An aviation data revision system based on artificial intelligence

Abstract

The present invention relates to the technical field of text processing, and specifically provides an aviation data revision system based on artificial intelligence, including: a text acquisition unit, which is used to acquire the target aviation data text, perform a clause splitting operation on the target aviation data text to obtain the first clause sequence of the target aviation data text, acquire the standard aviation data text, and perform a clause splitting operation on the standard aviation data text to obtain the second clause sequence of the target aviation data text. The present invention can quickly identify the parts that need to be revised from the target aviation data text through an automated text processing process, compare and revise them in combination with the standard aviation data, which can greatly reduce the time and cost of manual revision, improve the accuracy of revision, avoid human errors, and the system can better understand the semantics in the text by introducing a pre-trained large language model.

Claims

1. An artificial intelligence-based aviation information revision system, characterized in that: include: A text acquisition unit (1), the text acquisition unit (1) is used to acquire a target aviation information text, perform a sentence splitting operation on the target aviation information text to obtain a first clause sequence of the target aviation information text, acquire a standard aviation information text, perform a sentence splitting operation on the standard aviation information text to obtain a second clause sequence of the target aviation information; A sentence matching unit (2), the sentence matching unit (2) being used to perform semantic matching on each first clause in the first clause sequence with the second clause sequence to obtain a second clause matching the first clause, that is, to obtain a second clause set corresponding to the first clause sequence; A sentence interpretation unit (3), the sentence interpretation unit (3) being used to perform a clause interpretation on each second clause in the second clause set according to a pre-trained large language model, so as to obtain a clause interpretation corresponding to the second clause, that is, to obtain a clause interpretation set corresponding to the second clause set; A data revision unit (4), the data revision unit (4) being used to perform context-aware generation on the clause interpretation set to obtain a data revision text corresponding to the clause interpretation set; A data generation unit (5), the data generation unit (5) being used to perform image-text matching on the second clause set and the standard aerial image set to obtain the standard aerial image corresponding to the second clause, and to add the standard aerial image to the corresponding position in the data revision text to obtain the aerial image-text revision data; Context-aware generation is performed on the clause interpretation set to obtain a document revision text corresponding to the clause interpretation set, including: Performing dependency syntactic analysis on the clause interpretation set to generate a weighted directed graph; Performing semantic role labeling on the clause interpretation set to obtain a semantic role labeling result, and constructing a hyperedge set based on the semantic role labeling result; Constructing a hypergraph corresponding to the weighted directed graph based on the hyperedge set; Performing feature encoding on the hypergraph based on a graph attention network to obtain a context-aware feature vector corresponding to the hypergraph; constructing a prototype vector based on the context-aware feature vector; Calculating the semantic deviation of each clause interpretation in the clause interpretation set based on the prototype vector; Dynamically modulate the candidate clause interpretation representation based on the gating mechanism; When the semantic deviation is less than a preset deviation threshold, the candidate interpretation corresponding to the semantic deviation is used as a clause of the data revision text.

2. The aeronautical information revision system based on artificial intelligence according to claim 1, characterized in that: Semantically matching each first clause in the first clause sequence with the second clause sequence to obtain a second clause matching the first clause includes: Performing a word segmentation operation on the first clause and the second clause to obtain a first word sequence corresponding to the first clause and a second word sequence corresponding to the second clause; Performing a character segmentation operation on the first clause and the second clause to obtain a first character sequence corresponding to the first clause and a second character sequence corresponding to the second clause; Mapping the first word and the first character corresponding to the first word into a first word vector according to the weight of the pre-trained Word2vec model; mapping the second word and the second character corresponding to the second word into a second word vector according to the weight of the pre-trained Word2vec model; Regularizing the first word vector and the second word vector to obtain a first standard word vector corresponding to the first word vector and a second standard word vector corresponding to the second word vector.

3. The aeronautical information revision system based on artificial intelligence according to claim 2, characterized in that: Semantically matching each first clause in the first clause sequence with the second clause sequence to obtain a second clause matching the first clause includes: Performing difference encoding on the first standard word vector and the second standard word vector to obtain a difference vector between the first standard word vector and the second standard word vector; Interactively encoding the first standard word vector and the second standard word vector to obtain an interactive vector between the first standard word vector and the second standard word vector; The difference vector and the interaction vector are fused to obtain a fused vector, and the fused vector is classified to obtain a matching degree between the first clause and the second clause.

