Data processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a data processing method and device, electronic equipment and a storage medium, which are applied to a block chain network comprising a plurality of processing nodes, and the method comprises the following steps: sending to-be-translated audio data to at least one processing node in the block chain network, target translation content corresponding to the to-be-translated audio data is determined based on the processing nodes; for each processing node, receiving a target translation content fed back by the current processing node, and determining a target translation quality attribute corresponding to the target translation content, so as to determine target virtual resource information matched with the target translation quality attribute based on a pre-established corresponding relationship between the translation quality attribute and the virtual resource allocation amount; and performing uplink storage on the target translation content, the target translation quality attribute and the target virtual resource information of each processing node. According to the technical scheme of the invention, the objectivity of translation quality evaluation is enhanced, and the data security of the translation service is also enhanced by applying the block chain network.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a data processing method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of the Internet, the construction of a smart campus student management system can improve students' learning efficiency and school management level. At present, the existing smart campus student management system relies on centralized servers for management when grading students based on their feedback on translation tasks. The service quality depends on a single node, which has high data security risks and high subjectivity in student performance evaluation. Summary of the invention

[0003] The present invention provides a data processing method, device, electronic device and storage medium, so as to translate audio data through a blockchain network, determine virtual resource information matching the translation result according to the translation result, and store the recorded data generated during the translation process in the blockchain network, so as to enhance the objectivity of translation quality evaluation and enhance the data security of translation services.

[0004] In a first aspect, an embodiment of the present invention provides a data processing method, which is applied to a blockchain network, wherein the blockchain network includes multiple processing nodes, and the method includes:

[0005] Sending the audio data to be translated to at least one processing node in the blockchain network to determine target translation content corresponding to the audio data to be translated based on each processing node; wherein the target translation content and the audio data to be translated correspond to different languages;

[0006] For each processing node, receiving the target translation content fed back by the current processing node, and determining the target translation quality attribute corresponding to the target translation content, so as to determine the target virtual resource information matching the target translation quality attribute based on the pre-established correspondence between the translation quality attribute and the virtual resource allocation amount;

[0007] The target translation content, target translation quality attribute, and target virtual resource information corresponding to at least one processing node are stored on the chain.

[0008] In a second aspect, an embodiment of the present invention further provides a data processing device, which is applied to a blockchain network, wherein the blockchain network includes a plurality of processing nodes, and the device includes:

[0009] A translation result determination module, used to send the audio data to be translated to at least one processing node in the blockchain network, so as to determine the target translation content corresponding to the audio data to be translated based on each processing node; wherein the target translation content and the audio data to be translated correspond to different languages;

[0010] A virtual resource determination module is used to receive, for each processing node, the target translation content fed back by the current processing node, and determine the target translation quality attribute corresponding to the target translation content, so as to determine the target virtual resource information matching the target translation quality attribute based on the pre-established correspondence between the translation quality attribute and the virtual resource allocation amount;

[0011] The data storage module is used to store the target translation content, target translation quality attributes, and target virtual resource information corresponding to at least one processing node on the chain.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0013] one or more processors;

[0014] a storage device for storing one or more programs,

[0015] When one or more programs are executed by one or more processors, the one or more processors implement any data processing method as described in any embodiment of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute any data processing method as described in any of the embodiments of the present invention.

[0017] The technical solution of the embodiment of the present invention is applied to a blockchain network, wherein the blockchain network includes multiple processing nodes. The method includes: sending the audio data to be translated to at least one processing node in the blockchain network, determining the target translation content corresponding to the audio data to be translated based on each processing node, and then, for each processing node, receiving the target translation content fed back by the current processing node, and determining the target translation quality attribute corresponding to the target translation content, and determining the target virtual resource information matching the target translation quality attribute based on the correspondence between the pre-established translation quality attribute and the virtual resource allocation amount, and then, storing the target translation content, target translation quality attribute, and target virtual resource information corresponding to at least one processing node on the chain. The technical solution of the present invention translates the audio data through the blockchain network, determines the virtual resource information matching it according to the translation result, and stores the recorded data generated by the translation process in the blockchain network. This incentive mechanism not only ensures the translation quality, but also enhances the objectivity of the translation quality assessment, and also enhances the data security of the translation service by applying the blockchain network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required to describe the embodiments. Obviously, the drawings introduced are only drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flowchart of a data processing method provided by an embodiment of the present invention;

[0020] Figure 2 A flowchart of another data processing method provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of a data processing device provided by an embodiment of the present invention;

[0022] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] Figure 1 A flow chart of a data processing method provided in an embodiment of the present invention is provided. This embodiment is applicable to situations where translation, evaluation and tracing of translation tasks are required. The method can be executed by a data processing device, which can be implemented in the form of software and / or hardware. The hardware can be an electronic device, such as a mobile terminal, a PC or a server.

