A low-intervention and efficient regulation method and system for large language models in digital twin water conservancy systems

Abstract

The application provides a low-intervention and efficient regulation method and system for a large language model in a digital twin water conservancy system, comprising the following steps: (1) constructing a non-guided test set in a task-free and explicit target-free environment, performing multiple rounds of generation sampling on the model, and recording the discourse structure, theme progression and semantic coherence in the output; (2) performing semantic embedding and style embedding extraction and trajectory clustering analysis on the original output data to obtain a natural generation behavior path graph and a high-impact expression change point; (3) injecting a perturbation template into the high-impact expression change point and adjusting the language structure to obtain an optimized expression path of tone and structure trend; (4) comparing and analyzing the semantic theme changes of the optimized expression path and the original output data to evaluate the intervention effect of the perturbation insertion on the overall expression behavior. The application can minimize the intervention cost and accurately guide the model generation path, thereby improving the controllability and stability of the large model output in the digital twin water conservancy scenario.

Description

Technical Field

[0001] This invention relates to the fields of water conservancy engineering and artificial intelligence, specifically to a low-intervention, high-efficiency control method and system for large language models in digital twin water conservancy systems. Background Technology

[0002] Digital twin hydraulic engineering models suffer from insufficient controllability and stability under unguided conditions. With the deepening deployment of digital twin technology in hydraulic engineering systems, natural language generation capabilities based on large language models are widely used in key scenarios such as intelligent scheduling interpretation, monitoring data broadcasting, and emergency early warning response. However, in unguided states without explicit prompts or tasks, model output often lacks controllable directionality, easily exhibiting expression structure shifts, topic jumps, and semantic drift. Recent research, through analyzing the expression continuity, structural tendencies, and emergent behavioral inertia of multi-generation generation, has revealed the self-driven flow trend within the model. However, in the field of hydraulic digital twins, this process has not yet been observed and intervened in real time, leading to unpredictable deviations in global behavior caused by minor perturbations, thus increasing the risk of system operation.

[0003] Current technologies primarily rely on targeted retraining and reinforcement learning to shape the behavior of large models. For digital twin scenarios, to improve the controllability and style consistency of generated text, methods such as deterministic Promote injection, soft expression templates, or RLHF are commonly used for model retraining or incremental fine-tuning. These methods can, to some extent, calibrate the generation path, prevent semantic bias and style inconsistency, and improve the reliability of scheduling interpretations and warning response texts. Furthermore, researchers are constructing unsupervised test sets and combining them with trajectory clustering algorithms to identify high-impact expression turning points, enabling micro-adjustments at key nodes to maintain discourse coherence and thematic progression throughout multiple generation rounds.

[0004] However, existing methods suffer from drawbacks such as high cost, weak generalization ability, and lack of traceable intervention mechanisms. Targeted retraining and reinforcement learning retraining require large amounts of labeled data and computational resources, and are prone to overfitting on specific tasks, reducing their adaptability to various application scenarios. Furthermore, these methods often modify model weights only once, lacking systematic observation of internal flow structure gradients and refined perturbation strategies, making intervention costly and difficult to accurately assess. In addition, the lack of dynamic tracking and evaluation mechanisms for generated trajectories makes timely adjustments and optimizations difficult, leaving digital twin systems facing the dual challenges of semantic drift and style instability in actual operation. Therefore, there is an urgent need for a low-intervention, high-efficiency control method based on internal flow direction observation to achieve cost-effective, structurally traceable, and highly generalizable model behavior management, thereby improving the operational efficiency and safety of large-scale digital twin hydraulic models. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a low-intervention, high-efficiency control method and system for large language models in digital twin water conservancy systems. It constructs a control mechanism based on non-guided input triggering, trajectory analysis to identify key semantic nodes, and perturbation injection, which can minimize intervention costs and accurately guide the model generation path, improve the controllability and stability of large model output in digital twin water conservancy scenarios, and effectively solve the problems of high risk of model style drift and semantic deviation in existing technologies.

