A multi-scene application intelligent translation processing method and platform

Abstract

This invention relates to the field of intelligent translation, and more particularly to an intelligent translation processing method and platform for multi-scenario applications. The method includes the following steps: receiving a translation request file, identifying the interaction scenario to obtain the translation scenario type; evaluating the translation complexity based on the translation request file to obtain a complexity coefficient; identifying online translators on the current platform and constructing a translation capability profile; matching the translation capability profile based on the translation scenario type and complexity coefficient to generate a matching result; sending the translation request file to the online translator based on the matching result, the online translator translating the file and sending back a translated data packet, which is then sent to the requester to complete the translation processing task. This invention improves translation accuracy and professionalism by matching the optimal online translator for different translation scenarios.

Description

Technical Field

[0001] This invention relates to the field of intelligent translation, and in particular to an intelligent translation processing method and platform for multi-scenario applications. Background Technology

[0002] With the rapid development of globalization and information technology, translation needs are becoming increasingly diverse and real-time across various scenarios, including education, business, tourism, cross-border e-commerce, and international conferences. Current translation software largely relies on fixed machine translation models, typically employing general dictionaries and rules or pre-trained language models for text conversion, lacking the ability to adapt to different professional fields or specific scenarios. When faced with diverse translation scenarios such as medical, legal, technical documents, international conference dialogues, and outdoor navigation instructions, existing translation systems cannot dynamically adjust translation strategies and terminology selection based on context, industry terminology, or specific expressions, resulting in inaccurate, unprofessional, or awkward translations. Due to a lack of comprehensive consideration of task complexity, scenario characteristics, and translator capabilities, fixed machine translation systems struggle to achieve the same level of relevance and accuracy as professional translations, failing to meet the high-quality translation needs across multiple scenarios and fields. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes an intelligent translation processing method and platform for multi-scenario applications, thereby resolving at least one of the aforementioned technical issues.

[0004] To achieve the above objectives, the present invention provides an intelligent translation processing method for multi-scenario applications, comprising the following steps: Step S1: Receive the translation request file, determine the interaction scenario, and obtain the translation scenario type; Step S2: Evaluate the translation complexity based on the translation request file and obtain the complexity coefficient; Step S3: Identify online translators on the current platform and build a translation capability profile; Step S4: Match the translation capability profile based on the translation scenario type and complexity coefficient to generate matching results; Step S5: Based on the matching results, the translation request file is sent to an online translator, who translates it and sends back the translated data packet, which is then sent to the party requesting the translation, thus completing the translation process.

[0005] This specification provides an intelligent translation processing platform for multi-scenario applications, used to execute the intelligent translation processing method for multi-scenario applications as described above, including: The scene identification module is used to receive translation request files, identify the interactive scene, and obtain the translation scene type. The complexity assessment module is used to assess the translation complexity based on the translation request file and obtain the complexity coefficient. The capability calculation module is used to identify online translators on the current platform and build a translation capability profile. The matching module is used to match translation capability profiles based on translation scenario type and complexity coefficient, and generate matching results; The translation processing module is used to send the translation request file to an online translator based on the matching result. The online translator translates the file and sends back the translated data packet, which is then sent to the translation requester to complete the translation processing task.

[0006] The beneficial effects of this invention are specifically as follows: By identifying the application scenario of the translation content through interactive scenarios, pre-configuration of translation strategies is achieved, improving the accuracy and usability of the translation, while supporting multimodal task optimization and improving overall translation efficiency. Complexity assessment quantifies task difficulty, providing a basis for task grading and strategy adjustment, reducing ambiguity risks, improving the translation quality of high-difficulty tasks, and optimizing processing efficiency. Constructing translation capability profiles comprehensively understands translators' language abilities and professional backgrounds, enabling precise task allocation, improving translation accuracy and delivery efficiency, while optimizing platform resource scheduling. By matching scenario types and complexity coefficients, precise matching of tasks and translators' capabilities is achieved, improving translation quality, efficiency, and task load balancing, ensuring stable platform operation. Based on the matching results, tasks are distributed and translations are returned, achieving efficient and reliable translation processing, improving delivery speed and user satisfaction. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating the steps of an intelligent translation processing method for multiple applications according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0008] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0009] This application provides an intelligent translation processing method and platform for multi-scenario applications. The executing entities of the intelligent translation processing method and platform for multi-scenario applications include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-image management system, an information management system, and a cloud-based data management system.

[0010] Please see Figures 1 to 3 This invention provides an intelligent translation processing method for multiple application scenarios, including the following steps: Step S1: Receive the translation request file, determine the interaction scenario, and obtain the translation scenario type; Step S2: Evaluate the translation complexity based on the translation request file and obtain the complexity coefficient; Step S3: Identify online translators on the current platform and build a translation capability profile; Step S4: Match the translation capability profile based on the translation scenario type and complexity coefficient to generate matching results; Step S5: Based on the matching results, the translation request file is sent to an online translator, who translates it and sends back the translated data packet, which is then sent to the party requesting the translation, thus completing the translation process.

[0011] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of an intelligent translation processing method for multiple scenarios according to the present invention. In this example, the steps of the intelligent translation processing method for multiple scenarios include: Step S1: Receive the translation request file, determine the interaction scenario, and obtain the translation scenario type; In this embodiment, a file is received from the translation requester. This file may contain text, audio, video, or mixed multimodal data. Upon receipt, the file undergoes format standardization and integrity checks, such as unifying encoding methods, verifying file size and hash values, to ensure lossless file integrity. Multimodal parsing is then performed on the file content: text is processed through word segmentation and semantic vectorization, typically mapped to 768-dimensional or 1024-dimensional vectors; audio undergoes unified sampling rate (e.g., 16kHz), 25ms frame length, and 10ms frame shift for feature extraction; video is extracted through keyframe extraction at 2-4 frames per second, combined with optical flow analysis to capture dynamic scenes. After parsing, relationship modeling and semantic analysis are performed on the identified multimodal elements, including interaction relationships, pragmatic intent, and information flow direction. These elements and their associated features are then input into a scene classification model, such as a multimodal Transformer or GraphTransformer. The model captures global dependencies through an attention mechanism and outputs the probability distribution of each scene category. Common scene categories include meeting dialogues, outdoor navigation, human-computer interaction, and cross-language social scenarios. If the probability exceeds a set threshold, such as 0.7, the category is determined to be the translation scenario type of the current task.

