Water conservancy administrative law enforcement auxiliary method based on large model

Abstract

The invention relates to the technical field of environment monitoring, and provides a water conservancy administrative law enforcement auxiliary method based on a large model, the method is applied to a law enforcement terminal, the law enforcement terminal loads an auxiliary law enforcement large model for analyzing law enforcement field data, and the method comprises the following steps: extracting task distribution data of the law enforcement field data through a task arrangement framework; wherein different task distribution data in the task arrangement framework are configured with unique data pipelines; loading the task distribution data into an auxiliary law enforcement large model, and determining a child legal text and a corresponding parent legal text; wherein the child legal text is bound with first law enforcement tailoring reference information, and the parent legal text is bound with second law enforcement tailoring reference information; and calculating the vector similarity between the child law text and the parent law text and the task distribution data, and generating auxiliary law enforcement threshold information.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a water conservancy administrative law enforcement auxiliary method based on a large model. Background Technology

[0002] Currently, in the process of administrative law enforcement, law enforcement agencies combine terminal devices and big data model technology to achieve smart law enforcement. This is achieved by configuring case matching systems and administrative law enforcement assistants within the law enforcement terminals. These assistant programs primarily utilize Natural Language Processing (NLP) and Machine Learning (ML) technologies to automatically match laws and regulations and analyze cases. The specific execution methods are as follows:

[0003] (1) Keyword-matching-based legal consultation system:

[0004] We use keyword matching and rule engines to retrieve laws, regulations, and cases.

[0005] For example, when a user inputs a question, keywords are extracted, and legal provisions and cases containing these keywords are searched in the database. However, there are problems with semantic understanding and low relevance; when the user question involves polysemous words or complex sentence structures, mismatches or missed matches are likely to occur.

[0006] (2) Legal consultation system based on vector space model:

[0007] Law enforcement procedures use vector space models (TF-IDF or Word2Vec models) to improve matching accuracy by calculating cosine similarity to find the most relevant entries.

[0008] However, this method can only represent shallow semantics. For example, TF-IDF only focuses on word frequency and cannot capture contextual semantics. It can only achieve static matching and cannot dynamically associate legal provisions with discretionary rules, i.e., the calculation formula for fines or years of punishment.

[0009] (3) Deep learning-based legal consultation system:

[0010] Some law enforcement support systems use deep learning models (such as BERT and neural networks) for semantic matching. They transform legal provisions and cases into high-dimensional vectors using pre-trained language models, and then find the most relevant entries by calculating vector similarity.

[0011] However, it consumes a lot of computing resources. For example, the BERT model has high inference latency, making real-time interaction difficult, dynamic rule integration impossible, and the matching results cannot be combined with discretionary data in the structured database, i.e., the penalty coefficient table, for analysis.

[0012] (4) A discretionary evaluation system based on a rules engine:

[0013] The reasonableness of the fine amount is assessed through a rules engine, relying on predefined rules and calculation formulas.

[0014] However, it suffers from rigid logic and a lack of semantic connections. It cannot combine specific case backgrounds with dynamic rule adjustments, and there is a lack of semantic-level correlation analysis between the rules and legal provisions and cases.

[0015] (5) Knowledge graph-based legal consultation system:

[0016] There are also law enforcement assistance systems that can construct legal knowledge graphs, representing legal entities in a graph structure. The graph structure contains legal provisions, cases, types of penalties and their relationships of violation and reference. Matching with user queries can be achieved through graph embedding technology (such as TransE) or graph traversal algorithms (such as Neo4j Cypher query).

[0017] However, the construction cost is high, the legal entity relationships need to be manually marked, the real-time performance is poor, the map update requires batch processing, and it cannot respond to legal clause revisions in real time.

[0018] (6) Legal consultation system based on large model:

[0019] Recently, there has been a trend of directly using large language models (such as ChatGPT, Llama, etc.) for legal consultation and question answering. Various data processing methods are used, such as fine-tuning with specific legal datasets, retrieval enhancement and knowledge graph enhancement, and multimodal transformation. After the user inputs a question, the large model generates a response based on its pre-trained and fine-tuned knowledge.

[0020] While it performs well in natural language generation, its responses lack basis, resulting in results that deviate from specific legal provisions, incorrectly citing repealed regulations, and exhibiting high uncontrollability due to potential hallucinations. It generates fictitious legal clauses or cases, a common occurrence in large-scale law enforcement assistance systems.

[0021] In summary, traditional law enforcement systems and case matching systems have the following main technical shortcomings:

[0022] (1) Insufficient case matching accuracy: Traditional case matching systems are based on keyword matching or simple rule engines, which cannot handle complex natural language queries. The limitation of traditional case matching systems is that they cannot understand semantic context, the relevance of matching results is low, and mismatches or omissions often occur when dealing with polysemous words, synonyms or complex sentence structures.

[0023] (2) Insufficient correlation analysis between laws, regulations and cases: Traditional case matching systems usually process laws, regulations and cases separately, lacking or having an ineffective correlation analysis mechanism. In practice, even if relevant cases are matched, it is difficult to accurately determine which specific legal provisions are violated in the corresponding cases, resulting in users not being able to obtain a comprehensive legal analysis.

[0024] (3) Insufficient utilization of discretionary data: The existing system cannot dynamically combine specific laws, regulations and case backgrounds for comprehensive analysis, resulting in insufficient accuracy and rationality of discretionary results.

[0025] (4) Limited natural language generation capability: Natural language responses have templated fixed sentence patterns, resulting in stiff and unnatural responses that cannot meet the needs of high-quality administrative law enforcement.

[0026] (5) Poor system scalability and real-time performance: Traditional case matching systems lack real-time performance and scalability when data is frequently updated. Summary of the Invention

[0027] This application proposes a large-scale model-based auxiliary method for water conservancy administrative law enforcement. The data pipeline isolation ensures the independence of legal retrieval during multi-task parallel processing. Combined with the hierarchical attention mechanism of the large-scale model, it improves the efficiency of cross-level legal correlation in complex cases. The vectorized representation of discretionary benchmarks integrates similarity calculation with law enforcement experience parameters, enabling the recommendation of appropriate discretionary ranges for law enforcement personnel. Through lightweight model compression technology, it can generate penalty recommendations in real time, facilitating on-site law enforcement. This application can be applied to question-and-answer type law enforcement terminals and search history type law enforcement terminals, improving law enforcement efficiency.

[0028] Firstly, this application proposes a large-scale model-based auxiliary method for water conservancy administrative law enforcement, applied to an enforcement terminal. The enforcement terminal loads an auxiliary enforcement large-scale model that analyzes on-site enforcement data. The method includes:

[0029] Task distribution data is extracted from law enforcement field data through a task orchestration framework; different task distribution data in the task orchestration framework are configured with a unique data pipeline.

[0030] The task distribution data is loaded into the auxiliary law enforcement model to determine the sub-legal texts and their corresponding parent legal texts; the sub-legal texts are bound to the first law enforcement discretion benchmark information, and the parent legal texts are bound to the second law enforcement discretion benchmark information.

[0031] Calculate the vector similarity between the sub-legal text and the parent legal text and the task distribution data. Based on the vector similarity, generate auxiliary law enforcement threshold information on the law enforcement terminal.

