Construction engineering intelligent template processing and production method

Abstract

The invention discloses a building engineering intelligent template processing and production method, and relates to the technical field of template processing and production, and the method specifically comprises the following steps: under the condition that the edge of a template has an indentation recess, determining the area where the edge of the template has the indentation recess, and uniformly dividing the area into a plurality of sub-areas; analyzing each sub-region with the indentation on the edge of the template, evaluating the edge deformation degree of each sub-region with the indentation on the edge of the template, and dividing each sub-region into a standard cutting region, a region needing to be compensated for cutting and a region needing to be manually rechecked according to an evaluation result; and according to the classification result of each subarea, corresponding cutting path adjustment strategies are executed, and corresponding machining control instructions are generated and executed. According to the method, the problems that micro deformation of the edge of the template cannot be identified and the path cannot be dynamically adjusted are solved, and multi-region accurate classification and intelligent cutting precision optimization are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of template processing and production, and particularly relates to an intelligent template processing and production method for construction projects. Background Art

[0002] Intelligent template processing and production for construction projects refers to a production method that utilizes digital design, automated control, and intelligent communication technologies to achieve efficient collaboration and intelligent decision-making throughout the entire process from template design, processing to finished product output. This method integrates building structure information, engineering parameters, and template standard data, combines software systems to accurately calculate and automatically layout template requirements, and then automated equipment completes processing operations such as cutting, drilling, and marking. At the same time, various equipment during the production process realizes real-time linkage and data sharing through industrial communication protocols, thereby improving the accuracy, efficiency, and consistency of template processing. Traditional template processing generally has problems such as high dependence on manual labor, large dimensional errors, and difficult on-site adaptation, which seriously affect the construction quality and project duration. Intelligent template processing effectively solves these pain points. Through data-driven and process closed-loop management, it realizes flexible customization and rapid response of template processing, and is particularly suitable for modern construction projects with complex structures and variable design requirements, having significant economic benefits and application prospects.

[0003] Existing intelligent template processing and production technologies for construction projects usually rely on BIM (Building Information Modeling) or CAD drawings as the design source, parse building structure data and calculate template requirements through software systems, automatically generate template processing drawings and process parameters, and convert them into numerical control instructions for distribution to processing equipment. The entire processing and production process generally includes the import of template design data, template layout and nesting optimization, generation of numerical control processing instructions, automatic feeding and identification of materials, processing operations such as cutting, drilling, and grooving of templates, sorting and marking printing of finished products, and finally also includes the feedback of processing information and quality inspection. During this process, processing equipment such as CNC cutting machines, laser engraving machines, and automatic drilling machines maintains real-time communication with the control system to achieve efficient collaborative operations, and performs automatic calibration and error compensation through sensors and scanning modules. At the same time, some systems can also be integrated with enterprise ERP or MES systems to achieve work order management, progress tracking, and data archiving, forming a closed-loop intelligent template production process from design, processing to delivery, effectively improving the standardization, automation, and informatization levels of template manufacturing.

[0004] The existing technologies have the following deficiencies: In the case where the template board is manually transported and stacked multiple times, continuous but extremely fine indentations will form in the force application point area on the board edge due to repeated contact with the mechanical gripper. Although these indentations are difficult to identify with the naked eye, they will cause local thinning of the board edge thickness and slight bending of the shape, thus causing microscopic deformation of the board edge in terms of geometric structure. Since the template cutting path is usually set close to the board edge, when there is such an indented area, if the cutting path still executes according to the default linear trajectory, it will cause uneven force on the cutter head when entering the deformed area or incorrect path docking, resulting in problems such as torn cutting edges and linear deviation. The existing intelligent template processing and production technology for construction projects cannot adjust the starting position of the cutting path and the direction compensation angle according to the degree of microscopic deformation of the board edge in the case of indentations on the board edge. The system always cuts based on the ideal boundary and cannot perceive and respond to the actual state of the edge, which will cause inaccurate template boundary dimensions and non-straight cutting lines, and further lead to loose docking of the template assembly surface, affecting the assembly accuracy and appearance quality of components, and causing the error diffusion of batch template products, resulting in rework and material waste, and reducing the processing efficiency and good product rate of the template.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent template processing and production method for construction projects to solve the problems in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An intelligent template processing and production method for construction projects, specifically including the following steps: Calibrate the edge of the template placed on the cutting and processing platform, and perform real-time detection on the edge area of the template to determine whether there is an indentation. In the case where there is an indentation on the template edge, determine the area where the indentation appears on the template edge and evenly divide it into several sub-areas. Analyze each sub-area where the indentation appears on the template edge, evaluate the degree of edge deformation of each sub-area where the indentation appears on the template edge, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation results. According to the classification results of each sub-area, execute the corresponding cutting path adjustment strategy respectively, and generate and execute the corresponding processing control instructions. After cutting is completed, compare the error between the actual cutting trajectory and the corrected path, archive the cutting quality results and the processing data of the abnormal area, and analyze the archived data to dynamically optimize the edge deformation evaluation and path adjustment strategy in the subsequent template processing.

[0008] Preferably, in the case of indentation depressions on the template edge, determine the area where the indentation depressions appear on the template edge and evenly divide it into several sub-areas, specifically: In the case of indentation depressions on the template edge, by analyzing the contour scan data of the template edge area, identify continuous edge segments where the edge height change exceeds the set threshold as the indentation depression area, and based on the length information of the indentation depression area, evenly divide the indentation depression area into several sub-areas along the edge line direction. During the division process, calculate the start and end edge coordinate ranges of each indentation area and equally divide this range at a fixed length interval to obtain several sub-areas with continuous boundaries and consistent sizes.

[0009] Preferably, analyze each sub-area with indentation depressions on the template edge, evaluate the edge deformation degree of each sub-area with indentation depressions on the template edge, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation results. The specific steps include: Obtain in real time the edge deformation evaluation information of each sub-area with indentation depressions on the template edge and perform preprocessing after obtaining it; Extract the indentation depth distribution information and edge perturbation geometric information from the edge deformation evaluation information of each preprocessed sub-area, analyze them, and generate the indentation depth fluctuation index and edge perturbation expansion coefficient of each sub-area respectively; Construct an edge deformation evaluation model for the generated indentation depth fluctuation index and edge perturbation expansion coefficient of each sub-area, and generate the edge deformation coefficient of each sub-area through weighted summation; Determine the preset edge deformation coefficient threshold interval, compare it with the generated edge deformation coefficient of each sub-area after determination, evaluate the edge deformation degree of each sub-area with indentation depressions on the template edge according to the comparison result, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation result.