[0012] Step S2: Evaluate the translation complexity based on the translation request file and obtain the complexity coefficient; In this embodiment, a complexity analysis is performed on the document content to assess the translation difficulty. The text portion first undergoes semantic layering analysis, including lexical complexity, syntactic nesting depth, semantic ambiguity, and cross-cultural dependence. Lexical complexity is calculated using the proportion of low-frequency words or a lexical ranking table; syntactic nesting depth is extracted using dependency or constituent syntax tree structures; semantic ambiguity is quantified using context embedding variance or the proportion of polysemous words; and cross-cultural dependence is assessed through culture-specific vocabulary matching. For audio and video content, speech rate fluctuation, pause distribution, and semantic breakpoint frequency are calculated to analyze the rhythm of information delivery. These indicators are normalized and a weighted sum is used to generate a comprehensive complexity score, for example, a semantic complexity weight of 0.5, a speech rate-related indicator weight of 0.3, and a cross-cultural dependence weight of 0.2.

[0013] Step S3: Identify online translators on the current platform and build a translation capability profile; In this embodiment, translators in an online state on the platform are identified, and availability is determined by login status, heartbeat signals, and operational activity. For example, those who lose heartbeat for more than 60 seconds or have excessively high response delays are considered offline. Next, basic information about online personnel is extracted, including their language proficiency, certification qualifications, and self-reported scenario preferences; simultaneously, historical translation task logs are extracted, including task type, delivery timeliness, customer feedback, and translation quality rating. Then, historical scores are calculated based on this data; for example, translation quality score accounts for 0.5, delivery timeliness achievement rate accounts for 0.2, and customer feedback rating accounts for 0.3, forming a unified score mapping to the 0-1 range. The basic information and historical scores are integrated to form a multi-dimensional capability vector, including language ability, professional domain ability, scenario adaptability, and stability indicators, constructing a complete translation capability profile.

[0014] Step S4: Match the translation capability profile based on the translation scenario type and complexity coefficient to generate matching results; In this embodiment, after the competency profile is prepared, the task translation scenario type and complexity coefficient are matched with the competency vectors of candidate translators. An initial screening is performed, with candidates whose scenario suitability exceeds a threshold (e.g., 0.65) as the first candidate set. Subsequently, competency matching is performed on the complexity coefficient, calculating the comparison between the comprehensive competency score and the task complexity. For example, the competency score is required to be at least 10% higher than the complexity coefficient to ensure translation quality. A weighted scoring method can be used to calculate the final matching degree, for example, a scenario matching weight of 0.6 and a competency margin weight of 0.4. Further task load analysis is performed on the second candidate translators, predicting response time by querying the current task sequence and historical processing rates.

[0015] Step S5: Based on the matching results, the translation request file is sent to an online translator, who translates it and sends back the translated data packet, which is then sent to the party requesting the translation, thus completing the translation process.

[0016] In this embodiment, after matching is completed, the translation request file is sent to the selected translator via message push or task queue, and the task distribution time is recorded. A translation task tracking mechanism is established to periodically update the task status, including assigned, in progress, submitted, and completed. After completing the translation, the translator sends back the translation data package and records the submission timestamp. Upon receiving the translation, a multi-indicator score is performed using a preset translation quality assessment model, including semantic consistency, terminology correctness, fluency, and client requirement matching. The score results are compared with a passing threshold. If the translation score is passing, further intelligent adjustments and media synchronization processing are performed to generate adaptive translation content, including subtitles, dubbing, and text. This is then packaged into a translation delivery package; if it is failing, the translation is returned to the translator for revision. Finally, the translation delivery package is sent to the translation requester, and a translation report is generated, including task information, translation score, revision records, and multilingual output, achieving a complete closed-loop process from task reception, translation, quality control to final delivery.

[0017] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: The platform receives translation request files; performs multimodal content parsing on the translation request files to identify media content; Scene element identification is performed on media content, and multiple scene elements are labeled; Multiple scene elements are deconstructed to identify element association features; the element association features include interaction relationships, pragmatic intent, and information flow direction. Interaction scenarios are identified based on element association features to determine translation scenario types. These translation scenario types include meeting dialogue scenarios, outdoor navigation scenarios, human-computer interaction scenarios, and cross-language social scenarios.

[0018] In this embodiment, translation request files are received and standardized. Input formats include various data types such as text, images, audio, and video. A unified data encapsulation format is used for organization; text is stored using UTF-8 encoding, and image and video data are compressed. A multimodal parsing process is then performed: text content is preprocessed through word segmentation and semantic encoding, typically using a Transformer-based language representation model to map text into a fixed-dimensional semantic vector, such as 768 dimensions; image content is uniformly scaled to 224×224 pixels, and spatial features are extracted using a convolutional neural network; audio content is standardized, for example, by setting a sampling rate of 16kHz and segmenting it into frames with a frame length of 25ms and a frame shift of 10ms, then extracting MFCC features; video content is extracted as keyframes at 2-4 frames per second, and dynamic information is obtained using a temporal difference method. Finally, the features from different modalities are input into a multimodal fusion model, and cross-modal alignment is achieved through an attention mechanism, allowing information from different sources to be expressed in the same semantic space.

[0019] In one specific embodiment, the platform receives a translation request file containing video, audio, and text subtitles, and performs multimodal content parsing processing on the file. Media separation is performed on the input file, decoupling the video stream, audio stream, and text stream. Keyframes are extracted from the video stream according to time sequence, the audio stream is segmented, and the text stream is segmented at the sentence level. For the video data, the Faster R-CNN object detection model is called to perform visual object recognition on the keyframes, extracting visual elements such as people, road signs, and device interfaces, and generating region feature vectors and spatial location information. Simultaneously, an action recognition model is used to perform temporal analysis on continuous frames to identify behavioral semantics. For the audio data, the Wav2Vec 2.0 speech recognition model is used to transcribe the speech signal into a text sequence, extracting speaker features, speech rate, intonation, and pause information. For the text data, the pre-trained language model BERT-Based Chinese is used... Semantic encoding is performed to obtain context-dependent text vector representations. After completing the extraction of unimodal features, visual region features, speech text features, and original text features are input into the multimodal fusion model VisualBERT. Through a cross-modal attention mechanism, the alignment and fusion of visual and linguistic information are achieved, and a unified multimodal semantic representation is output. Based on the fused representation, people, environment, devices, and text sentences in the media content are uniformly identified to form a structured media content expression.

[0020] The visual object detection model chosen is Faster R-CNN. This model consists of two parts: a Region Proposal Network (RPN) and an object detection network. The backbone feature extraction network typically uses ResNet-50 or ResNet-101 to extract convolutional features from the input image. The RPN generates candidate regions (Region Proposals) on the feature map and outputs the candidate box positions and foreground probabilities. Subsequently, RoI Pooling / Align is used to align the candidate regions, inputting them into the classification and regression branches to achieve object category discrimination and bounding box refinement. This model is typically trained on the publicly available datasets MS COCO and Visual Genome. MS COCO provides 80 general object annotations, while Visual Genome provides finer-grained object and relation annotations, thereby enhancing scene understanding capabilities.

