Self-supervised learning contract template generation method based on trade system platform

Abstract

The invention provides a method for generating a self-supervised learning contract template based on a trade system platform, and aims to automatically generate and continuously optimize a personalized contract template by using a self-supervised learning technology and a Transform model. The method comprises the following steps: extracting unlabeled data through a historical contract data input module; utilizing a self-supervised learning module to identify the relevance of contract terms; modeling a long-term and short-term dependency relationship of the contract text through a multi-head self-attention mechanism of a Transform model; and generating a high-dimensional semantic embedding representation of the contract terms, and automatically generating a personalized contract template based on the high-dimensional semantic embedding representation. The system continuously optimizes a generation rule through historical data and real-time service feedback, and dynamically adjusts the priority and content sorting of contract terms. According to the method, the contract generation efficiency and accuracy are improved, the dependence on manual labeling is reduced, the method is suitable for the iron and steel industry and other large-scale contract management scenes, the contract template can be flexibly adjusted according to market changes and business requirements, and the individuation and adaptability of the contract are remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the fields of contract management, trade system platform and automatic generation, and belongs to the cross-technical field of natural language processing and contract management. Background Art

[0002] With the rapid development of my country's steel industry and the expansion of trade scale, enterprises are facing a large number of complex contract management needs in daily operations. Steel trade involves large-scale transactions, strict quality standards and complex delivery terms. Contract management has become a key link in the trade system. However, the existing contract generation and management methods mainly rely on traditional manual editing and pre-defined templates, which cannot meet the increasingly complex business needs and dynamically changing market environment.

[0003] In the steel industry, contract terms often involve complex elements such as multiple parties, price fluctuations, customized products, and transportation arrangements. The traditional contract template generation method is based on static rules and is difficult to adapt to frequently changing market conditions and personalized business needs. Especially when the contract terms are constantly changing, relying on manual annotation and template editing is not only inefficient, but also prone to errors or omissions of key terms, thereby increasing legal risks. In addition, steel trading companies usually face the problem of large-scale contract management, complex content, and diverse types, which makes the contract generation and review process cumbersome and time-consuming. With the increase in business volume, the template generation method in the existing system lacks intelligent processing capabilities, and it is difficult to quickly adjust the contract content to adapt to different transaction scenarios and contract entities. This manual-dependent model not only increases costs, but also seriously affects the company's response speed and market competitiveness.

[0004] The current steel industry and trade system platforms are facing many pain points such as inefficient contract management, lack of accuracy, and inability to quickly respond to personalized needs. There is an urgent need for a technical solution that can automatically and intelligently generate contract templates.

[0005] In the steel trading system platform, the complexity of contract management not only stems from the diversity and flexibility of the contract terms themselves, but is also closely related to the high interconnectivity of the trading system, data sharing requirements, and the need for cross-border transactions. Contract management in the steel industry is gradually showing a trend of being highly data-based and platform-based. The trading system platform needs to ensure the accuracy and compliance of the contract generation process while processing massive transaction data.

[0006] However, current contract management systems have several key problems in responding to these demands: Data silo problem: Contracts in the steel industry often involve multiple departments and external partners, and the data involved is scattered across different systems and databases. Existing contract management systems have difficulty achieving effective collaboration and integration between multiple data sources, resulting in inconsistent or delayed information during contract generation, affecting the timeliness and accuracy of contracts.

[0007] Complex dependencies of contract terms: Contract terms in the steel industry often have complex long-term and short-term dependencies, and the terms are highly correlated, but traditional contract management systems cannot fully identify and handle these dependencies. For example, price adjustment terms may need to depend on factors such as raw material price fluctuations and transportation costs, and these dependencies cannot be accurately reflected through simple template generation.

[0008] Personalization and compliance conflict: The laws, regulations, industry standards and business practices in different regions vary greatly, and the demand for personalized contract templates is also increasing. When generating personalized contracts, existing systems often find it difficult to meet compliance requirements at the same time, causing companies to face higher legal risks when handling cross-border transactions.

[0009] Lack of intelligent optimization and continuous learning capabilities: Most current contract management systems use predefined templates and lack intelligent optimization mechanisms. The system cannot automatically learn and optimize contract generation rules based on historical contract data, resulting in the generated contract templates being unable to timely reflect market changes or the latest developments in transaction terms. In addition, the lack of in-depth understanding of the correlation between contract terms affects the efficiency and accuracy of contract generation.

