Airborne asset cross-border compliance verification and mapping system, method and apparatus

Abstract

The application provides an aviation asset cross-border compliance verification and mapping system, method and equipment, the system comprises: a rule modeling module, a rule mapping module and a compliance verification module, wherein: the rule modeling module is used for collecting airworthiness rule data of a target aviation asset under a first regulatory system and a second regulatory system; the airworthiness rule data is divided according to a semantic model to obtain a target rule library; the rule mapping module is used for generating a target mapping sample based on a historical rule library; the target mapping model is trained according to the target mapping sample to obtain a target mapping model; the rule entries of the target rule library are associated and mapped based on the target mapping model to generate a rule mapping library; and the compliance verification module is used for performing compliance verification on the target aviation asset based on the rule mapping library to obtain a target verification result. The embodiment of the application can improve the efficiency of aviation asset cross-border compliance verification.

Description

Technical Field

[0001] This application relates to the field of aviation asset verification technology, and in particular to a cross-border compliance verification and mapping system, method and equipment for aviation assets. Background Technology

[0002] When aviation assets (such as aircraft, engines, and their critical components) are transferred or re-registered across borders, they must simultaneously meet the compliance requirements of the airworthiness regulatory systems of both the country of origin and the country of destination. Different countries or regions employ different airworthiness regulatory systems. For example, the airworthiness regulations issued by the Federal Aviation Administration (FAA) in the United States, the European Union Aviation Safety Agency (EASA), and the Civil Aviation Administration of China (CAAC) differ significantly in areas such as maintenance record requirements, component certification standards, and continuing airworthiness document formats.

[0003] Currently, cross-border compliance verification of aviation assets relies heavily on manual processes. Compliance experts manually compare the airworthiness provisions of the country of origin and the destination country, identify discrepancies, and guide asset owners to submit supplementary airworthiness evidence or conduct additional inspections based on these differences. Furthermore, existing technologies lack standardized, machine-readable mappings between airworthiness rules across different regulatory systems, hindering the automation of the compliance verification process and resulting in low efficiency in cross-border compliance verification of aviation assets.

[0004] Therefore, improving the efficiency of cross-border compliance verification of aviation assets is an urgent issue that needs to be addressed. Summary of the Invention

[0005] This application provides a system, method, and device for cross-border compliance verification and mapping of aviation assets, which improves the efficiency of cross-border compliance verification of aviation assets.

[0006] In a first aspect, embodiments of this application provide a cross-border compliance verification and mapping system for aviation assets, the system comprising: a rule modeling module, a rule mapping module, and a compliance verification module, wherein: The rule modeling module is used to collect airworthiness rule data under the first regulatory system and the second regulatory system corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed the compliance verification of the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; the airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule library; the target rule library includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0; The rule mapping module is used to generate target mapping samples based on a preset historical rule base; train a preset reference mapping model based on the target mapping samples to obtain a target mapping model; and perform association mapping on the rule entries of the first regulatory system and the second regulatory system in the target rule base based on the target mapping model to generate a rule mapping base. The compliance verification module is used to perform compliance verification on the target aviation asset based on the rule mapping library, and obtain the target verification result.

[0007] Secondly, embodiments of this application provide a method for cross-border compliance verification and mapping of aviation assets, the method comprising: Collect airworthiness rule data under the first and second regulatory systems corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; The airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule base; the target rule base includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0; Generate target mapping samples based on a preset historical rule base; The target mapping model is obtained by training a preset reference mapping model based on the target mapping samples; Based on the target mapping model, the rule entries of the first regulatory system and the second regulatory system in the target rule base are associated and mapped to generate a rule mapping base; Based on the rule mapping library, compliance verification is performed on the target aviation asset to obtain the target verification result.

[0008] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, the memory being used to store one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the second aspect of this application.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the second aspect of this application.

[0010] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the second aspect of embodiments of this application. The computer program product may be a software installation package.

[0011] As can be seen, the cross-border compliance verification and mapping system for aviation assets provided in this application firstly uses a rule modeling module to semantically segment airworthiness rule data from different regulatory systems, establishing a structured target rule base; then, a rule mapping module uses the historical rule base to train a target mapping model, automatically establishing mapping relationships between rule entries across regulatory systems and generating a rule mapping library; finally, a compliance verification module uses the rule mapping library to automatically perform compliance verification on the data to be verified for the target aviation asset and generate a target solution. This cross-border compliance verification and mapping system for aviation assets achieves automatic mapping of airworthiness rules across regulatory systems and automatic determination of compliance status, eliminating the need for manual comparison of airworthiness clauses from different regulatory systems, thereby improving the efficiency of cross-border compliance verification of aviation assets. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0013] Figure 1 This is a system architecture diagram of a cross-border compliance verification and mapping system for aviation assets provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the composition of a target rule base provided in an embodiment of this application; Figure 3 This is a schematic diagram of a model training process provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the composition of a rule mapping library provided in an embodiment of this application; Figure 5 This is a flowchart illustrating a process for determining a list of items to be verified, provided in an embodiment of this application. Figure 6 This is an application scenario diagram of an aviation asset cross-border compliance verification and mapping system provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 8 This is a flowchart illustrating a cross-border compliance verification and mapping method for aviation assets provided in an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0015] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0016] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0017] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0018] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] When aviation assets (such as aircraft, engines, and their critical components) are transferred or re-registered across borders, they must simultaneously meet the compliance requirements of the airworthiness regulatory systems of both the country of origin and the country of destination. Different countries or regions employ different airworthiness regulatory systems. For example, the airworthiness regulations issued by the Federal Aviation Administration (FAA) in the United States, the European Union Aviation Safety Agency (EASA), and the Civil Aviation Administration of China (CAAC) differ significantly in areas such as maintenance record requirements, component certification standards, and continuing airworthiness document formats.