[0024] The technical solution provided in this embodiment can use a peer-to-peer (P2P) network architecture to pre-build a blockchain network including multiple processing nodes. The technical solution of the following embodiment is implemented under the blockchain network architecture. Figure 1 As shown, the data processing method includes:

[0025] S110: Send the audio data to be translated to at least one processing node in the blockchain network to determine target translation content corresponding to the audio data to be translated based on each processing node.

[0026] The audio data to be translated is the audio data to be translated. Each processing node in the blockchain network can be understood as a server that can perform audio recognition and translation processing. The target translation content is the translation result obtained by translating the audio data to be translated. The specific representation form of the target translation content can be translation text and / or translation audio.

[0027] In this embodiment, the target translation content and the audio data to be translated correspond to different languages. For example, the language corresponding to the audio data to be translated is English, and the target translation content in Chinese is obtained by translating the audio data to be translated.

[0028] In a specific application process, the method for determining the audio data to be translated can be: obtaining original audio data in a first language, and encrypting the original audio data to obtain encrypted audio data; dividing the encrypted audio data into multiple audio sub-data based on a preset audio division rule, so as to determine at least one audio sub-data as the audio data to be translated.

[0029] The original audio data may be original speech collected during meetings or lectures.

[0030] In this embodiment, audio data at the scene can be collected when a meeting or lecture is held. These audio data include the content of the speech delivered by scholars, and these audio data can be called original audio data. After these original audio data are collected, they can be stored in a database for subsequent use. Thus, when it is necessary to determine the audio data to be translated, one or some original audio data can be obtained from the database. For any original audio data, it can be encrypted by a preset encryption algorithm to obtain encrypted audio data. Then, the encrypted audio data with a large data volume can be divided into multiple audio sub-data packets based on a pre-set audio division rule. For example, the audio division rule can be to divide 30 minutes of encrypted audio data into 180 audio sub-data packets containing 10 seconds of audio data. Thus, each audio sub-data packet can be called an audio data to be translated. In this embodiment, the processing method for each audio data to be translated is consistent, and any audio data to be translated is used as an example to illustrate.

[0031] Specifically, the audio data to be translated can be sent to one or more processing nodes in the blockchain network. For each processing node, the specific implementation method of determining the target translation content corresponding to the audio data to be translated is consistent, and any one of the processing nodes is used as the current processing node as an example for explanation. After receiving the audio data to be translated, the current processing node can convert the audio data to be translated into a text to be translated in text form; then, the specific implementation method of determining the target translation content can include at least two methods, the first method is to input the text to be translated into a pre-trained translation model, and the translation model outputs the target translation content corresponding to the audio data to be translated; the second method is to display the text to be translated in the display interface of the current processing node, and the user translates the text to be translated according to his own knowledge reserve, and uploads the target translation content corresponding to the audio data to be translated on the current processing node by uploading a file or keyboard input.

[0032] For example, in a certain school, a blockchain network can be built. After obtaining an audio data to be translated, the audio data to be translated can be sent to at least one processing node in the blockchain network. For a certain processing node, a student can log in the student's unique coding information on the processing node, and then translate the audio data to be translated and / or the text to be translated according to the student's knowledge reserve to obtain the target translation content.

[0033] S120. For each processing node, receive the target translation content fed back by the current processing node, and determine the target translation quality attribute corresponding to the target translation content, so as to determine the target virtual resource information matching the target translation quality attribute based on the pre-established correspondence between the translation quality attribute and the virtual resource allocation amount.

[0034] The target translation quality attribute is used to characterize the quality of the target translation content. For example, the target translation quality attribute can be characterized by a translation quality score. The larger the translation quality score, the better the translation result of the target translation content; the smaller the translation quality score, the worse the translation result of the target translation content. Virtual resources, for example, can be points, credits, virtual currency, etc. The target virtual resource information is used to characterize the reward information or penalty information that the translator (for example, a translation model, a student, a user, etc.) can obtain through the target translation content.

[0035] Among them, the correspondence between the translation quality attribute and the virtual resource allocation amount is used to characterize which translation quality attribute corresponds to which virtual resource allocation amount. For example, the correspondence can be expressed as follows: if the translation quality attribute meets the first preset condition (the first preset condition can be that the translation quality score is greater than or equal to the first preset score threshold), then the first amount of virtual resources can be obtained; if the translation quality attribute meets the second preset condition (the second preset condition can be that the translation quality score is less than or equal to the second preset score threshold), then the first amount of virtual resources needs to be deducted from the original virtual resources; if the translation quality attribute meets the third preset condition (the third preset condition can be that the translation quality score is between the first preset score threshold and the second preset score threshold), then neither the virtual resources will be obtained nor the original virtual resources will be deducted.

