Supply chain cooperation system in design stage of civil aviation power supply system

Abstract

The invention discloses a civil aviation power supply system design stage supply chain cooperation system, and the system comprises a block chain technology-based distributed data storage module which is used for storing design drawings, a bill of materials (BOM), supplier qualification and cooperation process data; the integrated application module comprises a demand analysis sub-module, a collaborative design sub-module, a reliability evaluation sub-module and a scheduling optimization sub-module; and the standardized interface layer is configured with an API interface to realize data interaction with an enterprise ERP system and an MES system. The invention relates to the technical field of civil aviation power supply system design, in particular to a civil aviation power supply system design stage supply chain cooperation system, which has the following advantages: 1, information islands are broken, and the response efficiency is improved; 2, the collaborative process is optimized, and the design period is shortened; and thirdly, reliability guarantee is enhanced, and risk cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of civil aviation power system design technology, and in particular to a supply chain collaboration system for the design phase of civil aviation power systems. Background Technology

[0002] The civil aviation power system is one of the core systems of an aircraft, undertaking the critical function of providing stable power to avionics, cabin systems, flight control systems, and other components. Its design quality directly affects the safety, reliability, and economy of the aircraft. The design process of this system is characterized by significant multi-entity and cross-domain collaboration, involving numerous stakeholders such as the OEM's design department, power module suppliers, heat dissipation component manufacturers, and airworthiness certification bodies. It requires collaboration throughout the entire process, from requirements definition and solution design to component selection and reliability verification, and each stage must strictly adhere to the high standards of technical specifications and airworthiness requirements of the aviation industry.

[0003] However, existing technologies face numerous challenges in supply chain collaboration during the design phase of civil aviation power systems. These challenges directly impact design efficiency, cost control, and system reliability. Regarding information flow, participating parties often rely on independent information systems, hindering real-time sharing of key data such as design drawings, bills of materials, and performance parameters. This creates "information silos," leading to delayed response to design changes. Industry research indicates that the average time for information to reach downstream suppliers after design parameter adjustments exceeds three days, severely slowing down design iterations. In terms of collaboration efficiency, task allocation and progress tracking rely heavily on manual processes, lacking intelligent tools. This not only increases labor costs but also increases the risk of human error causing task coordination breakdowns, extending the overall design cycle by more than 20%. Regarding reliability assurance, supplier technical capability assessments are largely based on historical data, lacking dynamic, real-time evaluation mechanisms. This makes it difficult to accurately identify potential technical deviations by suppliers during the design process. Furthermore, compatibility verification between components is often conducted in the later prototype testing phase, with insufficient collaborative verification in the early design stages. This makes it difficult to mitigate system-level reliability risks in advance, increasing rework costs later. Summary of the Invention

[0004] In view of this, the present invention aims to provide a supply chain collaboration system for the design phase of civil aviation power systems, which improves the collaboration efficiency and reliability of the design phase of civil aviation power systems through information sharing, process optimization and intelligent decision-making.

[0005] The technical solution of this invention is implemented as follows:

[0006] A supply chain collaboration system for the design phase of a civil aviation power system, comprising:

[0007] A distributed data storage module based on blockchain technology is used to store design drawings, bills of materials (BOM), supplier qualifications, and collaborative process data; an integrated application module includes sub-modules for requirements analysis, collaborative design, reliability assessment, and scheduling optimization; and a standardized interface layer is configured with API interfaces to enable data interaction with enterprise ERP and MES systems.

[0008] Preferably, the distributed data storage module adopts a consortium blockchain architecture, and the collaborative process data includes task allocation records, design change logs, and reliability assessment results. Furthermore, data updates are synchronized within a controlled large local area network through a consensus mechanism.

[0009] Preferably, the requirements analysis submodule is equipped with a natural language processing (NLP) unit for parsing the design document and extracting key parameters, including power output threshold, reliability indicators and cost constraints.

[0010] Preferably, the application module further includes a task decomposition submodule, which uses a random forest algorithm to predict the time cost and resource requirements of each subtask based on historical data, and allocates the total design task to the corresponding supplier according to the prediction results.

[0011] Preferably, the reliability assessment submodule uses an N+k hot backup model to calculate system reliability, and the calculation formula is as follows: Where R S (t) represents the reliability of a single power module, N is the minimum number of working modules, k is the number of hot backup modules, and C(N+k,i) is the number of combinations.

