Civil aviation industry standard data digital structure credible processing method and system

Abstract

The application discloses a kind of civil aviation industry standard data digital structure credible processing method and system, its method includes: S1, civil aviation industry standard data is obtained and is stored as structured standard database, structured standard database is constructed according to upper and lower level architecture;S2, standard data in structured standard database is carried out digital signature verification and block chain storage evidence credible processing;S3, all data items in structured standard database are divided into three categories of numerical class, requirement class and procedure class and are respectively identified and annotated and readable standardization processing;S4, access structured standard database and input field scene, name or / and object Single index word or combination index word retrieval and query and output relevant data item.The application can ensure the authenticity, integrity and effectiveness of digital civil aviation industry standard clauses, promote the real-time dynamic exchange, use and update of industry standard data, promote the credible flow of standard requirements within the industry.

Description

Technical Field

[0001] This invention relates to the field of trusted storage, search and processing of civil aviation data, and in particular to a trusted processing method and system for the digital structure of civil aviation industry standard data. Background Technology

[0002] A new round of technological revolution and industrial transformation, represented by next-generation information technology, is accelerating, making the digital transformation of the economy and society a trend of the times. Standards, as the technical support for economic activities and social development, are inevitably undergoing digital transformation. International and regional standardization organizations such as ISO, IEC, CEN, and CENELEC, as well as various countries, have incorporated the digital transformation of standards into their standardization strategies, pioneering research and application in fields such as industry, construction, and social governance. Currently, civil aviation standards mainly exist in the form of paper texts or electronic documents, which is inconvenient for relevant personnel to use. On the one hand, the specific practical processes at each stage of the entire lifecycle of civil aviation standards—from formulation and revision to implementation and application—heavily rely on the understanding and memory of the standards by relevant personnel. This leads to problems such as long processing cycles, low timeliness, inconvenience in use, and difficulty in guaranteeing accuracy. On the other hand, standards often involve nested references; a standard usually cites or references multiple standards, and the referenced standards will continue to cite even more standards. Managing the structural relationships and version changes between such long chains and numerous paper or electronic document standards is extremely difficult in practice. Furthermore, with the development of technology, standardization work is facing more and more integration and innovation across countries, regions, industries, departments, technologies, and fields, all of which make the digitalization of standards an urgent need in current standardization practice.

[0003] The civil aviation industry is complex in its operational technology field, involving the coordinated application of multiple professional technologies. The technical standards of these fields often overlap. Currently, the digital information processing of civil aviation standard data does not consider the rigor, reliability, and data security of civil aviation standard data, and can only address single technical fields and single needs. (For example, patent application CN117667890A discloses a method and system for constructing a knowledge base for standard digitization, which proposes methods such as knowledge data preprocessing, knowledge association levels, named data entities, and entity relationship extraction for low-carbon park carbon emission systems, ultimately resulting in a standard digitized knowledge base. Patent application CN116542627A discloses a method for digital management of technical standards, proposing related management system functions and implementation schemes, capable of fully integrating cigarette technical standard information.) The management efficiency of high-tech standards for cigarettes is improved. Patent application CN114116918A discloses a method, system, equipment, and storage medium for managing technical standards in power generation groups. This method can select the group's technical standards to be released and their status based on release requirements, and determine the standard distribution targets based on the status. This effectively reduces the workload of power generation companies' technical personnel in sorting through a large number of technical standards, facilitating standardized management of power plants according to the group's technical standards and avoiding equipment damage due to non-standard management. However, the aforementioned technologies are not applicable to the civil aviation industry (their functions are relatively limited compared to the civil aviation data field). Existing technologies do not address how to use fully digitized standards in the production process, or how to ensure cross-validation of standard requirements, thus failing to provide effective guidance for standardization in the digital production process. Furthermore, as a public transportation service industry, the civil aviation sector needs to ensure the safety and integrity of its production systems. While standard digitization promotes efficiency and innovation across industries, it also inevitably brings challenges in data security and privacy protection. Therefore, in the process of standard digitization in the civil aviation industry, it is crucial to address how digital standards can securely and reliably guide the operation of production systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for the credible processing of digital structured data in the civil aviation industry. This method involves the orderly processing of civil aviation industry standard data, including structured processing, digital signature verification and blockchain notarization, identification and annotation, and readable standardization. This process yields credible and machine-readable structured data items, enabling credible data querying and retrieval using single or combined index terms. It also outputs credible related data items, source link indexes, and related data items and indexes, thereby promoting the credible flow of industry standard data within the industry.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A reliable method for digital structure processing of standard data in the civil aviation industry, the method comprising:

[0007] S1. Obtain standard data from the civil aviation industry, perform structured processing, and store it as a structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being data items that are logically related to each other.