[0036] In the present embodiment, for each processing node, its specific processing process is the same, and any processing node is used as the current processing node as an example for explanation. A set of evaluation criteria for translation quality can be predefined, such as evaluation criteria can include accuracy (degree of conformity with the original text), fluency (naturalness of the translated text), professionalism (correct use of industry terms), etc., and these standards may be quantified as scores or grades for ease of calculation and comparison. For the current processing node, when receiving the target translation content fed back by the current processing node, the target translation quality attribute corresponding to the target translation content is determined by a semantic similarity algorithm, a grammatical error detection algorithm and a predefined evaluation criterion. Further, according to the corresponding relationship between the translation quality attribute and the virtual resource allocation amount and the target translation quality attribute, it can be determined that the target translation quality attribute corresponds to which virtual resource allocation amount in the corresponding relationship, and the virtual resource allocation amount is the target virtual resource information that matches the target translation quality attribute.

[0037] Based on the above example, the target translation content fed back by the current processing node is the translation result input by student A. By performing a quality assessment on the target translation content, it is determined that the target translation quality attribute is 91 points. At this time, according to the correspondence between the pre-defined translation quality attribute and the virtual resource allocation amount, it can be determined that: the target translation quality attribute meets the first preset condition (the first preset condition is that the translation quality score is greater than or equal to 90 points), and the first amount of virtual resources can be obtained at this time (for example, 0.5 credits can be obtained).

[0038] S130: storing target translation content, target translation quality attributes, and target virtual resource information corresponding to at least one processing node on a chain.

[0039] In this embodiment, the target translation content, target translation quality attributes, and target virtual resource information of each processing node can be stored on the blockchain network's data layer to form a decentralized database. In this way, an unalterable record can be formed. In the specific application process, the entire process of a certain translation task can be traced back through the blockchain network to verify the transparency and accuracy of the translation results.

[0040] On the basis of the above embodiment, for the case where an original audio data is divided into multiple audio data to be translated (each audio data to be translated can be called a data block), the data blocks can be linked together through hash pointer technology to form an unalterable chain structure. Each data block contains the hash value of the previous block. Any attempt to modify the early data will cause the subsequent hash value to not match, which will be recognized by the system, ensuring the integrity of the data and the authenticity of the historical records. The role of the blockchain data layer here is to provide a secure, transparent and persistent storage solution for the entire translation service, so that every link in the service process can be traced and verified.

[0041] A hash pointer is a special type of data structure that combines the concept of a traditional pointer with the characteristics of a hash function. In traditional computer science, a pointer is simply a variable that stores the address of a data element in memory, allowing the program to directly access the data at that address. A hash pointer adds a key feature: it not only stores the address (or location) of the data, but also stores the hash value of the data block after the hash function is used to calculate it.

[0042] In this embodiment, the principle of ensuring data security by linking data blocks by hash pointers mainly includes the following three points: First, the data block contains information and the hash value of the previous block: each data block contains actual data (such as data related to a section of audio translation process) and the hash value of the previous data block. This hash value is like a digital fingerprint of the previous block of data. Even if the previous block of data has a slight change, its hash value will be completely different. The second is the characteristics of the hash function: the hash function is unidirectional, which means that it is difficult to infer the original data from the hash value; at the same time, it is highly sensitive to the input. Even if the input data changes slightly, the output hash value will look completely random and different. The third is that since each data block is connected to each other by including the hash value of the previous block, a chain of hash pointers is formed. In this way, if someone tries to modify any early data block in the chain, the hash value of the block will change, which will cause the hash values ​​of all subsequent blocks to no longer match, because they are all calculated based on the correct hash value of the previous block. This design mechanism makes it almost impossible for any tampering with the data to go undetected. In summary, the mechanism of linking data blocks by hash pointers is the core of blockchain to ensure data integrity and immutability. It creates a database that is both distributed and highly secure, where every participant can verify the history of the data without relying on a central authority.

[0043] Based on the above embodiment, the metadata corresponding to each processing node can also be stored on the chain.

[0044] Metadata refers to auxiliary information related to audio recognition, translation process and translation result processing, which is essential to ensure data integrity and improve service transparency. Metadata may include at least one of timestamp information, object identification information, audio source information to be translated, node identification information, error log and exception report, and encryption authentication information. Timestamp information: records the specific time when audio data is received, processed, translated and stored, which helps to trace the processing history and efficiency evaluation of translation tasks. Object identification information: used to characterize the identity of the object that outputs the target translation content. For example, the object identification information can be the unique coding information of the student. Audio source information to be translated: used to indicate the source of audio data, such as speaker identity, lecture topic, classroom location or online conference code, so that users can understand the context of the translation content. Node identification information: records the node address or identifier that processes specific audio data to be translated, which helps to evaluate the work efficiency and reliability of the node. Error log and exception report: records any errors or exceptions encountered during the processing process, which is very critical for system maintenance and troubleshooting. Encryption and authentication information: encryption algorithm, key information, digital signature, etc., to ensure the security and integrity of data, and verify the source of data and that it has not been tampered with. In addition, metadata can also include: Task identifier: The task of a processing node processing a certain audio data to be translated can be called a translation task. Each translation task is assigned a unique identification code to facilitate tracking of task status and associating translation results with original voice data. Translation settings: including the translation language pairs used, translation preferences (such as formality, industry terminology, etc.), the translation model version used, etc. This information helps to reproduce the translation process and evaluate quality. Quality evaluation indicators: Quantitative data of evaluation results such as translation speed, accuracy, fluency, etc., are helpful for subsequent quality control and translator incentives.