[0010] Preferably, the acquisition logic of the indentation depth fluctuation index of each sub-area is as follows: Indentation depth distribution information is extracted from the edge deformation evaluation information of each preprocessed sub-region, specifically including the average indentation depth of all depth points, the standard deviation of the indentation depths of all depth points, and the maximum value of the indentation depths of all depth points in each sub-region where an indentation depression appears on the template edge, and they are respectively labeled as , and , represents the average indentation depth of all depth points in the -th sub-region where an indentation depression appears on the template edge, represents the standard deviation of the indentation depths of all depth points in the -th sub-region where an indentation depression appears on the template edge, represents the maximum value of the indentation depths of all depth points in the -th sub-region where an indentation depression appears on the template edge, , is a positive integer; The indentation depth fluctuation index of each sub-region is calculated, and the specific calculation formula is as follows: In the formula, is the indentation depth fluctuation index of the -th sub-region.

[0011] Preferably, the acquisition logic of the edge perturbation expansion coefficient of each sub-region is as follows: Edge perturbation geometric information is extracted from the edge deformation evaluation information of each preprocessed sub-region, specifically including the continuous length of the indentation perturbation segment, the average absolute value of the indentation curvature change, and the maximum change amount of the indentation curvature change value in each sub-region where an indentation depression appears on the template edge, and they are respectively labeled as , and , represents the continuous length of the indentation perturbation segment in the -th sub-region where an indentation depression appears on the template edge, represents the average absolute value of the indentation curvature change in the -th sub-region where an indentation depression appears on the template edge, represents the maximum change amount of the indentation curvature change value in the -th sub-region where an indentation depression appears on the template edge, , is a positive integer; The edge perturbation expansion coefficient of each sub-region is calculated, and the specific calculation formula is as follows: In the formula, is the Edge perturbation expansion coefficient of sub-regions.

[0012] Preferably, for the indentation depth fluctuation index of each generated sub-region and the edge perturbation expansion coefficient Construct an edge deformation evaluation model, and generate the edge deformation coefficient of each sub-region through weighted summation. The specific calculation formula is as follows: In the formula, is the edge deformation coefficient of the th sub-region, and are the non-zero weight coefficients of the indentation depth fluctuation index and the edge perturbation expansion coefficient of each sub-region respectively, and .

[0013] Preferably, determine a preset edge deformation coefficient threshold interval , and after determination, compare it with the edge deformation coefficient of each generated sub-region, evaluate the edge deformation degree of each sub-region where the edge of the template shows indentation depressions according to the comparison result, and divide each sub-region into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation result. The specific comparison analysis and division are as follows: If , the edge deformation degree of the sub-region where the edge of the template shows indentation depression is at a low level, and this sub-region is divided into the standard cutting area; If , the edge deformation degree of the sub-region where the edge of the template shows indentation depression is at a medium level, and this sub-region is divided into the cutting area requiring compensation; If , the edge deformation degree of the sub-region where the edge of the template shows indentation depression is at a high level, and this sub-region is divided into the area requiring manual review.

[0014] Preferably, according to the classification results of each sub-region, the corresponding cutting path adjustment strategies are respectively executed, specifically: for the sub-regions classified as standard cutting regions, the executed cutting path adjustment strategy is specifically: directly adopt the preset standard cutting path to perform the processing operation; for the sub-regions classified as regions requiring compensation cutting, the executed cutting path adjustment strategy is specifically: according to the edge deformation characteristics of the sub-region, adjust the starting position, cutting direction and feed angle of the cutting path to generate a compensation cutting path adapted to its edge micro-deformation state; for the sub-regions classified as regions requiring manual review, the executed cutting path adjustment strategy is specifically: stop the automatic cutting process, send a manual review prompt instruction to the operator, and mark the sub-region as a to-be-processed region, and then resume the cutting path planning after manual confirmation and trimming; And generate and execute the corresponding processing control instructions, specifically: according to the cutting path adjustment strategies corresponding to each sub-region, generate a set of processing control instructions matching them. The processing control instructions include parameters for controlling the cutting path trajectory, feed speed, feed depth and cutting direction, and after generation, send the processing control instructions to the numerical control system of the template processing equipment to control the tool to complete the cutting processing of the template according to the adjusted path.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The intelligent template processing and production method for construction engineering provided by the present invention can accurately identify the microscopic indentation depressions and deformation regions generated on the edges of template plates after multiple handling, stacking or the action of mechanical grippers, overcoming the problem in the prior art that the changes in the edge morphology cannot be perceived. By introducing a high-precision contour scanning and edge calibration algorithm, the system can obtain the morphological changes of the template edge in real time, divide the abnormal indentation region into multiple sub-regions, and then realize the partition modeling of edge perturbation information and indentation depth characteristics, providing a high-quality data basis for subsequent path adjustment and processing optimization, thereby significantly improving the perception ability and response accuracy of edge defects in template processing.

[0016] 2. The present invention constructs two core parameters, namely the "indentation depth fluctuation index" and the "edge perturbation expansion coefficient", which reflect the edge deformation characteristics in different dimensions, and generates an edge deformation coefficient based on weight fusion. The system can quantify the microscopic geometric abnormality degree of the template edge in different regions, and then realize the intelligent classification of sub-regions. For different classification results, the system respectively executes three different path strategies: standard cutting, compensation cutting or pause for review, and automatically generates and issues processing control instructions, dynamically adjusting the cutting path trajectory, feed speed and feed angle, fundamentally solving the problem that the existing system cannot actively correct the path when facing inconsistent actual edge states, and effectively avoiding rework and material waste caused by deformation mis-cutting, tool tearing or assembly surface errors.

[0017] 3. After the cutting is completed, the present invention also introduces an intelligent feedback mechanism of "trajectory error comparison - quality archiving - strategy optimization", which can automatically analyze the error between the actual processing result and the preset path, and continuously accumulate the causal data chain between sub-region classification, compensation strategy and cutting quality. With the modeling and learning of the archived data, the system can dynamically optimize the edge deformation evaluation model and cutting strategy parameters, realize the adaptive evolution of complex indentation patterns and curvature anomalies, and endow the whole template processing method with the ability of continuous learning and self-improvement. This solution not only improves the intelligence level and processing consistency of the template processing system, but also lays a technical foundation for the future construction of a transferable general template cutting decision-making system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flow chart of an intelligent template processing and production method for construction engineering according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that this disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] The present invention provides an intelligent template processing and production method for construction engineering as shown in Figure 1 and specifically includes the following steps: Calibrate the edge of the template placed on the cutting and processing platform, and perform real-time detection on the edge area of the template to determine whether there is an indentation depression. "Edge calibration of the template placed on the cutting processing platform" can be achieved through an automatic alignment mechanism controlled by software. Specifically, after the template is placed on the platform, the position point set of the actual edges of the four sides of the template is obtained through a linear array laser or a non-contact optical sensor arranged at the edge of the platform or at the end of the processing arm. The software system performs fitting processing on the collected edge coordinate data, establishes an actual boundary model of the plate, and compares it with the preset platform reference line to calculate the current offset and rotation angle of the template. Subsequently, the template position is adjusted under the control of the software's instructions through the platform's own micro-tightening mechanism or controllable sliding clamping unit so that it is automatically aligned to the processing reference origin. The entire edge calibration process consists of four parts: edge coordinate acquisition, error calculation, alignment feedback and position adjustment, all of which can be achieved by software closed-loop control.