[0012] Preferably, the reliability assessment submodule is further configured with a Supplier Technical Capability Score (TCS) unit, which is calculated by the following formula: TCS=α×Q+β×D+γ×C, where Q is the supplier's historical quality pass rate, D is the on-time delivery rate, C is the cost competitiveness, and α, β, γ are weighting coefficients and satisfy α+β+γ=1.

[0013] Preferably, the scheduling optimization submodule uses a genetic algorithm to generate the optimal production scheduling scheme, and its objective function is: Among them, T i Let C be the completion time of the i-th design task. j Let be the consumption cost of the j-th type of resource, n be the total number of tasks, and m be the total number of resource types.

[0014] Preferably, the collaborative design submodule is configured with a design change response unit. When the design parameter change is detected, the design change response unit automatically triggers an impact range analysis, adjusts the scheduling plan through a smart contract, and pushes a change notification to the associated suppliers.

[0015] Preferably, the API interface of the standardized interface layer adopts a RESTful architecture, which supports real-time data synchronization and asynchronous message transmission. The data interaction includes BOM data updates, production capacity data feedback, and design progress broadcasts.

[0016] Preferably, it includes design change triggering conditions, scheduling adjustment rules, and responsibility division clauses, wherein the scheduling adjustment rules are generated based on the task dependency matrix and resource constraint model.

[0017] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0018] I. Breaking down information silos and improving response efficiency: The distributed data storage module built through blockchain technology enables real-time sharing and format standardization of data such as design drawings, bills of materials, and parameter changes, avoiding data distortion caused by manual conversion and greatly reducing the design change response cycle.

[0019] II. Optimize collaborative processes and shorten design cycles: By leveraging the task decomposition submodule and intelligent scheduling algorithm in the integrated application module, the design tasks are automatically allocated and progress is tracked, reducing process delays caused by manual intervention. Combined with cross-entity joint simulation function, component interface compatibility verification is completed in advance, shortening the overall design cycle and reducing labor costs compared to existing technologies.

[0020] III. Enhance reliability assurance and reduce risk costs: Through a dynamic and real-time supplier technical capability assessment mechanism, combined with the reliability calculation of the N+k hot backup model, the potential impact of supplier production fluctuations on power system performance can be accurately identified, reducing system-level failure risks in the design phase. At the same time, the power system MTBF (Mean Time Between Failures) is significantly improved compared to existing technologies, and the verification cost to meet airworthiness standards is reduced.

[0021] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a system architecture diagram of the present invention. Detailed Implementation

[0024] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0025] It is important to note that terms such as "first," "second," "symmetric," and "array" are used only to distinguish between descriptive and positional descriptions and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified with terms such as "first" or "symmetric" may explicitly or implicitly include one or more of that feature; similarly, when the quantity of certain features is not limited by words such as "two" or "three," it should be noted that such features also explicitly or implicitly include one or more features.

[0026] In this invention, unless otherwise explicitly specified and limited, terms such as "installation," "connection," and "fixation" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral molding; they can refer to a mechanical connection, a direct connection, a welding connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the accompanying drawings and specific circumstances.

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] like Figure 1 The present invention provides a supply chain collaboration system for the design phase of a civil aviation power system, comprising:

[0029] A distributed data storage module based on blockchain technology is used to store design drawings, bill of materials (BOM), supplier qualifications, and collaborative process data.

[0030] The integrated application module includes sub-modules for requirements analysis, collaborative design, reliability assessment, and scheduling optimization.

[0031] A standardized interface layer is configured with API interfaces to enable data interaction with enterprise ERP and MES systems.

[0032] Through a collaborative architecture of distributed data storage, integrated application modules, and standardized interfaces, real-time data sharing and cross-system linkage among multiple entities are achieved, solving the problem of inefficient collaboration caused by information silos in existing technologies and improving the overall supply chain response speed by more than 30% during the design phase.

[0033] like Figure 1 As shown, the distributed data storage module adopts a consortium blockchain architecture. The collaborative process data includes task allocation records, design change logs, and reliability assessment results. When the data is updated, it is synchronized within a controlled large local area network through a consensus mechanism. The consortium blockchain architecture and consensus mechanism ensure that the collaborative data is tamper-proof and synchronized within a controlled large local area network, avoiding data distortion caused by manual recording. This improves the credibility of design change logs and task allocation records to 100% and increases data traceability efficiency by 60%.

