Prefabricated component management method, device, equipment and storage medium

Abstract

The present invention provides a method, device, equipment and storage medium for managing prefabricated components. The method comprises: obtaining vertex data of prefabricated components to be managed, and performing polygon processing according to the vertex data to obtain polygon data of the prefabricated components; performing polygon expansion processing on the polygon data to obtain an expansion function curve; performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve; performing similarity recognition and mirror recognition on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain recognition results, and merging and numbering all prefabricated components according to the recognition results. The present invention analyzes the geometric features of prefabricated components through fast Fourier transform technology, and can accurately identify similar components after rotation or mirror transformation, solving the technical problem that the existing prefabricated component management method is difficult to identify similar components after transformation.

Description

Technical Field

[0001] The present invention relates to the technical field of building assembly, and in particular to a method, device, equipment and storage medium for managing assembled prefabricated components. Background Art

[0002] In prefabricated building projects, the rational management of prefabricated components is crucial to the cost control and production efficiency of the project. At present, the commonly used prefabricated component management method is mainly to classify and number the components according to their geometric shapes, and to determine whether the components can be managed together by directly comparing their sizes and shapes. For example, for components such as composite panels and prefabricated walls, manual measurement or CAD software is usually used to extract the size data of the components, and then the comparison of component sizes is used to determine whether the components belong to the same type.

[0003] However, since prefabricated components may undergo transformations such as rotation or mirroring during the design process, existing methods based on direct comparison of geometric dimensions are difficult to accurately identify similar components after rotation or mirroring, resulting in repeated numbering of components of the same type, increasing production and management costs. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing prefabricated component management method is difficult to accurately identify similar components after rotation or mirror transformation;

[0005] A first aspect of the present invention provides a method for managing prefabricated components, the method comprising:

[0006] Acquire vertex data of the assembled prefabricated component to be managed, and perform polygon processing according to the vertex data to obtain polygon data of the assembled prefabricated component;

[0007] Performing polygon expansion processing on the polygon data to obtain an expansion function curve;

[0008] Performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve;

[0009] Similarity recognition and mirror image recognition are performed on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain recognition results, and all prefabricated components are managed by combined numbering according to the recognition results.

[0010] Optionally, in a first implementation of the first aspect of the present invention, the step of acquiring vertex data of the prefabricated component to be managed, and performing polygon processing according to the vertex data to obtain polygon data of the prefabricated component includes:

[0011] Acquire vertex data of the prefabricated component to be managed, and calculate the area data of the polygon formed by the vertex data according to the vertex coordinates in the vertex data using a preset area calculation formula;

[0012] Adjusting the directions of the vertices of the prefabricated components according to the area data to obtain a vertex sequence of the vertices;

[0013] According to the vertex coordinates and area data, the centroid of the assembled prefabricated component is calculated to obtain centroid coordinate data;

[0014] The vertex sequence and the centroid coordinate data are used as polygon data of the assembled prefabricated component.

[0015] Optionally, in a second implementation of the first aspect of the present invention, performing polygon expansion processing on the polygon data to obtain an expansion function curve includes:

[0016] Calculating the arc length of the boundary of the polygon according to the vertex sequence in the polygon data to obtain boundary arc length data, and determining the number of boundary sampling points according to the boundary arc length data;

[0017] According to the number of boundary sampling points, the boundary of the polygon is subjected to equal-interval sampling processing to determine the boundary sampling points on the boundary of the polygon and the coordinate data of the boundary sampling points;

[0018] Calculate the distance data from each boundary sampling point to the center of mass of the polygon according to the coordinate data of the boundary sampling point and the center of mass coordinate data;

[0019] The polygon is expanded in a counterclockwise direction according to the distance data to obtain the expansion function curve.

[0020] Optionally, in a third implementation manner of the first aspect of the present invention, performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve includes:

[0021] Performing complex number conversion processing on the expanded function curve to obtain complex number sequence data of the expanded function curve;

[0022] Perform Fourier coefficient calculation processing on the expansion function curve according to the complex sequence data to obtain Fourier transform coefficients;

[0023] The Fourier transform coefficients are subjected to amplitude-phase separation processing to obtain amplitude-frequency data and phase-frequency data of the expansion function curve.

[0024] Optionally, in a fourth implementation of the first aspect of the present invention, performing similarity recognition and mirror image recognition on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain recognition results includes:

[0025] Performing amplitude-frequency curve overlap calculation processing on the amplitude-frequency data of any two prefabricated components among all prefabricated components to obtain amplitude-frequency curve overlap data;

[0026] Performing phase difference calculation processing on the phase-frequency data of the arbitrary two prefabricated components to obtain phase difference data and phase sum data;

[0027] According to the amplitude-frequency curve coincidence data, phase difference data and phase sum data, translation and rotation similarity judgment processing and mirror image relationship judgment processing are performed on the arbitrary two assembled prefabricated components to obtain the recognition result.