[0045] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. For example, network requests are intercepted and processed with the authorization of the user.

[0046] The technical solution of the embodiment of the present invention is applied to a blockchain network, wherein the blockchain network includes multiple processing nodes. The method includes: sending the audio data to be translated to at least one processing node in the blockchain network, determining the target translation content corresponding to the audio data to be translated based on each processing node, and then, for each processing node, receiving the target translation content fed back by the current processing node, and determining the target translation quality attribute corresponding to the target translation content, and determining the target virtual resource information matching the target translation quality attribute based on the correspondence between the pre-established translation quality attribute and the virtual resource allocation amount, and then, storing the target translation content, target translation quality attribute, and target virtual resource information corresponding to at least one processing node on the chain. The technical solution of the present invention translates the audio data through the blockchain network, determines the virtual resource information matching it according to the translation result, and stores the recorded data generated by the translation process in the blockchain network. This incentive mechanism not only ensures the translation quality, but also enhances the objectivity of the translation quality assessment, and also enhances the data security of the translation service by applying the blockchain network.

[0047] Embodiment 2

[0048] Figure 2 This is a schematic diagram of a data processing method provided by an embodiment of the present invention. Based on the above embodiment, S110-S130 are described in detail. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0049] like Figure 2 As shown, the method specifically comprises the following steps:

[0050] S210: Send the audio data to be translated to at least one processing node in the blockchain network.

[0051] S220: For each processing node, convert the audio data to be translated into first text data based on the audio-to-text model deployed in the current processing node.

[0052] The languages ​​corresponding to the audio data to be translated and the first text data are the same, both being the first language. The audio-to-text model is used to convert the audio data into text data.

[0053] In this embodiment, an audio-to-text model can be pre-deployed in each processing node. For the current processing node, after receiving the audio data to be translated, the audio data to be translated can be input into the audio-to-text model to obtain the first text data corresponding to the audio data to be translated.

[0054] S230: When a translation result receiving instruction is detected, receiving target translation content fed back by a target object belonging to the current processing node.

[0055] The target translation content corresponds to the second language, and the target translation content is the translation text determined by the target object according to the first text data. The target object can be understood as the user corresponding to the current processing node, for example, the target object can be a student. The translation result receiving instruction mainly includes operations such as directly displaying the translated content on a specific platform, transmitting the translated character string back to the client that issued the request, and processing the dependency and inverse conversion of the translation result. These instructions ensure the correct reception and use of the translation result.

[0056] In this embodiment, the first text content can be displayed in the display page of the current processing node, and the target object affiliated with the current processing node can edit the translation text corresponding to the first text content or record the translation audio corresponding to the first text content according to the first text content and its own knowledge reserve, thereby generating the target translation content. When the target object completes the editing and triggers the pre-configured data sending control, a translation result receiving instruction can be generated at this time, so that the target translation content fed back by the target object affiliated with the current processing node can be received.

[0057] S240: For each processing node, receive the target translation content fed back by the current processing node, perform quality assessment on the target translation content based on at least one quality assessment dimension, and obtain at least one assessment attribute information; and determine the target translation quality attribute corresponding to the target translation content based on each assessment attribute information.

[0058] Among them, at least one quality assessment dimension includes a translation speed dimension, a translation result accuracy dimension and / or a translation result professionalism dimension. The translation speed dimension can be quantitatively characterized by the translation time. The translation result accuracy dimension can be characterized by determining the text similarity between the standard translation result and the target translation content, wherein the standard translation result can be determined in advance by professionals translating the audio data to be translated. The translation result professionalism dimension can be characterized by detecting the grammatical correctness of the target translation content. The evaluation attribute information can be understood as a translation quality score.