4. The aeronautical information revision system based on artificial intelligence according to claim 3, characterized in that: Performing difference encoding on the first standard word vector and the second standard word vector to obtain a difference vector between the first standard word vector and the second standard word vector includes: Performing context encoding on the first standard word vector and the second standard word vector according to a bidirectional GRU to obtain a first context vector corresponding to the first standard word vector and a second context vector corresponding to the second standard word vector; The distance between the first context vector and the second context vector is determined according to the Manhattan distance, and the first context vector and the second context vector are differentially encoded according to the distance to obtain a difference vector between the first standard word vector and the second standard word vector.

5. The aeronautical information revision system based on artificial intelligence according to claim 4, characterized in that: Interactively encoding the first standard word vector and the second standard word vector to obtain an interactive vector between the first standard word vector and the second standard word vector includes: The first standard word vector and the second standard word vector are interactively encoded according to the multi-head attention mechanism to obtain an interaction vector between the first standard word vector and the second standard word vector, wherein the expression of the interaction vector is as follows: ； in, represents the interaction vector, , and Indicates the query, key, and value projection matrices of the header, and represents the first standard word vector and the second standard word vector, represents the multi-head attention mechanism, Represents a concatenation operation.

6. The aeronautical information revision system based on artificial intelligence according to claim 5, characterized in that: The expression of the weighted directed graph is as follows: ； in, represents a weighted directed graph, Represents a collection of nodes. represents the set of dependency edges, represents a syntactic weight set, wherein the nodes in the node set correspond to the lexical units in the clause interpretation in the clause interpretation set, the dependency edges in the dependency edge set correspond to the dependency relationship types between the lexical units, and the syntactic weights in the syntactic weight set correspond to the syntactic association strength; The expression of the hyperedge in the hyperedge set is as follows: ； in, represents a hyperedge, It represents the 0th predicate-argument structure in the clause interpretation; The expression of the hypergraph is as follows: ； in, represents a hypergraph, Represents a set of hyperedges.

7. The aeronautical information revision system based on artificial intelligence according to claim 6, characterized in that: The expression of the prototype vector is as follows: ； in, represents the prototype vector, represents the average pooling operation, represents the context-aware feature vector; The calculation formula of the semantic deviation is as follows: ； in, Represents prototype vector and candidate clause interpretation The semantic deviation between Indicates the interpretation of candidate clauses The output vector of The expression of the dynamic modulation candidate interpretation representation is as follows: ； in, represents the dynamic modulation candidate interpretation representation, represents the activation function, represents the bias term, Represents the initialization of hyperedge connections in the hypergraph; When the semantic deviation is less than a preset deviation threshold, the candidate interpretation corresponding to the semantic deviation is used as a clause of the data revision text.

8. The aeronautical information revision system based on artificial intelligence according to claim 1, characterized in that: Performing image-text matching on the second clause set and the standard aerial image set to obtain the standard aerial image corresponding to the second clause includes: Extracting features from the standard aerial image according to bottom-up attention to generate a regional feature vector, dividing the standard aerial image into a plurality of blocks to obtain a position feature vector, and extracting features from the second clause according to Bi-GRU to obtain a text feature vector; The position feature vector is transformed into a query vector through a linear layer, and the region feature vector is used as a key and value vector for cross attention to obtain a visual feature vector; The text feature vector and the visual feature vector are sampled for similarity to obtain an overall similarity. If the overall similarity is higher than a preset similarity threshold, the second clause matches the standard aerial image.

9. The aeronautical information revision system based on artificial intelligence according to claim 8, characterized in that: The text feature vector and the visual feature vector are sampled for similarity to obtain overall similarity, including: Calculating the similarity between the text feature vector and the visual feature vector, wherein the similarity uses cosine similarity; Determining a semantic relationship between the text feature vector and the visual feature vector according to the similarity; Determining a weighted similarity of the block in the standard aerial image according to a semantic relationship between the text feature vector and the visual feature vector; The overall similarity between the text feature vector and the visual feature vector is determined according to the weighted similarity of the block in the standard aerial image.

10. The aeronautical information revision system based on artificial intelligence according to claim 9, characterized in that: The calculation formula of the semantic relationship is as follows: ； in, represents the semantic relationship between the second clause corresponding to the text feature vector and the standard aerial image corresponding to the visual feature vector, represents a mask that is equal to the input when it is positive, and 0 otherwise, represents the similarity between the text feature vector and the visual feature vector, Indicates the preset parameter threshold, represents the number of blocks in a standard aerial image, represents the activation function; The calculation formula of the weighted similarity is as follows: ； in, represents the weighted similarity of the block in the standard aerial image, A visual feature vector representing a block; The calculation formula of the overall similarity is as follows: ； in, represents the overall similarity between the text feature vector and the visual feature vector, Represents a text feature vector.

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