AI-driven cross-language big data semantic extraction and translation method and system

Abstract

The invention relates to the technical field of natural language processing, and discloses an AI-driven cross-language big data semantic extraction and translation method and system.The method comprises the steps that a semantic complexity online prediction model is constructed, a multi-level cache structure is achieved, a semantic importance scoring algorithm is applied, and a resource allocation strategy is optimized in combination with reinforcement learning; a hierarchical time window model is adopted for incremental semantic analysis, and cross-language real-time semantic extraction and translation are achieved. Through the semantic complexity online prediction model and the reinforcement learning optimization resource allocation strategy, the system can accurately predict the data processing difficulty and adjust resource allocation in advance, and the resource utilization rate is improved; in combination with a multi-level cache structure and incremental semantic analysis, the system can quickly retrieve and reuse the processed semantic information and is suitable for application scenes with high requirements on real-time performance; the resource utilization efficiency is effectively improved, the processing delay is remarkably reduced, and the peak value processing capacity is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing, and more specifically, to an AI-driven cross-language big data semantic extraction and translation method and system. Background Art

[0002] As globalization accelerates, cross-language information processing has become a major challenge facing modern computer technology. In scenarios such as multilingual social media monitoring, real-time translation of international news, and cross-language customer service, systems must simultaneously process large amounts of data streams in different languages, extract semantic information, and accurately translate them with low latency.

[0003] Existing cross-language processing systems typically employ fixed resource allocation strategies, which are unable to cope with sudden changes in data complexity. This leads to processing delays during periods of high load and resource waste during periods of low load. Furthermore, traditional batch processing approaches struggle to meet real-time requirements, and large-scale repetitive computations lead to system inefficiencies. Furthermore, existing systems lack mechanisms for assessing semantic importance, making it impossible to prioritize critical information when resources are limited.

[0004] Therefore, how to ensure real-time translation while ensuring accuracy, how to efficiently allocate computing resources to avoid waste and processing delays, and how to implement incremental semantic analysis to improve the overall processing efficiency of the system have become technical problems that need to be solved urgently. Summary of the Invention

[0005] The present invention provides an AI-driven cross-language big data semantic extraction and translation method and system to solve the technical problems in related technologies such as sudden changes in data complexity leading to resource waste and processing delays, difficulty in simultaneously ensuring translation accuracy and real-time performance, and low system processing efficiency.

[0006] The first aspect of the present invention discloses an AI-driven cross-language big data semantic extraction and translation method, comprising the following steps: Build an online semantic complexity prediction model to analyze historical processing data and current data characteristics, and predict the upcoming data processing difficulty in real time; Based on the prediction of upcoming data processing difficulty, a multi-level cache structure consisting of an immediate processing layer, a short-term cache layer, and a long-term knowledge base is implemented to optimize the data processing process; Based on the optimized data processing flow, a semantic importance scoring algorithm is applied to assign processing priorities to input data based on information content, novelty, and complexity; Based on the assigned processing priority, combined with reinforcement learning to optimize resource allocation strategies, the computing resources of each language processing module are adaptively adjusted according to the predicted data complexity and system load status; Based on the adjusted computing resources of each language processing module, a layered time window model is used for incremental semantic analysis to achieve real-time cross-language semantic extraction and translation.

[0007] Furthermore, the construction of the semantic complexity online prediction model includes: extracting features from the input multilingual data stream to obtain feature vectors; constructing a historical processing data memory module to store the processing data of multiple time windows in the past and their actual complexity; constructing an online prediction model based on the attention mechanism, taking current data features and historical data as input to predict the processing complexity of current data; establishing an online update mechanism for the prediction model to update the model parameters according to the actual processing complexity.

[0008] Furthermore, the implementation of the multi-level cache structure includes: building an immediate processing layer to directly process data that is determined to be high priority and cannot use the existing cache; building a short-term cache layer to use the least recently used strategy to store recently processed data and its semantic representation and translation results; building a long-term knowledge base to use a graph structure to store multilingual semantic knowledge; and implementing a multi-level cache collaboration mechanism to integrate the three-layer cache structure to form a unified data processing flow.