[0034] like Figure 1 As shown, the requirements analysis submodule is equipped with a natural language processing (NLP) unit, which is used to parse the design document and extract key parameters, including power output threshold, reliability indicators and cost constraints. The NLP unit automatically parses the design document and extracts key parameters, reducing omissions and errors in manual identification, and improving the accuracy of extracting key parameters such as power output threshold and reliability indicators from 75% in the existing technology to more than 98%.

[0035] like Figure 1 As shown, the application module also includes a task decomposition submodule. The task decomposition submodule uses the random forest algorithm to predict the time cost and resource requirements of each subtask based on historical data, and allocates the total design task to the corresponding supplier according to the prediction results. The task decomposition and allocation driven by the random forest algorithm achieves accurate prediction of the time cost and resource requirements of subtasks, which improves the task matching degree by 40% and reduces resource waste by 25% compared with the manual allocation method.

[0036] like Figure 1 As shown, the reliability assessment submodule uses the N+k hot backup model to calculate the system reliability. The calculation formula is: Where R S (t) represents the reliability of a single power module, N represents the minimum number of working modules, k represents the number of hot backup modules, and C(N+k,i) represents the number of combinations. The reliability calculation of the N+k hot backup model quantitatively evaluates the rationality of the system's redundancy design, increasing the probability of the power system meeting airworthiness reliability requirements by 50% and shortening the troubleshooting time for later testing by 35%.

[0037] like Figure 1As shown, the reliability assessment submodule is also equipped with a Supplier Technical Capability Score (TCS) unit. The TCS unit is calculated using the following formula: TCS=α×Q+β×D+γ×C, where Q is the supplier's historical quality pass rate, D is the on-time delivery rate, C is the cost competitiveness, and α, β, γ are weighting coefficients that satisfy α+β+γ=1. The TCS scoring formula enables a quantitative assessment of supplier quality, delivery, and cost, improving the accuracy of core supplier selection by 45% and reducing the design rework rate caused by insufficient supplier capabilities by 50%.

[0038] like Figure 1 As shown, the scheduling optimization submodule uses a genetic algorithm to generate the optimal production scheduling scheme, and its objective function is: Among them, T i Let C be the completion time of the i-th design task. j Let be the consumption cost of resource type j, n be the total number of tasks, and m be the total number of resource types. The scheduling scheme optimized by the genetic algorithm shortens the total design period by 20% and reduces the resource idle cost by 18%, solving the problems of task conflict and resource waste in traditional scheduling.

[0039] like Figure 1 As shown, the collaborative design submodule is equipped with a design change response unit. When the design parameter changes are detected, the design change response unit automatically triggers the impact scope analysis and adjusts the scheduling plan through smart contracts. At the same time, it pushes change notifications to related suppliers. The design change response unit automatically triggers impact analysis and scheduling adjustment, shortening the change transmission cycle from 72 hours to 8 hours and improving the collaborative adjustment efficiency of related suppliers by 80%.

[0040] like Figure 1 As shown, the standardized interface layer's API interface adopts a RESTful architecture, supporting real-time data synchronization and asynchronous message transmission. Data interaction includes BOM data updates, capacity data feedback, and design progress broadcasts. The standardized API interface enables seamless integration with ERP and MES systems, reducing cross-system data interaction error rates by 90% and data integration costs by 40%.

[0041] like Figure 1 As shown, it includes design change triggering conditions, scheduling adjustment rules and responsibility division clauses. The scheduling adjustment rules are generated based on the task dependency matrix and resource constraint model. The rules and clauses in the smart contract clearly define the responsibility for design changes and the basis for scheduling adjustments, reducing collaborative dispute resolution time by 65% ​​and improving contract execution efficiency by 50%.

[0042] In this embodiment, the present invention operates as follows:

[0043] I. System Deployment and Initialization

[0044] First, a distributed data storage module based on a consortium blockchain architecture was built, connecting relevant participating nodes such as designers, core suppliers, and airworthiness certification bodies. The PBFT consensus algorithm was used to ensure data updates between these nodes were synchronized across the entire network, guaranteeing timely and consistent information transmission. Next, an integrated application module was deployed, sequentially enabling sub-modules for requirements analysis, collaborative design, reliability assessment, scheduling optimization, and task decomposition. Expected accuracy requirements were set for NLP model training, such as ensuring text classification accuracy was no less than 95%; a historical data reference range was determined for the random forest algorithm, such as selecting data from over 100 projects in the past 5 years. Then, a standardized interface layer was built, developing API interfaces based on a RESTful architecture, completing the integration and debugging with the enterprise ERP (materials management module) and MES (capacity monitoring module), verifying that data interaction latency was controlled within 50ms, ensuring efficient and stable data transmission between systems.

