Digital assembly support management system and method for an aeronautical component

Abstract

The application discloses a kind of digital assembly support management systems and methods of aviation parts, related to aviation parts assembly field, comprising: configuration module, for each aviation parts allocation unique electronic identification, record its purchase batch, specification parameter, material attribute, design number, establish the one-to-one mapping of identification and component Association database;Modeling module, for the three-dimensional geometric parameters of aviation parts, assembly interface tolerance, process procedure logic, build data model containing assembly constraint relationship and process requirement, and synchronously bind to Association database;The application establishes the unique and tamper-proof association mapping for aviation parts, guarantees its full life cycle traceability and data security, and constructs accurate data model by normalizing related parameters, improves assembly data integration adaptation degree by combining standardized data processing, and dynamically generates optimal assembly scheme based on design rules, realizes the reasonable configuration of process, motion path and resource.

Description

Technical Field

[0001] This invention relates to the field of aerospace component assembly technology, specifically to a digital assembly support management system and method for aerospace components. Background Technology

[0002] Digitalization of aerospace components is based on 3D modeling and digital twins, integrating IoT and big data technologies to connect the data links of the entire process of design, manufacturing, testing, and operation and maintenance. This enables virtual simulation optimization, real-time control of the production process, and full life cycle traceability, significantly improving the precision, production efficiency, and reliability of components.

[0003] The invention patent application with publication number CN108229615B discloses a digital assembly support system and method for aero-engine components. The application aims to solve the problems that "currently, in the assembly and production of large aero-engines, the assembly process preparation time for these components is long, the assembly operation efficiency is low, and their overall assembly time accounts for a high proportion of the engine assembly cycle. At the same time, there are also problems with the installation errors and installation consistency of small components".

[0004] However, in existing intelligent assembly scenarios for aerospace components, the assembly process still relies on a certain amount of manual management support to ensure the stable progress of the assembly work. Such scenarios are in dire need of more intelligent related support systems to reduce the degree of manual involvement in the assembly work and ensure assembly accuracy.

[0005] To this end, we propose a digital assembly support management system and method for aerospace components. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a digital assembly support management system and method for aerospace components, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a digital assembly support management system for aerospace components, comprising: The system comprises the following modules: a configuration module, a modeling module, and an evaluation module. The configuration module assigns a unique electronic identifier to each aerospace component, recording its procurement batch, specifications, material properties, and design number, and establishing a database that maps identifiers to components. The modeling module standardizes the 3D geometric parameters, assembly interface tolerances, and process logic of aerospace components, constructing a data model that includes assembly constraints and process requirements, and simultaneously binding it to the database. The data acquisition module collects operating parameters of the assembly target equipment, component positioning data, and manual operation records, performs format conversion and standardization parsing on the collected data, and stores it in the database. The generation module dynamically generates the optimal assembly sequence, robotic arm motion path configuration strategy, and workstation resource allocation scheme based on the modeling data and parsing results in the database, combined with component assembly priority rules. The control module collects real-time component positioning accuracy, equipment operating status, and process completion progress at the assembly workstation, identifies deviations through threshold comparison, generates adjustment instructions and control signals, and simultaneously updates them to the database. The evaluation module iterates through the data stored in the database to quantitatively evaluate assembly quality matching and process safety. The configuration module is interconnected with the modeling module via a wireless network. The modeling module is interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the generation module via a wireless network. The generation module is interconnected with the control module and the evaluation module via a wireless network.

[0008] Furthermore, the electronic identifier assigned by the configuration module includes a unique traceability code for the entire lifecycle of the component. This traceability code forms an immutable mapping relationship with the purchase batch, specifications, material attributes, and design number in the associated database through a dynamic weighted hash algorithm. The dynamic weighted hash algorithm follows the following rules: ; In the formula: It is a hash encryption algorithm; These are the weighting coefficients; For each parameter, there is an independent hash function. For the procurement batch code, the corresponding numerical matrix, specification parameter quantization vector, material attribute feature value, and the unique integer corresponding to the design number; in, and obey .