[0009] Furthermore, the semantic importance scoring algorithm includes: calculating the information volume of multilingual data and measuring the effective information contained in the data; calculating the novelty of multilingual data and measuring the difference between the data and historical data; obtaining the processing complexity of the data; comprehensively considering the information volume, novelty and complexity to calculate the semantic importance score; and assigning processing priority to the input data based on the semantic importance score.

[0010] Furthermore, the combination of reinforcement learning to optimize resource allocation strategies includes: constructing a system state representation model to describe the current system's resource usage and processing capabilities; defining a resource allocation action space, including increasing, decreasing, or maintaining the resource allocation ratio of each language processing module; designing a reward function that takes into account throughput, latency, and resource utilization; constructing a resource allocation decision model based on deep Q learning to learn the optimal resource allocation strategy; and implementing a resource allocation executor to adjust the resource allocation of each language processing module according to the learned strategy.

[0011] Furthermore, the hierarchical time window model includes three levels: micro-window, meso-window and macro-window, which respectively process the immediate semantics, contextual relevance and recent semantic coherence, long-term semantic consistency and topic evolution of single sentences or short paragraphs.

[0012] Furthermore, the implementation of the incremental semantic analysis includes: implementing cross-language semantic embedding extraction to map multilingual inputs into a unified semantic space; developing an incremental semantic analysis mechanism to update semantic understanding without reprocessing all historical data; building a semantic-to-target language conversion model to achieve high-quality cross-language translation; and implementing a progressive refinement strategy to dynamically adjust the analysis granularity based on processing time and resource constraints.

[0013] Furthermore, the progressive refinement strategy performs coarse-grained, medium-grained or fine-grained analysis on the input data according to the current available processing time and available resources, giving priority to completing coarse-grained analysis when resources are tight, and gradually refining the analysis results when resources allow.

[0014] Furthermore, the long-term knowledge base adopts a graph structure, in which nodes represent concepts, edges represent relationships between concepts, and attribute sets include expressions in different languages.

[0015] The second aspect of the present invention discloses an AI-driven cross-language big data semantic extraction and translation system for executing the above-mentioned AI-driven cross-language big data semantic extraction and translation method, including: a semantic complexity prediction module for analyzing historical processing data and current data features, and predicting data processing difficulty in real time; a multi-level cache module, comprising a three-layer cache structure of an immediate processing layer, a short-term cache layer, and a long-term knowledge base; a semantic importance scoring module for assigning processing priority to input data based on information volume, novelty, and complexity; a resource allocation module for optimizing resource allocation strategies in combination with reinforcement learning, and adaptively adjusting the computing resources of each language processing module; a semantic analysis module for performing incremental semantic analysis using a hierarchical time window model to achieve cross-language real-time semantic extraction and translation.

[0016] The beneficial effects of the present invention are: through the online prediction model of semantic complexity and the resource allocation strategy optimized by reinforcement learning, the system can accurately predict the difficulty of data processing and adjust resource allocation in advance, thereby improving resource utilization; combined with the multi-level cache structure and incremental semantic analysis, the system can quickly retrieve and reuse processed semantic information, which is suitable for application scenarios with high real-time requirements; through predictive resource scheduling and multi-level cache mechanism, the system can smoothly process the load when data bursts, significantly reduce peak delays, and improve user experience, especially in scenarios such as hot events on social media and major international breaking news; the multi-level cache mechanism effectively retains language processing historical information and enhances cross-language semantic association capabilities; the modular design makes the system easy to expand horizontally, and new language processing modules can be dynamically added according to business needs to adapt to the ever-changing cross-language processing needs, and support seamless migration from ten languages to one hundred languages. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is the overall flow chart of the AI-driven cross-language big data semantic extraction and translation method of the present invention. DETAILED DESCRIPTION

[0018] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0019] This embodiment proposes an AI-driven cross-language semantic extraction method that combines adaptive resource scheduling with multi-level caching, such as Figure 1 As shown, the following steps are included: Step 1: Build an online semantic complexity prediction model, analyze historical processing data and current data features, and predict the upcoming data processing difficulty in real time; The specific steps are as follows: Step 1.1: Input multilingual data stream Perform feature extraction and obtain feature vectors ,in, Represents a multilingual data stream, 、 、 Respectively represent time points 1, 2, The input data, Indicates the current time point, Indicates the current time point Input data The eigenvector of .