[0008] S2. Perform digital signature verification and blockchain-based evidence storage on the standard data in the structured standard database to ensure its credibility.

[0009] S3. All data items in the structured standard database are divided into three categories: numerical, requirement, and program. For numerical items, identification, labeling, and readability normalization are performed according to domain scenario, name, object, data type, and content. For requirement items, identification, labeling, and readability normalization are performed according to domain scenario, name, object, and related domain. For program items, identification, labeling, and readability normalization are performed according to domain scenario, name, object, and program execution content. The structured standard database extracts the domain scenario, name, and object of the data items as index terms and labels them accordingly in the hierarchical architecture.

[0010] S4. Access the structured standard database and enter single or combined index terms of domain scenario, name and / or object to retrieve and query relevant data items.

[0011] To better implement the present invention, in method S4, a structured standard database is accessed and a single keyword or combination of keywords of the hierarchical architecture is input, and all relevant data items are output according to the hierarchical architecture.

[0012] Preferably, in method S1, during the structured processing of civil aviation industry standard data, a civil aviation semantic language model is constructed and trained using the civil aviation industry standard data. The civil aviation semantic language model identifies and classifies data items with logical relationships, identifies four types of data in the civil aviation industry standard data—text, mathematical expressions, tables, and graphs—and assigns them numbers. The numbering rules and methods are as follows:

[0013] Text data types use a segment-by-segment numbering method, with the numbering rule being: line number - W - segment sequence number, and the line numbering rule being: MH / T XXXX-XXXX; mathematical expressions use a sequential numbering method, with the numbering rule being: line number - E - expression sequence number; tables use a sequential numbering method, with the numbering rule being: line number - T - table sequence number; graphics use a sequential numbering method, with the numbering rule being: line number - P - graphic sequence number; all data in the civil aviation industry standard data are numbered, labeled, and assigned to a hierarchical structure.

[0014] Preferably, in method S2, the data that has passed digital signature verification is processed for electronic signature using the SM2 algorithm, and the data in the civil aviation industry standard data obtains a unique corresponding electronic signature value; the electronic signature processing first converts the object to be signed into a fixed-length format file, and then performs electronic signature processing in sequence.

[0015] Preferably, in method S2, after digital signature verification, the blockchain notarization process uses blockchain notarization technology to process the summary and / or attribute data of the data in the civil aviation industry standard data on the blockchain, obtains a unique notarization address information, and stores it in association with the electronic signature value.

[0016] Preferably, in method S3, the data type and content of the numeric class are judged and identified as follows:

[0017] If the data type is identified as an interval type, it is labeled as an interval type. The upper and lower limits of the interval type indicator are decomposed into two indicators, namely the upper limit and the lower limit of the indicator, and the end values ​​of the upper and lower limits are identified and then normalized for readability.

[0018] If the identified data type is a direction type, it is labeled as an indicator direction. The indicator direction includes less than, greater than, equal to, not less than, and not greater than, and is processed for readability normalization.

[0019] In the numeric class, the data type and content are converted to decimal or integer values.

[0020] Preferably, in method S3, the requirement class and the program class are processed by word segmentation, part-of-speech tagging and named entity recognition using the trained civil aviation semantic language model; the numerical class, requirement class and program class are converted into JSON or / and XML format text after readable normalization processing.

[0021] Preferably, the acquired civil aviation industry standard data includes existing data, incremental data, and revised data. For incremental and revised data, data consistency verification is required. This verification includes consistency verification of standardized terminology and consistency verification of numerical indicators. The method for verifying the consistency of standardized terminology is as follows: extract the standardized terminology library from the trusted structured standard database and perform consistency verification on related terms in the incremental and revised data, and output alarms. The method for verifying the consistency of numerical indicators is as follows: extract the numerical content from the numerical data items in the incremental and / or revised data, search for the corresponding data items in the existing standard from the trusted structured standard database, and ensure that the lower limit of the indicator in the incremental and / or revised data is not greater than the upper limit of the indicator in the existing standard, and the upper limit of the indicator is not less than the lower limit of the indicator in the existing standard.

[0022] Preferably, the structured standard database is stored in a distributed cluster. The structured standard database is networked and communicates with authorized access network terminals using a dedicated information interface. When the access network terminal performs a search query, it outputs reliable related data items, source link indexes, and related data items and indexes.