[0032] This application, through task distribution within a task orchestration framework and the one-way transmission function of a unique data pipeline, enables the separate connection of corresponding task data with different models during on-site law enforcement. This allows for the determination of the legal basis and similar cases for triggering measures against violators. By using the discretionary benchmarks of subordinate and superior laws, as well as the vector similarity between legal documents and events with parent-child relationship attributes, it ensures that the legal applicability complies not only with superior but also with subordinate laws. This data is then recommended to law enforcement personnel for reference in law enforcement.

[0033] In conjunction with the first aspect, the aforementioned auxiliary law enforcement big model is an AI integrated big model with a multi-model collaborative architecture; wherein, the multi-model collaborative architecture consists of an embedding model, a vector database, a structured database, and a large language model;

[0034] The multi-model collaborative architecture is configured with an incremental fine-tuning mechanism. When a feedback signal is received, the incremental fine-tuning mechanism corrects the gradient of the target model corresponding to the task distribution node according to the task distribution node corresponding to the feedback signal.

[0035] The feedback signal is generated by the consensus deviation of multiple model tasks in the AI ​​integrated large model.

[0036] The auxiliary law enforcement model proposed in this application is an AI integrated model with a multi-model collaborative architecture. It can integrate multiple large models, and different tasks can be handled by different models during law enforcement, thereby improving law enforcement efficiency. Furthermore, the model can be automatically adjusted based on feedback signals during the execution process, thereby improving the accuracy of law enforcement recommendation information.

[0037] In conjunction with the first aspect, the task orchestration framework is used to determine the data flow and topological invariants of task distribution data through a unique data pipeline;

[0038] Based on the data stream, determine the task feature vector;

[0039] Determine the task flow curvature based on topological invariants;

[0040] Based on the task feature vector and task flow curvature, the model nodes corresponding to the task flow are determined; among them, the model nodes are the task processing ports of different models in the auxiliary law enforcement big model.

[0041] In the process of processing task distribution data, this application uses a unique data pipeline to ensure the uniqueness of the model during task processing. However, the task processing also has the function of collaboratively processing load tasks, thereby improving task processing efficiency.

[0042] In conjunction with the first aspect, the task distribution data extraction process also includes:

[0043] Based on the unique data pipeline, identify the dynamic heterogeneous nodes of different models in the auxiliary law enforcement big model;

[0044] Based on dynamic heterogeneous nodes, the task description text of the law enforcement scene data is matched; the task description text includes associated feature data based on spatiotemporal dependencies, and the associated feature data is inference prompt data that conforms to the processing of the large-scale auxiliary law enforcement model;

[0045] Based on the task description text, generate multiple model task flow data.

[0046] In the process of task distribution data extraction, this application provides diverse computing resources for multiple subtasks of the task flow through dynamic heterogeneous nodes. The decomposition of task flow data, in turn, drives the dynamic adjustment of nodes, resulting in more accurate results when processing complex tasks.

[0047] In conjunction with the first aspect, the process of loading task distribution data into the auxiliary law enforcement model to determine sub-legal texts and their corresponding parent legal texts includes:

[0048] The task distribution data is parsed into semantic feature vectors and context feature vectors;

[0049] The semantic feature vectors are semantically matched using an auxiliary law enforcement model to generate a set of sub-legal texts; the set of sub-legal texts includes legal provisions, judicial interpretations, and historical precedents.

[0050] The context feature vectors are used to perform hierarchical backtracking based on the auxiliary law enforcement model to determine the corresponding initial parent legal text.

[0051] The set of sub-legal texts is compared with the initial parent legal text in terms of their legal effect hierarchy to determine the sub-legal texts with complete hierarchical chains and their corresponding parent legal texts; the complete hierarchical chain is used to represent the scope of authority and the time sequence of effectiveness of the legal texts.

[0052] This application constructs a complete hierarchical connection between sub-legal texts and parent legal texts by analyzing task distribution data and matching information such as case matching, legal stripe matching, and judicial interpretation in the auxiliary law enforcement big model, and determines the law enforcement basis documents for the target law enforcement opinion. The legal documents are correct in terms of law enforcement authority and effective time.

[0053] In conjunction with the first aspect, the process of loading task distribution data into the auxiliary law enforcement model to determine sub-legal texts and their corresponding parent legal texts also includes:

[0054] Configure a legislative intent analysis mechanism in the large-scale model for assisting law enforcement, determine the priority relationship between different legal texts, and generate a legal validity topology network based on statute of limitations markers;

[0055] In response to the comparison of validity hierarchy, the legal validity topology network is used to assign timeliness and validity weights to sub-legal texts with complete hierarchical chains and their corresponding parent legal texts, generating an applicable recommendation sequence.

[0056] This application provides a dynamic basis for time-limited authorization by using time-limited markers in the legal validity topology network. The resulting recommendation list can then promote the updating of the topology network when new legal texts are added, thereby improving the time-limited management of legal texts.

[0057] In conjunction with the first aspect, the process of loading task distribution data into the auxiliary law enforcement model to determine sub-legal texts and their corresponding parent legal texts also includes:

[0058] Based on the complete hierarchical chain, determine the decision bias of on-site law enforcement data;

[0059] Based on the decision-making deviation, determine the discretionary threshold for applicable legal deviation; wherein, the discretionary threshold conforms to the proviso and catch-all clauses in the legal text;

[0060] Based on the discretionary threshold, a hierarchical reasoning path based on feature reconstruction is generated to determine the implicit connection edges between the sub-legal texts and their corresponding parent legal texts.

[0061] Based on the correlation between implicit connection edges and on-site law enforcement data, a recommended sequence of sub-legal texts and their corresponding parent legal texts is generated.

[0062] This application can determine the degree of deviation between the legal text and the actual illegal act in terms of time, place, and circumstances by using the complete hierarchical chain of sub-legal texts and parent legal texts, thereby ranking the issued enforcement opinions.

[0063] In conjunction with the first aspect, the calculation of the vector similarity between the sub-legal text and the parent legal text and the task distribution data includes:

[0064] An interpretation consistency verification channel is constructed between the initially matched sub-legal text and the parent legal text, and the logical compatibility of the two in the expression of the constituent elements is detected by the legislative terminology alignment model.

[0065] When a deviation in direction is detected between the penalty magnitude vector of a sub-legal text and the authorization scope vector of the parent legal text, a validity hierarchy calibration mechanism is triggered to trace the source along the legal knowledge graph to find common superior concepts and perform vector space projection correction.

[0066] Establish vector boundary constraint rules for administrative discretion benchmarks to restrict the similarity calculation results to the effective area of ​​the hypercube formed by the parent legal text vector;

[0067] For similarity matching results that exceed the statutory discretionary authority, an overreach warning will be automatically generated and pushed to the administrative law enforcement rationality review interface.

[0068] This application, by judging the logic of sub-legal texts and parent legal texts, can prevent overreach of legal effect and ensure that enforcement opinions conform to the legal enforcement scope defined by sub-legal texts and parent legal texts in a multi-dimensional space when issuing enforcement opinions.

[0069] In conjunction with the first aspect, the generation of auxiliary law enforcement threshold information includes:

[0070] Based on vector similarity, a multi-dimensional feature extraction channel for water conservancy law enforcement scenarios is constructed.