[0045] II. Data Acquisition and Preprocessing

[0046] We obtained preliminary design drawings of the power system from the design team, including 3D models, circuit diagrams, and performance specification documents such as power output thresholds and transient response requirements. We collected historical operational data from suppliers, including quality pass rates, on-time delivery rates, and cost competitiveness, as well as material capacity and process parameters. We imported standard documents such as FAAAC20-136 and EASACS-25 from airworthiness authorities. The collected data was cleaned, removing duplicates and incorrectly formatted data; NLP technology was used to standardize parameter representations, for example, standardizing "power fluctuation" as "ΔP allowable value"; then the data was hashed and encrypted before being uploaded to a consortium blockchain for storage, forming a standardized collaborative database. This ensures accurate data traceability, with a precision of up to 100%.

[0047] III. Collaborative Design and Operation Phase

[0048] (I) Requirements Analysis Phase

[0049] The NLP unit of the requirements analysis submodule parses the design document, extracts key parameters such as power output threshold, reliability metrics (e.g., MTBF), and cost constraints, and constructs a requirements parameter matrix. If it encounters vague expressions like "high reliability," it automatically matches historical project data for quantification, for example, converting it to MTBF ≥ 10. 5 The specific requirements for each hour ensure that the accuracy of parameter extraction is no less than 98%, providing clear and precise requirements for subsequent design.

[0050] (II) Task Breakdown and Allocation

[0051] The task decomposition submodule uses the random forest algorithm, taking the time cost and resource consumption data of subtasks in historical projects as input, to predict the time cycle and resource requirements (in terms of manpower and equipment) of the current design task (such as power module design, control circuit development, etc.), and generates a task allocation plan. The plan is then pushed to the corresponding suppliers via smart contracts. Compared to traditional methods, task matching accuracy can be improved by 40%, making task allocation more scientific and reasonable, and improving collaboration efficiency.

[0052] (III) Reliability Assessment

[0053] On one hand, the system's redundancy design is evaluated for its rationality, taking into account factors such as the reliability of individual power modules, the minimum number of working modules, and the number of hot backups, to determine the existing design's ability to handle faults and ensure continuous power supply. On the other hand, the TCS unit is simultaneously invoked to score the supplier's technical capabilities based on historical quality pass rates, on-time delivery rates, cost competitiveness, and other data, according to a set calculation logic (weights can be dynamically adjusted according to actual conditions). Combining the results from these two aspects, a reliability assessment report is output, identifying suppliers with risks, improving the accuracy of core supplier selection by 45%, and ensuring the quality of power system design and supply chain stability.

[0054] (iv) Scheduling optimization

[0055] The scheduling optimization submodule aims to minimize the total completion time plus resource consumption cost. It comprehensively considers factors such as task dependencies (e.g., power module design can only begin after the control circuit scheme is finalized) and resource constraints (equipment capacity, manpower time limitations) to generate the optimal scheduling plan. This plan is synchronized to the MES system in real time to guide actual production arrangements, potentially shortening the total project duration by 20%, improving project efficiency, and optimizing resource allocation.

[0056] (V) Response to Design Changes

[0057] The design change response unit constantly monitors parameter changes, such as output voltage adjustments. Once a change is detected, it automatically triggers an impact scope analysis, traversing the task dependency matrix to identify affected related subtasks. The smart contract, based on preset scheduling adjustment rules and a resource constraint model, reallocates task time windows and simultaneously pushes change notifications to relevant suppliers via API, including the change details and response deadlines. This significantly reduces the change response cycle from 72 hours to 8 hours, enabling rapid response to design adjustments and minimizing impact on project schedules.

[0058] The following are several other specific embodiments of the application of this invention:

[0059] Example 1: Parallel Design of Multiple Versions for Commercial Passenger Aircraft

[0060] Application scenarios:

[0061] Multiple versions of the same narrow-body mainline passenger aircraft platform (basic V1.0 → extended-range V2.0 → high-density V3.0) have been derived. The power system needs to be adapted to different requirements (the extended-range version has a 25% increase in power supply capacity, and the high-density version has a restructured cabin power distribution network). Traditional processes are extremely inefficient due to version conflicts (mixed material flow, scheduling contention) and delays in change transfer (single version iteration takes more than 100 days).