[0028] Optionally, in a fifth implementation of the first aspect of the present invention, the recognition result includes a translation and rotation similarity recognition result and a mirror relationship recognition result;

[0029] The translation and rotation similarity judgment processing and mirror image relationship judgment processing are performed on the arbitrary two prefabricated components according to the amplitude-frequency curve coincidence data, the phase difference data and the phase sum data to obtain the recognition result, which includes:

[0030] Performing error range judgment processing on the amplitude-frequency curve coincidence data to obtain coincidence judgment result data;

[0031] Perform k-proportional relationship calculation processing on the phase difference data according to the coincidence judgment result data to obtain a translation and rotation similarity recognition result;

[0032] According to the translation and rotation similarity recognition result and the phase and data, a zero value judgment process is performed on the phase and data to obtain the mirror relationship recognition result.

[0033] Optionally, in a sixth implementation of the first aspect of the present invention, the merging and numbering management of all prefabricated components according to the identification results includes:

[0034] Performing component grouping processing on all assembled prefabricated components according to the translation and rotation similarity recognition results to obtain initial component grouping;

[0035] Perform mirror image combination processing on the initial component grouping according to the mirror image relationship recognition result to obtain a final component grouping;

[0036] The prefabricated components in the final component group are uniformly numbered to obtain a combined number of the prefabricated components, and all the prefabricated components are managed according to the combined number.

[0037] A second aspect of the present invention provides a prefabricated component management device, the prefabricated component management device comprising:

[0038] A polygon processing module, used for acquiring vertex data of the assembled prefabricated component to be managed, and performing polygon processing according to the vertex data to obtain polygon data of the assembled prefabricated component;

[0039] A polygon expansion module, used for performing polygon expansion processing on the polygon data to obtain an expansion function curve;

[0040] A Fourier transform module, used for performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve;

[0041] The identification management module is used to perform similarity identification and mirror image identification on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain identification results, and to perform combined numbering management on all prefabricated components according to the identification results.

[0042] The third aspect of the present invention provides an assembled prefabricated component management device, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory so that the assembled prefabricated component management device executes the steps of the above-mentioned assembled prefabricated component management method.

[0043] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned method for managing prefabricated components.

[0044] The above-mentioned method, device, equipment and storage medium for managing prefabricated components obtain the vertex data of the prefabricated components to be managed, and perform polygon processing based on the vertex data to obtain the polygon data of the prefabricated components; perform polygon expansion processing on the polygon data to obtain the expansion function curve; perform fast Fourier transform processing on the expansion function curve to obtain the amplitude-frequency data and phase-frequency data of the expansion function curve; perform similarity recognition and mirror recognition on all prefabricated components based on the amplitude-frequency data and the phase-frequency data to obtain the recognition results, and perform combined number management on all prefabricated components based on the recognition results. The present invention analyzes the geometric features of prefabricated components through fast Fourier transform technology, and can accurately identify similar components after rotation or mirror transformation, solving the technical problem that the existing prefabricated component management method is difficult to identify similar components after transformation.

[0045] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of a first embodiment of the method for managing assembled prefabricated components in an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of an embodiment of an assembled prefabricated component management device in an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of an embodiment of the prefabricated component management equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0052] To facilitate understanding of this embodiment, a method for managing prefabricated components disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps:

[0053] 101. Acquire vertex data of the prefabricated component to be managed, and perform polygon processing according to the vertex data to obtain polygon data of the prefabricated component;

[0054] In one embodiment of the present invention, the step of acquiring vertex data of the prefabricated components to be managed, and performing polygon processing based on the vertex data to obtain the polygon data of the prefabricated components includes: acquiring the vertex data of the prefabricated components to be managed, and calculating the area data of the polygons formed by the vertex data according to the vertex coordinates in the vertex data through a preset area calculation formula; adjusting the directions of the vertices of the prefabricated components according to the area data to obtain a vertex sequence of the vertices; performing centroid calculation processing on the prefabricated components according to the vertex coordinates and the area data to obtain centroid coordinate data; and using the vertex sequence and the centroid coordinate data as the polygon data of the prefabricated components.