[0071] Dynamic law enforcement response coefficients are generated by identifying the current law enforcement scenario through a geospatial correlation model;

[0072] A training strategy that pits legal discretion against ecological carrying capacity is adopted. When the ecological impact vector of illegal behavior exceeds the preset carrying capacity threshold, an emergency enforcement gradient enhancement mechanism is activated.

[0073] When outputting a spatiotemporally adaptive law enforcement threshold range, a set of derived decision parameters containing suggestions on enforcement intensity and guidance on remedial measures is generated simultaneously.

[0074] When assisting law enforcement, this application can provide suggestions on enforcement intensity and environmental remediation measures from multiple aspects, such as the enforcement scenario, the current environmental state, and the impact of enforcement results on the environment. This makes the punishment results related to environmental remediation and also has a deterrent effect.

[0075] In conjunction with the first aspect, the generation of auxiliary law enforcement threshold information further includes:

[0076] Pre-configure enforcement prompts, establish an enforcement decision-making effectiveness evaluation model, and conduct causal correlation analysis between the enforcement action results triggered by thresholds and water conservancy indicators;

[0077] Based on causal relationship analysis, personalized threshold suggestions with spatiotemporal markers are generated and synchronously updated to the law enforcement knowledge base of the law enforcement terminal.

[0078] This application can prevent the disconnect between punishment results and actual application of punishment measures through causal correlation analysis. Finally, it can realize real-time updates of positive disciplinary measures through prompt words and knowledge base, which can reflect the latest punishment results.

[0079] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0080] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0081] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0082] In the attached diagram:

[0083] Figure 1 This is a diagram illustrating the components of a water conservancy administrative law enforcement auxiliary method based on a large model, as described in an embodiment of the present invention.

[0084] Figure 2 This is a diagram illustrating the determination process of the law enforcement procedure in an embodiment of the present invention;

[0085] Figure 3 This is a flowchart illustrating the knowledge base initialization process for the law enforcement procedure in an embodiment of the present invention.

[0086] Figure 4 This is a flowchart illustrating question-and-answer type enforcement during an embodiment of the present invention.

[0087] Figure 5 This is a diagram showing the display interface of legal text during law enforcement in an embodiment of the present invention;

[0088] Figure 6 This is a display interface diagram showing specific violations of legal provisions during the law enforcement process according to an embodiment of the present invention;

[0089] Figure 7 This is a diagram showing the display interface of discretionary information during law enforcement in an embodiment of the present invention.

[0090] Figure 8 This is a cross-sectional view showing specific law enforcement and penalty information during the law enforcement process according to an embodiment of the present invention;

[0091] Figure 9 This is a system architecture diagram of the built-in water conservancy administrative law enforcement auxiliary system of the law enforcement terminal in an embodiment of the present invention. Detailed Implementation

[0092] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0093] Example 1:

[0094] See Figures 1 to 9This application proposes a water conservancy administrative law enforcement assistance method based on a large model, which is applied to a law enforcement terminal. The law enforcement terminal 1 integrates functions such as large model reasoning, task arrangement, and data processing. On-site law enforcement data collection 101 and threshold generation are performed. The law enforcement terminal 1 is connected to a cloud platform, which has a backend legal database. If the legal text database needs to be dynamically updated or has a large storage capacity, it can be accessed through the communication module of the law enforcement terminal 1.

[0095] The processor of law enforcement terminal 1 uses a high-performance ARM chip or edge computing GPU to support large model inference. The storage module of law enforcement terminal 1 locally stores legal text library and large model parameters. The camera, microphone and touch screen of law enforcement terminal 1 are used to collect law enforcement scene data and display the function interface of law enforcement processing.

[0096] The law enforcement terminal 1 in this application is an edge processing device. It does not rely on a remote server, but it can use a remote server. During law enforcement, it can reduce network latency and enable rapid on-site enforcement. Figure 1 During the law enforcement data collection process shown in step 101:

[0097] First, data from the law enforcement scene is collected through law enforcement terminal 1, such as... Figure 1 Step 1010, as shown, extracts on-site law enforcement data through a task orchestration framework and performs task distribution processing. Different task distribution data within the task orchestration framework are configured with a unique data pipeline; that is, a data pipeline is created when analyzing task data. Figure 1 As shown in step 1011, then determine whether the divided tasks have been distributed, such as... Figure 1 Step 1012 is shown;

[0098] When no distribution is required, it indicates that the current law enforcement task falls under the category of directly enforceable penalties with corresponding enforcement procedures and regulations, and law enforcement data will be generated directly, such as... Figure 1 Step 1013 is shown.

[0099] When task distribution is required, such as Figure 1 Step 102, as shown, will directly divide and distribute different task data to the corresponding models; for example... Figure 1 As shown in step 1021; after distributing the data to the corresponding model, perform large model inference, such as... Figure 1 Step 1022 is shown.

[0100] Specifically: The task orchestration framework adopts a lightweight microservice architecture, which directly connects to a single data pipeline to achieve data isolation and priority control. It divides the law enforcement field data into different task data, and each task data corresponds to any sub-model or sub-database in the law enforcement auxiliary big model. The law enforcement field data is directly transformed into tasks, and the tasks are then used to perform reasoning through the auxiliary law enforcement big model. The generated law enforcement tasks are distributed and processed to quickly generate suggested processing results and generate law enforcement basis documents for the processing results. For data that does not require task distribution, it indicates that direct judgment can be made, which belongs to fixed existing cases, and generates direct law enforcement data, which is displayed on the interface of law enforcement terminal 1.

[0101] This application loads task distribution data into a large-scale auxiliary law enforcement model to determine legal texts, which include sub-legal texts and their corresponding parent legal texts, such as... Figure 1 Step 1024 is shown. The sub-legal text is bound to the first enforcement discretion benchmark information, and the parent legal text is bound to the second enforcement discretion benchmark information, such as... Figure 1 As shown in step 1025; after confirming the legal text, law enforcement terminal 1 will report the law enforcement incident, such as... Figure 1 As shown in step 1031, the specific enforcement actions and penalty information are analyzed, such as... Figure 1 As shown in step 10311, during the law enforcement analysis process, all law enforcement data will be recorded, such as... Figure 1 Step 10312 is shown.

[0102] Specifically, the auxiliary law enforcement big data model is an integrated model for multi-task collaborative processing. While each model operates independently, they can collaboratively handle different tasks, all based on corresponding law enforcement analysis tasks generated from on-site law enforcement data. In this process, the auxiliary law enforcement big data model, combined with the topological interface of the legal knowledge graph, performs higher-level retrieval of legal texts to prevent legal conflicts and resolve the legal basis for law enforcement actions. The hierarchical association of legal texts and the dynamic binding of discretionary benchmarks in this application can output the reasoning process of the auxiliary law enforcement big data model, providing the basis for law enforcement, such as... Figure 1 Step 104 is shown.

[0103] The discretionary benchmark information in this application is quantitatively bound through vector space mapping, such as... Figure 1 As shown in step 1023, it will be transformed into a specific feature vector, such as Figure 1 As shown in step 1026, it will also be transformed into legal text and corresponding legal scenarios. In terms of law enforcement discretion, it can be combined with the generated mathematical expression to automatically calculate the law enforcement discretion and generate a recommended opinion on the law enforcement discretion. The recommended opinion can be a fine or other types of punishment.

