A data visualization processing method for inorganic binder testing

Abstract

This invention discloses a data visualization processing method for inorganic binder tests, belonging to the field of test data visualization technology. The method includes the following steps: acquiring material compaction test data curves, constructing a multi-primitive view, and uniformly mapping data coordinates and view coordinates; dynamically determining key parameter points of the curve and determining their annotation positions through distance analysis and visual analysis, generating dynamic annotation text boxes. This invention addresses the problems of low automation in binder test data visualization and unattractive annotation of key parameters.

Description

Technical Field

[0001] This invention relates to the field of experimental data visualization technology, and more specifically, to a data visualization processing method for inorganic binder experiments. Background Technology

[0002] Experimental data visualization is a discipline that uses computer graphics and image processing techniques to transform raw experimental data into charts, images, or animations to aid analysis. Current mainstream technologies range from basic two-dimensional statistical charts to complex interactive exploration interfaces. Technological trends are evolving towards integrating real-time big data computing to achieve streaming data visualization, and combining virtual reality for immersive data analysis. Current major challenges include the fusion and display of multi-source heterogeneous data and the degree of automation in visualization generation.

[0003] Inorganic binder testing mainly focuses on the performance testing system of road base materials stabilized by cementitious materials such as cement, lime, and fly ash. Its core technology includes determining the maximum dry density and optimum moisture content of the mixture through compaction tests, and then designing the mix proportions and evaluating the quality.

[0004] The existing technical solutions have the following technical problems: 1. The visualization and automation of data presentation for inorganic binder test data are insufficient; 2. Keypoint coordinates are typically in the data coordinate system, while text boxes are drawn in the screen pixel coordinate system. If scale changes, scaling transformations, or non-linear axis transformations are not processed synchronously, it will cause the annotation position to drift. 3. Ignoring the influence of the visual characteristics of the surrounding elements at the labeled location leads to unclear labeling of key parameters, affecting the visual experience.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a data visualization processing method for inorganic binder tests, aiming to solve the problems of low automation in the visibility of binder test data and unattractive labeling of key parameters.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A data visualization processing method for inorganic binder tests includes the following steps: acquiring material compaction test data curves, constructing multi-element views, and uniformly mapping data coordinates and view coordinates; dynamically determining key parameter points of the curves and determining their annotation positions through distance analysis and visual analysis, and generating dynamic annotation text boxes.

[0008] In a preferred embodiment, the step of dynamically determining the key parameter points of the curve and determining their annotation positions through distance analysis and visual analysis to generate dynamic annotation text boxes includes: performing least-squares fitting on the input discrete points of moisture content and dry density through nonlinear regression analysis to obtain the binder property fitting equation; taking the first derivative of the binder property fitting equation to obtain the optimum moisture content and maximum dry density; and generating dynamic text annotation boxes based on the positions of the optimum moisture content and maximum dry density parameter points in the material compaction test data curve.

[0009] In a preferred embodiment, generating a dynamic text annotation box includes: constructing a text box boundary potential function based on the acquired distance feature vector and primitive visual weights; determining the visual distance of the candidate text box based on the text box boundary potential function; obtaining an annotation cost function through the coverage area and the text box boundary potential function; and globally optimizing to obtain the optimal annotation position with the minimum annotation cost function as the optimization objective, thereby generating a dynamic text annotation box.

[0010] In a preferred embodiment, the unified mapping of data coordinates and view coordinates includes: mapping the data coordinates to view coordinates based on a preset combined mapping model, wherein the combined mapping model includes mapping correction, limiting the zoom range, and a view mapping function.

[0011] In a preferred embodiment, the method for obtaining the distance feature vector includes: obtaining the current view interface elements and constructing a set of primitives; calculating the minimum boundary distance between the candidate annotation text box and each primitive and constructing a distance feature vector.

[0012] In a preferred embodiment, the method for obtaining the visual weight of the graphic element includes: obtaining the boundary clarity, local structural complexity and visual saliency of each graphic element, and weighting them to obtain a visual score of the graphic element; and normalizing the visual score of the graphic element to obtain the visual weight of the graphic element at the position of the candidate annotation text box.