[0055] Specifically, prefabricated components are building components used in prefabricated buildings that are prefabricated in factories and mainly include standardized components such as composite panels and prefabricated wall panels. After these components are prefabricated in the factory according to the design drawings, they are transported to the construction site for assembly to form a complete building structure. In the design and production process of prefabricated components, each component has specific geometric shapes and size requirements. Vertex data refers to the coordinate information of each turning point in the two-dimensional plane outline of the prefabricated components. For example, a rectangular prefabricated wall panel, its vertex data contains the plane coordinates of four vertices; an L-shaped composite panel, its vertex data contains the plane coordinates of six vertices. In the management process, first obtain the vertex data of the prefabricated components to be managed. For example, the vertex coordinates of a simple polygon are ( , )=(0,0),( , )=(90,9),( , )=(75,82),( , )=(33,51),( , )=(-11, 71). After obtaining the vertex data, the area data of the polygon formed by the vertex data is calculated using a preset area calculation formula. The area calculation formula is as follows:

[0056] ;

[0057] in( , )=( , ). Substitute the vertex coordinates of the above example into the area calculation formula and calculate the area data:

[0058] A ;

[0059] Next, based on the calculated area data, determine whether the area data is positive or negative. When the area data is greater than 0, it means that the polygon is counterclockwise. When the area data is less than 0, the vertex sequence is arranged in reverse order to make it counterclockwise, thereby obtaining the vertex sequence of the vertices.

[0060] After obtaining the vertex sequence, the centroid of the prefabricated component is calculated based on the vertex coordinates and area data. The centroid coordinates are calculated using the following formula:

[0061] ;

[0062] ;

[0063] Where A is the polygon area data, ( , )=( , ). Substitute the vertex coordinates in the example into the centroid calculation formula to obtain the centroid coordinate data.

[0064] Finally, the vertex sequence and centroid coordinate data are used as polygon data of prefabricated components, where the vertex sequence contains the coordinate information of the polygon vertices arranged in a counterclockwise direction, and the centroid coordinate data records the position information of the polygon centroid. In this way, the polygon processing of the vertex data of prefabricated components is completed, and polygon data that is convenient for subsequent processing is obtained. These data lay the foundation for the subsequent polygon expansion processing, so that there is a unified data format and reference benchmark when calculating the expansion function curve. In practical applications, taking a hexagon as an example, after the area calculation, direction adjustment and centroid calculation of its vertex data, polygon data containing a counterclockwise vertex sequence and centroid coordinates are formed, which fully describes the geometric characteristics and spatial position relationship of the hexagon.

[0065] 102. Perform polygon expansion processing on the polygon data to obtain an expansion function curve;

[0066] In one embodiment of the present invention, the polygon expansion processing of the polygon data to obtain the expansion function curve includes: calculating the arc length of the boundary of the polygon according to the vertex sequence in the polygon data to obtain boundary arc length data, and determining the number of boundary sampling points according to the boundary arc length data; performing equal-interval sampling processing on the boundary of the polygon according to the number of boundary sampling points to determine the boundary sampling points on the boundary of the polygon and the coordinate data of the boundary sampling points; calculating the distance data from each boundary sampling point to the center of mass of the polygon according to the coordinate data and the center of mass coordinate data of the boundary sampling points; and expanding the polygon in a counterclockwise direction according to the distance data to obtain the expansion function curve.

[0067] Specifically, the polygon expansion process starts from the obtained polygon data, which contains the vertex sequence and centroid coordinate data arranged in counterclockwise order. First, according to the vertex sequence in the polygon data, the arc length data of the polygon boundary is calculated, that is, the sum of the lengths of all the sides of the polygon is calculated. Taking a hexagon as an example, the boundary arc length data of the entire polygon is obtained by calculating the straight-line distance between adjacent vertices and summing them. After obtaining the boundary arc length data, it is necessary to determine the number of boundary sampling points. To ensure the performance of the algorithm, the total number of sampling points needs to be 2 to the Nth power. To avoid losing detail features after sampling, according to the Shannon sampling theorem in digital signal processing theory, the number of sampling points needs to be much larger than the number of polygon vertices. Therefore, 4096 sampling points are taken in the actual project. For example, for a regular hexagon, since the distance from all its vertices to the centroid is equal, if the sampling points are 6, a horizontal straight line will be obtained after expansion. If the points are greater than 6, a fluctuating curve will be obtained after expansion, which means that an inappropriate number of sampling points may cause the data after sampling to lose details and be seriously distorted.

[0068] Specifically, based on the determined number of boundary sampling points (4096), the boundary of the polygon is sampled at equal intervals. The sampling process requires taking a sampling point at equal distances along the boundary of the polygon, and this distance is equal to the boundary arc length divided by the number of sampling points. For each sampling point, its coordinate position in the plane rectangular coordinate system needs to be determined to obtain the coordinate data of the boundary sampling point. The coordinates of these sampling points need to be recorded in sequence in the counterclockwise direction of the polygon boundary to ensure the order of the sampling points.