[0010] The existing steel industry contract management system is difficult to cope with the complexity, dynamics and personalized needs of contract terms. The traditional template generation method is inefficient and error-prone, and cannot fully utilize the existing contract history data to optimize the contract management process. These pain points have brought huge operational pressure to steel trading companies, and an intelligent, data-driven solution is urgently needed to improve contract management efficiency and reduce risks. Summary of the invention

[0011] The present invention aims to solve the problems of low efficiency, insufficient personalization, and insufficient handling of complex contract clause dependencies in existing contract management systems in the steel industry and trade system platforms, and proposes a contract template generation method based on self-supervised learning. The present invention realizes the automatic generation and continuous optimization of contract templates by utilizing the Transformer architecture and large-scale historical contract data of the steel industry.

[0012] The core technology of this method is to use self-supervised learning technology to extract effective information and the correlation between clauses from unlabeled historical contract data, without relying on manually labeled data, and automatically generate personalized contract templates that meet business needs. Self-supervised learning can automatically learn the long-term and short-term dependencies of contract clauses from massive historical data, ensuring that the generated contract template can accurately reflect the complex relationship between the various clauses of the contract. It is particularly suitable for complex contract scenarios in the steel industry with multiple parties involved and frequent dynamic adjustments.

[0013] One of the key innovations of this invention is the introduction of the Transformer model, which uses its powerful natural language processing capabilities to process long-term and short-term dependencies in the contract text. The Transformer model can capture the semantic relevance of the contract text when modeling contract terms, ensuring that the generated contract template can not only automatically fill in the terms, but also flexibly adjust the content of the terms according to different transaction scenarios and contract entities. This method ensures the accuracy and adaptability of contract generation in complex trade system platforms, and significantly improves the level of automation of contract management.

[0014] Another core innovation of the present invention is to combine self-supervised learning technology to automatically extract the implicit correlation between contract terms from unlabeled historical contract data, reducing the reliance on a large amount of manually labeled data. The historical contract data of the steel industry is rich and diverse, and traditional contract generation methods are difficult to effectively use these data to optimize the contract generation process. The present invention uses self-supervised learning technology to learn the correlation of terms and the potential rules of contract generation from these unlabeled contract data.

[0015] Self-supervised learning technology enables the present invention to effectively understand the complex contents of key clauses, restrictive clauses, agreed conditions, etc. in the contract, and automatically generate contract templates that meet business needs by semantically modeling the embedded representation of these clauses. In addition, through continuous learning with continuously updated historical data, the system can dynamically adjust the generation strategy, optimize the arrangement and content expression of contract clauses, and ensure the personalization and accuracy of the template.

[0016] On this basis, the present invention realizes the semantic embedding representation of contract terms, and vectorizes the contract terms through deep neural networks, further improving the accuracy and flexibility of generating contract templates. The embedded representation of contract terms can not only capture the surface semantics of the terms, but also identify the deep-level associations between the terms, which lays the foundation for the personalized generation of contract templates, enabling the system to generate more sophisticated contract templates that meet actual needs according to different business scenarios.

[0017] The present invention can not only automatically generate personalized contract templates, but also continuously improve the accuracy and adaptability of contract generation through continuous optimization mechanisms. The system is based on self-supervised learning of large-scale historical contract data, and iterative training is performed through continuously updated contract data, thereby dynamically adjusting the strategy for generating contracts to ensure that the generated contract templates can reflect the latest business needs and market changes.

[0018] In practical applications, the contract template generation method of the present invention can continuously optimize the content structure and arrangement order of contract clauses, especially when facing complex contract clauses in the steel industry, the system can automatically identify and optimize clauses related to business scenarios. For example, for complex content such as price fluctuation clauses, delivery time agreements, and transportation arrangements in the steel industry, the system can generate contract templates that comply with legal regulations and meet the personalized needs of customers based on the patterns and associations extracted from historical contracts.

[0019] In addition, the system can work in close coordination with business processes by integrating it into the steel industry's trade system platform. During the contract generation process, the system can automatically adjust the contract content based on the platform's real-time transaction data, inventory data, supply chain information, etc., to ensure that the generated contract matches the actual business situation. This integrated design enables the system to not only generate static contract templates, but also dynamically generate and update contracts in real time, improving the overall efficiency and accuracy of contract management.

[0020] In summary, the present invention solves the pain points in contract management in the steel industry by introducing self-supervised learning technology and Transformer architecture, combined with semantic embedding representation and dynamic optimization mechanism of contract terms, and realizes the automatic generation and continuous optimization of contract templates. The system can generate personalized contract templates according to actual business needs, reduce manual intervention, significantly improve the efficiency and accuracy of contract generation, and reduce potential legal risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Attached Figure 1 The contract template generation process based on self-supervised learning and Transformer model is demonstrated.

[0024] Attached Figure 2 Demonstrated the application of Transformer model in contract template generation.

[0025] Attached Figure 3 The process of generating semantic embedding representation of contract terms is demonstrated.

