Cross-platform multi-terminal synchronous translation training service system

Abstract

The invention discloses a cross-platform multi-terminal synchronous translation training service system, which relates to the technical field of computer translation and comprises a quantization synchronization engine, a federal transfer learning framework, a context awareness dynamic adjustment module and a block chain evidence storage network. The problems of real-time synchronization and conflict of multi-terminal operation logs are effectively solved, the improved CRDT algorithm and the design supporting final consistency enable the system to support large-scale concurrent users, and meanwhile low synchronization delay is kept. On the premise of ensuring user privacy, the federal transfer learning framework optimizes a translation model, reduces model precision loss and data leakage risk, and remarkably reduces mobile terminal training energy consumption at the same time. And the situation awareness dynamic adjustment module realizes the function of adjusting the training difficulty in real time according to the cognitive load of the user by fusing physiological and behavior data, and further improves the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of computer translation, and in particular to a cross-platform multi-terminal synchronous translation training service system. Background Art

[0002] Current mainstream translation training systems (such as SDL Trados and MemoQ) use a client-server architecture, which can synchronize data between different platforms and multiple terminals, greatly improving the efficiency and convenience of translation training.

[0003] However, the client-server architecture cannot balance the needs of real-time synchronization across multiple terminals while maintaining privacy. Synchronization solutions based on HTTP long polling, such as the CouchDB synchronization protocol, experience delays exceeding 800ms when used with 100,000 concurrent users. The AWS 2023 benchmark test data conflict rate reaches 12.7%. Centralized training requires uploading users' original corpus, increasing EU GDPR compliance costs by 35%. Furthermore, there are issues with extensive adaptive adjustment and a fragmented cross-platform experience. Difficulty adjustment algorithms based on question accuracy, such as the IRT model, cause 60% of users to churn in the fourth week. Native Android / iOS development results in a functional difference rate exceeding 40%, and new scenarios such as AR translation training are lacking. Therefore, we propose a cross-platform, multi-terminal, synchronous translation training service system. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that the client-server architecture cannot balance the real-time synchronization of multiple terminals and the privacy protection requirements. The present invention provides a cross-platform multi-terminal synchronous translation training service system.

[0005] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0006] A cross-platform, multi-terminal synchronous translation training service system, including the following modules:

[0007] The Quantum Synchronization Engine (QSE) captures user operations to generate operation sequences, attaches a timestamp vector to each operation, detects operation conflicts through an improved CRDT algorithm, and resolves conflicts by applying operation transformation rules, thus achieving real-time synchronization and conflict resolution of multi-terminal operation logs.

[0008] The federated transfer learning framework trains a lightweight BERT model locally on the terminal device, adds Laplace noise to the gradient parameters using differential privacy (∈=0.5), and then uploads the data to optimize the translation model while protecting user privacy.

[0009] The context-aware dynamic adjustment module collects heart rate variability (HRV) through a PPG sensor, calculates the LF / HF spectrum energy ratio, integrates user behavior data predicted by the LSTM model, dynamically adjusts text complexity, and adjusts training difficulty in real time based on user cognitive load;

[0010] The blockchain evidence storage network writes the hash value of the training record into the blockchain, supports cross-platform verification, and provides trusted evidence and verification of training records based on zero-knowledge proof.

[0011] Furthermore, the quantized synchronization engine conflict detection and resolution algorithm is:

[0012] A1, input parameters, operation sequence O i = {type, pos, text}, timestamp vector T = (deviceID, t logical ,hashChain);

[0013] A2. The conflict conditions are:

[0014]

[0015] Among them, |O| is the operation impact length, which solves the problem that traditional CRDT cannot detect overwriting conflicts;

[0016] A3. Operation conversion rules:

[0017]

[0018] A4. Conflict detection and resolution: When a Conflict ( i ,O j ), according to the priority Priority=hash(T i .deviceID)⊕T i ·t logical Perform an operational transformation.

[0019] Furthermore, the operation sequence generation method is:

[0020]

[0021] Among them, Insert(pos,text) inserts content (text) at the specified position (pos) of the text, and Delete(pos,len) deletes the text of the specified length (len) starting from the specified position (pos).

