Structured construction method for aeronautical part manufacturing tool data

Abstract

The invention relates to the technical field of aeronautical manufacturing tool data management, and discloses an aeronautical part manufacturing tool data structured construction method, which comprises the following steps of: embedding semantic anchor points containing unique identifiers for tool associated characteristics in a CAD (Computer Aided Design) model; automatically extracting geometric difference of anchor point features when a model change event is monitored; according to the method, source carrying of the design intention is achieved through the embedded semantic anchor points, tool data updating is converted into endogenous synchronization from external verification, and therefore cross-system semantic distortion is avoided; automatically adding a weight attenuation factor to guide manual checking when geometry dramatically changes, and constructing a safety protection mechanism under zero hardware cost; and meanwhile, the physical state of the tool is reversely deduced by multiplexing instruction stream data, so that digital and physical closed-loop feedback is formed.

Description

TECHNICAL FIELD

[0001] The application relates to a structural construction method of aviation part manufacturing tooling data, and belongs to the technical field of aviation manufacturing tooling data management. BACKGROUND

[0002] In the existing part design, geometric differences are identified through version comparison after changes, and then affected tooling units are manually associated by process personnel and the database is updated. This mode can still run in the conventional iteration, but in the high-frequency design iteration scene of aviation parts, its deep-seated bottleneck gradually appears. First, the semantic association of tooling function units and design features depends on external documents or manual experience maintenance. When the part topology changes (such as surface reconstruction), the system cannot automatically identify the association failure, resulting in tooling update lag or omission. Secondly, the existing technology lacks a prediction mechanism for geometric changes. When the support surface normal vector mutation exceeds the clamp adjustment threshold, the displacement compensation instruction is still mechanically executed, which may cause hidden risks such as abnormal clamping stress. Thirdly, the physical tooling wear state is completely separated from the digital system. The traditional scheme needs to install a sensor monitoring device to monitor the state, which significantly increases the deployment cost and maintenance complexity.

[0003] The industry has tried to optimize the synchronization efficiency by enhancing the geometric feature recognition algorithm or establishing a tooling knowledge graph, but it has not broken through the fundamental architectural constraints. First, the semantic mapping always exists as an external additional layer, and the association relationship still needs to be traced back across systems when changes are made. Second, the physical tooling health state still depends on an independent monitoring system, and the data value generated during the design change process cannot be reused.

[0004] Specifically, the existing technology has three inherent defects: 1. Cross-system transmission leads to design intent distortion; 2. Blind execution of automatic update when geometric changes occur; 3. Physical state monitoring and data system operate separately. Therefore, how to build a self-consistent semantic bearing mechanism at the data source to solve the cross-system mapping distortion, and establish a lightweight tooling data collaboration system that takes into account the risk warning of geometric changes and physical state self-sensing, has become a technical problem to be solved by the present application. SUMMARY

[0005] The present application provides an aviation part manufacturing tooling data structured construction method, which mainly aims to solve the problems of intent distortion caused by external semantic association, geometric change risk out of control and digital-physical system separation in aviation tooling data synchronization.

[0006] To achieve the above purpose, the aviation part manufacturing tooling data structured construction method provided by the present application comprises the following steps: Step a, in the computer-aided design model, embedding a non-geometric semantic anchor point for the geometric feature that has established a functional association with the tooling function unit; the semantic anchor point contains an identifier uniquely corresponding to the tooling function unit; Step b, monitoring a save or submit event of the computer-aided design model, and automatically identifying and extracting a current geometric state of all geometric features embedded with semantic anchors in the computer-aided design model when the event occurs; Step c, generating an atomized difference based on the current geometric state of the geometric feature and a previous version geometric state of the geometric feature, to represent a difference in geometric quantity of the geometric feature; Step d, generating an atomized difference instruction containing an adjustment action and an adjustment amount based on the unique identifier in the semantic anchor and the atomized difference; if the change angle of the surface normal vector of the geometric feature between the current geometric state and the previous version geometric state is greater than a set angle threshold, the atomized difference instruction additionally contains a semantic weight decay factor for representing a decrease in the automatic update credibility of the semantic anchor and a constraint redirection suggestion for prompting a constraint type check to be performed; Step e, sending the atomized difference instruction to the tooling data management system to drive the data of the corresponding tooling functional unit in the tooling data management system to be updated.

