292 results about "Scene analysis" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

The invention relates to the field of computer vision and the field of image processing, in particular to a blind guiding scene recognition method based on a multi-modal visual large model. Comprising the following steps: data acquisition: acquiring image or video data of a typical blind guiding scene, marking, constructing a data set, introducing an image prompting mechanism, and dividing a training set and a test set; constructing a multi-modal visual large model which comprises an image coding module, a semantic prompt coding module, a visual language fusion module and a scene analysis semantic decoding module; the model is tested and optimized, and a multi-task loss function is adopted for optimization; a blind guiding auxiliary system is constructed, image acquisition, visual understanding and voice broadcasting functions are integrated, and a closed-loop process is realized. According to the method, the problems of strong target dependence, poor universality and weak semantic comprehension ability of the existing blind guiding identification technology are solved, accurate identification and semantic feedback of complex scenes are supported, and the intelligent level and the environment adaptability of a blind guiding system are improved.

The invention discloses a big data platform asset intelligent sensing method based on LLM and a customizable MCP, and the method comprises the steps: defining a standard resource library of a model context protocol, constructing a model context protocol server, integrating a plurality of scanning tools, and carrying out the automatic adaption; receiving a natural language demand input by a user, and calling a pre-trained LLM model; the scene analysis module converts a natural language demand into a standardized scene description object; the intelligent scanning module is used for describing an object according to a standardized scene, generating an optimal tool execution chain, executing a complete scanning task, obtaining a task scanning result and unifying formats of heterogeneous data in the task scanning result, and the conflict resolution module is used for removing conflicts; the multi-source data association module fuses network topology resources and flow data captured by a probe to construct a dynamic asset atlas including asset attributes, service dependence and vulnerability information, and the report generation module generates a structured report. The method can provide a global risk perspective and decision support.

The invention relates to the technical field of artificial intelligence, in particular to a spatio-temporal context extraction method and system for real world model training, and the method comprises the steps: extracting all image information and text semantic information through content feature vectors; structural information such as spatial layout of a scene and relative positions of people and objects in the collected image is extracted through the spatial structure feature vector, and feature information such as appearance, posture and action of a target object is extracted through the entity feature vector; obtaining a time sequence feature vector according to the content feature vectors, the spatial structure feature vectors and the entity feature vectors in a plurality of preset time periods, capturing multi-dimensional dynamic changes of the target physical place in a period of time, and analyzing the extracted vectors to obtain scene analysis feature data; all feature information is integrated to obtain the spatio-temporal context, the representation richness and comprehensiveness of the target physical place are improved, and the representation degree and adaptability of the real world model obtained through subsequent training to the target physical place are achieved.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention discloses a self-adaptive sound effect management method and system with a scene analysis module, and relates to the field of self-adaptive sound effect management, and the method comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through the scene analysis module; s2, dynamically extracting user hearing preference parameters and scene classification labels; s3, constructing a virtual audition sweet zone matched with the physical space; and S4, dynamically adjusting the phase delay and the energy distribution proportion of each frequency band, and generating an optimal loudness equalization curve meeting the multi-scene constraint. According to the method, adaptive matching of sound field characteristics is realized in a complex dynamic scene through a multi-level perception-optimization closed-loop architecture, acoustic feature extraction and physical space modeling are deeply fused, the technical boundary of traditional pure signal processing is broken through, and the problem of multi-target conflict is solved based on cross-modal collaborative learning and a global optimization algorithm.

