545 results about "Pattern analysis" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention provides a monitoring early warning analysis method based on artificial intelligence and a server, and the method comprises the steps: firstly collecting a multi-source monitoring data stream of a target monitoring area, which comprises at least two kinds of real-time monitoring data and time-space label information, so as to determine a collection position and a timestamp, and then carrying out the spatial-temporal feature extraction, the method comprises the following steps: generating a spatial-temporal characteristic matrix with a hierarchical association relationship, analyzing a dynamic mode of the matrix through a preset anomaly recognition network, determining a potential anomaly event and an anomaly propagation path, and generating an adaptive dynamic early warning strategy including a differentiated trigger condition and a response instruction according to a path topological structure and event attribute parameters. And finally, optimizing and adjusting the strategy in real time by utilizing historical early warning feedback data, and outputting the strategy to a terminal equipment cluster, thereby comprehensively and accurately analyzing the monitoring data, and realizing efficient and intelligent monitoring and early warning.

The invention discloses a control method and system for remote monitoring of the Internet of Things, and relates to the technical field of intelligent monitoring of the Internet of Things, and the method comprises the steps: inputting an operation data set of Internet of Things equipment into a space-time diagram convolution model, carrying out the local space-time feature extraction of an edge layer, carrying out the cross-equipment cooperation mode analysis of a cloud layer, and generating an abnormal propagation path; the method comprises the following steps: mapping three-dimensional space coordinate parameters in an operation data set of Internet of Things equipment into nodes of a topological structure, mapping interaction data between the equipment into edges of the topological structure, constructing a dynamic knowledge graph, injecting an abnormal propagation path into the dynamic knowledge graph, and updating a fault influence weight between the nodes by applying an improved graph convolution fusion algorithm. Acquiring propagation risk nodes, and performing dynamic sorting and community clustering analysis on the propagation risk nodes. According to the invention, through the improved graph convolution fusion algorithm and the space-time graph convolution model, the capability of identifying the fault behavior in the Internet of Things equipment is enhanced, and the efficiency of edge and cloud collaborative analysis is improved at the same time.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention provides a telecommunication service fraud-related risk security assessment system based on a multi-modal fusion model, and the system comprises a multi-source data collection assembly which is responsible for obtaining original telecommunication service data in multiple ways, carrying out the preprocessing of the original telecommunication service data, and obtaining first telecommunication service data; performing feature extraction and behavior pattern analysis on the first telecommunication service data to obtain key features related to the telecommunication service; the assessment model construction component is responsible for constructing a fraud-related risk assessment model based on machine learning, inputting the key features into the fraud-related risk assessment model, outputting an intelligent assessment control matrix, classifying and rating fraud-related risks, and generating a risk assessment result; and the service collaborative linkage assembly is responsible for early warning the risk level of the telecommunication service system according to the risk assessment result, and taking prevention measures according to the risk level. According to the method, an objective evaluation standard is established, and the conversion of risk identification from experience judgment to data driving is realized.

The invention relates to the technical field of fishery management, in particular to an intelligent fishery supervision method and system based on multi-source data fusion, and the method comprises the steps: obtaining the multi-source trajectory data of a fishing boat, and carrying out the data fusion, and obtaining the precise trajectory data of the fishing boat; performing trajectory feature analysis, trajectory behavior pattern analysis and high-sensitivity point identification on the accurate trajectory data to obtain trajectory distribution of the fishing boat; obtaining multi-source fishery resource data and performing data fusion to obtain a fishery resource evaluation vector; the fishery resource prediction model processes the fishery resource evaluation vector to obtain fishery resource distribution; performing spatial coincidence analysis, time coincidence analysis and intensity coincidence analysis on the trajectory distribution of the fishing boats and the distribution of fishery resources to obtain analysis results; and an analysis result and management data are obtained and processed, and if abnormal behaviors exist, early warning is carried out.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring system based on the Internet of Things, which comprises a data quality checking module, a data exception correction module, a data association analysis module, an exception monitoring marking module and an environment early warning transmitting module. According to the method, the checking of data equipment numbers and time labels and the filtering of abnormal or discontinuous data fragments are emphasized, so that the integration of the data is improved, the calling and mode analysis of historical data are carried out, and the abnormity is identified and corrected, and the data better meet the requirements of a time sequence mode; according to the method, key monitoring item combinations can be accurately identified and recorded in data correlation analysis, so that the real-time analysis and response capability of monitoring data is remarkably improved, the adaptive capacity to environmental changes and the early warning accuracy are greatly enhanced through timely marking of abnormal states and rapid release of early warning information, and the early warning efficiency is improved. And the efficiency of environment monitoring and decision support is optimized.

The invention discloses an anti-collision monitoring system based on a depth estimation and instance segmentation fused three-dimensional model, particularly relates to the field of intelligent driving security and protection, is used for solving the problem of target recognition and anti-collision in an environment, realizes efficient feature extraction of global semantics and local textures through a multi-branch network structure, and ensures the accuracy and comprehensiveness of target segmentation. In combination with a cross-frame identity association and conflict detection mechanism, the consistency of target identities is effectively maintained, and the mismatching problem caused by shielding or similar appearances is reduced; multi-scale space-time shielding mode analysis and boundary motion consistency verification are adopted, a fusion algorithm is utilized to comprehensively evaluate shielding complexity and segmentation robustness, a high-risk shielding area is identified, secondary difference correction is executed, and space positioning information of a target is remarkably optimized; and finally, integrating the optimized segmentation and depth data into a global coordinate system, constructing a high-precision and coherent three-dimensional semantic scene, and providing stable and high-quality input data for collision detection and trajectory prediction.