[0009] Furthermore, the data model constructed by the modeling module includes dynamic weighting coefficients for assembly constraints. These weighting coefficients are used to quantify the coupled influence of three-dimensional geometric parameters, assembly interface tolerances, and process logic on assembly accuracy. The weighting coefficients are determined through the following objective function and constraint conditions: ; In the formula: To optimize the target value; include These are the weighting coefficients for three-dimensional geometric parameters, assembly interface tolerances, and process logic, respectively. This is the actual set of three-dimensional geometric parameters; A standard set of three-dimensional geometric parameters; This is the set of tolerances for the actual assembly interfaces; This refers to the standard assembly interface tolerance set. Quantification value of the logical sequence of actual process steps Quantization value of standard sequence KL divergence; These are the limit boundary values ​​of the geometric parameters; This represents the limit boundary value of the tolerance.

[0010] Furthermore, the acquisition module includes a vision sensor, a laser locator, and a force feedback sensor. When the acquisition module performs data standardization processing, it follows the following rules: ; In the formula: The data after purification; This is the original collected data; This represents the moving average value over the k-th data collection period. For adaptive sensitivity coefficients; The moving standard deviation is the value within the k-th data collection period. in, , Initial baseline mean, It is the moving average value during the (k-1)th data collection period.

[0011] Furthermore, the component assembly priority rule expression in the generation module is as follows: ; In the formula: Quantify the assembly priority value; Weights are assigned based on the criticality of components, assembly complexity, and supply timeliness. Assess the criticality of components; To quantify complex assembly values; This represents the deviation between the actual supply time and the planned supply time. The supply time benchmark threshold; The optimal assembly sequence is determined based on the quantified assembly priority value of each component. The generation module stores several shared and non-shared robot arm motion paths for each robot arm, and the quality level of each robot arm motion path is determined by its total stroke. When the generation module generates the optimal robot arm motion path configuration strategy, if the robot arm has a shared robot arm motion path, the configuration is based on the component assembly priority quantification value, so that the robot arm carrying the component with the larger assembly priority quantification value is given priority in configuring the optimal path; if the robot arm does not have a shared robot arm motion path, it enters the waiting queue. When generating the workstation resource allocation scheme in the generation module, the decision is based on the component assembly priority quantification value. A positive correlation matching mechanism between resource supply and priority quantification value is established. Workstation equipment resources, operator resources, and material delivery resources are prioritized to supply components with high assembly priority quantification values. At the same time, the supply allocation ratio is dynamically adjusted based on the resource carrying capacity limit of each workstation.

[0012] Furthermore, the threshold applied in the control module is determined based on the following logic: ; In the formula: Let t be the warning threshold at time t; Let be the threshold value exceeding the limit at time t; This is the initial baseline threshold; This is the threshold adjustment coefficient; The cumulative number of deviations before time t; This represents the total number of detections before time t. This refers to the cycle time of the assembly process; The adjustment command is generated based on the difference between the component positioning accuracy, equipment operating status, process completion progress and corresponding threshold of the assembly station. After the adjustment command is generated, it is synchronously converted into a control signal and sent to the corresponding assembly equipment in real time via industrial Ethernet. At the same time, the command content, control signal parameters and equipment execution feedback results are synchronously written into the associated database.

[0013] Furthermore, the quantitative evaluation logic for assembly quality matching degree in the evaluation module is as follows: ; In the formula: For quality matching metric quantification; The evaluation dimensions include dimensional accuracy, connection strength, and functional performance; is the weight coefficient for the v-th dimension; Let v be the sensitivity coefficient of the v-th dimension; This represents the actual detection value in the v-th dimension; is the standard value for the v-th dimension.

[0014] Furthermore, the security of the evaluation module process, when quantitatively evaluated, follows the following: ; In the formula: This is a quantitative value for process safety; the higher the value, the higher the safety. The evaluation dimensions include compliance of equipment operating parameters, standardization of operating procedures, and compliance of environmental safety parameters. Let z be the weight coefficient for the z-th dimension; This refers to the actual compliance rate, actual standardization rate, or actual compliance rate. For standard compliance, standardization rate, or compliance rate; for and Deviation; The preset security level threshold; To preset the hazard level threshold; and > .