[0059] In this embodiment, the reasons for the need to evaluate the quality of the target translation content are: Technical differences and errors: Different translation models or algorithms may have differences in processing specific language structures, slang, professional terms, etc., resulting in slightly different translation results. Even small differences may affect the accuracy of the content and the fluency of expression. Subjectivity of translation quality: Even if the translation result is correct at the technical level, there may still be differences in its expression, cultural adaptability, and whether it is faithful to the style of the original text. Target objects with different backgrounds may have different acceptance of the same translation. Incentive mechanism and continuous improvement: By scoring the quality of translation, translators and developers can be encouraged to continuously optimize the translation algorithm and improve the quality of translation. High-quality translations can get higher scores, thereby getting more rewards, forming a positive cycle. Ensure service reliability: The evaluation and scoring mechanism also plays a role in quality control, helping to identify and eliminate those occasional erroneous translations or substandard results, and ensuring the high quality and reliability of the overall translation service. Promote user participation: Introducing evaluators to participate in scoring can enhance the interactivity and participation of the decentralized network, making the entire system more decentralized and community-driven. Therefore, quality assessment of target translation content is a key component in promoting technological advancement and ensuring transparency of translation services.

[0060] In this embodiment, the target translation quality attribute may be determined by an evaluator or a crowdsourced evaluator scoring the target translation content; or the target translation quality attribute may be determined by a preset evaluation model.

[0061] Specifically, for the first method, the evaluator is responsible for reviewing the quality of the translation. They may be a group of certified users, experts, etc., who score the translation results according to the preset quality evaluation criteria. The evaluator's task is to ensure the accuracy, fluency and cultural adaptability of the translation, and their score directly affects the distribution of rewards received by the target object. Crowdsourcing evaluators refer to the broad group of members who participate in the evaluation task in the crowdsourcing project or platform. In the traditional professional or centralized evaluation model, the evaluation work is often completed by a few experts or internal teams. In the crowdsourcing model, this process is opened to a large number of users on the Internet, allowing ordinary people without professional background to participate in the evaluation activities. Specifically in the scenario of translation quality evaluation, crowdsourcing evaluators can be any individual who can understand the original and the translated text and judge the translation quality according to the preset criteria. They may be native speakers of the target language, professionals who understand knowledge in a specific field, or ordinary Internet users who have a keen sense of translation quality. These evaluators receive translation samples through the crowdsourcing platform, and score or provide feedback based on the evaluation criteria and algorithms defined in the smart contract. Their collective evaluation results are summarized and used to determine the translator's rewards or further translation quality improvements. The advantage of using crowdsourcing evaluation is that it can quickly gather a large number of opinions, increase the speed and breadth of evaluation, and reduce evaluation costs. However, in order to ensure the accuracy and reliability of the evaluation, it is usually necessary to combine certain quality control mechanisms, such as setting thresholds, adopting majority voting principles, or combining machine evaluation to ensure the objectivity and fairness of the final evaluation results.

[0062] For the second method, the preset evaluation model can perform quality evaluation on the target translation content from at least one quality evaluation dimension to obtain evaluation attribute information corresponding to each evaluation dimension. For example, for a certain target translation content, the first evaluation attribute information corresponding to the translation speed dimension is determined by recording the translation time; the second evaluation attribute information corresponding to the translation result accuracy dimension is determined by determining the text similarity between the standard translation result and the target translation content; the grammatical correctness of the target translation content is determined by the grammar detection algorithm, and the third evaluation attribute information corresponding to the translation result professionalism dimension is determined according to the grammatical correctness. Furthermore, the target translation quality attribute corresponding to the target translation content is determined according to the weighted sum of the first evaluation attribute information, the second evaluation attribute information and the third evaluation attribute information.

[0063] In specific applications, the target translation quality attribute may be determined by the first method and / or the second method.

[0064] S250: Determine target virtual resource information matching the target translation quality attribute based on the pre-established correspondence between the translation quality attribute and the virtual resource allocation amount.

[0065] For example, the target translation quality attributes and the target virtual resource information matching the target translation quality attributes can be determined by means of a smart contract engine. A translation quality assessment smart contract can be deployed in advance to define the assessment criteria, algorithm and incentive rules. The contract receives the assessment request, automatically executes the assessment logic, writes the results to the blockchain, and triggers the corresponding value transfer operation.