[0007] Preferably, the method further comprises: periodically acquiring and analyzing a historical atomized difference instruction stream for the same tooling functional unit; calculating a statistical dispersion value of the adjustment amount in the historical atomized difference instruction stream; comparing the statistical dispersion value with a preset health state judgment threshold to generate a state warning information containing physical health status or remaining adjustable margin information of the tooling functional unit.

[0008] Preferably, the semantic anchor further contains a constraint code for defining the constraint type between the geometric feature and the tooling functional unit.

[0009] Preferably, in step e, the atomized difference instruction is sent through a message queue based on a publish-subscribe mode.

[0010] Preferably, the method further comprises: after step b, performing a verification operation on the validity of the semantic anchor, the verification operation including verifying whether the tooling functional unit unique identifier pointed to by the semantic anchor exists validly in the tooling data management system and marking invalid associations.

[0011] Preferably, the geometric feature is a positioning hole, a supporting surface, a clamping edge, or a guide groove.

[0012] Preferably, the embedding method of the semantic anchor is realized through a custom attribute function or a metadata management function of the computer-aided design software.

[0013] Preferably, the format of the atomized difference instruction is JSON format or XML format.

[0014] Preferably, the method further includes: digitally signing the atomized differential instructions to ensure the integrity and source credibility of the instructions.

[0015] Preferably, the method further includes: when the tooling data management system receives an atomic differential instruction containing a semantic weight decay factor, it automatically converts the tooling functional unit data update task corresponding to the atomic differential instruction into a pending manual review status according to a preset review rule, and raises its priority in the manual review queue to the highest level.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. By embedding non-geometric semantic anchors in the CAD model, the association between tooling functional units and design features is internalized as a resolvable attribute of the model itself. When the design changes, the system only needs to extract the anchor identifier and geometric difference to generate an atomic instruction stream. There is no need to rely on external systems for complex geometric feature matching and intent inference. This mechanism changes the traditional data synchronization method from post-mapping verification to intent source carrying, avoiding semantic distortion of association in cross-system transmission and ensuring the essential consistency of tooling data updates.

[0017] 2. When the change angle of the surface normal vector of a geometric feature exceeds a threshold, the system automatically adds a semantic weight decay factor and constraint redirection suggestion to the differential instruction. This mechanism transforms drastic changes in geometric shape into actionable engineering decision signals: the weight decay factor dynamically reduces the credibility rating of automated updates, while the constraint redirection suggestion accurately locates the verification nodes that require manual intervention. By reusing the basic geometric attribute of the normal vector, the system builds a safety valve for extreme changes with zero additional hardware cost, transforming the risk of implicit association failure caused by drastic geometric changes into a manageable targeted review task.

[0018] 3. Based on the historical differential instruction stream of the same tooling unit, the system reverse-engineers the actual health status and adjustability margin of the physical tooling by calculating the statistical dispersion of the adjustment amount. This mechanism transforms the random fluctuation of the instruction sequence into information entropy that characterizes physical wear, making the seemingly disordered operation data a passive sensor for monitoring the performance of physical equipment. By reusing the existing digital instruction data stream, the system achieves the computability of the physical state of the tooling without the need to deploy physical sensors, thus achieving a closed-loop feedback between the digital space and the physical entity.

[0019] 4. By adopting a publish-subscribe model to transmit atomic differential instructions, the traditional tightly coupled database integration is decoupled into asynchronous communication based on message queues. Semantic anchors serve as data carriers, differential calculations serve as transformation engines, and message queues serve as transmission channels. These three elements work together to form a standardized, lightweight protocol. This protocol does not rely on a centralized chemical control system. Each subsystem only needs to follow a basic data format to achieve precise collaboration between heterogeneous systems, which greatly reduces the complexity of system integration and deployment costs. It is especially suitable for aerospace manufacturing scenarios with multi-supplier collaboration. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method for constructing structured data for aerospace parts manufacturing tooling in this invention; Figure 2 This is a diagram showing the relationship between the semantic weight attenuation factor and the surface normal vector change angle in this invention. Figure 3 This is a flowchart of the intelligent collaborative processing of tooling data for aerospace parts manufacturing, as described in this invention.