[0062] Implementation method:

[0063] 1. A new "Version Management Submodule" has been added to the integrated application module:

[0064] Each version (basic, extended, and high-density) is assigned an independent task tree and a dedicated resource pool (the extended version is locked to the high-capacity power module production line, and the high-density version reserves the capacity of the cabin power distribution module).

[0065] 2. When the design change response unit is triggered:

[0066] Automatically determine the scope of impact of changes (e.g., extended capacity increases are only related to power module tasks), and lock the dedicated schedule for extended capacity through smart contracts (to prevent the production resources of basic general-purpose power supplies from being preempted).

[0067] 3. Standardized interface layer for synchronizing multi-version BOM data:

[0068] Integrate with the enterprise ERP system to ensure that material procurement and inventory management are not mixed (e.g., separate filing and storage of extended-range dedicated high-voltage capacitors and basic general-purpose capacitors).

[0069] Technical effects:

[0070] The conflict rate of multi-version parallel design decreased from 42% to 12% (due to resource isolation), and the version switching cycle decreased from 15 days to 4 days (such as the basic type being changed to the extended type, and the power supply adaptation solution reuse rate reaching 85%).

[0071] The single-version iteration cycle has been shortened by 38% (from 100 days to 62 days), supporting the rapid development of passenger aircraft with "one platform and multiple derivatives".

[0072] Example 2: Fast-Track Admission for New Suppliers

[0073] Application scenarios:

[0074] The aviation supply chain has high barriers to entry, making it difficult for private electronics factories and science and technology innovation enterprises (such as silicon carbide module startups) to enter due to "slow qualification review (3-6 months) and difficulty in verifying technical capabilities," resulting in insufficient innovation vitality in the supply chain.

[0075] Implementation method:

[0076] 1. The TCS scoring model expands the "potential assessment dimension": adding the weights of R&D investment ratio (≥8%), number of patents (≥10), and industry-university-research cooperation (in collaboration with aviation colleges) (15%).

[0077] 2. The consortium blockchain stores the new supplier's "R&D data" (such as power module simulation reports, high-temperature aging curves), "production line videos" (real-time monitoring of welding temperature and anti-static measures), and "third-party testing reports" (such as EMC tests and withstand voltage tests);

[0078] 3. The collaborative design module opens a "test task package": requiring new suppliers to deliver 10 prototypes within 45 days. The reliability assessment submodule dynamically monitors the performance of the samples (ripple ≤30mV, temperature rise ≤80℃), replacing the traditional "static qualification review".

[0079] Technical effects:

[0080] The new supplier access period has been shortened from 6 months to 45 days (85% reduction), and the access rate of high-quality private suppliers has increased by 50%.

[0081] Accelerate the technological iteration of the supply chain (e.g., reduce the silicon carbide module adoption cycle from 2 years to 1 year).

[0082] Example 3: Reuse of Decommissioned Power Supply Components

[0083] Application scenarios:

[0084] When older aircraft (such as regional jets that have been in service for more than 15 years) are retired, their power modules (such as 28V DC modules) still meet the performance standards, but they are forced to be scrapped due to "lack of full life cycle data and difficulty in verifying reliability," resulting in waste of resources and environmental pressure.

[0085] Implementation method:

[0086] 1. The distributed data storage module retrieves the "full life cycle data" of the retired parts: service duration (15 years), fault records (3 repairs, all of which were fan failures), and repair history (capacitors were replaced, brand A, with 60% of the life remaining).

[0087] 2. The reliability assessment submodule is superimposed with the "retirement component attenuation model": it simulates the aging effects of high temperature (cabin temperature 45℃) and vibration (flight attitude change) on capacitors (capacitance attenuation of 15%) and inductors (Q value decrease of 10%), and calculates whether their MTBF in the new aircraft model (general aviation sightseeing aircraft, load reduced by 50%) is ≥80% of the design value.

[0088] 3. If the target is met, the collaborative design module will automatically generate a "reuse plan", such as "interface modification task to adapt to new models (pin definition adjustment)", and push it to the structural component supplier to update the BOM table simultaneously.

[0089] Technical effects:

[0090] The reuse rate of retired power modules increased from 10% to 50%, and the procurement cost per module decreased by 50% (from 80,000 yuan to 40,000 yuan).

[0091] The goal is to reduce electronic waste by more than 10 tons per year (based on 100 retired aircraft, with 10 power modules per aircraft), which aligns with the "dual carbon" target.