[0104] Regarding the specific results of the distribution process, such as Figure 1 Step 103 is shown. In the specific implementation process, the focus is on calculating the vector similarity between the sub-legal text and the parent legal text and the task distribution data, such as... Figure 1 Step 1033 is shown.

[0105] This process requires integrating the hierarchical weights of semantic similarity and the legal force of the legal text, such as... Figure 1 Step 1032 is shown; and on the law enforcement terminal 1, auxiliary law enforcement threshold information is generated, such as... Figure 1 Step 1034 is shown; enforcement threshold information is used to characterize recommendations during the enforcement process. The threshold information represents a range of enforcement discretion from the lowest to the highest; for example... Figure 1 As shown in step 1034; finally, the data is transmitted to law enforcement terminal 1 and displayed directly, such as... Figure 1 Step 1013 is shown.

[0106] Finally, this application employs a similarity algorithm, such as a hybrid similarity algorithm, to generate a fuzzy logic controller based on data such as cosine similarity, Euclidean distance, and the attenuation factor of legal effect. Based on the vector similarity between the sub-legal text, the parent legal text, and the task distribution data, it recommends feasible law enforcement actions for law enforcement personnel to refer to, and records the recommended law enforcement actions for display on the law enforcement terminal 1.

[0107] This application's data pipeline isolation ensures the independence of legal retrieval during multi-task parallel processing. Combined with the hierarchical attention mechanism of the auxiliary law enforcement model, it improves the efficiency of cross-level legal correlation in complex cases. The vectorized expression of discretionary benchmarks integrates similarity calculation with law enforcement experience parameters, which can recommend a more appropriate range of law enforcement discretion for law enforcement officers. Through lightweight model compression technology, it can generate penalty recommendations in real time, enabling on-site law enforcement. This application can be applied to question-and-answer type law enforcement terminal 1 and search record type law enforcement terminal 1, improving law enforcement efficiency.

[0108] For example, the task orchestration framework of this application extracts structured task distribution data through a unique pipeline, assisting the law enforcement big data model in matching child / parent legal texts based on the task distribution data. In specific implementation, the child legal text and the parent legal text have legal effect and are related to the superior and subordinate laws. Then, the quantitative correlation is combined with vector similarity to solve the problem in traditional law enforcement where raw law enforcement data is directly input into the model, which leads to biased recommended law enforcement opinions due to the mixture of information.

[0109] The core of this application is that the sub-legal text provides specific discretionary benchmarks and corresponding legal provisions, while the parent legal text is the superior law, providing the execution benchmarks for implementing the sub-legal text. By combining the similarity between the two, specific threshold information is generated, namely, law enforcement discretion information and law enforcement basis information, providing dual constraints and improving the rationality of triggering.

[0110] In practical implementation, this application also serves to improve the edge processing device, referring to the law enforcement terminal 1 of this application, by using the task distribution function of the task orchestration framework. It has local law enforcement and real-time law enforcement functions, does not need to upload to the cloud, reduces latency and improves security.

[0111] This application's task orchestration framework and unique data pipeline, while distributing tasks, reduce input noise to the large-scale law enforcement auxiliary model because the data has been partitioned by the task orchestration framework. This avoids processing large amounts of complex data and also solves the problem of context understanding. By binding different discretionary benchmarks to child / parent legal texts and combining similarity to generate thresholds, it uses the dual constraints of specific clauses and higher-level laws to avoid the problems of traditional law enforcement systems that can only output a single clause desired by law enforcement officers, which may lead to enforcement errors or a lack of legal basis.

[0112] Example 2:

[0113] See Figure 2 The large-scale AI model for assisting law enforcement is an AI integrated model with a multi-model collaborative architecture. The multi-model collaborative architecture is configured with an embedding model, a vector database, a structured database, and a large language model, and there are interaction ports between different models.

[0114] The multi-model collaborative architecture is configured with an incremental fine-tuning mechanism. When a feedback signal is received, the incremental fine-tuning mechanism corrects the model gradient of the model corresponding to the task distribution node according to the task distribution node corresponding to the feedback signal.

[0115] The feedback signal is determined by the consensus deviation of multiple model tasks in the AI ​​integrated large model.

[0116] This application presents a comprehensive model for mutual verification among multiple models. It features vectorization of data processing, data storage and identification, and data reasoning capabilities. The multi-model consensus determines the deviation and ensures the consistency of the output results. For example, the legal requirement for vector matching must be consistent with the legal reasoning. This addresses the output deviation of a single model. For instance, the Embedding model may match the wrong legal vector due to data noise, but the LLM discovers the conflict through semantic reasoning, i.e., the cause of the data noise, and generates a deviation signal to trigger correction.

[0117] Incremental fine-tuning is an auxiliary mechanism for model correction triggered by feedback signals. However, it only corrects the large model corresponding to the problematic node, rather than correcting all models simultaneously. It is a localized correction and collaborative technique, rather than correcting the whole model when there is a problem. This prevents the overall failure caused by group model correction errors. It is a targeted error handling, rather than correcting during training, which consumes less time and resources.

[0118] This application presents a heterogeneous integrated large model with a collaborative reasoning framework, enabling joint optimization of semantic understanding, structured feature extraction, and logical reasoning in legal texts.

[0119] In this application, the Embedding model is a hierarchical vector of legal text generated by domain-adaptive contrastive learning;

[0120] Vector databases achieve similarity searches of legal validity hierarchy constraints by constructing a hierarchical retrieval structure based on PQ quantization indexes.

[0121] Structured databases are relational storage based on legal knowledge graphs, enabling rapid association;

[0122] The large language model is an interpretable path for generating discretionary recommendations by fine-tuning legal instructions.

[0123] Structured databases, in practical implementation, encompass various types of structured data. Examples include: GBase8a, a novel analytical database suitable for the business analytics and business intelligence market; GBase 8s, a high-end transactional database suitable for OLTP applications; GBase8d, for large-scale directory service systems; and high-speed in-memory databases such as GBase 8m / AltiBase. In actual implementation, GBase8a can reduce I / O to 1 / 10 of traditional international databases and boasts speeds 10-200 times faster than general-purpose databases in handling massive amounts of data. GBase8s employs domestically developed cryptographic algorithms and hardware encryption technology.

[0124] In practical implementation:

[0125] The vector database uses Faiss, also known as dense vectors, to process massive amounts of data in high-dimensional space, perform similarity clustering, and configure retrieval methods.

[0126] The large-scale assisted law enforcement model adopts the Qwen2_7B model. This model also incorporates a reasoning-based large-scale model, based on the Transformer architecture, and integrates innovative technologies such as the Hybrid Expert (MoE) architecture, making it suitable for efficient reasoning and multi-task processing scenarios. The word embedding tool uses bge-large-zh-v1.5, focusing on retrieval-enhanced large-scale language models. Example 3:

[0127] The task orchestration framework is used to determine the data flow and topological invariants of task distribution data through a unique data pipeline. In specific implementation, the unique data pipeline has a routing isolation mechanism and a protocol encapsulation method. Each task distribution data is allocated an independent virtual channel. Through the hash consistency algorithm, the data flow path is dynamically mapped. Moreover, the protocol encapsulation method ensures the atomicity and consistency of data transmission. Combined with topological invariants, i.e., the non-selectability of task paths, there is no loss of corresponding data obtained by each sub-model and database during data transmission. During task processing, the task flow curvature remains unchanged.

