Ceramic product performance detection method and device under multi-source parameter analysis

Abstract

The invention discloses a ceramic product performance detection method and device under multi-source parameter analysis, and relates to the technical field of performance detection.The method comprises the steps that performance demand matching is conducted according to application scenes of ceramic accessory products, and K kinds of performance demand information are output; carrying out structure partitioning; performing cross-performance partition fusion, and outputting M multi-dimensional performance fusion partitions; performing dynamic connection detection to obtain M local multi-source performance parameters; performing partition performance quantification, and outputting M partition performance scores; performing multi-level performance gradient segmentation to obtain K three-dimensional performance gradient maps; and carrying out rendering fusion on the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map. The technical problems that in the prior art, performance analysis of the ceramic accessory product is not comprehensive and deep enough, evaluation is not systematic and visual display is not visual enough are solved, and the technical effect of improving performance analysis, evaluation and visual display of the ceramic accessory product is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of performance detection, and particularly to a method and device for detecting the performance of ceramic products under multi-source parameter analysis. Background Art

[0002] Currently, there are many deficiencies in the traditional ceramic fitting performance analysis technology. On the one hand, in the performance analysis link, only single performance indicators can be detected, and the synergistic effects of multiple performance dimensions of ceramic fittings in complex application scenarios cannot be comprehensively considered, resulting in one-sided analysis results and unable to provide a comprehensive basis for product design optimization. For example, in an environment of high temperature, high pressure and accompanied by chemical corrosion, only focusing on the thermal performance of ceramic fittings while ignoring their chemical stability and mechanical properties may cause the fittings to fail prematurely in actual use. On the other hand, in the evaluation process, there is a lack of a systematic and scientific evaluation system, and it mostly relies on empirical judgment, making it difficult to accurately measure the comprehensive performance level of products, resulting in uneven product quality. At the same time, the existing visualization display methods are too simple, often just presenting the chart of some performance data, unable to visually and comprehensively display the performance changes of products under different working conditions, and it is difficult to quickly obtain key information from them to assist in decision-making.

[0003] The existing technology has technical problems such as insufficiently comprehensive and in-depth analysis of the performance of ceramic fitting products, lack of systematicness in evaluation, and insufficiently intuitive visualization display. Summary of the Invention

[0004] This application provides a method and device for detecting the performance of ceramic products under multi-source parameter analysis, which are used to solve the technical problems in the existing technology such as insufficiently comprehensive and in-depth analysis of the performance of ceramic fitting products, lack of systematicness in evaluation, and insufficiently intuitive visualization display.

[0005] In view of the above problems, this application provides a method and device for detecting the performance of ceramic products under multi-source parameter analysis.

[0006] In the first aspect of this application, a method for detecting the performance of ceramic products under multi-source parameter analysis is provided. The method includes: Match the performance requirements according to the application scenarios of the ceramic fitting products, and output K kinds of performance requirement information; partition the structure of the ceramic fitting products based on the K kinds of performance requirement information to obtain K performance partition combinations; perform cross-performance partition fusion on the K performance partition combinations, and output M multi-dimensional performance fusion partitions, where the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers to obtain M local multi-source performance parameters; perform partition performance quantification based on the M local multi-source performance parameters, and output M partition performance scores; perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps; use the M partition performance scores to perform rendering fusion of the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map.

[0007] In the second aspect of the present application, a ceramic product performance detection device under multi-source parameter analysis is provided. The device includes: A performance requirement information output module, configured to match the performance requirements according to the application scenarios of the ceramic fitting products and output K kinds of performance requirement information; a performance partition combination acquisition module, configured to partition the structure of the ceramic fitting products based on the K kinds of performance requirement information to obtain K performance partition combinations; a multi-dimensional performance fusion partition output module, configured to perform cross-performance partition fusion on the K performance partition combinations and output M multi-dimensional performance fusion partitions, where the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; a local multi-source performance parameter acquisition module, configured to perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers to obtain M local multi-source performance parameters; a partition performance score output module, configured to perform partition performance quantification based on the M local multi-source performance parameters and output M partition performance scores; a three-dimensional performance gradient map acquisition module, configured to perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps; a product performance visualization map acquisition module, configured to use the M partition performance scores to perform rendering fusion of the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: Match the performance requirements according to the application scenarios of ceramic fitting products, and output K pieces of performance requirement information; partition the ceramic fitting products structurally to obtain K performance partition combinations; perform cross-performance partition fusion and output M multi-dimensional performance fusion partitions, where the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; perform dynamic connection detection on the M multi-dimensional performance fusion partitions to obtain M local multi-source performance parameters; perform partition performance quantification based on the M local multi-source performance parameters and output M partition performance scores; perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions to obtain K three-dimensional performance gradient diagrams; use the M partition performance scores to perform rendering fusion on the K three-dimensional performance gradient diagrams to obtain an interactive product performance visualization diagram. The technical effect of improving the performance analysis, evaluation and visualization display of ceramic fitting products is achieved. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0010] Figure 1 Schematic flowchart of the ceramic product performance detection method under multi-source parameter analysis provided by the embodiment of the present application; Figure 2 Schematic structural diagram of the ceramic product performance detection device under multi-source parameter analysis provided by the embodiment of the present application.

[0011] Description of the reference numerals: Performance requirement information output module 10, performance partition combination acquisition module 20, multi-dimensional performance fusion partition output module 30, local multi-source performance parameter acquisition module 40, partition performance score output module 50, three-dimensional performance gradient diagram acquisition module 60, product performance visualization diagram acquisition module 70. Detailed Embodiments

[0012] The present application provides a ceramic product performance detection method and device under multi-source parameter analysis, which are used to solve the technical problems in the prior art that the performance analysis of ceramic fitting products is not comprehensive and in-depth enough, the evaluation lacks systematicness, and the visualization display is not intuitive enough.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0014] Example 1, as Figure 1 shown, this application provides a method for detecting the performance of ceramic products under multi-source parameter analysis. The method includes: Step S100: Match the performance requirements according to the application scenario of the ceramic fitting product, and output K kinds of performance requirement information.

[0015] Specifically, match the performance requirements according to the application scenario of the ceramic fitting product, and output K kinds of performance requirement information. For example, if the ceramic fitting is used for high-temperature components in the aerospace field, it is necessary to focus on its performance requirements such as strength, thermal stability, and weight at high temperatures; if it is used for insulating components in electronic devices, then the focus is on its insulation performance, dielectric constant, etc. Through comprehensive analysis, accurately determine K corresponding kinds of performance requirement information, laying a foundation for subsequent detection work.

[0016] Step S200: Based on the K kinds of performance requirement information, perform structural zoning on the ceramic fitting product to obtain K performance zoning combinations.

