Intelligent law data processing method and system based on big data

Abstract

The invention discloses an intelligent legal data processing method and system based on big data, and the method comprises the steps: S1, constructing a legal data collection framework, and carrying out the unified coding and representation of different data forms through a multi-modal fusion technology; s2, performing hierarchical semantic analysis on the cross-domain legal clauses to generate a high-dimensional semantic representation vector with context sensing capability; s3, performing node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network; s4, mapping and optimization among terms are realized through semantic comparison and multi-level rule verification; s5, performing modeling analysis on the dynamic trend and the potential risk factors of the legal data, and outputting a risk prediction and early warning strategy of the legal event; and S6, improving cross-data-source cooperative computing capability and data analysis efficiency through a distributed optimization strategy. The method has the advantages of high semantic understanding depth, high risk prediction accuracy and high privacy protection and cooperative computing efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a smart legal data processing method and system based on big data. Background Art

[0002] With the continuous development of legal informatization, intelligent legal data processing has become an important technology to support legal services and decision-making. However, current legal data processing methods face huge challenges, especially in terms of data size, complexity and dynamics. Legal data covers multiple forms, including structured, semi-structured and unstructured, and involves a complex knowledge system that is multimodal, multi-domain and cross-domain, which puts higher requirements on existing technologies.

[0003] In the existing technology, traditional legal data processing methods mostly rely on rule engines, simple data parsing models or static semantic association methods. However, these methods show obvious limitations when facing the scale, dynamism and complexity of legal data. Specifically, traditional technologies have defects in the following aspects:

[0004] 1. Insufficient depth of semantic understanding: Traditional methods are usually based on keyword matching or shallow semantic analysis, which makes it difficult to accurately capture the deep semantic relationship between cross-domain legal clauses, resulting in insufficient accuracy and reliability of clause association mining.

[0005] 2. Difficulty in integrating heterogeneous data: Existing methods lack a unified fusion mechanism when processing structured, semi-structured and unstructured legal data, making it difficult to effectively integrate multimodal data and generate highly consistent data representations.

[0006] 3. Limited intelligent prediction capabilities: Traditional methods mostly rely on rule-driven prediction models, and lack the ability to model and analyze the dynamic changes of complex time series and potential legal risks, making it difficult to achieve effective prediction and early warning of future events.

[0007] 4. Inefficient collaborative computing: In distributed legal data processing, traditional methods lack privacy protection and cross-source collaborative optimization capabilities, resulting in inefficient data integration and joint analysis, and difficulty in meeting real-time and privacy security requirements.

[0008] 5. Insufficient dynamic adaptation: Traditional rule generation and optimization methods are usually based on static logic, which makes it difficult to dynamically adapt the semantic associations and rule updates between cross-domain legal clauses, restricting the ability to intelligently process legal data.

[0009] Therefore, how to provide an intelligent legal data processing method and system based on big data is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a smart legal data processing method and system based on big data. The present invention describes in detail the implementation method of intelligent processing and collaborative analysis of cross-domain legal data through dynamic semantic understanding and deep semantic reasoning, multi-objective evolutionary algorithm, legal knowledge mining based on graph neural network, time series modeling and federated learning and other technologies. It has the advantages of high depth of semantic understanding, strong data integration capability, high risk prediction accuracy, high privacy protection and high collaborative computing efficiency.

[0011] A smart legal data processing method based on big data according to an embodiment of the present invention comprises the following steps:

[0012] S1. Build a legal data collection framework to extract structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and use multimodal fusion technology to uniformly encode and represent different data forms;

[0013] S2. Using a multi-task learning framework that combines dynamic semantic modeling and deep semantic reasoning, we perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities.

[0014] S3, a legal knowledge association mining algorithm based on graph neural network, performs node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network;

[0015] S4. Based on the multi-objective evolutionary algorithm and dynamic weight adjustment mechanism, a set of cross-domain adaptive legal rules is automatically generated, and mapping and optimization between clauses are achieved through semantic comparison and multi-level rule verification;

[0016] S5. Use an intelligent algorithm that combines time series feature modeling with reinforcement learning to model and analyze the dynamic trends and potential risk factors of legal data, and output risk prediction and early warning strategies for legal events;

[0017] S6. Utilize distributed collaborative analysis technology based on federated learning, and on the basis of legal data privacy protection, improve the collaborative computing capabilities and data analysis efficiency across data sources through distributed optimization strategies.

[0018] Optionally, the S1 specifically includes:

[0019] S11. Through the multi-source heterogeneous legal data collection system, a real-time data interface is built based on a distributed architecture, and the judicial case library, legislative data source, and image and audio and video legal data streams are accessed through the network crawling module and the authorized data connection module to build a dynamic collection framework that supports multi-modal input;

[0020] S12. Use a feature alignment algorithm based on multi-scale semantic mapping to dynamically adjust the weights of field labels and classification dimensions in structured data to generate a cross-domain compatible multidimensional feature matrix A. ij :

[0021] A ij =α i ·D j +β ij ;

[0022] Among them, α i is the weight coefficient of the specific field, D j is the original data dimension, β ij is the bias parameter;

[0023] S13. Combine recursive parsing and regularization models to perform domain-specific semantic extraction on semi-structured data, use syntax tree matching technology to extract key tags and hierarchical relationships, and generate context-related multi-layer nested structures T k =(L k ,P k ), where L k is the label set, P k is the hierarchical relationship mapping matrix;

[0024] S14. Based on the self-supervised semantic parsing network, the terms, contextual relationships and complex sentences in unstructured data are decomposed step by step, and cross-modal embedding is generated through semantic comparison and aggregation strategies. Among them, M q and N q They are the content vector and semantic vector of unstructured data respectively;

[0025] S15, adopt the modality unified expression model based on adaptive fusion, perform efficient feature fusion on different data modalities, and apply the feature embedding function F k (v) represents the unified expression of modal data:

[0026] F k (v) = δ(U k Φ(v)+V k );

[0027] Among them, v is the input feature, Φ(v) is the feature extraction function, U k and V k are the modal weight matrix and bias matrix respectively, and δ is the fusion activation function;

[0028] S16. Construct a multimodal legal data storage and retrieval mechanism based on a multi-layer index tree, use a dynamic balancing algorithm to optimize the data retrieval path, and support the rapid storage and query of legal data through real-time update strategies.

