Hand plate mold detection process optimization management method and system

Abstract

The invention discloses a hand plate mold detection process optimization management method and system, relates to the technical field of mold detection, and aims to solve the problems of low efficiency and quality risk when a traditional detection process management system treats a novel composite material and a complex free-form surface structure hand plate mold. The method comprises the following steps: acquiring material characteristic data and geometric characteristic data of a to-be-detected mold; inputting the material characteristic data, the geometric characteristic data and the real-time state parameters and the dynamic capability parameters of the detection equipment into a preset virtual simulation module for detection process rehearsal; according to a rehearsal result output by the virtual simulation module, identifying a potential detection risk associated with the to-be-detected mold; the potential detection risks comprise the scanning blind area proportion, the number of interference points of the measuring head and the estimated detection time consumption deviation.

Description

Technical Field

[0001] This application relates to the field of mold inspection technology, and in particular to a method and system for optimizing the inspection process of prototype molds. Background Technology

[0002] In modern industrial production, the inspection of prototype molds is a crucial step in product development and quality control. As market demands evolve, manufacturing plants are increasingly facing orders for prototype molds made from new composite materials and those with complex free-form surface structures. Traditional inspection process management systems are often designed based on the factory's mainstream, relatively standardized mold types. This results in the system failing to fully consider the unique inspection challenges and required equipment capabilities when handling these "new faces," leading to a series of inefficiencies and quality risks. Summary of the Invention

[0003] This application provides a method and system for optimizing the inspection process of prototype molds, which aims to solve the problem that traditional inspection process management systems cannot fully consider the unique inspection difficulty and required equipment capabilities when dealing with prototype molds of new composite materials and complex free-form surface structures, thus leading to low efficiency and quality risks.

[0004] Firstly, to address the aforementioned technical problems, this invention provides a method for optimizing and managing the inspection process of a prototype mold. This method includes: acquiring material characteristic data and geometric characteristic data of the mold to be inspected; the material characteristic data includes at least one physical property of the mold surface, such as reflectivity, absorbance, and hardness; the geometric characteristic data characterizes at least one geometric structural property of the mold surface, such as the rate of curvature change, cavity depth, and slit width; inputting the material characteristic data, geometric characteristic data, and real-time status parameters and dynamic capability parameters of the inspection equipment into a preset virtual simulation module for pre-simulation of the inspection process; the real-time status parameters include the inspection... The equipment's current load, operating temperature, and time interval since the last calibration, along with dynamic capability parameters including the estimated scanning efficiency, measurement accuracy, and probe accessibility, are used. The virtual simulation module simulates the testing process on the mold under test based on material characteristic data, geometric characteristic data, real-time status parameters, and dynamic capability parameters. It outputs pre-test results including simulated testing coverage data, simulated probe accessibility data, and simulated testing cycle data. Based on the pre-test results output by the virtual simulation module, potential testing risks associated with the mold under test are identified. These potential risks include the scanning blind zone ratio, the number of probe interference points, and the deviation in estimated testing time.

[0005] Secondly, this application provides a process optimization and management system for prototype mold inspection. The system includes: an acquisition unit for acquiring material characteristic data and geometric characteristic data of the mold to be inspected; the material characteristic data includes at least one physical property of the mold surface, such as reflectivity, absorbance, and hardness; the geometric characteristic data characterizes at least one geometric structural property of the mold surface, such as the rate of curvature change, cavity depth, and slit width; and a processing unit for inputting the material characteristic data, geometric characteristic data, and real-time status parameters and dynamic capability parameters of the inspection equipment into a preset virtual simulation module for pre-simulating the inspection process; the real-time status parameters include the inspection equipment... The current load, operating temperature, and time interval since the last calibration are considered. Dynamic capability parameters include the estimated scanning efficiency, measurement accuracy, and probe accessibility of the testing equipment. The virtual simulation module simulates the testing process of the testing equipment on the mold to be tested based on material characteristic data, geometric characteristic data, real-time status parameters, and dynamic capability parameters, and outputs a pre-simulation result including simulated testing coverage data, simulated probe accessibility data, and simulated testing cycle data. The identification unit identifies potential testing risks associated with the mold to be tested based on the pre-simulation results output by the virtual simulation module. Potential testing risks include the scanning blind zone ratio, the number of probe interference points, and the deviation of the estimated testing time.

[0006] This application has at least the following beneficial effects: The hand-made mold inspection process optimization management method disclosed in this application obtains the material characteristic data and geometric characteristic data of the mold to be inspected, and combines them with the real-time status parameters and dynamic capability parameters of the inspection equipment, inputting them into a virtual simulation module to pre-simulate the inspection process. This virtual simulation module can simulate the inspection process of the inspection equipment on the mold to be inspected based on this comprehensive data, and output pre-simulation results including simulated inspection coverage data, simulated probe accessibility data, and simulated inspection cycle data. Subsequently, based on these pre-simulation results, the system identifies potential inspection risks associated with the mold to be inspected, including the scanning blind zone ratio, the number of probe interference points, and the deviation in estimated inspection time.

[0007] This method effectively solves the problems of inefficiency and quality risks in existing traditional inspection process management systems when handling prototype molds of novel composite materials and complex freeform surfaces, due to their failure to fully consider the unique inspection difficulties and required equipment capabilities. By conducting virtual simulation pre-testing before actual inspection, this application can identify potential scanning blind spots, probe interference, and time-consuming deviations in advance, thereby avoiding the improper task allocation and low inspection efficiency caused by information asymmetry in traditional methods. For example, for highly reflective or highly absorbent materials, the system can predict the risk of insufficient scanning coverage; for complex geometric structures with deep cavities or narrow slits, the system can predict probe interference or accessibility issues. This proactive risk identification mechanism enables factories to adjust inspection strategies, optimize equipment configuration, or generate targeted operation instructions in a timely manner based on the pre-testing results, thereby significantly improving the matching degree, efficiency, and accuracy of inspection tasks, reducing rework rates and production costs, and realizing intelligent and refined management of the inspection process. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating an optimized management method for the inspection process of a prototype mold, as provided in this application. Detailed Implementation

[0009] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0010] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0011] In modern industrial production, the inspection of prototype molds is a crucial step in product development and quality control. Traditional prototype mold inspection process management systems are often designed based on the mainstream, relatively standardized mold types prevalent in factories. This leads to a failure to fully consider the unique inspection challenges and required equipment capabilities when handling prototype molds made of new composite materials or with complex free-form surfaces, resulting in a series of inefficiencies and quality risks. If these problems are not addressed, the diversification of mold types will cause significant deviations in the original allocation logic's evaluation model of "equipment-task matching." This could lead to the system incorrectly assigning high-precision, highly reflective, or irregularly shaped molds to unsuitable inspection equipment, thus creating potential problems for future developments.

[0012] In view of the above problems, this application provides a method for optimizing and managing the inspection process of prototype molds. This application obtains the material and geometric feature data of the mold, combines it with the real-time status and dynamic capability parameters of the inspection equipment, and performs virtual simulation pre-runs to identify potential inspection risks. This effectively solves the problems of low efficiency and high quality risk in traditional methods when dealing with new and complex molds, and significantly improves the intelligence and optimization level of the inspection process.

[0013] The following specific embodiments will provide a detailed introduction and explanation of the precision fertilization program control method and system for intelligent agricultural equipment provided in this application.

[0014] Reference Figure 1 This application provides a method for optimizing and managing the inspection process of a prototype mold, which may include the following steps: S1. Obtain the material characteristic data and geometric characteristic data of the mold to be tested.

[0015] The material characteristic data includes at least one of the physical properties of the mold surface, such as reflectivity, absorbance, and hardness. The geometric characteristic data is used to characterize at least one of the geometric structural properties of the mold surface, such as the rate of curvature change, cavity depth, and slit width.

[0016] It should be noted that material characteristic data can refer to data describing the properties of the material of the mold to be tested, such as physical properties like surface reflectivity, absorbance, and hardness. This data is crucial for evaluating the performance of testing equipment on different materials.

[0017] Geometric feature data refers to data describing the geometry of the mold to be inspected, such as the rate of change of surface curvature, cavity depth, and slit width. This data helps evaluate the accessibility and measurement accuracy of the inspection equipment on complex geometries.

[0018] This application requires obtaining material characteristic data and geometric characteristic data of the mold to be tested. Material characteristic data may include at least one of the following physical properties of the mold surface: reflectivity, absorbance, and hardness. For example, this data can be obtained by manually consulting the mold material specifications or through simple physical tests. Geometric characteristic data can be used to characterize at least one of the following geometric structural properties of the mold surface: rate of curvature change, cavity depth, and slit width. For example, these geometric characteristics can be roughly assessed by manually measuring the key dimensions of the mold or through visual inspection.