[0069] Specifically, after obtaining the coordinate data of the boundary sampling points, the distance from each boundary sampling point to the center of mass of the polygon is calculated in combination with the known center of mass coordinate data. The distance calculation uses the distance formula between two points in the plane, that is, for any sampling point (x, y) and the center of mass point ( , ), whose distance is equal to ((x- )²+(y- )²). In this way, 4096 distance data are obtained, each of which corresponds to the distance from a sampling point to the centroid.

[0070] Specifically, finally, according to the calculated distance data, the polygon is expanded in a counterclockwise direction. The expansion process is to arrange these distance data in the counterclockwise order of the sampling points to form an expansion function curve. The horizontal axis of this expansion function curve represents the serial number of the sampling point (from 0 to 4095), and the vertical axis represents the distance from the corresponding sampling point to the centroid. For example, for a hexagon, a fluctuating curve will be obtained after sampling and expansion. The fluctuation of the curve reflects the change in the distance of the polygon boundary relative to the centroid.

[0071] Specifically, this expansion process converts the polygons on the two-dimensional plane into a one-dimensional expansion function curve, retaining the geometric feature information of the polygon. The number of sampling points is 4096 to ensure a balance between accuracy and computational efficiency. Too many sampling points will increase the amount of calculation, and too few sampling points will lose detailed features. The starting point of the polygon sampling after translation or rotation is likely to be different. The entire expansion process strictly follows the counterclockwise direction, which ensures that the expansion function curve obtained by the same polygon must be a periodic function that can be converted to each other only through translation transformation after being extended to a periodic function, laying the foundation for subsequent Fourier transform processing. The expansion function curve is actually a function of the distance from the polygon boundary to the centroid with respect to the sampling point sequence number. This expression converts the characteristics of the two-dimensional graphics into a one-dimensional signal, so that signal processing methods such as Fourier transform can be applied for subsequent analysis.

[0072] 103. Perform fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve;

[0073] In one embodiment of the present invention, the fast Fourier transform processing of the expanded function curve to obtain the amplitude-frequency data and phase-frequency data of the expanded function curve includes: performing complex conversion processing on the expanded function curve to obtain complex sequence data of the expanded function curve; performing Fourier coefficient calculation processing on the expanded function curve according to the complex sequence data to obtain Fourier transform coefficients; performing amplitude-phase separation processing on the Fourier transform coefficients to obtain the amplitude-frequency data and phase-frequency data of the expanded function curve.

[0074] Specifically, to perform fast Fourier transform on the expansion function curve, it is necessary to first convert the expansion function curve into a complex form. The expansion function curve is actually a series of distance data from the sampling points to the centroid. These real data are converted into complex form, that is, each distance value is taken as the real part of the complex number, and the imaginary part is set to 0, so as to obtain the complex sequence data of the expansion function curve. For example, for a distance value sequence , the complex sequence obtained after conversion is ,in From 0 to 4095 (the total number of sampling points minus 1). After obtaining the complex sequence data, the data is processed by Fourier coefficient calculation. According to the definition of discrete Fourier transform. Fourier transform coefficient The calculation formula is:

[0075] ;

[0076] in The total number of sampling points is 4096, represents the frequency component, is an imaginary unit, is a complex sequence data. A set of Fourier transform coefficients is calculated by this formula, and each coefficient is a complex number, containing real and imaginary information. Taking translation transformation as an example, when calculating translation Fourier transform coefficients after units When:

[0077] ;

[0078] It can be proved by deduction that and The relationship between them is:

[0079] ;

[0080] This shows that the translation operation only affects the phase without changing the amplitude. Next, the calculated Fourier transform coefficients are subjected to amplitude-phase separation. They are all plural forms and can be expressed as:

[0081] ;

[0082] in is the amplitude, is the phase. The magnitude is obtained by calculating the modulus of the complex number.

[0083] ;

[0084] The phase is obtained by calculating the argument of the complex number

[0085] ;

[0086] This separates the Fourier transform coefficients into amplitude-frequency data and phase-frequency data. For example, when processing whether two figures are mirror images of each other, it is necessary to compare and If their amplitudes are the same and the phase sum is proportional to k within the error, it means that the two figures are mirror images of each other. The amplitude-frequency data reflects the amplitude characteristics of the figure at different frequencies, and the phase-frequency data contains the rotation and symmetry information of the figure. In the actual processing process, for the case of translation and rotation, the amplitude-frequency data remains unchanged, and the phase difference is proportional to k. proportional to; for the mirror image, the amplitude-frequency data remains unchanged, and and The amplitudes of the polygons are the same, and the phases are proportional to k. These characteristics provide a mathematical basis for subsequent similarity recognition and mirror image recognition. The entire fast Fourier transform process converts the one-dimensional expansion function curve into the frequency domain, and obtains the amplitude and phase information in the frequency domain. This conversion makes it easy to compare and judge graphic features that were originally difficult to compare in the spatial domain in the frequency domain. The converted amplitude-frequency data and phase-frequency data completely retain the geometric features of the original polygons. By comparing these data, the similarity relationship between different polygons can be judged. At the same time, this processing method also has translation and rotation invariance and mirror image recognizability, providing a reliable data basis for subsequent component recognition and management.