[0026] Attached Figure 4 Demonstrates the dynamic optimization process of contract template generation. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship and movement of various components under a certain posture (as shown in the drawings). If the specific posture changes, the directional indication will also change accordingly.

[0029] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. The technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0030] In a high-concurrency trading system platform, system performance and reliability are crucial factors. When faced with a large number of contract generation requirements, how to effectively process data, improve template generation speed, and ensure contract accuracy and customization has become a core challenge in system design. Traditional contract generation systems often have difficulty in coping with diverse contract requirements, have slow generation speeds, and are unable to dynamically respond to market changes, which not only affects the operational efficiency of the enterprise, but also limits the scalability of the system.

[0031] To this end, this paper proposes a method for automatically generating contract templates based on self-supervised learning and the Transformer model, which can extract information from large-scale unlabeled historical contract data, generate and optimize personalized contract templates that meet business needs. This method can continuously self-learn and optimize, ensuring that contract templates can be generated automatically, efficiently, and accurately in a complex contract generation environment.

[0032] Next, various embodiments of the present invention will be described in detail. These embodiments will be combined with the accompanying drawings to show the technical details from contract data input, self-supervised learning, embedding representation generation to template output, and fully demonstrate the technical advantages of the present invention and its application scenarios.

[0033] Example 1

[0034] Contract template generation method based on self-supervised learning: Combined with Figure 1 (S101-S105), this embodiment provides a contract template generation method based on self-supervised learning technology, which aims to automatically generate personalized contract templates that meet business needs through large-scale historical contract data of the steel industry.

[0035] Historical contract data input (S101): The system first imports historical contract data of the steel industry through the trade platform interface. This data contains a large amount of unlabeled historical contract texts, involving various types of contract terms (such as price terms, delivery time terms, quality assurance terms, etc.). These unlabeled contract data are the core data source of the system.

[0036] Data preprocessing: At the input stage, historical contract texts are preprocessed to remove noise data, such as repeated clauses, irrelevant metadata, etc. Contract clause texts are standardized through word segmentation and stop word removal to ensure data quality in the subsequent learning process.

[0037] Self-supervised learning module (S102): This module extracts effective information from historical contract data through self-supervised learning. Specifically, it adopts a self-supervised learning algorithm based on contrastive learning, which gradually identifies and learns the potential correlation between different clauses by performing comparative training on sub-parts of similar contract texts.

[0038] The specific formula is as follows:

[0039] in, and is an embedded representation of the contract terms, is the similarity function, is the temperature parameter. By minimizing this loss function, the model can gradually learn the semantic similarity and correlation between contract terms.

[0040] In this process, the unlabeled clauses in the contract are judged for relevance through self-supervised learning without the need for manual labeling of data, greatly reducing the reliance on labeled data.

[0041] Transformer model application (S103): The Transformer model is the core model in the entire contract generation process. After the text of the contract terms is processed by position encoding, it is input into the Transformer model, and the long-term and short-term dependencies of the contract text are modeled through the multi-head self-attention mechanism. The multi-head attention mechanism in the model can capture the relationship between different terms in the contract and its connection with the context.

[0042] Input text for each contract clause After the Transformer model, the semantic representation of the terms is generated :

[0043] Through the parallel attention mechanism, Transformer can efficiently process a large number of contract clauses, ensuring that the semantic information and relevance of each clause are accurately captured, thus providing a basis for subsequent generation tasks.

[0044] Term embedding representation generation (S104): After the contract text is processed by Transformer, it will be further embedded into the contract terms through a deep neural network. The embedded representation of the terms not only contains the surface text information of the terms, but also combines the contextual dependencies of the contract text and the complex relationships between the terms.

[0045] The embedded representation uses the BERT model in deep learning to vectorize the contract terms. The generated term vector It expresses the semantic meaning and relevance of the terms, and the formula is as follows:

[0046] Contract template generation and optimization (S105): The system generates personalized contract templates that meet business needs based on the generated contract terms embedding representation and the results of the self-supervised learning model. The contract template generation process combines industry standardized terms and customer-specific terms, and generates templates that fit actual business scenarios by combining different terms.

[0047] Template optimization: The system continuously optimizes the generated contract templates through historical data feedback, gradually adjusts the weights and arrangement order of different clauses, and ensures that the generated contract templates meet the dynamic needs of the current market.

[0048] In this embodiment, the system's template generation efficiency is significantly improved, and the contract template generation time is shortened from several hours to several minutes. In addition, due to the introduction of self-supervised learning, the accuracy of template generation is also significantly improved, and the compliance of the generated contract terms is more than 15% higher than that of traditional methods.