[0006] The technical solution provided by this invention is a low-intervention, high-efficiency control method for large language models in digital twin water conservancy systems, comprising the following steps:

[0007] (1) Construct an unguided test set in a prompt environment without tasks or clear goals, perform multiple rounds of generation sampling on the model, record the discourse structure, topic progression and semantic coherence in its natural output, and capture the performance differences of the model under different generation paths by modeling the probability distribution of the output sequence, and systematically observe the natural generation ability of the model.

[0008] (2) Semantic embedding and style embedding extraction and trajectory clustering analysis are performed on the discourse structure, theme progression and semantic coherence data obtained in step (1) to obtain the natural generation behavior path map and high-influence expression change points.

[0009] (3) The high-impact expression change points obtained in step (2) are subjected to perturbation template injection and language structure adjustment to obtain an expression path with optimized tone and structural trend, which is used for intervention strategy implementation;

[0010] (4) Perform semantic topic change comparison analysis on the optimized expression path obtained in step (3) and the original output data obtained in step (1) to evaluate the intervention effect of perturbation insertion on the overall expression behavior.

[0011] Furthermore, the generation logic for constructing the unguided test set in step (1) by defining a random sampling function based on a uniform distribution is as follows:

[0012] in Indicates no task hint space Sampling in a uniform distribution, This refers to the size of the test set.

[0013] Furthermore, the probabilistic modeling method for multi-round generation sampling in step (1) is as follows:

[0014]

[0015] in Indicates the first The text sequence generated by the round, This represents the set of parameters for a large language model, specifically including model weights, bias terms, and learnable parameters related to the generation path. Given the sequence length, output diversity under different generation paths is modeled through sequence probability decomposition.

[0016] Furthermore, in step (1), to quantify the semantic coherence and topic progression stability of the content generated by the model, a semantic consistency index between generated texts is calculated based on embedding similarity. The semantic coherence quantification method is as follows:

[0017]

[0018] in The cosine similarity function is used. For the first The embedding vectors of semantic segments are used to evaluate the topic progression stability by the mean similarity between adjacent semantic segments.

[0019] Furthermore, the specific steps of step (2) are as follows:

[0020] (2-1) First, a dual-channel encoding architecture is used to achieve multi-dimensional behavioral representation, and its joint feature extraction function is defined as:

[0021]

[0022] in For semantic encoders, For style encoders, The operator represents the concatenation of the orthogonal vectors of the two, establishing an embedding space with both semantic depth and stylistic features for subsequent analysis;

[0023] (2-2) Joint feature extraction of semantic embedding and style embedding is performed on the original behavior sequence obtained in step (1). The original behavior sequence is the original text output sequence generated by the large language model under the unguided test set without any intervention, including its discourse structure, topic progression and semantic coherence. The semantic vector and expression style features of the generated text are mapped to a unified representation space. The trajectory clustering algorithm optimized by dynamic time warping is used to construct the natural generation behavior path map of the language model. The trajectory similarity is measured by defining a loss function based on DTW distance. The function expression is as follows:

[0024]

[0025] in For the first Trajectory clusters, Let be the cluster center vector. The number of clusters;

[0026] (2-3) In the constructed path graph, by calculating the node influence propagation coefficient and topological centrality index, and combining the semantic mutation detection algorithm of the generated text, the expression change points with global structural influence are identified, and an intervention node priority evaluation system is established. The key node determination method is as follows:

[0027]

[0028] in , where is the weighting coefficient, and KL divergence measures the distributional differences between adjacent semantic segments.

[0029] Furthermore, in step (3), at the key points of expression turning marked in the trajectory analysis, a set of perturbation templates are used to adjust the tone and structural trend of the subsequent expression path through minimal language insertion. The injection intensity of the perturbation templates is controlled by a gating function to achieve local path correction under low intervention conditions. The intensity control method of the perturbation templates is as follows:

[0030]

[0031] in As a key node, Embedding node features For the Sigmoid function, These are learnable parameters.