[0021] Currently, cross-border compliance verification of aviation assets relies heavily on manual processes. Compliance experts manually compare the airworthiness provisions of the country of origin and the destination country, identify discrepancies, and guide asset owners to submit supplementary airworthiness evidence or conduct additional inspections based on these differences. Furthermore, existing technologies lack standardized, machine-readable mappings between airworthiness rules across different regulatory systems, hindering the automation of the compliance verification process and resulting in low efficiency in cross-border compliance verification of aviation assets.

[0022] Therefore, improving the efficiency of cross-border compliance verification of aviation assets is an urgent issue that needs to be addressed.

[0023] To address the aforementioned issues, this application provides a system, method, and device for cross-border compliance verification and mapping of aviation assets. The system includes a rule modeling module, a rule mapping module, and a compliance verification module. The rule modeling module collects airworthiness rule data under a first regulatory system and a second regulatory system corresponding to a target aviation asset. The target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system. The first and second regulatory systems are airworthiness regulatory systems in different regions. The airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule library. The target rule library includes m first rule entries from the first regulatory system and n second rule entries from the second regulatory system; m and n are both integers greater than 0. The rule mapping module generates target mapping samples based on a preset historical rule library. A preset reference mapping model is trained using the target mapping samples to obtain a target mapping model. Based on the target mapping model, rule entries from the first and second regulatory systems within the target rule library are associated and mapped to generate a rule mapping library. The compliance verification module performs compliance verification on the target aviation asset based on the rule mapping library to obtain a target verification result. It is evident that by using this system, the efficiency of cross-border compliance verification of aviation assets can be improved.

[0024] For easier understanding, please refer to Figure 1 , Figure 1 This is a system architecture diagram of an aviation asset cross-border compliance verification and mapping system provided in an embodiment of this application. The system includes: a rule modeling module, a rule mapping module, and a compliance verification module, wherein: The rule modeling module is used to collect airworthiness rule data under the first regulatory system and the second regulatory system corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed the compliance verification of the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; the airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule library; the target rule library includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0.

[0025] In this embodiment, firstly, the first and second regulatory systems corresponding to the target aviation asset are determined. For example, if the target aviation asset (such as a CFM56-7B engine) needs to be transferred from the first region to the second region, then the regulatory system corresponding to the first region is the first regulatory system (such as the FAA), and the regulatory system corresponding to the second region is the second regulatory system (such as the CAAC). Then, airworthiness rule data under the first and second regulatory systems is collected from data sources such as official websites of regulatory agencies, regulatory databases, and airworthiness directive issuance platforms. This airworthiness rule data includes clauses from documents such as initial airworthiness regulations, continuing airworthiness regulations, airworthiness directives, technical standards, and advisory circulars of each regulatory system.

[0026] Next, the rule modeling module semantically segments the collected airworthiness rule data according to a preset semantic model to obtain the target rule base. This semantic model can be configured as a six-layer model, from top to bottom: regulatory system layer, chapter layer, clause layer, requirement layer, verification point layer, and evidence type layer. Specifically, the rule modeling module can decompose the airworthiness rule data layer by layer according to this semantic model: the regulatory system to which the regulation belongs is determined as the first-layer semantic unit; the chapter number of the regulation is determined as the second-layer semantic unit; the specific clauses under the chapter are determined as the third-layer semantic unit; the various airworthiness requirements stipulated in the clauses are determined as the fourth-layer semantic unit; the independently checkable compliance points in each requirement are determined as the fifth-layer semantic unit; and the document types used to prove that each compliance point is met are determined as the sixth-layer semantic unit. Unique identifiers can be generated based on the encoding of the first, second, and third-layer semantic units. The encoding rule can be set as "regulatory system code / regulation number / clause number," without specific limitations. Rule entries can be generated based on the association of fourth-level semantic units, fifth-level semantic units, and sixth-level semantic units. These rule entries include, but are not limited to: a set of airworthiness requirements composed of fourth-level semantic units, a set of verification points composed of fifth-level semantic units, and a set of evidence types composed of sixth-level semantic units.

[0027] Finally, the rule modeling module categorizes and summarizes all semantic units according to the regulatory system to form a target rule base. The target rule base contains m first rule entries for the first regulatory system and n second rule entries for the second regulatory system. Each rule entry includes a unique identifier and its content.

[0028] It is evident that by transforming airworthiness regulations in natural language form under different regulatory systems into a structured target rule base through the rule modeling module, a machine-readable data foundation is provided for subsequent automatic rule mapping and automatic compliance verification, thereby improving the automation and efficiency of cross-border compliance verification of aviation assets.

[0029] For easier understanding, please refer to Figure 2 , Figure 2 This is a schematic diagram of the composition of a target rule base provided in an embodiment of this application. The target rule base stores two types of rule entries according to the dimension of the regulatory system: m first rule entries belonging to the first regulatory system and n second rule entries belonging to the second regulatory system. Each rule entry is decomposed and structured by a semantic model and is accompanied by a unique identifier and multi-layer semantic unit information. The subsequent rule mapping module will perform semantic matching on the first rule entries and the second rule entries to construct a rule mapping relationship across regulatory systems.

[0030] The rule mapping module is used to generate target mapping samples based on a preset historical rule base; train a preset reference mapping model based on the target mapping samples to obtain a target mapping model; and perform association mapping on the rule entries of the first regulatory system and the second regulatory system in the target rule base based on the target mapping model to generate a rule mapping base.

[0031] In this embodiment, firstly, the rule mapping module calls a preset historical rule library to extract historical rule entries from different regulatory systems. Through semantic encoding and similarity calculation, it automatically filters out semantically related historical rule entry groups. After aviation compliance experts annotate the mapping type of each historical rule entry group, a target mapping sample is formed for model training. Then, the rule mapping module uses the historical rule entry groups in the target mapping sample as input and the annotated mapping types as supervision labels to construct a training set. This set is then used to supervise the training of a preset reference mapping model, enabling the model to automatically learn semantic matching patterns and mapping discrimination rules between rule entries across regulatory systems, thus obtaining the target mapping model. Finally, the rule mapping module extracts all rule entries from the target rule library for both the first and second regulatory systems. Through semantic similarity pre-screening, it identifies related rule entry groups, inputs these groups into the target mapping model for reasoning, and outputs the corresponding mapping type. For remaining rule entries in the second regulatory system that are not associated with any entry in the first regulatory system, the system directly determines their mapping type as rule supplements. Combining the model reasoning results and the system judgment results, a complete mapping relationship record is established and a rule mapping library is generated for the compliance verification module to use.