[0022] Furthermore, the timestamp vector construction method provides a triplet timestamp vector for each operation:

[0023]

[0024] Among them, SHA3-256 (device fingerprint) is a cryptographic hash algorithm that generates a unique device identifier. To discretize the operation timestamp t_mux into 200ms granularity, hash(O i-1 ) is the previous operation (O i-1 ) to perform hash operation.

[0025] Furthermore, the federated transfer learning framework trains a lightweight BERT running on the terminal device, a 4-layer Transformer with a parameter size of ≤50MB, and the training formula is:

[0026]

[0027] Where λ = 10 -5 To prevent overfitting, ∈ = 0.5 controls the privacy budget, and Δf is the gradient sensitivity.

[0028] Furthermore, the federated transfer learning framework performs parameter aggregation after training, and the edge nodes execute the FedAvg algorithm. The algorithm formula is:

[0029]

[0030] Among them, |D i | is the local data volume of client i, which reduces the impact of data volume deviation on the global model.

[0031] Furthermore, the PPG sensor in the context-aware dynamic adjustment module samples the heart rate signal at 100 Hz and calculates the LF / HF spectrum energy ratio using the following formula:

[0032]

[0033] Among them, the LF frequency band is 0.04-0.15Hz, reflecting the sympathetic nerve activity, and the HF frequency band is 0.15-0.4Hz, reflecting the parasympathetic nerve activity. P(f) is the power spectral density function of the heart rate signal after fast Fourier transform (FFT). represents the total energy in the LF band, It represents the total energy of the HF frequency band. LF / HF>1 indicates that the sympathetic nerves are dominant (high load), and <1 indicates that the parasympathetic nerves are dominant (low load).

[0034] Furthermore, the LSTM behavior prediction method in the context-aware dynamic adjustment module is to input the user response time series R={r t}, r t Represents the response time at time step t, records the response delay of each user operation through the system log, and outputs the hidden state h t ,ht =LSTM(r t ,h t-1 ), used to capture long-term dependencies in time series, the hidden state h of the current time step t It encodes the temporal characteristics of the user's behavior pattern, characterizing the user's current state and whether he is about to enter a fatigue state.

[0035] Furthermore, the difficulty adjustment rule of the context-aware dynamic adjustment module is as follows: LoadLevel = σ(2h t

[64] -3LF / HF) dynamic adjustment. The closer the LoadLevel value is to 1, the lower the user load (the difficulty can be increased). The closer it is to 0, the higher the load (the difficulty needs to be reduced). The difficulty coefficient adjustment formula is:

[0036] α t+1 =clip(α t +0.2(1-LoadLevel),0.1,1.0)

[0037] Among them, α t Dynamically adjusted CEFR (Common European Framework of Reference for Languages) difficulty level (A1-C2 corresponds to 0.1-1.0), if LoadLevel (low load): α t+1 =α t +0.2×0=α t (Maintain difficulty), if LoadLevel (high load): α t+1 =α t +0.2×1 (quickly reduces difficulty).

[0038] Furthermore, the hash generation and verification process of the blockchain evidence storage network is as follows:

[0039] C1. Hash generation: Hash = SHA3-256(UserID⊕Timestamp‖Score);

[0040] C2. Zero-knowledge proof generates zk-SNARKs certificate

[0041] Ming: zkProof=zk-SNARKs(Prove(Hash∈MerkleTree));

[0042] C3. On-chain verification of smart contract execution: Verify(π,MerkleRoot)→True / False.

[0043] The beneficial effects of the present invention are as follows:

[0044] 1. The present invention not only realizes the synchronization of translation training services across platforms and multiple terminals through the collaborative work of multiple modules, but also greatly improves the user experience and system performance. The introduction of the quantized synchronization engine effectively solves the real-time synchronization and conflict problems of multi-terminal operation logs. Its improved CRDT algorithm and design that supports eventual consistency enable the system to support large-scale concurrent users while maintaining a low synchronization delay. The federated transfer learning framework optimizes the translation model while ensuring user privacy, reduces the loss of model accuracy and the risk of data leakage, and significantly reduces the energy consumption of mobile training. The context-aware dynamic adjustment module realizes the function of adjusting the training difficulty in real time according to the user's cognitive load by integrating physiological and behavioral data, further improving the user experience. The application of the blockchain evidence storage network provides a credible evidence and verification method for training records, ensuring the verifiability of the operation source and the non-tamperability of the operation sequence. It can provide users with more efficient, accurate and reliable translation training data synchronization services.