[0032] Furthermore, in step (3), the predefined perturbation template embedding vector is fused with the original generated path features, and a sparse mask matrix is ​​used to constrain the scope of intervention. Under the premise of maintaining semantic coherence, the generated path is guided to shift in the expected direction. The fusion modeling method for path correction is as follows:

[0033]

[0034] in For the first Embedding vectors of semantic segments, It is a sparse mask matrix. For perturbation template embedding, This indicates element-wise multiplication;

[0035] Furthermore, in step (3), a bimodal alignment loss function is defined, while simultaneously constraining the semantic consistency of the generated path after correction and the magnitude of its deviation from the original path. The L1 regularization term is used to maintain the sparsity of the intervention operation, preventing model behavior distortion caused by excessive intervention. The loss function of the intervention effect optimization method is as follows:

[0036]

[0037] in Let represent the target embedding vector of the t-th semantic segment in the expected direction after perturbation intervention. The sparsity coefficient is... This indicates L1 regularization.

[0038] Furthermore, the specific steps of step (4) are as follows:

[0039] (4-1) First, a multidimensional quantitative index is constructed to evaluate the targeted control effect of perturbation insertion. The difference in semantic topic distribution before and after intervention is measured by cosine similarity and KL divergence. The semantic topic shift quantification method is as follows:

[0040]

[0041] in The cosine similarity function is used. , These represent the first and second days before and after the intervention, respectively. Embedding vectors of semantic segments, Indicates the first element in the generated text sequence. A semantic fragment, These represent the distribution of topics before and after the intervention;

[0042] (4-2) Based on the fluctuation characteristics analysis of the generated path after intervention, a dynamic stability evaluation function is introduced to analyze the fluctuation characteristics of the generated path. The path embedding covariance is calculated through a sliding window mechanism to measure the output stability of the model after intervention. The dynamic evaluation function for generation stability is as follows:

[0043]

[0044] in To adjust the sliding window size, The trace of the covariance matrix representing the path embedding. This represents the set of embedding vectors of continuous semantic segments from time step tw to t in the path generated after intervention;

[0045] (4-3) Combining the theme offset and stability indicators, an index-weighted comprehensive evaluation function is established to calculate the comprehensive intervention effect in a weighted manner, thereby achieving a quantitative grading of the intervention effect. The comprehensive intervention effect scoring method is as follows:

[0046]

[0047] in This is the normalization coefficient, used to balance the weights of the indicators.

[0048] Another technical solution provided by this invention: a low-intervention, high-efficiency control system for a large language model in a digital twin water conservancy system, comprising:

[0049] The unguided prompt sampling module is used to construct a non-task-oriented prompt input set to ensure the naturalness and non-goal-dependent nature of the language model's generation behavior. The output obtained through multiple rounds of sampling is used as raw behavioral data to analyze the model's natural expression tendency under uncontrolled conditions.

[0050] The generation trajectory modeling and inertial node recognition module is used to perform semantic modeling and behavior trajectory reconstruction on the original text generated by the model. Through sentence vector embedding, style embedding extraction and sequence clustering analysis, it identifies expression paths with stability and continuity in the model generation. Furthermore, by combining token-level attention weight changes, syntactic structure mutations and expression intensity changes, it marks structural nodes with potential behavior turning capabilities, namely expression inertia enhancement regions.

[0051] The perturbation strategy construction and insertion module calls the style fine-tuning morpheme library at the identified expression deflection nodes, selects structural semantic insertion words, and performs minimal language-level intervention.

[0052] The behavioral shift assessment module is used to quantify the changes in the model's expression before and after perturbation insertion. The assessment indicators cover multiple dimensions, including the degree of semantic shift, style trajectory changes, output topic migration, and structural consistency changes. Through visualization and trajectory comparison analysis, the magnitude and persistence of the shift in the overall generation behavior path caused by the micro-intervention are determined.

[0053] The beneficial effects of this invention are:

[0054] (1) This invention proposes a dynamic control mechanism based on real-time flow direction observation and key node perturbation. By introducing expression trajectory graph construction technology under unguided state and structured perturbation injection strategy, the adaptive intervention capability and global stability control capability of the model for generating behavioral paths are improved.