[0020] Features include but are not limited to: text length, vocabulary complexity, language type, terminology density, and grammatical structure complexity. Feature vector The calculation method is: ; in, 、 、 Respectively represent the 1st, 2nd, characteristic functions, Represents the total number of features.

[0021] Step 1.2: Build a historical processing data memory module to store past The processing data of each time window and its actual complexity form a historical data set: ; in, Indicates from the time point arrive A collection of historical data, Indicates the size of the historical time window, Indicates the current time point. Specifically, Indicates at a point in time The actual complexity value when processing data, Indicates at a point in time The actual complexity value when processing data, Indicates at a point in time The actual complexity value when processing data; Indicates at a point in time Input Data The eigenvector of Indicates at a point in time Input Data The eigenvector of Indicates at a point in time Input Data These feature vectors contain feature information such as text length, vocabulary complexity, and language type of the data at the corresponding time point.

[0022] Step 1.3: Build an online semantic complexity prediction model based on the attention mechanism, and integrate the current data features With historical data As input, predict the processing complexity of the current data The prediction model is defined as: ; in, is the processing complexity of the current data, represents the complexity prediction function, Indicates from the time point arrive A collection of historical data, Indicates the current time point Input data The eigenvector of The model uses attention weights Dynamically adjust the importance of historical data: ; in, is the complexity conversion function, For historical time points The actual processing complexity of Indicates a historical point in time Input data The eigenvector of Indicates the current time point Input data The eigenvector of is the attention weight, satisfying , calculated as follows: ; Represents the similarity function between feature vectors, which is used to calculate the similarity between the current data features and the historical data features; Indicates the current time point Input data The eigenvector of Indicates a historical point in time Input data The eigenvector of Indicates a historical point in time Input data The feature vector of , maps historical data and current features to complexity prediction values; represents the exponential function used to convert similarity into non-negative weights; Indicates the current time point; Indicates the size of the historical time window; arrive Indicates the historical time window range.

[0023] Step 1.4: Establish an online update mechanism for the prediction model. After each new data is processed, update the model parameters according to the actual processing complexity to ensure that the model can adapt to changes in data distribution. The online update formula is: ; in, represents the model parameters, is the learning rate, is the loss function, To deal with the actual complexity, represents the gradient of the loss function with respect to the model parameters, represents the model parameters before updating, Represents the updated model parameters. This formula implements an online learning process based on gradient descent, which continuously optimizes the model by minimizing the difference between the predicted complexity and the actual complexity.

[0024] Step 2: Based on the predicted difficulty of upcoming data processing, a multi-level cache structure consisting of an immediate processing layer, a short-term cache layer, and a long-term knowledge base is implemented to optimize the data processing process; The specific steps are as follows: Step 2.1: Build an immediate processing layer to handle time-sensitive data. This layer directly processes the input multilingual data stream without caching. It mainly targets data that is determined to be high priority after the semantic importance score in step 3 and cannot be cached. The processing function of the immediate processing layer is defined as: ; in, Indicates the immediate processing layer for input data The processing result, is the processing function, This formula describes how the immediate processing layer works: it processes input data directly without relying on cached information.

[0025] Step 2.2: Build a short-term cache layer to store recently processed data, its semantic representation, and translation results. The short-term cache layer uses an LRU (least recently used) strategy to maintain cache content, and the cache size is dynamically adjusted based on system resources. The data structure of the short-term cache layer is defined as: ; in, A data structure representing the short-term cache layer, 、 、 Respectively represent the 1st, 2nd, The key of the cache entry, The total number of keys representing cache entries; 、 、 Respectively represent 1, 2, The value of a cache entry, Represents the total number of values in the cache entries; 、 、 Respectively represent the 1st, 2nd, The last access timestamp of the cache entry, Indicates the total number of last access timestamps of cache entries.

[0026] The function to access the short-term cache is: ; in, Represents the input data Lookup results in the short-term cache, Indicates the cache value found, Indicates that a cache key exists , Represents the hash result of the input data feature vector, is the short-term cache matching threshold, is the characteristic hash function, is the similarity function. This formula indicates that when the similarity between the feature hash value of the input data and a cache key exceeds the threshold, the corresponding cache value is returned.

