Inorganic binder test curve intelligent fitting and parameter analysis method

Abstract

This invention discloses an intelligent fitting and parameter analysis method for inorganic binder test curves, relating to the fields of civil engineering material testing and computer application technology. The method includes the following steps: constructing a matrix interface to receive raw physical quantities and performing coupled real-time verification; using the least squares method to perform quadratic polynomial regression analysis to solve the characteristic function equation; mapping the data, equations, and fitted sequences to a rendering engine to perform dynamic redrawing; and automatically extracting key mechanical parameters using the first derivative method and performing adaptive annotation. This invention addresses the problems of fragmentation, strong subjectivity in fitted curves, non-standardized charts, and delayed interaction in existing inorganic binder test data processing and calculation processes. It achieves automated transformation from raw data to standardized engineering charts, significantly improving the objectivity of parameter determination and processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of civil engineering material testing and computer application technology, and more specifically, to a method for intelligent fitting and parameter analysis of test curves for inorganic binders. Background Technology

[0002] In highway engineering construction, the mechanical property testing of inorganic binder stabilized materials is a crucial step in ensuring project quality. According to the "Test Procedures for Inorganic Binder Stabilized Materials in Highway Engineering" (JTG 3441—2024), compaction tests and unconfined compressive strength tests are the core methods for evaluating the engineering performance of materials. With the advancement of digital transportation and smart laboratory construction, the industry has placed higher demands on the automated processing of test data, high-precision mechanical characteristic analysis, and the visual output of standardized reports.

[0003] Currently, the mainstream approach to processing test data for inorganic binders mainly relies on manual form filling and calculation, processing with general spreadsheets (such as Excel), and conventional plotting software. Technicians typically need to manually input raw physical quantities such as moisture content, density, and strength, perform basic numerical processing using general tools, and preliminarily determine the optimum moisture content and maximum dry density by manually visually observing the parabolic trend or using simple trend line fitting. Finally, the generated charts are exported and manually edited to meet the layout requirements of engineering reports.

[0004] However, the above-mentioned technical solutions have the following limitations in practical applications: First, the objectivity of key parameter determination is insufficient. Due to the lack of precise mathematical analysis methods, relying on manual visual estimation to fit the vertex of the parabola makes it difficult to accurately lock the coordinates, often accompanied by a 2%-5% human visual estimation bias. Second, it is difficult to achieve a leapfrog improvement in experimental processing efficiency. Because the calculation, fitting, feature extraction, and result export processes are disconnected and lack real-time interactive feedback, the data processing process is cumbersome, and its processing time is difficult to significantly reduce through simple optimization of general tools. Finally, the standardization of output results is low. Conventional plotting tools often produce garbled characters or non-standard layouts when processing Chinese labels, Greek letters, and complex unit superscripts, resulting in output charts often requiring complex manual post-processing enhancements before they can be used directly in reports, seriously affecting the professionalism and automation of the output results. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent fitting and parameter analysis method for inorganic binder test curves. By constructing a structured matrix input interface with coupling verification function, and integrating a quadratic polynomial nonlinear regression algorithm and a dynamic redrawing engine, the method achieves real-time synchronous mapping of test data, mechanical models, and standardized graphics, as well as self-analytical annotation of feature parameters. This solves the problems of fragmented calculation process, strong subjectivity in curve fitting, and disconnect between data and graphical feedback in existing inorganic binder tests.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for intelligent fitting and parameter analysis of experimental curves for inorganic binders includes the following steps: constructing a structured matrix data input interface to receive and organize raw physical quantity data from multiple sets of parallel experiments; performing coupled real-time verification on the raw physical quantity data, and outputting a standardized data set if the raw physical quantity data passes the verification of the built-in physical logic rule base; based on the standardized data set, performing quadratic polynomial nonlinear regression analysis using the least squares method to solve the characteristic function equation of dry density with respect to moisture content, and generating a smooth and continuous fitting curve coordinate sequence; mapping the standardized data set, the characteristic function equation, and the fitting curve coordinate sequence to an embedded scientific chart rendering engine to perform coordinate mapping and dynamic redrawing, generating a real-time responsive visualization; performing mathematical analysis on the characteristic function equation to automatically extract key mechanical characteristic parameters, and performing adaptive graphic annotation in the visualization.