[0015] On the other hand, a digital assembly support management method for aerospace components includes: Each aerospace component is assigned an electronic identifier containing a unique lifecycle traceability code, recording procurement batch, specifications, material properties, and design number information. An immutable mapping between the identifier and information is established using a dynamic weighted hash algorithm to construct an associated database. The three-dimensional geometric parameters, assembly interface tolerances, and process logic of aerospace components are standardized. Dynamic weight coefficients for assembly constraints are determined through objective functions and constraints, and a data model is constructed and bound to the associated database. The operating parameters of the assembly target equipment, component positioning data, and manual operation records are collected, and format conversion and standardized parsing are performed according to preset logic. The parsing results are then associated and stored in the associated database. Based on the data in the associated database and component assembly priority rules, the optimal assembly process sequence, robotic arm motion path configuration strategy, and workstation resource allocation scheme are dynamically generated. The positioning accuracy of the assembly workstation, equipment operating status, and process completion progress are collected in real time. Deviations are identified based on dynamically adjusted threshold comparisons, adjustment instructions and control signals are generated and issued, and synchronously updated to the associated database. The data stored in the associated database is traversed, and the assembly quality matching degree and process safety are evaluated according to preset quantitative logic, outputting the corresponding quantitative evaluation results.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a digital assembly support management system and method for aerospace components. During execution, the system and method establish a unique and tamper-proof association mapping for each aerospace component, ensuring traceability and data security throughout its entire lifecycle. Furthermore, it constructs a precise data model by standardizing relevant parameters, improves the integration and matching of assembly data through standardized data processing, and dynamically generates optimal assembly schemes based on design rules, achieving reasonable allocation of processes, movement paths, and resources. Real-time monitoring and threshold comparison promptly correct assembly deviations, ensuring assembly accuracy and process stability. Multi-dimensional quantitative evaluation controls assembly quality and process safety, effectively reducing assembly risks and failure probabilities. Overall, it improves the intelligence level and production efficiency of aerospace component assembly, shortens the assembly cycle, and reduces manpower and resource consumption. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a digital assembly support management system for aerospace components. Figure 2 This is a flowchart illustrating a digital assembly support management method for aerospace components. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment provides a digital assembly support management system for aerospace components, such as... Figure 1 As shown, it includes: The configuration module is used to assign a unique electronic identifier to each aerospace component, record its procurement batch, specifications, material properties, and design number, and establish a one-to-one mapping database between the identifier and the component. The electronic identifier assigned by the configuration module includes a unique traceability code for the entire lifecycle of the component. This traceability code is mapped immutably to the purchase batch, specifications, material attributes, and design number in the associated database using a dynamic weighted hash algorithm. The dynamic weighted hash algorithm follows the following rules: ; In the formula: It is a hash encryption algorithm; These are the weighting coefficients; For each parameter, there is an independent hash function. For the procurement batch code, the corresponding numerical matrix, specification parameter quantization vector, material attribute feature value, and the unique integer corresponding to the design number; The above formula combines four core information categories: procurement batch, specifications, material properties, and design number. By setting dynamically adjusted weight coefficients, the weight of each parameter is precisely matched with its importance to component traceability and assembly adaptation. At the same time, the SHA-512 algorithm is used to ensure the security of the hash value. A dedicated hash function based on the SM3 algorithm is used to process different types of parameters separately, realizing a unique mapping between electronic identification and component information and ensuring the security of the mapping relationship. in, and obey And each corresponds to a purchase batch, specification parameters, material properties, and design number, respectively. The value increases with the importance of traceability in component procurement batches and decreases with the decrease in traceability requirements, corresponding to the specifications and parameters. The influence of component specifications on assembly compatibility increases as the influence deepens and decreases as the influence weakens, corresponding to the material properties. The value increases as the material properties of the components play a more decisive role in assembly quality, and decreases as the role weakens, corresponding to the design number. The value increases as the need for unique identification of component design numbers increases, and decreases as the need for identification decreases; The SHA-512 hash encryption algorithm employs the complete implementation process of the standard SHA-512 encryption protocol, including four core steps: data padding, message expansion, compression function iteration, and result output, ensuring the collision resistance and irreversibility of the hash value; and independent hash functions for corresponding parameters. This is a parameter-specific hash function based on an improved SM3 algorithm, where... Used for hashing the purchase batch code after converting it from a string to a numerical matrix. Used for aggregate hashing of multi-dimensional quantization vectors of specification parameters. Used for single-value hashing of the physicochemical characteristic values ​​of material properties. Used to perform direct hashing on the unique integer corresponding to the design number, the output of each independent hash function is a 256-bit binary number, which is then converted and used in dynamic weighted summation. The modeling module is used to organize the three-dimensional geometric parameters, assembly interface tolerances, and process logic of aerospace components, construct a data model that includes assembly constraints and process requirements, and synchronously bind it to the associated database. The data model constructed by the modeling module includes dynamic weighting coefficients for assembly constraints. These weighting coefficients are used to quantify the coupled influence of 3D geometric parameters, assembly interface tolerances, and process logic on assembly accuracy. The weighting coefficients are determined through the following objective function and constraints: ; In the formula: To optimize the target value; include These are the weighting coefficients for three-dimensional geometric parameters, assembly interface tolerances, and process logic, respectively. This is the actual set of three-dimensional geometric parameters; A standard set of three-dimensional geometric parameters; This is the set of tolerances for the actual assembly interfaces; This refers to the standard assembly interface tolerance set. Quantification value of the logical sequence of actual process steps Quantization value of standard sequence KL divergence; These are the limit boundary values ​​of the geometric parameters; The limit boundary value of the tolerance; The above formula takes the deviation of three-dimensional geometric parameters, assembly interface tolerance, process logic and standard values ​​as optimization targets. By setting weight coefficients, the coupled influence of the three on assembly accuracy is quantified. The weight coefficient values ​​are dynamically adjusted according to the structural complexity of the parts, interface matching requirements and the closeness of process correlation. At the same time, the limit boundary constraints of parameters and tolerances are clearly defined, realizing the accurate quantification and dynamic optimization of key influencing factors of assembly. Among them, when the structural complexity of aerospace components is high and dimensional accuracy requirements are the core assembly indicators... The larger the value, the less the component structure is affected by dimensional deviations in the overall assembly effect. The smaller the value, the more likely it is that when the assembly interface of the parts is a precision fit structure, tolerance deviations will easily lead to assembly interference or functional failure. The larger the value, the more relaxed the interface fit requirements and the limited impact of tolerance fluctuations on assembly quality. The smaller the value, the better; when the assembly process includes multiple closely related critical processes, and a disordered process sequence will cause a chain of quality problems. The larger the value, the more independent the processes are, and the less the order adjustment has on the assembly result. The smaller the value; The data acquisition module is used to collect operating parameters of the target equipment, component positioning data, and manual operation records. It performs format conversion and standardized parsing on the collected data and stores it in the associated database. The data acquisition module includes a vision sensor, a laser positioner, and a force feedback sensor. When standardizing the data, the acquisition module follows the following rules: ; In the formula: The data after purification; This is the original collected data; This represents the moving average value over the k-th data collection period. For adaptive sensitivity coefficients; The moving standard deviation is the value within the k-th data collection period. in, , Initial baseline mean, This is the moving average value during the (k-1)th data collection period; The above formula constructs data purification rules based on the moving mean and moving standard deviation to classify and process the collected data. At the same time, it introduces an adaptive sensitivity coefficient, which not only effectively filters out abnormal data that exceeds the reasonable range, but also adapts to the dynamic changes in the data collection process, ensuring that the standardized data accurately reflects the actual assembly status. The generation module is used to dynamically generate the optimal assembly process sequence, robotic arm motion path configuration strategy, and workstation resource allocation scheme based on the modeling data and parsing results in the associated database, combined with the component assembly priority rules. The component assembly priority rule