[0019] S2. Input the material characteristic data, geometric characteristic data, and real-time status parameters and dynamic capability parameters of the testing equipment into the preset virtual simulation module to perform a pre-simulation of the testing process.

[0020] The real-time status parameters include the current load, operating temperature, and time interval since the last calibration of the testing equipment. The dynamic capability parameters include the estimated scanning efficiency, measurement accuracy, and probe accessibility of the testing equipment. The virtual simulation module is used to simulate the testing process of the testing equipment on the mold to be tested based on material characteristic data, geometric characteristic data, real-time status parameters, and dynamic capability parameters, and outputs the pre-simulation results including simulated testing coverage data, simulated probe accessibility data, and simulated testing cycle data.

[0021] It should be noted that real-time status parameters refer to the operating status data of the monitoring equipment at the current moment, such as the equipment's current load, operating temperature, and the time interval since the last calibration. These parameters reflect the equipment's immediate operating conditions.

[0022] Dynamic capability parameters refer to the performance indicators of a testing device under specific conditions, such as estimated scanning efficiency, measurement accuracy, and probe accessibility. These parameters reflect the adaptability and capability of the device in different testing tasks.

[0023] In this embodiment, the acquired material characteristic data, geometric characteristic data, and real-time status parameters and dynamic capability parameters of the testing equipment are input into a preset virtual simulation module for testing process rehearsal. Real-time status parameters may include the current load, operating temperature, and time interval since the last calibration of the testing equipment. For example, these parameters can be obtained by manually recording equipment operation logs or through simple sensor readings. Dynamic capability parameters may include the estimated scanning efficiency, measurement accuracy, and probe accessibility of the testing equipment. For example, these parameters can be manually input based on historical performance data of the equipment or specifications provided by the manufacturer. The virtual simulation module is used to simulate the testing process of the testing equipment on the mold to be tested based on these input data and outputs a pre-simulation result including simulated testing coverage data, simulated probe accessibility data, and simulated testing cycle data. For example, the virtual simulation module can be a simulation software based on a CAD model, simulating the probe path and scanning process through a preset algorithm.

[0024] S3. Based on the pre-simulation results output by the virtual simulation module, identify potential detection risks associated with the mold to be inspected.

[0025] Potential detection risks include the proportion of blind spots in the scan, the number of probe interference points, and deviations in the estimated detection time.

[0026] It should be noted that the virtual simulation module can be a software or hardware system used to simulate the inspection process of the inspection equipment on the mold to be inspected and output the simulation results.

[0027] Potential detection risks can refer to problems that may occur during the detection process and affect detection efficiency and quality, such as the proportion of scanning blind zones, the number of probe interference points, and the deviation of the estimated detection time.

[0028] This application embodiment identifies potential detection risks associated with the mold to be inspected based on the pre-simulation results output by the virtual simulation module. Potential detection risks may include the proportion of scanning blind spots, the number of probe interference points, and deviations in estimated detection time. For example, by manually analyzing the pre-simulation result report, areas with low coverage, points where probe collisions may occur, and differences between estimated time and empirical values ​​can be identified.

[0029] The overall working principle of this application lies in achieving early identification and warning of potential inspection risks through comprehensive virtual simulation pre-testing before the actual inspection task begins. Specifically, the method first acquires the material and geometric feature data of the mold to be inspected. This data provides the physical and geometric property basis for subsequent simulations. For example, for a high-reflectivity mold, its material feature data reflects this characteristic; for a mold with a deep cavity structure, its geometric feature data specifies the depth and width of the cavity. Next, this mold feature data, along with the real-time status parameters (such as current load and operating temperature) and dynamic capability parameters (such as estimated scanning efficiency and measurement accuracy) of the inspection equipment, are input into the virtual simulation module. The virtual simulation module uses this information to simulate the entire inspection process of the inspection equipment on the mold to be inspected, including the probe's movement trajectory, scanning path, and data acquisition. Through this simulation, the system can output detailed pre-testing results, such as simulated inspection coverage data, simulated probe accessibility data, and simulated inspection cycle data. These pre-testing results are crucial for identifying potential inspection risks. For example, if simulated inspection coverage data shows large scanning blind spots, or simulated probe reachability data shows probe interference in certain areas, or simulated inspection cycle data shows the estimated inspection time is much longer than expected, these will all be identified as potential inspection risks. In this way, this application can organically combine the characteristics of the mold, the condition of the equipment, and potential problems in the inspection process to form a closed-loop early warning and optimization mechanism. This allows for the identification and resolution of problems before the actual inspection task is executed, significantly improving the efficiency and reliability of the inspection process.

[0030] In some embodiments of this application, the acquisition of material characteristic data and geometric characteristic data of the mold to be tested may include the following specific steps: non-contact scanning of the surface of the mold to be tested using a spectral analysis device located at the entrance of the testing process to acquire reflectance spectral data; matching and analyzing the reflectance spectral data with a material characteristic spectral database to determine the material type of the mold to be tested; the material characteristic spectral database contains multiple material types, as well as standard reflectance spectral characteristic curves and preset material characteristic data corresponding to each material type; extracting corresponding material characteristic data from the material characteristic spectral database according to the determined material type; acquiring three-dimensional point cloud data of the mold to be tested using a three-dimensional scanning device; calculating the curvature distribution, cavity depth, and slit width of the surface of the mold to be tested based on the three-dimensional point cloud data, and generating the geometric characteristic data based on the calculation results.

[0031] Specifically, at the initial stage of the inspection process, a non-contact scanning method can be used to scan the mold surface using a spectral analysis device. This device emits a beam of light of a specific wavelength onto the surface of the mold to be inspected and receives the spectral data reflected back from the surface, i.e., reflectance spectral data. This non-contact scanning method avoids physical damage to the mold surface and improves the efficiency of data acquisition.

[0032] Furthermore, the acquired reflectance spectral data is input into a pre-defined material characteristic spectral database for matching analysis. This database pre-stores standard reflectance spectral characteristic curves for various known material types, along with associated material characteristic data such as reflectance, absorbance, or hardness. By comparing the reflectance spectral data of the mold to be tested with the standard curves in the database, the material type of the mold can be accurately determined. Once the material type is determined, the system can extract the pre-defined material characteristic data corresponding to that material type from the database.

[0033] Simultaneously, to obtain the geometric feature data of the mold, a 3D scanning device can be used to scan the mold under inspection, thereby obtaining its 3D point cloud data. 3D point cloud data is a digital representation of the mold surface geometry, containing a large amount of spatial coordinate point information. Based on this 3D point cloud data, key geometric structural attributes such as the curvature distribution, cavity depth, and narrow slot width of the mold surface can be further calculated. For example, by performing surface reconstruction and geometric analysis on the point cloud data, the degree of curvature in various regions of the mold surface can be quantified, the depth of deep recesses can be identified and measured, and the width of narrow slots can be detected and evaluated. Finally, these calculation results are integrated and generated as the geometric feature data.

[0034] This application's solution combines spectral analysis and 3D scanning technology to achieve comprehensive and accurate acquisition of mold material characteristic data and geometric feature data. The spectral analysis equipment acquires reflectance spectral data through non-contact scanning and matches it using a material characteristic spectral database, accurately identifying the mold material type and extracting corresponding material physical properties, avoiding the experience-based judgment errors that may exist in traditional methods. Simultaneously, the 3D point cloud data acquired by the 3D scanning equipment provides a high-precision geometric information foundation for subsequent accurate calculations of the mold surface curvature distribution, cavity depth, and narrow slot width. This dual data acquisition mechanism ensures the high accuracy and completeness of the initial data input to the virtual simulation module, thus laying a solid foundation for subsequent inspection process simulation and potential risk identification.

[0035] The above technical solution enables automated, high-precision, and non-contact acquisition of mold material characteristics and geometric features. This method not only improves the efficiency and accuracy of data acquisition, reduces manual intervention and potential errors, but also provides more realistic and comprehensive input parameters for the virtual simulation module through detailed material property identification and refined geometric structure analysis. This significantly enhances the simulation accuracy of subsequent inspection process rehearsals and the reliability of potential inspection risk identification, thus providing solid data support for optimizing the inspection process of prototype molds.