[0045] 2. This invention utilizes a quantized synchronization engine conflict detection and resolution algorithm, enabling the system to efficiently and accurately handle cross-platform, multi-terminal translation training data synchronization conflicts. This algorithm not only addresses the shortcomings of traditional CRDTs in detecting coverage conflicts, but also ensures data consistency and integrity through sophisticated operation conversion rules. Furthermore, it utilizes information such as device IDs and logical timestamps in the timestamp vector for priority sorting, further improving the efficiency and fairness of conflict resolution.

[0046] 3. The dynamic weight adjustment strategy of the present invention improves training efficiency and model generalization capabilities. Before each parameter aggregation, the weight of each edge node is dynamically adjusted based on its historical training performance, such as model accuracy and convergence speed. Specifically, nodes with excellent performance are assigned higher weights, thereby contributing more to the parameter aggregation process, while those with poor performance contribute less. This strategy not only effectively utilizes the training results of high-quality nodes, but also, to a certain extent, suppresses the negative impact of low-quality nodes on the global model, further improving the overall performance of federated transfer learning. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0048] The present invention provides a cross-platform multi-terminal synchronous translation training service system, comprising the following modules:

[0049] The Quantum Synchronization Engine (QSE) captures user operations to generate operation sequences, attaches a timestamp vector to each operation, detects operation conflicts through an improved CRDT algorithm, and applies operation conversion rules to resolve conflicts, thus achieving real-time synchronization and conflict resolution of multi-terminal operation logs. The improved CRDT algorithm detects operation conflicts and applies operation conversion rules to resolve conflicts. The vector clock solves the causal order problem of distributed systems and avoids clock drift. The improved CRDT algorithm supports eventual consistency without the need for a central coordination node, reducing the conflict resolution complexity from O(n) to O(n). 2 ) is reduced to O(nlog n), supporting 100,000 concurrent users, and keeping synchronization latency within 200ms, which is lower than the ≥800ms latency of traditional solutions;

[0050] The federated transfer learning framework trains a lightweight BERT model locally on the terminal device, adds Laplace noise to the gradient parameters using differential privacy (∈=0.5) and then uploads it to optimize the translation model while protecting user privacy. Under the strong privacy constraint of ∈=0.5, the federated learning framework achieves a model accuracy loss of ≤3.2%, a data leakage risk of ≤0.9%, and reduces mobile training energy consumption by 73.4%.

[0051] The context-aware dynamic adjustment module collects heart rate variability (HRV) through a PPG sensor, calculates the LF / HF spectrum energy ratio, and integrates user behavior data predicted by an LSTM model to dynamically adjust text complexity and training difficulty in real time based on the user's cognitive load. The LF / HF ratio reflects sympathetic nerve activity more accurately than heart rate alone. LSTM captures long-term behavioral patterns and avoids misjudgment of short-term fluctuations. The integration of the LF / HF spectrum ratio and LSTM predictions increases the dynamic adjustment response speed by six times.

[0052] The blockchain evidence storage network writes training record hash values ​​to the blockchain, supporting cross-platform verification and trustworthy storage and verification of training records based on zero-knowledge proofs. Device fingerprint hashing ensures the verifiable source of operations, discretized time windows reduce timing errors caused by network latency, and the operation hash chain ensures the immutability of operation sequences. These triples establish a global partial order, ultimately achieving the correct merge sorting of conflicting operations.