[0029] Optionally, the S2 specifically includes:

[0030] S21. Use dynamic semantic modeling units to divide cross-domain legal clauses into independent semantic segments and generate semantic dependency graphs G through semantic dependency analyzers. r =(V r ,E r ,W r ), where V r is the set of fragment nodes, E r is the set of dependency edges, W r is a weight matrix, used to represent the strength of semantic association;

[0031] S22, build a multi-layer recursive semantic reasoning network to semantic dependency graph G r Perform multi-step reasoning to generate a recursive embedding vector U r =f r (G r ,Θ r ), where f r is the recursive inference function, Θ r is the network parameter, U r Embedding representation of multi-dimensional features for semantic fragments;

[0032] S23, through the multi-task semantic optimization model, the recursive embedding vector U r Decompose into multiple task-specific sub-vector sets Y r ={y 1 ,y 2 ,…,y m}, where y i Represents the embedded feature vector of task i, combined with the task optimization function Ψ(y i ,T i ) Dynamically adjust each sub-vector;

[0033] S24, based on the cross-domain context perception module, calculate the contextual relevance between the semantic fragment and the task background, and generate a high-dimensional contextual semantic representation V through a weighted fusion model s :

[0034]

[0035] where β i is the context weighting coefficient, Γ is the context fusion function, C r is the cross-domain context matrix;

[0036] S25, through the hierarchical semantic generation mechanism, the high-dimensional context semantic representation V s Converted into a multi-level semantic vector set Z r,k :

[0037] Z r,k =σ(W k ·V s +∈ k );

[0038] Where W k is the layer weight matrix, ∈ k is the bias vector, σ is the nonlinear activation function;

[0039] S26, using the semantic fusion storage unit, the generated multi-level semantic vector set Z r,k Stored in a semantic knowledge base and jointly updated with dynamic association rules of cross-domain legal terms.

[0040] Optionally, the S3 specifically includes:

[0041] S31. Through the enhanced legal knowledge extraction module, multi-dimensional entities and deep semantic relationships are extracted from legal data to generate a weighted legal knowledge triple set Q = {(n i ,r ij ,n j ,p ij )}, where n i and n j Represents an entity node, r ij Indicates the relationship type, p ij represents the association weight;

[0042] S32. Constructing a dynamic weighted knowledge graph G based on a set of knowledge triples q =(N q ,L q ,P q ), where N q is the node set, L q is the edge set, P q is a weight matrix, which represents the multi-dimensional semantic relationship between nodes;

[0043] S33, using hierarchical graph embedding technology, embed the nodes and edges in the graph through a multi-level recursive mechanism to generate node embedding vectors and edge embedding vector

[0044]

[0045] Where ζ and ξ are the embedding activation functions of nodes and edges respectively, ψ is the embedding fusion function, and are the state matrices of nodes and edges respectively, M(n) is the neighbor set of node n, and is the bias term;

[0046] S34, using the association analysis module, through the dynamic relationship optimization function Ω(n i ,n j ,r)Compute node n i and n j The weight of the reinforcement relationship between:

[0047]

[0048] in represents the weight parameter of the relationship type, Γ is the node similarity calculation function, and λ r Strengthen bias parameters for relationships;

[0049] S35. Through the optimization module based on weight reinforcement and relational reasoning, the redundant relations in the knowledge graph are eliminated, and new high-confidence relation edges are generated through the self-supervision mechanism. The optimized knowledge graph is represented as G′ r =(N′ r ,L′ r ,P′ r );

[0050] S36. Combine the cross-domain association generation module to jointly analyze the optimized knowledge graph and multi-domain legal data to generate a cross-domain legal association matrix C t :

[0051]

[0052] Among them, W t is the cross-domain fusion weight matrix, is the Hadamard product of node and edge embedding, θ t is the bias vector, ν is the cross-domain activation function, and a multi-domain association network is dynamically generated.

[0053] Optionally, the S4 specifically includes:

[0054] S41, construct a multi-objective rule adaptation optimization model, set the optimization target set H = {h 1 (z),h 2 (z),…,h m (z)}, where z is the rule variable, h i (z) is the i-th optimization objective function, which optimizes the adaptability, semantic consistency and complexity of the rules at the same time;

[0055] S42, using the optimization algorithm based on multi-objective evolution strategy, generating the rule candidate set Z = {z 1 ,z 2 ,…,z n}, using the fitness function Select, crossover and mutate the candidate rules, where μ i Represents the weight of the objective function, and finally generates the optimal solution set;

[0056] S43. Design a dynamic weight adjustment module to calculate the dynamic changes of target weights in real time:

[0057]

[0058] in represents the weight value of the i-th target in the t+1 generation, κ is the learning rate, Represents the gradient of the optimization objective;

[0059] S44, through the rule adaptation generation module, extract the rules with the highest adaptability from the optimization solution set to generate the cross-domain legal rule set R f = {r 1 ,r 2 ,…,r p}, and merge the rules through the adaptive rule fusion algorithm;

[0060] S45. Based on the multi-dimensional semantic comparison mechanism, a rule semantic embedding model is constructed to calculate the semantic correlation between rules:

[0061] Φ(r i ,r j )=γ· <E(r i ),E(r j )>+∈;

[0062] Where γ is the contrast weight, E(r i ) and E(r j ) represent the rules r i and r j The embedding vector of , ∈ is the bias term;

[0063] S46, through the multi-level rule verification module, the rule set is verified layer by layer to establish the verification matrix M v , matrix element m ij Representation rule r i and r j The validation value:

[0064] m ij =σ(Φ(r i ,r j ))·α ij ;

[0065] Where σ is a nonlinear activation function, α ij is the verification coefficient;

[0066] S47. Use the dynamic rule optimization module to recursively optimize the verified rule set, and generate the final optimized cross-domain adaptation rule set R′ through weight update and relationship reinforcement. f , supporting semantic mapping and dynamic optimization between clauses.