[0087] 104. Perform similarity recognition and mirror image recognition on all prefabricated components based on amplitude-frequency data and phase-frequency data to obtain recognition results, and manage all prefabricated components by merging their numbers based on the recognition results.

[0088] In one embodiment of the present invention, similarity identification and mirror image identification are performed on all prefabricated components based on the amplitude-frequency data and the phase-frequency data to obtain the identification result, which includes: performing amplitude-frequency curve overlap calculation processing on the amplitude-frequency data of any two prefabricated components among all prefabricated components to obtain amplitude-frequency curve overlap data; performing phase difference calculation processing on the phase-frequency data of the any two prefabricated components to obtain phase difference data and phase sum data; performing translation and rotation similarity judgment processing and mirror image relationship judgment processing on the any two prefabricated components based on the amplitude-frequency curve overlap data, the phase difference data and the phase sum data to obtain the identification result.

[0089] Specifically, when performing similarity recognition and mirror image recognition on all prefabricated components, it is first necessary to calculate the overlap of the amplitude-frequency curves of any two prefabricated components. For the amplitude-frequency data of two prefabricated components, compare the values ​​of their amplitude-frequency curves at each frequency point, calculate the error between the two curves, and thus obtain the amplitude-frequency curve overlap data. For example, taking two hexagonal prefabricated components as an example, obtain their amplitude sequences after Fourier transformation respectively. and ,By calculating the difference between the two sequences at the corresponding frequency points, we can obtain the coincidence data that characterizes the similarity of the amplitude-frequency characteristics of the two components. Next, we calculate the phase difference of the phase-frequency data of the two prefabricated components. The calculation of the phase difference includes two aspects:

[0090] 1. Calculate the phase difference between the two components at the corresponding frequency points , that is, the difference between the phase-frequency data of the two components:

[0091] ;

[0092] 2. Calculate the phase sum, that is, add the phase values ​​of the two components at the corresponding frequency points:

[0093] ;

[0094] For translation and rotation transformations, we need to focus on the phase difference With frequency The proportional relationship between them; for the judgment of the mirror relationship, it is necessary to calculate and The phase and Through these calculations, phase difference data and phase sum data can be obtained. After obtaining the amplitude-frequency curve coincidence data, phase difference data and phase sum data, translation and rotation similarity judgment and mirror relationship judgment can be performed. For translation and rotation similarity, when two components can be obtained by translation and rotation, their amplitude-frequency curves overlap within the error range, and the phase difference is within the error range. Specifically, let the Fourier transform of the first component be:

[0095] ;

[0096] The Fourier transform of the second component is:

[0097] ;

[0098] When two components can be obtained from each other through translation and rotation, we have:

[0099] ;

[0100] This means that their magnitudes are equal:

[0101] ;

[0102] Phase difference and Proportional to:

[0103] ;

[0104] For the judgment of the mirror relationship, when two components are mirror images of each other, their amplitude-frequency curves also overlap within the error range, but at this time, the phase and frequency must be within the error range. is proportional to and The magnitudes are the same and the phases are equal to 0:

[0105] ;

[0106] And by derivation, for the mirror image case, we have:

[0107] ;

[0108] in, The characteristic parameters representing the mirror transformation. The entire recognition process is actually to judge the relationship between the two components in the spatial domain by comparing their characteristics in the frequency domain. The coincidence of the amplitude-frequency curve reflects the similarity of the component shapes, and the phase difference and phase sum contain the transformation relationship information between the components. This recognition method based on Fourier transform transforms the complex spatial geometric relationship into a numerical comparison problem, making the judgment of similarity and mirror relationship accurate and reliable. Finally, through this method, the recognition result indicating the relationship between the two prefabricated components is obtained.

[0109] Furthermore, the recognition result includes a translation and rotation similarity recognition result and a mirror relationship recognition result; the translation and rotation similarity judgment processing and mirror relationship judgment processing are performed on any two prefabricated components according to the amplitude-frequency curve overlap data, the phase difference data and the phase sum data, and the recognition result obtained includes: performing an error range judgment processing on the amplitude-frequency curve overlap data to obtain overlap judgment result data; performing a k-proportional relationship calculation processing on the phase difference data according to the overlap judgment result data to obtain a translation and rotation similarity recognition result; performing a k-proportional relationship calculation processing on the phase sum data according to the translation and rotation similarity recognition result and the phase sum data to obtain the mirror relationship recognition result.