[0023] A digital structured and trustworthy processing system for civil aviation industry standard data includes a data acquisition module, a structured standard database, a trustworthy processing module, a labeled and readable normalized processing module, and a network interface module. The data acquisition module acquires civil aviation industry standard data, performs structured processing, and stores it in the structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being logically related data items. The trustworthy processing module performs digital signature verification and blockchain notarization on the standard data in the structured standard database. The labeled and readable normalized processing module divides all data items in the structured standard database into three categories: numerical, requirement, and program. The system categorizes data types by domain scenario, name, object, data type, and content for identification, labeling, and readability standardization. For requirement types, it identifies, labels, and standardizes data based on domain scenario, name, object, and associated domain. For program types, it identifies, labels, and standardizes data based on domain scenario, name, object, and program execution content. The structured standard database extracts the domain scenario, name, and object of data items as index terms and labels them accordingly in the hierarchical architecture. The network interface module is used for data interaction; when accessing the structured standard database and inputting a single or combined index term (domain scenario, name, or / and object), it outputs the relevant data items retrieved and queried.

[0024] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0025] (1) The present invention obtains civil aviation industry standard data and performs structured processing, digital signature verification and blockchain evidence storage, identification and labeling and readable standardization processes in an orderly manner. It can obtain credible and machine-readable structured data items, realize credible query and retrieval of single or combined index terms, output credible related data items, source link index and related data items and indexes, and promote the credible flow of industry standard data within the industry.

[0026] (2) This invention covers requirement-type standard content, numerical standard content and procedural standard content, which can ensure the authenticity, completeness and validity of digital civil aviation industry standard clauses, promote the transformation of standard data management and use from single system database retrieval to real-time dynamic exchange, use and update of industry standard data, and promote the credible flow of standard requirements within the industry.

[0027] (3) The present invention can effectively perform conflict analysis of standard requirements and ensure the consistency of the same requirements, similar requirements and requirements with upstream and downstream relationships among digital standards.

[0028] (4) This invention is aimed at the coordinated application of multiple professional technologies in the civil aviation industry, realizing the complete digitization and cross-verification of various standard requirements, and effectively guiding the standardization of the civil aviation digital production process, solving the technical problem of providing safe and reliable guidance for the operation of the civil aviation production system through digital standards. Attached Figure Description

[0029] Figure 1 This is a flowchart of the digital structure reliability processing method of the present invention;

[0030] Figure 2 This is a simplified diagram illustrating the implementation principle of reliable processing for identifying existing standard data, as exemplified in this embodiment.

[0031] Figure 3 This is a schematic diagram illustrating the principle of readable normalization processing for numerical classes, requirement classes, and program classes in the embodiment. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to embodiments:

[0033] Example

[0034] like Figure 1 , Figure 2 As shown, a reliable processing method for the digital structure of civil aviation industry standard data includes the following steps:

[0035] S1. Obtain standard data from the civil aviation industry, perform structured processing, and store it as a structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being logically related data items.

[0036] In some embodiments, during the structured processing of civil aviation industry standard data in method S1, a civil aviation semantic language model is constructed and trained using the civil aviation industry standard data. The civil aviation semantic language model identifies and classifies data items with logical relationships, identifies four data types in the civil aviation industry standard data—text, mathematical expressions, tables, and graphs—and assigns them numbers. The numbering rules and methods are as follows:

[0037] Text data types use a segment-by-segment numbering method, with the numbering rule being: line number - W - segment sequence number, and the line numbering rule being: MH / T XXXX-XXXX; mathematical expressions use a sequential numbering method, with the numbering rule being: line number - E - expression sequence number; tables use a sequential numbering method, with the numbering rule being: line number - T - table sequence number; graphics use a sequential numbering method, with the numbering rule being: line number - P - graphic sequence number; all data in the civil aviation industry standard data are numbered, labeled, and assigned to a hierarchical structure.

[0038] In this embodiment, the structured standard database is stored in a distributed cluster. The structured standard database is networked and communicates with authorized network access terminals through a dedicated information interface. When the network access terminal performs a search query, it outputs trusted related data items, source link indexes, and related data items and indexes.

[0039] Method S1 primarily targets the structuring of existing and newly compiled civil aviation industry standard data. For existing civil aviation industry standards carried by paper or formatted documents, the standard text is structured using text recognition technology. For newly compiled civil aviation industry standards carried by formatted documents, the standard text is structured using text recognition technology. For newly compiled civil aviation industry standards carried by structured data, the data format is standardized according to the standard format to complete the structuring of the standard text.