[0022] "Real-time detection of the edge area of ​​the template to determine whether there is an indentation or not" can be achieved through a software process that integrates the edge profile scanning algorithm and the image processing algorithm. In this process, after the template calibration is completed, the edge inspection program is started to control the laser profiler or high-resolution industrial camera to slide at a uniform speed along the edge of the template to scan and sample the tiny depressions and deformations on the edge of the template. The collected edge height profile data or image profile data will be sent to the edge anomaly analysis module. The software will perform segment-by-segment fitting and discrimination processing on the scanned edge points by setting multiple detection dimensions such as local height change threshold, curvature change rate, and depression width. Once the curve fitting residual of a certain edge area exceeds the set threshold, it is judged that there is an indentation there, and its start and end coordinates are recorded. The entire process is automatically identified, judged, and marked by the software for the indentation area, which is suitable for high-precision recognition requirements of invisible micro-deformations.

[0023] The reason for edge calibration and indentation detection is that the template sheet is repeatedly handled and stacked in the early stage, and often forms indentations that are difficult to detect with the naked eye at the edge due to contact with mechanical clamps or stacking and extrusion. The cutting path of the template is usually set close to the edge of the template sheet. Therefore, if the real boundary position of the template is not accurately calibrated before processing, and these indentation areas are not identified, the cutting path will be executed according to the ideal boundary, resulting in path deviation, local tearing or contour deformation when the cutter head cuts into the indentation area, and ultimately causing serious consequences such as non-straight cutting lines, inaccurate dimensions, and loose splicing. The edge calibration achieved by software can ensure that the position and direction of the template accurately match the system coordinate system, and the indentation detection can detect the processing risk area in advance, thereby providing an accurate basis for subsequent path adjustment and classification response. It is a key pre-link to ensure template cutting accuracy and assembly quality.

[0024] In the case where an indentation occurs on the edge of the template, an area where the indentation occurs on the edge of the template is determined and evenly divided into a number of sub-areas; In this embodiment, in the case of indentation depressions appearing at the template edge, the area with indentation depressions at the template edge is determined and evenly divided into several sub-regions. Specifically: In the case of indentation depressions appearing at the template edge, by analyzing the contour scan data of the template edge region, continuous edge segments with edge height changes exceeding a set threshold are identified as the indentation depression regions. Based on the length information of the indentation depression regions, the indentation depression regions are evenly divided into several sub-regions along the edge line direction. During the division process, the start and end edge coordinate ranges of each indentation region are calculated, and this range is equally divided at fixed length intervals to obtain several sub-regions with continuous boundaries and consistent sizes.

[0025] In the case of indentation depressions appearing at the template edge, the contour scan data can be processed by means of software-based data analysis. Specifically, first, a laser displacement sensor or a linear array profiler is used to obtain the height contour data of the template edge, forming a set of continuous edge profile point sets. The software imports this point set into the edge analysis algorithm module. First, an ideal reference curve of the template edge is constructed through a fitting algorithm, and then the height value of each measurement point is compared with this reference curve to calculate the deviation value of the point from the curve. When the deviation values of multiple consecutive points exceed the system-pre-set height change threshold and continuously appear within a certain length range, the software determines that this region is an indentation depression region and records its start and end coordinate positions. In this way, edge abnormal regions with microscopic depressions that are imperceptible to the naked eye but actually exist can be identified with high precision, non-contact, and automated means, providing reliable boundary information for subsequent path adjustment. The reason why such processing is necessary is that the template plates often undergo multiple rounds of transportation and stacking before processing, and the edges are extremely prone to deformation due to stress. If these subtle deformations are not identified in time, it will directly cause the cutting path to deviate from the design boundary, thereby affecting the dimensional accuracy and assembly tightness of the template finished product. Therefore, software identification and processing based on contour data is a key precondition for ensuring processing accuracy.

[0026] After identifying the indentation depression area on the template edge, the length information of this indentation area can be analyzed and processed by software to achieve uniform division of sub-areas. The specific implementation method is as follows: The software first extracts the starting coordinates and ending coordinates of each indentation depression area, and calculates the actual length of this indentation area according to the position difference between the two in the direction of the template edge line; Subsequently, the user can set a standard division length (such as 10mm) in the software, and the software then determines the number of sub-areas that this area should be divided into according to the total length of the indentation area divided by this standard length; Then, the software automatically equally divides this coordinate segment according to the direction vector of the edge line and the coordinate spacing, generating several sub-region coordinate segments with continuous boundaries and consistent sizes. These sub-areas are subsequently used to evaluate the indentation degree and path interference level respectively. The reason for such division is that the indentation area itself may have uneven deformation distribution characteristics. Only by dividing it into multiple small unit areas and analyzing them independently can the interference degree of each local deformation on the cutting path be captured more granularly, so as to achieve accurate classification and dynamic compensation of the path strategy, and improve the overall accuracy and finished product consistency of template cutting. This division process does not require manual intervention, and the entire process can be automatically completed by software algorithms, which is suitable for the intelligent processing requirements of a large number of templates.

[0027] Analyze each sub-region with indentation depressions on the template edge, evaluate the edge deformation degree of each sub-region with indentation depressions on the template edge, and divide each sub-region into a standard cutting region, a cutting region requiring compensation, and a region requiring manual review according to the evaluation results; In this embodiment, analyzing each sub-region with indentation depressions on the template edge, evaluating the edge deformation degree of each sub-region with indentation depressions on the template edge, and dividing each sub-region into a standard cutting region, a cutting region requiring compensation, and a region requiring manual review according to the evaluation results specifically include the following steps: Obtain in real time the edge deformation evaluation information of each sub-region with indentation depressions on the template edge, and perform preprocessing after obtaining it; After the template completes edge calibration and identifies the indentation depression area, the system can obtain the edge contour data and corresponding geometric state parameters of each sub-region in the indentation depression area in real time through a software-controlled data stream management module. Specifically, the laser profiler or high-precision image acquisition device is controlled by software instructions to run along the indentation area trajectory, and the edge contours of each sub-region are scanned point by point to obtain raw data including edge height point sets, edge line fitting coordinates, edge curvature changes, etc. During the acquisition process, the software classifies the data synchronously according to the region number and encapsulates it into the edge deformation evaluation information corresponding to each sub-region, including the indentation depth distribution information for calculating the "indentation depth fluctuation index" subsequently, and the edge perturbation geometric information for calculating the "edge perturbation expansion coefficient". Since the entire process is based on a preset scanning strategy and sampling frequency, data acquisition, encapsulation, and synchronous push are all completed by the software within a millisecond-level response time, ensuring the continuity, integrity, and real-time nature of the evaluation information for each sub-region.