[0007] In a preferred embodiment, the coupled real-time verification of the original physical quantity data includes: when a change in the data state of any input unit in the input interface is detected, triggering a data capture subprocess to convert the input string sequence into a numerical type; calling the built-in physical logic rule library to perform real-time verification of the numerical type; the rule library includes positive and negative verification, range boundary verification, and process quantity logic relationship verification to automatically filter out abnormal data.

[0008] In a preferred embodiment, solving the characteristic function equation of dry density with respect to moisture content includes: extracting the moisture content data sequence and dry density data sequence from the standardized dataset; fitting the sequence using the least squares method to solve the characteristic function equation.

[0009] In a preferred embodiment, the execution of coordinate mapping and dynamic redrawing includes: mapping the original data points in the standardized dataset to discrete points in the coordinate system; mapping and connecting the fitted curve coordinate sequence to form a continuous and smooth function curve; and sending a redrawing command to the rendering engine whenever a new calculation result is generated, so as to clear the old graphic elements and re-execute the rendering based on the latest complete dataset, thereby ensuring the synchronization of graphics and underlying data models.

[0010] In a preferred embodiment, the automatic extraction of key mechanical characteristic parameters includes: obtaining the first derivative of the characteristic function equation with respect to moisture content; solving the equation where the first derivative is zero to obtain the vertex coordinates of the fitted parabola; defining the x-coordinate corresponding to the vertex coordinates as the optimal moisture content and the y-coordinate as the maximum dry density.

[0011] In a preferred embodiment, the adaptive graphic annotation includes: overlaying geometric marker points at the corresponding vertex positions of the fitted curve; dynamically calculating the optimal placement position of the annotation text box based on the visual coordinates of the marker points and the layout of surrounding elements, and generating a leader line to point the text box to the marker point.

[0012] In a preferred embodiment, an intelligent fitting and parameter analysis method for inorganic binder test curves further includes performing standardized semantic rendering on the visualized graphics: calling the academic symbol normalization rendering subprocess to identify the physical quantity codes in the charts and automatically convert them into Greek letter symbols that conform to academic norms; and using LaTeX typesetting syntax to perform superscript or subscript typesetting on the physical units.

[0013] In a preferred embodiment, an intelligent fitting and parameter analysis method for inorganic binder test curves further includes a font decoupling strategy for the execution interface and charts: the Chinese labels of the interactive interface are rendered using a first preset font; the numerical input and calculation result display area is rendered using a second preset font; wherein, the second preset font is a serif font of equal width, used to reduce fatigue when reading decimal points.

[0014] In a preferred embodiment, a method for intelligent fitting and parameter analysis of inorganic binder test curves further includes: constructing a curing age monitoring module, realizing dynamic updates of the experimental curing countdown through a timed triggering mechanism; and dynamically changing the display color of the countdown label according to the remaining time to achieve full-process visual monitoring from test preparation to later curing.

[0015] In a preferred embodiment, an intelligent fitting and parameter analysis method for inorganic binder test curves further includes: when rendering the calculation results to the interface, performing precision control through high-level formatting commands to ensure that the maximum dry density is precisely locked at the thousandths place and the optimum moisture content is retained to one decimal place.

[0016] The technical effects and advantages of the intelligent fitting and parameter analysis method for inorganic binder test curves of this invention are as follows: This invention constructs a structured matrix data input interface and a coupled real-time verification mechanism. Based on a standardized data set output from a built-in physical logic rule library, it performs least squares quadratic polynomial nonlinear regression analysis. An embedded scientific chart rendering engine dynamically maps and redraws the characteristic function equations and the coordinate sequence of the fitted curve. Combined with mathematical analysis of the characteristic function equations and adaptive graphical annotation of key mechanical parameters, it achieves automated transformation and closed-loop processing from raw physical quantities to standardized engineering charts. This results in a real-time interactive effect where the data model and graphical feedback are updated synchronously. By automatically extracting characteristic parameters and performing high-precision visualization graphics rendering, it helps eliminate subjective experience biases in traditional manual fitting and parameter interpretation processes. It effectively solves the technical problems of fragmented calculation processes, highly subjective fitted curves, and the disconnect between data calculation and chart generation in existing inorganic binder test data processing. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the intelligent fitting and parameter analysis method for inorganic binder test curves provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the moisture content calculation results provided in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of the binder calculation interface provided in an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of the maintenance age alarm clock interface provided in an embodiment of the present invention.