[0036] In some embodiments of this application, in order to obtain the geometric feature data of the mold to be tested more accurately, the calculation of the curvature distribution, deep cavity depth, and narrow slit width of the mold surface based on the three-dimensional point cloud data specifically includes the following steps: performing surface reconstruction on the three-dimensional point cloud data to generate a digital surface model of the mold; performing differential geometric analysis on the digital surface model to calculate the curvature value of each point on the surface, and statistically analyzing the proportion of regions where the curvature change exceeds a first preset threshold to obtain the curvature distribution; identifying concave regions in the digital surface model, calculating the ratio of the depth to the inlet width of each concave region, identifying regions where the ratio exceeds a second preset threshold as deep cavity structures, and recording the deep cavity depth; identifying gap regions in the digital surface model, calculating the minimum width of each gap region, identifying regions where the minimum width is less than a third preset threshold as narrow slit structures, and recording the narrow slit width.

[0037] Specifically, 3D point cloud data can be processed using various algorithms (such as Poisson reconstruction, moving least squares, or triangulation) to generate a continuous and smooth digital surface model. This model forms the basis for mold geometric feature analysis and accurately reflects the actual shape of the mold. Differential geometric analysis of the digital surface model involves using mathematical methods (such as calculating Gaussian curvature, mean curvature, or principal curvature) to quantify the degree of curvature at different points on the surface. By statistically analyzing the proportion of regions where curvature changes exceed a first preset threshold, areas with drastic geometric changes on the mold surface can be identified. These areas are often challenging during inspection, such as sharp angles, chamfers, or complex surface transition zones. The first preset threshold can be set according to the complexity of the mold and the required inspection accuracy to distinguish significant curvature changes.

[0038] In practical applications, the identification of concave regions can be achieved by analyzing the surface normal direction or using morphological operations. Calculating the ratio of the depth to the entrance width of each concave region quantifies the "depth-narrowness" of the cavity. When this ratio exceeds a second preset threshold, it indicates that the region is a challenging deep cavity structure, potentially making it difficult for the probe to reach or for the scanning beam to effectively cover it. The second preset threshold can be adjusted based on factors such as the probe size and scanning angle limitations of the detection equipment. Furthermore, the identification of gap regions can be achieved by analyzing the distance between adjacent surfaces or using connectivity analysis. Calculating the minimum width of each gap region assesses the narrowness of the gap. When the minimum width is less than a third preset threshold, the region is identified as a narrow slit structure, which may also pose a challenge to probe accessibility and scanning integrity. The third preset threshold can be set based on the minimum diameter of the probe or the size of the scanning spot.

[0039] This application's solution, through refined processing and analysis of 3D point cloud data, first generates a digital surface model of the mold through surface reconstruction, providing a high-fidelity foundation for subsequent geometric feature calculations. Next, differential geometric analysis is used to accurately calculate the curvature values ​​of each point on the surface and identify areas with drastic curvature changes, thereby quantifying the complexity of the mold surface. Simultaneously, by identifying concave regions and calculating the ratio of their depth to the inlet width, and identifying gap regions and calculating their minimum width, deep cavity structures and narrow slit structures can be accurately located and quantified. These detailed geometric feature calculation steps ensure that the complex geometric characteristics of the mold are comprehensively and accurately captured, providing reliable input data for subsequent virtual simulation modules to pre-simulate the inspection process.

[0040] The above technical solution overcomes the potential accuracy limitations of traditional methods when handling complex mold geometry. By refining the calculations of curvature distribution, cavity depth, and slit width, the geometric properties of the mold can be characterized more accurately, significantly improving the accuracy of the virtual simulation module in terms of simulated inspection coverage data, simulated probe accessibility data, and simulated inspection cycle data. Consequently, the identification of potential inspection risks becomes more precise, providing a solid data foundation for the subsequent generation of collaborative guidance schemes and enhanced standard operating procedures, ultimately enhancing the optimized management level of the prototype mold inspection process.

[0041] Specifically, when identifying potential detection risks associated with the mold to be inspected based on the pre-simulation results output by the virtual simulation module, the following methods can be used.

[0042] The aforementioned identification of potential detection risks associated with the mold to be inspected includes: determining the proportion of areas where the effective data point cloud coverage is lower than a preset coverage threshold due to excessively high material reflectivity or geometric occlusion based on simulated detection coverage data, thus obtaining the scanning blind zone proportion; counting the number of locations where virtual collisions occur between the probe or probe and the digital model of the mold, or where the probe cannot reach the measurement point in a standard posture, based on simulated probe accessibility data, thus obtaining the number of probe interference points; obtaining the simulation time required to complete the detection of all preset features of the mold to be inspected on the detection equipment based on simulated detection cycle data, comparing the simulation time with the historical average detection time of the detection equipment for processing standard molds, thus obtaining the estimated detection time deviation; and considering the scanning blind zone proportion, the number of probe interference points, and the estimated detection time deviation as potential detection risks.

[0043] Specifically, the scanning blind zone ratio refers to the proportion of the total inspection area where the inspection equipment cannot effectively collect data points due to the material characteristics (e.g., high reflectivity) or complex geometry (e.g., deep cavities, narrow slits) of the mold under inspection during virtual simulation. This ratio is calculated by analyzing simulated inspection coverage data to identify areas where the effective data point cloud coverage is lower than a preset coverage threshold, and then calculating the percentage of that area relative to the total area. The preset coverage threshold can be set according to specific inspection accuracy requirements and industry standards, for example, it can be set to 95% or 98%.

[0044] The number of probe interference points can be understood as the total number of locations where, during the simulated testing process, the probe of the testing equipment or its connecting rods physically collide with the digital model of the mold to be tested, or where the probe fails to reach the preset measurement point in its standard and effective measurement posture. This typically occurs when the mold has complex curved surfaces, narrow channels, or deep holes. Simulated probe reachability data is obtained by analyzing the probe's motion trajectory and the collision detection algorithm of the mold's geometric model to accurately count these interference points.

[0045] In practical applications, the predicted inspection time deviation specifically refers to the difference between the simulation time required to complete the inspection of all preset features of the mold to be inspected, obtained through the virtual simulation module, and the historical average inspection time accumulated by the inspection equipment when processing standard molds with similar complexity. This deviation can quantify the efficiency risk of the predicted inspection task. For example, if the simulation time is significantly longer than the historical average time, it indicates that the inspection of the mold may face efficiency challenges.

[0046] Therefore, by using the above three key indicators—the scanning blind zone ratio, the number of probe interference points, and the deviation of the estimated detection time—the potential detection risks associated with the mold to be inspected can be comprehensively and quantitatively constructed.

[0047] This application's solution, through detailed quantitative analysis of the pre-simulation results output by the virtual simulation module, can concretize abstract detection risks into measurable indicators. The calculation of the scanning blind zone ratio reveals detection incompleteness caused by materials or geometric structures, thus guiding subsequent adjustments to the detection strategy. The statistical analysis of the number of probe interference points directly reveals the physical accessibility limitations of the detection equipment on a specific mold, avoiding potential collisions or measurement failures in actual detection. The acquisition of estimated detection time deviation provides crucial time efficiency assessment data for scheduling detection tasks and allocating resources. It is through the precise identification and quantification of these specific risk indicators that this application provides a solid data foundation for subsequent risk avoidance and process optimization.

[0048] Through the above technical solution, this application can achieve refined and quantitative identification of potential detection risks. Compared with judgment based solely on experience or rough estimation, this solution can provide specific data support, such as the clear scanning blind zone area, the specific location and number of probe interference points, and the estimated detection time deviation. This quantitative risk identification method significantly improves the accuracy and objectivity of risk assessment, providing a clear direction and basis for subsequent development of targeted optimization strategies. This effectively avoids problems such as missed detections, collision damage, or low efficiency that may occur in actual testing, thereby improving the overall reliability and efficiency of the prototype mold inspection process.

[0049] In some of the embodiments described above in this application, although potential inspection risks associated with the mold to be inspected can be identified, simply identifying risks does not directly provide specific solutions or optimize the actual inspection process. Without further quantifying and assessing these potential risks and generating targeted operational guidance based on the assessment results, the actual inspection task may still face problems such as low efficiency, inaccurate inspection, or excessive time consumption. To address this, this application further proposes a method for optimizing and managing the inspection process of a prototype mold. This method quantifies potential inspection risks, generates collaborative guidance schemes, receives operator feedback and generates enhanced standard operating procedures, and ultimately controls the inspection equipment to execute actual inspection tasks, thereby achieving intelligent optimization and risk avoidance of the inspection process.