[0021] The speech recognition model used is Wav2Vec 2.0. This model consists of a convolutional feature encoder and a Transformer context network. First, the original speech waveform is encoded using multiple layers of one-dimensional convolutions to obtain a low-level speech feature representation. Then, context modeling is performed using a multi-layer Transformer structure, and self-supervised pre-training is conducted through a mask prediction task. In the fine-tuning stage, connection-time classification, CTC, and a loss function are introduced to achieve speech-to-text mapping. Training data sources include the large-scale speech corpus LibriSpeech (approximately 1000 hours of English speech) and the multilingual dataset Common Voice, which can be extended to cross-language speech recognition tasks.

[0022] The text semantic encoding model used is BERT-Based Chinese. This model consists of a 12-layer Transformer encoder, each layer containing a multi-head self-attention mechanism and a feedforward neural network structure. The input is the sum of Token Embedding, Segment Embedding, and Position Embedding. During the pre-training phase, a masked language model (MLM) and next-sentence prediction (NSP) tasks are employed to learn contextual semantic representations through a large-scale unsupervised corpus. Training data sources include ChineseWikipedia, news corpora such as THUCNews, and online text corpora, thereby achieving general Chinese semantic modeling capabilities.

[0023] The multimodal fusion model uses VisualBERT. This model extends the input layer on top of the BERT structure, using both visual region features and text tokens as input sequences. The visual features are extracted by Faster R-CNN. The model performs cross-modal joint encoding through multiple Transformer layers to achieve the alignment and fusion of visual and textual information. Its pre-training tasks include masked language modeling and image-text matching tasks to learn cross-modal semantic associations. Training data sources include image description data and visual question-answering data, thereby improving multimodal semantic understanding capabilities.

[0024] After parsing the multimodal data, fine-grained scene element recognition is performed to extract semantically meaningful basic units. For image and video content, object detection methods are used to identify people, objects, and environmental elements, with a detection confidence threshold set at 0.6, and duplicate targets are removed using non-maximum suppression. For text content, named entity recognition is used to extract information such as names, locations, organizations, and key events. For audio content, it is first transcribed into text and then semantically analyzed. During element labeling, each identified element is assigned a unique identifier, and its attribute information, including category label, spatial location, temporal information, and semantic feature vector, is recorded. A cross-modal matching mechanism is used to align elements expressing the same semantics in different modalities, such as associating keywords in speech with corresponding objects in images. To reduce noise interference, a minimum target area threshold can be set to filter invalid detection results.

[0025] A graph structure is constructed, with each element as a node and the relationships between nodes as edges, forming an initial relationship network. Multi-layered information propagation is typically performed using a graph neural network (2-4 layers). Node features are continuously updated during propagation, and edge weights are initially set in the range of 0.1-0.3. The system identifies interaction relationships between elements, dialogue relationships between characters, operational relationships between characters and devices, and perceptual relationships between characters and their environment. Further, semantic classification methods are used to identify pragmatic intent, mapping linguistic information to several intent categories, including requests, instructions, and descriptions. The number of categories is typically set to 10-20, and their confidence levels are represented by probability distributions. For information flow direction identification, temporal modeling methods are used to analyze the transmission paths of information between different elements, such as from speaker to receiver or from environment to individual, combined with attention weights, with a threshold of approximately 0.3 to filter key paths. Location encoding is introduced to enhance the modeling capabilities of spatial and temporal relationships.

[0026] The node and relation features in the graph structure are input into the scene classification model. The input dimension is generally N×D, where N represents the number of elements, usually not exceeding 128, and D represents the feature dimension, such as 768. A multi-head attention mechanism is used, setting 8-12 attention heads to model global dependencies, and a fully connected layer outputs the probability distribution of different scene categories. Preset translation scene types include meeting dialogue scenarios, outdoor navigation scenarios, human-computer interaction scenarios, and cross-language social scenarios. During the discrimination process, when the probability of a certain category exceeds a set threshold, such as 0.7, it is determined to be the target scene type. Dropout is introduced at a ratio of approximately 0.3 to improve the model's generalization ability, and class weights are adjusted within a range of 1.0-2.5 to alleviate class imbalance. Discrimination is based on different feature combinations; for example, when there is multi-person voice interaction and the semantics are mainly information exchange, it is judged as a meeting dialogue scenario; when it contains path indication and spatial location information, it is judged as an outdoor navigation scenario.

[0027] In this invention, the scene classification model can be constructed by combining a publicly available graph neural network with a multi-head attention fusion architecture, GraphAttention Network, and a Transformer attention mechanism to build a unified graph structure classification model. Its specific structure and training method are described below: The model consists of four core sub-modules. The first is the input representation layer, which constructs scene elements as a graph structure G=(V,E). Here, the node set V represents the identified scene elements, and the node feature vectors are derived from pre-processed multimodal encoding, such as text and visual fusion features, with a unified dimension of D=768. The edge set E represents the relationships between elements, and the edge features include interaction relationship type, pragmatic intent encoding, and information flow direction vectors. These are embedded and mapped to a fixed dimension, such as 128, and then fused and concatenated with the node features to form the input tensor. ,in .

[0028] The second layer is the graph attention encoding layer, which uses a multi-layer GAT structure to model local relationships. Each layer contains a multi-head attention mechanism with 8-12 heads, which performs weighted aggregation of node neighborhood information. Its core calculation is to calculate the attention coefficient between any node i and its neighboring node j. ; Where W is the linear transformation matrix. The attention weight vector is used; different relational subspace representations are learned in parallel through a multi-head mechanism, and the outputs of each head are concatenated or averaged to obtain the enhanced node representation.

[0029] Another global dependency modeling layer is introduced, which introduces a Transformer encoding layer based on the GAT output. This layer has the same structure as BERT and further models the long-distance dependencies between global nodes. This layer includes a multi-head self-attention mechanism and a feedforward neural network. The input dimension is kept to N×D. Through position encoding, graph structure encoding or node sorting encoding can be used to enhance the expression of structural information.

[0030] Subsequently, for the graph-level readout and classification layers, a global pooling strategy, such as MeanPooling or AttentionPooling, is employed to aggregate node-level representations into graph-level representation vectors. The input is fed into a fully connected classifier, a two-layer MLP structure, which outputs the probability distribution of four translation scenarios: conference dialogue, outdoor navigation, human-computer interaction, and cross-language social interaction. The classification layer uses the Softmax function, combined with cross-entropy loss for training, and introduces class weights of 1.0-2.5 to adjust the loss contribution of different classes.

[0031] Regarding the training strategy, a supervised learning approach is adopted, and the loss function is defined as weighted cross-entropy: ; in The class weights are used; Dropout is introduced in the GAT layer and the fully connected layer at a ratio of 0.3 to prevent overfitting, and L2 regularization is combined to improve generalization ability.

[0032] The training data is constructed and fused from publicly available multimodal and scene understanding datasets, including image and scene relationship data, providing object and relationship annotations, visual question answering data VQAv2, providing semantic interaction information, video behavior data, providing scene behavior labels, and voice dialogue data and multi-turn dialogue datasets, used to construct voice and interaction contexts. By uniformly annotating and mapping the above data, they are classified into four preset translation scene label systems to form training samples.