[0027] Step 2.3: Build a long-term knowledge base to store common semantic knowledge and translation patterns. The long-term knowledge base uses a graph structure to store multilingual semantic knowledge, with nodes representing concepts and edges representing relationships between concepts. The long-term knowledge base is defined as: ; in, represents the long-term knowledge base, is a set of concept nodes, is the set of relationship edges, is a set of attributes (including expressions in different languages). The function to access the long-term knowledge base is: ; in, represents access to a long-term knowledge base, is the correlation function, is the long-term knowledge base matching threshold, Represents a concept node in the knowledge base, Represents the set of all concept nodes, Indicates time The formula means retrieving all concept nodes from the long-term knowledge base whose relevance to the input data exceeds the threshold.

[0028] Step 2.4: Implement a multi-level cache coordination mechanism and integrate the three-layer cache structure to form a unified data processing flow. First, we try to get the processing results from the short-term cache layer. If there is no hit, we query the long-term knowledge base to extract relevant knowledge, and finally process it in the immediate processing layer. The collaborative processing function is defined as: ; in, Represents the input data The final processing result, Indicates a search in the short-term cache The result, represents an empty set (i.e. no match was found in the short-term cache), To process a function, when the short-term cache misses, the relevant knowledge in the long-term knowledge base is used to assist in processing. Indicates searching for the same Related knowledge.

[0029] In specific implementations, this multi-level cache structure is applied differently in different scenarios: Real-time news translation scenario: The short-term cache layer primarily stores translations of terms and entities related to recent hot events, while the long-term knowledge base stores multilingual representations of general domain knowledge and specialized terminology. For example, when processing news related to an emergency, the system first searches the short-term cache for translations of relevant entities (such as names of people, places, and events). If no translations are found, the system extracts translation patterns for similar concepts from the long-term knowledge base and performs instant translation based on the context.

[0030] In a multilingual customer service system scenario, the short-term cache layer stores recent user queries and their answers, while the long-term knowledge base stores product knowledge graphs and multilingual representations of FAQs. When a user asks a question in a non-native language, the system first checks the short-term cache for similar questions. If so, it directly returns a translated answer. If not, it extracts product knowledge from the long-term knowledge base and generates a targeted answer in the real-time processing layer.

[0031] In a cross-language social media monitoring scenario, a short-term cache layer stores semantic representations of recent hot topics and trends, while a long-term knowledge base stores commonly used expressions and sentiment patterns on social media. The system dynamically adjusts its caching strategy based on content novelty, prioritizing highly innovative content (such as new memes and terminology) in the short-term cache to facilitate faster semantic extraction of related content.

[0032] Step 3: Based on the optimized data processing flow, apply the semantic importance scoring algorithm to assign processing priorities to the input data based on information content, novelty, and complexity; The specific steps are as follows: Step 3.1: Calculate the information content of multilingual data and measure the effective information contained in the data. , its information volume The calculation method is: ; in, Represents input data The amount of information is used to quantify the richness of information contained in the data. The higher the amount of information, the greater the uncertainty contained in the data, that is, the richer the information content, and the more processing resources are required to extract the semantic information. Indicates time Input data at time, Represents the word in the data, Representation word Belong to input data , Expressive words The conditional probability in the context can be estimated by the language model, Represents the sum operation of all the words in the data, Represents a logarithmic function. The higher the amount of information, the greater the uncertainty contained in the data, that is, the richer the information content.

[0033] Step 3.2: Calculate the novelty of the multilingual data and measure the difference between the data and historical data. The calculation method is: ; in, is the similarity function, which calculates the difference between current data and past data. The maximum similarity of the data in the time window is calculated, and the novelty score is obtained by subtracting the value from 1. Indicates a past time point The input data, represents the number of historical time windows considered, arrive Indicates from The range from the time unit ago to the previous time unit, Indicates the maximum value operation. The higher the novelty, the more new information the data contains. The higher the novelty, the more new information the data contains.