[0021] Figure 5 This is a schematic diagram illustrating a data reading failure case provided in an embodiment of the present invention.

[0022] Figure 6 This is a schematic diagram of the fitting curve provided in an embodiment of the present invention.

[0023] Figure 7 This is a schematic diagram of a density calculation page provided in an embodiment of the present invention.

[0024] Figure 8 This is a schematic diagram of the stress-strain curve plotting interface provided in an embodiment of the present invention. Detailed Implementation

[0025] 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.

[0026] Example 1, Figure 1 The present invention provides a method for intelligent fitting and parameter analysis of inorganic binder test curves, including the following steps: S1 constructs a structured matrix data input interface to receive and organize raw physical quantity data from multiple sets of parallel experiments.

[0027] It should be noted that the raw physical quantity data includes at least two types: The first category consists of the original physical quantities used for compaction tests and fitting calculations, including the box mass, wet sample mass, and dry sample mass used to calculate moisture content, and the compaction cylinder volume, cylinder mass, and total mass of cylinder and wet soil used to calculate density. The second category consists of formulation parameters used for calculating the amount of material used in the specimens, including the target specimen size, compaction degree, binder content, and mass redundancy coefficient.

[0028] To handle complex experimental data entry, this method employs a tabular layout geometry manager to construct an 8-row by 5-column data entry matrix, such as... Figure 2 As shown, by generating data input fields in a loop and locating them precisely, a neat arrangement similar to an Excel spreadsheet is achieved, which facilitates batch data comparison and entry.

[0029] To accommodate multiple types of experimental logic within a limited display area, this embodiment also includes introducing tabbed page containers to implement multi-dimensional secondary navigation within specific experimental modules. Specifically, secondary navigation is implemented within the "Compact Test" and "Material Calculation" modules, allowing for one-click switching between "Cylindrical" and "Beam" calculations, thus accommodating twice the functional logic within a limited screen space. In this layout mode, the system achieves physical isolation of functional areas through containerized grouping and the collaboration of a grid system. Specifically, this visualization method extensively uses grouped box containers to divide functional areas; for example, in the "Beam Calculation" page, "Input Parameters" and "Calculation Results" are physically isolated, using visual boundaries to reduce the user's cognitive load. When the user switches tabs, the system achieves seamless instantaneous switching between functional pages through view state switching algorithms and coordinate smoothing mapping methods. This "non-pop-up" hierarchical management mechanism effectively avoids visual focus dispersion and memory resource redundancy caused by frequent pop-up sub-windows during multi-tasking, ensuring the interactive continuity of the entire experimental data processing process. The specific details of the secondary navigation interface and the "cylindrical" calculation layout of the relevant "Materials Calculation" module are as follows: Figure 3 As shown.

[0030] In the specific calculation logic, the formula for calculating the moisture content of the inorganic binder stabilized material is as follows: (1) In the formula, To stabilize the moisture content of inorganic binders; For box quality; For the quality of the wet sample in the box; For the quality of the boxed dry sample.

[0031] In this embodiment, an integrated management mechanism is used to achieve closed-loop monitoring from test preparation to data collection. It also includes: building a maintenance age monitoring module, using a timed triggering mechanism to dynamically update the countdown of experimental maintenance; and dynamically changing the display color of the countdown label according to the remaining time to achieve full-process visual monitoring from test preparation to later maintenance.