[0055] (2) The present invention can effectively capture the natural expression trajectory of the model and implement precise perturbation intervention, ensuring that the water conservancy digital twin system maintains semantic coherence and generation stability in a non-guided environment, and realizes low-cost and high-security intelligent decision support. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] This invention provides a low-intervention, high-efficiency control method for large language models in digital twin water conservancy systems, the specific steps of which include:

[0058] S1 Unguided State Test Pool Establishment

[0059] S2 Behavioral Trajectory Analysis and Feature Extraction

[0060] S3 Perturbation Injection

[0061] S4 Intervention Effectiveness Assessment

[0062] By establishing a mechanism through an unguided state test pool (S1), behavioral trajectory analysis and feature extraction (S2), perturbation injection (S3), and intervention effect evaluation (S4), precise monitoring and fine-tuning of the internal generation direction of a large-scale digital twin water conservancy model are achieved in an environment without explicit task prompts. Thus, based on capturing the model's natural expression trajectory, global behavioral shifts are effectively induced through key node perturbations of the minimum language template, significantly reducing control costs while ensuring the structural traceability and strong generalization ability of the model's control path. This enables quantitative evaluation and fine-grained control of the large-scale model's generation behavior in multiple scenarios such as water conservancy scheduling, monitoring data broadcasting, and emergency early warning, improving the scientific rigor and reliability of system early warning response and maintenance decisions.

[0063] In the technical solution of the present invention, the input of S1 is a prompting environment without tasks and without clear goals. The input is subjected to unguided test set construction and multiple rounds of generation sampling to obtain the discourse structure, topic progression and semantic coherence data of the model's natural output, which are used for behavioral path analysis in S2.

[0064] In a prompt-free environment without tasks or explicit goals, a non-instructive prompt evaluation set is constructed, and the model is sampled multiple times. The discourse structure, topic progression, and semantic coherence in its natural output are recorded to provide raw data support for subsequent behavioral path analysis. The generation logic of the non-instructive prompt evaluation set, based on a uniformly distributed random sampling function, is as follows:

[0065] in Indicates no task hint space Sampling in a uniform distribution, To ensure the test set size, random sampling was used to ensure the diversity and broad coverage of input scenarios.

[0066] A non-instructive prompt evaluation set is established in a prompt environment without tasks or explicit goals to systematically observe the model's natural generation capabilities. By defining a random sampling function, diverse test sets are generated under non-instructive input conditions to ensure coverage of a wide range of input scenarios. The probabilistic modeling method for multi-round generation sampling is as follows:

[0067]

[0068] in Indicates the first The text sequence generated by the round, This represents the set of parameters for a large language model, specifically including model weights, bias terms, and learnable parameters related to the generation path. Given the sequence length, output diversity under different generation paths is modeled through sequence probability decomposition.

[0069] Then, for each input sample, multiple rounds of generation sampling are performed, and the performance differences of the model under different generation paths are captured by modeling the probability distribution of the output sequence. To quantify the semantic coherence and topic progression stability of the generated content, a semantic consistency index between generated texts is further calculated based on embedding similarity, providing systematic data support for subsequent behavioral path analysis. The semantic coherence quantification method is as follows:

[0070]

[0071] in The cosine similarity function is used. For the first The embedding vectors of semantic segments are used to evaluate the topic progression stability by the mean similarity between adjacent semantic segments.

[0072] The input to S2 is the discourse structure, thematic progression, and semantic coherence data from S1. Semantic embedding and style embedding extraction, as well as trajectory clustering analysis, are performed on the input to obtain a naturally generated behavior path map and high-influence expression change points, which are used for the perturbation intervention structure node localization in S3.

[0073] Semantic embedding and style embedding are extracted from the original behavior sequence. A trajectory clustering algorithm is then used to construct a naturally generated behavior trajectory map for the language model. High-influence expression shifts are identified within this trajectory map and used as structural nodes for subsequent perturbation interventions. First, a dual-channel encoding architecture is used to achieve multi-dimensional behavior representation, with its joint feature extraction function defined as:

[0074]

[0075] in For semantic encoders, For style encoders, The operator represents the concatenation of orthogonal vectors of the two, establishing an embedding space with both semantic depth and stylistic characteristics for subsequent analysis.