[0034] Step 3.3: Use the semantic complexity prediction model in step 1 to obtain the processing complexity of the data The higher the complexity, the more difficult the data processing is and the more computing resources are required.

[0035] Step 3.4: Calculate the semantic importance score by combining information content, novelty and complexity. Scoring function Defined as: ; in, 、 and is the weight parameter, Represents input data The amount of information, is novelty, which indicates the degree of difference between the input data and historical data, Represents input data The computational complexity in the semantic extraction and translation process satisfies , used to balance the importance of various factors. The system dynamically adjusts these weights based on the application scenario to adapt to different types of data processing requirements.

[0036] Step 3.5: Assign processing priorities to input data based on semantic importance scores. Define the priority assignment function: ; in, and The priority threshold is dynamically adjusted based on system load. High-priority data receives priority processing resources, ensuring that critical information is not delayed due to high system load.

[0037] Step 4: Based on the assigned processing priority, the resource allocation strategy is optimized in combination with reinforcement learning. The computing resources of each language processing module are adaptively adjusted according to the predicted data complexity and system load status. The specific steps are as follows: Step 4.1: Build a system status representation model to describe the current system resource usage and processing capacity. System Status Includes the following components: ; in, Represents the load vector of each processing module at present, Represents the length vector of each priority queue, represents the available resource vector, Indicates the future The predicted complexity sequence of time windows (provided by the prediction model in step 1).

[0038] Step 4.2: Define the resource allocation action space, including increasing, decreasing, or maintaining the resource allocation ratio of each language processing module. Action Space Defined as: ; in, Indicates the total number of optional resource allocation schemes, 、 、 Respectively represent the 1st, 2nd, A resource allocation scheme is defined as the resource allocation vector of each language processing module: ; satisfy ,in Indicates the A resource allocation scheme is a resource allocation vector. 、 、 Respectively indicate the allocation to the 1st, 2nd, The resource ratio of language processing modules, The total number of resource proportions representing the language processing module; Indicates the sum of the resource ratios of all language processing modules to ensure that the total resource ratio is 1.

[0039] Step 4.3: Design a reward function to evaluate the effectiveness of the resource allocation strategy. Consider three aspects: throughput, latency, and resource utilization: ; in, 、 、 They are respectively represented as throughput weight parameter, delay weight parameter, and resource utilization weight parameter, represents the system throughput, Indicates processing delay, Indicates resource utilization, Indicates at a point in time The selected resource allocation action, Indicates the system status.

[0040] Step 4.4: Build a resource allocation decision model based on deep Q learning to learn the optimal resource allocation strategy. The Q function is approximately: ; in, Indicates the system status Take action The expected cumulative reward value that can be obtained, that is, the value function of the state-action pair, Indicates the system status, Indicates at a point in time The selected resource allocation action, is a parameterized Q network, are network parameters, and training is performed by minimizing the following loss function: ; in, is the discount factor, are the target network parameters, Indicates execution of an action The next system state after Indicates that among all possible actions Select the action that maximizes the Q value. Indicates the expected value, is the Q network loss function, is the reward function, Represents the target Q network, which is a fixed parameter network used for stable learning during training. Its parameters The main network parameters are regularly Copy updates.

[0041] Step 4.5: Implement the resource allocation executor to adjust the resource allocation of each language processing module according to the learned strategy. At each decision point, the system performs resource adjustments through the following steps: Get the current system status and future complexity predictions; Select the optimal action through the Q network: ; in, It means finding the action that maximizes the Q value , Indicates at a point in time the selected resource allocation action; Perform resource reallocation and adjust the computing resources of each language processing module; Observe the new system status and rewards ; Update the experience replay buffer and periodically update the Q network parameters.

[0042] In this way, the system can adjust resource allocation in advance based on the predicted data complexity and current load status, maintaining low processing latency and high resource utilization when data complexity changes suddenly.

[0043] In actual application scenarios, the dynamic resource allocation strategy has the following implementation details and application examples: International hot event reporting scenarios: When the system detects a sudden increase in data complexity for a specific language (such as Russian) (perhaps due to an international hot event), the dynamic resource allocation system anticipates future changes in data processing needs and proactively shifts computing resources from other language processing modules to the Russian processing module. For example, during an international sporting event, if the system detects a surge in Spanish and Portuguese content, it will automatically increase the resource allocation for these two language processing modules from 15% to 40%, while reducing resources allocated to other language modules.

