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CN-121981282-A - OpenClaw-based intelligent supervision method and system for Chinese white dolphin

CN121981282ACN 121981282 ACN121981282 ACN 121981282ACN-121981282-A

Abstract

The invention discloses a method and a system for intelligently supervising Chinese white dolphin based on OpenClaw, wherein the method comprises the steps of constructing a three-layer bi-directional semantic linkage protection knowledge system through OpenClaw agents, conducting space-time dimension alignment on multi-source heterogeneous data, generating cross-mode unified semantic representation based on double-constraint joint fusion, constructing a federal multi-mode joint model through OpenClaw agents based on the cross-mode unified semantic representation, conducting joint training to obtain a cloud large model, achieving migration of the cloud large model to an edge small model, conducting fitting of the cross-mode unified semantic representation based on OpenClaw agents to a noise intensity-behavior response reaction curve, determining a critical disturbance threshold, combining a causal graph model, constructing a causal inference model of ship activity-white dolphin population dynamics, constructing a multi-source heterogeneous data conflict resolution mechanism, conducting bi-dimensional consistency verification on a decision result output by the causal inference model, and completing semantic conflict resolution of the multi-source heterogeneous data.

Inventors

  • DAI YIYU

Assignees

  • 华侨大学

Dates

Publication Date
20260505
Application Date
20260409

Claims (10)