[0032] It should be noted that the rule mapping in this rule mapping library refers to the correspondence between rule clauses of different airworthiness regulatory systems. Specifically, it manifests as the determination of the mapping type between rule entries under the first regulatory system and rule entries under the second regulatory system at the level of verification items and specific requirements, including complete equivalence, partial equivalence, rule supplementation, and rule exclusivity. This rule mapping and asset data field mapping are collaborative but different technical means. Asset data field mapping typically refers to the alignment of asset information from different data sources or in different formats at the field level. For example, establishing a correspondence between the "completion date" field in a maintenance record and the "issue date" field in an airworthiness certificate, or matching fields in industry data sources with columns in the system's internal dismantling list. Rule mapping focuses on the semantic correspondence and difference identification of regulatory clauses between regulatory systems, while asset data field mapping focuses on the format alignment and information integration at the specific data level. The two can be used collaboratively in this system. For example, asset data field mapping can first achieve the field-level correspondence between the data to be verified and the verification points in the rule entries, and then rule mapping can be used to determine the compliance differences of the verification point between the two regulatory systems. However, rule mapping itself is independent of asset data field mapping. Optionally, in generating target mapping samples based on a preset historical rule base, the rule mapping module is specifically used to perform the following steps: S11. Extract multiple historical rule entries from the historical rule base under at least two different regulatory systems; the at least two different regulatory systems include at least the first regulatory system and the second regulatory system; S12. Semantically encode the multiple historical rule entries to generate multiple semantic vectors; S13. Calculate the semantic similarity between any two semantic vectors originating from different regulatory systems among the plurality of semantic vectors, and select a semantic similarity values ​​that are greater than a preset semantic similarity threshold; where a is an integer greater than 1. S14. Generate a group of historical rule entries based on the two historical rule entries corresponding to each of the a semantic similarities. S15. Label each of the a historical rule entry groups to obtain a reference mapping types; each historical rule entry group corresponds to a reference mapping type; the reference mapping type includes any of the following: complete equivalence, partial equivalence, and rule mutual exclusion; S16. Determine the target mapping sample based on the a groups of historical rule entries and the a reference mapping types.

[0033] In a specific embodiment, the rule mapping module extracts multiple historical rule entries from a pre-set historical rule base, representing at least two different regulatory systems. The historical rule base is a pre-built and stored rule library containing structured data of airworthiness regulations from multiple regulatory systems, including at least historical rule entries from the first and second regulatory systems. Then, the rule mapping module uses a pre-trained language model (such as a BERT-based pre-trained model) as a semantic encoder, inputting the rule entry content of each historical rule entry into the semantic encoder and outputting multiple semantic vectors of a pre-set dimension.

[0034] Next, the rule mapping module calculates the semantic similarity between every two semantic vectors originating from different regulatory systems and selects *a* semantic similarities that exceed a semantic similarity threshold. The semantic similarity can be calculated using a cosine similarity algorithm, and the threshold can be set to 0.7; no specific limitation is made here. For each of the *a* semantic similarities, the rule mapping module obtains its corresponding two historical rule entries and groups these two historical rule entries into a historical rule entry group, thereby generating *a* historical rule entry groups.

[0035] Then, the rule mapping module submits the generated 'a' historical rule entry groups to the system's annotation interface. Aviation compliance experts review and annotate each group, resulting in 'a' reference mapping types. These reference mapping types include three categories: complete equivalence (meaning two historical rule entries point to the same verification item, and their specific requirements are substantially the same); partial equivalence (meaning two historical rule entries point to the same verification item, but their specific requirements have identifiable differences); and mutual exclusion (meaning two historical rule entries point to the same verification item, but their specific requirements have irreconcilable conflicts). Finally, the 'a' historical rule entry groups and the 'a' reference mapping types are combined to form the target mapping sample.

[0036] It is evident that by combining automatic semantic similarity filtering with manual annotation and correction, high-quality target mapping samples can be efficiently generated from the historical rule base, providing a reliable training data foundation for subsequent training of a target mapping model that can automatically identify cross-regulatory system rule mapping relationships.

[0037] Optional, please refer to Figure 3 , Figure 3 This is a schematic diagram of a model training process provided in an embodiment of this application. In the step of training a preset reference mapping model based on the target mapping samples to obtain a target mapping model, the rule mapping module is specifically used to perform the following steps: S21. Using the a historical rule entry groups in the target mapping sample as input and the a reference mapping types as supervision labels, construct a training set; S22. Supervised training of the reference mapping model is performed based on the training set, and the parameters of the reference mapping model are adjusted to obtain the target mapping model; the reference mapping model automatically learns the features of each historical rule entry group during the training process and outputs the prediction result of the corresponding mapping type.

[0038] In a specific embodiment, firstly, for each historical rule entry group, the rule mapping module concatenates the rule entry contents of the two historical rule entries it contains according to a preset delimiter to form an input sequence; then, the reference mapping type corresponding to the historical rule entry group is converted into a digital label (i.e., a supervision label) as a supervision signal for the input sequence, thereby constructing a training set. The reference mapping types include three types: completely equivalent, partially equivalent, and mutually exclusive rules, each corresponding to different digital labels. After the training set is constructed, the rule mapping module divides the training set into a training subset and a validation subset according to a preset ratio (e.g., 8:2). The training subset is used for model parameter updates, and the validation subset is used for model performance evaluation during the training process.

[0039] Then, the rule mapping module performs supervised training on a pre-defined reference mapping model based on the training and validation subsets, adjusting the parameters of the reference mapping model to obtain the target mapping model. The reference mapping model can use a pre-trained language model based on the Transformer architecture as the base network, with a classification layer (i.e., a fully connected network) added on top. The base network performs semantic encoding and feature extraction on the input rule pairs; the classification layer maps the features output by the base network to predicted probabilities for each mapping type. The supervised training process consists of four iterative steps: forward propagation, loss calculation, backpropagation, and parameter update.