expression in the generation module is: ; In the formula: Quantify the assembly priority value; Weights are assigned based on the criticality of components, assembly complexity, and supply timeliness. Assess the criticality of components; To quantify complex assembly values; This represents the deviation between the actual supply time and the planned supply time. The supply time benchmark threshold; The above formula integrates three core factors: criticality of components, assembly complexity, and timeliness of supply. It distinguishes the degree of influence of each factor on assembly priority through weight coefficients. The weight values ​​are dynamically matched with the impact of components on core functions, assembly difficulty, and supply deviation. At the same time, it reasonably defines indicators such as criticality scores and complexity quantification values, realizing the scientific quantification of assembly priority and providing a reliable decision-making basis for subsequent optimal process sequence, path configuration, and resource allocation. All are positive numbers, and they follow the rule: the higher the influence of the component on the core function of the aviation equipment, the greater the influence of the component on the core function of the aviation equipment. The larger the value, the less directly related to or the weaker the impact on the core functionality. The smaller the value, the higher the number of assembly steps, the difficulty of interface matching, and the degree of tool dependence. The larger the value, the simpler the assembly process, the more standardized the interface, and the less need for special tools. The smaller the value, the smaller the deviation between the parts supply time and the planned assembly time. The larger the value, the longer the supply delay and the more significant the impact on the assembly schedule. The smaller the value; The higher the risk of loss of function or performance degradation of aviation equipment due to component failure, the larger the value; the smaller the value, the less the impact of failure or the ability to be quickly replaced. The higher the structural complexity of the component, the higher the assembly positioning accuracy requirements, and the higher the difficulty in avoiding interference with other components, the larger the value will be; the simpler the structure, the more relaxed the positioning requirements, and the lower the risk of assembly interference, the smaller the value will be. The optimal assembly sequence is determined based on the quantified assembly priority values ​​of each component. The generation module stores several shared and non-shared robot arm motion paths for each robot arm. Each robot arm motion path is graded as excellent based on its total stroke. When the generation module generates the optimal robot arm motion path configuration strategy, if the robot arm has a shared robot arm motion path, the configuration is based on the component assembly priority quantification value, so that the robot arm carrying the component with the larger assembly priority quantification value is given priority in configuring the optimal path; if the robot arm does not have a shared robot arm motion path, it enters the waiting queue. When generating the workstation resource allocation plan in the generation module, the decision is based on the component assembly priority quantification value. A positive correlation matching mechanism between resource supply and priority quantification value is established. Workstation equipment resources, operator resources, and material delivery resources are prioritized to supply components with high assembly priority quantification values. At the same time, the supply allocation ratio is dynamically adjusted based on the resource carrying capacity limit of each workstation. The control module is used to collect the positioning accuracy of parts, equipment operating status and process completion progress of the assembly station in real time. It identifies deviations through threshold comparison, generates adjustment instructions and control signals, and updates them to the associated database in a synchronous manner. The thresholds applied in the control module are determined based on the following logic: ; In the formula: Let t be the warning threshold at time t; Let be the threshold value exceeding the limit at time t; This is the initial baseline threshold; This is the threshold adjustment coefficient; The cumulative number of deviations before time t; This represents the total number of detections before time t. This refers to the cycle time of the assembly process; The above formula is based on the initial benchmark threshold, and dynamically adjusts the warning threshold by combining the cumulative deviation rate and the time node of the assembly process cycle. The threshold adjustment coefficient strengthens the impact of deviation accumulation and key assembly stages on the threshold. The value of the adjustment coefficient is flexibly set according to the deviation rate and assembly stage, so that the warning threshold can adapt to the dynamic changes in the assembly process, thereby improving the accuracy and timeliness of deviation identification. The adjustment command is generated based on the difference between the component positioning accuracy, equipment operating status, process completion progress and corresponding threshold of the assembly station. After the adjustment command is generated, it is synchronously converted into a control signal and sent to the corresponding assembly equipment in real time via industrial Ethernet. At the same time, the command content, control signal parameters and equipment execution feedback results are synchronously written into the associated database. in, The value range is preset to [0.1, 0.8], and the value is larger when the cumulative deviation count accounts for a higher proportion of the total number of inspections and the assembly stage is closer to the critical node, and smaller when the cumulative deviation count accounts for a lower proportion and the assembly is in the initial or final stage. The evaluation module is used to traverse the data stored in the associated database and quantitatively evaluate the assembly quality matching degree and process safety. The quantitative evaluation logic for assembly quality matching degree in the evaluation module is as follows: ; In the formula: For quality matching metric quantification; The evaluation dimensions include dimensional accuracy, connection strength, and functional performance; is the weight coefficient for the v-th dimension; Let v be the sensitivity coefficient of the v-th dimension; This represents the actual detection value in the v-th dimension; The standard value for the v-th dimension; The above formula distinguishes the importance of each dimension to the assembly quality by weighting coefficients, and introduces a sensitivity coefficient to amplify the impact of deviations in key dimensions. It transforms the deviation between the actual detection value and the standard value into a quality matching quantification result, thus achieving a comprehensive and accurate quantitative assessment of the assembly quality. in, ,and Corresponding to the dimension of size accuracy The value is larger when the dimensional fit compatibility requirements of the assembled aerospace components are higher, and smaller when the dimensional fit accuracy requirements are lower; this corresponds to the connection strength dimension. The value is larger when the assembly and connection relationship of components directly affects the load-bearing capacity or operational safety of the entire machine structure, and smaller when it only serves an auxiliary fixing function; corresponding to the size, function, and performance. The larger the value, the better; when a component's function directly determines the core operating performance of the entire machine, the smaller the value, and when it only performs an auxiliary function. Similarly, The preset value range is (0, 5), corresponding to the dimensional accuracy. The value is larger when the allowable deviation range for dimensional accuracy is narrower, and smaller when the dimensional deviation has a smaller impact on assembly quality; corresponding to the connection strength. The value is larger when a small deviation in connection strength might lead to assembly failure or safety hazards, and smaller when the connection strength redundancy is large; corresponding to functional performance. The larger the value, the more significant the impact of fluctuations in functional performance parameters on the overall operational stability; the smaller the value, the more significant the fluctuations in functional performance parameters. When assessing the security of the evaluation module process, the following criteria apply during quantitative evaluation: ; In the formula: This is a quantitative value for process safety; the higher the value, the higher the safety. The evaluation dimensions include compliance of equipment operating parameters, standardization of operating procedures, and compliance of environmental safety parameters. Let z be the weight coefficient for the z-th dimension; This refers to the actual compliance rate, actual standardization rate, or actual compliance rate. For standard compliance, standardization rate, or compliance rate; for and Deviation; The preset security level threshold; To preset the hazard level threshold; and > ; The above formula assesses process safety from three dimensions: compliance of equipment operating parameters, standardization of operating procedures, and compliance of environmental safety parameters. It balances the influence of each dimension through weighting coefficients, and calculates the safety of the assembly process by combining the ratio of actual compliance rate, standardization rate, and compliance rate to the standard value and the degree of deviation. Based on the quantitative results, it divides the process into three levels: safety, warning, and danger, and finally achieves a comprehensive quantification and intuitive judgment of the safety of the assembly process. in , The compliance of corresponding equipment operating parameters, the standardization of operating procedures, and the attainment of environmental safety parameters are recorded as follows: and obey And the initial default settings ; The actual compliance rate is calculated as the ratio of the number of core operating parameters of the equipment that meet the preset standard parameter range during the assembly process to the total number of core operating parameters; the actual standardization rate is calculated as the ratio of the number of assembly steps actually performed by the operator that meet the preset standard assembly process to the total number of steps in the standard assembly process; the actual compliance rate is calculated as the ratio of the number of environmental parameters at the assembly site that meet the preset environmental safety standards to the total number of environmental safety parameters. The configuration module interacts with the modeling module via a wireless network. The modeling module interacts with the acquisition module via a wireless network. The acquisition module interacts with the generation module via a wireless network. The generation module interacts with the control module and the evaluation module via a wireless network.

