Original information correlation analysis and cross-language binding system based on edge calculation AI
By deploying AI models at edge computing nodes and utilizing multi-head attention mechanisms, the problems of network latency and insufficient computing power in multi-language text processing are solved, and efficient and accurate text processing is achieved, which is suitable for real-time applications in multi-language environments.
Patent Information
- Application Number
- CN202311047326.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-18
- Publication Date
- 2025-02-21
AI Technical Summary
The existing text processing system faces the problems of network transmission delay and insufficient computing power in multilingual text processing, resulting in slow processing speed and improved accuracy.
Using text information grammatical analysis and cross-language binding system based on edge computing AI, we use AI models to deploy AI models on edge computing nodes to realize localized processing, reduce data transmission delay, and use multi-head attention mechanism to achieve cross-language binding.
It significantly improves the efficiency and accuracy of multilingual text processing, reduces data transmission delay, and is suitable for scenarios with high real-time requirements, such as real-time translation and real-time customer service.
Smart Images
Figure CN119494318A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of edge computing AI technology, and specifically to a raw information correlation analysis and cross-language binding system. Background Art
[0002] In recent years, with the development of global informatization, multilingual text processing has become an important challenge. However, existing text processing systems are often limited by network latency and computing power, resulting in slow processing speed and poor accuracy. To solve this problem, the present invention aims to combine edge computing and AI technology to provide an innovative solution. The system can deploy AI models on edge computing nodes, realize localized processing, reduce data transmission latency, and thus improve the efficiency of original information processing.
[0003] Current text processing systems often face problems with network transmission delays and insufficient computing power, especially when processing large-scale multilingual texts. Traditional cloud computing models may lead to slow data transmission, reduce processing efficiency, and may also be affected by network instability. Existing text processing systems still have room for improvement in processing efficiency and accuracy of multilingual texts. In particular, more precise technologies are needed to achieve fast and accurate processing in terms of grammatical analysis and cross-language binding.
[0004] This system can be widely used in linguistic research, machine translation, intelligent customer service, cross-language information search and other fields. Its efficient and accurate processing capabilities will significantly improve the efficiency and quality of multi-language original information processing, bringing users a more convenient, fast and accurate language processing experience. Summary of the invention
[0005] In view of the shortcomings of the existing technology, the present invention provides a text information grammatical analysis and cross-language binding system based on edge computing AI, which can meet the needs of multilingual communication, text processing efficiency improvement, intelligent service needs, linguistic research, etc., solves the technical challenges faced by the current field of multilingual text processing, and has important practical application value.
[0006] The technical solution adopted by the present invention is as follows:
[0007] To achieve the above objectives, the present invention implements applications based on edge computing Ai through the following technical solutions, including the implementation of multi-language grammar analysis algorithms, the implementation of cross-language binding functions, the implementation of cross-language binding by multi-head attention mechanisms, and the estimation of computational delays of AI models deployed on edge computing nodes.
[0008] The present invention provides a text information grammar analysis and cross-language binding system based on edge computing AI, which is characterized by comprising the following steps:
[0009] S1. Input text processing: The user inputs the text information to be processed, which can be in any language.
[0010] S2. Text preprocessing: In order to achieve fast and accurate analysis of multilingual original information, this system uses natural language processing technology to preprocess the input text and convert the text into word vector or character vector representation;
[0011] S3. Edge computing node deployment: Deploy AI models and algorithms on edge computing nodes to achieve localized processing, which helps reduce data transmission delays and increase processing speed.
[0012] D1. Edge computing AI technology. Edge computing is a new computing architecture that pushes computing and data processing capabilities to the edge of the network. This system uses edge computing nodes to deploy AI models and algorithms on devices closest to users, reducing data transmission delays and increasing processing speeds. This distributed computing method enables relevant analysis of raw information to be completed quickly locally without being limited by network connection stability and bandwidth.
[0013] D2, used for deploying AI models on edge computing nodes:
[0014]
[0015] Where T is the processing delay, is the size of the i-th data block, n is the number of data blocks, Bandwidth is the bandwidth of the edge computing node, T comp is the calculation time of the AI model. This formula is used to estimate the processing delay on the edge computing node and help the system select the optimal computing resources;
[0016] S4. Text encoding and feature extraction: Use deep learning models (such as Transformer) to encode and extract features from the preprocessed text to capture the semantic information of the text;
[0017] E1. Algorithm implementation. In order to better understand the algorithm implementation of this system, we give the following schematic diagram of the correlation analysis algorithm based on the Transformer model:
[0018]
[0019] Among them, Q is the query vector, K is the key vector, V is the value vector, and d k For the vector dimension, through the attention mechanism, the system can cross-encode the original information of different languages and achieve cross-language binding;
[0020] S5. Multilingual grammatical analysis: Using the weighted attention mechanism, the context of different languages is modeled to achieve grammatical analysis of multilingual texts. The system can accurately understand the grammatical structure of the text.