[0032] Specifically, such as Figure 4 As shown, this module uses a timed trigger mechanism to dynamically update the countdown. The countdown label changes color dynamically based on the remaining time, with red indicating maintenance in progress and green indicating completion, thus visually representing the passage of time. This maintenance countdown system, integrated in this invention and based on a lightweight data exchange format database, enhances the digitalization of laboratory management. Furthermore, to ensure real-time interactive feedback, an immersive status bar is set at the bottom of the interface, which, along with data-bound variables, displays the system's readiness status in real time, providing continuous feedback on the program's operation.

[0033] S2, perform coupled real-time verification on the original physical quantity data, and output a standardized data set if the verification passes.

[0034] In this embodiment, the coupled real-time verification of the original physical quantity data includes: S201, when a change in the data state of any input unit in the input interface is detected, a data capture sub-process is triggered to convert the input string sequence into a numeric type. Specifically, each independent data input unit in the interface is coupled with the real-time monitoring logic in the background. When the system detects a change in the data state of any input unit, a data capture and preprocessing sub-process is immediately triggered. This sub-process first converts the input string sequence into a numeric type, and then calls the built-in physical logic rule library for real-time verification.

[0035] S202, the built-in physical logic rule library is called to perform real-time verification of the numerical type; specifically, the system performs real-time verification through the built-in physical logic rule library to ensure the compliance of the input data, thereby automatically filtering out abnormal data and outputting a clean, standardized data set that can be used for subsequent curve fitting and material usage calculation.

[0036] The rule base includes positive / negative checks, range boundary checks, and process quantity logical relationship checks to automatically filter out abnormal data. To improve the user-friendliness and security of the interaction, this method incorporates an exception handling mechanism into the visual interaction. Instead of silently reporting errors in the background, this method uses pop-up windows to convert Python's exception stack traces into understandable prompts. Specifically, when the system detects data logic conflicts or read failures, it determines the specific error cause and provides feedback to the user. For example, when importing external experimental records, if the data format is incorrect, the system will pop up a feedback window indicating "No valid numbers detected." Examples of related data read failures can be found here. Figure 5 Through this real-time verification and anomaly feedback mechanism, the system can promptly detect abnormal experimental points, thereby enabling timely verification or re-testing and avoiding the accumulation of invalid experimental data.

[0037] S3, based on a standardized dataset, uses the least squares method to perform quadratic polynomial regression analysis, solves the characteristic function equation, and generates a sequence of coordinates for the fitted curve.

[0038] The process of solving the characteristic function equation of dry density with respect to moisture content includes: S301, Extract the moisture content data sequence and dry density data sequence from the standardized dataset; specifically, based on the standardized dataset obtained in step S1, the system automatically extracts the moisture content data sequence and dry density data sequence. In this step, the system uses the nonlinear regression analysis algorithm of the scientific computing module to organize the input discrete points of moisture content and dry density, thereby providing a basic data source for subsequent mathematical modeling.

[0039] S302, The sequence is fitted using the least squares method to solve the characteristic function equation; the specific steps are as follows: First, the least squares method is used to perform quadratic polynomial nonlinear regression analysis on the two sets of data sequences; Subsequently, the system solves the characteristic function equation of dry density with respect to moisture content using the optimal fitting method, and automatically constructs a quadratic polynomial equation.

[0040] The formula for calculating the characteristic function equation of dry density with respect to moisture content is as follows: (2) in For dry density, Moisture content, All are fitting coefficients.

[0041] To further generate a visually smooth and continuous fitted curve, the system performs automated interpolation calculations based on the characteristic function equation, within the interval defined by the minimum and maximum moisture content values ​​of the measured data, using a set high-density interpolation step size. Through this calculation process, the system generates a sequence consisting of a large number of dense coordinate points, which mathematically accurately represents the continuous geometric shape of the fitted curve.

[0042] In practical applications, this invention achieves a leap from discrete "points" to smooth "surfaces," and the resulting fitted curves are clearly visible. Figure 6 The density calculation visualization interface that integrates the fitting results is as follows: Figure 7 As shown in the figure. Through this regression analysis method, the system can effectively overcome the shortcomings of traditional manual operations in accurately determining the curve shape, and provide an accurate functional model for the subsequent automatic extraction of the optimal moisture content and maximum dry density.