The invention discloses a building facade three-dimensional surveying and mapping system and method based on an unmanned aerial vehicle and AI, and relates to the technical field of building surveying and mapping, and the system comprises a scene analysis module which is used for executing the information analysis of calibration scene information, and marking each surveying and mapping sub-region as a to-be-collected node; the acquisition planning module is used for calculating the node transition probability of each node to be acquired; the surveying and mapping search module is used for configuring a virtual unmanned aerial vehicle and performing selective iteration of surveying and mapping sub-regions by using pheromone concentration and heuristic factors; the clustering sharing module is used for carrying out three-dimensional surveying and mapping on the exterior facade of the building and establishing a shared information pool in a clustering manner; and the surveying and mapping optimization module is used for carrying out collaborative decision optimization on the local path and updating the acquisition path according to a collaborative decision optimization result. The technical problems of low surveying and mapping efficiency and large measurement error caused by difficulty in meeting large-scale and high-precision surveying and mapping requirements of modern buildings in the prior art are solved, and the technical effect of improving the surveying and mapping efficiency and precision of the buildings is achieved.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the technical field of power grid identification, and discloses a county power distribution network multi-dimensional operation mode identification method, system and device based on typical scene analysis, and a medium, and the method comprises the steps: obtaining the historical operation data of a county power distribution network, carrying out the extraction of multi-dimensional features based on the preprocessed historical operation data, and obtaining the multi-dimensional operation mode of the county power distribution network; carrying out clustering analysis on the operation data of the power distribution network through an improved time sequence clustering algorithm, and determining a typical scene of each category; and constructing a county power distribution network operation mode recognition model, classifying the operation states of the power distribution network, constructing a mapping relationship between the operation mode and a planning decision, and generating an optimal planning scheme. According to the invention, precise recognition of the operation mode of the county power distribution network is realized, the recognition accuracy of the operation mode of the power distribution network is improved, and precise planning is supported; the power distribution network planning cost is reduced and the resource utilization efficiency is improved.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention discloses a building surveying and mapping system based on an unmanned aerial vehicle remote sensing technology, and belongs to the technical field of building surveying and mapping. Comprising the following modules: a multi-sensor data acquisition module and an unmanned aerial vehicle data acquisition platform based on an integrated RGB camera, a laser radar and a multi-spectral sensor; the preliminary image processing module is used for generating standardized image data for scene analysis; the intelligent scene recognition module outputs a scene classification result and preliminary complexity grade evaluation; the self-adaptive decision module is used for generating an initial image processing parameter combination according to a scene recognition result; the dynamic image processing module is used for executing regional image registration, correction and seamless splicing based on the initial parameter combination to generate a building orthoimage; and the real-time quality evaluation module monitors and evaluates geometric accuracy and visual quality in the image processing process in real time.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention provides an environmental protection planning dynamic generation method and system based on a multi-modal large model. The method comprises the following steps: firstly, obtaining environmental protection planning target scene demand information covering text and image forms; calling the multi-modal large model to carry out cross-modal scene analysis, and generating an analysis result containing the core demand elements and priority ranking; generating a preliminary environmental protection planning scheme through a cross-modal planning content generation module based on the analysis result; collecting scheme pilot run feedback information and inputting the information into the multi-modal model for feedback adaptation analysis, and generating an adjustment rule; and finally, dynamically adjusting the initial scheme according to an adjustment rule to obtain a final scheme. According to the invention, scene requirements can be comprehensively and accurately captured, a scientific planning scheme is accurately generated, dynamic optimization is realized, and environmental protection planning efficiency and quality are improved.

The invention relates to a part generation method based on atomization disassembly and encapsulation data. The part generation method comprises the following steps: step 1, analyzing a demand scene; step 2, carrying out atomization disassembly; step 3, performing standardization verification; step 4, packaging the function logic layer; step 5, packaging the element part layer; 6, packaging an interface layer; according to the method, the problems of function solidification and difficult splitting of a traditional data product are solved, and efficient circulation and value release of data elements are promoted; and reusability and circulation efficiency of data parts are improved through the element part layer.

The invention discloses a road abnormal event real-time monitoring and warning system, and the system comprises a data processing module which is used for collecting and preprocessing multi-source monitoring data; the region segmentation module is used for performing multi-scale region segmentation on the multi-source monitoring data by utilizing a pyramid scene analysis network to form a region segmentation map; the weight calculation module is used for constructing a weighted relation graph of road scene areas and abnormal event types and calculating the weight of the abnormal event in each area; the state evaluation module is used for updating node states in the graph and evaluating the occurrence probability of road abnormal events in different areas; the classification labeling module is used for carrying out risk classification on the abnormal events according to the state updating result and generating real-time alarm information; and the feedback adjustment module is used for dynamically adjusting network parameters according to the real-time monitoring result and alarm information feedback. According to the method, the pyramid scene analysis network, the graph attention neural network and the Bellman equation are combined, so that efficient and accurate road abnormal event real-time monitoring and warning are realized.

