfit coefficients; based on the solution values ​​of the first and second unfit coefficients, as well as the first and second unfit equations, the building quality change rate function is determined.

[0058] According to one embodiment of the present invention, and For similar buildings, the most important structural nodes (e.g., main beam load-bearing points) are the historical node quality coefficients in the q-th historical inspection period. Let be the rate of decrease of the historical node quality coefficient in the q-th historical detection cycle. The value is dimensionless. This indicates a negative correlation between the historical rainfall pH value in the q-th historical monitoring period and the rate of decrease in the historical node quality coefficient. For example, the higher the historical rainfall pH value, the weaker the corrosive effect of the rainfall on the node, and the slower the rate of decrease in the historical node quality coefficient. This is the ratio of the historical relative humidity data for the q-th historical detection period to the preset relative humidity threshold, which can be set to 20%. This indicates a positive correlation between the historical relative humidity data of the q-th historical detection period and the rate of decrease of the historical node quality coefficient. For example, the higher the historical relative humidity data, the better the electrolyte solution provided for the electrochemical corrosion of the steel, resulting in a faster corrosion rate of screws, connecting plates, and light steel keel itself, and a relatively faster rate of decrease in the historical node quality coefficient. This is the ratio of the historical average sunshine duration to the preset sunshine duration threshold for the q-th historical detection period. It can be set to 1 hour. This indicates a positive correlation between the historical average sunshine duration of the q-th historical monitoring period and the rate of decline of the historical node quality coefficient. For example, the longer the average sunshine duration, the faster the pulverization and embrittlement of sealant, waterproof and breathable membrane, etc. at the node joints, which in turn accelerates metal corrosion from the inside, and the relatively faster the rate of decline of the historical node quality coefficient. This is the ratio of the historical seismic acceleration value in the q-th historical detection period to the preset seismic acceleration value threshold, which can be set to 1Gal. and All are dimensionless values. This indicates the severity of the earthquake in the q-th historical monitoring period. This indicates that the severity of the earthquake in the q-th historical monitoring period is positively correlated with the rate of decrease of the historical node quality coefficient. For example, the higher the earthquake acceleration value, the more severe the fatigue damage caused by a single earthquake, and the faster the rate of decrease of the historical node quality coefficient. The greater the number of historical earthquakes, the more severe the cumulative fatigue damage caused by multiple earthquakes, and the faster the rate of decrease of the historical node quality coefficient. Based on the above correlations, the first equation to be fitted for the rate of change function of building quality can be obtained.

[0059] According to one embodiment of the present invention, Let be the rate of decrease of the historical frame quality coefficient in the q-th historical detection cycle. The value is dimensionless. This indicates a negative correlation between the historical rainfall pH value in the q-th historical monitoring period and the rate of decrease in the historical frame quality coefficient. For example, the higher the historical rainfall pH value, the weaker the corrosiveness of the rainfall on the frame, and the slower the rate of decrease in the historical frame quality coefficient. This indicates a positive correlation between the historical relative humidity data of the q-th historical testing period and the rate of decrease in the historical frame quality coefficient. For example, higher historical relative humidity data provides a better electrolyte solution for the electrochemical corrosion of steel. As the steel cross-sections of all load-bearing components (beams and columns) gradually thin due to uniform corrosion, the overall load-bearing capacity of the frame decreases faster, and the historical frame quality coefficient decreases relatively faster. This indicates a positive correlation between the historical average sunshine duration in the q-th historical testing period and the rate of decline of the historical frame quality coefficient. For example, the longer the average sunshine duration, the faster the waterproofing and sealing systems of the exterior walls and roof age due to ultraviolet radiation, affecting the frame quality of the building, and the faster the historical frame quality coefficient declines. This indicates a positive correlation between the earthquake severity in the q-th historical monitoring period and the rate of decrease in the historical frame quality coefficient. For example, the higher the earthquake acceleration value, the more severe the fatigue damage caused by a single earthquake, and the faster the rate of decrease in the historical frame quality coefficient. The greater the number of historical earthquakes, the more severe the cumulative fatigue damage caused by multiple earthquakes, and the faster the rate of decrease in the historical frame quality coefficient. Based on the above correlations, the second equation to be fitted for the rate of change function of building quality can be obtained.