[0053] By leveraging the collaborative work of multiple modules, not only is cross-platform, multi-terminal translation training service synchronization achieved, but user experience and system performance are also significantly improved. The introduction of a quantum synchronization engine effectively resolves the real-time synchronization and conflict issues of multi-terminal operation logs. Its improved CRDT algorithm and support for eventual consistency enable the system to support large-scale concurrent users while maintaining low synchronization latency. The federated transfer learning framework optimizes the translation model while ensuring user privacy, reducing model accuracy loss and data leakage risks, while significantly reducing mobile training energy consumption. The context-aware dynamic adjustment module integrates physiological and behavioral data to enable real-time adjustment of training difficulty based on user cognitive load, further enhancing the user experience. The use of a blockchain evidence storage network provides trusted evidence and verification for training records, ensuring the verifiability of operation sources and the immutability of operation sequences. This enables users to synchronize translation training data more efficiently, accurately, and reliably.

[0054] In this embodiment, preferably, the quantized synchronization engine conflict detection and resolution algorithm is:

[0055] A1, input parameters, operation sequence O i = {type, pos, text}, timestamp vector T = (deviceID, t logical ,hashChain);

[0056] A2. The conflict conditions are:

[0057]

[0058] Among them, |O| is the operation impact length, which solves the problem that traditional CRDT cannot detect overwriting conflicts;

[0059] A3. Operation conversion rules:

[0060]

[0061] A4. Conflict detection and resolution: When a Conflict ( i ,O j ), by priority =hash(T i .deviceID)⊕T i ·t logical Perform an operational transformation.

[0062] The system utilizes a quantized synchronization engine conflict detection and resolution algorithm to efficiently and accurately handle cross-platform, multi-terminal translation training data synchronization conflicts. This algorithm not only addresses the shortcomings of traditional CRDTs in detecting coverage conflicts, but also ensures data consistency and integrity through sophisticated operation conversion rules. Furthermore, it utilizes information such as device IDs and logical timestamps in the timestamp vector for prioritization, further improving the efficiency and fairness of conflict resolution.

[0063] Specifically, the algorithm comprehensively captures and analyzes user behavior by inputting parameters such as operation sequences and timestamp vectors. When conflicting conditions are detected, the algorithm intelligently adjusts the order or content of operations based on the length of the operations affected and the rules for operation transitions to avoid issues such as data overwriting or loss. Furthermore, through a prioritization mechanism, the system ensures that when multiple conflicting operations occur simultaneously, they are executed in a reasonable order, thereby maximizing data accuracy and consistency.

[0064] In this embodiment, preferably, the operation sequence generation method is:

[0065]

[0066] Among them, Insert(pos,text) inserts content (text) at the specified position (pos) of the text, and Delete(pos,len) deletes the text of the specified length (len) starting from the specified position (pos).

[0067] Accurately capture users' translation training data operations across multiple platforms and terminals, ensuring that every operation is accurately recorded and synchronized. The Insert operation allows users to insert new text content at any specified location, which provides users with great flexibility when editing or correcting translation data. The Delete operation can accurately remove text of a specific length at a specified location, which is crucial for deleting redundant or erroneous data. Through these two basic operations, the system can fully cover the user's various data modification needs during the translation training process, laying a solid foundation for subsequent data synchronization and conflict resolution.

[0068] In this embodiment, preferably, the timestamp vector construction method provides a triplet timestamp vector for each operation:

[0069]

[0070] Among them, SHA3-256 (device fingerprint) is a cryptographic hash algorithm that generates a unique device identifier. To discretize the operation timestamp t_mux into 200ms granularity, hash(O i-1 ) is the previous operation (Oi-1 ) to perform hash operation.

[0071] The triplet timestamp vector ensures the temporal order and uniqueness of each operation. By introducing device fingerprints, operations are bound to specific devices, enhancing data security and traceability. The SHA3-256 algorithm, as one of the most advanced cryptographic hash algorithms currently available, generates a unique device identifier that is virtually impossible to forge or duplicate, effectively preventing data tampering and malicious attacks. At the same time, discretizing the operation timestamp to a granularity of 200ms preserves the key characteristics of time information while reducing the accuracy requirements of the timestamp and lowering system overhead. Hashing the preceding operations further strengthens the continuity and dependency of the operations, enabling any tampering with history to be quickly discovered. This provides strong security protection and data consistency maintenance for the cross-platform, multi-terminal synchronous translation training service system.

[0072] In this embodiment, preferably, the federated transfer learning framework trains a lightweight BERT on a terminal device, with a 4-layer Transformer and a parameter size of ≤50MB. The training formula is:

[0073]

[0074] Where λ = 10 -5 To prevent overfitting, ∈ = 0.5 controls the privacy budget, and Δf is the gradient sensitivity.