[0033] Through the above model structure and training data system, joint modeling of node features and relation features in graph structures is achieved, enabling high-precision discrimination of multiple scene types.

[0034] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Perform language recognition on media content to determine the required translation language; Semantic layering analysis is performed based on media content to obtain semantic layering features; the semantic layering features include lexical complexity, syntactic nesting depth, semantic ambiguity, and cross-cultural dependence. The speech rate fluctuation rate, pause distribution, and semantic breakpoint frequency are calculated based on the media content to obtain content speech rate information; The translation complexity is evaluated based on semantic hierarchical features and content speech rate information to obtain a complexity coefficient.

[0035] In this embodiment, the language type in media content is identified to determine the source language and target translation requirements. Differentiated methods are used for different modalities during processing: For text data, a preliminary judgment is made using a language recognition model based on character distribution and word frequency statistics, combined with a pre-trained language classification model and a multilingual recognition model based on a Transformer structure for fine-grained discrimination, mapping the input text to a language category probability distribution; for audio data, preprocessing is performed, including standardizing the sampling rate (e.g., 16kHz), denoising, increasing the signal-to-noise ratio to over 20dB, and voice activity detection (VAD), followed by extraction of acoustic features, such as MFCC (typically 13-40 dimensions), which are then input into a language recognition model, such as a language identification network based on CNN or RNN, for classification; for video content, a joint judgment is made by combining audio tracks and subtitle information. During the recognition process, a confidence threshold of 0.8 can be set; when the probability of a certain language category exceeds this threshold, it is determined to be the source language; if there is a mixture of multiple languages, segmented recognition is performed according to time segments, such as every 2 seconds. The final output includes the source language category, such as Chinese, English, Spanish, etc., and the target language requirement, forming a language mapping relationship based on preset rules or user specifications.

[0036] In the semantic hierarchical parsing process, the text sequence in the media content is first preprocessed and segmented into sentences, dividing continuous sentences into sentence-level and clause-level units. Based on the pre-trained language model BERT-BaseChinese, each sentence undergoes contextual semantic encoding to obtain word-level vector representations and sentence-level semantic representations. On this basis, the syntactic analysis tool Stanford CoreNLP is introduced for dependency parsing and constituent syntactic analysis, constructing a syntactic tree structure and statistically analyzing the syntactic nesting level. The nesting depth is represented by the number of clauses and the tree depth. Lexical complexity is addressed through word frequency statistics and word vector distribution calculation. The word frequency data can be... The complexity index is derived from the publicly available corpus ChineseWikipedia, with the proportion of low-frequency words and the percentage of specialized terms used as indicators. For semantic ambiguity, the entropy of word meaning distribution or the probability of polysemous words is calculated based on contextual semantic vectors, and the ambiguity score is obtained by measuring the vector variance of the same word in different contexts. For cross-cultural dependence, a culturally specific dictionary is constructed, including place names, idioms, and cultural symbols, and the frequency of occurrence of culturally specific entities is statistically analyzed in conjunction with named entity recognition results to obtain a cross-cultural dependence index. Finally, lexical complexity, syntactic nesting depth, semantic ambiguity, and cross-cultural dependence are combined to form a multi-dimensional semantic hierarchical feature vector.

[0037] In the process of calculating content speech rate information, the Wav2Vec2.0 speech recognition model is used to obtain aligned text and timestamp information for audio data. Based on the timestamp, the number of words or syllables per unit time is calculated to obtain the basic speech rate, words per second. Further, by using a sliding time window, such as 2-5 seconds, local speech rate changes are calculated to obtain speech rate fluctuation rate, standard deviation, or coefficient of variation. For pause distribution, by detecting silent intervals in the speech signal, based on energy threshold or VAD algorithm, pause duration and frequency are statistically analyzed, and a pause time distribution function is constructed. For semantic breakpoint frequency, the speech time axis is aligned with the text sentence segmentation results, and the frequency of pause occurrence is statistically analyzed at sentence boundaries or semantic segmentation points to obtain the degree of matching between semantic breakpoints and pauses. Finally, the speech rate fluctuation rate, pause distribution features, and semantic breakpoint frequency are combined to form the content speech rate information vector.

[0038] Lexical analysis is performed on the text to calculate lexical complexity. This is done, for example, through word frequency statistics and lexical ranking tables, such as common word frequency tables, to calculate the average word frequency index. The proportion of low-frequency words (those below the top 5000 words) is typically used as one indicator of complexity. Next, syntactic analysis is performed, using dependency parsing or constituent parsing to construct a syntactic tree structure and calculating the syntactic nesting depth, such as the maximum tree depth or average nesting levels, typically ranging from 2 to 10 levels to reflect sentence structure complexity. At the semantic level, context embedding models, such as... BERT analyzes semantic ambiguity, which can be measured by the vector variance of the same word in different contexts or the frequency of polysemous words, with a threshold set in the range of 0.3-0.6. Cross-cultural dependence is measured by identifying culture-specific expressions, such as idioms, slang, or regional expressions, which can be determined using cultural dictionary matching and semantic classification models. The proportion of these expressions is calculated, accounting for 5%-20% of the total number of words, ultimately forming a semantic hierarchical feature set, including lexical complexity index, syntactic nesting depth value, semantic ambiguity index, and cross-cultural dependence coefficient.

[0039] The speech signal is processed by framing, with a frame length of 25ms and a frame shift of 10ms. Speech activity detection distinguishes between speech segments and silence segments. For speech rate calculation, a basic speech rate value is obtained by counting the number of syllables or words per unit time, such as words per second (WPS), typically ranging from 2-5 words per second. The speech rate fluctuation rate is further calculated, which is the standard deviation of speech rate changes within a continuous time window, such as a 2-second window; a larger standard deviation indicates unstable speech rate. For pause distribution, the length of silence segments is detected; pauses exceeding 200ms are considered valid. The number of pauses and their time distribution are counted, and the average pause duration is calculated, typically 0.2-1 seconds. Semantic breakpoint frequency is determined by combining speech pauses with text punctuation information, such as pauses at periods, commas, or semantic transitions. The number of breakpoints per unit time is counted, such as 5-20 per minute. An energy threshold, such as below 30% of the maximum energy, can be set to assist in identifying pause regions.