[0066] A smart contract is a set of programming logic stored on the blockchain that can automatically execute predetermined terms and agreements when specific conditions are met. In the translation quality assessment scenario, smart contracts specifically specify the following aspects: The first is the assessment criteria, which define the specific requirements for translation quality, such as grammatical correctness, fidelity (consistency between the original text and the translated text), fluency, accuracy of professional terminology, etc. These criteria can be quantified into scores or grades to facilitate automatic evaluation by the system. The second is the algorithm. The smart contract has built-in or referenced external algorithms to automatically analyze the matching degree between the translated text and the original voice content, grammatical error detection, language style consistency, etc., to achieve objective scoring of translation quality. The third is the incentive rule: based on the evaluation results, the smart contract automatically determines the reward distribution between the translator and the evaluator. This includes but is not limited to giving different points rewards based on the quality of the translation, setting a threshold for reaching the standard to obtain basic rewards, and adjusting rewards based on community feedback. The specific implementation process involves the following: (1) Writing and deployment: Smart contracts are written in a programming language that supports blockchain. After writing, they are compiled into bytecode and deployed to the blockchain network, becoming an unchangeable part. (2) Trigger and Execution: When the target object submits the target translation content, the smart contract is triggered and the evaluation algorithm is executed to automatically score the translation quality. At the same time, the feedback submitted by the evaluator will also serve as a trigger condition, affecting the further execution of the smart contract. (3) State machine model: Smart contracts are usually based on the state machine model and switch between different states according to external events (such as translation submission, evaluation completion), ensuring that the operation of each link is orderly and in line with the expected logic. (4) Events and logs: During the execution of the contract, important events will be recorded as logs on the blockchain, such as evaluation completion, reward issuance, etc. These records are open and transparent and can be queried and verified by any network participant. (5) Security and audit: The smart contract code must undergo strict security audits to ensure that there are no vulnerabilities that can be exploited and to protect user assets and data security. The smart contract engine ensures the fair, transparent and efficient execution of translation quality evaluation and incentive distribution, reduces manual intervention, and improves the automation level and trust of the entire system.

[0067] S260: Perform consensus verification on the target translation content corresponding to each processing node based on a preset consensus algorithm, and determine a consensus verification result corresponding to each target translation content.

[0068] Among them, the consensus verification results include consensus verification pass results and consensus verification failure results. The consensus algorithm is a series of rules and processes used in blockchain technology to ensure that different nodes in a distributed system reach a consensus on a transaction or state. The core purpose of the consensus algorithm is to enable decentralized nodes to reach consensus on key operations such as transaction verification and block generation through specific rules and algorithms in a decentralized network environment. This consistency is the basis for the safe and reliable operation of blockchain technology, because it ensures that the data records of all honest nodes are consistent, thereby maintaining the security and stability of the entire system.

[0069] In blockchain, the implementation of consensus algorithms usually relies on mathematical principles and competition or voting mechanisms. For example, the preset consensus algorithms may include but are not limited to: Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPOS), etc.

[0070] In this embodiment, the specific implementation process of consensus verification of the target translation content corresponding to each processing node based on the preset consensus algorithm involves the following aspects: the first is comparison and voting, each processing node may compare their translation results (i.e., target translation content), or some processing nodes may be responsible for verifying the translation output of other nodes. This can be achieved through communication and comparison between nodes, or by using a more complex voting mechanism, in which the result agreed by the majority is regarded as the final result. The second is multiple rounds of verification: if the initial comparison finds inconsistency, the network may trigger additional verification or correction processes until most or all participating nodes reach a consensus. The third is quality assessment, which objectively evaluates the translation results through preset evaluation criteria and algorithms, and further promotes the achievement of consensus. Through these mechanisms, the consensus verification results of the target translation results corresponding to each processing node can be determined. In this way, the consistency of translation quality and results in the decentralized network can be guaranteed without central control, and the reliability and user experience of the system can be enhanced.

[0071] S270: Determine the processing node whose consensus verification result meets the preset conditions as the target processing node.

[0072] In this embodiment, the preset condition is that the consensus verification result is a consensus verification pass result. Based on this, the processing node whose formula verification result is a consensus verification pass result can be determined as the target processing node.

[0073] S280: storing the target translation content, target translation quality attributes, and target virtual resource information corresponding to the target processing node on a chain.

[0074] Based on the above embodiments, the target translation content, target translation quality attributes, and target virtual resource information corresponding to the target processing node can be stored on the chain. Through such a distributed processing method, not only the processing efficiency and robustness of the translation service are improved, but also the consensus mechanism is used to ensure the consistency and accuracy of the translation results between different processing nodes, thereby avoiding dependence on a single centralized server.

[0075] The technical solution of the embodiment of the present invention is to send the audio data to be translated to at least one processing node in the blockchain network, and for each processing node, convert the audio data to be translated into first text data based on the audio-to-text model deployed in the current processing node, and then receive the target translation content fed back by the target object belonging to the current processing node when the translation result reception instruction is detected. Further, the target translation content fed back by the current processing node is received, and the quality of the target translation content is evaluated based on at least one quality evaluation dimension to obtain at least one evaluation attribute information, and based on each evaluation attribute information, the target translation quality attribute corresponding to the target translation content is determined, and then, based on the pre-established correspondence between the translation quality attribute and the virtual resource allocation amount, the target virtual resource information matching the target translation quality attribute is determined. Finally, based on the preset consensus algorithm, consensus verification is performed on the target translation content corresponding to each processing node, and the consensus verification result corresponding to each target translation content is determined. The processing node whose consensus verification result meets the preset conditions is determined as the target processing node, and the target translation content, target translation quality attributes, and target virtual resource information corresponding to the target processing node are stored on the chain. Through such a distributed processing method, not only the processing efficiency and robustness of the translation service are improved, but also the consensus mechanism is used to ensure the consistency and accuracy of the translation results between different nodes, thereby avoiding dependence on a single centralized server.