[0017] Specifically, use 3D modeling software to construct a three-dimensional model of the fitting according to the design drawing and actual size parameters of the ceramic fitting product. In order to simulate its working state in the application scenario, various physical properties and environmental parameters are added to the model. For example, if the ceramic fitting is used for heat insulation components in an automobile engine, it is necessary to set high-temperature, high-pressure environmental conditions, as well as vibration parameters generated during engine operation in the model. Run the simulation program to make the model run under the set conditions, thereby obtaining an initial performance distribution model, which presents the preliminary performance distribution of the product in actual work. Extract K performance requirement indicators from the K kinds of performance requirement information. For example, for heat insulation components, the key indicators may be thermal conductivity, coefficient of thermal expansion, etc. Then, use data analysis algorithms to screen and analyze the data of the initial performance distribution model with these performance requirement indicators as constraints. Preset a performance deviation threshold, which is a standard for judging whether the performance difference is significant. By traversing each data point in the model and comparing the difference between its performance data and the performance requirement indicators, when the difference exceeds the performance deviation threshold, mark the point. Based on these marked points and combined with the spatial position relationship, divide the regions with similar performance, and finally obtain K performance zoning combinations, realizing a reasonable zoning of the structure of the ceramic fitting product based on performance.

[0018] Step S300: Perform cross-performance zoning fusion on the K performance zoning combinations, and output M multi-dimensional performance fusion zones, where the M multi-dimensional performance fusion zones have M performance requirement composition identifiers.

[0019] Specifically, according to the spatial distribution of K performance partition combinations in the ceramic fitting product, with the help of professional modeling and analysis tools such as ANSYS and MATLAB, spatial grid overlapping operations are performed on these performance partition combinations. The entire space of the ceramic fitting product is divided with a fine grid accuracy (for example, a grid size of 0.1mm×0.1mm×0.1mm), so that each performance partition combination intersects and overlaps with each other, thus forming a partitioned grid structure. In this structure, each tiny grid cell may converge the characteristics of multiple performance partitions. Grid extraction work is carried out on the generated partitioned grid structure. According to pre-established rules, such as based on the number and type differences of performance partitions covered by the grid, as well as their closeness in spatial position, grids with similar performance characteristics or closely connected in spatial position are merged, and finally M multi-dimensional performance fusion partitions are obtained. These fusion partitions no longer represent a single performance, but integrate the characteristics of multiple performance partitions, and can more comprehensively and comprehensively reflect the actual performance of different regions of the ceramic fitting product. Next, determine the performance requirement composition identifier for each multi-dimensional performance fusion partition. Taking the first multi-dimensional performance fusion partition as an example, it is carried out by analyzing its intersection relationship with K performance partition combinations. Specifically, check the grid cells covered by the first multi-dimensional performance fusion partition and judge which performance partition combinations these grid cells belong to at the same time. If there are grid cell overlaps (i.e., intersections) between the first multi-dimensional performance fusion partition and the thermal performance partition and the mechanical performance partition, further analyze whether the performance requirement dimensions (such as coefficient of thermal expansion, compressive strength, etc.) and performance requirement targets (such as the requirement for high temperature resistance greater than 1000°C, the requirement for compressive strength greater than 150MPa, etc.) in the intersection meet the corresponding standards. If satisfied, assign a clear performance requirement composition to it, such as "thermal - high expansion stability; mechanical - high compressive strength", and output the corresponding first performance requirement composition identifier. Then, in the same way, successively process the remaining M - 1 multi-dimensional performance fusion partitions. By analyzing the intersection relationship between each partition and the K performance partition combinations at the grid cell level, closely combined with the performance requirement dimensions and targets, assign performance requirement compositions to them one by one, and output M - 1 performance requirement composition identifiers. Through this series of operations, each of the M multi-dimensional performance fusion partitions has its own clear and definite performance requirement composition identifier, providing strong support for accurately grasping the product performance and optimizing the product design.

[0020] Step S400: Perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers to obtain M local multi-source performance parameters.

[0021] Specifically, first, preset an initial partition jump step in the system, for example, set it to 3 partition intervals. Use a random number generator to randomly select one from the M multi-dimensional performance fusion partitions as the second multi-dimensional performance fusion partition. Based on this initial jump step, screen out the third multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions, ensuring that there is a fourth multi-dimensional performance fusion partition between the two. With the help of detection devices, such as a universal material testing machine for testing mechanical properties and a thermal analyzer for testing thermal properties, etc., according to the second performance requirements of the second multi-dimensional performance fusion partition to form an identifier, conduct targeted local performance testing on the ceramic fitting product part in this partition to obtain the second local multi-source performance parameters; similarly, conduct testing on the third multi-dimensional performance fusion partition to obtain the third local multi-source performance parameters. Use data analysis software and adopt the Euclidean distance formula to calculate the deviation of these two sets of parameters on the same performance indicators (such as hardness, thermal conductivity, etc.) to obtain the first performance deviation coefficient. Compare this coefficient with the preset performance deviation scale. If the first performance deviation coefficient is less than the preset scale, it means that the performance difference between these two partitions is small. Update the initial partition jump step according to the pre-set step update formula (for example, new step = initial step + initial step × first performance deviation coefficient) to obtain the first partition jump step. At the same time, use an interpolation algorithm (such as linear interpolation) to fit and output the fourth local multi-source performance parameters of the fourth multi-dimensional performance fusion partition according to the second and third local multi-source performance parameters. If the first performance deviation coefficient is greater than the preset scale, directly conduct actual testing on the fourth multi-dimensional performance fusion partition to obtain its fourth local multi-source performance parameters. Then, take the third multi-dimensional performance fusion partition as the new starting point, and according to the updated first partition jump step, screen out the fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions, with H multi-dimensional performance fusion partitions linearly spaced between the two. Conduct local performance testing on the fifth multi-dimensional performance fusion partition again to obtain the fifth local multi-source performance parameters, calculate the second performance deviation coefficient (the method is the same as before), update the first partition jump step according to the comparison result with the preset scale to obtain the second partition jump step, and fit and output the H local multi-source performance parameters of the H multi-dimensional performance fusion partitions. Repeat this process until the M local multi-source performance parameters corresponding to the M multi-dimensional performance fusion partitions are obtained.

[0022] Step S500: Quantify the partition performance based on the M local multi-source performance parameters and output M partition performance scores.

[0023] Specifically, a comprehensive performance quantification evaluation system is constructed. For each performance index involved in ceramic accessory products (such as thermal performance, mechanical performance, chemical performance, etc.), the weight coefficient of each index is determined. This process requires comprehensive consideration of the product's application scenario, industry standards, and the importance of each performance index to the overall quality and function of the product. For example, for ceramic accessories used in the inner lining of high-temperature furnaces, the weight of thermal performance may be set to 0.5, the weight of mechanical performance to 0.3, and the weight of chemical performance to 0.2. According to the characteristics and measurement units of different performance indexes, a unified quantification standard is formulated. For example, for the thermal conductivity index, its value is normalized according to certain rules so that its value range is between 0 and 1; for the hardness index, according to different measurement methods such as Rockwell hardness and Brinell hardness, it is uniformly converted to a standard scale through a conversion formula. Using the above quantification standard, each performance data in the M local multi-source performance parameters is quantified. For example, if the measured value of thermal conductivity in a certain multi-dimensional performance fusion partition is x, it is converted into a quantified value x' according to the normalization formula. Then, for each multi-dimensional performance fusion partition, combined with the weight coefficients of each performance index, the partition performance score is calculated. Suppose the quantified value of thermal performance in a certain partition is a, the quantified value of mechanical performance is b, and the quantified value of chemical performance is c. According to the weights, the partition performance score S = 0.5a + 0.3b + 0.2c is calculated. Repeat the above steps to calculate each of the M multi-dimensional performance fusion partitions one by one, and finally output M partition performance scores, which can intuitively and quantitatively reflect the comprehensive performance level of each partition.