[0040] Each feature is normalized; for example, lexical complexity, syntactic depth, semantic ambiguity, and cross-cultural dependence are mapped to the 0-1 range. Speech rate-related indicators, such as speech rate fluctuation, pause density, and breakpoint frequency, are also standardized. A weighted evaluation model is then constructed, assigning weights to different features. For example, semantic ambiguity and cross-cultural dependence have higher weights (0.25-0.3), followed by lexical complexity and syntactic depth (0.15-0.2), while speech rate-related indicators account for 0.2-0.3 of the overall weight. A weighted summation method is used to calculate the overall score, yielding the initial complexity value. To enhance stability, a non-linear mapping function, such as the sigmoid function, can be introduced to smooth the results, stabilizing the complexity coefficient within the 0-1 range. When the complexity coefficient is close to 1, it indicates a higher translation difficulty, requiring more refined translation strategies, such as context enhancement or manual correction; when it is close to 0, it indicates relatively simple content.

[0041] In this embodiment, step S3 includes the following steps: Identify online translators on the current platform; Extract the basic information and historical translation task logs of the online translators; the basic information includes languages ​​they are proficient in, certification qualifications, and self-declared scenario preferences. The historical score is obtained by calculating the translation quality score, delivery timeliness achievement rate and customer feedback rating based on the historical translation task log. Based on the aforementioned basic information and historical scores, a translation ability assessment is conducted to obtain a translation ability profile.

[0042] In this embodiment, a fixed time window, such as 30 seconds, is used to receive status reports from translators. A heartbeat detection mechanism is used to determine online status; if a heartbeat is lost for more than two cycles (approximately 60 seconds), the translator is considered offline. Simultaneously, task response latency (average response time not exceeding 5 seconds) and recent activity time (operation records within 5 minutes) are used to further filter for valid online personnel. In multi-device login scenarios, device IDs are bound to user IDs to avoid duplicate entries. To improve recognition accuracy, a status confidence scoring mechanism can be introduced, weighting login status (0.4), behavioral activity (0.3), and response stability (0.3) in a weighted calculation. A comprehensive score exceeding 0.7 indicates a valid online translator. Concurrent task capacity can be considered, such as setting the maximum number of concurrent tasks to 3-5, to filter available resources.

[0043] After identifying online translators, their basic information and historical task data are extracted and structured. Basic information mainly includes their languages ​​of expertise, certifications, and self-declared scenario preferences. Language proficiency is standardized using a language tagging system, such as ISO language coding, and proficiency levels are recorded. Certifications are verified against certificate numbers and certification bodies and assigned weights, such as 0.4 for professional translation certificates and 0.3 for industry certifications. Scenario preferences are categorized using preset settings, such as conferences, medical, legal, and tourism, and the strength of these preferences is recorded as a value between 0 and 1. Regarding historical translation task logs, data from past tasks are collected, including task type, source and target languages, task duration, delivery time, and evaluation records. To ensure data validity, a time window can be set, such as the last 6 months or the most recent 100 task records, as the analysis scope. Abnormal data should be filtered out; for example, task durations exceeding the mean ± 3 standard deviations should be removed. All data should be converted to a structured format and normalized, such as using seconds as the time unit and a 0-5 rating range. The translation quality score is calculated using a multi-dimensional approach, combining automated evaluation metrics such as BLEU scores or semantic similarity scores with human review scores. The automated score is weighted at 0.4, and the human score at 0.6, ultimately normalized to the 0-1 range. Next, the delivery timeliness achievement rate is calculated, representing the percentage of tasks completed within a specified time. For example, if the deadline is T and the actual completion time is t, completion is considered achieved when t ≤ T. The percentage of completed tasks is then calculated; a value of 0.8 indicates 80% on-time completion. Finally, customer feedback ratings are calculated by averaging user reviews (1-5 stars) and incorporating sentiment analysis results (sentiment scores ranging from -1 to 1), weighted at a ratio of 0.7 and 0.3. To improve stability, a sliding window averaging method can be used, with a window size of 20 tasks, to smooth out score fluctuations.

[0044] Various features are standardized, for example, language proficiency, certification level, and scene preference intensity are uniformly mapped to the 0-1 range. Then, a capability assessment model is constructed, fusing basic information features with historical scoring features, where the weight of basic information can be set to 0.4 and the weight of historical scoring to 0.6. During capability modeling, a vectorized representation method can be used, representing each translator as a high-dimensional feature vector, such as 20-50 dimensions, including language ability vectors, professional domain vectors, and performance score vectors. Cluster analysis or similarity calculations, such as cosine similarity, can further identify their capability distribution characteristics. To enhance assessment accuracy, a non-linear mapping function, such as the sigmoid function, can be introduced to smooth the overall score. The output translation capability profile includes language ability level, professional scene adaptability, stability indicators, and overall capability score, represented in a structured form, such as vectors or a set of labels. In one specific embodiment, when the platform screens translators for the "Chinese-English, Engineering and Technical Conference Interpretation" task, the online scheduling module identifies in real time the set of translators currently available to accept orders. The identification method includes heartbeat detection, updating online status every 10 seconds, along with task occupancy markers and busy / idle flags. Only translators with valid and unoccupied heartbeats for 30 consecutive seconds are retained. Subsequently, basic information and historical translation task logs are extracted for each online translator, with the basic information represented in a structured vector. ,in This indicates proficiency in multiple languages, expressed as a numerical value mapped to CEFR levels: C2=1.0, C1=0.9, etc., with a weighted average for multiple languages. This indicates the certification level, such as national certification = 1.0, industry certification = 0.8, no certification = 0.5. The self-reporting scenario preference is represented by one-hot encoding or probability distribution, such as 0.7 for meeting dialogue and 0.2 for social interaction. The historical translation task log includes the most recent N=100 task records, or all records if there are fewer than 100. Each record contains information such as the original text, translation, submission time, deadline, and customer rating.

[0045] Calculate the historical score for each translator, specifically including a score for translation quality. Delivery timeliness achievement rate and customer feedback rating The translation quality score is calculated using an automated evaluation model, such as BLEU or BERTScore, using the following formula: Weights are assigned to different domains, such as a weight of 1.2 for the engineering domain, and the delivery timeliness achievement rate is defined as... ,in To ensure timely completion of tasks; customer feedback ratings are obtained by normalizing user scores from 1 to 5. The three indicators were then weighted and combined to obtain the historical score. The calculation formula is: The weights can be set to 0.5, 0.2, or 0.3. After obtaining basic information and historical scores, the system further constructs a translation ability assessment model to generate a comprehensive ability profile for translators. Specifically, a vector fusion method is used, defining the translator's ability vector as follows: Furthermore, normalization is used to ensure comparability across dimensions, while a scenario matching function is introduced to enhance the targeting of profiles, such as defining a matching degree for meeting dialogue scenarios. ,in The target scene vector is used as the final output; the translator's ability score is then output. For example, weights can be set to 0.5, 0.3, and 0.2, and combined with threshold filtering, such as... It generates a list of highly matched translators and outputs a translation ability profile including information such as "language proficiency level, areas of expertise, and historical performance stability." In this embodiment, the specific steps of step S4 are as follows: Based on the type of translation scenario, the translation ability profile is initially screened by scenario matching to extract the first candidate; The translation ability profiles of the candidate translators are retrieved; Based on the translation ability profile, the complexity coefficient is calculated using ability matching to obtain the second candidate. Perform a task load query on the second candidate to identify the task sequence of the current candidate; Based on the task sequence, response time is estimated to obtain the response time value for each person. Based on the response time value, optimal filtering is performed to generate matching results.