[0043] S4, the standardized data set, characteristic function equation and fitted curve coordinate sequence are mapped to the embedded scientific chart rendering engine to perform coordinate mapping and dynamic redrawing, and generate real-time responsive visualization graphics.

[0044] In this embodiment, the execution of coordinate mapping and dynamic redrawing includes: S401, the original data points in the standardized dataset are mapped to discrete points in a coordinate system; specifically, the system integrates an embedded scientific chart rendering engine and establishes a real-time data binding channel between the standardized dataset, characteristic function equations, and dense coordinate sequences and the engine. The engine, upon receiving instructions, maps the received original experimental data points to discrete points in a coordinate system.

[0045] S402, the fitted curve coordinate sequence is mapped and connected into a continuous and smooth function curve; specifically, the system maps and connects the dense coordinate sequence into a continuous and smooth function curve. This mapping process is driven by a vector graphics engine, ensuring that the graphics maintain strict geometric proportion consistency and high fidelity of text details at different resolutions.

[0046] S403, whenever a new calculation result is generated, a redraw command is sent to the rendering engine to clear the old graphic elements and re-execute the rendering based on the latest complete data set, ensuring the synchronization between the graphics and the underlying data model. The specific steps are as follows: The system establishes a real-time redraw mechanism. Whenever the original physical quantity data in step S1 changes or a new regression calculation result is generated in step S3, the system sends a redraw command to the rendering engine. This command drives the engine to automatically clear the old graphic elements in the current canvas and re-execute the above mapping and rendering process based on the latest complete data set. This mechanism ensures that the visualized graphics and the underlying data model always maintain strict synchronization, achieving a "data-driven, instant-response graphics" interactive effect.

[0047] Furthermore, to enhance the professionalism and compliance of charts, standardized semantic rendering of visual graphics is also implemented: The system invokes the academic symbol standardization rendering sub-process to identify physical quantity codes in charts and graphs and automatically convert them into Greek letter symbols that conform to academic standards. Specifically, the system invokes the academic symbol standardization rendering sub-process in the final graphic synthesis stage. This process can identify specific physical quantity codes in axis labels and legends and automatically convert them into Greek letter symbols that conform to academic publishing standards.

[0048] The system employs LaTeX typesetting syntax to handle superscript or subscript formatting of physical units. Specifically, it introduces the academically accepted LaTeX typesetting syntax, passing axis label definitions through raw string processing to ensure that instructions containing Greek letters or superscripts are passed to the underlying graphics rendering engine intact. For units commonly used in engineering, such as cubic centimeters (…),… This method uses the ^3 instruction to superscript physical units. Furthermore, the system introduces auxiliary grid lines with a semi-transparent dashed line design to facilitate rapid coordinate value reading. Related high-standard automated graphics output examples can be found... Figure 6 .

[0049] To further optimize the visual experience of human-computer interaction, a font decoupling strategy for the execution interface and charts is also included: The Chinese labels on the interactive interface are rendered using a first preset font. Specifically, the system constructs a global font mapping system during the initialization phase, and the interactive interface uses "Microsoft YaHei" as the main font. This first preset font has excellent smoothness under the Windows system, making it suitable for long-term office reading. At the same time, the system forces the software to call the system-level sans-serif font library when generating charts through the chart rendering parameter configuration module, ensuring the clear strokes of Chinese labels such as "moisture content" and "dry density".

[0050] The numerical input and calculation result display areas are rendered using a second preset font; this second preset font is a monospaced serif font, used to reduce eye strain when reading decimal points. Specifically, Times New Roman is preferentially used in the numerical input and calculation result display areas. As an internationally recognized standard font for numbers, the monospaced feel and serif design of this second preset font allow users to quickly locate the decimal point, reducing reading fatigue. Furthermore, Unicode superscript characters (such as...) are also used in the Tkinter interface tabs. This allows for a "what you see is what you get" display of calculation results.