[0128] Based on the data flow, determine the task feature vector; the data flow is a dynamic attribute.

[0129] The task flow curvature is determined based on topological invariants; topological invariants are structural properties.

[0130] Based on the task feature vector and task flow curvature, the model nodes corresponding to the task flow are determined; among them, the model nodes are the task processing ports of different models in the auxiliary law enforcement big model.

[0131] By combining dynamic and structural attributes, instead of just analyzing data structure and the number of nodes, we prevent matching deviations caused by separating dynamic data features from process structure features. The combination of the two drives the model nodes, eliminating the need to consider process complexity. We only quantify process complexity through curvature and dynamically adjust the number of model nodes. For example, when the curvature is high, we allocate large models, and when the curvature is low, we allocate lightweight sub-models, reducing the overall consumption of computing resources.

[0132] Finally, based on the task feature vector and task flow curvature, the corresponding model nodes are determined, ensuring uniqueness during model adaptation. (See also...) Figure 4 When reading unstructured data from a structured database, different databases and large models handle different tasks during the task processing process, achieving task collaboration while maintaining task independence. The results from different models can be mutually integrated, enabling rapid task processing.

[0133] Example 4:

[0134] The task distribution data extraction process in this application also includes:

[0135] Based on a single data pipeline, dynamic heterogeneous nodes of different models within the large-scale auxiliary law enforcement model are identified. These dynamic heterogeneous nodes are sub-model nodes with varying computational capabilities, and their type and number are dynamically adjusted according to the task. Examples include image reasoning nodes and voice reasoning nodes.

[0136] Based on dynamic heterogeneous nodes, task description text is matched in law enforcement scene data; the task description text includes correlation feature data based on spatiotemporal dependencies, and the correlation feature data is inference prompt data that conforms to the processing of the large-scale auxiliary law enforcement model; the task description text is law enforcement scene data, and the structured text generated according to the requirements of law enforcement tasks is used to characterize the correlation features of violation time, violation location and violation event.

[0137] Based on the task description text, task flow data of different models is generated. After the task description is fundamentally decomposed into multiple sub-tasks, model nodes of the execution sequence of the sub-tasks are generated, such as: spatial positioning node, event verification node, legal matching node, and discretion calculation node.

[0138] The unique data pipeline of this application can connect to different models in the large-scale auxiliary law enforcement model, so dynamic heterogeneous nodes can be identified. Dynamic heterogeneous nodes are processing nodes for different models to handle their respective tasks. Through the processing nodes, the different data extracted by different models through the unique data pipeline will match the corresponding task description text, thereby determining the law enforcement case and law enforcement provisions corresponding to the incident scene data. These law enforcement cases and law enforcement provisions are generated by reasoning prompts to realize the task description text and generate task flow data.

[0139] This application combines the dynamic adjustment computing power of the data pipeline load in dynamic heterogeneous nodes with the task requirement information in the task description text to prevent analysis delays and errors caused by mismatch between node capabilities and task requirements.

[0140] This application uses reasoning hints based on task description text to determine the sub-tasks to be prioritized during task flow decomposition, thereby avoiding the problem of blind task flow decomposition leading to reasoning errors in the large-scale auxiliary law enforcement model.

[0141] This application provides diverse computing resources for multiple subtasks in a task flow through dynamic heterogeneous nodes. The decomposition of task flow data, in turn, drives the dynamic adjustment of nodes, resulting in more accurate results when processing complex tasks.

[0142] Example 5:

[0143] This application loads task distribution data into an auxiliary law enforcement model to determine sub-legal texts and their corresponding parent legal texts, including:

[0144] The task distribution data is parsed into semantic feature vectors and context feature vectors. The semantic feature vectors are low-dimensional vectors that reflect the core semantics of illegal behavior during task distribution, while the context feature vectors are vectors that reflect the contextual information of the task data, mainly used to indicate the event, location, and related behaviors of the illegal event.

[0145] The semantic feature vectors are semantically matched using an auxiliary law enforcement model to generate a sub-legal text set. The sub-legal text set includes legal provisions, judicial interpretations, and historical precedents. This application directly matches the data semantics of the task distribution data with the relevant legal provisions. In actual implementation, it is necessary to combine specific legal provisions, judicial interpretations, historical precedents, and other information to match the semantic feature vectors and legal text vectors based on semantic similarity.

[0146] The context feature vectors are used to perform hierarchical backtracking based on the hierarchical structure of the auxiliary law enforcement model to determine the corresponding initial parent legal text. The hierarchical structure represents the effectiveness level of the legal system and is used to distinguish between local laws and national unified superior laws. The backtracking is triggered by the sub-legal text and searches upwards for the basis for the formulation of the sub-legal text or the related superior legal text, and finally determines the mismatched superior legal text.

[0147] This application compares the hierarchy of legal texts with the initial parent legal text to identify sub-legal texts with complete hierarchical chains and their corresponding parent legal texts. The complete hierarchical chain represents the scope of authority and the sequence of effective dates of the legal texts. The hierarchy comparison in this application is tailored to specific legal provisions and judicial interpretations, comparing the legal texts' rank of authority. For example, the order is: Constitution > Conventional Law > Administrative Law > Local Regulations. This comparison determines the hierarchy of legal texts and the sequence of their effective dates, establishing the relationship between the sub-legal texts and parent legal texts. The scope of authority indicates the applicable geographical area, subject matter, and applicable illegal acts of the legal text, and also determines the order of the legal texts' effective dates, preventing the use of outdated laws that are inapplicable to current illegal events.

[0148] This application, based on semantic feature vectors, ensures a strong semantic correlation between the set of sub-legal texts and the core data of law enforcement events. Contextual feature vectors, through backtracking to higher-level laws, identify the associated parent legal texts. Dual-vector parsing and dual-path matching prevent incomplete legal text coverage. The set of sub-legal texts provides the triggering basis, while the initial parent legal text provides the higher-level law. By comparing the hierarchy of legal force, conflicts in legal force levels and temporal order are prevented, thus avoiding unreasonable enforcement recommendations or contradictory penalty bases. The association between contextual features and parent legal texts binds the spatiotemporal and plot details of the data with the additional constraints of the law, resulting in more accurate recommendations for both the basis and outcome of penalties.

[0149] Specifically, in determining the sub-legal texts and parent legal texts, the task distribution data is parsed into semantic feature vectors and contextual feature vectors. For example, semantic feature vectors for enforcement scenarios and enforcement types are used. Contextual feature vectors are used to determine the causal relationship before and after enforcement, matching corresponding enforcement cases and generating a collection of legal texts for those cases. This collection includes case text data and case data for legal provisions, forming a set of spontaneous legal texts. During this process, it is also necessary to consider whether the use of the sub-legal texts in the current enforcement scenario would lead to legal contradictions or inconsistencies between higher and lower-level laws. By tracing back to higher-level laws, the parent legal text is determined. Through a comparison of the effectiveness of the parent and sub-legal texts, a complete hierarchical chain between the two sub-legal texts and the parent legal text is generated, ensuring that there are no errors in the scope of authority and effective time of the law. For specific implementation details, please refer to [link to relevant documentation]. Figure 5 By analyzing fine information and violated legal provisions, the reasonableness of legal disciplinary measures can be determined. The sub-legal texts and corresponding cases can be displayed on the enforcement terminal 1's screen.