[0050] In some embodiments, the method further includes: performing dimensionless processing on the scanning blind zone ratio, the number of probe interference points, and the estimated detection time deviation to obtain corresponding first risk scores, second risk scores, and third risk scores; weighting the first risk scores, second risk scores, and third risk scores to obtain a risk index corresponding to the potential detection risk; generating and outputting a collaborative guidance scheme when the risk index corresponding to the identified potential detection risk exceeds a preset risk threshold; the collaborative guidance scheme includes at least one recommended operation strategy for the potential detection risk; receiving operator feedback instructions for the collaborative guidance scheme; generating an enhanced standard operating procedure based on the operator feedback instructions; the enhanced standard operating procedure includes a three-dimensional visualization measurement path determined according to the selected recommended operation strategy, preset values ​​of equipment operating parameters, and instructions for using auxiliary tools; and controlling the detection equipment to perform the actual detection task on the mold to be detected according to the enhanced standard operating procedure.

[0051] Specifically, after identifying potential detection risks, in order to uniformly assess and compare different types of risks, it is necessary to perform dimensionless processing on the scanning blind zone ratio, the number of probe interference points, and the estimated detection time deviation. Dimensionless processing refers to converting these risk indicators with different dimensions and numerical ranges into uniform, dimensionless values ​​through mathematical transformations. Methods such as min-max normalization, Z-score standardization, or logarithmic transformation can be used. From this, corresponding first, second, and third risk scores can be obtained, reflecting the relative severity of each risk.

[0052] Furthermore, to comprehensively assess the overall potential detection risks, the aforementioned first, second, and third risk scores are weighted. Weighting involves assigning different weight coefficients to different risk indicators based on their importance or priority in impacting the detection task, and then summing the weighted scores to obtain a risk index corresponding to the potential detection risk. For example, if the number of probe interference points is considered to have the greatest impact on detection accuracy and equipment safety, it can be assigned a higher weight. This risk index provides a quantitative and comprehensive risk assessment result.

[0053] When the risk index corresponding to a identified potential detection risk exceeds a preset risk threshold, the system will automatically generate and output a collaborative guidance plan. This collaborative guidance plan aims to provide operators with targeted risk mitigation suggestions, which may include at least one recommended operational strategy for the potential detection risk. For example, for scanning blind spots, recommended strategies may include adjusting the scanning angle, using auxiliary illumination, or replacing the scanning equipment; for probe interference, recommended strategies may include replacing with a smaller probe, optimizing the probe path, or segmented measurement; for estimated detection time deviation, recommended strategies may include optimizing the scanning strategy, parallel processing, or adjusting equipment parameters.

[0054] After the collaborative guidance scheme is output, the system receives operator feedback instructions for the scheme. These instructions allow operators to select, modify, or supplement the recommended operational strategies based on their experience and the actual situation, ensuring that the final solution not only conforms to the system's optimization suggestions but also considers the feasibility and flexibility of practical operation.

[0055] Based on the operator feedback instructions, the system will generate an enhanced standard operating procedure (SOP). This enhanced SOP is an optimized and customized inspection process that includes a 3D visualization measurement path determined according to the selected recommended operating strategy, preset values ​​for equipment operating parameters, and instructions for using auxiliary tools. The 3D visualization measurement path visually displays the precise movement trajectory of the inspection equipment (such as a probe or scanning beam) on the mold to avoid interference or ensure comprehensive coverage. The preset values ​​for equipment operating parameters can be automatically adjusted according to the mold material characteristics or geometry, such as scanning speed and laser power. The instructions for using auxiliary tools guide the operator on when and where to use auxiliary tools such as fixtures, positioning blocks, or surface treatment agents.

[0056] Finally, the testing equipment will perform the actual testing task on the mold to be tested according to the enhanced standard operating procedure. This means that the testing equipment will operate automatically or semi-automatically according to the optimization scheme jointly determined by the system and the operator, thereby achieving efficient, accurate and risk-controlled mold testing.

[0057] This application's solution quantifies potential detection risks, elevating risk management from qualitative identification to quantitative analysis, thus providing an objective basis for subsequent decision-making. When the risk index reaches a preset threshold, the system proactively generates collaborative guidance plans, transforming the detection process from passively responding to risks to proactively preventing and optimizing them. By introducing operator feedback instructions, this application's solution fully integrates the computational power of the automated system with the experience and wisdom of human operators, ensuring the practicality and effectiveness of the solution. Furthermore, the generated enhanced standard operating procedures concretize optimization strategies into executable instructions, including 3D visualization measurement paths, preset values ​​for equipment operating parameters, and instructions for using auxiliary tools. This directly guides the actual operation of the detection equipment, effectively mitigating potential risks and improving detection efficiency and accuracy.

[0058] The aforementioned technical solution not only enables precise quantification and assessment of potential inspection risks but also allows for the intelligent generation and optimization of inspection strategies based on the assessment results. This human-machine collaborative model allows the inspection process to flexibly adapt to the complexity and diversity of different molds, significantly reducing inspection errors and rework caused by insufficient human experience or improper planning. Consequently, inspection efficiency is significantly improved, the inspection cycle is shortened, and the accuracy and reliability of inspection results are ensured, thus providing a more efficient and intelligent management method for the quality control of prototype molds.

[0059] In some preferred embodiments, it is assumed that a prototype mold to be inspected has a complex internal deep cavity structure and some highly reflective surfaces. According to the above method, the inspection process is first pre-simulated using a virtual simulation module to identify a high proportion of scanning blind zones (due to high reflectivity and deep cavity obstruction), a large number of probe interference points (due to the narrowness of the deep cavity), and a long deviation in estimated inspection time. Subsequently, these risk indicators are dimensionless and weighted to obtain a high risk index. Since this risk index exceeds a preset risk threshold, the system automatically generates a collaborative guidance scheme. This scheme may recommend the following operational strategies: for high reflectivity areas, it is suggested to apply a peelable matte coating before inspection; for deep cavity structures, it is suggested to use an extended probe and a multi-angle tilting scanning strategy; for time-consuming issues, it is suggested to optimize the scanning path and reduce repeated scanning areas. After receiving these recommended strategies, the operator selects the strategy of applying a matte coating and using an extended probe for multi-angle scanning based on the actual situation, and fine-tunes some aspects of the scanning path. Based on operator feedback, the system generates an enhanced standard operating procedure (SOP), which includes detailed spraying instructions, a 3D visualized motion path for the extended probe, scanning speed adjustment parameters for specific areas, and precise measurement points inside the deep cavity. Ultimately, following this enhanced SOP, the inspection equipment efficiently and accurately completed the inspection of the complex prototype mold, effectively avoiding scanning blind spots and probe interference, and significantly shortening the inspection time.

[0060] In some embodiments described above, a collaborative guidance scheme is generated and output when the risk index corresponding to the identified potential detection risk exceeds a preset risk threshold. However, in its implementation, simply generating recommended operation strategies may not be sufficient to ensure that operators can efficiently and accurately understand and execute these strategies. Traditionally, operators may need to spend additional time interpreting complex textual instructions, or, in the absence of intuitive guidance, find it difficult to accurately locate risk areas and implement recommended operations, which may lead to inefficient decision-making or operational errors.

[0061] To address this, this application further proposes generating and outputting a collaborative guidance scheme when the risk index corresponding to the identified potential detection risk exceeds a preset risk threshold. Specifically, this includes: when the risk index corresponding to the identified potential detection risk exceeds the preset risk threshold, retrieving at least one set of candidate operation strategies from a preset multi-level solution knowledge base based on the identified potential detection risk type; each set of candidate operation strategies includes a primary recommended strategy and at least one alternative strategy; integrating the retrieved candidate operation strategy sets, the expected effect of each strategy, and a summary of the tools and steps required to implement each strategy to generate a structured guidance document; presenting the guidance document through a graphical user interface, and visually displaying the potential detection risk area and the targeted operation positions suggested by the candidate operation strategies on the 3D model of the mold to be tested in the form of highlights, annotations, or animations.

[0062] Specifically, when the risk index corresponding to a potential detection risk exceeds a preset risk threshold, the system will intelligently retrieve at least one set of candidate operation strategies from a preset multi-level solution knowledge base based on the identified specific risk type, such as an excessively high proportion of scanning blind spots, an excessive number of probe interference points, or an excessively large deviation in estimated detection time. This knowledge base can pre-store solutions for different risk types and severity levels. For example, for scanning blind spots, strategies such as adjusting the scanning path, changing the probe type, or increasing the auxiliary scanning angle can be provided; for probe interference, strategies such as optimizing the probe posture, selecting a smaller probe, or segmented measurement can be provided. Each set of candidate operation strategies typically includes a primary recommended strategy, which is considered the most effective or economical solution based on historical data and expert experience, and at least one alternative strategy to provide operators with more choices and flexibility.