[0013] In a preferred embodiment, obtaining the annotation cost function through the coverage area and the text box boundary potential function further includes: defining a local radius with the current candidate annotation position as the center, and statistically analyzing the primitive set within this radius; calculating the average radius, standard deviation of the radius, and maximum radius based on the historical sample radius set, and determining the adaptive correction parameter; constructing the radius control parameter based on the current local radius; and linearly correcting the cost adjustment parameter of the annotation cost function through the adaptive correction parameter and the radius control parameter.

[0014] The technical effects and advantages of the data visualization processing method for inorganic binder testing of the present invention are as follows: This invention effectively eliminates annotation drift caused by inconsistencies between data coordinates and view coordinates by uniformly mapping the material compaction test data curve to the view coordinate system. Based on this, distance feature vectors and visual weights of primitives are obtained, and a text box boundary potential energy function is constructed, thereby obtaining the annotation cost function. Based on the annotation cost function, the optimal position of the annotation text box is obtained, taking into account spatial collision and visual perception. While avoiding overlap between text boxes and primitives, the visual effect of the text box annotation position under the visual influence of nearby primitives is fully considered, effectively solving the problems of low automation of the visibility of binder test data and unsightly annotation of key parameters. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of a data visualization processing method for inorganic binder testing provided in an embodiment of the present invention.

[0016] Figure 2 The graph shows the calculated moisture content results provided in an embodiment of the present invention.

[0017] Figure 3 The diagram shows the density calculation results, binomial fitting, and dynamic text annotation box provided in the embodiments of the present invention.

[0018] Figure 4 This is a schematic diagram of material compaction test data curves and key parameter points provided in the embodiments of the present invention. Detailed Implementation

[0019] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, Figure 1 This invention provides a data visualization processing method for inorganic binder testing, comprising the following steps: S1. Obtain the material compaction test data curve, construct a multi-element view, and uniformly map the data coordinates and view coordinates; S2 dynamically determines the key parameter points of the curve and determines their annotation positions through distance analysis and visual analysis, generating dynamic annotation text boxes.

[0021] The unified mapping of data coordinates and view coordinates described in S1 includes: Data coordinates are mapped to view coordinates based on a preset combined mapping model, which includes mapping correction, limiting zoom range, and view mapping function.

[0022] Map and correct the original coordinates of the graphic: , , in, and To correct the data coordinates, and The original data coordinates, , and , These represent the lower and upper limits of the x-axis values ​​of the original data, and the lower and upper limits of the y-axis values ​​of the original data, respectively.

[0023] Limit zoom range: , In the formula, and For mapping constraint parameters, This is a preset limit range.

[0024] It should be noted that by limiting the range of the mapping, floating-point cumulative errors and high-magnification scaling distortion can be avoided during the mapping process.

[0025] Optionally, the composite mapping model includes a view mapping function: , in, , , In the formula, For view coordinate vectors, This is an axis nonlinear transformation function, which can be selected as a logarithmic function. For time affine matrix, and This represents the amount of graphic translation.

[0026] S2 describes the dynamic determination of key curve parameter points and the determination of their annotation positions through distance and visual analysis, generating dynamic annotation text boxes, including: S21. Through nonlinear regression analysis, the least squares fit is performed on the discrete points of the input moisture content and dry density to obtain the fitting equation of the binder properties. S22, take the first derivative of the fitting equation for the properties of the binder to obtain the optimal moisture content and maximum dry density; S23, based on the position of the optimum moisture content and maximum dry density parameter points in the material compaction test data curve, generates a dynamic text annotation box.

[0027] Optionally, binomial fitting: using a nonlinear regression analysis algorithm, least squares regression analysis is performed on the discrete points of the input moisture content and dry density to generate a smooth fitting curve.