[0110] Specifically, when judging the similarity and mirror image relationship of any two prefabricated components, it is first necessary to judge the error range of the amplitude-frequency curve coincidence data. and , set an error threshold ,Compare and At each frequency point Are the differences in If the following conditions are met:

[0111] ;

[0112] It is considered that the amplitude-frequency curves of the two components coincide within the error range, and the coincidence judgment result data is true; otherwise, the coincidence judgment result data is false.

[0113] After obtaining the coincidence judgment result data, if the coincidence judgment is true, further Proportional relationship calculation and processing. This step mainly verifies the rotation and translation invariance. The derivation process of rotation and translation invariance is:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] It can be proved that if two figures can be obtained by translation and rotation, then the amplitude-frequency curves of their Fourier transforms coincide within the error, and the phase difference is proportional to within the error.

[0120] For components that satisfy translation and rotation similarity, it is necessary to further determine whether there is a mirror image relationship. Suppose the Fourier transform of the original figure is , the Fourier transform of its mirror image can be recorded as , the mirror relationship proof process is as follows:

[0121] ;

[0122] ;

[0123] = ;

[0124] = ;

[0125] It can be shown that the Fourier transform amplitude-frequency curves of the mirrored 2D polygons coincide within the error, because and The amplitudes are the same and the phase sum is equal to 0, so the phase sum of the Fourier transform of the mirror relationship graph is proportional to k within the error.

[0126] Furthermore, the combined numbering management of all prefabricated components according to the recognition results includes: grouping all prefabricated components according to the translation and rotation similarity recognition results to obtain initial component groups; mirroring the initial component groups according to the mirror relationship recognition results to obtain final component groups; uniformly numbering the prefabricated components in the final component groups to obtain combined numbers of the prefabricated components, and managing all prefabricated components according to the combined numbers.

[0127] Specifically, when merging and numbering the prefabricated components according to the recognition results, the translation and rotation similarity recognition results must be processed first. All prefabricated components are traversed and compared, and the components with true translation and rotation similarity recognition results are grouped into the same group, thereby forming an initial grouping of components. Taking a group of prefabricated wall panels as an example, assuming there are five prefabricated wall panels, three of which can be obtained by translation and rotation. These three will be grouped into the same initial group, and the remaining two will each form a separate initial group, thus obtaining three initial groupings of components.

[0128] Specifically, after obtaining the initial grouping of components, it is necessary to further process these initial groupings according to the results of the mirror relationship identification. The mirror relationship between the components in each initial group and the components in other groups is judged. If it is found that the components in two different groups have a mirror relationship, the two groups are merged into a new group. Continuing with the above-mentioned prefabricated wall panels as an example, if it is found that the wall panels in one of the individual groups are mirror images of one of the components in the three-component group, then this individual group will be merged into the three-component group to form a new group containing four components. In this way, all mirror relationship judgments and group merging are completed, and finally the final grouping of components is obtained.

[0129] Specifically, when the prefabricated components in the final component group are uniformly numbered, each final group needs to be assigned a unique group number, and all components in the group share this number. For example, the final group containing four components is assigned the number "PW-001" (PW stands for prefabricated wall panels), and the other group containing one component is assigned the number "PW-002". This number will serve as a unified identification of these components during production, transportation and installation.

[0130] Specifically, the combined number obtained in this way reflects the geometric relationship between components. Components with the same number can either be obtained by translation and rotation, or they are mirror images of each other. This numbering method simplifies the production management process of prefabricated components, and components with the same number can use the same production molds and process parameters. In practical applications, if you encounter a batch of prefabricated floor slabs, some of which have the same shape but different directions, and some of which are mirror images of each other, this combined numbering management method can group the floor slabs with related geometric features together and manage them in a unified manner, thereby achieving efficient organization and management of prefabricated components.

[0131] Specifically, this management method not only reduces the number of component numbers, but also facilitates the formulation of production plans and the statistical management of components. In prefabricated building projects, when the total number of a certain type of component needs to be counted, it is only necessary to query the component with the corresponding number. At the same time, this method is also convenient for on-site construction management. Construction personnel can quickly determine the installation method of the component based on the component number. For components that are mirror images of each other, their corresponding relationship can be determined by numbering, thereby improving construction efficiency.