[0049] Example 2

[0050] Application of Transformer architecture in contract template generation: Combined with Figure 2 (S201-S206), this embodiment describes in detail how the Transformer model is applied to various links in contract template generation, especially capturing the complex dependencies and contextual relevance between contract terms through the multi-head self-attention mechanism.

[0051] Contract terms input layer (S201): The system imports various contract terms from the historical contract database or trading platform. These terms involve price, delivery conditions, payment terms, etc. The input data is presented in the form of structured contract text.

[0052] In order to improve the model's comprehension ability, the system pre-processes the contract text and uses natural language processing (NLP) technology to perform word segmentation, remove stop words, and perform part-of-speech tagging, making the contract terms more standardized for subsequent model processing.

[0053] Position encoding layer (S202): Since the Transformer model does not have natural sequence processing capabilities, this paper uses positional encoding to introduce the order information of contract clauses. Through positional encoding, each clause contains not only its textual semantic information, but also its relative position in the contract.

[0054] The mathematical formula for position encoding is:

[0055] in, is the location information of the clause, is the dimension index, is the dimension size of the model. The position encoding vector is added to the term embedding vector and then input into the Transformer, so that the model can capture the sequential dependencies of the terms.

[0056] Multi-head self-attention mechanism (S203): The multi-head self-attention mechanism is the core component of the Transformer model, which can calculate the interrelationships between contract terms in parallel through multiple attention heads. Each attention head models the dependencies between terms through different semantic dimensions.

[0057] For each term embedding, we compute the query, key, and value, and based on these we compute the similarity between terms:

[0058] in, , , Represents query, key and value respectively, is the dimension size of the key. Through parallel processing of multiple attention heads, the system can capture the long-term and short-term dependencies between different clauses in multiple dimensions, ensuring that the generated contract template not only conforms to the global semantic logic, but also can handle the dependencies between local clauses.

[0059] The outputs of multiple attention heads are aggregated into a single representation vector through a concatenation operation to capture the full context of the contract terms.

[0060] The terms are embedded in the presentation layer (S204): After the contract terms are processed by the multi-head self-attention mechanism, their embedded representations (ClauseEmbedding) are generated. These embedded representations not only contain the semantic information of the terms, but also carry the association information with other terms.

[0061] At this point, the embedding representation of each clause can be regarded as a high-dimensional vector, indicating its position and role in the contract text. For example, the payment terms of a clause may depend on the terms of the delivery date, and its embedding representation captures these dependencies through the self-attention mechanism, so that the generated contract template can be more consistent with business logic.

[0062] Clause generation layer (S205): The clause generation layer generates personalized contract clauses based on the clause embedding representation and the customer's specific business needs. This process uses the semantic embedding representation of contract clauses to dynamically combine and generate contract templates that meet specific scenarios.

[0063] The specific generation strategy is to adjust the order, priority and content of contract terms based on the input historical contract data and real-time business needs. For example, for a specific customer order, if market conditions change, the system can adjust the terms content based on the latest embedded representation to ensure that the contract terms accurately reflect the current market conditions.

[0064] Output contract template (S206): Finally, the generated personalized contract template is output from the clause generation layer, and the system passes the template to the business platform for subsequent processing. The generated contract template is highly dynamic and personalized, and can automatically adapt to different transaction scenarios and customer needs.

[0065] The output contract template is not only a combination of standardized contract clauses, but also fine-tunes the content of the clauses based on transaction history and the latest embedded representation. This contract template can be directly used for automated contract signing in the trading system or further manual adjustment.

[0066] Through the multi-head self-attention mechanism, the accuracy and consistency of the system in clause generation have been significantly improved, and the matching degree between clause generation and actual contract business needs has been improved by more than 20%. At the same time, the generation time has been reduced by 30% compared with traditional methods, enhancing the real-time response capability of contract generation.

[0067] Example 3

[0068] Generation and optimization of semantic embedding representation of contract terms: Combined with Figure 3 (S301-S304), this embodiment describes in detail how to generate semantic embedding representations of contract terms through deep neural networks, and use these embedding representations to improve the accuracy and adaptability of contract template generation.

[0069] Contract terms input (S301): This module receives input contract clause texts from historical contract data, which may include standard clauses and personalized clauses for the steel industry. The input data may not have a uniform structure, so the system will standardize each clause to ensure the accuracy of semantic analysis and subsequent modeling.

[0070] Data preprocessing includes the following steps: standardizing the format of contract terms (such as paragraph segmentation, adding structured tags), removing redundant information, processing special characters, and normalizing common professional terms in contracts. This step helps improve the model's understanding of the terms and modeling effect.

[0071] Semantic analysis layer (S302): The system uses natural language processing (NLP) technology to perform semantic analysis on the input contract terms. This layer maps each word or phrase in the contract terms to its corresponding semantic category through technologies such as word segmentation, part-of-speech tagging, and dependency syntax analysis.