[0028] Preprocessing the edge deformation evaluation information is a crucial step to ensure the accuracy of subsequent evaluations and the robustness of the model. Because there are often problems such as sensor errors, edge mutation values, noise points, and inconsistent sampling intervals in the original acquisition data. If directly participating in parameter calculations without preprocessing, it will lead to distorted evaluation indices or even model divergence. The preprocessing mainly includes three aspects: First, the software performs denoising processing on the height data and curvature data of each sub-region, often using sliding median filtering or wavelet denoising algorithms to remove abnormal points; Second, data normalization processing is performed to convert various input data into a unified dimension range to avoid affecting the weighted summation result due to scale differences; Third, interpolation and resampling of the edge coordinate point sets are carried out to make the number of data points in each sub-region consistent, improving the consistency and stability of the model input. These preprocessing operations are all automatically completed by the software and are executed in real time in the processing thread after data acquisition without manual intervention, ensuring that each sub-region in the indentation area has a high-quality and structurally unified data input basis before entering the evaluation model, thus laying a good foundation for the accurate evaluation of the edge deformation degree.

[0029] Extract the indentation depth distribution information and edge perturbation geometric information from the edge deformation evaluation information of each preprocessed sub-region, and analyze them to generate the indentation depth fluctuation index and edge perturbation expansion coefficient of each sub-region respectively; After completing the preprocessing of the edge deformation evaluation information for each sub-region, the software can perform a structured analysis on the data of each sub-region through the feature extraction module, and extract the indentation depth distribution information and edge perturbation geometry information for subsequent parameter calculation. Specifically, the software first performs a segmented scan on the indentation depth point set of each sub-region, calculates the average value, standard deviation, and extreme values of all height values in this region, and encapsulates them as the indentation depth distribution information to describe the deformation fluctuation trend of this sub-region in the vertical direction; subsequently, the software fits and calculates the derivative of the edge line coordinate points of each sub-region, and extracts the continuous length, average curvature value, and maximum amplitude of the curvature change of the edge contour. These results are encapsulated as the edge perturbation geometry information to reflect the perturbation expansion degree of the edge line in the horizontal direction. The entire extraction process is based on the calibrated region numbers, and the software establishes a one-to-one data mapping relationship, automatically identifying and classifying during the structured processing process, ensuring that each type of information is accurately associated with its corresponding sub-region, and finally passing it into the subsequent model evaluation stage in the form of a structured information block, which is completely automatically completed by the software during the data transfer process without manual intervention.

[0030] Construct an edge deformation evaluation model for the indentation depth fluctuation index and edge perturbation expansion coefficient of each generated sub-region, and generate the edge deformation coefficient of each sub-region through weighted summation; Determine the preset edge deformation coefficient threshold interval, and compare it with the edge deformation coefficients of each generated sub-region after determination. According to the comparison results, evaluate the edge deformation degree of each sub-region with an indentation depression on the template edge, and divide each sub-region into a standard cutting region, a cutting region requiring compensation, and a region requiring manual review according to the evaluation results.

[0031] The pre-set threshold interval of the edge deformation coefficient can be automatically determined by software through statistical modeling and analysis based on historical processing data. Specifically, the software first performs data mining on the correspondence between the sub-region evaluation coefficients recorded in a large number of existing template cutting tasks and the actual cutting effects, and extracts the typical distribution characteristics of the evaluation coefficients at different quality levels. The software uses statistical methods such as cluster analysis, density estimation, or K-S test to identify the numerical demarcation points of the evaluation coefficients among states such as "good cutting effect", "acceptable slight edge deviation", and "significant deviation requiring intervention"; then, these demarcation points are used as threshold boundaries to automatically generate a three-segment threshold interval for the edge deformation coefficient. During the threshold determination process, the software also combines influencing factors such as the sheet material, thickness, and deformation sensitivity of different types of templates to adapt personalized interval parameters for different batches or projects, ensuring the dynamic adaptability of the division criteria and the accuracy of evaluation. The entire threshold setting process is completed by the software during the background model training stage and is loaded as static configuration parameters into the formal evaluation process without manual intervention by the user, which not only improves the evaluation efficiency but also ensures the consistency and repeatability of the judgment basis.

[0032] In this embodiment, the acquisition logic of the indentation depth fluctuation index for each sub-region is as follows: Extract the indentation depth distribution information from the edge deformation evaluation information of each pre-processed sub-region, specifically including the average indentation depth of all depth points, the standard deviation of the indentation depths of all depth points, and the maximum value of the indentation depths of all depth points in each sub-region where indentation depressions appear at the template edge, and calibrate them respectively as 、 and , represents the average indentation depth of all depth points in the -th sub-region where indentation depressions appear at the template edge, represents the standard deviation of the indentation depths of all depth points in the -th sub-region where indentation depressions appear at the template edge, represents the maximum value of the indentation depths of all depth points in the -th sub-region where indentation depressions appear at the template edge, , is a positive integer; The average indentation depth, the standard deviation of the indentation depth, and the maximum value of the indentation depth for all depth points in each sub-region where indentation depressions appear at the template edge can all be automatically obtained through real-time processing of high-precision contour scanning data by software. Specifically, after the template is placed on the cutting and processing platform and edge calibration is completed, the system scans point by point along the indentation area of the template edge using a laser displacement sensor or a line-scanning profiler to obtain a continuous set of height data points in this area, and these point sets are allocated in real time under a preset sub-region index. After receiving the data, the software starts the data processing process. First, it uniformly normalizes all the height data in each sub-region, and then uses a statistical algorithm to calculate the vertical indentation values of all measurement points in this area relative to the original plane reference to form an indentation depth sequence. The average indentation depth refers to the arithmetic mean of the indentation depths of all points in this sub-region, which is used to reflect the normal trend of the overall depression; the standard deviation of the indentation depth is used to measure the degree of fluctuation of the indentation depth in this area, that is, whether the depression differences at different positions are drastic; and the maximum value represents the deepest single-point indentation in this area, which is a key indicator for judging the existence of local extreme deformation. The extraction and statistical calculation of all data are completed by the software in a millisecond-level data flow processing thread, without relying on manual intervention, and can realize real-time, parallel, and structured monitoring and quantification of the indentation states of multiple sub-regions, providing basic data support for subsequent indentation evaluation.