[0150] In the specific reasoning process:

[0151] Law enforcement officers use the camera on enforcement terminal 1 to collect on-site photos / videos, the microphone to collect interrogation recordings, and the touchscreen to input written transcripts, thus collecting on-site data.

[0152] Examples include: illegal water extraction volume, location of river channel damage, and statements from parties involved. Data types include structured numerical data, unstructured text, and images.

[0153] The task orchestration framework cleans, denoises, and unifies the format of the raw data before distributing tasks.

[0154] During the data pipeline configuration process, for example: based on the data type (image, text) or law enforcement scenario (illegal water extraction permit, illegal river sand mining), assign a unique data pipeline to each type of task. The unique data pipeline IDs are: PIPE_001 - image, PIPE_002 - text, to ensure data isolation and priority scheduling, with emergency scenario data being processed first.

[0155] Task distribution: The cleaned data is packaged into task distribution data, including scene tags, timestamps, and data pipeline IDs, and pushed to the inference module of the large-scale auxiliary law enforcement model.

[0156] The auxiliary law enforcement large-scale model inference module loads task distribution data and uses a large model, such as a finely tuned LLaMA-3 model for the legal domain, to perform semantic understanding and legal knowledge matching. The matching includes:

[0157] Sub-legal text matching: Identify keywords of violations in task distribution data, such as "without water extraction permit" and "excessive water extraction", and retrieve sub-legal texts bound to the first discretionary benchmark from the legal text library, down to the specific legal provisions.

[0158] Parent legal text association: Based on the superior law or judicial interpretation of the child legal text, associate the parent legal text and extract the bound second discretionary benchmark information, which includes: the penalty range for excessive water withdrawal is 1-3 times the water resource fee payable.

[0159] The vector similarity calculation module of this application vectorizes the task distribution data and the child / parent legal text. For example, the specific process includes:

[0160] Data Encoding: Using Sentence-BERT or CLIP models, the task distribution data and on-site photos show a riverbed sand extraction volume of 500m³. 3 Encode the legal text with the child / parent text, for example: unauthorized sand mining exceeding 300m. 3 This is a serious violation and should be encoded as a high-dimensional vector, such as 768-dimensional.

[0161] Similarity calculation: The similarity value between the two sets of vectors is calculated using the cosine similarity formula. Under normal circumstances, the range is 0-1. The output is the similarity of the sub-legal text S1 = 0.85; the similarity of the parent legal text S2 = 0.78.

[0162] Finally: Based on the similarity S1 of the sub-legal text and the similarity S2 of the parent legal text, combined with the discretionary benchmark information bound to the legal text, the specific auxiliary law enforcement threshold information is determined and displayed on the terminal interface:

[0163] Example: Threshold rule: If S1≥0.8 and S2≥0.7, generate a suggestion with a high correlation threshold, such as: suggest the maximum penalty according to the first discretionary benchmark; if S1<0.6, generate a suggestion with a low correlation threshold, and supplementary on-site evidence is required.

[0164] Finally, in the visualization output, that is, when displayed on the law enforcement terminal 1, the threshold information is displayed through the touch screen of the law enforcement terminal 1. The red highlight is the highly relevant label + specific discretionary suggestions, and it can be exported to PDF / Word format as an auxiliary basis for law enforcement documents.

[0165] Example 6:

[0166] This application loads task distribution data into an auxiliary law enforcement model to determine sub-legal texts and their corresponding parent legal texts. During this process, it also generates specific recommended sequences that are displayed on law enforcement terminal 1, including:

[0167] Configure a legislative intent analysis mechanism in the large-scale model for assisting law enforcement, determine the priority relationship between different legal texts, and generate a legal validity topology network based on statute of limitations markers;

[0168] In this application, the legislative intent analysis mechanism uses natural language processing to analyze the legislative purpose and policy orientation of legal texts, for example: development priority and ecological protection. Based on the legislative intent, it determines the applicability priority between legal texts, and then constructs a graph-structured legal effect topology network using the time attributes of the legal texts' effective date, amendment date, and repeal date, with legal texts as nodes and priority relationships and time limit markers as edges. This application can display legal texts, their corresponding enacting authorities, and implementation dates by retrieving and analyzing data. See also... Figure 6 You can find more specific legal text information.

[0169] In response to the comparison of validity hierarchy, the legal validity topology network is used to assign timeliness and validity weights to sub-legal texts with complete hierarchical chains and their corresponding parent legal texts, generating an applicable recommendation sequence.

[0170] This application assigns different weights to legal texts based on the comparison of their legal validity ranks, generates a list of legal texts ranked according to their weights, and recommends them to law enforcement personnel. For example, an administrative regulation, although of lower legal validity rank, may have a legislative intent of "emergency management." In a scenario of sudden flooding, it should take precedence over higher-ranking general laws. This solution accurately identifies this intent through the intent marking and weighting mechanism of the topology network and then recommends it to law enforcement personnel. Furthermore, this application provides a dynamic basis for timeliness weighting through the timeliness marking of the legal validity topology network. The resulting recommendation list can further promote topology network updates when new legal texts are added, improving the timeliness management of legal texts.

[0171] This application integrates a complete hierarchical chain with a legal effect topology network to supplement the policy guidance of legislative intent, so that the recommended enforcement opinions in the enforcement process take into account policy needs and conform to the legal structure.

[0172] In specific implementation, this application is exemplified by:

[0173] A legal validity quantification model is constructed, with the basic weight of the superior law being 1.0, and the weight decreasing by 0.2 for each lower level of validity, for example: administrative regulations 0.8, local regulations 0.6; thus, in the process of law enforcement, the recommended legal cases and legal texts are the optimal solution set.

[0174] Example 7:

[0175] When loading task distribution data into the auxiliary law enforcement model and determining the sub-legal texts and their corresponding parent legal texts, this application not only generates applicable recommendation sequences, but also generates different phenomena such as sub-legal texts having multiple parent legal texts and parent legal texts having multiple sub-legal texts.

[0176] Based on the complete hierarchical chain, the decision bias of the law enforcement site data is determined. First, after the complete hierarchical chain is a legal text association chain with legal validity hierarchy and effective time sequence, the degree of deviation in time, location, and illegal circumstances is determined by the differences between the characteristics of the law enforcement site data and the preset scenarios of the legal text in the complete hierarchical chain.

[0177] Based on decision-making deviations, a discretionary threshold for applicable legal deviations is determined. This discretionary threshold conforms to the proviso and catch-all clauses in the legal text. In this application, the discretionary threshold conforms to the proviso, which is an exception in the legal text that excludes a specific act, used to increase or decrease penalties, or waive penalties, in special circumstances. The catch-all clause refers to other general situations stipulated in the legal text, such as: other acts that seriously damage the river's ecology will be punished in accordance with this clause. Furthermore, through keyword matching and clause matching, the punitive measures for violations are determined, and the scope of penalties is quantified.

[0178] Based on the discretionary threshold, a hierarchical reasoning path based on feature reconstruction is generated, determining the implicit connections between sub-legal texts and their corresponding parent legal texts. Feature reconstruction involves recombining the features of the law enforcement field data to match the specific reasoning requirements of the legal texts, such as triggering proviso clauses and catch-all clauses. This combines the reasoning steps from law enforcement field data to legal texts to generate a reasoning path. Finally, based on the unstated but existing connections between legal texts, a determination is made, and based on the strength of the implicit connections and the degree of matching of the law enforcement field data features, a specific recommendation sequence for the sub-legal texts and their corresponding parent legal texts is generated. In other words, a recommendation sequence for sub-legal texts and their corresponding parent legal texts is generated based on the correlation between implicit connections and law enforcement field data.