[0028] Optionally, the x-coordinate of the curve vertex, i.e., the optimum moisture content (OMC), is calculated by taking the first derivative of the fitted equation; in the graph, the vertex is marked with a striking green dot and accompanied by dynamic text annotations, intuitively presenting the spatial location of the maximum dry density (MDD).

[0029] The moisture content calculation results obtained based on material compaction test data are as follows: Figure 2 As shown, the density calculation results and binomial fitting are as follows: Figure 3 As shown.

[0030] Calculating moisture content and density based on material compaction test data is a prior art in this field and will not be elaborated upon here.

[0031] Material compaction test data curves and key parameter points are as follows: Figure 4 As shown.

[0032] The method for obtaining the distance feature vector includes: Get the elements of the current view interface and construct a primitive set; Calculate the minimum boundary distance between the candidate labeled text boxes and each graphic element, and construct the distance feature vector.

[0033] Optionally, obtain the current view interface elements and construct a primitive set. This includes curve boundaries, coordinate axis boundaries, legend boundaries, and parameter text box boundaries; Calculate the minimum boundary distance between the i-th candidate labeled text box and each primitive, and construct the distance feature vector; , The distance feature vector of the i-th candidate labeled text box is: , In the formula, For the text box border, The total number of primitives, This represents the boundary distance between the text box and the graphic element. For distance feature vectors, For elements in the primitive set, for and The distance function.

[0034] The method for obtaining the visual weights of the primitives includes: Obtain the boundary clarity, local structural complexity, and visual saliency of each primitive, and then weight them to obtain a visual score for the primitive. The visual weights of the primitives at the candidate labeled text box positions are obtained by normalizing the visual scores of the primitives.

[0035] Optionally, the boundary sharpness is calculated based on the degree of change in the geometric curvature of the primitive boundary; Optionally, a local spatial analysis region can be constructed within the primitive neighborhood to calculate the density of visual structures within the neighborhood and obtain the local structural complexity. Optionally, based on the brightness contrast relationship, the importance of primitives in the visual structure of the view is quantified to obtain visual saliency; The visual weights of primitives are obtained by normalizing the visual scores of primitives.

[0036] The specific formula for the visual scoring of the graphic element is as follows: , in, Assess the visual score for primitives. For boundary clarity, For local structural complexity, For visual saliency, , and These are the preset scoring feature weights.

[0037] The generation of dynamic text annotation boxes described in S23 includes: Based on the acquired distance feature vector and primitive visual weights, a text box boundary potential function is constructed. The visual distance to the candidate text box is determined based on the text box boundary potential energy function; The annotation cost function is obtained by using the coverage area and the text box boundary potential function; Using the minimum annotation cost function as the optimization objective, the optimal annotation position is obtained through global optimization, and dynamic text annotation boxes are generated.

[0038] Based on distance feature vectors and primitive visual weights, a text box boundary potential function is constructed. , In the formula, Let be the boundary potential function of text box i. For primitive visual weights, For boundary distance, This is a smoothing term.

[0039] The visual distance to the candidate text box is determined based on the text box boundary potential energy function; , in, , In the formula, Visual distance, The distance between the centroid of the primitive and the text box. This is the centroid position of the text box. The location of the graphic element closest to the text box. It is a second-order norm. The preset ratio parameter is used to represent the boundary potential energy and spatial distance.

[0040] It should be noted that the text box boundary potential function indicates that a dense boundary allows the text box to be far from the key parameter points, while a loose boundary is close to the key parameter points.

[0041] The annotation cost function is obtained by combining the coverage area and the text box boundary potential function. , In the formula, For the labeled cost function, For coverage area, Visual distance, and These are the preset cost adjustment parameters.

[0042] The optimal annotation location is selected globally with the minimum annotation cost function as the optimization objective.

[0043] , In the formula, For the optimal annotation position, It is a global optimization algorithm. To optimize the objective.

[0044] Optionally, a particle swarm optimization algorithm can be used for global optimization.