[0063] Furthermore, to facilitate operator understanding and decision-making, the system integrates the retrieved candidate operational strategy groups, the expected effects of each strategy (e.g., by what percentage the scanning blind zone can be reduced, by how much the detection time can be shortened after implementing the strategy), and a summary of the tools and steps required to implement each strategy (e.g., which fixtures to use, which probes to replace, and the general operating procedures) into a structured guidance document. This document aims to provide comprehensive and clear information to help operators quickly evaluate the advantages and disadvantages of different strategies.

[0064] Furthermore, to enhance intuitiveness and ease of use, the guidance documentation is presented through a graphical user interface. Simultaneously, on the 3D model of the mold to be inspected, potential detection risk areas are visualized through highlighting, annotation, or animation; for example, blind spots are highlighted in red, and probe interference points are marked with specific icons. More importantly, the targeted operation locations suggested by the candidate operation strategies are also visualized on the 3D model, for example, by simulating probe path adjustments through animation, or by using arrows to indicate areas requiring manual intervention, thus enabling the operator to intuitively understand the location of the risks and how to specifically implement the recommended operation strategies.

[0065] This application's solution effectively addresses the comprehension and execution efficiency issues that may exist in the "collaborative guidance scheme" of the basic solution by structuring, quantifying, and visually presenting solutions for potential detection risks. Specifically, by retrieving candidate operational strategies from a multi-level solution knowledge base, it ensures that professional and diverse solutions can be provided for different risk types. Each strategy group includes a primary recommended strategy and alternative strategies, providing operators with decision-making flexibility. Integrating the expected effects of the strategies and the implementation steps into the structured guidance document allows operators to fully understand the potential benefits and implementation costs of each strategy, thus making more informed choices. Crucially, the graphical user interface provides a visual representation of risk areas and operational locations on the 3D model of the mold to be inspected through highlighting, annotation, or animation, greatly reducing the cognitive burden on operators to understand complex risks and operational instructions. This enables operators to intuitively identify problems and accurately execute solutions, thereby significantly improving the efficiency and accuracy of human-machine collaboration.

[0066] Through the above technical solution, this application provides a more intuitive, efficient, and user-friendly method for generating and presenting collaborative guidance solutions. Compared to simply outputting recommended operation strategies, this solution greatly enriches the operator's decision-making information by providing multi-level candidate strategies, quantified expected effect evaluation, and detailed implementation step summaries. In particular, by visualizing the solution on a 3D model, operators can clearly understand the specific location of potential risks and the implementation details of recommended operation strategies. This significantly improves the operator's understanding and response speed to complex detection risks, effectively avoiding misoperations or inefficiencies caused by unclear information or misunderstandings. Consequently, it enhances the optimization and management level of the entire prototype mold inspection process and the execution efficiency of inspection tasks.

[0067] As a specific implementation, suppose that during the pre-simulation of the inspection process for a prototype mold with a complex curved surface and deep cavity structure, the pre-simulation results output by the virtual simulation module show that a certain deep cavity area of ​​the mold has a high proportion of scanning blind spots, and some narrow slit areas have a potential inspection risk of too many probe interference points, causing the risk index to exceed a preset threshold. At this time, the system will retrieve corresponding candidate operation strategies from a preset multi-level solution knowledge base based on these risk types. For example, for scanning blind spots, the system may recommend the main strategy as "adjusting the scanning path and increasing the tilt scanning angle," with the alternative strategy as "replacing with a smaller diameter probe for local supplementary scanning"; for probe interference, the main strategy may be "optimizing the probe posture and adopting multi-angle segmented measurement," with the alternative strategy as "using a flexible probe or laser scanner for non-contact measurement."

[0068] Subsequently, the system will quantitatively evaluate these candidate operation strategy groups and the expected effects of each strategy. For example, adjusting the scan path can reduce the blind zone ratio from 15% to 5%, but will increase the detection time by 10 minutes; replacing the probe with a smaller diameter probe can reduce the blind zone to 2%, but will require an additional 30 minutes of replacement and calibration time. The system will also provide a summary of the tools and steps required to implement each strategy. For example, adjusting the scan path requires modifying the G code in the CAM software, and replacing the probe requires manual operation and recalibration. The system will then integrate these into a structured guidance document.

[0069] Finally, the guidance document will be presented to the operator through the testing equipment's user interface. On the interface, a 3D model of the mold to be tested will be loaded, with blind spots highlighted in red and probe interference points marked with yellow icons. Simultaneously, the system will simulate the probe's movement trajectory after adjusting the scanning path through animation, or use arrows to indicate areas requiring special attention after probe replacement, intuitively demonstrating the effects and specific operational locations of different strategies to the operator. Based on this information, combined with practical experience, the operator can select the most appropriate strategy for subsequent operations.

[0070] In some of the embodiments described above in this application, a collaborative guidance scheme for generating an enhanced standard operating procedure (SOP) is proposed, based on operator feedback instructions. However, in its implementation, simply generating a SOP may not be sufficient to address the complexity and diversity of prototype mold inspection. If the generated SOP lacks sufficient detail, intuitive guidance, or direct executableness, operators may still face difficulties in understanding, inaccurate operation, or low execution efficiency in actual operation, thereby affecting the accuracy of the inspection results and the optimization effect of the overall process.

[0071] To address this, this application further proposes generating an enhanced standard operating procedure (SOP) based on the operator feedback instructions, including: parsing the operator feedback instructions to determine the final operating strategy selected by the operator from the collaborative guidance scheme or a customized operating strategy modified by the operator; generating an operation step list based on the final operating strategy or the customized operating strategy; the operation step list includes equipment preparation steps, mold clamping and positioning steps, measurement program loading and parameter setting steps, and data acquisition and saving steps; associating corresponding 3D visualization guidance animations or static illustrations with key steps in the operation step list; the 3D visualization guidance animations or static illustrations are used to display the motion trajectory of the testing equipment, the movement path of the probe or scanning beam, and the precise location and method where the operator needs to manually intervene; integrating and encapsulating the operation step list, the associated visualization guidance, and the preset equipment control instruction code to generate an enhanced SOP file that can be directly parsed and executed by the testing equipment control system.

[0072] Specifically, parsing operator feedback instructions refers to the system receiving and understanding instructions input by operators through a graphical user interface or other interactive methods. These instructions may include the selection or modification of recommended strategies in a collaborative guidance scheme, or custom strategies proposed by operators based on experience. The purpose is to accurately capture the operator's intent as a basis for subsequently generating enhanced standard operating procedures.

[0073] Generating an operation step list refers to the system automatically planning and listing all the specific operation steps required to complete the testing task based on the determined final operation strategy or a custom operation strategy. This list is broken down into multiple stages, such as equipment preparation steps, mold clamping and positioning steps, measurement program loading and parameter setting steps, and data acquisition and saving steps, to ensure the comprehensiveness and logical consistency of the operation.

[0074] The system includes 3D visual guidance animations or static illustrations associated with key steps in the operation procedure list. These can be understood as the system providing intuitive visual assistance for each critical operation step. The visualizations clearly show the movement trajectory of the detection equipment, the movement path of the probe or scanning beam, and the precise location and method of manual intervention required by the operator at specific stages. The aim is to reduce operational difficulty, minimize human error, and improve operational accuracy and efficiency.

[0075] In practical applications, integrating and encapsulating the operation step list, associated visual guidance, and preset equipment control command codes to generate an enhanced standard operating procedure file that can be directly parsed and executed by the control system of the tested equipment means packaging all necessary information and instructions into a unified, executable file format. This file can be directly read and executed by the control system of the tested equipment, thereby achieving automated or semi-automated operation of the testing task.

[0076] This application's solution effectively addresses the ambiguity and incompleteness inherent in traditional operating procedures by meticulously analyzing operator feedback instructions and generating a structured list of operational steps. Furthermore, by associating key steps with 3D visualization guidance, complex inspection operations become intuitive and easy to understand, significantly lowering the operator's comprehension threshold and execution difficulty. This allows operators to more accurately understand and execute recommended operational strategies, avoiding inspection deviations caused by misunderstandings or improper operation. Finally, all operational information and equipment control instructions are integrated and encapsulated into a directly executable file, ensuring standardized and automated execution of the operating procedures, thereby overcoming the inconsistencies and inefficiencies that can arise from manual operation.