[0179] Example 8:

[0180] In the process of determining discretionary authority on the terminal device, this application also processes the statutory discretionary authority in the following way, thereby generating specific information recommended to the user.

[0181] In calculating the vector similarity between the sub-legal text and the parent legal text and the task distribution data:

[0182] First, an interpretation consistency verification channel is constructed between the initially matched sub-legal texts and the parent legal texts. A legislative terminology alignment model is used to detect the logical compatibility of their statements regarding the constituent elements. This interpretation consistency verification channel is used to compare the sub-legal texts and the service legal texts to determine logical consistency in interpretation, preventing contradictory legal clauses from leading to incorrect opinions issued to law enforcement or queried enforcement opinions. The legislative alignment model is an NLP model constructed by aligning legislation through word embeddings and knowledge graphs. For example, the NLP model aligns illegal sand mining and river ecological damage as related legislative terms; illegal sand mining necessarily implies river ecological damage. Logical compatibility means that there are no contradictions in the statements of the constituent elements—subject, behavior, and consequences—between the sub-legal texts and the parent legal texts.

[0183] This application triggers a validity hierarchy calibration mechanism when it detects a directional deviation between the penalty magnitude vector of a sub-legal text and the authorization range vector of a parent legal text. This mechanism traces the legal knowledge graph back to find common overarching concepts and performs vector space projection correction. The trigger magnitude vector represents the numerical vector indicating the penalty range of the sub-legal text, such as the amount of the fine. The authorization range vector represents the discretionary range allowed by the parent legal text. That is, the parent legal text stipulates different illegal acts that result in either aggravated or mitigated punishment, possessing a discretionary range. The sub-legal text is used to recommend specific enforcement values ​​to law enforcement officers and displays them on the law enforcement terminal 1 for their reference. A directional deviation indicates that the penalty magnitude vector exceeds the authorization range vector, and the two have no intersection. In this case, it is considered a directional deviation, and neither the corresponding sub-legal text nor the parent legal text can be selected, creating a contradiction. At this point, through the effectiveness hierarchy calibration mechanism, that is, by tracing the source of the legal knowledge graph to the superior law, the vector deviation is corrected, thereby determining the target parent legal text. The vector space projection correction data is determined by tracing the source of the legal knowledge graph. After determining the target parent legal text, the deviation trigger amplitude vector is projected onto the authorization range of the parent legal text.

[0184] The vector boundary constraint rules for administrative discretion benchmarks are established to restrict the similarity calculation results to the effective area of ​​the hypercube formed by the parent legal text vector. The vector boundary constraint rules for administrative discretion benchmarks are based on the discretionary scope boundary stipulated by law, and corresponding constraint conditions are set accordingly. For example, the fine shall not exceed three times the illegal gains. The effective area of ​​the hypercube is the legal range defined by the parent legal text vector in the multidimensional space. That is, the vector similarity between the child legal text and the parent legal text and the task distribution data is consistent. The legal range defined by the child legal text and the parent legal text for each distribution task in the multidimensional space is consistent. That is, the final illegal opinion and illegal punishment behavior both conform to the effective area of ​​the hypercube.

[0185] Finally, for similarity matching results that exceed the statutory discretionary authority, an overreach warning is automatically generated and pushed to the administrative law enforcement reasonableness review interface. This application uses the overreach warning mechanism to automatically generate an overreach warning when a sub-legal text and a parent legal text are matched and the similarity exceeds the statutory discretionary authority, i.e., exceeds the statutory punitive measures. The warning outputs that the similarity matching result exceeds the authorized punitive scope of the parent legal text, and finally pushes the warning information to the review module connected to the law enforcement system, which is configured with an API port.

[0186] Example 9:

[0187] In the process of generating auxiliary law enforcement threshold information, this application:

[0188] This application constructs a multi-dimensional feature extraction channel for water conservancy law enforcement scenarios based on vector similarity. This application will generate a multi-dimensional feature parallel extraction link based on time, space, behavior and consequences in the water conservancy law enforcement scenario through the similarity between parent legal text and child legal text, that is, the semantic correlation between legal text and task distribution data. For example: time dimension - the time period when the illegal behavior occurs; spatial dimension - the ecologically sensitive period of the river; behavioral dimension - the type of sand mining tool.

[0189] This application identifies dynamic law enforcement response coefficients for current law enforcement scenarios using a geospatial correlation model. The geospatial correlation model is a geographic information system that analyzes spatial data and the relationship between law enforcement responses. In the current law enforcement scenario, it determines the response strength and impact of spatial characteristics of the law enforcement scenario on the ecological environment by analyzing the spatial correlation between illegal behavior, the current law enforcement scenario, and geographic space. It is used to characterize whether the area corresponding to the current law enforcement scenario needs to increase or decrease law enforcement efforts, and is integrated into the calculation of discretionary benchmarks to determine whether the discretionary value should be increased or decreased.

[0190] This application employs an adversarial training strategy between legal discretionary benchmarks and ecological carrying capacity. When the ecological impact vector of a detected illegal act exceeds a preset carrying capacity threshold, an emergency enforcement gradient enhancement mechanism is activated. Through legal discretionary benchmarks (i.e., the magnitude of fines) and ecological carrying capacity (i.e., the river's self-recovery ability), dynamic adversarial learning is achieved to optimize the threshold calculation training method—that is, a strategy that maximizes legal effectiveness while minimizing ecological damage. Furthermore, during the inspection process, if illegal acts are found, the multi-dimensional impact vector of the illegal act on the ecological environment is assessed, such as suspended solids concentration and biodiversity. Consequently, when the ecological impact exceeds the limit, enforcement intensity and enforcement rules are automatically increased.

[0191] When this application outputs a spatiotemporally adaptive enforcement threshold range, it simultaneously generates a set of derived decision parameters containing suggestions on enforcement intensity and guidance on remediation measures. In actual implementation, it also combines time and space to determine a spatiotemporally adaptive enforcement threshold range. The enforcement threshold range is dynamically adjusted according to time and space. After adjustment, a set of derived local measurement parameters is generated, including discretionary thresholds and additional decision information, such as river dredging and vegetation planting for ecological restoration or other derived decision information, in order to carry out adaptive restoration for reparable ecological environments.

[0192] Example 10:

[0193] When generating auxiliary law enforcement threshold information, this application provides law enforcement opinions that are simultaneously updated to the law enforcement knowledge base of law enforcement terminal 1. The opinions are also analyzed to ensure their accuracy and reliability. Specifically:

[0194] Pre-configured enforcement prompts are used to establish an enforcement decision-making effectiveness evaluation model. Causal correlation analysis is performed between the results of threshold-triggered enforcement actions and water conservancy indicators. The enforcement prompts serve as structured texts to guide and assist the large-scale enforcement model in generating specific enforcement recommendations. Through these prompts, a machine learning model is constructed to quantify the impact of enforcement actions on water conservancy indicators. Water conservancy indicators are quantitative parameters reflecting the state of the water conservancy system, such as suspended solids concentration and biodiversity index. Then, by triggering enforcement threshold recommendations, specific enforcement actions and their results are determined. A causal inference algorithm is used to determine the causal relationship between enforcement actions and water conservancy indicators, i.e., the causal relationship between punitive measures and water conservancy improvements.