[0033] Calculate the indentation depth fluctuation index for each sub-region. The specific calculation formula is as follows: In the formula, is the indentation depth fluctuation index of the

[0034] th sub-region. The design of the calculation formula for this indentation depth fluctuation index aims to comprehensively measure the degree of indentation deformation in the sub-region from multiple dimensions and express its interference risk to the stability of the cutting path. First, in the formula represents the square of the standard deviation of the indentation depth in this sub-region, which is used to quantify the degree of fluctuation of the indentation. The larger the standard deviation, the more drastic the change in the indentation depth in this area. The square operation further enhances the influence of the high-fluctuation area in the index, making it easier to be recognized as a "high-risk" area by the evaluation mechanism. Second, is the exponential reciprocal term of the average indentation depth in this area. Among them, the larger it is, the deeper the overall indentation. Taking the inverse of its exponent and then taking the reciprocal is equivalent to , which means that the deeper the average depression depth, the higher the index value, thus overall amplifying the numerical output. This non-linear enhancement processing simulates the sharp impact of deeper collapse of the sheet material on the path matching of the cutting tool head. Finally, To characterize the ability of local extreme indentations to regulate the results, The larger the value, the deeper the individual indentation points appear. Although the affected range is small, the risk is extremely high. Taking the natural logarithm can compress the abnormal extreme values and prevent them from dominating the overall evaluation and causing misjudgment. The three combinations express the coupling effects among the volatility, overall trend, and local extreme values of the indentations, making It can comprehensively and accurately reflect the actual interference degree of the edge deformation on the template processing path, providing a solid data basis for the subsequent adjustment of the cutting strategy.

[0035] The indentation depth fluctuation index of the nth sub-region The magnitude of the value has a significant positive correlation with the degree of edge deformation in that sub-region. Specifically, The larger the value, the stronger the volatility (large standard deviation), deeper average indentation trend (large average value), and more significant local extreme indentations (large maximum value) in the overall deformation of the indentation depth in that sub-region. The combined effect of these three reflects the instability degree of the edge geometry in that region. By introducing the square of the standard deviation, exponential enhancement of the average value, and logarithmic regulation of the extreme value, It is possible to integrate the indentation characteristics in multiple dimensions into a single index to reflect the uncertainty and interference intensity that the region may impose on the cutting path. Therefore, The larger the value, the more severe the edge deformation in that sub-region, and the more likely it is to cause deviations, tears, or joining errors in the cutting path, thus being identified as a high-risk region that requires compensation or manual review in the evaluation model. Conversely, if

[0036] In this embodiment, the acquisition logic of the edge perturbation expansion coefficient for each sub-region is as follows: Extract the edge perturbation geometric information from the edge deformation evaluation information of each pre-processed sub-region, specifically including the continuous length of the indentation perturbation segment, the average absolute value of the indentation curvature change, and the maximum change amount of the indentation curvature change in each sub-region where indentation depressions appear on the template edge, and respectively label them as , and , represents the continuous length of the indentation perturbation segment in the nth sub-region where indentation depressions appear on the template edge, represents the average absolute value of the indentation curvature change in the nth sub-region where indentation depressions appear on the template edge, represents the maximum change amount of the indentation curvature change in the The maximum change in the indentation curvature change value in the sub-region , is a positive integer; In each sub-region where an indentation depression appears at the template edge, the three types of data, namely the continuous length of the indentation perturbation segment, the average absolute value of the indentation curvature change, and the maximum change in the indentation curvature change value, can all be automatically extracted and calculated through the real-time processing of the high-precision contour scanning data of the template edge by software. After the template is placed on the cutting and processing platform and the edge calibration is completed, the system uses a laser contour sensor or a structured light vision scanning device to perform point-by-point scanning on the edge area and obtain high-density two-dimensional or three-dimensional edge contour point cloud data. After the software receives the original edge coordinate point sequence, it first performs contour fitting on the indentation area through an edge reconstruction algorithm, reconstructs the continuous geometric line of the indentation segment, and performs perturbation feature extraction based on this. The continuous length of the indentation perturbation segment refers to the linear length of the continuous indentation perturbation segments existing on the edge line, which can be obtained by identifying and measuring the intervals of continuous paragraphs in the edge contour whose curvature significantly deviates from the reference curvature range; the average absolute value of the indentation curvature change refers to the average value of the absolute values of the local curvature change amplitudes of all sampling points in the indentation perturbation segment, which is used to reflect the overall fluctuation complexity of the edge contour of this segment; and the maximum change in the indentation curvature change value refers to the maximum difference in the curvature change values within this segment, that is, the position of the local strongest mutation, which is used to reveal whether there are sharp structural interference points. Throughout the process, edge extraction, curvature calculation, and feature statistics are all automatically completed by the built-in contour analysis algorithm of the software, and are dynamically bound through the scanning data number and sub-region index, so that each sub-region can be dynamically assigned complete edge perturbation geometric information without manual intervention, ensuring a high degree of consistency in the real-time performance, accuracy, and data closed-loop of the indentation perturbation assessment.

[0037] Calculate the edge perturbation expansion coefficient of each sub-region. The specific calculation formula is as follows: In the formula, is the edge perturbation expansion coefficient of the th sub-region.

[0038] This edge perturbation expansion coefficient 's calculation formula aims to comprehensively evaluate the expansion trend, perturbation complexity, and mutation risk of indentation perturbation in the horizontal direction in each sub-region of the template edge. In the formula, represents the continuous length of the indentation perturbation segment in this sub-region. Adding 1 can prevent zero values, and the square operation strengthens the influence of the region with a larger perturbation range, highlighting its potential threat to the continuity of the cutting path; is the average absolute value of the change in indentation curvature. Adding 1 is also used for numerical stability while retaining the positive amplification effect where the greater the curvature and the more complex and curved the edge, to reflect the basic morphological volatility of the overall edge perturbation; represents the exponential enhancement of the maximum change in the value of the indentation curvature change, aiming to magnify the recognition ability for local edge mutations (such as sharp corners, turns, etc.), as they are often the key risk sources causing machining path deviation and tool chatter. Finally, the natural logarithm function is nested outside the overall product term. On the one hand, it is used to compress the abnormal expansion caused by extreme values, and on the other hand, it enhances the non-linear expression ability of the overall model, enabling the coefficient values to accurately reflect the perturbation intensity level in this area when multiple perturbation factors are superimposed simultaneously. Therefore, through the three-dimensional coupling modeling of "perturbation range × curvature fluctuation × mutation intensity", this formula ensures the physical rationality and engineering operability of the evaluation logic while maintaining mathematical complexity, providing solid data support for the subsequent dynamic adjustment of the cutting path.