[0040] This embodiment takes the standard digitization of certain clauses in the data requirements of "General Requirements for Logistics Operation of Civil Unmanned Aerial Vehicle Systems Part 1: Island Scenarios" (MH / T 2014-2023) as an example, and provides an implementation example of using the present invention for reliable standard digitization. During the structured processing of method S1, the structured processing results of the following data are as follows:

[0041] MH / T 2014-2023-W-9.3.1: "Operator data shall meet the relevant requirements of MH / T 2011";

[0042] MH / T2014-2023-W-9.3.2: "Data from unmanned aerial vehicle (UAV) systems shall meet the relevant requirements of MH / T2011";

[0043] MH / T 2014-2023-T-001.001: "Real name of maintenance personnel, not exceeding 128 characters, int format";

[0044] MH / T 2014-2023-E-001: "The signal packet loss rate in island scenarios shall not exceed 0.1%";

[0045] MH / T 2014-2023-E-002: "Data transmission delay shall not exceed 2000ms".

[0046] S2. Perform digital signature verification and blockchain notarization on the standard data in the structured standard database to ensure its credibility (perform credibility processing on the structured standard data to solve the integrity of the transmission and storage of structured standard data, and to determine the authenticity of the structured standard data, thereby ensuring the credibility of the structured standard data).

[0047] In some embodiments, in method S2, the data that has passed digital signature verification is processed using the SM2 algorithm for electronic signature processing (electronic signature processing can ensure the authenticity and integrity of industry standard terms), and the data in the civil aviation industry standard data obtains a unique corresponding electronic signature value; the electronic signature processing first converts the object to be signed into a fixed-length format file (to improve the efficiency of signature calculation, the object to be signed can be converted into a fixed-length format file first), and then performs electronic signature processing sequentially. For example, text can be extracted into a digest, and images can be converted into a base64-bit format file. Therefore, the calculation of the electronic signature value of the i-th signature object is expressed as follows:

[0048] Sign_Value(i)=SM2(Hash(Object(i), Base64(Object(i)))).

[0049] In the example of digitizing certain clauses regarding data requirements in the standard "General Requirements for Logistics Operation of Civil Unmanned Aerial Vehicle Systems Part 1: Island Scenarios" (MH / T 2014-2023), encryption using the SM2 algorithm with an asymmetric key can yield a digital signature. A unique association is then established between the digital signature and the standard clause, as shown in the example below:

[0050] MH / T 2014-2023-W-9.3.1:Sign_Value(1);

[0051] MH / T 2014-2023-W-9.3.2:Sign_Value(2);

[0052] MH / T 2014-2023-T-001.001:Sign_Value(3);

[0053] MH / T 2014-2023-E-001:Sign_Value(4);

[0054] MH / T 2014-2023-E-002:Sign_Value(5).

[0055] Preferably, after digital signature verification, blockchain-based notarization employs blockchain notarization technology to store the summary and / or attribute data of the civil aviation industry standard data on the blockchain. This ensures the version and effective date of the industry standard clauses, obtains a unique notarization address, and stores it in association with the electronic signature value. The calculation for notarization of the i-th industry standard data entry is expressed as follows:

[0056] address(i)=BK(Hash(Object(i))).

[0057] In the example of digitizing certain clauses in the data requirements section of the standard "General Requirements for Logistics Operation of Civil Unmanned Aerial Vehicle Systems Part 1: Island Scenarios" (MH / T 2014-2023), the source of the digitized standard requirement clauses (i.e., the identity of the signer) can be confirmed through the signature value, and the completeness of the received clauses can also be verified. If the signature verification is successful, blockchain technology can be used for data notarization, and a unique standard clause notarization address can be obtained, as shown in the example below:

[0058] MH / T 2014-2023-W-9.3.1:address(1);

[0059] MH / T 2014-2023-W-9.3.2:address(2);

[0060] MH / T 2014-2023-T-001.001:address(3);

[0061] MH / T 2014-2023-E-001:address(4);

[0062] MH / T 2014-2023-E-002:address(5).