[0040] In the forward propagation phase, the rule mapping module inputs the text sequence corresponding to a rule entry group from the training subset into the reference mapping model. The base network jointly encodes the two input rule entries, and through a pre-defined multi-layer self-attention mechanism, the model automatically learns and extracts the deep semantic matching features of the rule entry group, including but not limited to keyword overlap, semantic vector similarity, structured correspondence of clause numbers, and whether there are additional requirements or conflicting expressions. Then, the feature vector output by the base network is input into the classification layer, processed by the corresponding Softmax function, and outputs the predicted probability distribution of the rule entry group belonging to each mapping type. In the loss calculation phase, the rule mapping module uses a pre-defined cross-entropy loss function to calculate the difference between the predicted probability distribution output by the model and the numerical label of the sample, obtaining the loss value. The smaller the loss value, the closer the model's prediction result is to the manually labeled result. In the backpropagation phase, the rule mapping module calculates the gradient of the loss value with respect to each parameter of the model layer by layer from the output layer to the input layer based on the loss value using a pre-defined chain rule. During the parameter update phase, the rule mapping module uses a preset gradient descent optimization algorithm to update all parameters (such as weight matrix and bias vector) of the base network and classification layer based on the calculated gradient and preset learning rate, so that the model can output a prediction result that is closer to the digit label in the next forward propagation.

[0041] In this process, after each training epoch, the rule mapping module evaluates the classification accuracy of the reference mapping model on a validation subset. As the number of training epochs increases, the loss value of the reference mapping model on the training subset gradually decreases, while the accuracy on the validation subset gradually increases. When the classification accuracy no longer improves within three consecutive epochs or reaches the preset maximum number of training epochs (e.g., 100), the rule mapping module stops training, saves the current model parameters, and obtains the target mapping model. The trained target mapping model has the ability to automatically determine the mapping type of rule entries across regulatory systems. For any two rule entries belonging to different regulatory systems, the target mapping model receives the content of the rule entries as input, automatically extracts semantic matching features, and outputs the prediction result of the corresponding mapping type.

[0042] It is evident that by using manually labeled mapping samples for supervised training, the model can automatically learn the semantic matching rules between rule entries across the regulatory system, thereby obtaining a target mapping model that can automatically output mapping types for any pair of rule entries, thus improving the automation level and consistency of rule mapping.

[0043] Optionally, the rule mapping library includes n mapping records. In generating the rule mapping library by associating and mapping the rule entries of the first regulatory system and the second regulatory system within the target rule library based on the target mapping model, the rule mapping module specifically performs the following steps: S31. Select k first rule entries and k second rule entries that meet the preset pairing conditions from the m first rule entries and the n second rule entries respectively; the preset pairing conditions are that the semantic similarity between the second rule entries and the first rule entries is greater than the semantic similarity threshold; k is an integer greater than 0 and less than or equal to n; S32. Determine k target rule entry groups based on the k first rule entries and the k second rule entries; S33. Input the k target rule entry groups into the target mapping model for reasoning, and output k target mapping types; S34. Generate k mapping records based on the k target rule entry groups and the k target mapping types; S35. Obtain nk second rule entries from the n second rule entries, excluding the k second rule entries; S36. Determine that all nk target mapping types corresponding to the nk second rule entries are rule supplements; S37. Generate nk mapping records based on the nk second rule entries and the nk target mapping types.

[0044] In a specific embodiment, firstly, the rule mapping module performs semantic encoding on m first rule entries and n second rule entries respectively, generating corresponding semantic vectors. Then, it iterates through all vector pairs of semantic vectors of the first rule entries and all vector pairs of semantic vectors of the second rule entries, calculating the semantic similarity of each vector pair. K vector pairs with semantic similarity greater than a semantic similarity threshold are retained, and k first rule entries and k second rule entries are extracted from them. Next, each first rule entry and its corresponding second rule entry are combined into a target rule entry group, resulting in k target rule entry groups. Then, the k target rule entry groups are input one by one into the target mapping model for inference, outputting k target mapping types. Specifically, for each target rule entry group, the target mapping model extracts the rule entry content of two rule entries, performs joint semantic encoding through a base network, automatically learns and extracts the semantic matching features of the rule entry group, and then the classification layer calculates the semantic matching features, outputting the predicted probability that the target rule entry group belongs to one of three mapping types: completely equivalent, partially equivalent, or mutually exclusive. The mapping type with the highest probability is taken as the target mapping type corresponding to the target rule entry group. Then, based on the k target rule entry groups and their respective target mapping types, k mapping records are generated.

[0045] Next, obtain the remaining nk second rule entries out of the n second rule entries, excluding the k second rule entries. Then, determine the target mapping type of all nk remaining second rule entries as rule supplements. A rule supplement indicates that the verification item specified by the second rule entry has no corresponding provision in the first regulatory system and is an additional requirement of the second regulatory system. Then, based on the nk second rule entries and their corresponding target mapping types, generate nk mapping records.

[0046] As can be seen, by generating paired mapping records through semantic similarity pre-screening combined with model reasoning, and automatically determining the remaining rule entries that have not been successfully paired as rule supplements, the mapping relationship of all rule entries in the second regulatory system is fully covered, realizing the automated construction of the rule mapping library and providing a complete mapping data foundation for subsequent compliance verification.