[0021] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] To make the technical solution and beneficial effects of the present invention clearer, the present invention will be further described in detail below. It should be understood that the specific applications described herein are intended to illustrate the core principles of the present invention, and not to limit its scope of protection.

[0023] This invention discloses a method for constructing structured data for manufacturing tooling for aerospace parts. The technical solution is implemented in a software system deployed on a server or equivalent computing platform. This system integrates three core, mutually cooperating components in its functional architecture: a semantic anchor management module deeply integrated into the computer-aided design environment; an independently operating geometric state change monitoring and differential analysis engine; and an atomic instruction distribution service based on industry-standard message queues. These three components together construct an event-driven closed-loop data path that begins at the source of design intent and ends with tooling data updates. In the specific execution process, to mitigate the inherent risk of distortion and loss of semantic relationships during cross-system transmission caused by the separate management of the design model and the tooling database, the initial step of this solution is placed at the very beginning of the design process, i.e., directly constructing self-interpretable data links within the computer-aided design model. Specifically, for specific aerospace parts, such as aircraft... When planning tooling functions for key geometric features such as positioning holes, support surfaces, clamping edges, or guide grooves of the reinforcing frame or wing beam, the system does not rely on external documents or databases. Instead, it uses the custom attribute function or metadata management function provided by the CAD software itself to directly embed a non-geometric semantic anchor point into the geometric feature. This semantic anchor point is represented by a structured metadata text, which can be configured in JSON or XML format and contains at least two key fields: a unique identifier that precisely points to the associated tooling functional unit, and a constraint code that defines the type of functional constraint between the two. In this way, the functional and constraint relationship between the part's geometric features and the tooling functional unit is internalized as an inseparable inherent attribute of the part's 3D model, and is self-consistently transmitted with the version iteration and flow of model data, thus avoiding the fragility of external mapping relationships in the traditional mode.

[0024] To ensure timely capture of design changes and accurate and reliable subsequent data synchronization, a continuously running monitoring service is deployed in the system background. This service closely monitors version change events such as saving or submitting CAD model libraries. Once such an event is successfully detected, the differential analysis engine is triggered and first performs a preliminary validation operation to verify the validity of semantic anchors. This operation extracts the unique corresponding identifier contained in all semantic anchors in the model and initiates a real-time query to the database service interface of the tooling data management system to verify that the tooling functional unit pointed to by the identifier is still valid. Any association pointing to invalid or obsolete units will be immediately marked and subsequent processes will be stopped, thus eliminating the blindness of data updates. After passing the validity validation, the engine automatically extracts each semantic anchor carrying a valid semantic anchor. The system calculates the current geometric state of the geometric feature and compares it precisely with the geometric state of the same feature in the previous version cached by the system. This generates a quantifiable atomic difference, such as a three-dimensional translation vector for the displacement of the center point of the positioning hole. Based on this difference, the system further constructs a structured atomic difference instruction. This instruction encapsulates the unique identifier corresponding to the target tooling unit, the adjustment action and adjustment amount representing the adjustment direction and magnitude, and adds a digital signature using an asymmetric encryption algorithm to ensure the integrity and non-repudiation of the instruction during transmission. Finally, this instruction is asynchronously pushed to the corresponding topic through a message queue channel built on a publish-subscribe model, and is securely received, parsed, and executed by the tooling data management system that has subscribed to the topic.