[0024] Step S600: Perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps.

[0025] Specifically, perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps. Analyze the performance parameters from multiple dimensions, and based on the changes in different performance indexes, perform multi-level partitioning on the multi-dimensional performance fusion partitions in space. For example, according to different dimensions such as thermal performance and mechanical performance, the partition is divided into regions with different performance gradients to form a three-dimensional performance gradient map. These gradient maps visually show the change trend of the product's performance in different regions, and a total of K such three-dimensional performance gradient maps are obtained.

[0026] Step S700: Use the M partition performance scores to perform rendering fusion on the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map.

[0027] Specifically, the data of the performance scores of M partitions and the K stereoscopic performance gradient maps are normalized. The partition performance scores are mapped to the interval [0, 1] according to their maximum and minimum values, and the data of each dimension of the stereoscopic performance gradient map are also normalized. The stereoscopic performance gradient map data is converted into an image data structure convenient for processing, and the performance gradient value is used to map the gray level or color of the image pixels. The watershed algorithm is applied. The image is regarded as a terrain surface, the performance gradient value is used as the altitude, multiple "seed points" are set and the "water injection" process is simulated. The boundaries formed when the water converges divide different performance characteristic regions, and then combined with the partition performance scores, color coding or transparency settings are performed on each region to highlight regions with different performance levels. Then, a graphics rendering engine (such as OpenGL, DirectX, or Three.js, etc.) is used for rendering and fusion. The K performance heat distribution maps are three-dimensionally reconstructed according to the spatial positions in the ceramic product, the surface material or color of the model is updated in real time according to the partition performance scores, different layers are set for each map and the transparency and display order are adjusted according to the importance of the scores, and at the same time, color mixing or lighting effects are processed considering the interaction relationship of different performance dimensions. Finally, interactive functions such as zooming, rotating, and clicking to view specific performance data are added to the generated visualization diagram, so as to obtain an interactive product performance visualization diagram.

[0028] In a possible implementation manner, step S200 further includes: Step S210: Construct a three-dimensional model of the ceramic accessory product.

[0029] Step S220: Obtain an initial performance distribution model by simulating the working state of the application scenario for the three-dimensional model of the accessory.

[0030] Step S230: Extract K performance requirement indicators from the K types of performance requirement information, and perform one-dimensional performance extraction on the initial performance distribution model with the K types of performance requirement indicators as constraints to obtain K one-dimensional performance distribution models.

[0031] Step S240: Preset a performance deviation threshold.

[0032] Step S250: Use the performance deviation threshold and the K types of performance requirement information to traverse the K one-dimensional performance distribution models to delimit the performance partition boundaries and obtain the K performance partition combinations.

[0033] Specifically, with the help of professional 3D modeling software, such as SolidWorks, UG NX, etc., according to the design drawings, precise dimensions, and internal structures and other detailed information of the ceramic accessory product, the three-dimensional model of the accessory is constructed. During the modeling process, strictly according to the actual situation of the product, accurately draw the shapes, positions, and connection relationships of each component to ensure the accuracy and integrity of the model.

[0034] Using simulation analysis software, such as ANSYS, import the constructed three-dimensional model of the fitting into it, and set the corresponding working state parameters according to the application scenarios of the ceramic fitting products. For example, if the fitting is applied to mechanical seals in high-temperature environments, set simulation conditions such as high temperature, high pressure, and relative motion. Under these simulation conditions, the software analyzes and calculates the model by solving complex physical equations, simulating the physical process of the ceramic fitting in actual work, so as to obtain the initial performance distribution model, which reflects the performance distribution of each part of the product in the initial state.

[0035] Extract K performance requirement indicators from the K kinds of performance requirement information, such as the coefficient of thermal expansion and high-temperature resistance in terms of thermal performance, the compressive strength and flexural strength in terms of mechanical performance, the corrosion resistance and oxidation resistance in terms of chemical performance, and the surface hardness and wear resistance in terms of surface performance. Then, taking these indicators as constraints, screen the data in the initial performance distribution model data, sort and visualize the corresponding data for each indicator respectively, and obtain K single-dimensional performance distribution models for thermal performance, mechanical performance, chemical performance, surface performance, etc. These models can respectively display the distribution of the ceramic fitting under the corresponding performance dimensions, providing a basis for in-depth analysis of product performance.

[0036] Preset the performance deviation threshold. This needs to be determined by combining the application requirements of the ceramic fitting products, industry standards, and actual production experience. For example, in the production of a certain specific ceramic product, according to its quality control standards and feedback from actual use effects, set the hardness performance deviation threshold to ±5 (Brinell hardness value), that is, allow the hardness values of different parts of the product to fluctuate within 5 Brinell hardness units above and below the standard hardness value.

[0037] Use the preset performance deviation threshold and the K kinds of performance requirement information to perform a traversal analysis on the K single-dimensional performance distribution models. During the traversal process, for each single-dimensional performance distribution model, compare the performance values of each data point in the model with the standard values of the corresponding performance requirement indicators. When the performance value of a data point exceeds the performance deviation threshold range, mark the data point. Based on these marked points, combined with the structural and spatial position information of the product, delimit the boundaries of regions with similar performance, and finally obtain K performance partition combinations, realizing the performance-based structural partition of the ceramic fitting products.

[0038] In a possible implementation manner, step S300 further includes: Step S310: According to the spatial distribution of the K performance partition combinations, perform grid overlapping on the K performance partition combinations to form a partitioned grid structure.

[0039] Step S320: Extract grids from the partitioned grid structure to obtain the M multi-dimensional performance fusion partitions.

[0040] Step S330: According to the intersection relationship between the first multi-dimensional performance fusion partition and the combination of the K performance partitions, perform allocation of the performance requirement composition for the first multi-dimensional performance fusion partition, and output the first performance requirement composition identifier.

[0041] Step S340: And so on, according to the intersection relationship between the M - 1 multi-dimensional performance fusion partitions and the combination of the K performance partitions, perform allocation of the performance requirement composition for the M - 1 multi-dimensional performance fusion partitions, and output M - 1 performance requirement composition identifiers.

[0042] Specifically, based on the distribution of the combination of K performance partitions in the ceramic fitting product space, use 3D modeling or data analysis software (such as ANSYS, MATLAB, etc.) to perform spatial grid overlapping operations on these performance partition combinations. For example, arrange different performance partitions in three-dimensional space according to their actual positions, and then divide the entire ceramic fitting product space with a certain grid accuracy (such as a grid size of 0.1mm×0.1mm×0.1mm), so that each performance partition combination overlaps with each other, thereby forming a partition grid structure, which divides the product space into numerous small grid units.