[0076] Embodiment 3

[0077] Figure 3 This is a structural diagram of a data processing device provided by an embodiment of the present invention. The device includes: a translation result determination module 310, a virtual resource determination module 320 and a data storage module 330.

[0078] The translation result determination module 310 is used to send the audio data to be translated to at least one processing node in the blockchain network, so as to determine the target translation content corresponding to the audio data to be translated based on each processing node; wherein the target translation content and the audio data to be translated correspond to different languages;

[0079] The virtual resource determination module 320 is used to receive the target translation content fed back by the current processing node for each processing node, and determine the target translation quality attribute corresponding to the target translation content, so as to determine the target virtual resource information matching the target translation quality attribute based on the correspondence between the translation quality attribute and the virtual resource allocation amount established in advance;

[0080] The data storage module 330 is used to store the target translation content, target translation quality attributes, and target virtual resource information corresponding to at least one processing node on a chain.

[0081] On the basis of the above device, optionally, the data processing device further includes: a module for determining audio data to be processed; the module for determining audio data to be processed specifically includes:

[0082] An original audio acquisition unit, used to acquire original audio data in a first language, and encrypt the original audio data to obtain encrypted audio data;

[0083] The to-be-processed audio data determining unit is used to divide the encrypted audio data into a plurality of audio sub-data based on a preset audio division rule, so as to determine at least one audio sub-data as the to-be-translated audio data.

[0084] Based on the above device, optionally, the virtual resource determination module 320 also includes: a translation quality attribute determination unit; the translation quality attribute determination unit is specifically used to perform quality assessment on the target translation content based on at least one quality assessment dimension to obtain at least one assessment attribute information; based on each assessment attribute information, determine the target translation quality attribute corresponding to the target translation content.

[0085] Based on the above device, optionally, the at least one quality assessment dimension includes a translation speed dimension, a translation result accuracy dimension and / or a translation result professionalism dimension.

[0086] Based on the above device, optionally, the data storage module 330 includes:

[0087] A consensus verification unit, used to perform consensus verification on the target translation content corresponding to each processing node based on a preset consensus algorithm, and determine a consensus verification result corresponding to each target translation content;

[0088] A target node determination unit, used to determine a processing node whose consensus verification result meets a preset condition as a target processing node;

[0089] The on-chain storage unit is used to store the target translation content, target translation quality attributes, and target virtual resource information corresponding to the target processing node on the chain.

[0090] Based on the above device, optionally, the data storage module 330 is also used to store the metadata corresponding to each processing node on the chain; wherein the metadata includes at least one of timestamp information, object identification information, audio source information to be translated, node identification information, error log and exception report, and encrypted authentication information

[0091] The technical solution of the embodiment of the present invention is applied to a blockchain network, wherein the blockchain network includes multiple processing nodes. The method includes: sending the audio data to be translated to at least one processing node in the blockchain network, determining the target translation content corresponding to the audio data to be translated based on each processing node, and then, for each processing node, receiving the target translation content fed back by the current processing node, and determining the target translation quality attribute corresponding to the target translation content, and determining the target virtual resource information matching the target translation quality attribute based on the correspondence between the pre-established translation quality attribute and the virtual resource allocation amount, and then, storing the target translation content, target translation quality attribute, and target virtual resource information corresponding to at least one processing node on the chain. The technical solution of the present invention translates the audio data through the blockchain network, determines the virtual resource information matching it according to the translation result, and stores the recorded data generated by the translation process in the blockchain network. This incentive mechanism not only ensures the translation quality, but also enhances the objectivity of the translation quality assessment, and also enhances the data security of the translation service by applying the blockchain network.

[0092] The data processing device provided in the embodiment of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0093] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0094] Embodiment 4

[0095] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 40 suitable for implementing exemplary embodiments of the present invention is shown. Figure 4 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0096] like Figure 4As shown, the electronic device 40 is in the form of a general computing device. The components of the electronic device 40 may include but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).

[0097] Bus 403 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0098] The electronic device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 40, including volatile and non-volatile media, removable and non-removable media.

[0099] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. Electronic device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 403 via one or more data medium interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of various embodiments of the present invention.

[0100] A program / utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 407 generally perform the functions and / or methods of the embodiments described herein.