[0046] In this embodiment, a mapping relationship is constructed between scene tags and capability profiles. Each scene requirement is broken down into several capability dimensions. For example, the meeting scene emphasizes real-time response and semantic coherence, while the navigation scene emphasizes instruction comprehension and spatial expression capabilities. Scene adaptability indicators in the translation capability profile are matched and calculated, typically using similarity calculation methods such as cosine similarity or weighted matching scores, aligning the scene requirement vector with the personnel capability vector. The weights of scene-related features are set to 0.5-0.7 to highlight the importance of scene matching, and a matching threshold, such as 0.65, is set to retain only personnel with scores above this threshold as candidates. To ensure the quality of the screening, the upper limit of the number of candidates can be limited, such as the top 20% or a maximum of 50 people. For each primary candidate, their complete translation ability profile data is retrieved. This process mainly includes data reading and structured integration, unifying scattered ability information, such as language proficiency, professional domain ability, historical scores, and stability indicators, into a standardized feature vector. Caching mechanisms, such as recently accessed cache, can be employed to improve retrieval efficiency. Each candidate's ability profile is represented as a fixed-dimensional vector, such as 32-dimensional or 64-dimensional. During data processing, data from different sources is normalized; for example, scoring indicators are uniformly mapped to the 0-1 range, and categorical data, such as language and domain, are converted into one-hot encoding or embedded representations. To avoid data bias, historical scores can be subject to time decay processing, such as a recent task weight of 0.7 and a historical task weight of 0.3. Incomplete candidate information can be filtered out by setting a data integrity threshold, such as missing fields not exceeding 10%.

[0047] A capability-complexity matching model is constructed, using a complexity coefficient (range 0-1) as a task difficulty indicator, and comparing it with the comprehensive capability score and sub-capability indicators in the capability profile. The matching method can employ a weighted scoring mechanism, for example, setting the comprehensive capability score weight to 0.4, the semantic processing capability weight to 0.3, and the scenario experience weight to 0.3, and calculating the weighted total score. Subsequently, a capability threshold is set, for example requiring the candidate's capability score to be higher than 1.1 times the complexity coefficient, i.e., leaving a 10% capability redundancy to ensure stable translation quality.

[0048] Based on platform logs, task queries are performed on candidate personnel to extract a list of currently executing and pending tasks for each candidate, and a task sequence is constructed in chronological order. Each task record includes information such as task start time, estimated completion time, task complexity, and priority. During processing, a maximum number of concurrent tasks can be set, such as 3-5, and the current task occupancy rate is calculated as the number of assigned tasks divided by the maximum number of tasks. Task pressure is assessed by calculating the remaining time of tasks, such as the estimated completion time minus the current time. To enhance accuracy, task weighting coefficients can be introduced, for example, a weight of 1.5 for high-complexity tasks and 1.0 for ordinary tasks, to calculate a weighted load value. Task distribution density can be analyzed through time windows, such as the next 30 minutes or 1 hour, to identify any task concentration. Finally, the task sequence and load indicators for each candidate are output, providing basic data for response timeliness assessment.

[0049] The estimated idle time, i.e., the earliest available time after all tasks are completed, is calculated based on the current task sequence. This is combined with historical average processing times, such as words processed per minute or average time per task, to estimate the completion time of new tasks. The response time can be defined as the estimated start time plus the estimated processing time. The estimated processing time can be adjusted according to task complexity, such as by multiplying the complexity coefficient by the baseline processing time. In experimental parameters, a baseline processing rate of 150-250 words per minute can be set, and a fluctuation correction factor, such as ±10%, can be introduced to reflect actual instability. The average latency is calculated using historical response data, such as an average latency of 2-5 seconds, and corrected accordingly. Regression models, such as linear regression or simple neural networks, can be used to predict response time and improve estimation accuracy.

[0050] Response timeliness is used as the core indicator, combined with capability matching scores for multi-objective optimization. A comprehensive scoring function can be constructed, for example, Comprehensive Score = α × Capability Matching Degree + β × 1 / Response Timeliness, where the weights of α and β can be set to 0.6 and 0.4 respectively to balance quality and efficiency. During the ranking process, priority is given to personnel with the shortest response timeliness and the highest capability matching degree; when multiple candidates have similar scores, such as a difference of less than 0.05, further refinement can be made by referring to stability indicators or historical performance. To avoid frequently assigning the same personnel, a load balancing mechanism can be introduced, imposing a penalty coefficient, such as 0.9, on personnel with a high recent task assignment frequency. A response timeliness cap can be set, such as not exceeding 1.5 times the expected time; candidates exceeding this range will be eliminated.

[0051] In a specific embodiment, for a translation task involving a "multi-party engineering meeting dialogue video," the platform first performs a preliminary scene matching screening based on the translation scenario type and meeting dialogue scenario identified in the aforementioned steps, using the translation capability profiles of all online translators. Specifically, the system uses the scene preference vector from each translator's capability profile... Scene vector of the current task For example, "conference dialogue = [1,0,0,0]" represents conference dialogue, outdoor navigation, human-computer interaction, and cross-language social interaction, and similarity calculation is performed. and set a threshold Select the first candidate set If 12 translators are selected, The system retrieves a complete translation capability profile of the primary candidate, including language proficiency, certifications, historical scores, and areas of expertise, and then bases this profile on the task complexity coefficient. Perform capability matching calculations. Task complexity can be comprehensively quantified using indicators such as task text length, density of technical terms, number of participants in the dialogue, and speaking speed. Where L is the average sentence length, T is the percentage of terms, N is the number of speakers, and S is the speech rate in words per minute. To normalize the weights, for example, 0.3, 0.3, 0.2, 0.2, the competency matching degree for each translator is calculated as follows: ,in The translators' overall abilities were scored, and a second set of candidates who could match the task complexity was selected. For example, 6 translators After obtaining the second candidate, the system performs a real-time task load query. Each translator's task sequence includes a list of ongoing and pending tasks, recording the estimated completion time and priority of each task. Based on the task sequence, the system calculates the response time value. ,in CT represents the estimated completion time of the translator's current task. The maximum tolerable waiting time for a task is set, such as 5 minutes, to ensure that the response time does not exceed the acceptable range.

[0052] The response timeliness value is used to optimally select the second candidate, and a comprehensive priority score is defined. ,in To prevent division by zero, Take 0.7 and 0.3 respectively, and then... The final matching results are generated by sorting the results from highest to lowest. The final output includes 1-2 of the best translators as task assignment recipients, along with information on response time, skill matching, and areas of expertise.