[0060] According to one embodiment of the present invention, fitting can be performed based on multiple parameters involved in the first and second equations to be fitted, namely, fitting based on historical node quality coefficients, historical frame quality coefficients, historical relative humidity data, historical rainfall pH values, historical average sunshine duration, historical earthquake frequency, and historical earthquake acceleration values, thereby solving for the aforementioned multiple first and second coefficients to be fitted. There are nine first and nine second coefficients to be fitted, respectively. , , , , , , , and as well as , , , , , , , and Based on the historical node quality coefficients, historical frame quality coefficients, historical relative humidity data, historical rainfall pH values, historical average sunshine duration, historical earthquake frequency, and historical earthquake acceleration values ​​from at least nine historical monitoring cycles, the above nine first-to-fit coefficients and nine second-to-fit coefficients are fitted to obtain the solution values ​​of the above nine first-to-fit coefficients and nine second-to-fit coefficients. The solution values ​​of the above nine first-to-fit coefficients and nine second-to-fit coefficients are then substituted into the first-to-fit equation and the second-to-fit equation, respectively, to determine the rate function of change of building mass.

[0061] In this way, the rate function of housing quality change can be determined based on historical housing use environment data, historical node quality coefficients, and historical frame quality coefficients. This accurately describes the impact of housing use environment data on node quality coefficients and frame quality coefficients, and improves the objectivity of the rate function of housing quality change.

[0062] According to an embodiment of the present invention, in step S7, the predicted building maintenance time is determined based on the building quality change rate function, the building usage environment data, the node quality coefficient, and the frame quality coefficient.

[0063] For example, by substituting the building's environmental data, node quality coefficient, and frame quality coefficient into the building quality change rate function, the predicted decline rate of the node quality coefficient and frame quality coefficient is obtained. Based on the predicted decline rate of the node quality coefficient and frame quality coefficient, as well as preset node quality coefficient safety thresholds (which can be set to 0.9) and preset frame quality coefficient safety thresholds (which can be set to 1.9), the first maintenance time when the node quality coefficient drops to the preset node quality coefficient safety threshold and the second maintenance time when the frame quality coefficient drops to the preset frame quality coefficient safety threshold are determined. The smaller of the first maintenance time and the second maintenance time is set as the predicted building maintenance time.

[0064] According to one embodiment of the present invention, in step S8, an inspection report is generated based on the node quality coefficient, the frame quality coefficient, and the predicted building maintenance time.

[0065] For example, if the node quality coefficient is less than 0.9, it indicates that the node has quality problems and needs maintenance; otherwise, it indicates that the node is of normal quality. If the frame quality coefficient is less than 1.9, it indicates that the frame of the house has quality problems and needs maintenance; otherwise, it indicates that the frame is of normal quality. If the predicted maintenance time for the house is less than 1 year, it indicates that the house may have quality problems under the current environmental conditions and needs to be strengthened for maintenance; otherwise, it indicates that the quality of the house is normal.

[0066] The method for inspecting the splicing quality of prefabricated light steel frames for rural houses according to embodiments of the present invention can accurately acquire node inspection data and column inspection data of the house, and evaluate the node quality and frame quality based on the node inspection data and column inspection data to determine the node quality coefficient and frame quality coefficient. Furthermore, it can accurately analyze the changes in node quality and frame quality under the current usage environment, determine the predicted house maintenance time, and then generate an inspection report based on the predicted house maintenance time, node quality coefficient, and frame quality coefficient, thus improving the comprehensiveness and accuracy of the quality inspection of prefabricated light steel frames for rural houses. When determining the node quality coefficient, it can be determined based on preset weighting coefficients, node torque, node screw angle, node coating thickness, actual number of node screws, design torque, design number of node screws, and node structural similarity. During the calculation process, it can analyze whether there are obvious serious quality problems at the nodes. Furthermore, when there are no obvious serious quality problems at the nodes, a weighted evaluation is performed based on four aspects: node torque, node screw angle, node structural similarity, and node coating thickness to determine the node quality coefficient, thus improving the comprehensiveness and accuracy of the node quality coefficient. When determining the frame quality coefficient, it can be based on relative verticality deviation, column verticality deviation, column distance, and the first standard deviation. During the calculation process, it's possible to analyze whether there are any abnormalities in the verticality deviation of individual columns. If no abnormalities are found in the verticality deviation of individual columns, further analysis is conducted based on the overall degree of deviation disorder and the degree of distortion between any local structures, improving the comprehensiveness and accuracy of the frame quality coefficient. When determining the rate of change function of building quality, it can be based on historical building usage environment data, historical node quality coefficients, and historical frame quality coefficients. This accurately describes the impact of building usage environment data on node quality coefficients and frame quality coefficients, improving the objectivity of the rate of change function of building quality.