[0043] Extract the grids in the above partition grid structure. According to the pre-set rules (such as according to the number and type of performance partitions contained in the grid), merge the grids with similar performance characteristics or closely connected spatial positions into a new area. In this way, M multi-dimensional performance fusion partitions are obtained, and each fusion partition synthesizes the characteristics of multiple performance partitions.

[0044] Take the first multi-dimensional performance fusion partition as an example to analyze its intersection relationship with the combination of K performance partitions. Specifically, it is to check which of the grid cells involved in the first multi-dimensional performance fusion partition overlap with the grid cells involved in each other performance partition combination. If it is found that there are overlapping parts (i.e., intersections) with the thermal performance partition and the mechanical performance partition, then further analyze the performance requirement dimensions in the intersection, such as the coefficient of thermal expansion, compressive strength, etc., and the performance requirement targets, such as the high-temperature resistance requirement is greater than 1000°C, the compressive strength requirement is greater than 150MPa, etc. If these indicators meet the corresponding requirements, a clear performance requirement composition will be allocated to the first multi-dimensional performance fusion partition, such as "Thermal - High expansion stability; Force - High compressive strength", and the corresponding first performance requirement composition identifier will be output. This identifier is like a unique "identity label", clearly indicating the performance characteristics of this fusion partition.

[0045] Process the remaining M - 1 multi - dimensional performance fusion partitions in sequence, analyze the intersection relationship between each partition and the combinations of K performance partitions one by one, closely combine the performance requirement dimensions and goals, allocate the performance requirement compositions for them one by one, and output M - 1 performance requirement composition identifiers. Through this operation, each of the M multi - dimensional performance fusion partitions has a clear and definite performance requirement composition identifier, which provides strong support for accurately grasping the performance of ceramic accessory products and subsequent optimization of product design, and can improve the product more pertinently and enhance the product quality.

[0046] In a possible implementation manner, step S400 further includes: Step S410: Preset an initial partition jump step size.

[0047] Step S420: After randomly selecting a second multi - dimensional performance fusion partition from the M multi - dimensional performance fusion partitions, screen a third multi - dimensional performance fusion partition from the M multi - dimensional performance fusion partitions with the initial partition jump step size as a constraint, where there is a fourth multi - dimensional performance fusion partition linearly spaced between the second multi - dimensional performance fusion partition and the third multi - dimensional performance fusion partition.

[0048] Step S430: After performing local performance detection on the second multi - dimensional performance fusion partition and the third multi - dimensional performance fusion partition, update the initial partition jump step size according to the detection results to output a first partition jump step size, and fit and output the fourth local multi - source performance parameters of the fourth multi - dimensional performance fusion partition according to the detection results.

[0049] Step S440: Starting from the third multi - dimensional performance fusion partition, screen a fifth multi - dimensional performance fusion partition from the M multi - dimensional performance fusion partitions with the first partition jump step size as a constraint, where there are H multi - dimensional performance fusion partitions linearly spaced between the fifth multi - dimensional performance fusion partition and the third multi - dimensional performance fusion partition.

[0050] Step S450: After performing local performance detection on the fifth multi - dimensional performance fusion partition, update the first partition jump step size according to the detection results of the fifth multi - dimensional performance fusion partition and the third multi - dimensional performance fusion partition to output a second partition jump step size, and fit and output the H local multi - source performance parameters of the H multi - dimensional performance fusion partitions according to the detection results.

[0051] Step S460: And so on, update the initial partition jump step size according to the performance deviation coefficient of the spaced multi - dimensional performance fusion partitions, and solve the local multi - source performance parameters of the M multi - dimensional performance fusion partitions according to the step size update results until the M local multi - source performance parameters of the M multi - dimensional performance fusion partitions are obtained.

[0052] Specifically, when conducting the dynamic connection detection of M multi-dimensional performance fusion partitions to obtain local multi-source performance parameters, an initial partition jump step is preset according to experience and the characteristics of ceramic fitting products. This step represents the number of interval partitions between two partitions. For example, if it is set to 2, it means that each jump will skip 2 partitions.

[0053] Use a random number generator to randomly select one from the M multi-dimensional performance fusion partitions as the second multi-dimensional performance fusion partition. Then, with the preset initial partition jump step as the limiting condition, select the third multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions, so that there is a fourth multi-dimensional performance fusion partition between the second multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition.

[0054] Adopt high-precision performance detection instruments, such as a hardness tester for detecting hardness and a thermal conductivity meter for detecting thermal conductivity, etc., to comprehensively detect the performance indicators of the second multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition, and obtain detailed data including thermal performance, mechanical performance, chemical performance, etc. Import these detection data into data analysis software (such as Python with NumPy and Pandas libraries), calculate the difference between the two partitions in the same performance indicator, and then obtain the performance deviation coefficient. Set an adjustment function for the performance deviation coefficient and the partition jump step, such as a linear function. According to the calculated performance deviation coefficient, substitute it into this function to calculate the new partition jump step, that is, the first partition jump step. For fitting and outputting the fourth local multi-source performance parameters of the fourth multi-dimensional performance fusion partition, use an interpolation algorithm, such as cubic spline interpolation. Take the performance data of the second and third multi-dimensional performance fusion partitions as known data points, and use the spatial position information of the ceramic fitting product as the coordinate basis. In three-dimensional space, according to the known data points and spatial position relationship, calculate the parameter values of the fourth multi-dimensional performance fusion partition in each performance dimension through the cubic spline interpolation algorithm, so as to obtain the fourth local multi-source performance parameters. In this way, both the partition jump step is updated and the performance parameters of the interval partition are fitted, providing a more accurate basis for subsequent detection and analysis.

[0055] Take the third multi-dimensional performance fusion partition as the new starting point, and with the first partition jump step as the constraint condition, select the fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions. At this time, there are H multi-dimensional performance fusion partitions linearly spaced between the fifth multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition.

[0056] Perform local performance detection on the fifth multi-dimensional performance fusion partition. According to the detection results of the fifth multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition, update the first partition jump step again according to the established rules, and output the second partition jump step. Also use the interpolation algorithm to fit and output the H local multi-source performance parameters of the H multi-dimensional performance fusion partitions based on the detection results of these two partitions.

[0057] By analogy with the previous operation mode, after each interval detection, update the partition jump step according to the performance deviation coefficient of the interval multi-dimensional performance fusion partition. With the update of the step size, continuously solve the local multi-source performance parameters of the M multi-dimensional performance fusion partitions, and iterate this process continuously until all the M local multi-source performance parameters of the M multi-dimensional performance fusion partitions are successfully obtained, so as to comprehensively master the performance of each partition.

[0058] In a possible implementation manner, step S430 further includes: Step S431: Perform local performance detection on the ceramic fitting product in the second multi-dimensional performance fusion partition according to the second performance requirement to obtain the second local multi-source performance parameter.