[0053] In this embodiment, the specific steps of step S5 are as follows: Based on the matching results, the translation request file is sent to the matched online translator to establish a translation task tracking mechanism; The system detects the translated data packets sent back by online translators and calculates the timestamp of the transmission. The preset translation quality assessment model is invoked to automatically score the translation data package using multiple indicators, and the translation score results are output. If the translation is rated as substandard, it will be returned to the online translator for revision. When the translation score is deemed satisfactory, the translation data package is layered and encapsulated, sent to the translation requester, and a translation report is generated simultaneously, completing the translation processing task.

[0054] In this embodiment, the translation request file is encapsulated into a task, generating a unique task identifier, such as a UUID, along with task metadata, including the source language, target language, translation scenario type, complexity coefficient, and estimated completion time. Subsequently, the task is sent to the target user's terminal via a message push mechanism, such as a long-lived connection or task queue, and the sending timestamp is recorded with millisecond-level precision. For task tracking, a state transition model is constructed, classifying tasks into states such as assigned, received, in progress, submitted, and completed. Periodic status reporting, such as every 10 seconds, updates the task progress. To ensure traceability, a logging mechanism can be set up to record key operations, such as task reception confirmation, editing start, and periodic saving. Timeout control parameters can be introduced, such as a 30-second timeout for task reception and a 2-minute timeout for starting processing. If a timeout occurs, a reassignment or reminder mechanism is triggered.

[0055] When a translated data packet is uploaded, data integrity is verified by checking file size, hash value, or structural format (e.g., JSON field integrity) to ensure data validity. The return timestamp is recorded with millisecond precision and compared with the task sending timestamp to calculate the total processing time. Further subdivision of time metrics is possible, such as the preparation time from task receipt to translation start and the processing time from translation start to translation submission, resulting in more granular time analysis data. Experimental parameters can be set to a time recording error range of no more than ±5 milliseconds to ensure accuracy. A sliding time window, such as the last 10 tasks, can be used to calculate the average response time for subsequent performance evaluation. Abnormal situations, such as a return time exceeding a preset limit by 1.5 times, can be flagged and recorded as a delay event.

[0056] A pre-defined translation quality assessment model is used, typically composed of multiple evaluation metrics, including automated metrics and semantic consistency metrics. The source and target texts are aligned at the sentence or paragraph level using text alignment methods. Then, automated evaluation metrics are calculated, such as BLEU score (usually ranging from 0 to 1), semantic similarity (calculated using vector cosine similarity, with a threshold of 0.7 or higher for high consistency), language fluency score (measured by perplexity, typically controlled below 30), terminology consistency detection (matching a pre-defined terminology database with an accuracy target of ≥90%), and grammatical error detection (with an error rate not exceeding 5%). In the score fusion stage, each metric is weighted, for example, BLEU weighted at 0.3, semantic similarity at 0.3, fluency at 0.2, and terminology consistency at 0.2, and the results are normalized to the 0-1 range. To enhance stability, moving averages or exponential weighting methods can be used to smooth score fluctuations.

[0057] Set a quality acceptance threshold, such as an overall score ≥ 0.75. Translations scoring below this threshold are considered unacceptable and trigger a revision process. Generate quality feedback information, including specific problem locations such as sentences with low semantic consistency, terminology errors, and grammatical issues, and return this information to the translator in a structured format, such as a problem list with suggested explanations. Simultaneously, record the scoring result and problem type for subsequent competency assessments. During the revision process, a maximum number of revisions can be set, such as no more than 2-3 times, and a time limit for each revision, such as 50% of the original task timeframe. To improve revision efficiency, problem areas can be highlighted and reference prompts, such as standard terminology or recommended expressions, can be provided. In this embodiment, the specific steps for completing the translation processing job—namely, when the translation score is satisfactory, layering and encapsulating the translation data packet, sending it to the translation requester, and simultaneously generating a translation report—are as follows: When the translation score is satisfactory, media type analysis is performed based on the media content, the translation data package is adjusted accordingly, and an intelligently adjusted translation is output. The intelligently adjusted translation is aligned with the media stream timing to generate adaptive translation content; the adaptive translation content includes multilingual subtitles, dubbing synthesis results, and translated text; The adaptive translation content is layered and encapsulated to obtain the translation delivery package; The translated delivery package is sent to the translation requester, a translation report is generated simultaneously, and the translation processing is completed.

[0058] In this embodiment, the source media content undergoes type analysis, including text, audio, video, and image / subtitle combinations. Multimodal feature detection is used to determine media structural features. For example, if a video contains lip-sync information and background noise, the translation needs to be optimized for the dubbing. For text documents, the focus is on layout and terminology consistency; for audio files, speech rate and pause information must be considered. Subsequently, differentiated adjustments are made based on media type: for subtitles or text, length adaptation and segmentation optimization are performed to ensure each line contains 15-20 characters for easy reading and synchronized display; for dubbing generation, sentence rhythm and pauses are adjusted to ensure the translated speech rate, stress, and intonation match the original media content, typically with the target speech rate differing from the original by no more than 10%, and fine-tuning is achieved through resampling or time stretching methods; for multilingual output, the translation is localized based on grammatical and cultural characteristics to improve cross-language comprehensibility.

[0059] Frame-level temporal analysis is performed on the video or audio, mapping each translated sentence to a specific time window, and adjusting the time offset using dynamic time warping (DTW) or acoustic feature-based alignment algorithms. Subtitle output must be synchronized with the visuals or lip movements, typically with each subtitle display time no less than 1.5 seconds and no more than 5 seconds, while ensuring a reading speed of approximately 300-400 words per minute. For voice-over synthesis, the adjusted translation is input into the TTS (Text-to-Speech) engine, where timbre parameters, speech rate (±10% of the original rate), and pause control are set to achieve synchronization with the audio and video. The text content undergoes paragraph rearrangement and punctuation optimization to ensure the reading order aligns with the media content logic.

[0060] Subtitles, dubbing, and text content are stored as independent layers, with metadata such as language version, timestamp information, media type, and version number recorded. The subtitle layer typically uses standard subtitle formats such as SRT or ASS, the dubbing layer is stored as audio files such as WAV or MP3, and the text layer saves the original text and translation in JSON or TXT format. The delivered package is also validated to ensure file integrity, encoding consistency, and time synchronization accuracy. Subtitle time deviation is no more than 50ms, and dubbing / video frame deviation is no more than 100ms. The package can be further compressed and packaged, such as into ZIP or multimedia container formats like MP4 / MKV, for convenient transmission and storage.

[0061] The translation delivery package is sent to the requesting party via a secure transmission channel, while a translation report is generated to document the translation process and quality information. The transmission process includes data encryption, integrity verification, and status confirmation to ensure that the delivered documents are not tampered with or lost during transmission. The translation report includes task information, source language, target language, translation scenario, translation quality score, task time, revision history, intelligent adjustment information, and multilingual output details. The report can be in a structured format, such as JSON or PDF, for easy viewing and archiving, and includes a version number and timestamp for traceability.