  1. 1. OpenClaw-based intelligent supervision method for Chinese white dolphin is characterized by comprising the following steps of: S1, constructing a three-layer two-way semantic linkage protection knowledge system of a core ontology relation layer-knowledge map layer-rule base layer based on species mechanism and protection expert knowledge of a Chinese white dolphin through a OpenClaw intelligent agent scheduling field large model, and realizing linkage increment updating of the protection knowledge system based on an active learning strategy of semantic deviation detection; S2, automatically accessing multi-source heterogeneous data related to the protection of the Chinese white dolphin through a standardized interface of OpenClaw intelligent bodies, realizing the alignment of space-time dimensions of the multi-source heterogeneous data through a space-time registration technology of ontology semantic constraint, and calling a marine multi-mode large model to realize the dual-constraint joint fusion of multi-mode features extracted from the multi-source heterogeneous data in an ontology semantic space to generate cross-mode unified semantic representation; S3, constructing a federal multi-mode joint model fused with the semantic hard constraint of the ontology by using OpenClaw intelligent agents on the basis of the cross-mode unified semantic representation, realizing the joint training of the cross-mechanism to obtain a cloud large model on the premise of not sharing original data, and realizing the lightweight migration from the cloud large model to an edge small model positioned at an edge by combining with a semantic anchoring knowledge distillation technology, thereby realizing the self-adaptive calculation and the energy efficiency optimization of the edge; S4, based on the cross-modal unified semantic characterization, constructing a quantitative association model of ship noise-white dolphin behavior response guided by body semantics through OpenClaw intelligent agents, fitting a noise intensity-behavior response dose-response curve according to the quantitative association model, determining a critical disturbance threshold, and constructing a causal inference model of ship activity-white dolphin population dynamics by combining a causal graph model; S5, constructing a multi-source heterogeneous data conflict resolution mechanism based on ontology semantic reasoning, carrying out two-dimensional consistency check on a decision result output by a causal inference model through OpenClaw intelligent agent scheduling ocean multi-mode large model, completing semantic conflict resolution of multi-source heterogeneous data through conflict root positioning, hierarchical semantic resolution and countermeasure training optimization steps in sequence, and reversely feeding back resolved monitoring samples and decision execution effects to the protection knowledge system, the federal multi-mode joint model and the causal inference model to realize full-flow closed-loop optimization.
  2. 2. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, wherein step S1 specifically includes: S11, automatically extracting entity and relation of data related to the protection of the middle Hua Bai dolphin through a OpenClaw agent scheduling field large model, defining core bodies, and constructing semantic association relations and hierarchical structures among the core bodies to form a core semantic framework for protecting a knowledge system, wherein 768-dimensional semantic anchoring vectors are generated for each core body to serve as unified semantic references; S12, based on semantic association relations among core bodies, carrying out automatic triad extraction on unstructured field monitoring logs, historical observation cases, ship violation records and protection disposal cases through OpenClaw intelligent agent scheduling field large models, and fusing association relations of long-term monitoring data of Chinese dolphin population and ship activity space-time data to construct a protection knowledge graph of Chinese dolphin standardized by entity-relation-attribute, wherein a verification process is automatically executed through OpenClaw intelligent agents, each entity in the protection knowledge graph carries out cosine similarity verification with semantic anchoring vectors corresponding to the core bodies, and the entity with similarity lower than 0.85 is required to be subjected to field large model reparse and protection expert verification and then put in storage; S13, based on experience of a protection expert and a species behavior mechanism, converting an association relation in a protection knowledge graph into an executable generation type management and control rule through a OpenClaw agent scheduling field large model, wherein the premise and conclusion of each rule must be mapped to the entity relation between a core entity and the protection knowledge graph, the relation weight of the rule confidence and the protection knowledge graph and the semantic similarity of the core entity are updated in a linkage way, the successful triggering of the rule can synchronously update the association weight of a corresponding entity in the protection knowledge graph through OpenClaw agents, and the misjudgment of the rule can trigger the relation correction of the core entity semantic verification and the protection knowledge graph, so that the real-time bidirectional linkage of a three-layer framework is realized; S14, through OpenClaw agents, a two-dimensional active learning strategy based on uncertainty sampling and semantic deviation detection is designed, semantic deviation samples, newly added observation cases and management and control treatment cases with the similarity of semantic anchoring vectors of output low confidence samples, features and core bodies lower than a threshold value are automatically screened and pushed to protection domain experts for labeling, after labeling is completed, dynamic incremental updating of entities, relations and rules is completed through OpenClaw agent scheduling domain big models, and three-layer linkage updating logic of 'first semantic anchoring vectors, then protection of knowledge map entity mapping and finally rule base constraint' is followed during updating, so that semantic consistency of an updated three-layer framework is ensured.
  3. 3. The OpenClaw-based intelligent supervision method for white dolphin according to claim 2, wherein in step S14, the model formula of the dynamic incremental update is as follows: Wherein, the Representing the state of a protection knowledge system at the time t, wherein the state comprises a core ontology concept, a semantic anchoring vector, a protection knowledge map triplet and the full content of a rule base; Representing the state of the protection knowledge system updated at the time t+1; Updating the step length for the knowledge; a knowledge updating action for the time t; Checking a bonus function for knowledge consistency; Is a semantic similarity function.
  4. 4. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, wherein step S2 specifically includes: performing standardized preprocessing on the acquired multi-source heterogeneous data through OpenClaw intelligent agents, and simultaneously finishing ontology semantic tag binding; aiming at the problem of space-time dislocation of multi-source heterogeneous data, a space-time registration model guided by ontology semantics is constructed through OpenClaw intelligent agents, so that the space-time dimension accurate alignment of the multi-source heterogeneous data is realized; Aiming at the preprocessed and space-time aligned data of different modes, an adaptive depth feature extraction network with body semantic constraint is constructed through OpenClaw intelligent agents, so that the extraction and semantic anchoring of the features of each mode are realized, and the multi-mode features are obtained; And mapping the multi-modal features to a unified ontology semantic space by using the semantic anchoring vector of the protection knowledge system as a benchmark and dispatching the ocean multi-modal large model through OpenClaw intelligent agents, constructing a dual-constraint joint fusion mechanism of intermodal contrast learning fusion and ontology semantic anchoring fusion, and generating a cross-modal unified semantic representation.