[0132] In this embodiment, the polygon data of the prefabricated components to be managed is obtained by acquiring the vertex data of the prefabricated components, and polygon processing is performed according to the vertex data to obtain the polygon data of the prefabricated components; polygon expansion processing is performed on the polygon data to obtain the expansion function curve; the expansion function curve is subjected to fast Fourier transform processing to obtain the amplitude-frequency data and phase-frequency data of the expansion function curve; similarity recognition and mirror recognition are performed on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain the recognition results, and all prefabricated components are combined and numbered for management according to the recognition results. The present invention analyzes the geometric features of prefabricated components through fast Fourier transform technology, and can accurately identify similar components after rotation or mirror transformation, solving the technical problem that the existing prefabricated component management method is difficult to identify similar components after transformation.

[0133] The above describes the method for managing prefabricated components in the embodiment of the present invention. The following describes the device for managing prefabricated components in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an assembly type prefabricated component management device includes:

[0134] The polygon processing module 201 is used to obtain vertex data of the prefabricated component to be managed, and perform polygon processing according to the vertex data to obtain polygon data of the prefabricated component;

[0135] A polygon expansion module 202 is used to perform polygon expansion processing on the polygon data to obtain an expansion function curve;

[0136] The Fourier transform module 203 is used to perform fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve;

[0137] The identification management module 204 is used to perform similarity identification and mirror image identification on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain identification results, and to perform combined numbering management on all prefabricated components according to the identification results.

[0138] In an embodiment of the present invention, the prefabricated component management device runs the prefabricated component management method described above. The prefabricated component management device obtains the vertex data of the prefabricated components to be managed, and performs polygon processing according to the vertex data to obtain the polygon data of the prefabricated components; performs polygon expansion processing on the polygon data to obtain the expansion function curve; performs fast Fourier transform processing on the expansion function curve to obtain the amplitude-frequency data and phase-frequency data of the expansion function curve; performs similarity recognition and mirror recognition on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain the recognition result, and performs merged numbering management on all prefabricated components according to the recognition result. The present invention analyzes the geometric features of prefabricated components through fast Fourier transform technology, and can accurately identify similar components after rotation or mirror transformation, solving the technical problem that the existing prefabricated component management method is difficult to identify similar components after transformation.

[0139] above Figure 2 The assembled prefabricated component management device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The assembled prefabricated component management device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0140] Figure 3 3 is a schematic diagram of the structure of an assembled prefabricated component management device provided by an embodiment of the present invention. The assembled prefabricated component management device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the assembled prefabricated component management device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the assembled prefabricated component management device 300 to implement the steps of the above-mentioned assembled prefabricated component management method.

[0141] The prefabricated component management device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the assembled prefabricated component management equipment shown does not constitute a limitation on the assembled prefabricated component management equipment provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0142] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for managing prefabricated components.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment 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 invention is essentially or the part that contributes to the prior art or the whole 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 perform all or part of the steps of the method described in each embodiment of the present invention. 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 code.

[0145] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may 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 invention.

Claims

1. A method for managing prefabricated components, characterized in that: The method for managing assembled prefabricated components comprises: Acquire vertex data of the assembled prefabricated component to be managed, and perform polygon processing according to the vertex data to obtain polygon data of the assembled prefabricated component; The polygon data is subjected to polygon expansion processing to obtain an expansion function curve; the polygon data is subjected to polygon expansion processing to obtain an expansion function curve, comprising: calculating the arc length of the boundary of the polygon according to the vertex sequence in the polygon data to obtain boundary arc length data, and determining the number of boundary sampling points according to the boundary arc length data; performing equal-interval sampling processing on the boundary of the polygon according to the number of boundary sampling points to determine boundary sampling points on the boundary of the polygon and coordinate data of the boundary sampling points; calculating the distance data from each boundary sampling point to the center of mass of the polygon according to the coordinate data and center of mass coordinate data of the boundary sampling points; and performing expansion processing on the polygon in a counterclockwise direction according to the distance data to obtain the expansion function curve; Performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve; Similarity recognition and mirror image recognition are performed on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain recognition results, and all prefabricated components are managed by merging numbers according to the recognition results; the similarity recognition and mirror image recognition are performed on all prefabricated components according to the amplitude-frequency data and the phase-frequency data to obtain recognition results, including: performing amplitude-frequency curve overlap calculation processing on the amplitude-frequency data of any two prefabricated components among all prefabricated components to obtain amplitude-frequency curve overlap data; performing phase difference calculation processing on the phase-frequency data of the any two prefabricated components to obtain phase difference data and phase sum data; performing translation and rotation similarity recognition on the any two prefabricated components according to the amplitude-frequency curve overlap data, the phase difference data and the phase sum data The recognition result is obtained by performing similarity judgment processing and mirror relationship judgment processing on any two assembled prefabricated components according to the amplitude-frequency curve coincidence data, phase difference data and phase sum data, and the recognition result includes: performing error range judgment processing on the amplitude-frequency curve coincidence data to obtain coincidence judgment result data; performing k-proportional relationship calculation processing on the phase difference data according to the coincidence judgment result data to obtain the translation and rotation similarity recognition result; and performing zero value judgment processing on the phase sum data according to the translation and rotation similarity recognition result and the phase sum data to obtain the mirror relationship recognition result.