[0067] Optionally, the S5 specifically includes:

[0068] S51. Use the data preprocessing module to time-series the legal data and construct a multidimensional time series data set in represents the jth characteristic variable corresponding to time t;

[0069] S52. Based on time series feature modeling technology, the long short-term memory network model is used to extract features from time series data and generate a time embedding representation Z. t :

[0070] Z t =φ(A z ·Z t-1 +B z ·y t +c z );

[0071] Among them A z and B z are the state weight matrix and the input weight matrix respectively, c z is the bias term, φ is the activation function;

[0072] S53. Define the state variable V through the reinforcement learning framework t , action set U t , reward function R t , and use the policy optimization function to update the policy π(u|v):

[0073]

[0074] Where δ is the discount factor, R t is the reward value at time t;

[0075] S54. Combine time series embedding representation with reinforcement learning strategy to construct risk factor weight matrix W r =[w ij ], where the matrix element w ij for:

[0076]

[0077] where λ ij is the risk factor influence coefficient, is the gradient of the feature variable with respect to the temporal embedding representation;

[0078] S55, using the risk prediction module, based on the embedded representation Z t and the risk factor matrix W r , through the risk scoring function S r =η·Z t +∑w ij Conduct quantitative analysis of potential risks;

[0079] S56. Generate a dynamic risk warning strategy for legal events, build a warning plan based on the risk score ranking results, and output the risk classification and trend prediction results of legal events.

[0080] Optionally, the S6 specifically includes:

[0081] S61. Build an enhanced federated learning framework and define the participating node set Q = {q 1 ,q 2 ,…,q m}, where each node q j Local storage of legal data D j , and encrypt the data through differential privacy protection mechanism;

[0082] S62, design an adaptive local training module, each participating node based on its local data D j Update the model parameters φ using an adaptive optimization algorithm j :

[0083]

[0084] in represents the local model parameters of the tth iteration, α j is the node-specific dynamic learning rate, Represents the objective function gradient based on local data;

[0085] S63, through the distributed model aggregation module, the model parameters uploaded by the node are aggregated using the federated aggregation method based on weight optimization. Aggregation generates global model parameters Φ (t+1) :

[0086]

[0087] where κ j Represents node q j The weight coefficient of is dynamically determined by its data quality and training performance;

[0088] S64, through the collaborative optimization module, the global model parameter Φ (t+1) Analyze the contribution of each node and generate the cross-domain collaboration matrix C o=[c uv ], where the matrix element c uv Represents node q u and q v The collaborative analysis results:

[0089] c uv =ξ uv ·Φ (t+1) Δ uv ;

[0090] where ξ uv represents the collaborative weight factor between nodes, Δ uv Represents the difference parameter between nodes;

[0091] S65. Using the Collaboration Matrix C o Build structured analysis reports and transform collaborative computing results into multidimensional analysis models to provide optimized support for legal data association mining and intelligent decision-making across data sources.

[0092] A smart legal data processing system based on a smart legal data processing method based on big data, comprising:

[0093] The data collection module extracts structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and uniformly encodes and represents the data through multimodal fusion technology;

[0094] The semantic parsing module combines dynamic semantic modeling and deep semantic reasoning to perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities;

[0095] The legal knowledge graph module mines legal knowledge based on the graph neural network algorithm and dynamically updates the legal knowledge graph through node embedding, relationship analysis and reasoning optimization;

[0096] The rule generation and verification module uses a multi-objective evolutionary algorithm and a dynamic weight adjustment mechanism to automatically generate a set of cross-domain adaptive legal rules, and achieves rule optimization and clause mapping through semantic comparison and multi-level rule verification;

[0097] The risk prediction module uses a method that combines time series feature modeling with reinforcement learning to model the dynamic trends of legal data and generate quantitative analysis of potential risk factors and risk warning strategies;

[0098] The distributed collaborative analysis module uses a distributed optimization strategy based on federated learning to improve the collaborative computing capabilities and data analysis efficiency across data sources while ensuring data privacy.

[0099] The system control module coordinates the interaction of the above modules, manages data flow, model training and update process, and generates comprehensive legal analysis reports and decision support recommendations.

[0100] The beneficial effects of the present invention are:

[0101] (1) The present invention combines dynamic semantic understanding, deep semantic reasoning, legal knowledge mining based on graph neural networks, and time series modeling to achieve deep analysis and association mining of cross-domain legal data. It can efficiently mine the semantic associations and dynamic changes between complex legal clauses, especially for the processing of multimodal and multi-domain legal data, and improve the system's understanding and adaptability to complex legal issues.

[0102] (2) This invention uses a multi-objective evolutionary algorithm and federated learning technology to dynamically generate and optimize a cross-domain adaptive legal rule set while ensuring data privacy and security, and combines distributed collaborative computing methods to improve the efficiency of cross-data source collaborative analysis. This not only significantly reduces the time for legal data processing, but also improves the accuracy and comprehensiveness of data analysis, providing efficient and intelligent support for legal services.