[0047] For easier understanding, please refer to Figure 4 , Figure 4This is a schematic diagram illustrating the composition of a rule mapping library provided in this application embodiment. The k mapping records are generated by the target mapping model from k target rule entry groups, resulting in k target mapping types. These k target mapping types include at least one of complete equivalence, partial equivalence, and rule exclusivity. Complete equivalence indicates that two rule entries have no substantial difference in verification matters and requirements; partial equivalence indicates that two rule entries address the same verification matter but differ in specific requirements; rule exclusivity indicates that the requirements of two rule entries for the same verification matter are irreconcilable. The nk mapping records are generated from the remaining second rule entries among n second rule entries that failed to pair successfully with any first rule entry. Their corresponding target mapping types are all determined to be rule supplements. Rule supplements indicate that the verification matter corresponding to the rule entry has no corresponding provision in the first regulatory system and belongs to the additional requirements of the second regulatory system. The rule mapping library contains a total of n mapping records. Each mapping record contains at least a unique identifier of the second rule entry, a target mapping type, and, when the target mapping type is partially equivalent or rule exclusivity, recorded differences or mutually exclusive content for use by the compliance verification module.

[0048] The compliance verification module is used to perform compliance verification on the target aviation asset based on the rule mapping library, and obtain the target verification result.

[0049] Optionally, regarding the compliance verification of the target aviation asset based on the rule mapping library to obtain the target verification result, the compliance verification module is specifically used to perform the following steps: S41. Obtain the data to be verified for the target aviation asset; the data to be verified includes at least: basic asset information, ownership history, maintenance records, and airworthiness certificate information; the basic asset information includes the subject type of the target aviation asset; S42. Extract b mapping records corresponding to the subject type from the n mapping records in the rule mapping library; b is a positive integer less than or equal to n; S43. Determine the list to be verified based on the b mapping records; the list to be verified includes a list of differences and a list of mutually exclusive items; S44. Perform compliance verification on the data to be verified according to the list of data to be verified, and obtain the target verification result.

[0050] In a specific embodiment, firstly, the compliance verification module extracts the data to be verified from the digital passport of the target aviation asset. The target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system, and its digital passport stores all airworthiness compliance evidence documents of the target aviation asset under the first regulatory system. The compliance verification module reads basic asset information through the digital passport interface, including the entity type, model, serial number, and production date; reads ownership history, including information on previous operators, ownership transfer time, and registration number change records; reads maintenance records, including descriptions of all maintenance work since it was put into operation, maintenance completion dates, maintenance unit information, and maintenance release signature documents; and reads airworthiness certificate materials, including airworthiness approval labels, type certificate data sheets, and airworthiness directive execution records.

[0051] Then, the compliance verification module uses the subject type as the search criterion to filter out b mapping records corresponding to the subject type from n mapping records in the rule mapping library. Specifically, the compliance verification module extracts all b mapping records applicable to the target aviation asset by matching the asset type range applicable to the second rule entry in the n mapping records with the subject type of the target aviation asset.

[0052] Next, based on the target mapping type corresponding to each mapping record in the b mapping records, it is categorized into a list of differences or a list of mutual exclusions, thus obtaining a list to be verified. Then, the compliance verification module performs compliance verification on the data to be verified based on the list of differences and the list of mutual exclusions in the list to be verified, thereby obtaining the target verification result.

[0053] As can be seen, by automatically extracting mapping records that match the subject type and dividing them into a list of differences and a list of mutually exclusive items, compliance verification can focus on the differences and conflicts to be processed, eliminating the need for manual comparison of all airworthiness clauses, thereby improving the efficiency and relevance of cross-border compliance verification of aviation assets.

[0054] Optional, please refer to Figure 5 , Figure 5 This is a flowchart illustrating a process for determining a list to be verified, provided in an embodiment of this application. Specifically, in determining the list based on the b mapping records, the compliance verification module performs the following steps: S51. Determine the first target mapping type corresponding to the first mapping record; the first mapping record is any one of the b mapping records; S52. When the first target mapping type is the partial equivalence or the rule supplement, determine that the first mapping record is an item in the list of differences; S53. When the first target mapping type is the rule mutual exclusion, determine that the first mapping record is an item in the mutual exclusion item list.

[0055] In a specific embodiment, the compliance verification module traverses b mapping records, obtaining the target mapping type recorded in each record. The first mapping record is any one of the b mapping records. The first target mapping type corresponding to the first mapping record is obtained, such as complete equivalence, partial equivalence, mutually exclusive rules, or supplementary rules.

[0056] When the first target mapping type is partially equivalent, it indicates that the second regulatory system and the first regulatory system stipulate the same verification item, but the specific requirements differ. In this case, the first mapping record is added to the difference item list. When the first target mapping type is rule supplement, it indicates that the verification item in the second regulatory system has no corresponding provision in the first regulatory system and is an additional requirement of the second regulatory system. In this case, the first mapping record is added to the difference item list. When the first target mapping type is rule exclusion, it indicates that the requirements of the second and first regulatory systems for the same verification item have an irreconcilable conflict. In this case, the first mapping record is added to the exclusion item list. It should be noted that when the first target mapping type is completely equivalent, the compliance verification module does not process it and directly determines that the verification item is compliant and passes, and it is not included in the pending verification list.

[0057] As can be seen, by automatically classifying mapping records into a list of differences and a list of mutually exclusive items based on the mapping type, the compliance verification module can adopt differentiated processing strategies for different types of issues, thereby improving the granularity and processing efficiency of compliance verification.

[0058] Optionally, the target verification result includes at least one of the following: a first verification result and a second verification result. Regarding the compliance verification of the data to be verified according to the list to be verified to obtain the target verification result, the compliance verification module is specifically used to perform the following steps: S61. Traverse each mapping record in the list of differences, extract the difference content according to the target mapping type corresponding to the mapping record, and generate an evidence supplementation plan; S62. Perform compliance verification on the data to be verified according to the evidence supplementation scheme to obtain the first verification result; S63. Traverse each mapping record in the list of mutually exclusive items, extract the mutually exclusive content recorded in the mapping record, and generate a risk management plan in combination with the preset conflict resolution strategy; the conflict resolution strategy includes at least one of the following: the principle of priority of target supervision system, the principle of priority of bilateral agreement, and the principle of manual adjudication; S64. Perform compliance verification on the data to be verified according to the risk handling plan to obtain the second verification result.