[0045] Optionally, obtaining the annotation cost function through the coverage area and the text box boundary potential function further includes: Define a local radius with the current candidate label position as the center, and count the primitive set within this radius; Calculate the average radius, standard deviation of radius, and maximum radius of the sample based on the historical sample radius set, and determine the adaptive correction parameters; Construct radius control parameters based on the current local radius; The cost adjustment parameters of the annotation cost function are linearly corrected by adaptive correction parameters and radius control parameters.

[0046] Optionally, , , , , , , , , , In the formula, The average radius of the sample. The standard deviation of the radius, The maximum radius of the sample. and To adaptively adjust parameters, For radius control parameters, and The upper and lower limits of the preset radius range, The sample radius index. The total number of sample radius indices. The current local radius, and The adjusted cost parameters are as follows. This is the corrected annotation cost function.

[0047] It should be noted that this embodiment uses adaptive correction parameters and radius control parameters to linearly correct the cost adjustment parameters. Furthermore, it uses intelligent adaptive methods to clarify the differences in the impact of coverage area and visual distance on different target areas in actual application scenarios, reducing the dependence on manual hyperparameters and improving the actual optimization effect of determining the position of text annotation boxes.

[0048] The final generated dynamic text annotation box is as follows: Figure 3 As shown.

[0049] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0051] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0052] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0053] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0054] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data visualization processing method for inorganic binder testing, characterized in that, Includes the following steps: Obtain material compaction test data curves, construct multi-element views, and uniformly map data coordinates and view coordinates; The key parameter points of the curve are dynamically determined, and their annotation positions are determined through distance analysis and visual analysis, generating dynamic annotation text boxes.

2. The data visualization processing method for inorganic binder testing according to claim 1, characterized in that, The process of dynamically determining key curve parameter points and identifying their annotation positions through distance and visual analysis, and generating dynamic annotation text boxes, includes: By performing least squares fitting on the discrete points of input moisture content and dry density through nonlinear regression analysis, the fitting equation for the binder properties is obtained. The first derivative of the fitted equation for the properties of the binder is obtained to determine the optimal moisture content and maximum dry density. Dynamic text annotation boxes are generated based on the positions of the optimum moisture content and maximum dry density parameter points in the material compaction test data curve.

3. The data visualization processing method for inorganic binder testing according to claim 2, characterized in that, The generation of dynamic text annotation boxes includes: Based on the acquired distance feature vector and primitive visual weights, a text box boundary potential function is constructed. The visual distance to the candidate text box is determined based on the text box boundary potential energy function; The annotation cost function is obtained by using the coverage area and the text box boundary potential function; Using the minimum annotation cost function as the optimization objective, the optimal annotation position is obtained through global optimization, and dynamic text annotation boxes are generated.

4. The data visualization processing method for inorganic binder testing according to claim 1, characterized in that, The unified mapping of data coordinates and view coordinates includes: Data coordinates are mapped to view coordinates based on a preset combined mapping model, which includes mapping correction, limiting zoom range, and view mapping function.

5. The data visualization processing method for inorganic binder testing according to claim 3, characterized in that, The method for obtaining the distance feature vector includes: Get the elements of the current view interface and construct a primitive set; Calculate the minimum boundary distance between the candidate labeled text boxes and each graphic element, and construct the distance feature vector.

6. The data visualization processing method for inorganic binder testing according to claim 3, characterized in that, The method for obtaining the visual weights of the primitives includes: Obtain the boundary clarity, local structural complexity, and visual saliency of each primitive, and then weight them to obtain a visual score for the primitive. The visual weights of the primitives at the candidate labeled text box positions are obtained by normalizing the visual scores of the primitives.

7. The data visualization processing method for inorganic binder testing according to claim 3, characterized in that, The method of obtaining the annotation cost function through the coverage area and the text box boundary potential function also includes: Define a local radius with the current candidate label position as the center, and count the primitive set within this radius; Calculate the average radius, standard deviation of radius, and maximum radius of the sample based on the historical sample radius set, and determine the adaptive correction parameters; Construct radius control parameters based on the current local radius; The cost adjustment parameters of the annotation cost function are linearly corrected by adaptive correction parameters and radius control parameters.

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