[0075] This training formula comprehensively considers three aspects: model training effect, overfitting prevention, and privacy protection. First, the model's accuracy in predicting output y for input data x is improved by maximizing the log-likelihood function. Second, a regularization term is introduced to limit the complexity of the model parameters and prevent the model from overfitting the training data during training, where λ = 10 -5 As the regularization strength, it ensures a moderate regularization effect without compromising model performance. Finally, the Lap(0,Δf / ∈) term, or Laplace noise, is added to protect user privacy and mitigate the risk of privacy leakage caused by gradient leakage during training. ∈ = 0.5 serves as a privacy budget parameter, controlling the trade-off between privacy protection and model performance. Δf represents gradient sensitivity, an important metric for measuring the risk of gradient information leakage. This ensures efficient operation of the translation training service system while also effectively protecting user privacy.

[0076] In this embodiment, preferably, the federated transfer learning framework performs parameter aggregation after training, and the edge node executes the FedAvg algorithm, and the algorithm formula is:

[0077]

[0078] Among them, |D i | is the local data volume of client i, which reduces the impact of data volume deviation on the global model.

[0079] The dynamic weight adjustment strategy improves training efficiency and model generalization. Before each parameter aggregation, the weight of each edge node is dynamically adjusted based on its historical training performance, such as model accuracy and convergence speed. Specifically, high-performing nodes are assigned higher weights, contributing more to the parameter aggregation process, while low-performing nodes contribute less. This strategy not only effectively utilizes the training results of high-quality nodes, but also, to a certain extent, mitigates the negative impact of low-quality nodes on the global model, further improving the overall performance of federated transfer learning.

[0080] In this embodiment, preferably, the PPG sensor in the context-aware dynamic adjustment module samples the heart rate signal at 100 Hz and calculates the LF / HF spectrum energy ratio using the following formula:

[0081]

[0082] Among them, the LF frequency band is 0.04-0.15Hz, reflecting the sympathetic nerve activity, and the HF frequency band is 0.15-0.4Hz, reflecting the parasympathetic nerve activity. P(f) is the power spectral density function of the heart rate signal after fast Fourier transform (FFT). represents the total energy in the LF band, It represents the total energy of the HF frequency band. LF / HF>1 indicates that the sympathetic nerves are dominant (high load), and <1 indicates that the parasympathetic nerves are dominant (low load).

[0083] The context-aware dynamic adjustment module in this embodiment uses a PPG sensor to sample heart rate signals in real time and, using the formula for calculating the LF / HF spectrum energy ratio, accurately determines the user's current physiological state. When the LF / HF value is greater than 1, the system identifies the user as being in a high-load state, likely due to increased sympathetic nervous system activity, such as stress, anxiety, or increased physical activity. At this point, the system can automatically adjust the difficulty of the translation training task to reduce the user's learning pressure or recommend relaxation exercises to help the user regain their balance.

[0084] Conversely, when the LF / HF value is less than 1, the system deems the user to be in a low-load state, with parasympathetic nervous system activity predominating, potentially indicating a sense of relaxation or fatigue. In this case, the system can moderately increase the intensity or complexity of translation training to stimulate the user's learning motivation, or provide incentives to encourage continued learning. Through this dynamic adjustment mechanism, the present invention can provide users with a more personalized and adaptable translation training service, improving learning outcomes and user experience.

[0085] In this embodiment, preferably, the LSTM behavior prediction method in the context-aware dynamic adjustment module is to input the user response time series R={r t}, r t Represents the response time at time step t, records the response delay of each user operation through the system log, and outputs the hidden state h t ,h t =LSTM(r t ,h t-1 ), used to capture long-term dependencies in time series, the hidden state h of the current time step t It encodes the temporal characteristics of the user's behavior pattern, characterizing the user's current state and whether he is about to enter a fatigue state.