[0051] Also includes: When rendering the calculation results to the interface, high-level formatting commands are used to control precision, ensuring that the maximum dry density is precisely locked to the thousandths place and the optimum moisture content is retained to one decimal place. Specifically, this method uses high-level formatting commands such as :.3f and :.1f when rendering the calculation results to the interface. This ensures that regardless of how many decimal places the floating-point number calculated in the background has, the final displayed "maximum dry density" will be precisely locked to the thousandths place, while the "optimal moisture content" will be retained to one decimal place. The generated integrated display string is in the form of "OMC:12.5%|MDD:2.150g / cm". 3 ".

[0052] S5, perform mathematical analysis on the characteristic function equation to automatically extract key mechanical characteristic parameters, and perform adaptive graphic annotation in the visualization graph.

[0053] In this embodiment, the automatic extraction of key mechanical feature parameters includes: S501, Calculate the first derivative of the characteristic function equation with respect to moisture content; specifically, the system's built-in feature analysis module performs mathematical analysis on the characteristic function equation obtained in step S2. This process utilizes the principles of analytical geometry, aiming to locate the geometric extrema of the curve through mathematical means, thereby eliminating the 2%-5% human bias caused by traditional manual visual fitting. The formula for calculating the first derivative of the characteristic function equation is as follows: (3) in, Let dry density be a function of the rate of change of moisture content. Moisture content, and These are the fitting coefficients mentioned in step S3.

[0054] S502, Solve the equation where the first derivative is zero to obtain the vertex coordinates of the fitted parabola; the steps are as follows: The system automatically sets the above first derivative function... This allows for the solution of equations with a derivative of zero, automatically and accurately calculating the vertex coordinates of the fitted parabola. The formula for calculating the vertex x-coordinate is: (4) In the formula, To fit the x-coordinate of the vertex of the parabola, this value represents the moisture content state corresponding to the point where the dry density reaches its maximum value.

[0055] S503, the x-coordinate corresponding to the vertex coordinates is defined as the optimum moisture content, and the y-coordinate is defined as the maximum dry density. Specifically, the x-coordinate and y-coordinate values ​​corresponding to this vertex are defined as the optimum moisture content and maximum dry density of the material, respectively. Under this logic, the system will solve for... The optimum moisture content (OMC) is assigned as the value, and this value is substituted into the original characteristic function equation to obtain the ordinate, i.e., the maximum dry density (MDD). The system automatically determines the optimum moisture content and maximum dry density using this first-order derivative method, achieving automatic tracking of extreme values. For the specific fitting results and parameter values, please refer to [reference needed]. Figure 7 As shown.

[0056] Furthermore, adaptive graphic annotation is performed, including: S504, geometric marker points are superimposed and drawn at the corresponding vertex positions of the fitted curve; specifically, after the calculation is completed, the system drives the graphics rendering engine to superimpose and draw a conspicuous geometric marker point at the corresponding vertex position of the fitted curve generated in step S3. In the graph, this vertex is marked with a conspicuous green dot to visually represent the spatial location of the maximum dry density (MDD). See [link to graph description] for the specific graphical representation. Figure 6 .

[0057] S505, based on the visual coordinates of the marker point and the layout of surrounding elements, the optimal placement position of the annotation text box is dynamically calculated, and a leader line is generated to point the text box to the marker point. Specifically, the system activates an adaptive annotation algorithm: this algorithm dynamically calculates the optimal placement position and visual style of the annotation text box based on the coordinates of the marker point in the view, the graphic scale, and the layout of surrounding elements, and generates a leader line to point the text box to the marker point. Through this coordinate-based dynamic pointing annotation technology, the system can clearly and unobstructedly annotate the names and specific values ​​of key mechanical parameters in the chart, coupled with dynamic text annotations, realizing intelligent recognition and engineering semantic annotation of curve peak feature points. This annotation technology is also applicable to stress-strain analysis, and can automatically retrieve the maximum value in the stress sequence and accurately extract the peak strain. The related stress-strain curve annotation effect is visible. Figure 8 Through the aforementioned adaptive annotation, this invention effectively avoids human interpretation errors and improves the automation level of experimental data feature extraction.