[0059] Step S432: Perform local performance detection on the ceramic fitting product in the third multi-dimensional performance fusion partition according to the third performance requirement to obtain the third local multi-source performance parameter.

[0060] Step S433: Calculate the deviation of the same performance index between the second local multi-source performance parameter and the third local multi-source performance parameter using the Euclidean distance to obtain the first performance deviation coefficient.

[0061] Step S434: If the first performance deviation coefficient is less than the preset performance deviation scale, update the initial partition jump step according to the first performance deviation coefficient and output the first partition jump step.

[0062] Step S435: Use the second local multi-source performance parameter and the third local multi-source performance parameter to perform performance parameter fitting on the fourth multi-dimensional performance fusion partition and output the fourth local multi-source performance parameter.

[0063] Specifically, an identifier is formed according to the second performance requirement. This identifier clarifies the specific performance requirements such as thermal performance, mechanical performance, and chemical performance involved in the second multi-dimensional performance fusion zone (such as requirements in terms of coefficient of thermal expansion, compressive strength, etc.). Using targeted testing equipment (such as a dilatometer for thermal performance testing, a universal material testing machine for mechanical performance testing, etc.), precise local performance testing of the ceramic fitting products is carried out in the second multi-dimensional performance fusion zone, so as to obtain comprehensive and detailed second local multi-source performance parameters, which cover the performance data of this zone in different performance dimensions.

[0064] An identifier is formed according to the third performance requirement. Similarly, corresponding testing means are used to carry out local performance testing of the ceramic fitting products in the third multi-dimensional performance fusion zone, and then third local multi-source performance parameters are obtained, reflecting the performance conditions of this zone.

[0065] The second local multi-source performance parameters and the third local multi-source performance parameters are sorted into the form of a multi-dimensional data vector, and the Euclidean distance formula is used to calculate the deviation of the same performance indicators (such as the coefficient of thermal expansion, compressive strength, etc. of the two groups of parameters). Taking the coefficient of thermal expansion as an example, by calculating the Euclidean distance of the coefficient of thermal expansion values of the two, the deviation value of this performance indicator is obtained. After such calculations are carried out for all the same performance indicators, the first performance deviation coefficient is comprehensively obtained to measure the difference degree of performance between the two zones.

[0066] The first performance deviation coefficient is compared with the preset performance deviation scale. If the first performance deviation coefficient is less than the preset performance deviation scale, it indicates that the second multi-dimensional performance fusion zone and the third multi-dimensional performance fusion zone are relatively similar in performance. According to the pre-set step adjustment rule (such as an inverse relationship with the first performance deviation coefficient), the initial partition jump step length is updated based on the first performance deviation coefficient, and the first partition jump step length is output, so that the subsequent detection jump interval changes accordingly.

[0067] In view of the similar performance of the second multi-dimensional performance fusion zone and the third multi-dimensional performance fusion zone, for the performance fitting of the interval partition, the same-index performance of these two zones is used for interval partition performance fitting. For the performance not involved, the preset performance requirements of the product are used to fill. Using these two groups of parameters, the average value of the performance when connecting adjacent partitions with similar performance is calculated, and the performance parameters of the fourth multi-dimensional performance fusion zone are fitted. For example, for the performance parameter of the coefficient of thermal expansion, the average value of the coefficient of thermal expansion of the second and third zones is taken as the fitting value of the coefficient of thermal expansion of the fourth multi-dimensional performance fusion zone, and so on. All performance parameters are fitted, and finally the fourth local multi-source performance parameters are output to complete the estimation of the performance of the interval partition.

[0068] In a possible implementation manner, step S435 further includes: Step S4351: If the first performance deviation coefficient is greater than the preset performance deviation scale, perform compensation detection on the fourth multi-dimensional performance fusion partition and output the fourth local multi-source performance parameters.

[0069] Step S4352: Starting from the fourth multi-dimensional performance fusion partition and constrained by the initial partition jump step, screen out the fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions.

[0070] Step S4353: After performing local performance detection on the fifth multi-dimensional performance fusion partition, update the initial partition jump step according to the detection results of the fifth multi-dimensional performance fusion partition and the fourth multi-dimensional performance fusion partition.

[0071] Specifically, when the calculated first performance deviation coefficient is greater than the preset performance deviation scale, it means that the performance difference between the second multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition is large, and the performance of the fourth multi-dimensional performance fusion partition cannot be simply determined by fitting. Compensation detection needs to be performed on the fourth multi-dimensional performance fusion partition. Using detection equipment such as hardness testers and thermal conductivity meters, and constructing the performance indicators involved in the identification according to the performance requirements corresponding to this partition, perform comprehensive and accurate local performance detection on the ceramic fitting products in this partition to obtain accurate fourth local multi-source performance parameters.

[0072] After completing the detection of the fourth multi-dimensional performance fusion partition, using the fourth multi-dimensional performance fusion partition as a new starting point and based on the preset initial partition jump step, screen out the fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions. This screening process strictly follows the number of interval partitions specified by the initial partition jump step to ensure that the fifth multi-dimensional performance fusion partition maintains a specific distance relationship with the fourth multi-dimensional performance fusion partition in space.

[0073] According to the performance requirement composition identifier corresponding to the fifth multi-dimensional performance fusion partition, use detection equipment, such as a universal material testing machine for detecting mechanical properties, a thermal imager for detecting thermal properties, a spectral analyzer for detecting chemical properties, etc., to conduct a comprehensive and detailed local performance detection of the ceramic fitting product in this partition. The detection process strictly follows the established operation specifications and standard procedures to ensure accurate and reliable performance data of the fifth multi-dimensional performance fusion partition. Subsequently, compare and analyze the detection results of the fifth multi-dimensional performance fusion partition with the detection results of the previously obtained fourth multi-dimensional performance fusion partition. For each same performance index, such as coefficient of thermal expansion, compressive strength, corrosion resistance, etc., calculate the difference between the two, and further obtain a performance deviation coefficient that can comprehensively reflect the performance difference degree between the two partitions through a weighted average algorithm. Finally, update the initial partition jump step according to the pre-set step update rule. If the calculated performance deviation coefficient is large, it indicates that the performance difference between adjacent two partitions is obvious. In order to more accurately grasp the performance of each partition and avoid missing key information due to too large a jump step, it is necessary to appropriately reduce the jump step at this time; on the contrary, if the performance deviation coefficient is small, it indicates that the performance of adjacent partitions is relatively similar. On the premise of ensuring the detection accuracy, the jump step can be appropriately increased to improve the detection efficiency. By dynamically adjusting the partition jump step in this way, the subsequent detection process of the remaining multi-dimensional performance fusion partitions becomes more scientific and reasonable, and the performance detection work of M multi-dimensional performance fusion partitions can be completed in a more efficient and accurate manner, providing a solid data basis for comprehensively evaluating the performance of ceramic fitting products.

[0074] In a possible implementation manner, step S450 further includes: Step S451: Perform local performance detection on the ceramic fitting product in the fifth multi-dimensional performance fusion partition according to the fifth performance requirement composition identifier to obtain fifth local multi-source performance parameters.