[0039] Edge perturbation expansion coefficient of the m-th sub-region The magnitude of the value is significantly positively correlated with the degree of edge deformation of this sub-region in the horizontal direction. Specifically, the larger the value, the more significant the performance of this sub-region in terms of the continuity, complexity, and local mutability of edge perturbation. Among them, the longer the continuous length of the perturbed segment means that the edge indentation is not sporadic but persists in patches, with stronger geometric interference; the larger the average absolute value of the curvature change indicates that the overall edge shows a more complex or irregular fluctuation trend and is difficult to fit perfectly with the standard cutting path; and the larger the maximum curvature mutation amount, it reflects that there may be local high-risk structures such as sharp turns and sudden protrusions in this area, which are extremely likely to cause cutting errors, tool tip chatter, or path tearing. By combining and calculating the above three factors, it can effectively quantify the expansion risk and structural instability of the edge perturbation of this sub-region. Therefore, the larger it is, the more serious the degree of edge deformation of this sub-region, the greater the potential impact on the cutting path planning and machining quality, and the more necessary it is to implement intervention measures such as path compensation or manual review.

[0040] In this embodiment, for the indentation depth fluctuation index and the edge perturbation expansion coefficient of each generated sub-region, an edge deformation evaluation model is constructed, and the edge deformation coefficient of each sub-region is generated through weighted summation. The specific calculation formula is as follows: In the formula, is the edge deformation coefficient of the -th sub-region, and Indentation depth fluctuation indices for each sub-region and edge perturbation expansion coefficients with non-zero weight coefficients, and .

[0041] In the actual implementation process, the generation of the edge deformation coefficient is completed by software through the fusion calculation of the indentation depth fluctuation indices and edge perturbation expansion coefficients for each sub-region during the template processing and modeling stage. Specifically, after the system completes the independent calculations for each sub-region and , it automatically calls the edge deformation evaluation model, linearly combines the two types of indices through the set weighting parameters, and generates the value for each sub-region. The and in the weighting parameters are respectively used to control the relative weights of the indentation depth factor and the edge perturbation factor in the final evaluation result. Both are non-zero real numbers and satisfy to ensure that the result after weighted summation is within a unified evaluation scale range. The setting of the weight coefficients can be obtained through model training based on historical template processing data, or configured according to different application scenarios (such as template material differences, edge smoothness requirements, structure fitting accuracy levels, etc.): if the processing process is more sensitive to the influence of indentation depth, can be appropriately increased; if the edge profile perturbation has a greater impact on path control, can be appropriately increased. This calculation method not only realizes the comprehensive perception of the multi-dimensional characteristics of the template edge deformation, but also provides a stable quantitative basis for the accurate classification and path intervention of subsequent sub-regions.

[0042] In this embodiment, a pre-set edge deformation coefficient threshold interval is determined, and after determination, it is compared with the generated edge deformation coefficients of each sub-region. According to the comparison result, the edge deformation degree of each sub-region with an indentation depression on the template edge is evaluated, and each sub-region is respectively divided into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation result. The specific comparison analysis and division are as follows: If , the edge deformation degree of the sub-region with an indentation depression on the template edge is at a low level, and this sub-region is divided into the standard cutting area; This situation indicates that the edge state of this area is overall stable, the indentation depth fluctuates slightly, the edge contour perturbation is slight, and there are no obvious curvature mutations or structural abnormalities. Such areas generally mean that the template edge has not been significantly deformed during the actual processing, has good geometric consistency and edge continuity, and the cutting path can be executed according to the original design without compensation or offset. This type of area belongs to the low-risk processing area evaluated by the system, and the standard tool path can be directly used for linear cutting, so as to ensure the processing efficiency and template accuracy, while reducing the system resource consumption and the computational cost of path adjustment.

[0043] If , the degree of edge deformation of the sub-area where the indentation depression appears on the template edge is medium, and this sub-area is classified as an area requiring compensated cutting; This situation shows that the indentation deformation in this area has certain perturbation characteristics, manifested as medium fluctuation of the indentation depth, a certain deviation of the contour curvature, or limited mutation changes in local edge segments. Although such areas do not lead to serious cutting risks, they may already affect the accurate edge-following of the tool head feed path, and then cause problems such as connection gaps, component misalignment, or loose assembly in subsequent assembly. Therefore, path compensation measures need to be implemented for this type of area. The software can adjust the cutting start point, path angle, or cutting depth based on the deformation characteristics to achieve fine adaptation of the cutting trajectory to the local edge state, so as to improve the forming quality and suppress the error diffusion caused by mild deformation.

[0044] If , the degree of edge deformation of the sub-area where the indentation depression appears on the template edge is high, and this sub-area is classified as an area requiring manual review.

[0045] This situation indicates that a relatively serious indentation deformation phenomenon has occurred in this area, which may include continuous depth depressions, significant edge perturbations, and prominent curvature mutation points, and the geometric shape has deviated significantly from the compensable range. In such areas, there is usually a situation where the processing path cannot adapt. If the cutting is still forced to be executed, it may lead to serious misalignment of the tool head running trajectory, incision tearing, edge breakage, and even scrapping of the entire template segment. Therefore, the system identifies this area as an area requiring manual review, suspends the automatic cutting process, and the operator performs manual judgment and adjustment processing on this area, or performs pre-trimming operations to ensure that the template edge state is controllable and then re-incorporates it into the processing path planning, so as to avoid large-area errors or rework costs caused by high-risk areas.

[0046] According to the classification results of each sub-area, corresponding cutting path adjustment strategies are respectively executed, and corresponding processing control instructions are generated and executed to complete the cutting process of the template; In this embodiment, according to the classification results of each sub-region, corresponding cutting path adjustment strategies are executed respectively. Specifically: for the sub-regions classified as standard cutting regions, the executed cutting path adjustment strategy is specifically: directly adopt the preset standard cutting path to perform the machining operation; for the sub-regions classified as regions requiring compensation cutting, the executed cutting path adjustment strategy is specifically: according to the edge deformation characteristics of the sub-region, adjust the starting position, cutting direction and feed angle of the cutting path to generate a compensation cutting path adapted to its edge micro-deformation state; for the sub-regions classified as regions requiring manual review, the executed cutting path adjustment strategy is specifically: stop the automatic cutting process, send a manual review prompt instruction to the operator, and mark the sub-region as a to-be-processed region, and then resume the cutting path planning after manual confirmation and trimming; For the sub-regions classified as standard cutting regions, the edge geometric state is stable, without obvious indentation depth fluctuations or significant curvature perturbations. Therefore, the system can directly call the standard cutting path defined in the original template design to perform the machining operation. After the software identifies this type of region, it will automatically associate the region label with the "standard path" mode, without executing path offset or compensation algorithms, directly extract the corresponding design geometric boundary points of the region, generate equal-precision path coordinates through interpolation fitting, etc., and then output them to the device controller in the form of numerical control instructions. The advantage of this process is that there is no need to reconstruct the path, saving system computing resources and instruction generation time, while maximizing the retention of the geometric accuracy of the template design, suitable for batch automation processing, and it is the most efficient and simplest type of region path strategy in the intelligent template machining process.