[0077] Through the above technical solutions, this application can significantly improve the practicality and executability of the enhanced standard operating procedure. Specifically, by providing clear, step-by-step operation guidelines and intuitive 3D visualization guidance, operators can perform inspection tasks more accurately and efficiently, reducing errors caused by improper operation or misunderstanding. Furthermore, integrating the operating procedure with equipment control commands allows the inspection equipment to directly parse and execute them, thereby automating and standardizing the inspection process. This greatly improves inspection efficiency and data acquisition accuracy, effectively avoiding errors and inconsistencies that may occur in traditional manual operations, and thus enhancing the overall quality and reliability of prototype mold inspection.

[0078] As a specific implementation, suppose that when inspecting a prototype mold with a complex deep cavity structure, the virtual simulation module identifies a high number of probe interference points and generates a collaborative guidance scheme, recommending the use of a specific small probe and adjustment of the scanning angle. After receiving the scheme, the operator selects and confirms the recommended strategy through the graphical user interface. The system first parses the operator's confirmation instruction to determine the final operation strategy. Subsequently, based on this strategy, the system automatically generates a detailed list of operation steps, including: equipment preparation (e.g., changing to a small probe), mold clamping and positioning (e.g., ensuring the precise position of the mold on a specific fixture), measurement program loading and parameter setting (e.g., loading preset scanning paths and speed parameters), and data acquisition and saving.

[0079] Furthermore, for key steps such as "replacing the small probe" and "adjusting the scanning angle," the system will associate and display 3D visual guidance animations. For example, when replacing the probe, the animation will show the precise movements of taking the probe out of the tool library and installing it on the probe holder; when adjusting the scanning angle, the animation will simulate the movement trajectory of the probe of the inspection equipment in the deep cavity area of ​​the mold, highlight the recommended scanning angle and path, and even prompt the operator on how to make manual fine adjustments at specific locations.

[0080] Ultimately, all these operational steps, visual guidance, and corresponding equipment control command codes (e.g., G-codes or specific API calls) are integrated and packaged into an enhanced standard operating procedure (SOP) file. When an operator initiates an inspection task, the inspection equipment's control system directly loads and executes this file, automatically guiding the equipment to complete probe replacement, mold positioning, and scanning according to the visual path. It also prompts the operator for manual intervention when necessary, thus ensuring the accurate and efficient execution of complex mold inspection tasks.

[0081] In some embodiments described above in this application, a virtual simulation module is used to rehearse the testing process and identify potential testing risks based on estimated dynamic capability parameters, thereby generating an enhanced standard operating procedure. However, the dynamic capability parameters of the testing equipment may drift due to long-term operation, wear, or environmental changes, leading to discrepancies between the estimated parameters and actual performance. If these dynamic capability parameters are not calibrated and updated in a timely manner, the prediction accuracy of the virtual simulation module will be affected, potentially reducing the optimization effect of the generated enhanced standard operating procedure and even leading to unforeseen risks during the actual testing process.

[0082] In response, this application further proposes to collect real-time operation data of the testing equipment during the execution of the actual testing task; the real-time operation data includes data from the internal sensors of the equipment and the actual testing time; the real-time operation data is compared with the corresponding predicted data in the pre-test results, and the dynamic capability parameters are updated based on the comparison results.

[0083] Specifically, real-time operational data refers to various operational indicators acquired in real time by the equipment itself or an external monitoring system while the testing equipment is performing actual testing tasks. Internal sensor data may include, but is not limited to, the temperature of the moving shaft, motor load, vibration frequency, power supply voltage, and the operating status of the probe or scanning module. This data directly reflects the physical performance and health status of the testing equipment under current operating conditions. Actual testing time refers to the actual time spent by the testing equipment from the start of executing the enhanced standard operating procedure to completing the testing of the entire mold to be tested. The corresponding predicted data in the pre-test results, such as simulated testing cycle data, are predicted values ​​output by the virtual simulation module during the pre-test phase based on estimated dynamic capability parameters. By comparing the actual testing time with the simulated testing cycle data, the accuracy of the virtual simulation module's predictions can be quantified. Updating the dynamic capability parameters based on the comparison results aims to improve the accuracy of subsequent pre-tests by ensuring that the dynamic capability parameters used by the virtual simulation module (such as estimated scanning efficiency, measurement accuracy, and probe accessibility) more accurately reflect the current actual performance of the testing equipment through a feedback mechanism.

[0084] This application's solution constructs a closed-loop feedback mechanism by continuously monitoring and collecting real-time operational data of the testing equipment during actual testing tasks. This real-time data, including data from internal sensors and actual testing time, accurately reflects the performance of the testing equipment under current operating conditions. Subsequently, this actual operational data is compared with the predicted data generated by the virtual simulation module during the pre-simulation phase. If the comparison results show a significant deviation, it indicates that the dynamic capability parameters used by the virtual simulation module may no longer be accurate. Therefore, the system can adjust and update the dynamic capability parameters accordingly based on the comparison results. For example, if the actual testing time is significantly longer than the simulated testing cycle, it may be necessary to lower the estimated scanning efficiency parameter; if the internal sensor data shows overheating or abnormal vibration, it may be necessary to adjust the measurement accuracy or probe reachability parameters. This dynamic update mechanism enables the virtual simulation module to continuously learn and adapt to the actual performance changes of the testing equipment, ensuring that its prediction model always maintains high accuracy and high reliability.

[0085] Through the above technical solution, this application effectively solves the prediction bias problem caused by the staticization of dynamic capability parameters in traditional methods. By collecting data in real time and comparing it with the simulation results, the system can promptly detect and correct the mismatch between dynamic capability parameters and the actual performance of the equipment, thereby significantly improving the prediction accuracy of the virtual simulation module. This not only makes the identification of potential detection risks more accurate, but also ensures that the generated enhanced standard operating procedures can more effectively guide actual detection tasks, reducing unexpected situations and rework that may occur in actual operation, thereby improving the efficiency and quality of the overall detection process.

[0086] In some preferred embodiments, it is assumed that a coordinate measuring machine (CMM) is performing an inspection task on a prototype mold. Before the task begins, a virtual simulation module performs a pre-simulation of the inspection process based on preset dynamic capability parameters (e.g., estimated scanning efficiency of 50 mm / s and measurement accuracy of ±5 μm), predicting that the entire inspection task will take 120 minutes. During the actual inspection, the system continuously collects real-time operational data of the CMM, including the actual scanning speed of its probe in different areas, the real-time temperature of the motion axis, and the actual time taken to complete the entire task. If, after the actual inspection is completed, the system records an actual time of 140 minutes, and the actual scanning speed in some complex curved areas is only 40 mm / s, while the temperature of the motion axis remains consistently high, the system will compare these real-time operational data with the predicted data in the pre-simulation results. Since the actual time and scanning speed are both lower than the predicted values, the system will update the dynamic capability parameters of the CMM based on the comparison results, for example, correcting the estimated scanning efficiency parameter downward to 45 mm / s, and possibly adjusting its measurement accuracy parameter in high-temperature environments based on temperature data. These revised dynamic capability parameters will be stored and used in subsequent virtual simulations, making future predictions more closely resemble actual conditions and further optimizing the detection process.

[0087] In some of the embodiments described above in this application, a method is proposed to update the dynamic capability parameters of the testing equipment by comparing real-time running process data with pre-simulation results during the actual execution of the testing task. However, in its implementation, if only simple comparison and updating are performed, it may not be possible to accurately identify the specific causes of deviations, nor is it easy to optimize the equipment performance for specific mold types or specific testing scenarios, thereby limiting the system's ability to adaptively learn and continuously improve.

[0088] In response, this application further proposes the following steps for updating dynamic capability parameters based on the comparison results: comparing the key indicator sequence in the collected real-time operation data with the corresponding predicted indicator sequence in the above-mentioned pre-simulation results point by point or segment by segment, and calculating the root mean square error or mean absolute percentage error between the two as a deviation metric; when the above-mentioned deviation metric exceeds a preset deviation threshold, determining that the current task execution process is an abnormal event that needs to be learned, and extracting the above-mentioned material feature data, above-mentioned geometric feature data and the identification of the detection equipment used for the current task; according to the magnitude and direction of the above-mentioned deviation metric, correcting downward the corresponding sub-parameters in the above-mentioned dynamic capability parameters of the detection equipment when processing molds with the same or similar material and geometric feature combinations as the current task, with the correction magnitude being positively correlated with the above-mentioned deviation metric; synchronizing the corrected dynamic capability parameters to the capability parameter database of the above-mentioned virtual simulation module, for providing updated equipment capability assessment basis for subsequent pre-simulation of the detection process for new molds to be tested.