[0022] In this embodiment, the configuration module assigns a unique electronic identifier to each aerospace component, records its procurement batch, specifications, material properties, and design number, and establishes a one-to-one mapping database between the identifier and the component. The modeling module then runs to organize the three-dimensional geometric parameters, assembly interface tolerances, and process logic of the aerospace component, constructing a data model that includes assembly constraints and process requirements, and simultaneously binding it to the associated database. The acquisition module further collects the operating parameters of the assembly target equipment, component positioning data, and manual operation records, performs format conversion and standardized parsing on the collected data, and stores it in the associated database. Then, the generation module dynamically generates the optimal assembly process sequence, robotic arm motion path configuration strategy, and workstation resource allocation scheme based on the modeling data and parsing results in the associated database, combined with component assembly priority rules. The control module collects the component positioning accuracy, equipment operating status, and process completion progress of the assembly workstation in real time, identifies deviations through threshold comparison, generates adjustment instructions and control signals, and simultaneously updates them to the associated database. Finally, the evaluation module traverses the data stored in the associated database to quantitatively evaluate the assembly quality matching degree and process safety.

[0023] In the above embodiments, the system can achieve full-process traceability of aerospace components, accurately match assembly requirements and resource allocation, dynamically optimize assembly processes and operation paths, monitor assembly accuracy and operating status in real time, and effectively reduce quality hazards and safety risks by quickly correcting deviations, significantly improving assembly efficiency and quality stability, and providing conditions for digital, precise and efficient management and control of aerospace component assembly.