[0103] (3) The present invention uses an intelligent risk prediction module, combined with time series feature modeling and reinforcement learning algorithms, to achieve accurate prediction of the dynamic trends of legal data and quantitative analysis of potential risks. It can generate early warning strategies and trend prediction results for legal events in real time, reducing reliance on human intervention, while improving the system's prediction capabilities and response efficiency, significantly enhancing the intelligence level and practical application value of legal services. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0105] Figure 1 This is a general framework diagram of a smart legal data processing method and system based on big data proposed in the present invention. DETAILED DESCRIPTION

[0106] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0107] refer to Figure 1 , a smart legal data processing method based on big data, comprising the following steps:

[0108] S1. Build a legal data collection framework to extract structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and use multimodal fusion technology to uniformly encode and represent different data forms;

[0109] In this implementation, S1 specifically includes:

[0110] S11. Through the multi-source heterogeneous legal data collection system, a real-time data interface is built based on a distributed architecture, and the judicial case library, legislative data source, and image and audio and video legal data streams are accessed through the network crawling module and the authorized data connection module to build a dynamic collection framework that supports multi-modal input;

[0111] S12. Use a feature alignment algorithm based on multi-scale semantic mapping to dynamically adjust the weights of field labels and classification dimensions in structured data to generate a cross-domain compatible multidimensional feature matrix A. ij :

[0112] A ij =α i ·D j +β ij ;

[0113] Among them, α i is the weight coefficient of the specific field, D j is the original data dimension, β ij is the bias parameter;

[0114] S13. Combine recursive parsing and regularization models to perform domain-specific semantic extraction on semi-structured data, use syntax tree matching technology to extract key tags and hierarchical relationships, and generate context-related multi-layer nested structures T k =(L k ,P k ), where L k is the label set, P k is the hierarchical relationship mapping matrix;

[0115] S14. Based on the self-supervised semantic parsing network, the terms, contextual relationships and complex sentences in unstructured data are decomposed step by step, and cross-modal embedding is generated through semantic comparison and aggregation strategies. Among them, M q and N q They are the content vector and semantic vector of unstructured data respectively;

[0116] S15, adopt the modality unified expression model based on adaptive fusion, perform efficient feature fusion on different data modalities, and apply the feature embedding function F k (v) represents the unified expression of modal data:

[0117] F k(v) = δ(U k Φ(v)+V k );

[0118] Among them, v is the input feature, Φ(v) is the feature extraction function, U k and V k are the modal weight matrix and bias matrix respectively, and δ is the fusion activation function;

[0119] S16. Construct a multimodal legal data storage and retrieval mechanism based on a multi-layer index tree, use a dynamic balancing algorithm to optimize the data retrieval path, and support the rapid storage and query of legal data through real-time update strategies.

[0120] S2. Using a multi-task learning framework that combines dynamic semantic modeling and deep semantic reasoning, we perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities.

[0121] In this implementation, S2 specifically includes:

[0122] S21. Use dynamic semantic modeling units to divide cross-domain legal clauses into independent semantic segments and generate semantic dependency graphs G through semantic dependency analyzers. r =(V r ,E r ,W r ), where V r is the set of fragment nodes, E r is the set of dependency edges, W r is a weight matrix, used to represent the strength of semantic association;

[0123] S22, build a multi-layer recursive semantic reasoning network to semantic dependency graph G r Perform multi-step reasoning to generate a recursive embedding vector U r =f r (G r ,Θ r ), where f r is the recursive inference function, Θ r is the network parameter, U r Embedding representation of multi-dimensional features for semantic fragments;

[0124] S23, through the multi-task semantic optimization model, the recursive embedding vector U r Decompose into multiple task-specific sub-vector sets Y r ={y 1 ,y 2 ,…,y m}, where y i Represents the embedded feature vector of task i, combined with the task optimization function Ψ(y i ,Ti ) Dynamically adjust each sub-vector;

[0125] S24, based on the cross-domain context perception module, calculate the contextual relevance between the semantic fragment and the task background, and generate a high-dimensional contextual semantic representation V through a weighted fusion model s :

[0126]

[0127] where β i is the context weighting coefficient, Γ is the context fusion function, C r is the cross-domain context matrix;

[0128] S25, through the hierarchical semantic generation mechanism, the high-dimensional context semantic representation V s Converted into a multi-level semantic vector set Z r,k :

[0129] Z r,k =σ(W k ·V s +∈ k );

[0130] Where W k is the layer weight matrix, ∈ k is the bias vector, σ is the nonlinear activation function;

[0131] S26, using the semantic fusion storage unit, the generated multi-level semantic vector set Z r,k Stored in a semantic knowledge base and jointly updated with dynamic association rules of cross-domain legal clauses.

[0132] S3, a legal knowledge association mining algorithm based on graph neural network, performs node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network;

[0133] In this implementation, S3 specifically includes:

[0134] S31. Through the enhanced legal knowledge extraction module, multi-dimensional entities and deep semantic relationships are extracted from legal data to generate a weighted legal knowledge triple set Q = {(n i ,r ij ,n j ,p ij )}, where n i and n j Represents an entity node, r ij Indicates the relationship type, p ij represents the association weight;

[0135] S32. Constructing a dynamic weighted knowledge graph G based on a set of knowledge triples q =(N q ,L q ,P q ), where N q is the node set, L q is the edge set, P q is a weight matrix, which represents the multi-dimensional semantic relationship between nodes;

[0136] S33, using hierarchical graph embedding technology, embed the nodes and edges in the graph through a multi-level recursive mechanism to generate node embedding vectors and edge embedding vector

[0137]

[0138] Where ζ and ξ are the embedding activation functions of nodes and edges respectively, ψ is the embedding fusion function, and are the state matrices of nodes and edges respectively, M(n) is the neighbor set of node n, and is the bias term;

[0139] S34, using the association analysis module, through the dynamic relationship optimization function Ω(n i ,n j ,r)Compute node n i and n j The weight of the reinforcement relationship between:

[0140]

[0141] in represents the weight parameter of the relationship type, Γ is the node similarity calculation function, and λ r Strengthen bias parameters for relationships;

[0142] S35. Through the optimization module based on weight reinforcement and relational reasoning, the redundant relations in the knowledge graph are eliminated, and new high-confidence relation edges are generated through the self-supervision mechanism. The optimized knowledge graph is represented as G′ r =(N′ r ,L′ r ,P′ r );

[0143] S36. Combine the cross-domain association generation module to jointly analyze the optimized knowledge graph and multi-domain legal data to generate a cross-domain legal association matrix C t :

[0144]

[0145] Among them, W t is the cross-domain fusion weight matrix, is the Hadamard product of node and edge embedding, θ t is the bias vector, ν is the cross-domain activation function, and a multi-domain association network is dynamically generated.