[0047] For the sub-regions classified as regions requiring compensation cutting, the system identifies that although the region does not reach the high-risk level, there are certain degrees of micro-deformations at its edges, including mild indentation depth fluctuations, local contour curvature offsets, etc. At this time, the software needs to start the path compensation algorithm module based on the edge deformation characteristic parameters extracted in the early stage, such as the indentation deformation quantity curve, local perturbation direction and intensity. This module dynamically adjusts the starting position of the cutting path (i.e., the initial contact position of the tool head with the edge), the cutting direction (to optimize the fitting of the boundary trend), and the feed angle (to reduce the sudden change in force when the tool head contacts the irregular edge), while ensuring that the cutting profile shape fits the actual edge state, avoiding problems such as cutting edge offset and tearing. The adjusted path is regenerated in the form of multiple micro-compensation vectors and is kept consistent with the standard path format to ensure compatibility with the downstream control system for execution. This strategy realizes the path refinement and optimization without manual intervention through the closed-loop processing of "perception - modeling - adjustment" of the local contour state by the software, significantly improving the machining accuracy and qualification rate of the regions with slightly deformed edges.

[0048] For sub-regions classified as areas requiring manual review, the system evaluates and determines that there are significant edge anomalies in these areas, such as excessive indentation depth, long continuous disturbance length, severe curvature mutation, etc. Such areas are very likely to cause serious problems such as tool path breakage, over-cutting, or deformation amplification during tool head machining, which have exceeded the software path compensation capability. To avoid the risk of mis-cutting, after identifying such areas, the software will automatically interrupt the current machining instruction stream and send a review prompt instruction to the human-machine interaction system through the interface to prompt the operator to intervene. This area will also be marked in a high-risk state in the system interface, and a structured information package including area number, position coordinates, details of abnormal parameters, etc. will be generated to facilitate rapid manual positioning and judgment. After the operator completes edge pre-trimming, glue filling, or contour pre-treatment, the operator can restart the path planning for this area through the system confirmation instruction. The software then re-executes the path calculation based on the trimmed edge state or calls the manually revised path template to ensure that the subsequent cutting accuracy and template quality are not affected by the damaged area. This process ensures through the software and manual collaboration mechanism that high-risk areas are not automatically mis-cut by the system, effectively preventing the spread of local problems in mass production.

[0049] And generate and execute corresponding machining control instructions, specifically: according to the cutting path adjustment strategy corresponding to each sub-region, generate a matching set of machining control instructions. The machining control instructions include parameters for controlling the cutting path trajectory, feed speed, cutting depth, and cutting direction, and after generation, the machining control instructions are sent to the numerical control system of the template machining equipment to control the tool to complete the cutting of the template according to the adjusted path.

[0050] During the template processing, for the cutting path adjustment strategies adopted for different sub-regions, the system needs to automatically generate and execute corresponding processing control instructions through software to achieve precise control of the tool behavior. The specific implementation method is as follows: After the path adjustment is completed in each sub-region, the system first standardizes each generated path data, including steps such as path coordinate point serialization, node angle calculation, and boundary fitting detection. Then, based on the edge feature state and the selected strategy (such as standard cutting, path compensation, or reconstructed path after review) of each sub-region, it automatically matches the corresponding processing parameter template to generate a control instruction set including cutting path trajectory, feed speed, cutting depth, and cutting direction. The path trajectory data is output in G-code or device-adapted format. The feed speed parameter is set differentially according to the deformation degree of the sub-region to ensure that the tool head runs at a lower speed in the high-deformation region to improve control accuracy; the cutting depth adjusts the feed distance of the tool head along the Z-axis according to the edge micro-deformation to prevent the cutting edge from tearing or over-cutting; the cutting direction is determined by calculating the main curvature direction of the edge to optimize the tool entry angle and reduce local uneven stress. After completing the assembly of the control instructions, the software packages this instruction set and sends it to the numerical control controller interface of the processing equipment in real time, and the equipment performs the cutting operation according to this instruction. This method can ensure that the execution effect after path adjustment is accurately restored, and at the same time allocate processing resources as needed in different regions to achieve fast cutting of standard regions and fine control of complex regions, effectively improving the overall processing quality and product consistency of the template.

[0051] After cutting is completed, the error between the actual cutting trajectory and the corrected path is compared, and the cutting quality results and the processing data of the abnormal region are archived, and the archived data is analyzed to dynamically optimize the edge deformation evaluation and path adjustment strategy in the subsequent template processing.

[0052] After the cutting process is completed, to improve the accuracy of subsequent processing and the system's adaptive ability, the software system needs to automatically compare and analyze the error between the actual cutting trajectory and the preset corrected path. This process first records the actual running trajectory data of the tool in real time through a high-precision encoder, laser displacement sensor, or cutting head displacement feedback system on the processing equipment, converts it into a point sequence or vector path, and then matches it one by one with the corrected path originally generated in the software system. The software calculates the point-to-point offset between the two paths through a path registration algorithm, identifies the regions with large offsets and their position indexes, and extracts characteristic indexes such as the average value, maximum value, and volatility of the error curve. These error data can not only be used to calibrate whether there are problems such as tool head offset, workpiece slip, or processing environment change during the cutting process, but also help the system judge whether a specific compensation strategy is effective in a certain type of edge deformation region, thus forming a processing accuracy evaluation mechanism based on actual feedback.

[0053] After completing the error comparison, the system will uniformly archive the cutting quality results and the abnormal area processing data to construct a processing history dataset, including multi-dimensional information such as sub-region classification, path strategy type, evaluation coefficient, execution instruction parameters, actual trajectory error, and final cutting quality level. Through the built-in data attribution analysis module, the software trains and mines the archived data based on machine learning or statistical modeling methods to identify which types of edge deformations correspond to which error patterns, and which strategy combinations perform better in different sub-regions. Finally, the system can dynamically optimize the edge deformation evaluation model and path adjustment strategy in the next round of template processing, such as automatically adjusting the sub-region division threshold, updating the determination interval of the evaluation coefficient, or optimizing the cutting path planning logic. Through this feedback closed-loop mechanism, the system can continuously self-correct and self-learn, enabling the entire set of template processing methods to have stronger generalization ability and adaptability in complex edge deformation scenarios, and significantly improving the template yield and overall processing efficiency.