[0024] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of the digital assembly support management system for aerospace components in Example 1 is provided below: A digital assembly support management method for aerospace components, comprising: Each aviation component is assigned an electronic identifier containing a unique traceability code throughout its entire lifecycle, recording procurement batch, specifications, material properties, and design number information. An immutable mapping between the identifier and the information is established through a dynamic weighted hash algorithm to build an associated database. The three-dimensional geometric parameters, assembly interface tolerances and process logic of aerospace components are organized, and the dynamic weight coefficients of assembly constraints are determined through objective functions and constraints. A data model is constructed and bound to the associated database. Collect the operating parameters of the target equipment, component positioning data, and manual operation records, perform format conversion and standardized parsing according to preset logic, and then store the parsing results in the associated database. Based on the associated database data and component assembly priority rules, the optimal assembly process sequence, robotic arm motion path configuration strategy and workstation resource allocation scheme are dynamically generated. The system collects data on the positioning accuracy of the assembly station, the operating status of the equipment, and the progress of the process in real time. It identifies deviations based on dynamically adjusted thresholds, generates adjustment instructions and control signals, and sends them out, while simultaneously updating the associated database. Iterate through the data stored in the associated database, evaluate the assembly quality matching degree and process safety according to the preset quantitative logic, and output the corresponding quantitative evaluation results.