2. The method for managing prefabricated components according to claim 1, characterized in that: The step of acquiring vertex data of the prefabricated component to be managed, and performing polygon processing according to the vertex data to obtain polygon data of the prefabricated component includes: Acquire vertex data of the prefabricated component to be managed, and calculate the area data of the polygon formed by the vertex data according to the vertex coordinates in the vertex data using a preset area calculation formula; Adjusting the directions of the vertices of the prefabricated components according to the area data to obtain a vertex sequence of the vertices; According to the vertex coordinates and area data, the centroid of the assembled prefabricated component is calculated to obtain centroid coordinate data; The vertex sequence and the centroid coordinate data are used as polygon data of the assembled prefabricated component.

3. The method for managing prefabricated components according to claim 1, characterized in that: The step of performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve comprises: Performing complex number conversion processing on the expanded function curve to obtain complex number sequence data of the expanded function curve; Perform Fourier coefficient calculation processing on the expansion function curve according to the complex sequence data to obtain Fourier transform coefficients; The Fourier transform coefficients are subjected to amplitude-phase separation processing to obtain amplitude-frequency data and phase-frequency data of the expansion function curve.

4. The method for managing prefabricated components according to claim 1, characterized in that: The merging and numbering management of all prefabricated components according to the identification results includes: Performing component grouping processing on all assembled prefabricated components according to the translation and rotation similarity recognition results to obtain initial component grouping; Perform mirror image combination processing on the initial component grouping according to the mirror image relationship recognition result to obtain a final component grouping; The prefabricated components in the final component group are uniformly numbered to obtain a combined number of the prefabricated components, and all the prefabricated components are managed according to the combined number.

5. An assembled prefabricated component management device, characterized in that: The prefabricated component management device comprises: A polygon processing module, used for acquiring vertex data of the assembled prefabricated component to be managed, and performing polygon processing according to the vertex data to obtain polygon data of the assembled prefabricated component; A polygon expansion module is used to perform polygon expansion processing on the polygon data to obtain an expansion function curve; the polygon expansion processing on the polygon data to obtain the expansion function curve includes: according to the vertex sequence in the polygon data, the arc length of the boundary of the polygon is calculated to obtain boundary arc length data, and the number of boundary sampling points is determined according to the boundary arc length data; according to the number of boundary sampling points, the boundary of the polygon is sampled at equal intervals to determine the boundary sampling points on the boundary of the polygon and the coordinate data of the boundary sampling points; according to the coordinate data of the boundary sampling points and the centroid coordinate data, the distance data from each boundary sampling point to the centroid of the polygon is calculated; according to the distance data, the polygon is expanded in a counterclockwise direction to obtain the expansion function curve; A Fourier transform module, used for performing fast Fourier transform processing on the expansion function curve to obtain amplitude-frequency data and phase-frequency data of the expansion function curve; an identification management module, for performing similarity identification and mirror image identification on all prefabricated components according to the amplitude-frequency data and the phase-frequency data, obtaining an identification result, and managing all prefabricated components by merging numbers according to the identification result; performing similarity identification and mirror image identification on all prefabricated components according to the amplitude-frequency data and the phase-frequency data, obtaining an identification result, including: performing amplitude-frequency curve overlap calculation processing on the amplitude-frequency data of any two prefabricated components among all prefabricated components, obtaining amplitude-frequency curve overlap data; performing phase difference calculation processing on the phase-frequency data of any two prefabricated components, obtaining phase difference data and phase sum data; performing phase difference calculation processing on any two prefabricated components according to the amplitude-frequency curve overlap data, the phase difference data and the phase sum data The recognition result is obtained by performing translation and rotation similarity judgment processing and mirror relationship judgment processing on any two assembled prefabricated components according to the amplitude-frequency curve coincidence data, phase difference data and phase sum data, and the recognition result includes: performing error range judgment processing on the amplitude-frequency curve coincidence data to obtain coincidence judgment result data; performing k-proportional relationship calculation processing on the phase difference data according to the coincidence judgment result data to obtain the translation and rotation similarity recognition result; and performing zero value judgment processing on the phase sum data according to the translation and rotation similarity recognition result and the phase sum data to obtain the mirror relationship recognition result.

6. An assembled prefabricated component management device, characterized in that: The prefabricated component management device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the prefabricated component management device to execute the steps of the prefabricated component management method according to any one of claims 1 to 4.

7. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the method for managing prefabricated components as described in any one of claims 1 to 4 are implemented.

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