[0067] Figure 5An exemplary block diagram of a rural prefabricated light steel frame splicing quality inspection system according to an embodiment of the present invention is shown. The system includes: an environmental data module for acquiring housing usage environment data; an inspection data module for acquiring node inspection data, housing design information, and column inspection data; a historical data module for acquiring historical housing usage environment data, historical node inspection data, and historical column inspection data for historical inspection periods; a node quality module for determining a node quality coefficient based on the node inspection data and the housing design information; a frame quality module for determining a frame quality coefficient based on the column inspection data; a change rate module for determining a housing quality change rate function based on the historical housing usage environment data, the historical node inspection data, and the historical column inspection data; a time prediction module for determining a predicted housing maintenance time based on the housing quality change rate function, the housing usage environment data, the node quality coefficient, and the frame quality coefficient; and an inspection report module for generating an inspection report based on the node quality coefficient, the frame quality coefficient, and the predicted housing maintenance time.

[0068] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0069] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A rural house assembled light steel frame splicing quality detection method, characterized in that, The method comprises: acquiring house use environment data, acquiring node detection data, house design information and column detection data, acquiring historical house use environment data, historical node detection data and historical column detection data of a historical detection period, determining a node quality coefficient according to the node detection data and the house design information, determining a frame quality coefficient according to the column detection data, determining a house quality change speed function according to the historical house use environment data, the historical node detection data and the historical column detection data, and determining a predicted house maintenance time according to the house quality change speed function, the house use environment data, the node quality coefficient and the frame quality coefficient; generating a detection report according to the node quality coefficient, the frame quality coefficient and the predicted house maintenance time.

2. The rural house fabricated light steel frame splicing quality detection method according to claim 1, characterized in that, According to the node detection data and the house design information, the node quality coefficient is determined, including: determining the node coating thickness, the node torque and the node image according to the node detection data; determining the node screw angle and the actual node screw quantity according to the node image; determining the design torque, the design node screw quantity and the standard connecting piece image according to the house design information; determining the node structure similarity according to the standard connecting piece image and the node image; determining the node type of each node according to the house design information; determining the preset weight coefficient according to the node type, wherein the preset weight coefficient includes: the first preset weight, the second preset weight, the third preset weight and the fourth preset weight; and determining the node quality coefficient according to the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual node screw quantity, the design torque, the design node screw quantity and the node structure similarity.

3. The rural house fabricated light steel frame splicing quality detection method according to claim 2, characterized in that, According to the node type, the preset weight coefficient is determined, including: if the node type is a high corrosion environment node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.6, 0.15, 0.05 and 0.2 respectively; if the node type is a standard indoor environment node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.65, 0.2, 0.1 and 0.05 respectively; if the node type is a main load-bearing node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.7, 0.19, 0.09 and 0.02 respectively; and if the node type is a lateral force resisting node, the first preset weight, the second preset weight, the third preset weight and the fourth preset weight are 0.25, 0.15, 0.55 and 0.05 respectively.

4. The rural house fabricated light steel frame splicing quality detection method according to claim 2, characterized in that, According to the preset weight coefficient, the node torque, the node screw angle, the node coating thickness, the actual node screw quantity, the design torque, the design node screw quantity and the node structure similarity, a node quality coefficient is determined, including: according to the formula: determining the node quality coefficient of the i th node of the house , wherein if is a conditional function, or is a logical operator of "or", max is a maximum value function, is a preset coefficient, is a first preset weight, is a second preset weight, is a third preset weight, is a fourth preset weight, is the node torque of the i th node, is the design torque of the i th node, is the actual node screw quantity of the i th node, is the design node screw quantity of the i th node, is the node coating thickness of the i th node, is a preset node coating thickness threshold value, is the node structure similarity of the i th node, is a preset node structure similarity threshold value, is the node screw angle of the j th screw of the i th node, m is the screw quantity of the node, j≤m, j and m are both positive integers.