[0044] Daily cyclical fluctuations: Through long-term learning, the system has discovered that data volumes for different languages fluctuate cyclically (e.g., differences between weekdays and weekends, and differences in active hours across time zones). The dynamic resource allocation model can predict these cyclical patterns and make proactive resource adjustments. For example, the system has learned that processing demand for Asian languages (such as Chinese, Japanese, and Korean) increases late at night in North American time zones (during daytime in Asia), while European languages have varying resource requirements at different times. The system proactively adjusts computing resource allocation across language servers in different regions accordingly.

[0045] Emergency response scenarios: When a sudden increase in the processing queue for a particular language is detected (e.g., due to an information outburst caused by natural disaster reports), the system immediately triggers an emergency resource allocation strategy, rapidly allocating additional resources to the language processing module while reducing resource usage in non-critical modules. This strategy is triggered by monitoring a combination of queue length, wait time, and data importance, ensuring that critical information is not delayed due to excessive system load.

[0046] Step 5: Based on the adjusted computing resources of each language processing module, a layered time window model is used to perform incremental semantic analysis to achieve real-time cross-language semantic extraction and translation. The specific steps are as follows: Step 5.1: Construct a hierarchical time window model to capture semantic dependencies at different time scales. The model consists of three levels: micro-window, meso-window, and macro-window: Microscopic window: length is , processing the immediate semantics of a single sentence or short paragraph of text; Mesoscopic window: length is , dealing with contextual relevance and recent semantic coherence; Macro window: length is , dealing with long-term semantic consistency and topic evolution.

[0047] Satisfaction relationship: ,The three-layer window structure allows the system to capture semantic ,dependencies at different time scales.

[0048] Step 5.2: Implement cross-language semantic embedding extraction to map multilingual inputs into a unified semantic space. , construct its semantic representation : ; in, is the semantic embedding function, Indicates time Input data at time, is the source language type. To enhance cross-language generalization capabilities, a contrastive learning method is used to learn the semantic embedding function: ; in, for Human translation reference, is the temperature parameter, is the similarity function, is the contrast loss function, For input The semantic embedding representation of is the semantic embedding representation of other samples in the batch.

[0049] Step 5.3: Develop an incremental semantic analysis mechanism to update semantic understanding without reprocessing all historical data. Define the incremental semantic update function: ; in, Indicates a time point The cumulative semantic representation of Indicates a time point The cumulative semantic representation of represents the semantic embedding of the current input, Represents the currently active time window level. The Update function is an intelligent update mechanism that avoids reprocessing all historical data by selectively integrating new information with historical semantic representations. The core function of this function is to efficiently integrate the semantic content of new input while preserving historical semantic information, achieving continuity and consistency in semantic understanding. The update function uses an attention mechanism to dynamically adjust the weight based on the relevance of the new input and historical semantics: ; ; in, Indicates a time point The cumulative semantic representation of is the sigmoid function, is the attention scoring function, The sigmoid function maps the attention score to the range 0-1. Calculate the relevance of new input and historical semantics for the attention scoring function, Indicates a time point The cumulative semantic representation of represents the semantic embedding of the current input, Indicates the currently active time window level.

[0050] Step 5.4: Build a semantic to target language conversion model to achieve high-quality cross-language translation. Defined as: ; in, Translate the results into the target language, is the target language type. The conversion model combines the semantic embedding of the current input and cumulative semantic representation , generating context-sensitive translation results.

[0051] Step 5.5: Implement a progressive refinement strategy to dynamically adjust the analysis granularity based on processing time and resource constraints. Define the progressive refinement function: ; in, is the current available processing time, is the amount of available resources, and are the minimum and maximum thresholds for processing time, and are the minimum and maximum thresholds of resource amounts, is the current input data, 、 and denote the coarse-grained, medium-grained and fine-grained analysis functions respectively, The function is a progressive refinement function. Through progressive refinement, the system can prioritize coarse-grained analysis when resources are limited, and gradually refine the analysis results when resources allow.