[0025] To proactively address the drastic geometric changes that can easily occur in scenarios such as the iteration of complex curved surfaces of aerospace parts, and to mitigate the physical clamping risks that automated update mechanisms may introduce when facing nonlinear changes, this solution incorporates an adaptive risk warning and tiered intervention mechanism. Simultaneously with the generation of atomized differentials by the differential analysis engine, the system specifically calculates a key risk assessment indicator: the angle of change of the surface normal vector of the affected geometric features between the current geometric state and the previous version's geometric state. The angle threshold is set here. It is not a static empirical parameter, but rather pre-calibrated through procedures: During the system deployment phase, finite element analysis tools are used to simulate a series of benchmark assembly scenarios. By gradually increasing the deflection angle of the normal vector of the key support surface, the assembly stress changes on the clamping body and parts are monitored. The critical deflection angle at which the stress value first exceeds the preset safety factor, such as 85% of the material's yield strength, is defined as the set angle threshold for this constraint type. In the actual operation of the system, once a change in angle is detected... The system then determines that this change is a drastic change and automatically injects two additional semantic markers into the generated atomic differential instructions: one is a semantic weight decay factor used to characterize the reduced credibility of the automatic update of semantic anchors. The first is that its value can be configured to dynamically decay as the angle exceeds the threshold. The second is a constraint redirection suggestion used to indicate that a constraint type check is needed. Correspondingly, when the tooling data management system receives an instruction containing this semantic weight decay factor, its internal preset review rules are triggered, automatically changing the data update task from automatic execution to pending manual review, and placing it in the highest priority in the manual review queue. This transforms a hidden risk of geometrical change into an explicit, controllable, high-priority manual decision-making event, building a safety barrier at zero hardware cost.

[0026] To ultimately break down the information barrier between the digital instruction space and the physical tooling entity, and to achieve implicit closed-loop feedback between the digital twin and the physical entity, this solution further integrates a state prediction module that performs in-depth mining of historical instruction data. This module collects and analyzes all historical atomized differential instruction streams pointing to the same tooling functional unit at a preset period (e.g., daily or weekly), and uses statistical algorithms to calculate the statistical dispersion value of its adjustment sequence. The main approach is as follows: a healthy and stable physical fixture should exhibit low dispersion in its adjustment sequence. Conversely, if the fixture experiences wear, loosening, or other physical performance degradation, its adjustment process will inevitably exhibit greater randomness and volatility to achieve the same positioning accuracy, leading to a significant increase in dispersion. The system will calculate the statistical dispersion value in real time. A preset health status judgment threshold was established based on statistical modeling of massive amounts of historical health equipment data. A comparison will be made, and once discovered... It automatically generates and pushes a status warning message. This message not only includes a warning of the possible decline in the physical health of the tooling unit, but also calculates the remaining adjustable margin by combining its historical total adjustment. Thus, without relying on any additional physical sensors, it can effectively use existing digital command stream data to reverse deduce the health status and wear trend of the physical entity, providing reliable data support for predictive maintenance.

[0027] Example 1: In a specific industrial application, during the later testing phase of a wide-body passenger aircraft, to optimize aerodynamic efficiency within a specific flight envelope, an urgent nonlinear reconstruction involving complex freeform surfaces was required for a large fairing at the wing-fuselage junction. This design change involved massive geometric modifications, and the project cycle was compressed to its limit, posing a severe challenge to the timeliness and accuracy of downstream tooling data synchronization. Under such circumstances, traditional work methods relying on manual comparison or external document mapping are difficult to control in terms of error risk and time cost. However, the mechanism built into this technical solution fundamentally reshapes the workflow in this scenario; when carrying new surface data... When the CAD model is submitted and saved, the system's background monitoring service immediately captures the change event and automatically triggers the subsequent chain-like processing flow. First, the semantic anchor points pre-embedded in the geometric features of each key support surface and positioning hole of the fairing ensure that no matter how the surface topology evolves, the logical connection between the design intent and the tooling functional units remains stable. The system can instantly lock all affected tooling units without starting any time-consuming and uncertain geometric comparison and feature matching algorithms. This step is a redefinition of the traditional problem of identifying the impact domain of changes, transforming a complex post-mapping problem into a deterministic native attribute reading operation.