[0063] By parsing the evidence storage address, information such as the standard evidence storage time, content, storage holder, and uploader can be obtained, as shown in the example below:

[0064]

[0065] S3. All data items in the structured standard database are divided into three categories: numerical, requirement, and program. The existing industry standard data that has undergone structured processing, along with the evidence address information, is processed item by item (such as text, mathematical expressions, tables, or graphics) for machine readability. For numerical data, identification, labeling, and readability normalization are performed according to domain scenario, name, object, data type, and content. For requirement data, identification, labeling, and readability normalization are performed according to domain scenario, name, object, and related domain. For program data, identification, labeling, and readability normalization are performed according to domain scenario, name, object, and program execution content. The structured standard database extracts the domain scenario, name, and object of each data item as index terms and labels them accordingly in the hierarchical architecture. For structured standard data that has undergone trustworthiness processing, compilation methods are used to translate it into a text format that computer programs can read and understand. As industry standards need to be used in complex civil aviation production scenarios, the machine readability process also needs to meet the consistency of indicators between different professional standards and the ease of retrieval and use.

[0066] In some embodiments, the data type and content of a numeric class are judged and identified as follows:

[0067] If the identified data type is an interval type, it is labeled as such. The upper and lower limits of the interval type indicator are decomposed into two indicators, namely the upper limit and lower limit of the indicator, and the endpoints of the upper and lower limits are identified. Readability normalization processing is then performed. In the example of digitizing certain clauses regarding data requirements in "General Requirements for Logistics Operation of Civil Unmanned Aerial Vehicle Systems Part 1: Island Scenarios" (MH / T 2014-2023), taking the standard requirement of "MH / T 2014-2023-E-001" as an example, the example is as follows:

[0068] <root>

[0069] <no> MH / T2014-2023-E-001< / no> (Standard Number)

[0070] <text>The signal packet loss rate in island scenarios should not exceed 0.1% (Standard No.)

[0071] <name> Signal packet loss rate< / name> (Indicator Name)

[0072] <ulimit> 0.0001< / ulimit> (Indicator ceiling)

[0073] <ue> True< / ue> (Can it be equal to the upper limit?)

[0074] <llimit> 0< / llimit> (Lower limit of the indicator)

[0075] <le> True< / le> (Can it be equal to the lower limit?)

[0076] <field> Unmanned aerial vehicles / islands< / field> (Business Areas)

[0077] <0bject>C2 Link< / 0bject><!--0bject--> (Standardized Objects)

[0078] <pfield> Unmanned aerial vehicles (UAVs), flight control, operation< / pfield> (Domain Reference Tags)

[0079] <pobject> Communication link, control link, monitoring, navigation, communication< / pobject> (Object reference tag)

[0080] <signature> *******< / signature> (Signature data)

[0081] <address>*********< / address> (Evidence storage address)

[0082] < / text> < / root>

[0083] The XML file above contains information such as indicator name, standardized object, application field, upper and lower limits of indicator, authenticity verification data, and integrity verification data.

[0084] If the identified data type is a direction type, it is labeled as an indicator direction. The indicator direction includes less than, greater than, equal to, not less than, and not greater than, and is processed for readability normalization.

[0085] In the numeric class, the data type and content are converted to decimal or integer values.

[0086] like Figure 3As shown, the method for numerical data identification, annotation, and readability normalization includes annotation metadata, indicator decomposition, indicator direction annotation, indicator extraction and standardization, and format conversion, ultimately resulting in machine-readable numerical standard data. Examples of the composition of annotation metadata according to domain scenario, name, object, data type, and content are shown in the table below:

[0087]

[0088] During format conversion, data is uniformly converted to JSON or / and XML format text. For numerical industry standard data, a certain information interface is generally used to compare the consistency between the information generated in the production process and the standard requirements, and return the standard comparison results and the standard source index. (Because there are many standards in the civil aviation industry and the production process is very complex, the civil aviation enterprise's system transmits the standard range to be retrieved through the interface (e.g., for fatigue management, it can automatically use the aforementioned metadata to construct air traffic control + fatigue management + dispatcher) to retrieve the standard clause requirements, and input directly related standard requirements and peripheral requirements (e.g., inputting the fatigue management requirements for pilots and maintenance release personnel)).

[0089] In some embodiments, the requirement class and the procedure class are processed by word segmentation, part-of-speech tagging and named entity recognition using a trained civil aviation semantic language model; the data of the numerical class, requirement class and procedure class are converted into JSON or / and XML format text after readable normalization processing.

[0090] like Figure 3 As shown, the methods for class identification annotation and readability normalization include annotation metadata, natural language processing of the civil aviation semantic language model, and format conversion, ultimately obtaining standard data for machine-readable text classes (specifically, the required classes). Examples of the annotation metadata structure based on domain scenario, name, object, and associated domain are shown in the table below:

[0091]

[0092] During format conversion, the data is uniformly converted to JSON or / and XML format text. For data requiring industry standards, a specific information interface is typically used to provide feedback on the specific indicator requirements for the standardized object and the index of the standard source.