The invention provides a real-time video monitoring intelligent analysis system based on deep learning, and relates to the technical field of computers, and the system comprises a video collection unit which is used for obtaining a real-time monitoring video stream to be analyzed, and extracting a target video frame sequence from the real-time monitoring video stream; the scene analysis unit is used for processing the target video frame sequence by using a pre-trained scene analysis model to obtain a dynamic object mask and a scene semantic graph corresponding to the real-time monitoring video stream; the acquisition unit is used for acquiring thermal infrared characteristics and dynamic environment variable information of a monitoring area corresponding to the real-time monitoring video stream; and the semantic segmentation unit is used for inputting the dynamic object mask, the scene semantic graph, the thermal infrared features and the dynamic environment variable information into a pre-trained semantic segmentation model to obtain a semantic segmentation result output by the semantic segmentation model. By applying the method provided by the invention, the monitoring video can be accurately analyzed.

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.

The invention belongs to the field of vehicle networking communication, and particularly relates to a hybrid network communication system for network connection cooperative driving, which comprises the following steps: analyzing urban road straightaway and highway straightaway scenes including scene composition and scene traffic flow; deploying RSU and MEC calculation units according to scene analysis; establishing a multi-mode communication and hybrid network topology structure; a hybrid network cooperative lane changing communication system is established according to multimode communication and a hybrid network topology structure, and comprises a vehicle end system, a road side system and a cloud control platform; based on a hybrid network cooperative lane changing communication system, executing a CAV cooperative lane changing driving service; the continuity of cross-network communication is realized, and an efficient, low-delay and high-reliability cooperative driving task is realized.

The invention discloses a new-generation emergency command system scene analysis system integrated with a convolutional neural network, particularly relates to the technical field of computer vision and intelligent emergency command, and is used for solving the problem that the dynamic association and situation evolution logic analysis capability among emergency scene elements is insufficient in the prior art. Visual element features are extracted from a video stream through a feature extraction module by using a convolutional neural network, an association graph construction module constructs a space-time association graph based on the features, a movement evaluation module calculates element group movement feasibility indexes, and a situation analysis module analyzes divergence features and antagonism degrees in combination with movement track convergence. And the risk identification module identifies a derived risk aggregation mode according to the situation prediction, and the semantic generation module finally outputs high-level semantic description information containing situation prediction to realize closed-loop analysis from scene perception to cognitive decision.

The invention relates to the field of advertisement putting optimization, in particular to an advertisement putting parameter adaptation method and system fused with scene analysis, and the method comprises the steps: obtaining multi-dimensional features of a plurality of known scenes in historical advertisement putting data; in combination with a preset feature embedding model, mapping the multi-dimensional features of the plurality of known scenes into high-dimensional vectors, and generating a known scene vector set to construct a scene vector space; in response to the received new advertisement putting request, obtaining multi-dimensional features of a new scene; based on the same feature embedding model, mapping the multi-dimensional features of the new scene into a high-dimensional vector, and generating a new scene vector; determining at least one optimal associated scene from the known scenes according to the new scene vector; generating an initial advertisement putting parameter of the new scene in combination with the historical advertisement putting parameter of the at least one optimal associated scene; and obtaining effect feedback data of the initial advertisement putting parameters in real time, and generating optimized advertisement putting parameters according to the initial advertisement effect feedback data.

The invention discloses a network data dynamic encryption updating method, system and device and a storage medium, and relates to the technical field of network security, and the method comprises the steps: generating an encryption key in a mode of combining a quantum random number generator and a machine learning algorithm; monitoring and analyzing an application scene of the original network data in real time to obtain a scene analysis result; evaluating the encryption algorithm in real time to obtain an algorithm evaluation result; selecting an encryption algorithm according to the scene analysis result and the algorithm evaluation result to obtain a target encryption algorithm; encrypting the original network data based on the encryption key to obtain encrypted network data; monitoring security threats in the network in real time to obtain alarm information; determining whether to update an encryption mode according to the alarm information; and continuing to encrypt the original network data under the condition of determining to update the encryption mode. Therefore, the security of the data is improved.