  5. 5. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, wherein step S3 specifically includes: Deploying local nodes at each data holding mechanism through OpenClaw agents, deploying trusted global nodes at a cloud end, and constructing a federal multi-mode joint model of semantic constraint, wherein the federal multi-mode joint model comprises a local feature extractor, a local predictor, a global aggregator and an ontology semantic constraint branch; through OpenClaw intelligent agent automatic scheduling federation training process, a safe encryption transmission channel is established between a local feature extractor and a global aggregator, and a three-stage semantic perception alternating training mechanism is designed to obtain a global cloud large model; based on the cloud large model, invoking a semantic anchoring knowledge distillation technology by OpenClaw agents, and migrating knowledge of the cloud large model to an edge end to obtain a light-weight edge small model; Aiming at the battery and calculation force constraint of the edge end, an adaptive calculation strategy of ontology semantic guidance is constructed through OpenClaw intelligent agents, so that the dynamic balance of the reasoning precision and the energy consumption of the edge small model is realized.
  6. 6. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, wherein step S4 specifically comprises: Acquiring semantic association of ship noise-white dolphin behaviors based on semantic association relationship among core bodies, performing FFT (fast Fourier transform) spectrum analysis on noise data acquired by passive sonar through OpenClaw intelligent agents, extracting noise intensity, frequency and duration, constructing an association model, and quantitatively analyzing the influence of ship noise parameters on the white dolphin behaviors; Based on the output of the association model, combining with a survival analysis method, determining a critical disturbance threshold by OpenClaw intelligent body fitting a ship noise intensity-white dolphin behavior response/population-affected dose-response curve; Based on semantic association relation among core bodies, constructing a causal graph model through OpenClaw intelligent agents, and constructing a causal inference model between ship activities, habitat changes, environmental factors and white dolphin population dynamics according to the causal graph model; Based on a critical disturbance threshold and a causal inference model, a hierarchical management and control decision system guided by ontology semantics is constructed through OpenClaw intelligent agents by combining a rule base of a protection knowledge system.
  7. 7. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, wherein step S5 specifically includes: aiming at white dolphin identification and ship disturbance evaluation results obtained by multi-source heterogeneous data obtained by monitoring the same sea area at the same moment, carrying out two-dimensional conflict detection by OpenClaw intelligent agents, and judging whether conflict samples exist; Aiming at the detected conflict sample, a monitoring model of a three-step resolution mechanism of root-cause positioning-level resolution-semantic verification is constructed through OpenClaw intelligent agents so as to achieve resolution of the conflict sample; And taking the high confidence coefficient sample after conflict resolution as an countermeasure training sample, carrying out fine adjustment optimization on the monitoring model through OpenClaw agents, constructing a three-branch full-link closed-loop feedback mechanism, realizing automatic scheduling through OpenClaw agents, and realizing synchronous iterative optimization of the protection knowledge system, the federal multi-mode joint model and the causal inference model.
  8. 8. The OpenClaw-based intelligent supervision method for white dolphin according to claim 1, further comprising: S6, constructing a core intelligent agent cooperative management and control system based on OpenClaw intelligent agents, and realizing end-to-end automatic execution and multi-role man-machine cooperative interaction of the whole protection process of the Chinese dolphin.
  9. 9. The OpenClaw-based intelligent supervision method for white dolphin according to claim 8, wherein step S6 specifically includes: constructing a collaborative scheduling model of a data acquisition agent, a monitoring reasoning agent, a management and control decision agent and a man-machine interaction agent through OpenClaw agents; Constructing a decision-making interpretability model based on ontology semantics through OpenClaw intelligent agents, and performing automatic semantic interpretation on supervision decisions of the model; through OpenClaw intelligent agents, visual interaction interfaces adapting to different roles are developed, and information sharing and collaborative management and control of multiple roles are achieved.
  10. 10. OpenClaw-based intelligent supervision system for Chinese white dolphin is characterized by comprising: The knowledge system construction unit is used for constructing a three-layer two-way semantic linked protection knowledge system of a core ontology relation layer-knowledge map layer-rule base layer through a OpenClaw intelligent agent scheduling field large model based on species mechanism and protection expert knowledge of the dolphin, and realizing linkage increment updating of the protection knowledge system based on an active learning strategy of semantic deviation detection; The joint fusion unit is used for automatically accessing multi-source heterogeneous data related to the protection of the Chinese white dolphin through a standardized interface of OpenClaw intelligent bodies, realizing the alignment of space-time dimensions of the multi-source heterogeneous data through a space-time registration technology of ontology semantic constraint, and calling a marine multi-mode large model to realize the double-constraint joint fusion of multi-mode features extracted from the multi-source heterogeneous data in an ontology semantic space so as to generate cross-mode unified semantic representation; The migration unit is used for constructing a federal multi-mode joint model fused with the semantic hard constraint of the ontology by using OpenClaw intelligent agents on the basis of the cross-mode unified semantic representation, realizing the joint training of the cross-mechanism to obtain a cloud large model on the premise of not sharing original data, and realizing the light-weight migration from the cloud large model to an edge small model positioned at an edge by combining a semantic anchoring knowledge distillation technology, thereby realizing the self-adaptive calculation and the energy efficiency optimization of the edge; The causal inference unit is used for constructing a quantitative association model of ship noise-white dolphin behavior response guided by body semantics through OpenClaw intelligent agents on the basis of the cross-modal unified semantic characterization, determining a critical disturbance threshold according to a dose-response curve of the quantitative association model fitting noise intensity-behavior response, and constructing a causal inference model of ship activity-white dolphin population dynamics by combining a causal graph model; The conflict resolution unit is used for constructing a multi-source heterogeneous data conflict resolution mechanism based on ontology semantic reasoning, carrying out two-dimensional consistency check on a decision result output by the causal inference model through OpenClaw intelligent agent scheduling ocean multi-mode large model, completing semantic conflict resolution of multi-source heterogeneous data through conflict root positioning, hierarchical semantic resolution and countermeasure training optimization steps in sequence, and reversely feeding back resolved monitoring samples and decision execution effects to the protection knowledge system, the federal multi-mode joint model and the causal inference model to realize full-flow closed-loop optimization.