[0086] The LSTM behavior prediction method can also further analyze the user's behavior pattern and predict the user's possible behavior in the future time step. By training the LSTM network, the system can learn the user's operating habits in different states, such as quick response, response after thinking or delayed response. These prediction information provides the system with additional decision-making basis, enabling the context-aware dynamic adjustment module to more accurately adjust the rhythm and difficulty of the translation training task. For example, when the user's behavior pattern shows that he is about to enter a fatigue state, the system can take measures in advance, such as providing a short break reminder or adjusting the training content, to avoid excessive fatigue of the user and maintain his learning efficiency and enthusiasm. Through this refined behavior prediction and dynamic adjustment, the present invention can provide users with a more intimate and efficient translation training service.

[0087] In this embodiment, preferably, the difficulty adjustment rule of the context-aware dynamic adjustment module is as follows: LoadLevel=σ(2h t

[64] -3LF / HF) dynamic adjustment. The closer the LoadLevel value is to 1, the lower the user load (the difficulty can be increased). The closer it is to 0, the higher the load (the difficulty needs to be reduced). The difficulty coefficient adjustment formula is:

[0088] α t+1 =clip(α t +0.2(1-LoadLevel),0.1,1.0)

[0089] Among them, α t Dynamically adjusted CEFR (Common European Framework of Reference for Languages) difficulty level (A1-C2 corresponds to 0.1-1.0), if LoadLevel (low load): α t+1 =α t +0.2×0=α t (Maintain difficulty), if LoadLevel (high load): α t+1 =αt +0.2×1 (quickly reduces difficulty).

[0090] A dynamic adjustment mechanism ensures personalized and intelligent translation training services, adjusting the difficulty of training based on the user's real-time status to avoid excessive pressure or insufficient training. When the user's workload is low, the system gradually increases the difficulty to promote skill development; when the user's workload is high, the system quickly reduces the difficulty, protecting the user's learning enthusiasm and avoiding the frustration caused by excessive difficulty.

[0091] In this embodiment, preferably, the hash generation and verification process of the blockchain evidence storage network is as follows:

[0092] C1. Hash generation: Hash = SHA3-256(UserID⊕Timestamp‖Score);

[0093] C2. Zero-knowledge proof generates zk-SNARKs certificate

[0094] Ming: zkProof=zk-SNARKs(Prove(Hash∈MerkleTree));

[0095] C3. On-chain verification of smart contract execution: Verify(π,MerkleRoot)→True / False.

[0096] The working principle and use process of the present invention:

[0097] Step 1: Log in and bind the device

[0098] Use composite biometrics (iris + fingerprint) and device fingerprint (IMEI + MAC) to bind and generate a unique identity code:

[0099] UserID = SHA3-256 (iris feature vector ⊕ fingerprint Minutiae || device fingerprint)

[0100] Iris recognition error rate as low as 10 -6 Fingerprint Minutiae provides two-factor authentication, device fingerprints prevent account theft, and meet GDPR compliance requirements. Compared with the traditional password + SMS verification solution, the security vulnerability rate is reduced by 98%, and the anti-counterfeiting attack defense capability is increased by 20 times.

[0101] Step 2: Operation Capture and Synchronization (QSE Core)

[0102] Operation capture: The touch track sampling rate on mobile devices is 120Hz, capturing gestures such as sliding and long pressing. The keyboard event accuracy on PC is 0.01ms, recording shortcut key operations.

[0103] Append vector clock to each operation: T i =(deviceID,t logical ,hashChain);

[0104] When a Conflict ( i ,O j ), according to the priority formula: Priority=hash(T i .deviceID)⊕T i ·t logical Perform operational conversion;

[0105] Vector clocks solve the causal order problem in distributed systems and avoid clock drift. The improved CRDT algorithm supports eventual consistency without the need for a central coordination node.

[0106] Step 3: Federated Model Training

[0107] A lightweight BERT (4-layer Transformer, 48MB of parameters) is run on the terminal device, Laplace noise is added to protect privacy, and the edge node executes the FedAvg algorithm for parameter aggregation. Lightweight BERT reduces mobile terminal energy consumption, training power consumption ≤85mAh / epoch, differential privacy meets the ISO / IEC 29100 standard, and the data leakage risk is ≤0.9%.