[0075] Step S452: Calculate the deviation of the same performance index between the second local multi-source performance parameters and the fifth local multi-source performance parameters using the Euclidean distance to obtain a second performance deviation coefficient.

[0076] Step S453: If the second performance deviation coefficient is less than the preset performance deviation scale, update the first partition jump step according to the second performance deviation coefficient and output the second partition jump step.

[0077] Step S454: Use the third local multi-source performance parameters and the fifth local multi-source performance parameters to perform performance parameter fitting of the H multi-dimensional performance fusion partitions and output H local multi-source performance parameters.

[0078] Step S455: Screen the sixth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions with the second partition jump step as a constraint, where there are W multi-dimensional performance fusion partitions linearly spaced between the fifth multi-dimensional performance fusion partition and the sixth multi-dimensional performance fusion partition.

[0079] Specifically, based on the identification of the fifth performance requirement composition, clarify the specific requirements of this partition in terms of thermal performance, mechanical performance, chemical performance, etc. Use professional testing equipment, such as a heat flow meter for thermal performance testing and a fatigue testing machine for mechanical performance testing, to conduct detailed local performance testing on the ceramic fitting products in the fifth multi-dimensional performance fusion partition, and obtain comprehensive fifth local multi-source performance parameters, which cover the performance data of this partition in each performance dimension.

[0080] After the testing is completed, organize the previously obtained second local multi-source performance parameters and the newly obtained fifth local multi-source performance parameters into the form of a multi-dimensional data vector, and use the Euclidean distance formula to calculate the deviation of the same performance indicators in the two groups of parameters, such as thermal conductivity, flexural strength, etc. For example, for thermal conductivity, calculate the Euclidean distance of the thermal conductivity values of the two, obtain the deviation value of this performance indicator, and comprehensively calculate the deviation results of all the same performance indicators to obtain the second performance deviation coefficient, so as to quantify the difference in performance between the two partitions.

[0081] Compare the second performance deviation coefficient with the preset performance deviation scale. If the second performance deviation coefficient is less than the preset performance deviation scale, it indicates that the second multi-dimensional performance fusion partition and the fifth multi-dimensional performance fusion partition are relatively close in performance. According to the preset step adjustment rule, update the first partition jump step in combination with the second performance deviation coefficient, and output the second partition jump step, providing a basis for adjusting the detection jump interval in the subsequent process.

[0082] After completing the local performance detection of the fifth multi-dimensional performance fusion partition and obtaining the fifth local multi-source performance parameters, when the second performance deviation coefficient is less than the preset performance deviation scale, the performance parameters of H multi-dimensional performance fusion partitions can be fitted using the third local multi-source performance parameters and the fifth local multi-source performance parameters. Since the third multi-dimensional performance fusion partition and the fifth multi-dimensional performance fusion partition are considered to have a certain correlation in performance detection and the performance deviation between them is small, it indicates that the performance transition between them is relatively smooth. At this time, based on the continuity in spatial position, these two partitions can be regarded as boundary points, and the performance parameter estimation is performed for the H multi-dimensional performance fusion partitions in the linear interval between them. For each performance index, such as thermal conductivity, compressive strength, etc., linear interpolation is used, with the third local multi-source performance parameters and the fifth local multi-source performance parameters as known data points, to calculate the values of the H multi-dimensional performance fusion partitions under this performance index. By performing such fitting calculations for all performance indexes, finally H local multi-source performance parameters are output, thereby efficiently obtaining the approximate performance conditions of these H partitions, improving the detection efficiency while ensuring a certain detection accuracy.

[0083] After completing the fitting of the performance parameters of the H multi-dimensional performance fusion partitions and outputting the relevant parameters, using the calculated second partition jump step as the screening constraint condition, the sixth multi-dimensional performance fusion partition is determined from the M multi-dimensional performance fusion partitions. Since the second partition jump step specifies the number of partitions in the linear interval between two partitions, during the screening process, starting from the fifth multi-dimensional performance fusion partition, in accordance with the established linear direction, following the interval rule determined by the second partition jump step, W multi-dimensional performance fusion partitions are skipped, thereby accurately locating the sixth multi-dimensional performance fusion partition. This screening method can ensure that in the entire detection process, the detection range is dynamically adjusted according to the performance differences between partitions, improving the detection efficiency while ensuring the integrity and accuracy of the detection data, and laying a foundation for the subsequent local performance detection of the sixth multi-dimensional performance fusion partition and further improving the comprehensive evaluation of the performance of the M multi-dimensional performance fusion partitions.

[0084] Embodiment 2, based on the same inventive concept as the ceramic product performance detection method under multi-source parameter analysis in the foregoing embodiment, as Figure 2 shown, the present application provides a ceramic product performance detection device under multi-source parameter analysis. The device in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the device includes: The performance requirement information output module 10 is configured to perform performance requirement matching according to the application scenarios of ceramic fitting products and output K types of performance requirement information; the performance partition combination obtaining module 20 is configured to perform structural partitioning on the ceramic fitting products based on the K types of performance requirement information to obtain K performance partition combinations; the multi-dimensional performance fusion partition output module 30 is configured to perform cross-performance partition fusion on the K performance partition combinations and output M multi-dimensional performance fusion partitions, where the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; the local multi-source performance parameter obtaining module 40 is configured to perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers to obtain M local multi-source performance parameters; the partition performance score output module 50 is configured to perform partition performance quantization based on the M local multi-source performance parameters and output M partition performance scores; the three-dimensional performance gradient map obtaining module 60 is configured to perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps; the product performance visualization map obtaining module 70 is configured to perform rendering fusion of the K three-dimensional performance gradient maps by using the M partition performance scores to obtain an interactive product performance visualization map.

[0085] Further, the performance partition combination obtaining module 20 further includes: The fitting three-dimensional model construction unit is configured to construct a fitting three-dimensional model of the ceramic fitting product; the initial performance distribution model obtaining unit is configured to obtain an initial performance distribution model by simulating the working state of the application scenario of the fitting three-dimensional model; the single-dimensional performance distribution model obtaining unit is configured to extract K types of performance requirement indicators from the K types of performance requirement information and perform single-dimensional performance extraction on the initial performance distribution model with the K types of performance requirement indicators as constraints to obtain K single-dimensional performance distribution models; the performance deviation threshold presetting unit is configured to preset a performance deviation threshold; the performance partition boundary demarcation unit is configured to traverse the K single-dimensional performance distribution models by using the performance deviation threshold and the K types of performance requirement information to demarcate performance partition boundaries and obtain the K performance partition combinations.