[0195] Finally, based on causal correlation analysis, a personalized threshold suggestion scheme with spatiotemporal markers is generated and synchronously updated to the law enforcement knowledge base of law enforcement terminal 1. This application determines the temporal and spatial attributes of the threshold suggestion through causal correlation analysis, combines the customized threshold with spatiotemporal characteristics, i.e., the customized law enforcement opinion, and finally stores the law enforcement opinion in the law enforcement knowledge base.

[0196] This application utilizes the guidance function of enforcement prompts to verify the effectiveness of punitive measures in improving the actual water environment and can recommend enforcement data that better reflects the enforcement situation. Causal analysis also prevents a disconnect between the punitive results and the actual application of the punitive measures. Finally, through prompts and a knowledge base, positive punitive measures can be updated in real time to reflect the latest punitive outcomes.

[0197] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assisting water conservancy administrative law enforcement based on a large model, characterized in that, Applied to law enforcement terminals, the law enforcement terminals load auxiliary law enforcement large-scale models that analyze on-site law enforcement data. The method includes: Task distribution data is extracted from law enforcement field data through a task orchestration framework; different task distribution data in the task orchestration framework are configured with a unique data pipeline. The task distribution data is loaded into the auxiliary law enforcement model to determine the sub-legal texts and their corresponding parent legal texts; the sub-legal texts are bound to the first law enforcement discretion benchmark information, and the parent legal texts are bound to the second law enforcement discretion benchmark information. Calculate the vector similarity between the sub-legal text and the parent legal text and the task distribution data. Based on the vector similarity, generate auxiliary law enforcement threshold information on the law enforcement terminal.

2. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The aforementioned auxiliary law enforcement big model is an AI integrated big model with a multi-model collaborative architecture; wherein, the multi-model collaborative architecture consists of an embedding model, a vector database, a structured database, and a large language model; The multi-model collaborative architecture is configured with an incremental fine-tuning mechanism. When a feedback signal is received, the incremental fine-tuning mechanism corrects the gradient of the target model corresponding to the task distribution node according to the task distribution node corresponding to the feedback signal. The feedback signal is generated by the consensus deviation of multiple model tasks in the AI ​​integrated large model.

3. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The task orchestration framework is used to determine the data flow and topological invariants of task distribution data through a unique data pipeline; Based on the data stream, determine the task feature vector; Determine the task flow curvature based on topological invariants; Based on the task feature vector and task flow curvature, the model nodes corresponding to the task flow are determined; among them, the model nodes are the task processing ports of different models in the auxiliary law enforcement big model.

4. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The task distribution data extraction process also includes: Based on the unique data pipeline, identify the dynamic heterogeneous nodes of different models in the auxiliary law enforcement big model; Based on dynamic heterogeneous nodes, the task description text of the law enforcement scene data is matched; the task description text includes associated feature data based on spatiotemporal dependencies, and the associated feature data is inference prompt data that conforms to the processing of the large-scale auxiliary law enforcement model; Based on the task description text, generate multiple model task flow data.

5. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The process of loading task distribution data into the auxiliary law enforcement model and determining sub-legal texts and their corresponding parent legal texts includes: The task distribution data is parsed into semantic feature vectors and context feature vectors; The semantic feature vectors are semantically matched using an auxiliary law enforcement model to generate a set of sub-legal texts; the set of sub-legal texts includes legal provisions, judicial interpretations, and historical precedents. The context feature vectors are used to perform hierarchical backtracking based on the auxiliary law enforcement model to determine the corresponding initial parent legal text. The set of sub-legal texts is compared with the initial parent legal text in terms of their legal effect hierarchy to determine the sub-legal texts with complete hierarchical chains and their corresponding parent legal texts; the complete hierarchical chain is used to represent the scope of authority and the time sequence of effectiveness of the legal texts.

6. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 5, characterized in that, The step of loading task distribution data into the auxiliary law enforcement model and determining the sub-legal texts and their corresponding parent legal texts also includes: Configure a legislative intent analysis mechanism in the large-scale model for assisting law enforcement, determine the priority relationship between different legal texts, and generate a legal validity topology network based on statute of limitations markers; In response to the comparison of validity hierarchy, the legal validity topology network is used to assign timeliness and validity weights to sub-legal texts with complete hierarchical chains and their corresponding parent legal texts, generating an applicable recommendation sequence.

7. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 5, characterized in that, The step of loading task distribution data into the auxiliary law enforcement model and determining the sub-legal texts and their corresponding parent legal texts also includes: Based on the complete hierarchical chain, determine the decision bias of on-site law enforcement data; Based on the decision-making deviation, determine the discretionary threshold for applicable legal deviation; wherein, the discretionary threshold conforms to the proviso and catch-all clauses in the legal text; Based on the discretionary threshold, a hierarchical reasoning path based on feature reconstruction is generated to determine the implicit connection edges between the sub-legal texts and their corresponding parent legal texts. Based on the correlation between implicit connection edges and on-site law enforcement data, a recommended sequence of sub-legal texts and their corresponding parent legal texts is generated.

8. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The calculation of the vector similarity between the sub-legal text and the parent legal text and the task distribution data includes: An interpretation consistency verification channel is constructed between the initially matched sub-legal text and the parent legal text, and the logical compatibility of the two in the expression of the constituent elements is detected by the legislative terminology alignment model. When a deviation in direction is detected between the penalty magnitude vector of a sub-legal text and the authorization scope vector of the parent legal text, a validity hierarchy calibration mechanism is triggered to trace the source along the legal knowledge graph to find common superior concepts and perform vector space projection correction. Establish vector boundary constraint rules for administrative discretion benchmarks to restrict the similarity calculation results to the effective area of ​​the hypercube formed by the parent legal text vector; For similarity matching results that exceed the statutory discretionary authority, an overreach warning will be automatically generated and pushed to the administrative law enforcement rationality review interface.

9. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The generation of auxiliary law enforcement threshold information includes: Based on vector similarity, a multi-dimensional feature extraction channel for water conservancy law enforcement scenarios is constructed. Dynamic law enforcement response coefficients are generated by identifying the current law enforcement scenario through a geospatial correlation model; A training strategy that pits legal discretion against ecological carrying capacity is adopted. When the ecological impact vector of illegal behavior exceeds the preset carrying capacity threshold, an emergency enforcement gradient enhancement mechanism is activated. When outputting a spatiotemporally adaptive law enforcement threshold range, a set of derived decision parameters containing suggestions on enforcement intensity and guidance on remedial measures is generated simultaneously.

10. The water conservancy administrative law enforcement auxiliary method based on a large model as described in claim 1, characterized in that, The generation of auxiliary law enforcement threshold information also includes: Pre-configure enforcement prompts, establish an enforcement decision-making effectiveness evaluation model, and conduct causal correlation analysis between the enforcement action results triggered by thresholds and water conservancy indicators; Based on causal relationship analysis, personalized threshold suggestions with spatiotemporal markers are generated and synchronously updated to the law enforcement knowledge base of the law enforcement terminal.

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