[0093] like Figure 3 As shown, the methods for program class identification, annotation, and readability normalization include annotation metadata, natural language processing of the civil aviation semantic language model, and format conversion, ultimately yielding machine-readable program class standard data. Examples of the annotation metadata structure based on domain scenario, name, object, and program execution content are shown in the table below:

[0094]

[0095]

[0096] During format conversion, the data is uniformly converted to JSON or / and XML text format. For industry standard data requiring certain types of data, and for industry standard data requiring certain types of programs, when performing program compliance verification, a certain information interface is generally used to compare the program steps of the standardized object in the standard, and to return the comparison results and the standard source index; when providing program compliance guidance, a certain information interface is generally used to return the next work content of the program steps of the standardized object returned in the standard, and the standard source index. This invention uses the formation of a signature value to complete the verification of the authenticity and integrity of the standard requirement data, and uses the formed evidence storage address to complete the verification of the validity of the standard requirement.

[0097] S4. Access the structured standard database and enter single or combined index terms of domain scenario, name and / or object to retrieve and query relevant data items.

[0098] In some embodiments, in method S4, a structured standard database is accessed and a single keyword or combination of keywords for the hierarchical architecture is input, and all relevant data items are output according to the hierarchical architecture.

[0099] Preferably, under the premise of ensuring data transmission security and consistency with standard requirements, machine-readable standard requirements are transformed into contractual form, thereby enabling a large number of civil aviation enterprises to directly use the standard content in their production information systems, reducing human intervention, avoiding human error, and ensuring the overall safety of civil aviation operations; at the same time, for standard revision needs generated during the production process, suggestions for standard use are promptly fed back to ensure the timeliness and accuracy of the evaluation of the implementation effect of civil aviation industry standards, and to achieve the self-improvement of civil aviation industry standards.

[0100] In some embodiments, the acquired civil aviation industry standard data includes existing data, incremental data, and revised data. Incremental and revised data require data consistency verification, which includes consistency verification of normative terminology and consistency verification of numerical indicators. The normative terminology consistency verification method is as follows: extract the normative terminology library from the trusted structured standard database and perform consistency verification and output alarms for related terms in the incremental and revised data. The numerical indicator consistency verification method is as follows: extract the numerical content from the numerical data items in the incremental and / or revised data, search for the corresponding data items in the existing standard from the trusted structured standard database, and ensure that the lower limit of the indicator in the incremental and / or revised data is not greater than the upper limit of the indicator in the existing standard, and the upper limit of the indicator is not less than the lower limit of the indicator in the existing standard. During consistency verification, the new standard or newly revised numerical indicators generally need to meet the following expression requirements:

[0101] θ n+1∈(θ 1 ,θ 2 ,θ 3 , …, θ n )

[0102] That is, a certain numerical indicator θ in the new standard n+1 The requirements for this numerical indicator should be included in the existing n standards. Smaller indicators should not exceed the maximum value in the existing standards, and larger indicators should not be less than the minimum value in the existing standards. In case of conflicts, the standard administrator needs to manually determine whether to modify the current standard value or propose revisions to the existing standard based on the actual situation.

[0103]

[0104] A large amount of information is generated during civil aviation production, all of which must conform to certain standards and specifications. Through the processing of this invention, civil aviation industry standards, primarily in text or paper form, are transformed into machine-readable and verifiable standard entries. Therefore, information in civil aviation production can be retrieved and used using certain interfaces to meet standard requirements. Because there are numerous civil aviation industry standards and the production process is complex, civil aviation companies' systems can retrieve the required standard scope (e.g., for fatigue management, the aforementioned metadata can be used to construct air traffic control + fatigue management + dispatcher) through interfaces to search for standard clause requirements, and input directly related standard requirements and related requirements (e.g., inputting pilot fatigue management and maintenance release personnel fatigue management requirements). During policy formulation, if it is necessary to compare with existing standard requirements, the consistency analysis of requirements is completed according to the data consistency verification of this invention, guiding the revision of policy documents or proposing standard revision requirements. Consistency analysis mainly targets numerical standards; for example, a new standard's requirement for a certain value should not be lower than the existing standard, and even less so than the requirements of policy documents. If it is lower than the existing standard or policy document, an alarm confirmation is output, initiating the revision procedure of the existing standard or policy document, or adjusting the current standard. If the standard or policy document is higher than the existing standard or policy document, an alarm will be output to confirm whether the standard or policy document should be revised.