5. The rural house fabricated light steel frame splicing quality detection method according to claim 1, characterized in that, According to the column detection data, the frame quality coefficient is determined, including: determining column verticality deviation of multiple columns according to the column detection data; determining column distance between multiple columns according to the column detection data; determining first deviation standard deviation and relative verticality deviation between any two columns according to the column verticality deviation of multiple columns; determining frame quality coefficient according to the relative verticality deviation, the column verticality deviation, the column distance and the first deviation standard deviation.

6. The rural house fabricated light steel frame splicing quality detection method according to claim 5, characterized in that, According to the relative verticality deviation, the column verticality deviation, the column distance and the first deviation standard deviation, the frame quality coefficient is determined, including: according to the formula: determining a frame quality coefficient wherein if is a conditional function, max is a maximum function, is a column verticality deviation of the kth column, is a preset column verticality deviation threshold value, is a first deviation standard deviation, is a relative verticality deviation between the kth column and the rth column, is a column distance between the kth column and the rth column, is a preset distance deviation ratio value, K and R are the number of columns, r≤R, k≤K, r, R, k and K are all positive integers.

7. The rural house fabricated light steel frame splicing quality detection method according to claim 1, characterized in that, According to the historical house use environment data, the historical node detection data and the historical column detection data, the house quality change speed function is determined, including: determining historical node quality coefficient according to the historical node detection data; determining historical frame quality coefficient according to the historical column detection data; determining house quality change speed function according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, wherein the historical house use environment data includes: historical relative humidity data, historical rainfall PH value, historical average sunshine time, historical earthquake times and historical earthquake acceleration value.

8. The rural house fabricated light steel frame splicing quality detection method according to claim 7, characterized in that, According to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, a house quality change speed function is determined, including: according to formula: A first to-be-fitted equation and a second to-be-fitted equation of the house quality change speed function are determined, wherein, 、 、 、 、 、 、 、 and is a first to-be-fitted coefficient, 、 、 、 、 、 、 、 and is a second to-be-fitted coefficient, is a length of the qth historical detection period, is a historical node quality coefficient at a starting moment of the qth historical detection period, is a historical node quality coefficient at an ending moment of the qth historical detection period, is a historical frame quality coefficient at a starting moment of the qth historical detection period, is a historical frame quality coefficient at an ending moment of the qth historical detection period, is historical relative humidity data of the qth historical detection period, is a preset relative humidity threshold, is historical rainfall PH value of the qth historical detection period, is historical earthquake acceleration value of the qth historical detection period, is a preset earthquake acceleration value threshold, is a historical earthquake frequency of the qth historical detection period, is historical average sunshine time of the qth historical detection period, is a preset sunshine time threshold; according to the historical house use environment data, the historical node quality coefficient and the historical frame quality coefficient, the first to-be-fitted coefficient and the second to-be-fitted coefficient are solved, to obtain a solving value of the first to-be-fitted coefficient and the second to-be-fitted coefficient; and according to the solving value of the first to-be-fitted coefficient and the second to-be-fitted coefficient, and the first to-be-fitted equation and the second to-be-fitted equation, the house quality change speed function is determined.

9. A rural house assembled light steel frame splicing quality detection method, characterized in that, For performing the method of any one of claims 1-8, including: an environment data module for acquiring house use environment data; a detection data module for acquiring node detection data, house design information and column detection data; a historical data module for acquiring historical house use environment data, historical node detection data and historical column detection data of a historical detection period; a node quality module for determining node quality coefficient according to the node detection data and the house design information; a frame quality module for determining frame quality coefficient according to the column detection data; a change speed module for determining house quality change speed function according to the historical house use environment data, the historical node detection data and the historical column detection data; a time prediction module for determining predicted house maintenance time according to the house quality change speed function, the house use environment data, the node quality coefficient and the frame quality coefficient; a detection report module for generating detection report according to the node quality coefficient, the frame quality coefficient and the predicted house maintenance time.

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