[0086] Further, the multi-dimensional performance fusion partition output module 30 further includes: A partition grid structure forming unit, configured to perform grid overlapping on the K combinations of performance partitions according to the spatial distribution of the K combinations of performance partitions, so as to form a partition grid structure; a multi-dimensional performance fusion partition obtaining unit, configured to perform grid extraction on the partition grid structure to obtain the M multi-dimensional performance fusion partitions; a first performance requirement composition identification output unit, configured to perform performance requirement composition allocation for the first multi-dimensional performance fusion partition according to the intersection relationship between the first multi-dimensional performance fusion partition and the K combinations of performance partitions, and output a first performance requirement composition identification; a performance requirement composition identification unit, configured to, by analogy, perform performance requirement composition allocation for the M-1 multi-dimensional performance fusion partitions according to the intersection relationship between the M-1 multi-dimensional performance fusion partitions and the K combinations of performance partitions, and output M-1 performance requirement composition identifications.

[0087] Further, the local multi-source performance parameter obtaining module 40 further includes: An initial partition jump step preset unit, configured to preset an initial partition jump step; a third multi-dimensional performance fusion partition obtaining unit, configured to, after randomly selecting a second multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions, screen out a third multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions with the initial partition jump step as a constraint, where a fourth multi-dimensional performance fusion partition is linearly spaced between the second multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition; a fourth local multi-source performance parameter output unit, configured to, after performing local performance detection on the second multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition, update the initial partition jump step according to the detection result to output a first partition jump step, and fit and output the fourth local multi-source performance parameter of the fourth multi-dimensional performance fusion partition according to the detection result; a fifth multi-dimensional performance fusion partition obtaining unit, configured to, starting from the third multi-dimensional performance fusion partition, screen out a fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions with the first partition jump step as a constraint, where H multi-dimensional performance fusion partitions are linearly spaced between the fifth multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition; H local multi-source performance parameter output units, configured to, after performing local performance detection on the fifth multi-dimensional performance fusion partition, update the first partition jump step according to the detection results of the fifth multi-dimensional performance fusion partition and the third multi-dimensional performance fusion partition to output a second partition jump step, and fit and output the H local multi-source performance parameters of the H multi-dimensional performance fusion partitions according to the detection result; a local multi-source performance parameter obtaining unit, configured to, by analogy, update the initial partition jump step according to the performance deviation coefficient of the spaced multi-dimensional performance fusion partitions, and solve the local multi-source performance parameters of the M multi-dimensional performance fusion partitions according to the step update result until the M local multi-source performance parameters of the M multi-dimensional performance fusion partitions are obtained.

[0088] Further, the fourth partial multi-source performance parameter output unit further includes: A second partial multi-source performance parameter acquisition unit, configured to perform local performance detection on the ceramic fitting product in the second multi-dimensional performance fusion partition according to the second performance requirement composition identifier, and obtain second partial multi-source performance parameters; a third partial multi-source performance parameter acquisition unit, configured to perform local performance detection on the ceramic fitting product in the third multi-dimensional performance fusion partition according to the third performance requirement composition identifier, and obtain third partial multi-source performance parameters; a first performance deviation coefficient acquisition unit, configured to calculate the deviation of the same performance index between the second partial multi-source performance parameters and the third partial multi-source performance parameters by using the Euclidean distance, and obtain a first performance deviation coefficient; a first partition jump step output unit, configured to update the initial partition jump step according to the first performance deviation coefficient and output the first partition jump step if the first performance deviation coefficient is less than a preset performance deviation scale; a performance parameter fitting unit, configured to perform performance parameter fitting on the fourth multi-dimensional performance fusion partition by using the second partial multi-source performance parameters and the third partial multi-source performance parameters, and output the fourth partial multi-source performance parameters.

[0089] Further, the performance parameter fitting unit further includes: A compensation detection unit, configured to perform compensation detection on the fourth multi-dimensional performance fusion partition and output the fourth partial multi-source performance parameters if the first performance deviation coefficient is greater than a preset performance deviation scale; a multi-dimensional performance fusion partition screening unit, configured to start from the fourth multi-dimensional performance fusion partition and screen the fifth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions with the initial partition jump step as a constraint; an initial partition jump step update unit, configured to update the initial partition jump step according to the detection results of the fifth multi-dimensional performance fusion partition and the fourth multi-dimensional performance fusion partition after performing local performance detection on the fifth multi-dimensional performance fusion partition.

[0090] Further, the H partial multi-source performance parameter output units further include: The local performance detection unit is used to perform local performance detection on the ceramic fitting product according to the fifth performance requirement in the fifth multi-dimensional performance fusion partition to obtain the fifth local multi-source performance parameters; the same performance index deviation calculation unit is used to calculate the deviation of the same performance index between the second local multi-source performance parameters and the fifth local multi-source performance parameters by using the Euclidean distance to obtain the second performance deviation coefficient; the second partition jump step output unit is used to update the first partition jump step according to the second performance deviation coefficient and output the second partition jump step if the second performance deviation coefficient is less than the preset performance deviation scale; the H performance parameter fitting units are used to perform performance parameter fitting on the H multi-dimensional performance fusion partitions by using the third local multi-source performance parameters and the fifth local multi-source performance parameters to output H local multi-source performance parameters; the sixth multi-dimensional performance fusion partition screening unit is used to screen the sixth multi-dimensional performance fusion partition from the M multi-dimensional performance fusion partitions with the second partition jump step as a constraint, where there are W multi-dimensional performance fusion partitions at a straight-line interval between the fifth multi-dimensional performance fusion partition and the sixth multi-dimensional performance fusion partition.

[0091] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0093] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A ceramic product performance testing method under multi-source parameter analysis, characterized in that: The method comprises: Match performance requirements according to the application scenarios of ceramic accessories products and output K types of performance requirement information; Structural partitioning of the ceramic accessory product based on the K types of performance requirement information to obtain K performance partition combinations; Performing cross-performance partition fusion on the K performance partition combinations, and outputting M multi-dimensional performance fusion partitions, wherein the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; Perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers to obtain M local multi-source performance parameters; quantifying partition performance based on the M local multi-source performance parameters, and outputting M partition performance scores; Perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps; The M partition performance scores are used to perform rendering fusion of the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map.

2. The ceramic product performance detection method under multi-source parameter analysis according to claim 1, characterized in that: Based on the K types of performance requirement information, the ceramic accessory product is structurally partitioned to obtain K performance partition combinations, and the method includes: Constructing a three-dimensional model of the ceramic accessory product; By simulating the working state of the application scenario of the three-dimensional model of the accessory, an initial performance distribution model is obtained; Extracting K performance requirement indicators from the K types of performance requirement information, and performing single-dimensional performance extraction on the initial performance distribution model with the K types of performance requirement indicators as constraints to obtain K single-dimensional performance distribution models; Preset performance deviation threshold; The K single-dimensional performance distribution models are traversed using the performance deviation threshold and K types of performance requirement information to define performance partition boundaries, thereby obtaining the K performance partition combinations.

3. The ceramic product performance detection method under multi-source parameter analysis according to claim 2, characterized in that: The K performance partition combinations are cross-performance partition fusion performed to output M multi-dimensional performance fusion partitions, wherein the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers, and the method includes: According to the spatial distribution of the K performance partition combinations, grid overlapping is performed on the K performance partition combinations to form a partition grid structure; Performing grid extraction on the partitioned grid structure to obtain the M multi-dimensional performance fusion partitions; According to the intersection relationship between the first multi-dimensional performance fusion partition and the K performance partition combinations, a performance requirement composition is allocated to the first multi-dimensional performance fusion partition, and a first performance requirement composition identifier is output; By analogy, according to the intersection relationship between the M-1 multi-dimensional performance fusion partitions and the K performance partition combinations, performance requirement composition allocation is performed for the M-1 multi-dimensional performance fusion partitions, and M-1 performance requirement composition identifiers are output.