[0021] S6. Cross-language binding: Using the multi-head attention mechanism to achieve cross-language binding, this system supports binding and converting original information in different languages. Users can enter text in one language, and the system will automatically convert it into the corresponding text in other languages, realizing cross-language communication and information sharing. This function has a wide range of practical application value in a multilingual environment;
[0022] F1. Cross-language binding of original information for the system:
[0023] Multi-Head Attemtiom(Q,K,V)
[0024] =Concat(head1, head2,..., head i )W 0
[0025] in, is the query, key, and value matrix of the ith attention head, and hh is the number of attention heads. Through the multi-head attention mechanism, the system can simultaneously focus on the contextual information between different languages and achieve more accurate cross-language binding;
[0026] F2. Schematic diagram of the cross-language binding algorithm:
[0027] Encoding(x)=TransformenrEncoder(x)
[0028]
[0029] Decoding(x)=TransformerDecoder(x)
[0030] where x i and x j They represent input texts in different languages respectively. TransformerEncoder(x) and TransformerDecoder(x) are the encoder and decoder of Transformer respectively. is the query, key, and value matrix in the cross-language binding, is the output matrix. Through the three steps of encoding, cross-language binding and decoding, the system can convert the input text from one language to another language, realizing the cross-language binding function;
[0031] S7. Result output: Output the text results after syntax analysis and cross-language binding. Users can obtain accurate analysis and converted text information.
[0032] Beneficial effects of the present invention:
[0033] The present invention provides a text information grammatical analysis and cross-language binding system for edge computing AI. Compared with the prior art, it has many benefits and advantages, making it have significant value and competitiveness in the field of multilingual text processing. The AI model is deployed using edge computing nodes to achieve localized processing, which greatly reduces data transmission delays. Compared with the traditional cloud computing model, the system can process text information more quickly, and is particularly suitable for scenarios with high real-time requirements, such as real-time translation, real-time customer service, etc. Utilizing the edge computing architecture, the system can make full use of local computing resources and improve the efficiency of text information processing. Compared with methods that rely on remote cloud servers, the processing speed is faster and users can get results faster. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION
[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0036] The present invention provides a text information grammar analysis and cross-language binding system based on edge computing AI, which specifically includes the following steps:
[0037] S1. Input text processing: The user inputs the text information to be processed, which can be in any language;
[0038] S2. Text preprocessing: In order to achieve fast and accurate analysis of multilingual original information, this system uses natural language processing technology to preprocess the input text and convert it into word vector or character vector representation;
[0039] S3. Edge computing node deployment: Deploy AI models and algorithms on edge computing nodes to achieve localized processing. This helps reduce data transmission latency and increase processing speed;
[0040] D1. Edge computing AI technology. Edge computing is a new computing architecture that pushes computing and data processing capabilities to the edge of the network. This system uses edge computing nodes to deploy AI models and algorithms on the devices closest to the user, reducing data transmission delays and increasing processing speed. This distributed computing method allows the relevant analysis of raw information to be completed quickly locally without being limited by network connection stability and bandwidth;
[0041] D2, used for deploying AI models on edge computing nodes:
[0042]
[0043] Where T is the processing delay, is the size of the i-th data block, n is the number of data blocks, Bandwidth is the bandwidth of the edge computing node, T comp is the calculation time of the AI model; this formula is used to estimate the processing delay on the edge computing node and help the system select the optimal computing resources;
[0044] S4. Text encoding and feature extraction: Use deep learning models (such as Transformer) to encode and extract features from the preprocessed text to capture the semantic information of the text;
[0045] E1. Algorithm implementation. In order to better understand the algorithm implementation of this system, we give the following schematic diagram of the correlation analysis algorithm based on the Transformer model:
[0046]
[0047] Among them, Q is the query vector, K is the key vector, V is the value vector, and d k is the vector dimension. Through the attention mechanism, the system can cross-encode the original information of different languages and achieve cross-language binding;
[0048] S5. Multilingual grammatical analysis: Using the weighted attention mechanism, the context of different languages is modeled to achieve grammatical analysis of multilingual texts. The system can accurately understand the grammatical structure of the text;
[0049] S6. Cross-language binding: Using the multi-head attention mechanism to achieve cross-language binding, this system supports binding and converting original information in different languages. Users can enter text in one language, and the system will automatically convert it into the corresponding text in other languages to achieve cross-language communication and information sharing. This function has a wide range of practical application value in a multilingual environment;
[0050] F1. Cross-language binding of original information for the system:
[0051] Multi-Head Attention(Q,K,V)
[0052] =Concat(head1, head2,..., head i )W 0
[0053] in, is the query, key, and value matrix of the ith attention head, and hh is the number of attention heads. Through the multi-head attention mechanism, the system can simultaneously focus on the contextual information between different languages and achieve more accurate cross-language binding;
[0054] F2. Schematic diagram of the cross-language binding algorithm:
[0055] Encoding(x)=TransformenrEncoder(x)
[0056]
[0057] Decodiing(x)=TransformerDecoder(x)
[0058] where x i and x j They represent input texts in different languages, TransformerEncoder(x) and TransformerDecoder(x) are the encoder and decoder of Transformer respectively. is the query, key, and value matrix in the cross-language binding, is the output matrix. Through the three steps of encoding, cross-language binding and decoding, the system can convert the input text from one language to another and realize the cross-language binding function.