Description

OpenClaw-based intelligent supervision method and system for Chinese white dolphin Technical Field The invention relates to the field of marine ecology, in particular to a OpenClaw-based intelligent supervision method and system for Chinese white dolphins. Background Along with the continuous aggravation of human activities such as offshore shipping, port economy, coastal travel and the like, the overlapping rate of the habitat of the Chinese white dolphin and the human sea-related activity area is continuously improved, and the Chinese white dolphin faces serious threats such as broken habitat, slow population growth, low survival rate of young animals and the like. The Chinese white dolphin is used as a key indicator for protecting wild animals, the living state of the white dolphin is a core indicator for the health of a offshore marine ecosystem, and the protection work of the white dolphin is an important component of marine ecological civilization construction. Traditional Chinese white dolphin protection mainly relies on modes such as manual lookout, cruising of patrol boats, manual field observation and the like, has core defects such as limited monitoring range, poor real-time performance, high labor cost, insufficient data accuracy and the like, is greatly influenced by natural conditions such as weather, water quality, illumination and the like, is difficult to realize continuous and comprehensive population monitoring and protection management and control, and cannot meet the core requirements of current marine ecology refined protection. Ext> withext> theext> developmentext> ofext> theext> AIext> technologyext>,ext> theext> currentext> Chinaext> whiteext> dolphinext> protectionext> technologyext> iteratesext> towardsext> theext> directionsext> ofext> largeext> modelext> semanticext> drivingext>,ext> multiext> -ext> sourceext> informationext> fusionext>,ext> 5ext> Gext> -ext> Aext> senseext> integrationext> andext> cloudext> edgeext> endext> collaborativeext> deploymentext>,ext> semanticext> modelingext> ofext> protectionext> knowledgeext>,ext> multiext> -ext> sourceext> dataext> fusionext>,ext> modelext> lightweightext> adaptationext> andext> scientificext> managementext> andext> controlext> decisionext>,ext> andext> becomesext> aext> coreext> linkext> forext> determiningext> protectionext> systemext> performanceext>,ext> practicabilityext> andext> largeext> -ext> scaleext> landingext> capabilityext>.ext> However, the current mainstream intelligent monitoring protection technology has introduced deep learning, transfer learning, internet of things and other technologies, and can meet the basic white dolphin identification requirements in a fixed sea area and a good environment, but the following defects still exist: Firstly, the deep fracture of the semantic layer exists between the knowledge system in the protection field and the data driving model. The ontology, the knowledge graph and the protection rule base in the prior art are only static structured storage carriers, a bidirectional semantic linkage and verification mechanism is absent among three layers of frameworks, dynamic semantic anchoring is not realized by the whole flow of multi-source data fusion, target detection, behavior analysis and management and control decision, the knowledge cannot provide whole-course semantic constraint for model training, so that knowledge and model 'two sheets' cannot be really converted into core supervision capability of the model by expert protection experience, and the problem of mismatching of knowledge semantics is extremely easy to occur in the process of trans-sea and trans-scene migration. Secondly, multi-source heterogeneous data fusion and multi-mode detection lack the hard constraint and automatic scheduling capability of the semantics in the protection field, and the fusion precision and the detection robustness are insufficient. The existing multi-source data fusion method based on feature splicing and space-time alignment mostly only realizes the unification of data formats and shallow alignment of space-time dimensions, does not introduce the hard semantic constraint of knowledge in the field of Chinese white dolphin protection, also lacks a unified intelligent body framework to realize the automatic access, pretreatment and fusion scheduling of multi-source data, easily solves the problems that white dolphin core discrimination features are weakened and the environment and ship redundancy features are excessively aligned in the feature fusion process, and meanwhile, under complex underwater environment (turbidity and low illumination), vision and acoustic multi-mode detection are simply fused on the feature level, unified characterization based on protection body semantics is not realized, and the detection precision of young dolphin and long-distance individuals is insufficient, so that the high-precision supervision requirement cannot be met. Thirdly, the model is optimized to be a