[0076] Joint feature extraction using semantic embedding and style embedding is performed on the original behavior sequence obtained in S1. The original behavior sequence is the uninterrupted raw text output sequence generated by the large language model on an unsupervised test set, including its discourse structure, topic progression, and semantic coherence. The semantic vectors and expression style features of the generated text are mapped to a unified representation space. A trajectory clustering algorithm optimized by Dynamic Time Warping (DTW) is used to construct the natural generation behavior path map (expression trajectory map) of the language model. Trajectory similarity is measured by defining a loss function based on DTW distance, expressed as follows:

[0077]

[0078] in For the first Trajectory clusters, Let be the cluster center vector. This represents the number of clusters.

[0079] Then, in the constructed path graph, by calculating the node influence propagation coefficient and topological centrality index, and combining this with a semantic mutation detection algorithm for the generated text, high-influence expression shifts with global structural influence are identified, and an intervention node priority evaluation system is established. The key node determination method is as follows:

[0080]

[0081] in , where is the weighting coefficient, and KL divergence measures the distributional differences between adjacent semantic segments.

[0082] The input of S3 is the high-impact expression change point in S2. By injecting perturbation templates and adjusting the language structure of the input, an expression path with optimized tone and structural trends is obtained, which is used for the implementation of intervention strategies.

[0083] At key points of expression shifts marked in the generative trajectory analysis, a set of perturbation templates (such as soft expressions, negotiated connection structures, and syntactic reconstructions) are used to adjust the tone and structural trend of subsequent expression paths through minimal language insertion. First, the injection intensity of the perturbation templates is controlled by a gating function to achieve local path correction under low-intervention conditions. The method for controlling the intensity of the perturbation templates is as follows:

[0084]

[0085] in As a key node, Embedding node features For the Sigmoid function, These are learnable parameters.

[0086] The predefined perturbation template embedding vector is fused with the original generated path features for calculation. A sparse mask matrix is ​​used to constrain the scope of intervention, guiding the generated path to shift in the expected direction while maintaining semantic coherence. The fusion modeling method for path correction is as follows:

[0087]

[0088] in For the first Embedding vectors of semantic segments, It is a sparse mask matrix. For perturbation template embedding, This indicates element-wise multiplication.

[0089] Then, by defining a bimodal alignment loss function, and simultaneously constraining the semantic consistency of the corrected generated path and its deviation from the original path, an L1 regularization term is used to maintain the sparsity of the intervention operation, preventing model behavior distortion caused by excessive intervention. The intervention effect optimization method (loss function) is as follows:

[0090]

[0091] in Let represent the target embedding vector of the t-th semantic segment in the expected direction after perturbation intervention. This vector is generated using domain knowledge or a pre-set semantic template and is used to constrain the consistency between the corrected generation path and the expected semantic trend. The sparsity coefficient is... This indicates L1 regularization.

[0092] Table 1 Examples of perturbation templates

[0093]

[0094] The input to S4 is the optimized expression path in S3 and the original output data in S1. The semantic topic change comparison analysis is performed on the input to evaluate the intervention effect of perturbation insertion on the overall expression behavior.

[0095] By comparing the changes in semantic topics before and after the intervention, the intervention effect of perturbation insertion on overall expressive behavior is evaluated. First, a multidimensional quantitative index is constructed to assess the targeted regulatory effect of perturbation insertion. Cosine similarity and KL divergence are jointly used to measure the difference in semantic topic distribution before and after the intervention. The semantic topic shift quantification method is as follows:

[0096]

[0097] in The cosine similarity function is used. Represents the embedding vector of the t-th semantic segment before intervention; Represents the embedding vector of the t-th semantic segment after intervention; This represents the t-th semantic segment (such as a sentence or phrase) in the generated text sequence, used to quantify the difference in the distribution of local semantics before and after the intervention. These represent the distribution of topics before and after the intervention.