4. The ceramic product performance detection method under multi-source parameter analysis according to claim 2, characterized in that: According to the M performance requirement composition identifiers, the M multi-dimensional performance fusion partitions are dynamically connected to detect each other to obtain M local multi-source performance parameters. The method includes: Preset the initial partition jump step size; After randomly selecting a second multidimensional performance fusion partition from the M multidimensional performance fusion partitions, a third multidimensional performance fusion partition is obtained by screening from the M multidimensional performance fusion partitions with the initial partition jump step length as a constraint, wherein a fourth multidimensional performance fusion partition is executed between the second multidimensional performance fusion partition and the third multidimensional performance fusion partition; After performing local performance detection on the second multidimensional performance fusion partition and the third multidimensional performance fusion partition, updating the initial partition jump step size to output the first partition jump step size according to the detection result, and fitting and outputting the fourth local multi-source performance parameter of the fourth multidimensional performance fusion partition according to the detection result; Taking the third multidimensional performance fusion partition as a starting point and the first partition jump step as a constraint, a fifth multidimensional performance fusion partition is obtained by screening from the M multidimensional performance fusion partitions, wherein a straight line interval between the fifth multidimensional performance fusion partition and the third multidimensional performance fusion partition is H multidimensional performance fusion partitions; After performing local performance detection on the fifth multidimensional performance fusion partition, updating the first partition jump step size and outputting the second partition jump step size according to the detection results of the fifth multidimensional performance fusion partition and the third multidimensional performance fusion partition, and fitting and outputting H local multi-source performance parameters of the H multidimensional performance fusion partitions according to the detection results; By analogy, the initial partition jump step is updated according to the performance deviation coefficient of the interval multidimensional performance fusion partition, and the local multi-source performance parameters of the M multidimensional performance fusion partitions are solved according to the step update result until the M local multi-source performance parameters of the M multidimensional performance fusion partitions are obtained.

5. The ceramic product performance detection method under multi-source parameter analysis according to claim 4, characterized in that: After performing local performance detection on the second multidimensional performance fusion partition and the third multidimensional performance fusion partition, updating the initial partition jump step size to output the first partition jump step size according to the detection result, and fitting and outputting the fourth local multi-source performance parameter of the fourth multidimensional performance fusion partition according to the detection result, the method includes: Performing local performance testing on the ceramic accessory product in the second multi-dimensional performance fusion partition according to the second performance requirement composition identifier to obtain a second local multi-source performance parameter; Performing local performance testing on the ceramic accessory product in the third multi-dimensional performance fusion partition according to the third performance requirement composition identifier, obtaining a third local multi-source performance parameter; Using Euclidean distance to calculate the same performance index deviation of the second local multi-source performance parameter and the third local multi-source performance parameter to obtain a first performance deviation coefficient; If the first performance deviation coefficient is less than a preset performance deviation scale, updating the initial partition jump step size according to the first performance deviation coefficient, and outputting the first partition jump step size; The second local multi-source performance parameter and the third local multi-source performance parameter are used to fit the performance parameters of the fourth multi-dimensional performance fusion partition, and the fourth local multi-source performance parameter is output.

6. The ceramic product performance detection method under multi-source parameter analysis according to claim 5, characterized in that: The method further comprises: If the first performance deviation coefficient is greater than a preset performance deviation scale, performing compensation detection on the fourth multi-dimensional performance fusion partition and outputting the fourth local multi-source performance parameter; Taking the fourth multidimensional performance fusion partition as a starting point and the initial partition jump step as a constraint, the fifth multidimensional performance fusion partition is obtained by screening from the M multidimensional performance fusion partitions; After performing a local performance detection on the fifth multi-dimensional performance fusion partition, the initial partition jump step is updated according to the detection results of the fifth multi-dimensional performance fusion partition and the fourth multi-dimensional performance fusion partition.

7. The ceramic product performance detection method under multi-source parameter analysis according to claim 5, characterized in that: After performing local performance detection on the fifth multidimensional performance fusion partition, updating the first partition jump step size and outputting the second partition jump step size according to the detection results of the fifth multidimensional performance fusion partition and the third multidimensional performance fusion partition, and fitting and outputting H local multi-source performance parameters of the H multidimensional performance fusion partitions according to the detection results, the method includes: Performing local performance testing on the ceramic accessory product in the fifth multi-dimensional performance fusion partition according to the fifth performance requirement composition identifier to obtain a fifth local multi-source performance parameter; Using Euclidean distance to calculate the same performance index deviation of the second local multi-source performance parameter and the fifth local multi-source performance parameter to obtain a second performance deviation coefficient; If the second performance deviation coefficient is less than a preset performance deviation scale, updating the first partition jump step size according to the second performance deviation coefficient, and outputting the second partition jump step size; Using the third local multi-source performance parameter and the fifth local multi-source performance parameter to perform performance parameter fitting of the H multi-dimensional performance fusion partitions, and outputting H local multi-source performance parameters; Taking the jump step of the second partition as a constraint, a sixth multidimensional performance fusion partition is obtained by screening from the M multidimensional performance fusion partitions, wherein there are W multidimensional performance fusion partitions in a straight line interval between the fifth multidimensional performance fusion partition and the sixth multidimensional performance fusion partition.

8. Ceramic product performance testing device under multi-source parameter analysis, characterized in that: The device is used to implement the ceramic product performance detection method under multi-source parameter analysis according to any one of claims 1 to 7, and the device comprises: A performance requirement information output module is used to match performance requirements according to application scenarios of ceramic accessory products and output K types of performance requirement information; A performance partition combination acquisition module, used to perform structural partitioning on the ceramic accessory product based on the K types of performance requirement information to obtain K performance partition combinations; A multi-dimensional performance fusion partition output module, used to perform cross-performance partition fusion on the K performance partition combinations, and output M multi-dimensional performance fusion partitions, wherein the M multi-dimensional performance fusion partitions have M performance requirement composition identifiers; A local multi-source performance parameter acquisition module, configured to perform dynamic connection detection on the M multi-dimensional performance fusion partitions according to the M performance requirement composition identifiers, and obtain M local multi-source performance parameters; A partition performance score output module, used to quantify partition performance based on the M local multi-source performance parameters and output M partition performance scores; A three-dimensional performance gradient map acquisition module is used to perform multi-level performance gradient segmentation on the M multi-dimensional performance fusion partitions according to the M local multi-source performance parameters to obtain K three-dimensional performance gradient maps; The product performance visualization map acquisition module is used to use the M partition performance scores to perform rendering fusion of the K three-dimensional performance gradient maps to obtain an interactive product performance visualization map.

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