[0059] In a specific embodiment, the compliance verification module iterates through each mapping record in the discrepancy list, extracts the discrepancy content based on its corresponding target mapping type, and generates a supplementary evidence scheme accordingly. Specifically, for mapping records where the target mapping type is partially equivalent, the compliance verification module extracts the differentiated requirements of the second regulatory system relative to the first regulatory system from this mapping record, compares it with existing first regulatory system evidence documents in the data to be verified, and identifies specific items in the existing evidence that are missing or do not meet the additional requirements as discrepancy content. For example, if the first regulatory system evidence document includes a description of the maintenance work, the completion date, and the signatory's signature, while the second regulatory system's additional requirement is the affixing of the maintenance unit's official seal, then the discrepancy content is "missing maintenance unit's official seal." Based on this discrepancy content, a corresponding supplementary evidence item is generated, specifying the type of supplementary evidence and the corresponding regulatory basis. Specifically, for mapping records where the target mapping type is rule supplementation, the compliance verification module extracts the additional requirements of the second regulatory system from this mapping record, i.e., the discrepancy content. Based on this discrepancy content, a corresponding supplementary evidence item is generated, specifying the type and content specifications of airworthiness evidence to be submitted according to the requirements of the second regulatory system. Then, the compliance verification module summarizes all the supplementary evidence items and generates a supplementary evidence plan.

[0060] For each evidence supplement item in the evidence supplementation plan, the compliance verification module checks whether there is already evidence document in the data to be verified that meets the requirements of that item. If evidence document that meets the requirements can be extracted from the data to be verified, the verification of that item is deemed to have passed; if there is no corresponding evidence document in the data to be verified or the existing evidence does not meet the requirements, the verification of that item is deemed to have failed, and the item is marked as pending supplementation. After all evidence supplement items have been verified, the compliance verification module summarizes and generates a first verification result, which includes the verification status of each evidence supplement item in the evidence supplementation plan.

[0061] The compliance verification module iterates through each mapping record in the mutual exclusion list, extracting the mutually exclusive content recorded in that record. This mutually exclusive content describes the conflict between the specific requirements of the first and second regulatory systems for the same verification matter. Based on this mutually exclusive content and a pre-defined conflict resolution strategy, the compliance verification module generates a corresponding conflict resolution item for each mutually exclusive mapping record. Then, the compliance verification module aggregates all conflict resolution items to generate a risk management plan.

[0062] The conflict resolution strategies include the following three: Priority principle of the target regulatory system: The requirements of the second regulatory system shall be the final judgment standard. Conflict resolution items shall be instructed to submit compliance evidence in accordance with the requirements of the second regulatory system. The compliance status of the first regulatory system shall not be recognized.

[0063] Bilateral agreement priority principle: Check whether there is a valid bilateral airworthiness agreement between the first regulatory system and the second regulatory system. If the bilateral airworthiness agreement contains an equivalent acceptance clause for the verification matter, the conflict resolution item shall cite the specific clause of the bilateral airworthiness agreement as the basis for equivalent acceptance and instruct that it be handled in accordance with the agreement.

[0064] Manual adjudication principle: When neither of the above two principles can be applied, the conflict resolution item generates an adjudication work order, which pushes the description of the mutually exclusive rules, the cause of the conflict and the optional handling solutions to the designated review node, whereby a third-party regulatory agency or authorized expert will make a manual adjudication.

[0065] For each conflict resolution item in the risk management plan, the compliance verification module performs corresponding compliance verification operations based on its selected conflict resolution strategy. When the conflict resolution strategy prioritizes the target regulatory system, if the data to be verified contains evidence that satisfies the conflict resolution item (i.e., evidence that meets the requirements of the second regulatory system), the verification is deemed successful; otherwise, it fails. When the conflict resolution strategy prioritizes bilateral agreements, if the data to be verified meets the equivalent acceptance conditions in the bilateral agreement, the verification is deemed successful, and the bilateral agreement clause is recorded as the basis for success; otherwise, it fails. When the strategy prioritizes manual adjudication, the item is marked as pending adjudication, and the verification result is updated after manual adjudication is completed. After all conflict resolution items have been verified, the compliance verification module generates a second verification result, which includes the verification status of each conflict resolution item in the risk management plan.

[0066] It is evident that by automatically generating supplementary evidence plans and risk management plans, and then conducting differentiated compliance verification on the data to be verified accordingly, targeted handling guidelines and verification conclusions are provided for discrepancies and mutually exclusive items, eliminating the need for manual evaluation of each item, thereby improving the automation level and processing efficiency of compliance verification.

[0067] In one possible embodiment, the compliance verification module is further configured to acquire the digital archive of the target aviation asset, perform completeness verification on the digital archive according to a preset document checklist, obtain a completeness verification result, and extract the data to be verified from the digital archive based on the completeness verification result. The compliance verification module is also configured to intelligently classify the documents in the data to be verified and extract structured fields from the documents to assist in completeness verification.

[0068] Specifically, the compliance verification module can obtain the digital file corresponding to the target aviation asset through the digital passport interface. This digital file is an electronic collection of airworthiness compliance evidence documents for the target aviation asset throughout its entire lifecycle under the first regulatory framework. It includes all airworthiness-related documents from delivery to the present, such as maintenance records, ownership change documents, airworthiness approval labels, and airworthiness directive execution records. The document checklist is a pre-configured verification list based on the basic document requirements for cross-border transfer of aviation assets, listing all the necessary document types that must be submitted during the cross-border transfer of the target aviation asset and their corresponding verification requirements.

[0069] The compliance verification module iterates through each required document type in the document checklist, searching the digital archive for a matching document. The search criteria include file name, file type tag, and file content characteristics. If a matching document is found, the item is marked as complete; otherwise, it is marked as missing. After the iteration, a completeness verification result is generated. This result includes a list of complete documents, a list of missing documents, and guidelines for submitting supplementary information for each missing document. If the completeness verification result is successful (meaning all required document types listed in the document checklist in the digital archive are complete), the compliance verification module extracts the documents corresponding to this result from the digital archive, forming the data to be verified. This data is a subset of evidence documents in the digital archive directly related to the current cross-border compliance verification task, including at least basic asset information, ownership history, maintenance records, and airworthiness certificate information. If the completeness verification result is that the verification fails, that is, there are missing documents, the compliance verification module generates a missing document supplement notification and returns it to the asset holder. The asset holder then submits the missing documents and triggers the completeness verification again.