[0089] Specifically, during the comparison, the key indicator sequence in the real-time running process data can include actual scanning efficiency, actual measurement accuracy, and actual probe accessibility, while the predicted indicator sequence in the pre-simulation results corresponds to simulated scanning efficiency, simulated measurement accuracy, and simulated probe accessibility. Point-by-point comparison refers to comparing each corresponding point on the time series, while segmented comparison compares the average value or trend over a specific time period. Root mean square error (RMSE) or mean absolute percentage error (MAPE) are commonly used statistical methods to quantify the degree of deviation between predicted and actual values, providing an objective measure of deviation.

[0090] The preset deviation threshold can be set according to actual application needs and the system's requirements for prediction accuracy. When the deviation metric exceeds this threshold, it indicates that the prediction of the virtual simulation module differs significantly from the actual situation, requiring the system to learn and adjust. At this time, the system not only records the deviation but also extracts the contextual information that caused the deviation, including the material feature data and geometric feature data of the mold to be inspected, as well as the identification of the inspection equipment performing the task, in order to perform targeted learning.

[0091] In practical applications, the correction of dynamic capability parameters is not done blindly, but rather based on the magnitude and direction of the deviation metric. For example, if the actual scanning efficiency is significantly lower than the estimated scanning efficiency, it indicates that the estimated scanning efficiency parameter of the detection equipment may be overly optimistic when processing molds with similar materials and geometric features, and needs to be corrected downwards. The correction magnitude is positively correlated with the deviation metric, meaning that the larger the deviation, the larger the correction magnitude, thereby accelerating the system's learning and adaptation process. This correction mechanism ensures the rationality and effectiveness of parameter adjustments.

[0092] Furthermore, the revised dynamic capability parameters will be synchronously updated in the capability parameter database of the virtual simulation module. This means that when new molds to be inspected enter the inspection process, the virtual simulation module will use these learned and revised latest dynamic capability parameters to perform a pre-test, thereby providing more accurate and reliable inspection process predictions and risk assessments.

[0093] This application's solution addresses the shortcomings of traditional simple comparison updates, such as insufficient accuracy and poor adaptability, by introducing a refined comparison mechanism and an anomaly-based learning strategy. First, by comparing real-time operational data with pre-simulation results point-by-point or segment-by-segment, and calculating the root mean square error or mean absolute percentage error, the deviation between prediction and reality can be quantified, providing an objective basis for subsequent parameter correction. Second, when the deviation exceeds a preset threshold, the system identifies it as an anomaly requiring learning and extracts relevant material characteristic data, geometric characteristic data, and testing equipment identification. This mechanism enables the system to associate specific performance deviations with specific mold characteristics and equipment states, achieving targeted learning. Therefore, based on the magnitude and direction of the deviation, the dynamic capability parameters of specific testing equipment when handling molds with similar characteristics are corrected downwards and synchronized to the capability parameter database of the virtual simulation module. Thus, the virtual simulation module can continuously learn from actual testing tasks, constantly optimizing its assessment of the dynamic capabilities of testing equipment, ensuring the accuracy and reliability of its predictions, thereby improving the intelligence level of the entire testing process.

[0094] Through the above technical solution, this application enables adaptive and refined updating of the dynamic capability parameters of the testing equipment. Compared to simple comparison updates, this solution significantly improves the accuracy and reliability of the virtual simulation module's predictions by introducing deviation measurement, abnormal event identification, and parameter correction based on specific mold characteristics. This allows the system to more effectively identify and learn the actual performance of the testing equipment when handling molds with different materials and geometric features, thereby avoiding potential testing risks caused by inaccurate parameters. Furthermore, the corrected parameters can be promptly fed back into the capability parameter database of the virtual simulation module, ensuring that subsequent testing process simulations are based on the latest, practically verified equipment capabilities, further enhancing the intelligence, predictability, and robustness of the entire prototype mold testing process.

[0095] As a specific implementation, suppose a testing device is inspecting a mold with a high reflectivity surface and a complex deep cavity structure. The virtual simulation module estimates the scanning efficiency to be 100 mm / s. However, during the actual inspection, due to the high reflectivity requiring multiple power adjustments by the laser scanner and severe light attenuation inside the deep cavity, the actual scanning efficiency is only 70 mm / s. At this point, the system collects real-time data on the actual scanning efficiency and compares it with the predicted scanning efficiency in the simulation results. Calculations show that the mean absolute percentage error (MAPE) between the two is 30%, far exceeding the preset 10% deviation threshold. The system then determines this task as an abnormal event requiring learning and extracts the high reflectivity material feature data, deep cavity geometric feature data, and the identification of the testing device. Based on this, the system will adjust the corresponding sub-parameter of the dynamic capability parameter "estimated scanning efficiency" for the testing device when processing molds with high reflectivity and deep cavity features downwards, according to the 30% deviation metric. The adjustment magnitude is positively correlated with the 30% deviation. For example, the estimated scanning efficiency parameter for this type of mold can be adjusted to 80 mm / s. The corrected parameter will be immediately synchronized to the capability parameter database of the virtual simulation module. When another mold with similar high reflectivity and deep cavity structure enters the inspection process, the virtual simulation module will use the corrected 80 mm / s as the estimated scanning efficiency of the equipment during the inspection process rehearsal. This provides a more realistic prediction of the inspection cycle and risk assessment, effectively avoiding inspection delays or missed risks due to overestimating equipment capabilities.

[0096] In modern industrial production, the inspection of prototype molds is a crucial step in product development and quality control. Traditional prototype mold inspection process management systems are often designed based on the mainstream, relatively standardized mold types prevalent in factories. This leads to a failure to fully consider the unique inspection challenges and required equipment capabilities when handling prototype molds made of new composite materials or with complex free-form surfaces, resulting in a series of inefficiencies and quality risks. If these problems are not addressed, the diversification of mold types will cause significant deviations in the original allocation logic's evaluation model of "equipment-task matching." This could lead to the system incorrectly assigning high-precision, highly reflective, or irregularly shaped molds to unsuitable inspection equipment, thus creating potential problems for future developments.

[0097] In some embodiments, this application proposes a prototype mold inspection process optimization management system, comprising: an acquisition unit for acquiring material characteristic data and geometric characteristic data of the mold to be inspected; the material characteristic data includes at least one material physical property selected from reflectivity, absorbance, and hardness of the mold surface to be inspected, and the geometric characteristic data is used to characterize at least one geometric structural property selected from curvature change rate, cavity depth, and slit width of the mold surface to be inspected; and a processing unit for inputting the material characteristic data, geometric characteristic data, and real-time status parameters and dynamic capability parameters of the inspection equipment into a preset virtual simulation module for inspection process rehearsal; the real-time status parameters include the inspection equipment... The current load, operating temperature, and time interval since the last calibration are considered. Dynamic capability parameters include the estimated scanning efficiency, measurement accuracy, and probe accessibility of the testing equipment. The virtual simulation module simulates the testing process of the testing equipment on the mold to be tested based on material characteristic data, geometric characteristic data, real-time status parameters, and dynamic capability parameters, and outputs a pre-simulation result including simulated testing coverage data, simulated probe accessibility data, and simulated testing cycle data. The identification unit identifies potential testing risks associated with the mold to be tested based on the pre-simulation results output by the virtual simulation module. Potential testing risks include the scanning blind zone ratio, the number of probe interference points, and the deviation of the estimated testing time.

[0098] This application's system, through the collaborative work of its acquisition unit, processing unit, and identification unit, effectively solves the problems of inefficiency and quality risks inherent in traditional prototype mold inspection processes. The acquisition unit is responsible for comprehensively collecting the material and geometric characteristics of the mold to be inspected, laying the foundation for subsequent accurate evaluation. The processing unit combines these mold characteristics with the real-time status and dynamic capability parameters of the inspection equipment, and uses a virtual simulation module to pre-simulate the inspection process, thereby generating detailed pre-simulation results. Finally, based on these pre-simulation results, the identification unit intelligently identifies potential inspection risks, enabling problems to be detected and avoided before the actual inspection task begins, significantly improving the intelligence, accuracy, and reliability of the inspection process.