[0098] Further analysis of the fluctuation characteristics of the generated path after intervention is conducted, and a dynamic stability evaluation function is introduced to analyze the fluctuation characteristics of the generated path. The path embedding covariance is calculated using a sliding window mechanism to measure the output stability of the model after intervention. The dynamic evaluation function for generation stability is as follows:

[0099]

[0100] in To adjust the sliding window size, The trace of the covariance matrix representing the path embedding. This indicates that in the path generated after intervention, from the time step... arrive The set of continuous semantic fragment embedding vectors is used to dynamically analyze the local fluctuation characteristics of the generated path after intervention. The trace of the covariance matrix is ​​calculated through a sliding window mechanism (window size W) to evaluate the output stability of the model.

[0101] Finally, combining thematic shift and stability indicators, an index-weighted comprehensive evaluation function is established to calculate the overall intervention effect, thereby achieving a quantitative grading of the intervention effect. The comprehensive intervention effect scoring method is as follows:

[0102]

[0103] in This is the normalization coefficient, used to balance the weights of the indicators.

[0104] Table 2 Parameters for evaluating intervention effectiveness

[0105]

[0106] Throughout this invention, we systematically designed a low-intervention, high-efficiency control system for a large-scale digital twin water conservancy model. First, by constructing an unguided test pool, we sampled the large model multiple times in a prompting environment without explicit tasks or objectives, comprehensively collecting discourse structure, topic evolution, and semantic coherence from its natural output, providing rich raw data support for subsequent behavioral path analysis. Next, in the behavioral trajectory analysis and feature extraction stage, we extracted semantic and style embeddings from the collected expression sequences, constructed an expression trajectory map using a trajectory clustering algorithm, and identified highly influential key nodes in the trajectory map, providing precise location for perturbation injection. Subsequently, in the perturbation injection stage, we introduced a set of carefully designed perturbation templates at the key nodes, effectively guiding the model to shift its global structure and semantic trends in subsequent generation through minimal language insertion. Finally, in the intervention effect evaluation stage, by comparing the changes in topic distribution, structural evolution, and semantic coherence of the generated text before and after intervention, we quantitatively and qualitatively evaluated the impact of perturbation on the model's behavioral path, verifying the superior performance of this method in achieving low-cost, high-stability large-scale model control.

[0107] After completing the entire process, a large-scale digital twin water conservancy model with low-intervention and high-efficiency regulation capabilities was successfully constructed. Through multiple rounds of natural generation sampling and expression trajectory analysis of the model under unguided conditions, the system accurately identified key semantic turning points and applied perturbation injections at these locations, achieving efficient directional adjustment of the global generation path at minimal cost. Comparative evaluation before and after intervention showed that the model maintained high semantic coherence and style stability during unconstrained iteration, significantly reducing the risk of semantic bias and enhancing the transparency and traceability of the generation process. The optimized large-scale model demonstrated excellent stability and reliability in intelligent scheduling, data broadcasting, and emergency early warning scenarios within the water conservancy digital twin system, significantly improving regulation efficiency and operational safety, and providing low-cost, highly versatile, and highly secure technical support for decision support in water conservancy projects.

[0108] To achieve the above technical objectives, the proposed method has the following system framework:

[0109] Module 1 Non-Guided Sampling Module

[0110] This module is used to construct a non-task-oriented input set to ensure the naturalness and non-goal-dependent nature of the language model's generation behavior. The input pool includes open-ended contextual quotations, sentence start points without specific task instructions, and semi-structured language trigger words, used to guide the model into a zero-shot generation state. The output obtained through multiple rounds of sampling serves as raw behavioral data to analyze the model's natural expression tendencies under uncontrolled conditions.

[0111] Module 2: Trajectory Modeling and Inertial Node Recognition Module

[0112] This module performs semantic modeling and behavior trajectory reconstruction on the original text generated by the model. Through sentence vector embedding, style embedding extraction, and sequence clustering analysis, it identifies expression paths with stability and continuity in the model generation. Furthermore, by combining token-level attention weight changes, syntactic structural mutations, and expression intensity changes, it identifies structural nodes with potential behavior shift capabilities, i.e., expression inertia enhancement regions.

[0113] Module 3: Perturbation Strategy Construction and Insertion

[0114] At the identified expression deflection points, the module calls the style fine-tuning morpheme library and selects structural semantic insertions (such as sentence-initial softening structures, logical transitions, and negotiation expression frameworks) for minimal language-level intervention. The insertion strategy does not change the original task structure, nor does it introduce additional semantic load. Instead, it guides the model's generation path to a natural shift through tone control and rhythm adjustment.