[0070] The compliance verification module can also intelligently classify and extract structured fields from documents in the data to be verified to assist in completeness verification. For example, through a pre-trained document classification model, it automatically identifies the document type of each document in the data to be verified and categorizes them into asset basic information, ownership history, maintenance records, airworthiness certificates, or other categories. It can also extract key structured fields from various types of documents using natural language processing technology. For example, it can extract asset model and serial number from asset basic information documents, the most recent maintenance date and maintenance unit name from maintenance record documents, and airworthiness approval label number and validity period from airworthiness certificate documents. The compliance verification module can automatically match and compare the extracted structured fields with the required items in the document checklist, thereby further improving the efficiency and accuracy of completeness verification.

[0071] In one possible embodiment, after the completeness verification is passed, the compliance verification module can combine the rule mapping library to perform cross-regulatory system compliance verification on the data to be verified, identify discrepancies and mutually exclusive items, and generate the corresponding target verification results.

[0072] Specifically, the compliance verification module first determines the completeness verification result. If missing documents are found, a missing document supplementation notification is generated and returned to the asset holder. After the missing documents are supplemented, a completeness verification is performed again. If the completeness verification passes, the compliance verification process is initiated. The compliance verification process includes the following steps: The compliance verification module obtains the subject type of the target aviation asset and extracts b mapping records corresponding to the subject type from n mapping records in the rule mapping library. It then iterates through the b mapping records, classifying each record into a difference list or a mutual exclusion list based on its target mapping type. Specifically, when the target mapping type is partially equivalent or a rule supplement, the mapping record is classified into the difference list; when the target mapping type is mutually exclusive, the mapping record is classified into the mutual exclusion list; when the target mapping type is fully equivalent, compliance is directly determined, and the record is not included in the pending verification list.

[0073] After determining the lists of discrepancies and mutually exclusive items, the compliance verification module performs compliance verification comparisons on both lists separately. For each mapping record in the discrepancy list, corresponding evidence documents are extracted from the data to be verified and compared with the verification point requirements of the second regulatory system to identify discrepancies. For each mapping record in the mutually exclusive item list, corresponding evidence documents are extracted from the data to be verified and compared with the verification point requirements of the second regulatory system to identify conflicts. After the comparisons are completed, the compliance verification module summarizes and generates the target verification results, including the discrepancy list and its corresponding descriptions of discrepancies, as well as the mutually exclusive item list and its corresponding descriptions of conflicts. Then, the compliance verification module can generate target solutions based on the target verification results, including evidence supplementation plans for the discrepancy list and risk management plans for the mutually exclusive item list.

[0074] It is evident that by conducting completeness verification and compliance verification, invalid comparisons caused by missing documents are avoided, and fully equivalent items are directly judged as passing. The focus is on the identification and handling of differences and mutually exclusive items, thereby improving the logical rigor and processing efficiency of the cross-border compliance verification process.

[0075] For easier understanding, please refer to Figure 6 , Figure 6This is an application scenario diagram of an aviation asset cross-border compliance verification and mapping system provided in this application embodiment. The target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system. The system inputs its data to be verified, including basic asset information, ownership history, maintenance records, and airworthiness certificate information. Internally, the system first trains a target mapping model based on a preset historical rule base. Then, it uses the target mapping model to match and associate rule entries from the first and second regulatory systems in the pre-built target rule base, generating a rule mapping library. Next, based on the rule mapping library, it obtains the mapping type and differences between each rule entry in the second regulatory system and its corresponding entry in the first regulatory system. Finally, it performs differentiated compliance verification on the data to be verified according to the mapping type, generating the target verification result.

[0076] The following is combined Figure 7 The electronic devices in the embodiments of this application will be described. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown, the electronic device includes one or more processors, a memory, a communication interface, and one or more programs. The processor is connected to the memory and the communication interface via an internal communication bus.

[0077] The one or more programs are stored in the aforementioned memory and configured to be executed by the aforementioned processor, and the one or more programs include instructions for performing any of the steps in the above embodiments.

[0078] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0079] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0080] It is understood that the electronic device may include more or fewer structural elements than those shown in the block diagram above, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device may incorporate elements such as... Figure 1 The system architecture described above.

[0081] For easier understanding, please refer to Figure 8 , Figure 8 This is a flowchart illustrating a cross-border compliance verification and mapping method for aviation assets provided in this application embodiment, specifically including the following steps: S801. Collect airworthiness rule data under the first and second regulatory systems corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; S802. The airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule base; the target rule base includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0; S803. Generate target mapping samples based on a preset historical rule base; S804. Train the preset reference mapping model based on the target mapping sample to obtain the target mapping model; S805. Based on the target mapping model, perform association mapping on the rule entries of the first regulatory system and the second regulatory system in the target rule base to generate a rule mapping base; S806. Based on the rule mapping library, perform compliance verification on the target aviation asset to obtain the target verification result.

[0082] It is evident that by transforming airworthiness regulations from different regulatory systems into a structured rule base and using machine learning to automatically establish cross-system rule mappings, and then performing automated compliance verification of target aviation assets on this basis, the traditional method of manually comparing airworthiness clauses one by one is replaced, significantly improving the efficiency and accuracy of cross-border compliance verification of aviation assets.

[0083] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of the cross-border compliance verification and mapping method for aviation assets as described in the above embodiments, wherein the computer includes an electronic device.