[0146] S4. Based on the multi-objective evolutionary algorithm and dynamic weight adjustment mechanism, a set of cross-domain adaptive legal rules is automatically generated, and mapping and optimization between clauses are achieved through semantic comparison and multi-level rule verification;

[0147] In this implementation, S4 specifically includes:

[0148] S41, construct a multi-objective rule adaptation optimization model, set the optimization target set H = {h 1 (z),h 2 (z),…,h m (z)}, where z is the rule variable, h i (z) is the i-th optimization objective function, which optimizes the adaptability, semantic consistency and complexity of the rules at the same time;

[0149] S42, using the optimization algorithm based on multi-objective evolution strategy, generating the rule candidate set Z = {z 1 ,z 2 ,…,z n}, using the fitness function Select, crossover and mutate the candidate rules, where μ i Represents the weight of the objective function, and finally generates the optimal solution set;

[0150] S43. Design a dynamic weight adjustment module to calculate the dynamic changes of target weights in real time:

[0151]

[0152] in represents the weight value of the i-th target in the t+1 generation, κ is the learning rate, Represents the gradient of the optimization objective;

[0153] S44, through the rule adaptation generation module, extract the rules with the highest adaptability from the optimization solution set to generate the cross-domain legal rule set R f = {r 1 ,r 2 ,…,r p}, and merge the rules through the adaptive rule fusion algorithm;

[0154] S45. Based on the multi-dimensional semantic comparison mechanism, a rule semantic embedding model is constructed to calculate the semantic correlation between rules:

[0155] Φ(r i ,r j )=γ· <E(r i ),E(r j )>+∈;

[0156] Where γ is the contrast weight, E(r i ) and E(r j ) represent the rules r i and r j The embedding vector of , ∈ is the bias term;

[0157] S46, through the multi-level rule verification module, the rule set is verified layer by layer to establish the verification matrix M v , matrix element m ij Representation rule r i and r j The validation value:

[0158] m ij =σ(Φ(r i ,r j ))·α ij ;

[0159] Where σ is a nonlinear activation function, α ij is the verification coefficient;

[0160] S47. Use the dynamic rule optimization module to recursively optimize the verified rule set, and generate the final optimized cross-domain adaptation rule set R′ through weight update and relationship reinforcement. f , supporting semantic mapping and dynamic optimization between clauses.

[0161] S5. Use an intelligent algorithm that combines time series feature modeling with reinforcement learning to model and analyze the dynamic trends and potential risk factors of legal data, and output risk prediction and early warning strategies for legal events;

[0162] In this implementation, S5 specifically includes:

[0163] S51. Use the data preprocessing module to time-series the legal data and construct a multidimensional time series data set in represents the jth characteristic variable corresponding to time t;

[0164] S52. Based on time series feature modeling technology, the long short-term memory network model is used to extract features from time series data and generate a time embedding representation Z. t :

[0165] Z t =φ(A z ·Z t-1 +Bz ·y t +c z );

[0166] Among them A z and B z are the state weight matrix and the input weight matrix respectively, c z is the bias term, φ is the activation function;

[0167] S53. Define the state variable V through the reinforcement learning framework t , action set U t , reward function R t , and use the policy optimization function to update the policy π(u|v):

[0168]

[0169] Where δ is the discount factor, R t is the reward value at time t;

[0170] S54. Combine time series embedding representation with reinforcement learning strategy to construct risk factor weight matrix W r =[w ij ], where the matrix element w ij for:

[0171]

[0172] where λ ij is the risk factor influence coefficient, is the gradient of the feature variable with respect to the temporal embedding representation;

[0173] S55, using the risk prediction module, based on the embedded representation Z t and the risk factor matrix W r , through the risk scoring function S r =η·Z t +∑w ij Conduct quantitative analysis of potential risks;

[0174] S56. Generate a dynamic risk warning strategy for legal events, build a warning plan based on the risk score ranking results, and output the risk classification and trend prediction results of legal events.

[0175] S6. Utilize distributed collaborative analysis technology based on federated learning, and on the basis of legal data privacy protection, improve the collaborative computing capabilities and data analysis efficiency across data sources through distributed optimization strategies.

[0176] In this implementation, S6 specifically includes:

[0177] S61. Build an enhanced federated learning framework and define the participating node set Q = {q 1 ,q 2 ,…,q m}, where each node q j Local storage of legal data D j , and encrypt the data through differential privacy protection mechanism;

[0178] S62, design an adaptive local training module, each participating node based on its local data D j Update the model parameters φ using an adaptive optimization algorithm j :

[0179]

[0180] in represents the local model parameters of the tth iteration, α j is the node-specific dynamic learning rate, Represents the objective function gradient based on local data;

[0181] S63, through the distributed model aggregation module, the model parameters uploaded by the node are aggregated using the federated aggregation method based on weight optimization. Aggregation generates global model parameters Φ (t+1) :

[0182]

[0183] where κ j Represents node q j The weight coefficient of is dynamically determined by its data quality and training performance;

[0184] S64, through the collaborative optimization module, the global model parameter Φ (t+1) Analyze the contribution of each node and generate the cross-domain collaboration matrix C o =[c uv ], where the matrix element c uv Represents node q u and q v The collaborative analysis results:

[0185] c uv =ξ uv ·Φ (t+1) ·Δ uv ;

[0186] where ξ uv represents the collaborative weight factor between nodes, Δ uv Represents the difference parameter between nodes;

[0187] S65. Using the Collaboration Matrix C oBuild structured analysis reports and transform collaborative computing results into multidimensional analysis models to provide optimized support for legal data association mining and intelligent decision-making across data sources.