[0054] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0055] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0056] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0057] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0058] In several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0059] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0061] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An intelligent template processing and production method for construction projects, characterized in that, Specifically, it includes the following steps: Perform edge calibration on the template placed on the cutting and processing platform, and perform real-time detection on the edge area of the template to determine whether there is an indentation; In the case of an indentation on the template edge, determine the area with the indentation on the template edge and evenly divide it into several sub-areas; Analyze each sub-area with an indentation on the template edge, evaluate the edge deformation degree of each sub-area with an indentation on the template edge, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation results; According to the classification results of each sub-area, execute the corresponding cutting path adjustment strategy respectively, and generate and execute the corresponding processing control instructions; After cutting, compare the error between the actual cutting trajectory and the corrected path, archive the cutting quality results and the processing data of the abnormal area, and analyze the archived data to dynamically optimize the edge deformation evaluation and path adjustment strategy in the subsequent template processing process.

2. The intelligent template processing and production method for construction engineering according to claim 1, wherein In the case of an indentation on the template edge, determine the area with the indentation on the template edge and evenly divide it into several sub-areas, specifically: In the case of an indentation on the template edge, by analyzing the contour scan data of the template edge area, identify the continuous edge segments with the edge height change exceeding the set threshold as the indentation area, and based on the length information of the indentation area, evenly divide the indentation area into several sub-areas along the edge line direction. During the division process, calculate the start and end edge coordinate ranges of each indentation area and equally divide the range at a fixed length interval to obtain several sub-areas with continuous boundaries and consistent sizes.

3. The method for intelligent template processing and production of a construction project according to claim 2, characterized in that, Analyze each sub-area with an indentation on the template edge, evaluate the edge deformation degree of each sub-area with an indentation on the template edge, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation results. Specifically, it includes the following steps: Obtain the edge deformation evaluation information of each sub-area with an indentation on the template edge in real time and perform preprocessing after obtaining it; Extract the indentation depth distribution information and edge perturbation geometric information from the edge deformation evaluation information of each preprocessed sub-area, analyze them, and generate the indentation depth fluctuation index and edge perturbation expansion coefficient of each sub-area respectively; Construct an edge deformation evaluation model for the generated indentation depth fluctuation index and edge perturbation expansion coefficient of each sub-area, and generate the edge deformation coefficient of each sub-area through weighted summation; Determine the preset edge deformation coefficient threshold interval, and after determination, compare it with the generated edge deformation coefficient of each sub-area. Evaluate the edge deformation degree of each sub-area with an indentation on the template edge according to the comparison result, and divide each sub-area into a standard cutting area, a cutting area requiring compensation, and an area requiring manual review according to the evaluation results.

4. A method for intelligent template processing and production in construction engineering according to claim 3, characterized in that, The acquisition logic of the indentation depth fluctuation index of each sub-area is as follows: Indentation depth distribution information is extracted from the edge deformation evaluation information of each pre-processed sub-region, specifically including the average indentation depth of all depth points, the standard deviation of the indentation depths of all depth points, and the maximum value of the indentation depths of all depth points in each sub-region where an indentation depression appears at the template edge, and they are respectively calibrated as , and , represents the average indentation depth of all depth points in the -th sub-region where an indentation depression appears at the template edge, represents the standard deviation of the indentation depths of all depth points in the -th sub-region where an indentation depression appears at the template edge, represents the maximum value of the indentation depths of all depth points in the -th sub-region where an indentation depression appears at the template edge, , is a positive integer; Calculate the indentation depth fluctuation index of each sub-area. The specific calculation formula is as follows: In the formula, is the indentation depth fluctuation index of the th sub-region.

5. A method for intelligent template processing and production in construction engineering according to claim 4, characterized in that, The acquisition logic of the edge perturbation expansion coefficient of each sub-region is as follows: Extract the edge perturbation geometric information from the edge deformation evaluation information of each preprocessed sub-region, specifically including the continuous length of the indentation perturbation segment, the average absolute value of the indentation curvature change, and the maximum change amount of the indentation curvature change value in each sub-region where indentation depressions appear on the template edge, and calibrate them respectively as , and , represents the continuous length of the indentation perturbation segment in the -th sub-region where indentation depressions appear on the template edge, represents the average absolute value of the indentation curvature change in the -th sub-region where indentation depressions appear on the template edge, represents the maximum change amount of the indentation curvature change value in the -th sub-region where indentation depressions appear on the template edge, , is a positive integer; Calculate the edge perturbation expansion coefficient of each sub-region. The specific calculation formula is as follows: In the formula, is the edge perturbation expansion coefficient of the th sub-region.

6. The method for intelligent template processing and production of a construction project according to claim 5, wherein, Indentation depth fluctuation index of each generated sub-region and edge perturbation expansion coefficient Construct an edge deformation evaluation model, and generate an edge deformation coefficient for each sub-region through weighted summation. The specific calculation formula is as follows: In the formula, is the edge deformation coefficient of the th sub-region, and are the indentation depth fluctuation index and the edge perturbation expansion coefficient of each sub-region respectively, and .

7. A method for intelligent template processing and production in construction engineering according to claim 6, characterized in that, Determine the pre-set threshold interval of the edge deformation coefficient , and after determination, compare it with the edge deformation coefficients of the generated sub-regions , evaluate the edge deformation degree of the sub-regions with indentation depressions on the template edge according to the comparison results, and divide each sub-region into a standard cutting region, a cutting region requiring compensation, and a region requiring manual review according to the evaluation results. The specific comparison analysis and division are as follows: If , the edge deformation degree of the sub-region with indentation depression at the template edge is at a low level, and this sub-region is divided into a standard cutting region; If , the edge deformation degree of the sub-region where indentations appear on the template edge is at a medium level, and this sub-region is divided into a cutting region that needs to be compensated If , the edge deformation degree of the sub-region with indentation on the template edge is high, and this sub-region is divided into the region requiring manual review.

8. A method for intelligent template processing and production in construction engineering according to claim 7, characterized in that, According to the classification results of each sub-region, execute the corresponding cutting path adjustment strategy respectively. Specifically: for the sub-region classified as the standard cutting area, the cutting path adjustment strategy to be executed is: directly adopt the preset standard cutting path to perform the processing operation; for the sub-region classified as the cutting area requiring compensation, the cutting path adjustment strategy to be executed is: according to the edge deformation characteristics of the sub-region, adjust the starting position, cutting direction and feed angle of the cutting path to generate a compensation cutting path adapted to its edge micro-deformation state; for the sub-region classified as the area requiring manual review, the cutting path adjustment strategy to be executed is: stop the automatic cutting process, send a manual review prompt instruction to the operator, and mark the sub-region as the area to be processed, and then resume the cutting path planning after manual confirmation and trimming; And generate and execute the corresponding processing control instructions. Specifically: according to the cutting path adjustment strategy corresponding to each sub-region, generate a matching set of processing control instructions. The processing control instructions include parameters for controlling the cutting path trajectory, feed speed, feed depth and cutting direction, and after generation, send the processing control instructions to the numerical control system of the template processing equipment to control the tool to complete the cutting processing of the template according to the adjusted path.

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