[0084] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of the cross-border compliance verification and mapping method for aviation assets as described in the above embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0085] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0086] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0087] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0088] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0089] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0090] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0091] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A cross-border compliance verification and mapping system for aviation assets, characterized in that, The system includes: a rule modeling module, a rule mapping module, and a compliance verification module, wherein: The rule modeling module is used to collect airworthiness rule data under the first regulatory system and the second regulatory system corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed the compliance verification of the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; the airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule library; the target rule library includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0; The rule mapping module is used to generate target mapping samples based on a preset historical rule base; train a preset reference mapping model based on the target mapping samples to obtain a target mapping model; and perform association mapping on the rule entries of the first regulatory system and the second regulatory system in the target rule base based on the target mapping model to generate a rule mapping base. The compliance verification module is used to perform compliance verification on the target aviation asset based on the rule mapping library, and obtain the target verification result.

2. The system as described in claim 1, characterized in that, In generating target mapping samples based on a preset historical rule base, the rule mapping module is specifically used for: From the historical rule base, extract multiple historical rule entries from at least two different regulatory systems; the at least two different regulatory systems include at least the first regulatory system and the second regulatory system; Semantic encoding is performed on each of the multiple historical rule entries to generate multiple semantic vectors; Calculate the semantic similarity between any two semantic vectors originating from different regulatory systems among the plurality of semantic vectors, and select a semantic similarity values ​​that are greater than a preset semantic similarity threshold; where a is an integer greater than 1. Based on the two historical rule entries corresponding to each of the a semantic similarities, generate a group of historical rule entries; Each of the a historical rule entry groups is labeled to obtain a reference mapping types; each historical rule entry group corresponds to a reference mapping type; the reference mapping type includes any of the following: complete equivalence, partial equivalence, and rule mutual exclusion; The target mapping sample is determined based on the a groups of historical rule entries and the a reference mapping types.

3. The system as described in claim 2, characterized in that, In the process of training a preset reference mapping model based on the target mapping samples to obtain a target mapping model, the rule mapping module is specifically used for: The training set is constructed by taking the a historical rule entry groups in the target mapping sample as input and the a reference mapping types as supervision labels; The reference mapping model is trained under supervision based on the training set, and the parameters of the reference mapping model are adjusted to obtain the target mapping model. During the training process, the reference mapping model automatically learns the features of each historical rule entry group and outputs the prediction result of the corresponding mapping type.

4. The system as described in claim 2, characterized in that, The rule mapping library includes n mapping records. In generating the rule mapping library by associating and mapping the rule entries of the first regulatory system and the second regulatory system within the target rule library based on the target mapping model, the rule mapping module is specifically used for: From the m first rule entries and the n second rule entries, select k first rule entries and k second rule entries that meet the preset pairing conditions; the preset pairing conditions are that the semantic similarity between the second rule entries and the first rule entries is greater than the semantic similarity threshold; k is an integer greater than 0 and less than or equal to n; Based on the k first rule entries and the k second rule entries, determine k target rule entry groups; The k target rule entry groups are input into the target mapping model for reasoning, and k target mapping types are output. Generate k mapping records based on the k target rule entry groups and the k target mapping types; Obtain nk second rule entries from the n second rule entries, excluding the k second rule entries; It is determined that the nk target mapping types corresponding to the nk second rule entries are all rule supplements; Generate nk mapping records based on the nk second rule entries and the nk target mapping types.

5. The system as described in claim 4, characterized in that, In the process of performing compliance verification on the target aviation asset based on the rule mapping library to obtain the target verification result, the compliance verification module is specifically used for: Obtain the data to be verified for the target aviation asset; the data to be verified includes at least: basic asset information, ownership history, maintenance records, and airworthiness certificate information; the basic asset information includes the subject type of the target aviation asset; Extract b mapping records corresponding to the subject type from the n mapping records in the rule mapping library; b is a positive integer less than or equal to n. A list to be verified is determined based on the b mapping records; the list to be verified includes a list of differences and a list of mutually exclusive items. The data to be verified is subjected to compliance verification based on the list of data to be verified, and the target verification result is obtained.

6. The system as described in claim 5, characterized in that, In determining the list to be verified based on the b mapping records, the compliance verification module is specifically used for: Determine the first target mapping type corresponding to the first mapping record; the first mapping record is any one of the b mapping records. When the first target mapping type is the partial equivalence or the rule supplement, the first mapping record is determined to be an item in the list of differences; When the first target mapping type is the rule mutual exclusion, the first mapping record is determined to be an item in the list of mutual exclusion items.

7. The system as described in claim 6, characterized in that, The target verification result includes at least one of the following: a first verification result, a second verification result. In the process of performing compliance verification on the data to be verified according to the list to be verified to obtain the target verification result, the compliance verification module is specifically used for: Iterate through each mapping record in the list of differences, extract the difference content according to the target mapping type corresponding to the mapping record, and generate an evidence supplementation plan; The data to be verified is subjected to compliance verification according to the evidence supplementation scheme, and the first verification result is obtained. Each mapping record in the list of mutually exclusive items is traversed, the mutually exclusive content recorded in the mapping record is extracted, and a risk management plan is generated by combining it with a preset conflict resolution strategy; the conflict resolution strategy includes at least one of the following: the principle of priority of target regulatory system, the principle of priority of bilateral agreement, and the principle of manual adjudication; The data to be verified is subjected to compliance verification according to the risk management plan, and the second verification result is obtained.

8. A method for cross-border compliance verification and mapping of aviation assets, characterized in that, The method includes: Collect airworthiness rule data under the first and second regulatory systems corresponding to the target aviation asset; the target aviation asset is an aviation asset that has completed compliance verification under the first regulatory system; the first regulatory system and the second regulatory system are airworthiness regulatory systems in different regions; The airworthiness rule data is semantically segmented according to a preset semantic model to obtain a target rule base; the target rule base includes m first rule entries of the first regulatory system and n second rule entries of the second regulatory system; m and n are both integers greater than 0; Generate target mapping samples based on a preset historical rule base; The target mapping model is obtained by training a preset reference mapping model based on the target mapping samples; Based on the target mapping model, the rule entries of the first regulatory system and the second regulatory system in the target rule base are associated and mapped to generate a rule mapping base; Based on the rule mapping library, compliance verification is performed on the target aviation asset to obtain the target verification result.

9. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in claim 8.

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