[0025] In summary, the systems and methods described in the above embodiments, during execution, ensure the traceability and data security of aerospace components throughout their entire lifecycle by establishing a unique and tamper-proof association mapping. Furthermore, they construct precise data models by standardizing relevant parameters, improve the integration and matching of assembly data through standardized data processing, and dynamically generate optimal assembly schemes based on design rules. This enables the rational allocation of processes, movement paths, and resources. Real-time monitoring and threshold comparison promptly correct assembly deviations, ensuring assembly accuracy and process stability. Multi-dimensional quantitative evaluation controls assembly quality and process safety, effectively reducing assembly risks and failure probabilities. Overall, this improves the intelligence level and production efficiency of aerospace component assembly, shortens the assembly cycle, and reduces manpower and resource consumption.

[0026] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital assembly support management system for aerospace components, characterized in that, include: The configuration module is used to assign a unique electronic identifier to each aerospace component, record its procurement batch, specifications, material properties, and design number, and establish a one-to-one mapping database between the identifier and the component. The modeling module is used to organize the three-dimensional geometric parameters, assembly interface tolerances, and process logic of aerospace components, construct a data model that includes assembly constraints and process requirements, and synchronously bind it to the associated database. The data model constructed by the modeling module includes dynamic weighting coefficients for assembly constraints. These weighting coefficients are used to quantify the coupled influence of three-dimensional geometric parameters, assembly interface tolerances, and process logic on assembly accuracy. The weighting coefficients are determined through the following objective function and constraint conditions: ； In the formula: To optimize the target value; These are the weighting coefficients for three-dimensional geometric parameters, assembly interface tolerances, and process logic. This is the actual set of three-dimensional geometric parameters; A standard set of three-dimensional geometric parameters; This is the set of tolerances for the actual assembly interfaces; This refers to the standard assembly interface tolerance set. Quantification value of the logical sequence of actual process steps Quantization value of standard sequence KL divergence; for; These are the limit boundary values ​​of the geometric parameters; The limit boundary value of the tolerance; The data acquisition module is used to collect operating parameters of the target equipment, component positioning data, and manual operation records. It performs format conversion and standardized parsing on the collected data and stores it in the associated database. The generation module is used to dynamically generate the optimal assembly process sequence, robotic arm motion path configuration strategy, and workstation resource allocation scheme based on the modeling data and parsing results in the associated database, combined with the component assembly priority rules. The component assembly priority rule expression in the generation module is: ； In the formula: Quantify the assembly priority value; Weights are assigned based on the criticality of components, assembly complexity, and supply timeliness. Assess the criticality of components; To quantify complex assembly values; This represents the deviation between the actual supply time and the planned supply time. The supply time benchmark threshold; The optimal assembly sequence is determined based on the quantified assembly priority value of each component; The generation module stores several shared and non-shared robot arm motion paths for each robot arm, and the quality level of each robot arm motion path is determined by its total stroke. When the generation module generates the optimal robot arm motion path configuration strategy, if the robot arm has a shared robot arm motion path, the configuration is based on the component assembly priority quantification value, so that the robot arm carrying the component with the larger assembly priority quantification value is given priority in configuring the optimal path; if the robot arm does not have a shared robot arm motion path, it enters the waiting queue. When generating the workstation resource allocation scheme in the generation module, the decision is based on the component assembly priority quantification value. A positive correlation matching mechanism between resource supply and priority quantification value is established. Workstation equipment resources, operator resources, and material delivery resources are prioritized to supply components with high assembly priority quantification values. At the same time, the supply allocation ratio is dynamically adjusted based on the resource carrying capacity limit of each workstation. The control module is used to collect the positioning accuracy of parts, equipment operating status and process completion progress of the assembly station in real time. It identifies deviations through threshold comparison, generates adjustment instructions and control signals, and updates them to the associated database in a synchronous manner. The threshold applied in the control module is determined based on the following logic: ； In the formula: Let t be the warning threshold at time t; Let be the threshold value exceeding the limit at time t; This is the initial baseline threshold; This is the threshold adjustment coefficient; The cumulative number of deviations before time t; This represents the total number of detections before time t. This refers to the cycle time of the assembly process; The adjustment command is generated based on the difference between the component positioning accuracy, equipment operating status, process completion progress and corresponding threshold of the assembly station. After the adjustment command is generated, it is synchronously converted into a control signal and sent to the corresponding assembly equipment in real time via industrial Ethernet. At the same time, the command content, control signal parameters and equipment execution feedback results are synchronously written into the associated database. The evaluation module is used to traverse the data stored in the associated database and quantitatively evaluate the assembly quality matching degree and process safety.