[0072] The main goal of semantic analysis is to extract the contract elements implied in the clauses. For example, in the delivery time clause, the system can identify key elements such as "delivery date", "delay compensation", "acceptable extension", etc., and mark them as important contract conditions. These elements will be used for subsequent contract clause generation and template optimization.

[0073] Deep neural network layer (S303): Based on semantic analysis, the system further processes the text of the contract terms through a deep neural network (DNN) to generate a high-dimensional semantic embedding representation of the terms. The multi-layer structure of the deep neural network can effectively capture the complex semantic information in the contract terms and generate a compact embedding representation.

[0074] This embedding representation is not just the surface text information, it also contains the contextual semantics of the clause and its potential correlation with other clauses. For example, the dependency between the amount clause and the delivery clause in the payment terms clause will be captured and reflected through the embedding representation. This semantic embedding representation can improve the accuracy of clause combination in the contract template generation process.

[0075] The embedded representation also allows the system to perform more intelligent clause retrieval and matching. When generating a new contract template, the system can quickly retrieve semantically similar clauses from historical contracts and generate contract content that adapts to current business needs.

[0076] The term embedding representation output (S304): Finally, after the contract terms are processed by a deep neural network, the generated embedding representations serve as the semantic vectors of the contract terms. These embedding representations not only capture the content of the terms, but also incorporate the contextual relevance of the terms, providing high-dimensional semantic support for the automatic generation of contract templates.

[0077] The generated term embedding representation can be used in the following two application scenarios: Dynamic generation of contract templates: By combining and optimizing the semantically embedded representations of contract terms, the system can generate personalized contract templates that meet business needs. Especially in multi-party contracts, the system can automatically adjust the order and priority of the terms to ensure that the contract terms meet the business needs of all parties.

[0078] Semantic search and retrieval of contract terms: The system can also perform intelligent search and term matching based on the embedded representation of terms. For example, when a user needs to generate a specific type of contract, the system can retrieve semantically similar terms from historical contracts and generate a new contract template after personalized adjustments.

[0079] After generating embedded representations of contract terms through deep neural networks, the system has significantly improved the accuracy and adaptability of contract generation. In particular, in multi-clause contracts, the correlation between different clauses is effectively modeled and applied, making the personalized generation of contracts more intelligent and efficient.

[0080] Experimental data shows that the accuracy of contract templates generated by this semantic embedding model has been improved by more than 15%, while the generation time has been reduced by about 20%. This enables the system to quickly respond to large-scale contract generation needs, especially in a highly complex trading environment such as the steel industry.

[0081] Example 4

[0082] Dynamic optimization and continuous learning of contract template generation: Combined with Figure 4 (S401-S406), this embodiment introduces how to continuously improve the generation process of contract templates through dynamic optimization and continuous learning mechanisms to ensure that the system can generate high-quality personalized contract templates in an ever-changing business environment.

[0083] Initial generation of contract template (S401): The initial contract template is generated based on historical contract data and self-supervised learning models. The system generates the first version of the contract template based on the input transaction conditions, customer needs and standardized contract terms. This template can meet conventional business needs, but in order to adapt to personalized needs and the ever-changing market environment, the system needs to optimize the generated contract template.

[0084] The generation of the initial contract template is based on the embedded representation of contract terms and historical contract templates in the system. The system generates a basic version of the contract by combining and sorting the terms. This initial contract template will serve as the basis for subsequent optimization.

[0085] Historical data feedback (S402): The system extracts relevant data from historical contracts through a feedback mechanism and continuously optimizes the generation process of contract templates. The system analyzes the execution results of previous contracts, the frequency of use of clauses, contract performance and other information to determine which clauses have shown high applicability and stability in actual business.

[0086] The system will automatically analyze which contract terms frequently appear in historical contracts, which terms are highly correlated, or which terms cause disputes or need to be modified during the performance of the contract. Based on these historical data feedback, the system can identify which terms need to be optimized or updated to better meet current business needs.

[0087] Real-time data feedback (S403): In addition to historical data, the system can also obtain feedback from the real-time business data of the trading platform to further optimize the contract generation strategy. Real-time data may include the latest market information, raw material price fluctuations, dynamic customer needs, supply chain conditions, etc., which can affect the content and priority of contract terms.

[0088] The system dynamically adjusts contract terms based on real-time data. For example, in the steel industry, the system can automatically adjust price terms based on real-time steel price fluctuations, or adjust delivery time terms based on transportation delays in the supply chain. Real-time data feedback ensures that the generated contract can respond to market changes in a timely manner and avoid contract disputes caused by market fluctuations.