[0028] Following this, when the differential analysis engine calculates the atomic differences of each feature, its integrated risk warning mechanism begins to operate. For one of the core load-bearing support surfaces, the system calculates the angle of change of its surface normal vector. This significantly exceeds the preset angle threshold determined through finite element simulation. This objectively quantifiable fact was judged by the system as a high-risk, drastic change. At this point, a decisive synergistic relationship was formed between the two key technical features of the solution: the semantic anchor provided the precise identity of the change, indicating the physical tooling unit that needed adjustment, while the exceeding judgment of the change angle gave this change a clear risk characterization, indicating that this adjustment instruction should not be executed automatically without conditions. Based on this, the system generated a special atomic differential instruction, which, in addition to containing the adjustment amount, also carried a semantic weight decay factor. The corresponding constraint redirection suggestions were distributed to the tooling data management system via a publish-subscribe message queue. This process resolved the inherent conflict between efficiency and safety in automated processes. For non-drastic routine geometric fine-tuning, the instructions did not contain attenuation factors, and the system executed them at high speed in fully automatic mode, ensuring iteration efficiency. For the drastic changes identified this time, the instructions containing attenuation factors were automatically directed to the highest priority of the manual review queue by preset rules. Without sacrificing overall efficiency, process nodes requiring manual review were set for high-risk points. Ultimately, the senior process engineer received not a vague change notification, but a precise task encapsulating the specific tooling unit identifier, suggested adjustment amount, and clear risk level. This allowed them to complete the review and confirmation of this specific constraint type in a very short time, thereby avoiding stress anomalies or physical interference between the tooling and parts that might be caused by drastic changes in the normal vector, ensuring the smooth progress of the production plan. At the same time, all atomic differential instructions of this operation were recorded and archived by the system, providing another effective data sample for subsequent evaluation of the physical health of the fairing tooling unit by calculating statistical dispersion values.

[0029] Example 2: To objectively quantify the effectiveness of the technical solution of this invention in handling design changes of different natures, this comparative verification experiment was designed and executed. The experimental platform was built in a virtualized engineering environment, which consisted of computer-aided design software running on a standard graphics workstation, a simulated tooling data management system database, and an integrated version control system to reproduce the real industrial design and manufacturing data interaction link. Two parallel software instances were set up in the experiment: one was a control group, which used automated synchronization logic based on geometric topology comparison to perform indiscriminate direct data updates on all identified geometric changes; the other was the experimental group, which fully deployed the technical solution of this invention, including the implantation of semantic anchors, differential analysis based on event listening, and an atomic differential instruction generation and distribution mechanism with built-in risk judgment. The key parameter setting in the experiment was an angle threshold. The value of follows the deterministic procedure described in the aforementioned specific implementation method. The technical consideration of this procedure lies in achieving an engineering balance between the sensitivity and false alarm rate of change identification. That is, an overly sensitive threshold will introduce unnecessary manual review, while an overly insensitive threshold may miss risks. In this experiment, for standard aerospace aluminum parts and typical tooling clamping forces, the minimum normal vector deflection angle that would cause assembly stress to exceed the safety factor was calibrated through finite element analysis simulation. Finally, this set angle threshold was determined. It was determined to be 7.5 degrees.

[0030] The experiment employed a predefined design change sequence comprising 10 steps, applied to a standard aerospace structural stiffener CAD model. This sequence included 7 routine geometric fine-tunings and 3 drastic geometric changes. The control and experimental groups were configured to process the same change sequence synchronously. During the experiment, the control group showed consistent data synchronization time across all 10 steps. However, for change sequences 4, 7, and 9, which involved drastic geometric changes, the updated models experienced assembly interference or constraint failures in subsequent virtual assembly verifications due to data errors. In contrast, the experimental group's synchronization time for the routine change sequences was comparable to that of the control group. However, when processing change sequences 4, 7, and 9, the system automatically identified risks and added a semantic weight attenuation factor to the atomic differential command, subsequently transferring the task to manual review. Although this action introduced additional review time, the virtual assembly verification success rate reached 100%. Specific quantitative data comparisons are shown in Table 1.

[0031] Table 1: A comparison of performance data between the experimental group and the control group when processing representative altered sequences.

[0032]

[0033] The data discrepancies presented in Table 1 are directly attributable to the technical reason that the experimental group's technical solution precisely identified the object of change through semantic anchors and further calculated the angle of change. With the set angle threshold The comparison gives the system the inherent ability to distinguish the nature of changes, enabling its automated processes to transform from indiscriminate blind execution to selective hierarchical processing; the synchronous failure of the control group, on the other hand, is the inherent vulnerability of the automated processes when faced with unexpected operating conditions in the absence of this risk assessment capability.