2. The digital assembly support management system for aerospace components according to claim 1, characterized in that, The electronic identifier assigned by the configuration module includes a unique traceability code for the entire lifecycle of the component. This traceability code forms an immutable mapping relationship with the purchase batch, specifications, material attributes, and design number in the associated database through a dynamic weighted hash algorithm. The dynamic weighted hash algorithm follows the following rules: ； In the formula: It is a hash encryption algorithm; These are the weighting coefficients; For each parameter, there is an independent hash function. For the procurement batch code, the corresponding numerical matrix, specification parameter quantization vector, material attribute feature value, and the unique integer corresponding to the design number; in, and obey .

3. The digital assembly support management system for aerospace components according to claim 1, characterized in that, The data acquisition module includes a vision sensor, a laser locator, and a force feedback sensor. When standardizing the data, the acquisition module follows the following rules: ； In the formula: The data after purification; This is the original collected data; This represents the moving average value over the k-th data collection period. For adaptive sensitivity coefficients; The moving standard deviation is the value within the k-th data collection period. in, , Initial baseline mean.

4. The digital assembly support management system for aerospace components according to claim 1, characterized in that, The quantitative evaluation logic for assembly quality matching degree in the evaluation module is as follows: ； In the formula: This is a metric for quality matching; The evaluation dimensions include dimensional accuracy, connection strength, and functional performance; is the weight coefficient for the v-th dimension; Let v be the sensitivity coefficient of the v-th dimension; This represents the actual detection value in the v-th dimension; This is the standard value for the v-th dimension.

5. The digital assembly support management system for aerospace components according to claim 4, characterized in that, The security of the assessment module process, when quantitatively assessed, shall comply with the following: ； In the formula: This is a quantitative value for process safety; the higher the value, the higher the safety. The evaluation dimensions include compliance of equipment operating parameters, standardization of operating procedures, and compliance of environmental safety parameters. Let z be the weight coefficient for the z-th dimension; This refers to the actual compliance rate, actual standardization rate, or actual compliance rate. For standard compliance, standardization rate, or compliance rate; for and Deviation; The preset security level threshold; To preset the hazard level threshold; and > .

6. The digital assembly support management system for aerospace components according to claim 1, characterized in that, The configuration module is interconnected with the modeling module via a wireless network. The modeling module is interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the generation module via a wireless network. The generation module is interconnected with the control module and the evaluation module via a wireless network.

7. A digital assembly support management method for aerospace components, wherein the method is an implementation method of the digital assembly support management system for aerospace components as described in any one of claims 1-6, characterized in that, include: Each aviation component is assigned an electronic identifier containing a unique traceability code throughout its entire lifecycle, recording procurement batch, specifications, material properties, and design number information. An immutable mapping between the identifier and the information is established through a dynamic weighted hash algorithm to build an associated database. The three-dimensional geometric parameters, assembly interface tolerances and process logic of aerospace components are organized, and the dynamic weight coefficients of assembly constraints are determined through objective functions and constraints. A data model is constructed and bound to the associated database. Collect the operating parameters of the target equipment, component positioning data, and manual operation records, perform format conversion and standardized parsing according to preset logic, and then store the parsing results in the associated database. Based on the associated database data and component assembly priority rules, the optimal assembly process sequence, robotic arm motion path configuration strategy and workstation resource allocation scheme are dynamically generated. The system collects data on the positioning accuracy of the assembly station, the operating status of the equipment, and the progress of the process in real time. It identifies deviations based on dynamically adjusted thresholds, generates adjustment instructions and control signals, and sends them out, while simultaneously updating the associated database. Iterate through the data stored in the associated database, evaluate the assembly quality matching degree and process safety according to the preset quantitative logic, and output the corresponding quantitative evaluation results.

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