[0105] In the example of standard digitization of certain clauses in the data requirements section of "General Requirements for Logistics Operation of Civil Unmanned Aerial Vehicle Systems Part 1: Island Scenarios" (MH / T2014-2023), consistency checks are performed. Consistency analysis is conducted with existing valid digitized standard content in two dimensions: name and indicators. If conflicts are confirmed through manual intervention during the analysis, revisions are made, such as optimizing the standardized object name and modifying the standardized indicators. If no conflicts exist, the digitized standard requirements are entered into the database and published. After the civil aviation industry digitized standards are entered into the database and published, industry unit information systems can obtain the content of the digitized standards through standard interfaces and further edit and apply them within the system. The main aspect relevant to this method is the handling of situations where the authenticity of the standard text is questioned. This invention provides three levels of credibility assurance measures to ensure the credibility of the digitized standard data, facilitating its widespread use within the industry.

[0106] When standard clause data is maliciously tampered with, it can be detected through the electronic signature data attached to the standard XML in the Structured Standards Database (as in the example above). <signature>The electronic signature data will be verified; if the standard terms data is indeed tampered with, the electronic signature verification will indicate an error.

[0107] When both the standard terms data and the electronic signature data are maliciously altered and replaced, the original accompanying data must be used alone. <signature>Data verification alone cannot verify the authenticity and completeness of the data terms. Therefore, it is necessary to use them together with the data. <address>The data undergoes on-chain consistency determination and verification.

[0108] When standard terms data, electronic signature data, and address data are all maliciously altered and replaced, it can be used... <address>The data verification process verifies the time the data is uploaded to the blockchain and utilizes the distributed ledger technology in blockchain to complete the final verification of authenticity and data integrity.

[0109] A digital structured and trustworthy processing system for civil aviation industry standard data includes a data acquisition module, a structured standard database, a trustworthy processing module, a labeled and readable normalized processing module, and a network interface module. The data acquisition module acquires civil aviation industry standard data, performs structured processing, and stores it in the structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being logically related data items. The trustworthy processing module performs digital signature verification and blockchain notarization on the standard data in the structured standard database. The labeled and readable normalized processing module divides all data items in the structured standard database into three categories: numerical, requirement, and program. The system categorizes data types by domain scenario, name, object, data type, and content for identification, labeling, and readability standardization. For requirement types, it identifies, labels, and standardizes data based on domain scenario, name, object, and associated domain. For program types, it identifies, labels, and standardizes data based on domain scenario, name, object, and program execution content. The structured standard database extracts the domain scenario, name, and object of data items as index terms and labels them accordingly in the hierarchical architecture. The network interface module is used for data interaction; when accessing the structured standard database and inputting a single or combined index term (domain scenario, name, or / and object), it outputs the relevant data items retrieved and queried.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.< / address> < / address> < / signature> < / signature>

Claims

1. A reliable method for digital structure processing of standard data in the civil aviation industry, characterized in that: The methods include: S1. Obtain standard data from the civil aviation industry, perform structured processing, and store it as a structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being data items that are logically related to each other. S2. Perform digital signature verification and blockchain notarization on the standard data in the structured standard database to ensure its trustworthiness; after digital signature verification, the blockchain notarization technology is used to process the summary and / or attribute data of the data in the civil aviation industry standard data on the blockchain to obtain a unique notarization address information and store it in association with the electronic signature value. S3. Divide all data items in the structured standard database into three categories: numerical, requirement, and program. For numerical items, identify, label, and normalize them according to domain scenario, name, object, data type, and content. For the data type and content within the numerical category, perform the following data type judgment and identification processing: If the data type is identified as an interval type, it is labeled as an interval type. The upper and lower limits of the interval type indicator are decomposed into two indicators, namely the upper limit and the lower limit of the indicator, and the end values ​​of the upper and lower limits are identified and then normalized for readability. If the identified data type is a direction type, it is labeled as an indicator direction. The indicator direction includes less than, greater than, equal to, not less than, and not greater than, and is processed for readability normalization. Convert the data type and content of the numeric class to decimal or integer values; For requirement classes, identification, annotation, and readability normalization are performed according to domain scenario, name, object, and associated domain; for program classes, identification, annotation, and readability normalization are performed according to domain scenario, name, object, and program execution content; the structured standard database extracts the domain scenario, name, and object of data items as index terms and annotates them accordingly in the hierarchical architecture; The acquired civil aviation industry standard data includes existing data, incremental data, and revised data. For incremental and revised data, data consistency verification is required. This verification includes consistency verification of standardized terminology and consistency verification of numerical indicators. The method for verifying standardized terminology consistency is as follows: extract the standardized terminology library from the trusted structured standard database and perform consistency verification and output alarms for related terms in the incremental and revised data. The method for verifying numerical indicators consistency is as follows: extract the numerical content from the numerical data items in the incremental and / or revised data, search for the corresponding data items in the existing standard from the trusted structured standard database, and ensure that the lower limit of the indicators in the incremental and / or revised data is not greater than the upper limit of the indicators in the existing standard, and the upper limit of the indicators is not less than the lower limit of the indicators in the existing standard. S4. Access the structured standard database and enter single or combined index terms of domain scenario, name and / or object to retrieve and query relevant data items.