[0052] Real application examples of this embodiment In order to verify the effectiveness and practicality of the present invention, the specific application of this embodiment will be demonstrated below through the real-time translation scenario of multilingual reports on major international sporting events.

[0053] Application scenarios: During an international sports event, it was necessary to translate and extract semantic information from real-time reports from multiple media outlets to enable global audiences and analysts to obtain timely event information. This scenario has the following characteristics: High data burstiness: The amount of information surges during the event, with data peaks occurring at key moments of the game (such as goals and finals); Multilingual Concurrency: Reports from all over the world use different languages, including English, Spanish, French, German, Russian, Japanese, Arabic, etc. Terminology-heavy: Sports coverage contains a lot of jargon, athlete names, and team names; High timeliness requirements: Translation delay must be controlled within 3 seconds to ensure that global audiences can obtain information simultaneously.

[0054] Implementation process example: The specific implementation process of this embodiment in this scenario is as follows: Application of semantic complexity prediction model: The system first extracts features from multilingual reporting data, including text length, language type, and term density. By analyzing historical data from the two weeks leading up to the event, the system learns the complexity patterns of reports in different languages and builds a predictive model. Table 1 shows examples of feature vectors for different language types: Table 1: Example feature vectors of reports in different languages

[0055] Based on these features, the system was able to predict changes in data complexity during the event. As shown in Table 2, the system successfully predicted the complexity peaks before and after the finals: Table 2: Comparison of complexity prediction and actual values at different times during the event

[0056] Implementation of multi-level cache structure: The system has built a special multi-level cache structure for sports events: Real-time processing layer: handles high-priority content at key game moments, such as goals, red and yellow cards, injuries, and other emergency reports; Short-term cache layer: stores translations of event-specific vocabulary such as athlete names, team names, and competition terms, and dynamically updates them based on usage frequency using a LRU strategy; Long-term knowledge base: stores a general knowledge graph in the field of sports, including the rules, terminology and multilingual expressions of different sports.

[0057] Table 3 shows the changes in the hit rate of the short-term cache layer during the competition: Table 3: Performance indicators of short-term cache at different stages

[0058] Application of semantic importance scoring and resource allocation strategy: The system combines semantic importance scoring and dynamic resource allocation strategies to achieve efficient resource allocation. Table 4 shows the semantic importance scores of different types of content and the system's resource allocation strategies at different stages of the competition: Table 4: Semantic importance scores and resource allocation for different types of content

[0059] Application of incremental semantic analysis: In the translation of sports reports, the system uses a layered time window model for incremental semantic analysis: Micro window (5 seconds): handles the instant translation of a single report; Meso-window (5 minutes): Maintain the coherence of the reporting sequence and ensure consistent terminology; Macro window (entire game): Maintain the overall context of the event and correctly translate pronoun references and omitted content.

[0060] Table 5 shows the application of the progressive refinement strategy under different resource conditions: Table 5: Progressive refinement effects under different resource conditions

[0061] Through the practice of application scenarios in international sports events, this implementation method has achieved significant technical effects. The following focuses on verifying two key effects: Verification of the effect of improving resource utilization efficiency: Table 6 compares the resource utilization efficiency of the traditional fixed resource allocation method and this implementation in the translation of sports reports: Table 6: Comparison of resource utilization efficiency

[0062] Verification of the effect of reducing processing delay: Table 7 compares the processing delays of the traditional batch processing method and this implementation in different scenarios: Table 7: Processing delay comparison

[0063] The above verification results show that this implementation method has achieved significant advantages in the two key indicators of resource utilization efficiency and processing delay, achieving the technical effect of increasing resource utilization by more than 60% and reducing processing delay by 95%, while maintaining a translation quality of more than 90%.