[0062] In this embodiment, an intelligent translation processing platform for multi-scenario applications is provided, including a scenario discrimination module, a complexity assessment module, a capability calculation module, a matching module, and a translation processing module. The scenario discrimination module is responsible for parsing translation request files, extracting multimodal features, and determining the translation scenario type; its output is directly used as input to the complexity assessment module and the matching module. The complexity assessment module generates a complexity coefficient by analyzing the vocabulary, syntax, semantics, and speech rate features of text or speech; this coefficient, along with the scenario type, is provided to the matching module. The capability calculation module independently identifies online translators and constructs capability profiles, including language... The translation module considers the translator's language proficiency, professional domain familiarity, and real-time availability. Its output is correlated with the scenario type and complexity coefficient in the matching module to achieve intelligent matching between translators and tasks. The matching module comprehensively analyzes the translation scenario type, complexity coefficient, and ability profile, calculates the matching degree through algorithms, and generates matching results. Technically, it acts as a bridge, connecting task requirements with personnel capabilities. The translation processing module sends the translation request file to the selected translator based on the matching results and completes the output after the translated text is returned, ensuring that task execution and feedback are traceable. This is used to execute the intelligent translation processing method for multi-scenario applications described above, including: The scene identification module is used to receive translation request files, identify the interactive scene, and obtain the translation scene type. The complexity assessment module is used to assess the translation complexity based on the translation request file and obtain the complexity coefficient. The capability calculation module is used to identify online translators on the current platform and build a translation capability profile. The matching module is used to match translation capability profiles based on translation scenario type and complexity coefficient, and generate matching results; The translation processing module is used to send the translation request file to an online translator based on the matching result. The online translator translates the file and sends back the translated data packet, which is then sent to the translation requester to complete the translation processing task.

[0063] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0064] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent translation processing of multi-scenario applications, characterized in that, Includes the following steps: Step S1: Receive the translation request file, determine the interaction scenario, and obtain the translation scenario type; Step S2: Evaluate the translation complexity based on the translation request file and obtain the complexity coefficient; Step S3: Identify online translators on the current platform and build a translation capability profile; Step S4: Match the translation capability profile based on the translation scenario type and complexity coefficient to generate matching results; Step S5: Based on the matching results, the translation request file is sent to an online translator, who translates it and sends back the translated data packet, which is then sent to the party requesting the translation, thus completing the translation process.

2. The intelligent translation processing method for multi-scenario applications according to claim 1, characterized in that, The specific steps of step S1 are as follows: The platform receives translation request files; performs multimodal content parsing on the translation request files to identify media content; Scene element identification is performed on media content, and multiple scene elements are labeled; Deconstruct multiple scene elements and identify element association features; The element association features include interaction relationships, pragmatic intent, and information flow direction; Interaction scene discrimination is performed based on element association features to obtain the translation scene type.

3. The intelligent translation processing method for multi-scenario applications according to claim 2, characterized in that, The translation scenarios include conference dialogue scenarios, outdoor navigation scenarios, human-computer interaction scenarios, and cross-language social scenarios.

4. The intelligent translation processing method for multi-scenario applications according to claim 2, characterized in that, The specific steps of step S2 are as follows: Perform language recognition on media content to determine the required translation language; Semantic layering analysis is performed based on media content to obtain semantic layering features; the semantic layering features include lexical complexity, syntactic nesting depth, semantic ambiguity, and cross-cultural dependence. The speech rate fluctuation rate, pause distribution, and semantic breakpoint frequency are calculated based on the media content to obtain content speech rate information; The translation complexity is evaluated based on semantic hierarchical features and content speech rate information to obtain a complexity coefficient.

5. The intelligent translation processing method for multi-scenario applications according to claim 4, characterized in that, Step S3 is as follows: Identify online translators on the current platform; Extract the basic information and historical translation task logs of the online translators; the basic information includes languages ​​they are proficient in, certification qualifications, and self-declared scenario preferences. The historical score is obtained by calculating the translation quality score, delivery timeliness achievement rate and customer feedback rating based on the historical translation task log. Based on the aforementioned basic information and historical scores, a translation ability assessment is conducted to obtain a translation ability profile.

6. The intelligent translation processing method for multi-scenario applications according to claim 5, characterized in that, The specific steps of step S4 are as follows: Based on the type of translation scenario, the translation ability profile is initially screened by scenario matching to extract the first candidate; The translation ability profiles of the candidate translators are retrieved; Based on the translation ability profile, the complexity coefficient is calculated using ability matching to obtain the second candidate. Perform a task load query on the second candidate to identify the task sequence of the current candidate; Based on the task sequence, response time is estimated to obtain the response time value for each person. Based on the response time value, optimal filtering is performed to generate matching results.

7. The intelligent translation processing method for multi-scenario applications according to claim 6, characterized in that, The specific steps of step S5 are as follows: Based on the matching results, the translation request file is sent to the matched online translator to establish a translation task tracking mechanism; The system detects the translated data packets sent back by online translators and calculates the timestamp of the transmission. The preset translation quality assessment model is invoked to automatically score the translation data package using multiple indicators, and the translation score results are output. If the translation is rated as substandard, it will be returned to the online translator for revision. When the translation score is deemed satisfactory, the translation data package is layered and encapsulated, sent to the translation requester, and a translation report is generated simultaneously, completing the translation processing task.

8. The intelligent translation processing method for multi-scenario applications according to claim 7, characterized in that, The specific steps for completing the translation processing job, when the translation score is deemed satisfactory, are as follows: The translation data package is layered and encapsulated, sent to the translation requester, and a translation report is generated synchronously. When the translation score is satisfactory, media type analysis is performed based on the media content, the translation data package is adjusted accordingly, and an intelligently adjusted translation is output. The intelligently adjusted translation is aligned with the media stream timing to generate adaptive translation content; the adaptive translation content includes multilingual subtitles, dubbing synthesis results, and translated text; The adaptive translation content is layered and encapsulated to obtain the translation delivery package; The translated delivery package is sent to the translation requester, a translation report is generated simultaneously, and the translation processing is completed.

9. A multi-scenario intelligent translation processing platform, characterized in that, The intelligent translation processing method for performing multi-scenario applications as described in claim 1 includes: The scene identification module is used to receive translation request files, identify the interactive scene, and obtain the translation scene type. The complexity assessment module is used to assess the translation complexity based on the translation request file and obtain the complexity coefficient. The capability calculation module is used to identify online translators on the current platform and build a translation capability profile. The matching module is used to match translation capability profiles based on translation scenario type and complexity coefficient, and generate matching results; The translation processing module is used to send the translation request file to an online translator based on the matching result. The online translator translates the file and sends back the translated data packet, which is then sent to the translation requester to complete the translation processing task.

Images