[0089] Model update and optimization (S404): Based on the feedback of historical and real-time data, the system automatically updates the contract generation model and continuously optimizes the generation strategy of contract templates. This process includes retraining the self-supervised learning model, updating the clause embedding representation, and optimizing the generation rules.

[0090] In the specific optimization process, the system uses the newly added contract data as a training set and regularly iterates and updates the model. By continuously learning new contract terms, the correlation between terms, and market changes, the system can keep the model up to date and ensure that the generated contract templates can reflect the current market needs and the latest requirements of laws and regulations.

[0091] After the model is optimized, the system will automatically apply new generation rules to improve the accuracy, rationality and flexibility of contract template generation. This continuous optimization mechanism enables the system to maintain efficient operation as the business develops and the market environment changes.

[0092] Dynamically generate rule adjustment (S405): Based on the model update, the system dynamically adjusts the contract generation rules. The process of adjusting the rules is based on the latest embedded representation, historical and real-time data feedback, and customer personalized needs. The rule adjustment includes the priority arrangement of clauses, the dynamic update of clause content, and the adjustment of the overall structure of the contract.

[0093] For example, the system may adjust the length and complexity of contract terms based on real-time transaction frequency, or adjust payment terms based on the credit status of a specific customer. This rule adjustment is dynamic, and the system will update the generated rules at any time based on the latest data to ensure that the generated contract template always meets current business requirements.

[0094] Optimized contract template generation (S406): Finally, the contract template generated by the system can better match business needs after multiple feedback and optimization of historical data and real-time data. The optimized contract template not only has a standardized clause structure, but also combines personalized needs, reflecting a high degree of adaptability to the business environment.

[0095] The optimized contract templates will be pushed to the trading platform for users to use directly or make further manual adjustments. These contract templates can significantly improve the operational efficiency of enterprises, reduce the workload of manual contract modification, and reduce the risk of contract disputes.

[0096] Through dynamic optimization and continuous learning mechanisms, the accuracy of contract templates generated by the system has increased by more than 20%, especially in complex transaction scenarios, the personalized matching degree of template generation has increased by 30%. In addition, the speed of contract generation has been further optimized, which can respond to a large number of contract generation needs in a relatively short time.

[0097] Example 5

[0098] In order to further improve the technical implementation scheme of each module of the present invention and keep consistent with the contents in the specification and claims, the specific implementation and technical details of the historical contract data input module (S101), the Transformer model module (S103), the clause embedding representation generation module (S104) and the contract template generation and optimization module (S105) are described in detail. Based on the above technical scheme, this embodiment deeply explores the interactive relationship between these modules and their role in the entire contract generation process, showing the integrity and dynamic adaptability of the system.

[0099] In the entire contract generation system, the historical contract data input module (S101) is the starting point for contract template generation. It not only provides the basic data required for the initial learning of the system, but also lays a foundation for the quality assurance of the processing of subsequent modules. Through a customized data interface, the system can extract historical contract texts from multiple data sources, including but not limited to scanned copies in PDF format, structured database records, and semi-structured XML files. In order to ensure the availability of data, the module performs a series of preprocessing operations after data import, including the cleaning of redundant data, automatic segmentation of contract terms, and standardization of contract glossary. In particular, for industry terms in the content of the terms, the system introduces a synonym matching mechanism based on a word vector model, so that the preprocessed data can retain the industry characteristics of the original contract and have universality. Furthermore, the module uses a pre-trained language model to semantically annotate the input contract, automatically distinguish between main terms and additional terms, and provide high-quality input for subsequent term relevance modeling.

[0100] After the preprocessed data flows into the Transformer model module (S103), the system models the complex correlations and contextual dependencies between contract terms through this module. The design of the Transformer model fully considers the long-term and short-term dependency characteristics of contract terms. These dependencies are particularly critical in large-scale contracts in the steel industry. For example, price adjustment terms may depend on changes in delivery terms and transportation terms. To this end, the Transformer model processes the interactive information between multiple terms in parallel through a multi-head self-attention mechanism. At the model input layer, the text content of the terms is weighted by word embedding and positional encoding. The latter introduces positional information to the order of the terms, thereby retaining the orderliness and logic of the contract text during the model processing. The output of the model expresses the dependency strength between contract terms in the form of a specific attention weight matrix. These weights directly affect the priority ranking of the terms in subsequent generation. It is worth mentioning that the decoding part of the Transformer model ensures that both global semantics and local details are retained in the model by gradually generating predictions for the contract terms.