Claims

1. An AI-driven cross-language big data semantic extraction and translation method, characterized by: The following steps are involved: Build an online semantic complexity prediction model to analyze historical processing data and current data characteristics, and predict the upcoming data processing difficulty in real time; Based on the prediction of upcoming data processing difficulty, a multi-level cache structure consisting of an immediate processing layer, a short-term cache layer, and a long-term knowledge base is implemented to optimize the data processing process; Based on the optimized data processing flow, a semantic importance scoring algorithm is applied to assign processing priorities to input data based on information content, novelty, and complexity; Based on the assigned processing priority, combined with reinforcement learning to optimize resource allocation strategies, the computing resources of each language processing module are adaptively adjusted according to the predicted data complexity and system load status; Based on the adjusted computing resources of each language processing module, a layered time window model is used for incremental semantic analysis to achieve real-time cross-language semantic extraction and translation.

2. The method according to claim 1, characterized in that The construction of the semantic complexity online prediction model includes: Perform feature extraction on the input multilingual data stream to obtain feature vectors; Build a historical processing data memory module to store the processing data and actual complexity of multiple time windows in the past; Build an online prediction model based on the attention mechanism, taking the current data features and historical data as input to predict the processing complexity of the current data; Establish an online update mechanism for the prediction model and update the model parameters according to the actual processing complexity.

3. The method according to claim 1, characterized in that The implementation of the multi-level cache structure includes: Build an immediate processing layer to directly process data that is determined to be high priority and cannot be processed using existing cache; Build a short-term cache layer that uses a least recently used strategy to store recently processed data, its semantic representation, and translation results; Build a long-term knowledge base and use graph structure to store multilingual semantic knowledge; Implement a multi-level cache collaboration mechanism and integrate the three-layer cache structure to form a unified data processing flow.

4. The method according to claim 1, wherein The semantic importance scoring algorithm includes: Calculate the information content of multilingual data and measure the effective information contained in the data; Calculate the novelty of multilingual data and measure the difference between the data and historical data; The processing complexity of acquiring data; The semantic importance score is calculated by comprehensively considering the amount of information, novelty and complexity; Assign processing priorities to input data based on semantic importance scores.

5. The method according to claim 1, wherein The combination of the reinforcement learning optimization resource allocation strategy includes: Build a system status representation model to describe the current system resource usage and processing capacity; Define the resource allocation action space, including increasing, decreasing, or maintaining the resource allocation ratio of each language processing module; Design a reward function that takes into account throughput, latency, and resource utilization; Build a resource allocation decision model based on deep Q learning to learn the optimal resource allocation strategy; Implement a resource allocation executor to adjust the resource allocation of each language processing module according to the learned strategy.

6. The method according to claim 1, characterized in that The hierarchical time window model includes three levels: micro-window, meso-window and macro-window, which respectively process the immediate semantics, context relevance and recent semantic coherence, long-term semantic consistency and topic evolution of single sentences or short paragraphs.

7. The method according to claim 1, characterized in that The implementation of the incremental semantic analysis includes: Implement cross-language semantic embedding extraction to map multilingual input into a unified semantic space; Developing incremental semantic analysis mechanisms to update semantic understanding without reprocessing all historical data; Build a semantic-to-target language conversion model to achieve high-quality cross-language translation; Implement a progressive refinement strategy to dynamically adjust the analysis granularity based on processing time and resource constraints.

8. The method according to claim 7, characterized in that The progressive refinement strategy performs coarse-grained, medium-grained, or fine-grained analysis on the input data according to the current available processing time and available resources. When resources are tight, the coarse-grained analysis is completed first, and the analysis results are gradually refined when resources allow.

9. The method according to claim 3, characterized in that The long-term knowledge base adopts a graph structure, in which nodes represent concepts, edges represent relationships between concepts, and attribute sets include expressions in different languages.

10. An AI-driven cross-language big data semantic extraction and translation system, characterized by: The method for executing the AI-driven cross-language big data semantic extraction and translation method according to any one of claims 1 to 9 comprises: Semantic complexity prediction module, used to analyze historical processing data and current data characteristics, and predict data processing difficulty in real time; Multi-level cache module, including three-layer cache structure: immediate processing layer, short-term cache layer and long-term knowledge base; A semantic importance scoring module to assign processing priorities to input data based on information content, novelty, and complexity; The resource allocation module is used to optimize resource allocation strategies by combining reinforcement learning and adaptively adjust the computing resources of each language processing module; The semantic analysis module is used to perform incremental semantic analysis using a layered time window model to achieve real-time cross-language semantic extraction and translation.

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