[0188] A smart legal data processing system based on a smart legal data processing method based on big data, comprising:

[0189] The data collection module extracts structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and uniformly encodes and represents the data through multimodal fusion technology;

[0190] The semantic parsing module combines dynamic semantic modeling and deep semantic reasoning to perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities;

[0191] The legal knowledge graph module mines legal knowledge based on the graph neural network algorithm and dynamically updates the legal knowledge graph through node embedding, relationship analysis and reasoning optimization;

[0192] The rule generation and verification module uses a multi-objective evolutionary algorithm and a dynamic weight adjustment mechanism to automatically generate a set of cross-domain adaptive legal rules, and achieves rule optimization and clause mapping through semantic comparison and multi-level rule verification;

[0193] The risk prediction module uses a method that combines time series feature modeling with reinforcement learning to model the dynamic trends of legal data and generate quantitative analysis of potential risk factors and risk warning strategies;

[0194] The distributed collaborative analysis module uses a distributed optimization strategy based on federated learning to improve the collaborative computing capabilities and data analysis efficiency across data sources while ensuring data privacy.

[0195] The system control module coordinates the interaction of the above modules, manages data flow, model training and update process, and generates comprehensive legal analysis reports and decision support recommendations.

[0196] Embodiment 1:

[0197] In order to verify the feasibility of the present invention, the big data-based smart legal data processing method and system of the present invention are applied in the legal affairs department of a large multinational company B. Since company B involves legal clauses in multiple countries, its legal data types are complex, including a large amount of structured contract data, as well as unstructured legal documents and multimodal legal reference materials. In addition, the legal affairs department of company B often needs to respond quickly to legal risks and provide efficient cross-domain legal clause mapping and risk prediction services. However, traditional legal data processing systems are difficult to meet their needs, mainly manifested in slow processing speed, low accuracy of clause association mining, and low risk prediction accuracy. In order to meet these challenges, company B decided to deploy the smart legal data processing system of the present invention.

[0198] During the deployment process, the system first extracts structured contracts, semi-structured legal process records, and unstructured legal document texts from the legal databases of Company B distributed around the world through the data collection module. Through multimodal fusion technology, the system uniformly encodes these data and generates cross-domain comparable semantic representations. In this process, the dynamic semantic understanding module automatically classifies relevant legal clauses and annotates semantic relevance through deep semantic modeling and reasoning. For example, in the tax contract submitted by the European branch, the system automatically identified the clauses involving VAT calculation rules and performed high-precision semantic matching with similar tariff clauses of the Asian branch.

[0199] The system then built a legal knowledge graph specific to the enterprise through the legal knowledge graph module, and dynamically updated it based on the graph neural network algorithm. In a typical scenario, the system found that there were differences in the legal clauses on data privacy between the US and European branches. Through the embedded representation and reasoning optimization functions of the graph neural network, the system mined the specific differences between the two legal clauses in terms of data protection scope and penalty standards, and provided targeted adjustment suggestions for the enterprise.

[0200] In terms of risk prediction, the system uses a combination of time series feature modeling and reinforcement learning to identify legal risks that companies may face in their operations in multiple countries. In one example, the system analyzed contract dispute data in Europe over the past five years and detected through a time series model that the frequency of contract disputes increased significantly after certain tax policy changes. By constructing a risk factor matrix, the system predicts the risk of contract disputes that may occur in the next year and generates corresponding early warning strategies to help companies adjust contract terms in advance.

[0201] In addition, the system realizes collaborative analysis across data sources through the federated learning module. During the implementation process, the system integrated the legal data of Company B distributed around the world, and improved the analysis efficiency through distributed optimization strategies while ensuring data privacy. For example, the system automatically generates a unified template from the privacy protection contract data extracted from subsidiaries in different regions to guide the standardized operation of contracts worldwide. This process significantly reduces the time and cost of contract review. In order to quantify the actual effect of the present invention, the efficiency and accuracy of legal affairs before and after the deployment of the system are compared, and the experimental results are shown in the following table:

[0202] Table 1B Enterprise Legal Affairs Efficiency Improvement Report

[0203]

[0204] As can be seen from Table 1, after deploying the system of the present invention, the efficiency and accuracy of legal affairs of Company B have been significantly improved. The accuracy of the system's cross-domain legal clause mapping has increased from 72.4% to 94.8%, and the processing time per million legal data has been shortened from 48 hours to 6 hours. In terms of risk prediction, the accuracy rate has increased to 96.2%, and the time to generate a comprehensive risk warning has been shortened to 10 minutes. In particular, in terms of data privacy protection and contract review efficiency, the system has achieved a dual improvement in data compliance and efficiency through distributed collaborative analysis technology.

[0205] These data prove that the present invention not only effectively solves the problems of insufficient semantic understanding depth, difficulty in integrating heterogeneous data, and limited risk prediction capabilities in traditional legal data processing methods, but also significantly improves the efficiency and accuracy of legal affairs processing through intelligent technology, providing strong technical support for enterprises' global legal services.

[0206] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart legal data processing method based on big data, characterized in that: The steps include: S1. Build a legal data collection framework to extract structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and use multimodal fusion technology to uniformly encode and represent different data forms; S2. Using a multi-task learning framework that combines dynamic semantic modeling and deep semantic reasoning, we perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities. S3, a legal knowledge association mining algorithm based on graph neural network, performs node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network; S4. Based on the multi-objective evolutionary algorithm and dynamic weight adjustment mechanism, a set of cross-domain adaptive legal rules is automatically generated, and mapping and optimization between clauses are achieved through semantic comparison and multi-level rule verification; S5. Use an intelligent algorithm that combines time series feature modeling with reinforcement learning to model and analyze the dynamic trends and potential risk factors of legal data, and output risk prediction and early warning strategies for legal events; S6. Utilize distributed collaborative analysis technology based on federated learning, and on the basis of legal data privacy protection, improve the collaborative computing capabilities and data analysis efficiency across data sources through distributed optimization strategies.