[0101] Based on the weight matrix and clause features generated by the Transformer model, the contract clauses enter the clause embedding representation generation module (S104), which vectorizes the contract clauses through a deep neural network (Deep Neural Network, DNN) to generate a high-dimensional semantic representation (Clause Embedding). Unlike traditional methods, the present invention introduces a contextual awareness mechanism (Contextual Awareness Mechanism) so that the embedding representation of each clause can not only reflect its own semantic information, but also capture its potential relevance to other clauses. For example, the embedding representation will automatically extract the time dependency between the adjustment formula in the price clause and the transportation clause, providing more accurate input for contract template generation. The embedding generation process is completed in a hierarchical manner in the neural network, and each layer of the network extracts the grammatical features, semantic features and logical relevance of the clauses respectively. This hierarchical design ensures the stability and robustness of the embedding representation. In practical applications, the module can also dynamically adjust the dimension size of the embedding vector to adapt to contract types of different complexities.

[0102] After completing the generation of embedded clauses, the data flows into the contract template generation and optimization module (S105), which realizes the transformation process from semantic representation to final contract template. The module first converts the embedded representation into specific contract clauses through a preset generation strategy. The core of the generation strategy is to comprehensively utilize the successful model of historical contracts and current real-time business needs. For example, when market prices fluctuate violently, the system will give priority to generating contract templates with floating price clauses. During the generation process, the module also combines the data of the user preference database to ensure that the template content meets the personalized needs of different customers. After the contract generation is completed, the module performs multiple rounds of optimization on the generated template, and its optimization mechanism is based on historical data feedback and real-time transaction information. For example, the module will automatically adjust the priority of the delivery clause according to the fulfillment record of the historical contract, or dynamically modify the content of the transportation clause according to the real-time inventory status. Through this dynamic optimization, the module can generate highly adaptable contract templates, significantly reducing the workload of manual modification by users.

[0103] Each module in this embodiment not only independently completes its own function, but also realizes overall collaborative optimization through dynamic interaction of data streams. For example, the output of the historical contract data input module directly determines the training efficiency of the Transformer model module, and the feedback data of the contract template generation and optimization module will react to the embedded representation generation module to form a complete closed-loop optimization process. The design of the entire system fully reflects the integration of modularization and intelligence, so that the contract template generation method of the present invention can show extremely high efficiency and adaptability in a complex business environment.

[0104] (1) Self-supervised learning: Self-supervised learning is a machine learning method that can automatically extract useful information from unlabeled data for model generation. It reduces the reliance on manually labeled data by generating pseudo-labels or targets. The present invention uses self-supervised learning to extract the implicit correlation between contract terms from historical contract data, thereby realizing the automatic generation and continuous optimization of contract templates.

[0105] (2) Transformer model: Transformer is a deep learning model originally used for natural language processing tasks. It can process long-term and short-term dependencies in input sequences in parallel through a self-attention mechanism. In this invention, the Transformer model is used to process the long-term and short-term dependencies of the contract text to ensure that the generated contract template can accurately capture the complex correlations between the clauses.

[0106] (3) Multi-Head Self-Attention Mechanism: The multi-head self-attention mechanism is a core component of the Transformer model. It captures the correlation between different parts of the input data by calculating multiple sets of attention weights simultaneously. Each attention head models the contract terms in different semantic dimensions, thereby improving the model's ability to understand complex dependencies.

[0107] (4) Contract Clause Embedding: Contract clause embedding is a high-dimensional vector representation generated by processing contract clauses through a deep neural network. These embeddings contain not only the surface text information of the contract clauses, but also the semantic meaning of the clauses, contextual information, and the relevance between clauses. The present invention uses the embedded representation of contract clauses to ensure that clauses can be appropriately combined according to their business semantics during the template generation process.

[0108] (5) Contract Template Generation: Contract Template Generation refers to the process of automatically combining historical contract data, real-time business requirements, and embedded representations to generate personalized contract templates. This generation process is based on a machine learning model that combines standard contract terms with personalized requirements, so that the generated contract templates comply with industry standards and meet the specific needs of customers.

[0109] (6) Historical Data Feedback: Historical data feedback is the process by which the system analyzes historical contract data, extracts information such as the frequency of use of contract terms and execution results, and uses this feedback information to optimize the generation of contract templates. Through historical data feedback, the system can continuously update the terms generation rules to ensure that the generated contract templates meet market demand and the best practices of contract terms.

[0110] (7) Real-Time Data Feedback: Real-Time Data Feedback means that the system adjusts the content and priority of contract terms in real time by analyzing the real-time business data of the trading system platform (such as market fluctuations, changes in customer demand, raw material prices, etc.). This feedback mechanism ensures that the generated contract template can quickly respond to the dynamically changing market environment.

[0111] (8) Deep Neural Network (DNN): A deep neural network is a machine learning model consisting of multiple layers of neurons that is good at processing nonlinear and high-dimensional data. In the present invention, a deep neural network is used to generate a semantic embedding representation of contract terms, capturing the complex semantics and associated information of contract terms, thereby providing strong semantic support for contract template generation.