[0101] The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboards, pointing devices, displays 810, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 40, and / or may communicate with any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 411. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 412. As shown, the network adapter 412 communicates with other modules of the electronic device 40 via the bus 403. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0102] The processing unit 401 executes various functional applications and page processing by running the programs stored in the system memory 402, for example, implementing the data processing method provided by the embodiment of the present invention.

[0103] Embodiment 5

[0104] An embodiment of the present invention further provides a storage medium containing computer executable instructions, which are used to execute a data processing method when executed by a computer processor, and is applied to a blockchain network, wherein the blockchain network includes multiple processing nodes, and the method includes:

[0105] Sending the audio data to be translated to at least one of the processing nodes in the blockchain network to determine target translation content corresponding to the audio data to be translated based on each of the processing nodes; wherein the target translation content and the audio data to be translated correspond to different languages;

[0106] For each of the processing nodes, receiving the target translation content fed back by the current processing node, and determining a target translation quality attribute corresponding to the target translation content, so as to determine target virtual resource information matching the target translation quality attribute based on a pre-established correspondence between the translation quality attribute and the virtual resource allocation amount;

[0107] The target translation content, the target translation quality attribute, and the target virtual resource information corresponding to at least one of the processing nodes are stored on a chain.

[0108] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0109] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0110] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0111] Computer program code for performing the operation of embodiments of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A data processing method, characterized in that: Applied in a blockchain network, the blockchain network includes multiple processing nodes, including: Sending the audio data to be translated to at least one of the processing nodes in the blockchain network to determine target translation content corresponding to the audio data to be translated based on each of the processing nodes; wherein the target translation content and the audio data to be translated correspond to different languages; For each of the processing nodes, receiving the target translation content fed back by the current processing node, and determining a target translation quality attribute corresponding to the target translation content, so as to determine target virtual resource information matching the target translation quality attribute based on a pre-established correspondence between the translation quality attribute and the virtual resource allocation amount; The target translation content, the target translation quality attribute, and the target virtual resource information corresponding to at least one of the processing nodes are stored on a chain.

2. The method according to claim 1, characterized in that Also includes: Acquire original audio data in a first language, and encrypt the original audio data to obtain encrypted audio data; The encrypted audio data is divided into a plurality of audio sub-data based on a preset audio division rule, so as to determine at least one of the audio sub-data as audio data to be translated.

3. The method according to claim 1, characterized in that The determining, based on each of the processing nodes, target translation content corresponding to the audio data to be translated comprises: For each of the processing nodes, converting the audio data to be translated into first text data based on an audio-to-text model deployed in the current processing node; wherein the audio data to be translated and the first text data correspond to a first language; When a translation result receiving instruction is detected, a target translation content fed back by a target object belonging to the current processing node is received; wherein the target translation content corresponds to a second language, and the target translation content is a translation text determined by the target object according to the first text data.

4. The method according to claim 1, characterized in that: The determining of a target translation quality attribute corresponding to the target translation content includes: Performing a quality assessment on the target translation content based on at least one quality assessment dimension to obtain at least one assessment attribute information; Based on each of the evaluation attribute information, a target translation quality attribute corresponding to the target translation content is determined.

5. The method according to claim 4, characterized in that The at least one quality assessment dimension includes a translation speed dimension, a translation result accuracy dimension and / or a translation result professionalism dimension.

6. The method according to claim 1, characterized in that The storing the target translation content, the target translation quality attribute, and the target virtual resource information corresponding to at least one of the processing nodes on a chain includes: Performing consensus verification on the target translation content corresponding to each of the processing nodes based on a preset consensus algorithm, and determining a consensus verification result corresponding to each of the target translation content; Determine the processing node whose consensus verification result meets the preset conditions as the target processing node; The target translation content, the target translation quality attribute, and the target virtual resource information corresponding to the target processing node are stored on a chain.

7. The method according to claim 1, characterized in that Also includes: The metadata corresponding to each of the processing nodes is stored on the chain; wherein the metadata includes at least one of timestamp information, object identification information, audio source information to be translated, node identification information, error log and exception report, and encrypted authentication information.

8. A data processing device, characterized in that: Applied in a blockchain network, the blockchain network includes multiple processing nodes, including: A translation result determination module, used to send the audio data to be translated to at least one processing node in the blockchain network, so as to determine the target translation content corresponding to the audio data to be translated based on each of the processing nodes; wherein the target translation content and the audio data to be translated correspond to different languages; A virtual resource determination module, configured to receive, for each of the processing nodes, the target translation content fed back by the current processing node, and determine a target translation quality attribute corresponding to the target translation content, so as to determine target virtual resource information matching the target translation quality attribute based on a pre-established correspondence between the translation quality attribute and the virtual resource allocation amount; The data storage module is used to store the target translation content, the target translation quality attribute, and the target virtual resource information corresponding to at least one of the processing nodes on a chain.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the data processing method according to any one of claims 1 to 7 when executed.

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