[0034] Example 3: This example combines Figures 1 to 3 This paper describes the implementation of a data structure construction method for aerospace part manufacturing tooling, such as... Figure 1 As shown, semantic anchors are first embedded in the geometric features (positioning holes / support surfaces, etc.) of the CAD model. These semantic anchors contain unique identifiers and constraint codes. Then, the system listens for CAD model save / submit events. When an event occurs, the current geometric state is extracted and compared with the previous version's geometric state. Before this, the validity of the semantic anchors is checked. If valid, the process continues; if invalid, invalid associations are marked. Based on the comparison of geometric states, the system generates atomic differences. Next, the normal vector change angle is calculated. ,if > If the judgment result is yes, an differential instruction containing a weight decay factor is generated, which includes a suggestion to add constraints and redirect. If the judgment result is no, a regular differential instruction is generated, which is then signed with a key and published via a message queue (publish-subscribe mode). The instruction is sent to the tooling data management system. If a regular instruction is received, the tooling data is automatically updated. Simultaneously, these regular instructions are also sent to the historical differential instruction stream analysis module, which calculates the statistical dispersion. ,when > When this happens, a physical health status warning will be generated. If the tooling data management system receives an instruction containing a decay factor, it will identify the decay factor, transfer it to manual review (highest priority), and notify the process engineer to review and confirm the adjustment for the task to be reviewed.

[0035] like Figure 2 As shown, Figure 2 This diagram illustrates the relationship between the semantic weight decay factor and the change angle of the surface normal vector. A safety threshold line is also marked. When the change angle of the surface normal vector is between 0° and 7.5°, the semantic weight decay factor remains at 1.0. Once the change angle exceeds 7.5° (i.e., the safety threshold line in the diagram), the semantic weight decay factor gradually decreases with increasing angle. For example, it is approximately 0.8 at 10°, approximately 0.6 at 15°, approximately 0.4 at 20°, approximately 0.2 at 25°, and close to 0.1 at 30°. This indicates that when the system detects that the change angle of the surface normal vector exceeds the preset threshold, it automatically reduces the reliability of automated updates, thereby guiding manual verification. This preset angle threshold... It was determined to be 7.5 degrees.

[0036] like Figure 3As shown, this process involves roles such as a differential analysis engine, a risk assessment module, a message queue, a tooling data management system, a manual review queue, and process engineers. First, the differential analysis engine sends geometric differential data to the risk assessment module. The risk assessment module calculates the surface normal vector change angle and compares the change angle with a threshold. If the change angle exceeds the threshold, a semantic weight decay factor is generated and a constraint redirection suggestion is added. Then, a high-risk marked instruction is returned to the differential analysis engine. The differential analysis engine attaches a digital signature to the high-risk instruction and publishes the high-risk instruction to the message queue. By pushing the instruction to the tooling data management system, the tooling data management system identifies the decay factor and transfers the task to the highest priority manual review queue. At the same time, it notifies the process engineer to review and confirm the adjustment. If the change angle does not exceed the threshold (i.e., a regular change), the risk assessment module returns a regular instruction to the differential analysis engine. The differential analysis engine attaches a digital signature to the regular instruction and publishes the regular instruction to the message queue. By pushing the instruction to the tooling data management system, the tooling data management system automatically executes the update upon receiving the regular instruction.

[0037] Example 4: In a specific implementation of the technical solution of the present invention, the parameter calibration and operation of its built-in risk model and health status assessment model, for the semantic weight decay factor The calculation, in which the key parameter is the attenuation constant. The value of is constrained by a deterministic procedure based on engineering risk tolerance; this procedure is based on the assumption that the angle exceeds the basic set threshold. Then, a predefined angle of high alertness With a clearly defined semantic weight decay factor target value The correspondence is used to uniquely determine the constant. The value of makes the response sensitivity of the risk model directly related to a quantifiable engineering decision.

[0038] The deduction of the physical health status of the tooling is achieved through an algorithmic process that includes data preprocessing, feature extraction, and threshold determination. This process operates on a specific functional unit of the tooling, based on the most recent... The next operation adjusts the sample sequence and performs a median filtering preprocessing step on the sample sequence to remove extreme outliers before calculating the dispersion. After preprocessing, the process calculates the interquartile range of the clean sample sequence as the core feature index. The corresponding preset health status judgment threshold During the tooling learning period, baseline adjustment data is collected and its baseline divergence value is calculated. And it is determined that the threshold is set to Among them, health sensitivity factors In this specific implementation, it is set to 3; when the current dispersion value is calculated during continuous system operation. satisfy When the conditions are met, a status warning message containing an alert on the physical health status of the tooling is generated and pushed.