[0112] (9) Positional Encoding: Positional encoding is a mechanism that introduces position information into the Transformer model to solve the problem that the Transformer cannot directly process sequence information. By adding position information to each clause, the model can capture the sequential dependencies of clauses in the contract text, thereby generating a template that conforms to the contract logic.

[0113] (10) Contract Template Dynamic Optimization: Contract Template Dynamic Optimization means that the system continuously adjusts the rules and strategies for generating contract templates based on feedback from historical data and real-time data. Through dynamic optimization, the system can improve the accuracy, personalization, and real-time adaptability of contract templates to meet rapidly changing market and business needs.

[0114] (11) Clause Retrieval and Matching: Clause retrieval and matching refers to the system automatically searching and matching relevant clauses from historical contract data based on the semantic embedding representation of the clauses. This mechanism ensures that when generating a contract template, clauses that meet current business needs can be found quickly, reducing manual intervention.

[0115] (12) Continuous Learning: Continuous learning means that the system continuously learns from new contract data and optimizes the model to adapt to changes in the market, laws, and business needs. Through continuous learning, the system can gradually improve the accuracy and efficiency of clauses during the contract generation process.

[0116] (13) Long- and Short-Term Dependencies of Contract Clauses: Long- and Short-Term Dependencies of Contract Clauses refer to the correlation between different clauses in a contract, including both short-term contextual dependencies (such as the relationship between adjacent clauses) and long-term dependencies across multiple clauses (such as the correlation between payment clauses and delivery clauses). The present invention models these dependencies through the Transformer model to ensure that the generated contract template can accurately reflect the actual correlation between the various clauses in the contract.

[0117] The explanation of these technical terms helps to fully understand the technical implementation and innovation of the present invention, and ensures that in different implementation scenarios, the system can automatically, efficiently and intelligently generate contract templates that meet business needs.

Claims

1. A method for generating a contract template based on self-supervised learning in a trade system platform, characterized in that: The following steps are involved: (1) Importing unlabeled historical contract data through the historical contract data input module (S101) and preprocessing the data, including word segmentation, removal of stop words, and format standardization; (2) Using the self-supervised learning module (S102), extracting implicit correlations between contract terms; (3) Through the Transformer model module (S103), the contract text is processed using the multi-head self-attention mechanism and position encoding to model the long-term and short-term dependencies between its clauses; (4) Generate a high-dimensional semantic embedding representation of the contract terms through a terms embedding representation generation module (S104); (5) Through the contract template generation and optimization module (S105), a personalized contract template is generated according to the embedded representation, and the generation rules and template content are continuously optimized based on historical data and real-time feedback.

2. The generation method according to claim 1, characterized in that: The historical contract data input module (S101) pre-processes the contract text data, including deleting duplicate data, segmenting words, removing noise and formatting.

3. The generation method according to claim 1, characterized in that: The self-supervised learning module (S102) extracts semantic relevance from unlabeled contract data through a contrastive learning method to generate a preliminary semantic representation of the terms.

4. The generation method according to claim 1, characterized in that: The Transformer model module (S103) captures the contextual semantic relationship of the contract terms based on a multi-head self-attention mechanism, and introduces the sequential information of the terms through position encoding.

5. The generation method according to claim 1, characterized in that: The clause embedding representation generation module (S104) generates a semantic embedding representation of the contract clauses through a deep neural network, and the representation content includes the semantic information of the clauses and their contextual associations.

6. The generation method according to claim 1, characterized in that: The contract template generation and optimization module (S105) dynamically adjusts clause priorities, arrangement order and content generation rules based on the embedded representation of contract clauses and real-time business data feedback to achieve template optimization.

7. A contract template generation system, characterized in that: include: (1) Historical contract data input module, used to import unlabeled contract data and perform preprocessing; (2) A self-supervised learning module to extract implicit semantic associations between contract terms; (3) Transformer model module, which is used to model the long-term and short-term dependencies of clauses in the contract text based on the multi-head self-attention mechanism; (4) A term embedding representation generation module, which is used to generate high-dimensional semantic embedding representations of terms; (5) Contract template generation and optimization module, which is used to generate personalized contract templates based on the embedded representation of clauses and realize dynamic optimization.

8. The system according to claim 7, characterized in that The contract template generation and optimization module dynamically adjusts the generation rules and clause content sorting through feedback from historical contract performance data and real-time business data to meet market changes and business needs.

9. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores a computer program, and when the program is executed by the processor, the generation method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The medium stores a computer program, and when the program is executed by a processor, the generation method according to any one of claims 1 to 6 is implemented.

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