[0059] S7. Result output: Output the text results after syntax analysis and cross-language binding. Users can obtain accurate analysis and converted text information.
[0060] In summary, the original information correlation analysis and cross-language binding system based on edge computing AI is an innovative technology with important application value. This system deploys AI models through edge computing nodes to achieve localized processing, reduce data transmission delays, and improve the efficiency of original information processing. At the same time, the system's multilingual support and cross-language binding functions bring new solutions to multilingual text processing and expand the application field of language processing technology. The present invention is of great significance to promoting multilingual communication, cross-cultural communication and global information sharing, and is expected to have a wide range of application effects in multiple fields. Through this patented technology, we believe that it will bring users a more convenient, efficient and accurate language processing experience, and promote further innovation in multilingual information processing technology.
[0061] In this application document, the innovative advantages of this patent based on edge computing AI are mainly reflected in that by deploying AI models on edge computing nodes, this system achieves efficient localized original information processing, avoiding the problems of data transmission delay and network instability in the traditional cloud computing model. At the same time, the system's multilingual correlation analysis algorithm and cross-language binding function provide a new solution for multilingual text processing, effectively improving processing efficiency and accuracy.
[0062] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.
[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. Text information grammar analysis and cross-language binding system based on edge computing AI, characterized by: It includes the application of edge computing AI technology, the implementation of multi-language syntax analysis algorithms, the implementation of cross-language binding functions, the multi-head attention mechanism to achieve cross-language binding, and the estimation of computing delays for deploying AI models on edge computing nodes.
2. The text information grammar analysis and cross-language binding system based on edge computing AI according to claim 1, characterized in that: The specific steps include: S1. Input text processing: The user inputs the text information to be processed, which can be in any language. S2. Text preprocessing: In order to achieve fast and accurate analysis of multilingual original information, this system uses natural language processing technology to preprocess the input text and convert the text into word vector or character vector representation; S3. Edge computing node deployment: Deploy AI models and algorithms on edge computing nodes to achieve localized processing, which helps reduce data transmission latency and improve processing speed; D1. Edge computing AI technology. Edge computing is a new computing architecture that pushes computing and data processing capabilities to the edge of the network. This system uses edge computing nodes to deploy AI models and algorithms on devices closest to users, reducing data transmission delays and increasing processing speeds. This distributed computing method enables relevant analysis of raw information to be completed quickly locally without being limited by network connection stability and bandwidth. D2, used for deploying AI models on edge computing nodes: Where T is the processing delay, is the size of the i-th data block, n is the number of data blocks, Bandwidth is the bandwidth of the edge computing node, T comp is the calculation time of the AI model. This formula is used to estimate the processing delay on the edge computing node and help the system select the optimal computing resources; S4. Text encoding and feature extraction: Use deep learning models (such as Transformer) to encode and extract features from the preprocessed text to capture the semantic information of the text; E1. Algorithm implementation. In order to better understand the algorithm implementation of this system, we give the following schematic diagram of the correlation analysis algorithm based on the Transformer model: Among them, Q is the query vector, K is the key vector, V is the value vector, and d k For the vector dimension, through the attention mechanism, the system can cross-encode the original information of different languages and achieve cross-language binding; S5. Multilingual grammatical analysis: Using the weighted attention mechanism, the context of different languages is modeled to achieve grammatical analysis of multilingual texts. The system can accurately understand the grammatical structure of the text. S6. Cross-language binding: The multi-head attention mechanism is used to achieve cross-language binding. This system supports binding and converting original information in different languages. Users can enter text in one language, and the system will automatically convert it into the corresponding text in other languages, realizing cross-language communication and information sharing. This function has a wide range of practical application value in a multilingual environment. F1. Cross-language binding of original information for the system: Multi-HeadAttention(Q,K,V) =Concat(head1,head2,…,head i )W 0 in, is the query, key, and value matrix of the i-th attention head, and hh is the number of attention heads. Through the multi-head attention mechanism, the system can simultaneously pay attention to the contextual information between different languages and achieve more accurate cross-language binding; F2. Schematic diagram of the cross-language binding algorithm: Encoding(x)=TransformenrEncoder(x) Decoding(x)=TransformerDecoder(x) where x i and x j They represent input texts in different languages, TransformerEncoder(x) and TransformerDecoder(x) are the encoder and decoder of Transformer respectively. is the query, key, and value matrix in the cross-language binding, To output the matrix, through the three steps of encoding, cross-language binding and decoding, the system can convert the input text from one language to another, realizing the cross-language binding function; S7. Result output: Output the text results after syntax analysis and cross-language binding. Users can obtain accurate analysis and converted text information.