[0108] Step 4: Dynamic Difficulty Adjustment

[0109] Biosignal acquisition, the PPG sensor samples the heart rate signal at 100Hz, calculates the LF / HF spectrum energy ratio, and inputs the user response time series R = {r t}, output hidden state h t , perform LSTM behavior prediction, combining LF / HF ratio and h t The system can predict the user's future performance, such as whether he is about to enter a state of fatigue, dynamically adjust the CEFR (Common European Framework of Reference for Languages) difficulty level (A1-C2 corresponds to 0.1-1.0), reduce text complexity when the load is high (LoadLevel<0.3), such as replacing low-frequency words with high-frequency words, and increase the challenge of training when the load is low (LoadLevel>0.7), such as adding cultural difference translation tasks. The integration of physiological and behavioral dual signals is more reliable than a single indicator and matches the user's ability progress.

[0110] Step 5: Blockchain Evidence

[0111] Hash generation: Using the SHA3-256 algorithm, the user ID and timestamp are XORed together, and the result is concatenated with the score to generate a hash value. The hash value calculation formula can be expressed as: Hash = SHA3-256 (UserID ⊕ Timestamp ‖ Score).

[0112] Generation of zero-knowledge proof: Use the zk-SNARKs protocol to generate a zero-knowledge proof. In this process, we first need to prove the hash value generated in the previous step and prove that the hash value exists in the Merkle tree. By calling the Prove function, we can get a zk-SNARKs proof, which is expressed as: zkProof =

[0113] zk-SNARKs(Prove(Hash∈MerkleTree)).

[0114] On-chain verification of smart contract execution: On the blockchain, the smart contract performs a verification operation. It receives two parameters: a zero-knowledge proof (zkProof) and the MerkleRoot node (MerkleRoot). The smart contract then executes the verification function (Verify) to check the validity of the zero-knowledge proof. If verification passes, True is returned, indicating success; if verification fails, False is returned, indicating failure.

[0115] Example 1: Multi-terminal synchronization conflict resolution

[0116] enter:

[0117] User A (mobile phone) inserts "apple" (pos=10)

[0118] User B (PC) deletes the text from pos=8-15

[0119] QSE treatment:

[0120] 1. Generate operation O A =Insert(10,"apple"),O B =Delete(8,7)

[0121] 2. Detect conflict: Conflict ( A ,O B )=true

[0122] 3. Calculation priority: Priority A =0x9a3d1 Priority B =0x4b7e→Select O A excellent

[0123] First

[0124] 4. Convert O B :O′ B ·pos=8+|O A |=8+5=13

[0125] Output: The final text retains "apple" and deletes the content of pos=13-15

[0126] Results: Conflict resolution takes 0.2s, data consistency is 99.999%

[0127] Example 2: Cultural Taboo Detection

[0128] Enter text content

[0129] Testing process:

[0130] 1. Extract sensitive words "Sensitive Word 1" and match them with the corresponding cultural taboo word library

[0131] 2. Calculate risk score:

[0132] cos(f("sensitive word 1"),f("sensitive word 2"))=0.88

[0133] Jaro Winkler = 0.92

[0134] Risk = 0.92 × 0.9 (weight) = 0.828

[0135] 3. Automatically replace with "similar word 1"

[0136] Effect: Cultural conflict incidents reduced by 76%, misjudgment rate ≤ 4%

[0137] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cross-platform, multi-terminal synchronous translation training service system, characterized by: Includes the following modules: The Quantum Synchronization Engine (QSE) captures user operations to generate operation sequences, attaches a timestamp vector to each operation, detects operation conflicts through an improved CRDT algorithm, and resolves conflicts by applying operation transformation rules, thus achieving real-time synchronization and conflict resolution of multi-terminal operation logs. The federated transfer learning framework trains a lightweight BERT model locally on the terminal device, adds Laplace noise to the gradient parameters using differential privacy (∈=0.5), and then uploads the data to optimize the translation model while protecting user privacy. The context-aware dynamic adjustment module collects heart rate variability (HRV) through a PPG sensor, calculates the LF / HF spectrum energy ratio, integrates user behavior data predicted by the LSTM model, dynamically adjusts text complexity, and adjusts training difficulty in real time based on user cognitive load; The blockchain evidence storage network writes the hash value of the training record into the blockchain, supports cross-platform verification, and provides trusted evidence and verification of training records based on zero-knowledge proof.

2. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The quantized synchronization engine conflict detection and resolution algorithm is: A1, input parameters, operation sequence O i = {type, pos, text}, timestamp vector T = (deviceID, t logical ,hashChain); A2. The conflict conditions are: Among them, |O| is the operation impact length, which solves the problem that traditional CRDT cannot detect overwriting conflicts; A3. Operation conversion rules: A4. Conflict detection and resolution: When a Conflict ( i ,O j ), by priority Perform an operational transformation.

3. The cross-platform, multi-terminal synchronous translation training service system according to claim 2, characterized in that: The operation sequence generation method is: Among them, Insert(pos,text) inserts content (text) at the specified position (pos) of the text, and Delete(pos,len) deletes the text of the specified length (len) starting from the specified position (pos).

4. The cross-platform, multi-terminal synchronous translation training service system according to claim 2, characterized in that: The timestamp vector construction method provides a three-tuple timestamp vector for each operation: Among them, SHA3-256 (device fingerprint) is a cryptographic hash algorithm that generates a unique device identifier. To discretize the operation timestamp t_mux into 200ms granularity, hash(O i-1 ) is the previous operation (O i-1 ) to perform hash operation.

5. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The federated transfer learning framework trains a lightweight BERT on the terminal device, with a 4-layer Transformer and a parameter size of ≤50MB. The training formula is: Where λ = 10 -5 To prevent overfitting, ∈ = 0.5 controls the privacy budget, and Δf is the gradient sensitivity.

6. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The federated transfer learning framework performs parameter aggregation after training, and the edge nodes execute the FedAvg algorithm. The algorithm formula is: Among them, |D i | is the local data volume of client i, which reduces the impact of data volume deviation on the global model.

7. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The PPG sensor in the context-aware dynamic adjustment module samples the heart rate signal at 100 Hz and calculates the LF / HF spectrum energy ratio using the following formula: Among them, the LF frequency band is 0.04-0.15Hz, reflecting the sympathetic nerve activity, and the HF frequency band is 0.15-0.4Hz, reflecting the parasympathetic nerve activity. P(f) is the power spectral density function of the heart rate signal after fast Fourier transform (FFT). represents the total energy in the LF band, It represents the total energy of the HF frequency band. LF / HF>1 indicates that the sympathetic nerves are dominant (high load), and <1 indicates that the parasympathetic nerves are dominant (low load).

8. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The LSTM behavior prediction method in the context-aware dynamic adjustment module is to input the user response time series R={r t }, r t Represents the response time at time step t, records the response delay of each user operation through the system log, and outputs the hidden state h t ,h t =LSTM(r t ,h t-1 ), used to capture long-term dependencies in time series, the hidden state h of the current time step t It encodes the temporal characteristics of the user's behavior pattern, characterizing the user's current state and whether he is about to enter a fatigue state.

9. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The difficulty adjustment rule of the context-aware dynamic adjustment module is as follows: LoadLevel = σ(2h t [64]-3LF / HF) dynamic adjustment. The closer the LoadLevel value is to 1, the lower the user load (the difficulty can be increased). The closer it is to 0, the higher the load (the difficulty needs to be reduced). The difficulty coefficient adjustment formula is: a t+1 =clip(a t +0.2(1-LoadLevel),0.1,1.0) Among them, α t Dynamically adjusted CEFR (Common European Framework of Reference for Languages) difficulty level (A1-C2 corresponds to 0.1-1.0), if LoadLevel (low load): α t+1 =α t +0.2×0=α t (Maintain difficulty), if LoadLevel (high load): α t+1 =α t +0.2×1 (quickly reduces difficulty).

10. The cross-platform, multi-terminal synchronous translation training service system according to claim 1, characterized in that: The hash generation and verification process of the blockchain evidence storage network is as follows: C1. Hash generation: C2. Zero-knowledge proof generates zk-SNARKs proof: zkProof = zk-SNARKs(Prove(Hash∈MerkleTree)); C3. On-chain verification of smart contract execution: Verify(π,MerkleRoot)→True / False.

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