[0099] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing and managing the inspection process of a prototype mold, characterized in that, include: Acquire material characteristic data and geometric characteristic data of the mold to be tested; the material characteristic data includes at least one of the material physical properties of the mold surface, such as reflectivity, absorbance and hardness; the geometric characteristic data is used to characterize at least one of the geometric structural properties of the mold surface, such as the rate of curvature change, cavity depth and slit width. The material characteristic data, the geometric characteristic data, and the real-time status parameters and dynamic capability parameters of the detection equipment are input into a preset virtual simulation module to perform a pre-simulation of the detection process; the real-time status parameters include the current load, operating temperature, and time interval since the last calibration of the detection equipment; the dynamic capability parameters include the estimated scanning efficiency, measurement accuracy, and probe reachability of the detection equipment. The virtual simulation module is used to simulate the detection process of the detection equipment on the mold to be tested based on the material characteristic data, the geometric characteristic data, the real-time state parameters and the dynamic capability parameters, and output the pre-simulation results including simulated detection coverage data, simulated probe accessibility data and simulated detection cycle data. Based on the pre-simulation results output by the virtual simulation module, potential detection risks associated with the mold to be tested are identified; the potential detection risks include the scanning blind zone ratio, the number of probe interference points, and the deviation of the estimated detection time.

2. The method according to claim 1, characterized in that, The acquisition of material characteristic data and geometric characteristic data of the mold to be tested includes: The surface of the mold to be tested is scanned non-contactly by a spectral analysis device installed at the entrance of the testing process to obtain reflectance spectral data; The reflectance spectral data is matched and analyzed with a material characteristic spectral database to determine the material type of the mold to be tested; the material characteristic spectral database contains multiple material types, as well as standard reflectance spectral characteristic curves and preset material characteristic data corresponding to each material type; Based on the determined material type, extract the corresponding material characteristic data from the material characteristic spectral database; The three-dimensional point cloud data of the mold to be inspected is obtained by a three-dimensional scanning device; Based on the three-dimensional point cloud data, the curvature distribution, cavity depth, and slit width of the surface of the mold to be inspected are calculated, and the geometric feature data are generated based on the calculation results.

3. The method according to claim 2, characterized in that, The step of calculating the curvature distribution, cavity depth, and slit width of the surface of the mold to be inspected based on the three-dimensional point cloud data includes: The three-dimensional point cloud data is reconstructed to generate a digital surface model of the mold; Differential geometry analysis is performed on the digital surface model to calculate the curvature value of each point on the surface, and the proportion of regions where the curvature change exceeds a first preset threshold is statistically analyzed to obtain the curvature distribution. In the digital surface model, concave regions are identified, the ratio of the depth of each concave region to the inlet width is calculated, regions with a ratio exceeding a second preset threshold are identified as deep cavity structures, and the depth of the deep cavity is recorded. In the digital surface model, gap regions are identified, the minimum width of each gap region is calculated, regions with a minimum width less than a third preset threshold are identified as narrow slit structures, and the width of the narrow slit is recorded.

4. The method according to claim 1, characterized in that, The step of identifying potential detection risks associated with the mold to be inspected based on the pre-simulation results output by the virtual simulation module includes: Based on the simulated detection coverage data, the proportion of areas where the effective data point cloud coverage is lower than a preset coverage threshold due to excessively high material reflectivity or geometric occlusion is determined, thus obtaining the scanning blind zone proportion; Based on the simulated probe reachability data, the number of virtual collisions or failures to reach the measurement point in a standard posture between the probe or probe rod and the digital model of the mold is counted to obtain the number of probe interference points. Based on the simulated detection cycle data, the simulated time required to complete the detection of all preset features of the mold to be tested on the detection equipment is obtained. The simulated time is compared with the historical average detection time of the detection equipment for processing standard molds to obtain the estimated detection time deviation. The potential detection risk is defined by the scanning blind zone ratio, the number of probe interference points, and the estimated detection time deviation.

5. The method according to claim 1, characterized in that, The method further includes: The scanning blind zone ratio, the number of probe interference points, and the estimated detection time deviation are respectively processed into dimensionless values ​​to obtain the corresponding first risk score, second risk score, and third risk score; The first risk score, the second risk score, and the third risk score are weighted to obtain the risk index corresponding to the potential detection risk; When the risk index corresponding to the identified potential detection risk exceeds a preset risk threshold, a collaborative guidance scheme is generated and output; the collaborative guidance scheme includes at least one recommended operation strategy for the potential detection risk; Receive operator feedback instructions regarding the collaborative guidance scheme; Based on the operator feedback instructions, an enhanced standard operating procedure is generated; the enhanced standard operating procedure includes a three-dimensional visualization measurement path determined according to the selected recommended operating strategy, preset values ​​of equipment operating parameters, and instructions for using auxiliary tools; The control and testing equipment performs the actual testing task on the mold to be tested according to the enhanced standard operating procedure.

6. The method according to claim 5, characterized in that, When the risk index corresponding to the identified potential detection risk exceeds a preset risk threshold, a collaborative guidance scheme is generated and output, including: When the risk index corresponding to the identified potential detection risk exceeds the preset risk threshold, at least one set of candidate operation strategies is retrieved from the preset multi-level solution knowledge base according to the type of identified potential detection risk; each set of candidate operation strategies includes a main recommended strategy and at least one alternative strategy. The retrieved candidate operation strategy groups, the expected effect of each strategy group, and the summary of the tools and steps required to implement each strategy are integrated to generate a structured guidance document. The guidance document is presented through a graphical user interface, and potential detection risk areas and the targeted operation positions suggested by the candidate operation strategies are visualized on the 3D model of the mold to be inspected in the form of highlights, annotations or animations.

7. The method according to claim 5, characterized in that, The step of generating an enhanced standard operating procedure based on the operator's feedback instructions includes: Analyze the operator feedback instructions to determine the final operation strategy selected by the operator from the collaborative guidance scheme or the customized operation strategy modified by the operator; Based on the final operation strategy or a custom operation strategy, an operation step list is generated; the operation step list includes equipment preparation steps, mold clamping and positioning steps, measurement program loading and parameter setting steps, and data acquisition and saving steps; The key steps in the operation steps list are associated with corresponding 3D visualization guidance animations or static illustrations; the 3D visualization guidance animations or static illustrations are used to show the motion trajectory of the detection equipment, the movement path of the probe or scanning beam, and the precise location and method where the operator needs to manually intervene; The operation step list, associated visual guidance, and preset equipment control command codes are integrated and encapsulated to generate an enhanced standard operating procedure file that can be directly parsed and executed by the detection equipment control system.

8. The method according to claim 5, characterized in that, The method further includes: During the execution of the actual detection task, real-time operational data of the detection equipment is collected; the real-time operational data includes data from internal sensors and the actual detection time. The real-time operation data is compared with the corresponding predicted data in the pre-simulation results, and the dynamic capability parameters are updated based on the comparison results.

9. The method according to claim 8, characterized in that, The step of updating the dynamic capability parameters based on the comparison results includes: The key indicator sequence in the collected real-time operation process data is compared point by point or segment by segment with the corresponding predicted indicator sequence in the pre-simulation results, and the root mean square error or mean absolute percentage error between the two is calculated as the deviation measure. When the deviation measurement value exceeds the preset deviation threshold, the current task execution process is determined to be an abnormal event that needs to be learned, and the material feature data, geometric feature data and the identification of the detection equipment used are extracted for this task. Based on the magnitude and direction of the deviation metric, the corresponding sub-parameters in the dynamic capability parameters of the detection equipment when processing molds with the same or similar material and geometric feature combinations for this task are corrected downwards, and the correction magnitude is positively correlated with the deviation metric. The revised dynamic capability parameters are synchronized to the capability parameter database of the virtual simulation module, so as to provide updated equipment capability assessment basis when conducting test process simulations for new molds to be tested.

10. A process optimization and management system for prototype mold inspection, characterized in that, include: The acquisition unit is used to acquire material feature data and geometric feature data of the mold to be tested; the material feature data includes at least one material physical property among reflectivity, absorbance and hardness of the surface of the mold to be tested, and the geometric feature data is used to characterize at least one geometric structural property among curvature change rate, deep cavity depth and narrow slit width of the surface of the mold to be tested. The processing unit is used to input the material characteristic data, the geometric characteristic data, and the real-time status parameters and dynamic capability parameters of the detection equipment into a preset virtual simulation module to perform a pre-simulation of the detection process; the real-time status parameters include the current load, operating temperature, and time interval since the last calibration of the detection equipment; the dynamic capability parameters include the estimated scanning efficiency, measurement accuracy, and probe reachability of the detection equipment. The virtual simulation module is used to simulate the detection process of the detection equipment on the mold to be tested based on the material characteristic data, the geometric characteristic data, the real-time state parameters and the dynamic capability parameters, and output the pre-simulation results including simulated detection coverage data, simulated probe accessibility data and simulated detection cycle data. The identification unit is used to identify potential detection risks associated with the mold to be inspected based on the pre-simulation results output by the virtual simulation module; the potential detection risks include the scanning blind zone ratio, the number of probe interference points, and the deviation of the estimated detection time.

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