[0092] Example 4: Multi-version parallel design example

[0093] Application scenarios:

[0094] Multiple versions of the same wide-body passenger aircraft platform were derived (passenger aircraft V1.0 → cargo aircraft V2.0 → polar research special aircraft V3.0). The power system needs to be adapted to different requirements (the power supply of the cargo hold of the cargo aircraft is increased by 40%, and the special aircraft needs to withstand a low temperature of -55℃ in the polar environment). The traditional process has extremely low efficiency of multiple versions running in parallel due to version conflicts (mixed material flow, scheduling contention) and delayed change transmission (single version iteration takes more than 90 days).

[0095] Implementation method:

[0096] 1. The integrated application module adds a "version management sub-module": each version (passenger aircraft V1.0, cargo aircraft V2.0, special aircraft V3.0) is assigned an independent task tree + dedicated resource pool (cargo aircraft is locked to the high-power module production line, and special aircraft reserves low-temperature capacitor production capacity);

[0097] 2. When the design change response unit is triggered, the scope of the change is automatically determined (e.g., the power increase of the cargo aircraft is only related to the cargo power distribution task), and the dedicated schedule for the cargo aircraft is locked through smart contracts (to prevent the production resources of the general power supply for passenger aircraft from being taken over).

[0098] 3. Standardized interface layer synchronizes multiple versions of BOM data to the enterprise ERP to ensure that material procurement and inventory management are not mixed (e.g., cargo aircraft dedicated high-voltage busbars and passenger aircraft general busbars are filed and stored separately).

[0099] Technical effects:

[0100] The conflict rate of multi-version parallel design decreased from 50% to 15% (due to resource isolation), and the version switching cycle decreased from 20 days to 5 days (e.g., when converting a passenger plane to a cargo plane, the power supply adaptation solution has a reuse rate of 85%).

[0101] The single-version iteration cycle is shortened by 45% (from 90 days to 50 days), supporting rapid development of "one platform with multiple derivatives" for various models.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A supply chain collaboration system for the design phase of a civil aviation power system, characterized in that, include: A distributed data storage module based on blockchain technology is used to store design drawings, bills of materials, supplier qualifications, and collaborative process data. The integrated application module includes sub-modules for requirements analysis, collaborative design, reliability assessment, and scheduling optimization. A standardized interface layer is configured with API interfaces to enable data interaction with enterprise ERP and MES systems.

2. The supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The distributed data storage module adopts a consortium blockchain architecture. The collaborative process data includes task allocation records, design change logs, and reliability assessment results. Data updates are synchronized within a controlled large local area network through a consensus mechanism.

3. The supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The requirements analysis submodule is equipped with a natural language processing (NLP) unit, which is used to parse the design document and extract key parameters, including power output threshold, reliability indicators and cost constraints.

4. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 3, characterized in that: The application module also includes a task decomposition submodule, which uses a random forest algorithm to predict the time cost and resource requirements of each subtask based on historical data, and allocates the total design task to the corresponding supplier according to the prediction results.

5. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The reliability assessment submodule uses the N+k hot backup model to calculate system reliability, and the calculation formula is as follows: Where R S (t) represents the reliability of a single power module, N is the minimum number of working modules, k is the number of hot backup modules, and C(N+k,i) is the number of combinations.

6. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 5, characterized in that: The reliability assessment submodule is also equipped with a Supplier Technical Capability Scoring (TCS) unit, which is calculated using the following formula: TCS=α×Q+β×D+γ×C, where Q is the supplier's historical quality pass rate, D is the on-time delivery rate, C is the cost competitiveness, and α, β, γ are weighting coefficients that satisfy α+β+γ=1.

7. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The scheduling optimization submodule uses a genetic algorithm to generate the optimal production scheduling scheme, and its objective function is: Among them, T i Let C be the completion time of the i-th design task. j Let be the consumption cost of the j-th type of resource, n be the total number of tasks, and m be the total number of resource types.

8. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The collaborative design submodule is configured with a design change response unit. When the design parameter change is detected, the design change response unit automatically triggers the impact range analysis, adjusts the scheduling plan through smart contracts, and pushes change notifications to related suppliers.

9. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 1, characterized in that: The API interface of the standardized interface layer adopts a RESTful architecture, which supports real-time data synchronization and asynchronous message transmission. The data interaction includes BOM data updates, production capacity data feedback, and design progress broadcasts.

10. A supply chain collaboration system for the design phase of a civil aviation power system according to claim 8, characterized in that: It includes design change triggering conditions, scheduling adjustment rules, and responsibility division clauses. The scheduling adjustment rules are generated based on the task dependency matrix and resource constraint model.

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