2. The method for reliable digital structure processing of civil aviation industry standard data according to claim 1, characterized in that: In method S4, a structured standard database is accessed and a single keyword or combination of keywords for the hierarchical architecture is input, and all relevant data items are output according to the hierarchical architecture.

3. A reliable processing method for the digital structure of civil aviation industry standard data according to claim 1 or 2, characterized in that: In method S1, during the structured processing of civil aviation industry standard data, a civil aviation semantic language model is constructed and trained using the civil aviation industry standard data. The civil aviation semantic language model identifies and classifies data items with logical relationships, identifies four data types in the civil aviation industry standard data—text, mathematical expressions, tables, and graphs—and assigns them numbers. The numbering rules and methods are as follows: Text data types use a segment-by-segment numbering method, with the numbering rule being: line number - W - segment sequence number, and the line numbering rule being: MH / T XXXX-XXXX; mathematical expressions use a sequential numbering method, with the numbering rule being: line number - E - expression sequence number; tables use a sequential numbering method, with the numbering rule being: line number - T - table sequence number; graphics use a sequential numbering method, with the numbering rule being: line number - P - graphic sequence number; all data in the civil aviation industry standard data are numbered, labeled, and assigned to a hierarchical structure.

4. The method for reliable processing of digital structured civil aviation industry standard data according to claim 3, characterized in that: In method S2, the data that has passed digital signature verification is processed for electronic signature using the SM2 algorithm, and the data in the civil aviation industry standard data obtains a unique corresponding electronic signature value; the electronic signature processing first converts the object to be signed into a fixed-length format file, and then performs electronic signature processing in sequence.

5. The method for reliable digital structure processing of civil aviation industry standard data according to claim 1, characterized in that: In method S3, the trained civil aviation semantic language model is used to perform word segmentation, part-of-speech tagging, and named entity recognition on the requirement class and the procedure class; the data of the numerical class, requirement class, and procedure class are converted into JSON or / and XML format text after readable normalization processing.

6. The method for reliable digital structure processing of civil aviation industry standard data according to claim 1, characterized in that: The structured standard database is stored in a distributed cluster. The structured standard database is networked and communicates with authorized network access terminals through a dedicated information interface. When the network access terminal performs a search, it outputs reliable related data items, source link indexes, and related data items and indexes.

7. A digital structure trusted processing system for civil aviation industry standard data that implements the digital structure trusted processing method of claim 1, characterized in that: It includes a data acquisition module, a structured standard database, a trustworthy processing module, a labeled and readable normalization processing module, and a network interface module. The data acquisition module acquires civil aviation industry standard data, performs structured processing, and stores it in the structured standard database. The structured standard database is constructed according to a hierarchical architecture, with the smallest level being logically related data items. The trustworthy processing module is used to perform digital signature verification and blockchain notarization on the standard data in the structured standard database. The labeled readability normalization processing module divides all data items in the structured standard database into three categories: numerical, requirement, and program. For numerical items, it performs identification, labeling, and readability normalization processing according to domain scenario, name, object, data type, and content. For requirement items, it performs identification, labeling, and readability normalization processing according to domain scenario, name, object, and related domain. For program items, it performs identification, labeling, and readability normalization processing according to domain scenario, name, object, and program execution content. The structured standard database extracts the domain scenario, name, and object of the data items as index terms and labels them accordingly in the hierarchical architecture. The network interface module is used for data interaction. When accessing a structured standard database and inputting a single or combined index term for a domain scenario, name, or / and object, it will output the relevant data items retrieved and queried.

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