[0058] In summary, by working together in steps S1 to S5, this invention integrates the originally independent calculation, fitting, labeling, and export processes into a real-time response system, thereby achieving intelligent and standardized processing of inorganic binder test data for highway engineering.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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 method for intelligent fitting and parameter analysis of experimental curves for inorganic binders, characterized in that, Includes the following steps: Construct a structured matrix data input interface to receive and organize raw physical quantity data from multiple sets of parallel experiments; The raw physical quantity data is subjected to coupled real-time verification. If the raw physical quantity data passes the verification of the built-in physical logic rule base, a standardized data set is output. Based on the standardized dataset, a quadratic polynomial nonlinear regression analysis was performed using the least squares method to solve the characteristic function equation of dry density with respect to moisture content, and a smooth and continuous fitted curve coordinate sequence was generated. The standardized dataset, feature function equations, and fitted curve coordinate sequences are mapped to an embedded scientific chart rendering engine to perform coordinate mapping and dynamic redrawing, generating real-time responsive visualization graphics. The characteristic function equation is mathematically analyzed to automatically extract key mechanical characteristic parameters, and adaptive graphic annotation is performed in the visualization graph.

2. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, The coupled real-time verification of the original physical quantity data includes: When a change in the data state of any input unit in the input interface is detected, a data capture subprocess is triggered to convert the input string sequence into a numeric type. The built-in physical logic rule library is invoked to perform real-time verification of the numerical type; The rule base includes positive / negative verification, range boundary verification, and process quantity logical relationship verification to automatically filter out abnormal data.

3. The intelligent fitting and parameter analysis method for inorganic binder test curves according to claim 1, characterized in that, The process of solving the characteristic function equation of dry density with respect to moisture content includes: Extract the moisture content data sequence and dry density data sequence from the standardized dataset; The sequence is fitted using the least squares method, and the characteristic function equation is solved: ; in, Represents dry density, Represents moisture content. represents the fitting coefficient.

4. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, The execution of coordinate mapping and dynamic redrawing includes: Map the original data points in the standardized dataset to discrete points in a coordinate system; The fitted curve coordinate sequence is mapped and connected to form a continuous and smooth function curve; Whenever a new calculation result is generated, a redraw command is sent to the rendering engine to clear the old graphic elements and re-execute the rendering based on the latest data set, ensuring the synchronization of graphics and underlying data model.

5. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, The automatic extraction of key mechanical feature parameters includes: Take the first derivative of the characteristic function equation with respect to water content; Solve the equation whose first derivative is zero to obtain the vertex coordinates of the fitted parabola; The x-coordinate corresponding to the vertex coordinates is defined as the optimum moisture content, and the y-coordinate is defined as the maximum dry density.

6. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 5, characterized in that, The execution of adaptive graphic annotation includes: Geometric marker points are superimposed at the corresponding vertex positions of the fitted curve; Based on the visual coordinates of the marker point and the layout of surrounding elements, the optimal placement position of the annotation text box is dynamically calculated, and a leader line is generated to point the text box to the marker point.

7. The intelligent fitting and parameter analysis method for inorganic binder test curves according to claim 1, characterized in that, This also includes performing standardized semantic rendering on visualized graphics: The academic notation standardization rendering subprocess is invoked to identify the physical quantity codes in the charts and automatically convert them into Greek letter symbols that conform to academic standards. Use LaTeX typesetting syntax to format physical units using superscripts or subscripts.

8. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, This also includes a strategy for decoupling the fonts of the user interface and charts: The Chinese labels on the interactive interface are rendered using the first preset font; The numerical input and calculation result display area is rendered using the second preset font; The second preset font is a monospaced serif font, used to reduce fatigue when reading decimal points.

9. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, Also includes: A maintenance age monitoring module was constructed, and the countdown to experimental maintenance was dynamically updated through a timed triggering mechanism; The countdown label's display color is dynamically changed based on the remaining time, enabling full-process visual monitoring from test preparation to post-construction maintenance.

10. The method for intelligent fitting and parameter analysis of inorganic binder test curves according to claim 1, characterized in that, Also includes: When rendering the calculation results to the interface, precision control is performed through high-level formatting commands to ensure that the maximum dry density is precisely locked at the thousandths place and the optimum moisture content is retained to one decimal place.

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