[0115] Module four lines offset evaluation module

[0116] This module quantifies the changes in model expression before and after perturbation insertion. Evaluation metrics cover multiple dimensions, including embedding shift, style divergence, output topic shift, and changes in structural consistency. Through visualization and trajectory comparison analysis, the magnitude and persistence of the shift in the overall generation behavior path caused by the perturbation are determined.

[0117] Through these modules, precise path control and semantic risk prevention ultimately achieve low-intervention and efficient control over the generation behavior of the digital twin water conservancy large model. This system framework enables real-time observation of natural expression trajectories and the collaborative operation of directional perturbations at key nodes, providing technical support for semantic stability enhancement and dynamic risk avoidance in multiple scenarios such as water conservancy scheduling interpretation, monitoring data broadcasting, and emergency early warning response.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-intervention, high-efficiency control method for large language models in digital twin water conservancy systems, characterized in that, Includes the following steps: (1) Construct an unguided test set in a prompt environment without tasks or clear goals, perform multiple rounds of generation sampling on the model, record the discourse structure, topic progression and semantic coherence in its natural output, and capture the performance differences of the model under different generation paths by modeling the probability distribution of the output sequence, and systematically observe the natural generation ability of the model. (2) Semantic embedding and style embedding extraction and trajectory clustering analysis are performed on the discourse structure, theme progression and semantic coherence data obtained in step (1) to obtain the natural generation behavior path map and high-influence expression change points. (3) For the high-impact expression change points obtained in step (2), perform perturbation template injection and language structure adjustment to obtain an expression path with optimized tone and structural trend, which can be used for intervention strategy implementation; (4) Perform semantic topic change comparison analysis on the optimized expression path obtained in step (3) and the original output data obtained in step (1) to evaluate the intervention effect of perturbation insertion on the overall expression behavior. In step (1), to quantify the semantic coherence and topic progression stability of the content generated by the model, a semantic consistency index between the generated texts is calculated based on embedding similarity. The semantic coherence quantification method is as follows: ， in The cosine similarity function is used. For the first The embedding vectors of semantic segments are used to evaluate the topic progression stability by the mean similarity between adjacent semantic segments.

2. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 1, characterized in that, The generation logic for constructing the unguided test set in step (1) by defining a random sampling function based on a uniform distribution is as follows: , in Indicates no task hint space Sampling in a uniform distribution This refers to the size of the test set.

3. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 1, characterized in that, The probabilistic modeling method for multi-round generation sampling in step (1) is as follows: ， in Indicates the first The text sequence generated by the round, Represents the set of parameters for a large language model. Given the sequence length, output diversity under different generation paths is modeled through sequence probability decomposition.

4. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 1, characterized in that, The specific steps of step (2) are as follows: (2-1) First, a dual-channel encoding architecture is used to achieve multi-dimensional behavioral representation, and its joint feature extraction function is defined as: ， in For semantic encoders, For style encoders, The operator represents the concatenation of the orthogonal vectors of the two, establishing an embedding space with both semantic depth and stylistic features for subsequent analysis; (2-2) Joint feature extraction of semantic embedding and style embedding is performed on the original behavior sequence obtained in step (1). The semantic vector and expression style features of the generated text are mapped to a unified representation space. The trajectory clustering algorithm optimized by dynamic time warping is used to construct the natural generation behavior path graph of the language model. The trajectory similarity is measured by defining a loss function based on DTW distance. The function expression is as follows: ， in For the first Trajectory clusters, Let be the cluster center vector. The number of clusters; (2-3) In the constructed path graph, by calculating the node influence propagation coefficient and topological centrality index, and combining the semantic mutation detection algorithm of the generated text, the expression change points with global structural influence are identified, and an intervention node priority evaluation system is established. The key node determination method is as follows: ， in , where is the weighting coefficient, and KL divergence measures the distributional differences between adjacent semantic segments.

5. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 1, characterized in that, In step (3), at the key points of expression turning marked in the trajectory analysis, a set of perturbation templates are used to adjust the tone and structural trend of the subsequent expression path through minimal language insertion. The injection intensity of the perturbation templates is controlled by a gating function to achieve local path correction under low intervention conditions. The intensity control method of the perturbation templates is as follows: ， in As a key node, Embedding node features For the Sigmoid function, These are learnable parameters.

6. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 5, characterized in that, In step (3), the predefined perturbation template embedding vector is fused with the original generated path features. A sparse mask matrix is ​​used to constrain the scope of intervention. While maintaining semantic coherence, the generated path is guided to shift in the expected direction. The fusion modeling method for path correction is as follows: ， in For the first Embedding vectors of semantic segments, It is a sparse mask matrix. For perturbation template embedding, This indicates element-wise multiplication.

7. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 6, characterized in that, In step (3), a bimodal alignment loss function is defined, while simultaneously constraining the semantic consistency of the generated path after correction and the magnitude of its deviation from the original path. The L1 regularization term is used to maintain the sparsity of the intervention operation, preventing model behavior distortion caused by excessive intervention. The loss function of the intervention effect optimization method is as follows: ， in For the first after perturbation intervention The target embedding vector of each semantic fragment in the expected direction The sparsity coefficient is... This indicates L1 regularization.

8. The low-intervention, high-efficiency control method for a large language model in a digital twin water conservancy system according to claim 1, characterized in that, The specific steps of step (4) are as follows: (4-1) First, a multidimensional quantitative index is constructed to evaluate the targeted control effect of perturbation insertion. The difference in semantic topic distribution before and after intervention is measured by cosine similarity and KL divergence. The semantic topic shift quantification method is as follows: ， in The cosine similarity function is used. , These represent the first and second days before and after the intervention, respectively. Embedding vectors of semantic segments, Indicates the first element in the generated text sequence. A semantic fragment, These represent the distribution of topics before and after the intervention; (4-2) Based on the fluctuation characteristics analysis of the generated path after intervention, a dynamic stability evaluation function is introduced to analyze the fluctuation characteristics of the generated path. The path embedding covariance is calculated through a sliding window mechanism to measure the output stability of the model after intervention. The dynamic evaluation function for generation stability is as follows: ， in To adjust the sliding window size, The trace of the covariance matrix representing the path embedding. This represents the set of embedding vectors of continuous semantic segments from time step tw to t in the path generated after intervention; (4-3) Combining the theme offset and stability indicators, an index-weighted comprehensive evaluation function is established to calculate the comprehensive intervention effect in a weighted manner, thereby achieving a quantitative grading of the intervention effect. The comprehensive intervention effect scoring method is as follows: ， in This is the normalization coefficient, used to balance the weights of the indicators.

9. A low-intervention, high-efficiency control system for a large language model in a digital twin water conservancy system, implemented according to any one of claims 1-8, characterized in that, include: The unguided prompt sampling module is used to construct a non-task-oriented prompt input set to ensure the naturalness and non-goal-dependent nature of the language model's generation behavior. The output obtained through multiple rounds of sampling is used as raw behavioral data to analyze the model's natural expression tendency under uncontrolled conditions. The generation trajectory modeling and inertial node recognition module is used to perform semantic modeling and behavior trajectory reconstruction on the original text generated by the model. Through sentence vector embedding, style embedding extraction and sequence clustering analysis, it identifies expression paths with stability and continuity in the model generation. Furthermore, by combining token-level attention weight changes, syntactic structure mutations and expression intensity changes, it marks structural nodes with potential behavior turning capabilities, namely expression inertia enhancement regions. The perturbation strategy construction and insertion module calls the style fine-tuning morpheme library at the identified expression deflection nodes, selects structural semantic insertion words, and performs minimal language-level intervention. The behavioral shift assessment module is used to quantify the changes in the model's expression before and after perturbation insertion. The assessment indicators cover multiple dimensions, including the degree of semantic shift, style trajectory changes, output topic migration, and structural consistency changes. Through visualization and trajectory comparison analysis, the magnitude and persistence of the shift in the overall generation behavior path caused by the micro-intervention are determined.

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