[0039] Example 5: In a specific application scenario, when the system receives computer-aided design models from external design partners or different design departments, the system will execute a standardized model reception and anchor integrity verification procedure. Before any new version of the CAD model is included in the official version library that can trigger atomic differential instructions, it is placed in a temporary isolation zone. The verification service automatically triggered by the system will perform structural verification and association validity verification on all semantic anchors in the model. Only when all semantic anchors in the model pass the verification will the model be marked as verified and removed from the isolation zone. Otherwise, the system will reject the model version and return specific error information. This procedure ensures that the data sources entering the automated process are consistent and valid in terms of structure and semantics in advance.

[0040] Throughout the entire lifecycle of each atomic differential instruction, from generation to execution, its source credibility and content integrity are guaranteed by a set of instruction signing and verification procedures based on asymmetric encryption technology. As the sole legitimate issuer of the instruction, the differential analysis engine is configured with a unique private key and uses this private key to sign the hash value of each instruction, while the tooling data management system has its corresponding public key pre-configured. Upon receiving any instruction, the tooling data management system's first action is to verify the validity of its digital signature using the public key. If the verification fails, the instruction will be immediately discarded and a security alarm will be triggered. This mechanism provides a technical implementation method for instruction security and operational traceability in automated data links, all of which are extended implementation methods known to those skilled in the art.

[0041] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing structured data for aerospace component manufacturing tooling, characterized in that, Includes the following steps: Step a: In the computer-aided design model, embed non-geometric semantic anchors for geometric features that have established functional associations with tooling functional units. Semantic anchors contain identifiers that uniquely correspond to the tooling functional units; Step b: Listen for the save or submit event of the computer-aided design model, and when the event occurs, automatically identify and extract the current geometric state of all geometric features with embedded semantic anchors in the computer-aided design model; Step c: Based on the current geometric state of the geometric feature and the previous version of the geometric state of the geometric feature, generate an atomized difference to characterize the difference in geometric quantities of the geometric feature. Step d: Based on the unique identifier and atomic difference in the semantic anchor, generate an atomic difference instruction containing adjustment action and adjustment amount; if the change angle of the surface normal vector of the geometric feature between the current geometric state and the previous version geometric state is greater than a set angle threshold, the atomic difference instruction additionally includes a semantic weight decay factor used to characterize the reduced confidence of the automatic update of the semantic anchor and a constraint redirection suggestion used to indicate the need for constraint type checking. Step e: Send the atomization differential instruction to the tooling data management system to drive the data of the corresponding tooling functional unit in the tooling data management system to be updated.

2. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The method also includes: periodically acquiring and analyzing historical atomized differential instruction streams for the same tooling functional unit; calculating the statistical dispersion value of the adjustment amount in the historical atomized differential instruction streams; and generating a status warning message containing information on the physical health status or remaining adjustable margin of the tooling functional unit based on a comparison of the statistical dispersion value with a preset health status judgment threshold.

3. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The semantic anchor also contains constraint code that defines the constraint type between geometric features and tooling functional units.

4. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, In step e, the atomic differential instruction is sent via a message queue based on a publish-subscribe pattern.

5. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The method further includes: after step b, performing a validation operation on the validity of the semantic anchor. The validation operation includes verifying whether the unique identifier of the tooling functional unit pointed to by the semantic anchor exists validly in the tooling data management system, and marking invalid associations.

6. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The geometric features include positioning holes, support surfaces, clamping edges, or guide grooves.

7. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The atomic differential instruction is in JSON or XML format.

8. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The method also includes digitally signing the atomic differential instructions.

9. The method for constructing structured data of aerospace part manufacturing tooling according to claim 1, characterized in that, The method also includes: when the tooling data management system receives an atomic differential instruction containing a semantic weight decay factor, it automatically converts the tooling functional unit data update task corresponding to the atomic differential instruction into a pending manual review status according to a preset review rule, and raises its priority in the manual review queue to the highest level.

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