2. According to the big data-based intelligent legal data processing method of claim 1, it is characterized in that: The S1 specifically includes: S11. Through the multi-source heterogeneous legal data collection system, a real-time data interface is built based on a distributed architecture, and the judicial case library, legislative data source, and image and audio and video legal data streams are accessed through the network crawling module and the authorized data connection module to build a dynamic collection framework that supports multi-modal input; S12. Use a feature alignment algorithm based on multi-scale semantic mapping to dynamically adjust the weights of field labels and classification dimensions in structured data to generate a cross-domain compatible multidimensional feature matrix A. ij : A ij =a i @D j +b ij ; Among them, α i is the weight coefficient of the specific field, D j is the original data dimension, β ij is the bias parameter; S13. Combine recursive parsing and regularization models to perform domain-specific semantic extraction on semi-structured data, use syntax tree matching technology to extract key tags and hierarchical relationships, and generate context-related multi-layer nested structures T k =(L k ,P k ), where L k is the label set, P k is the hierarchical relationship mapping matrix; S14. Based on the self-supervised semantic parsing network, the terms, contextual relationships and complex sentences in unstructured data are decomposed step by step, and cross-modal embedding is generated through semantic comparison and aggregation strategies. Among them, M q and N q They are the content vector and semantic vector of unstructured data respectively; S15, adopt the modality unified expression model based on adaptive fusion, perform efficient feature fusion on different data modalities, and apply the feature embedding function F k (v) represents the unified expression of modal data: F k (v)=δ(U k ·Φ(v)+V k ); Among them, v is the input feature, Φ(v) is the feature extraction function, U k and V k are the modal weight matrix and bias matrix respectively, and δ is the fusion activation function; S16. Construct a multimodal legal data storage and retrieval mechanism based on a multi-layer index tree, use a dynamic balancing algorithm to optimize the data retrieval path, and support the rapid storage and query of legal data through real-time update strategies.

3. According to the big data-based intelligent legal data processing method of claim 1, it is characterized in that: The S2 specifically includes: S21. Use dynamic semantic modeling units to divide cross-domain legal clauses into independent semantic segments and generate semantic dependency graphs G through semantic dependency analyzers. r =(V r ,E r ,W r ), where V r is the set of fragment nodes, E r is the set of dependency edges, W r is a weight matrix, used to represent the strength of semantic association; S22, build a multi-layer recursive semantic reasoning network to semantic dependency graph G r Perform multi-step reasoning to generate a recursive embedding vector U r =f r (G r ,Θ r ), where f r is the recursive inference function, Θ r is the network parameter, U r Embedding representation of multi-dimensional features for semantic fragments; S23, through the multi-task semantic optimization model, the recursive embedding vector U r Decompose into multiple task-specific sub-vector sets Y r ={y1,y2,…,y m }, where y i Represents the embedded feature vector of task i, combined with the task optimization function Ψ(y i ,T i ) Dynamically adjust each sub-vector; S24, based on the cross-domain context perception module, calculate the contextual relevance between the semantic fragment and the task background, and generate a high-dimensional contextual semantic representation V through a weighted fusion model s : where β i is the context weighting coefficient, Γ is the context fusion function, C r is the cross-domain context matrix; S25, through the hierarchical semantic generation mechanism, the high-dimensional context semantic representation V s Converted into a multi-level semantic vector set Z r,k : WITH r,k =σ(W k ·V s +∈ k ); Where W k is the layer weight matrix, ∈ k is the bias vector, σ is the nonlinear activation function; S26, using the semantic fusion storage unit, the generated multi-level semantic vector set Z r,k Stored in a semantic knowledge base and jointly updated with dynamic association rules of cross-domain legal clauses.

4. According to the big data-based intelligent legal data processing method of claim 1, it is characterized in that: The S3 specifically includes: S31. Through the enhanced legal knowledge extraction module, multi-dimensional entities and deep semantic relationships are extracted from legal data to generate a weighted legal knowledge triple set Q = {(n i ,r ij ,n j ,p ij )}, where n i and n j Represents an entity node, r ij Indicates the relationship type, p ij represents the association weight; S32. Constructing a dynamic weighted knowledge graph G based on a set of knowledge triples q =(N q ,L q ,P q ), where N q is the node set, L q is the edge set, P q is a weight matrix, which represents the multi-dimensional semantic relationship between nodes; S33, using hierarchical graph embedding technology, embed the nodes and edges in the graph through a multi-level recursive mechanism to generate node embedding vectors and edge embedding vector Where ζ and ξ are the embedding activation functions of nodes and edges respectively, ψ is the embedding fusion function, and are the state matrices of nodes and edges respectively, M(n) is the neighbor set of node n, and is the bias term; S34, using the association analysis module, through the dynamic relationship optimization function Ω(n i ,n j ,r)Compute node n i and n j The weight of the reinforcement relationship between: where θ r represents the weight parameter of the relationship type, Γ is the node similarity calculation function, and λ r Strengthen bias parameters for relationships; S35. Through the optimization module based on weight reinforcement and relational reasoning, the redundant relations in the knowledge graph are eliminated, and new high-confidence relation edges are generated through the self-supervision mechanism. The optimized knowledge graph is represented as G′ r =(N′ r ,L′ r ,P′ r ); S36. Combine the cross-domain association generation module to jointly analyze the optimized knowledge graph and multi-domain legal data to generate a cross-domain legal association matrix C t : Among them, W t is the cross-domain fusion weight matrix, is the Hadamard product of node and edge embedding, θ t is the bias vector, ν is the cross-domain activation function, and a multi-domain association network is dynamically generated.