The invention provides an analysis method based on a visual scene analysis algorithm model and AI glasses, and relates to the technical field of scene analysis. The method comprises the steps of collecting audience image data of a speech scene through a high-definition camera of AI glasses, and extracting audience feature information including expressions, body actions and attention distribution; and inputting the feature information into a visual scene analysis algorithm model which is trained through deep learning in advance, and generating scene analysis data such as an expression distribution thermodynamic diagram and an attention concentration area distribution diagram. The system carries out time sequence analysis on the data and generates a trend report containing an audience emotion change curve and an attention fluctuation curve. And finally, in combination with a preset speech evaluation model, generating real-time feedback suggestions such as speech rhythm adjustment, interaction opportunity and key content emphasis, and presenting the suggestions to a speaker in a visual manner through a display screen. According to the scheme, real-time and comprehensive monitoring of large-scale audience group responses is achieved, and then the speech effect is improved.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

The invention relates to the technical field of explosion-proof transformers, in particular to a nitrogen filling control system for an explosion-proof transformer, which comprises a data acquisition module, a feature extraction module, a full-scene analysis module, a dynamic response module and a feedback optimization module. The system is connected with a multi-node transformer through a short-distance communication technology, acquires running state data in real time, generates a multi-dimensional evaluation value by utilizing a catalytic combustion sensor, load prediction and external scene prediction, judges an abnormal event in combination with a global analysis model, dynamically adjusts the nitrogen charging and discharging rate, and achieves the purpose of monitoring the nitrogen charging and discharging rate. And meanwhile, the prediction precision and the response efficiency are improved through a closed-loop optimization mechanism. According to the application, multi-node cooperative monitoring and accurate prediction can be realized, the safety and reliability of the explosion-proof transformer are significantly improved, and the occurrence of potential critical conditions is effectively avoided.

The invention discloses a smart home control method and system based on graph retrieval enhanced generation and multi-agent gaming, and the method comprises the steps: constructing a basic knowledge graph based on a current smart home environment, carrying out the semantic enrichment of service entities in the basic knowledge graph, generating an enhanced knowledge graph, and storing the enhanced knowledge graph in a vector database; constructing an example library containing a plurality of formalized observation-logic hypothesis pairs; acquiring a situation snapshot of the user, and outputting a structured scene analysis report based on the situation snapshot and the vectorization database through a front-end LLM; collaborative review is carried out by combining a factor-tracing reasoning agent, a user preference agent and a conflict detection agent with a scene analysis report and an example library, and a final decision is output by a core decision LLM and mapped into an equipment control instruction to call corresponding equipment for execution. According to the method, the fuzzy instruction of the user is combined with the knowledge graph, so that the intention recognition accuracy is improved; the multi-agent parallel auditing mechanism improves the robustness and safety of decision making.

The invention discloses an HR3C scene-based fatigue prediction method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting HR3C pipeline operation data to construct an application scene data set, carrying out the scene analysis to establish a ground state scene, and generating a limit state scene after key features are recognized; establishing environment change rate characteristics based on ground state and limit state scenes; and obtaining pipeline information to configure a dynamic fatigue prediction model, inputting related data to execute fatigue prediction, and performing pipeline management according to a prediction result. The technical problem that the prediction result and the actual service state have large deviation due to the fact that the fatigue evolution process of the HR3C pipeline under the variable working condition and the environment disturbance condition is difficult to accurately reflect in the prior art is solved, and the aim that the fatigue evolution process of the HR3C pipeline under the variable working condition and the environment disturbance condition is obtained by constructing the scene-based fatigue modeling system and introducing the dynamic environment change characteristics. And the accuracy of HR3C pipeline fatigue damage prediction is improved.