5. According to the big data-based intelligent legal data processing method of claim 1, it is characterized in that: The S4 specifically includes: S41, construct a multi-objective rule adaptation optimization model, set the optimization target set H = {h1(z), h2(z), ..., h m (z)}, where z is the rule variable, h i (z) is the i-th optimization objective function, which optimizes the adaptability, semantic consistency and complexity of the rules at the same time; S42, using the optimization algorithm based on multi-objective evolution strategy, generating the rule candidate set Z = {z1, z2, ..., z n }, using the fitness function Select, crossover and mutate the candidate rules, where μ i Represents the weight of the objective function, and finally generates the optimal solution set; S43. Design a dynamic weight adjustment module to calculate the dynamic changes of target weights in real time: in represents the weight value of the i-th target in the t+1 generation, κ is the learning rate, Represents the gradient of the optimization objective; S44, through the rule adaptation generation module, extract the rules with the highest adaptability from the optimization solution set to generate the cross-domain legal rule set R f ={r1,r2,…,r p }, and merge the rules through the adaptive rule fusion algorithm; S45. Based on the multi-dimensional semantic comparison mechanism, a rule semantic embedding model is constructed to calculate the semantic correlation between rules: Φ(r i ,r j )=γ· <E(r i ),E(r j )>+∈; Where γ is the contrast weight, E(r i ) and E(r j ) represent the rules r i and r j The embedding vector of , ∈ is the bias term; S46, through the multi-level rule verification module, the rule set is verified layer by layer to establish the verification matrix M v , matrix element m ij Representation rule r i and r j The validation value: m ij =σ(Φ(r i ,r j ))·a ij ; Where σ is a nonlinear activation function, α ij is the verification coefficient; S47. Use the dynamic rule optimization module to recursively optimize the verified rule set, and generate the final optimized cross-domain adaptation rule set R′ through weight update and relationship reinforcement. f , supporting semantic mapping and dynamic optimization between clauses.

6. The method for processing intelligent legal data based on big data according to claim 1 is characterized in that: The S5 specifically includes: S51. Use the data preprocessing module to time-series the legal data and construct a multidimensional time series data set in represents the jth characteristic variable corresponding to time t; S52. Based on the time series feature modeling technology, the long short-term memory network model is used to extract the features of the time series data and generate the time embedding representation Z t : Z t =φ(A z ·Z t-1 +B z ·y t +c z ); Among them A z and B z are the state weight matrix and the input weight matrix respectively, c z is the bias term, φ is the activation function; S53. Define the state variable V through the reinforcement learning framework t , action set U t , reward function R t , and use the policy optimization function to update the policy π(u|v): Where δ is the discount factor, R t is the reward value at time t; S54. Combine time series embedding representation with reinforcement learning strategy to construct risk factor weight matrix W r =[w ij ], where the matrix element w ij for: where λ ij is the risk factor influence coefficient, is the gradient of the feature variable with respect to the temporal embedding representation; S55, using the risk prediction module, based on the embedded representation Z t and the risk factor matrix W r , through the risk scoring function S r =η·Z t +∑w ij Conduct quantitative analysis of potential risks; S56. Generate a dynamic risk warning strategy for legal events, build a warning plan based on the risk score ranking results, and output the risk classification and trend prediction results of legal events.

7. The method for processing intelligent legal data based on big data according to claim 1 is characterized in that: The S6 specifically includes: S61. Build an enhanced federated learning framework and define the participating node set Q = {q1, q2, …, q m }, where each node q j Local storage of legal data D j , and encrypt the data through differential privacy protection mechanism; S62, design an adaptive local training module, each participating node based on its local data D j Update the model parameters φ using an adaptive optimization algorithm j : in represents the local model parameters of the tth iteration, α j is the node-specific dynamic learning rate, Represents the objective function gradient based on local data; S63, through the distributed model aggregation module, the model parameters uploaded by the node are aggregated using the federated aggregation method based on weight optimization. Aggregation generates global model parameters Φ (t+1) : where κ j Represents node q j The weight coefficient of is dynamically determined by its data quality and training performance; S64, through the collaborative optimization module, the global model parameter Φ (t+1) Analyze the contribution of each node and generate the cross-domain collaboration matrix C o =[c uv ], where the matrix element c uv Represents node q u and q v The collaborative analysis results: c uv =ξ uv ·F (t+1) ·D uv ; where ξ uv represents the collaborative weight factor between nodes, Δ uv Represents the difference parameter between nodes; S65. Using the Collaboration Matrix C o Build structured analysis reports and transform collaborative computing results into multidimensional analysis models to provide optimized support for legal data association mining and intelligent decision-making across data sources.

8. The smart legal data processing system of the smart legal data processing method based on big data according to any one of claims 1 to 7, characterized in that: include: The data collection module extracts structured, semi-structured and unstructured data from multi-source heterogeneous legal data, and uniformly encodes and represents the data through multimodal fusion technology; The semantic parsing module combines dynamic semantic modeling and deep semantic reasoning to perform hierarchical semantic parsing of cross-domain legal clauses and generate high-dimensional semantic representation vectors with context-aware capabilities; The legal knowledge graph module mines legal knowledge based on the graph neural network algorithm and dynamically updates the legal knowledge graph through node embedding, relationship analysis and reasoning optimization; The rule generation and verification module uses a multi-objective evolutionary algorithm and a dynamic weight adjustment mechanism to automatically generate a set of cross-domain adaptive legal rules, and achieves rule optimization and clause mapping through semantic comparison and multi-level rule verification; The risk prediction module uses a method that combines time series feature modeling with reinforcement learning to model the dynamic trends of legal data and generate quantitative analysis of potential risk factors and risk warning strategies; The distributed collaborative analysis module uses a distributed optimization strategy based on federated learning to improve the collaborative computing capabilities and data analysis efficiency across data sources while ensuring data privacy. The system control module coordinates the interaction of the above modules, manages data flow, model training and update process, and generates comprehensive legal analysis reports and decision support recommendations.

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