The invention relates to the technical field of image recognition, and provides a video target recognition method and device based on artificial intelligence and a storage medium, and the method comprises the steps: obtaining a video frame data sequence of a target video, carrying out the multi-dimensional analysis of the video frame data sequence, obtaining global video frame information and local region-of-interest information, and generating a target feature map based on the global video frame information and the local region-of-interest information, then carrying out time sequence mode analysis to obtain time sequence evolution features, combining the generated semantic representation vector, inputting the semantic representation vector into a preset adaptive Transform model to carry out target recognition, and obtaining a target recognition result. A semantic representation vector is generated through feature fusion, and a self-adaptive Transform model is used for target recognition, so that the target recognition precision in a complex scene is improved, and the problems of low detection precision and insufficient time sequence information utilization during complex scene processing, dynamic change and long-time sequence analysis are solved.

The invention relates to the technical field of high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to the field of network security, and discloses a network abnormal traffic monitoring and attack defense system based on artificial intelligence, which comprises a data preprocessing module used for receiving original traffic data, executing cleaning, duplicate removal and normalization processing, and dividing a processing result into data fragments according to a preset time window; the network flow analysis module is used for receiving the data fragments, performing feature extraction and mode analysis and outputting an analysis result representing the flow abnormal degree, and the analysis result at least comprises a score value representing the overall abnormal degree and a feature vector representing the flow mode feature; the attack judgment module is used for comparing the score value with a preset abnormal threshold value so as to judge whether network abnormal traffic exists or not, if yes, the feature vector is further matched with a pre-constructed abnormal traffic type feature library, and a specific abnormal traffic type is determined according to a matching result; and the attack defense module is used for automatically triggering and executing a corresponding defense strategy.

The invention discloses an industrial control network anomaly detection method considering priori knowledge, and the method comprises the steps: enabling a spatial feature extraction module to consider the function attributes, communication specifications and topological structure features of equipment in an industrial control network, introducing equipment role perception coding and topological perception position coding, improving the self-attention through protocol constraint, and achieving the detection of the anomaly of the industrial control network. The spatial dependency relationship of an industrial control scene can be better understood, and the recognition capability of the model on key spatial features is improved. Meanwhile, a periodic sensing door is arranged in the time sequence feature extraction module, historical state information is aggregated to obtain scene context vectors, so that an attention mechanism is updated, weighted features are obtained, features of different time scales are subjected to weighted fusion in combination with a multi-scale time window, and finally, the three features are spliced together, so that a scene is obtained. And final time feature representation is formed. According to the method, the prior knowledge can be effectively utilized, the interaction mode between devices is captured, the time sequence dependency relationship is analyzed, and the anomaly detection effect is improved.

The invention relates to the technical field of remote monitoring, and discloses a coal unloader remote monitoring system based on AI learning and a cloud platform, and the system comprises an impact force monitoring module, a vibration mode analysis module, an operation state evaluation module, a health diagnosis module, and an intelligent early warning module. According to the method, through calculation of the magnitude, the direction, the stress time and the stress frequency of the impact force, fine-grained recognition of coal flow impact force distribution and fusion of vibration data and impact force data are enhanced, comprehensiveness and multi-dimensional data cross analysis of fault recognition of the coal unloader are improved, the abnormal misjudgment rate is reduced, intelligent calculation based on AI learning is achieved, and the fault recognition accuracy of the coal unloader is improved. According to the invention, a health assessment result can be dynamically adjusted, misjudgment caused by single-point abnormity is avoided, a cloud data processing mode enables remote management to be converted from data collection to intelligent early warning, the prediction capability of equipment operation abnormity is enabled to be more timely, remote monitoring is combined with intelligent learning, multi-level assessment is formed, and the accuracy and decision efficiency of remote management are improved.

Aspects of the disclosure relate to a network traffic monitoring platform. The platform may train an attack pattern analysis model to output a behavior profile and a cumulative attack score. The platform may identify a password failure rate spike. The platform may extract a password hash from the network traffic. The platform may generate the behavior profile based on the password hash. The platform may generate the cumulative attack score based on the behavior profile. The platform may compare the cumulative attack score to a threshold. Based on identifying that the cumulative attack score is below the threshold, the platform may identify the password hash as a secure hash. Based on identifying that the cumulative attack score meets or exceeds the threshold, the platform may and generate password complexity rules. The platform may refine the attack pattern analysis model based on the attacked hash and the cumulative attack score.

The invention discloses a two-phase immersed liquid cooling liquid level intelligent dynamic regulation and control system and method, and relates to the technical field of liquid cooling intelligent processing. The two-phase immersion type liquid cooling liquid level intelligent dynamic regulation and control method comprises the steps that S1, state observation data, execution control data and log data in the liquid cooling liquid level regulation and control process are collected and preprocessed, and a standardized liquid cooling state data set is constructed; s2, the synchronism of the evaporation strengthening trend is evaluated, and the opening degree of an electronic control valve is adjusted; s3, analyzing a control feasible region under gas-liquid balance constraint, and optimizing the working mode of the two-way pump; and S4, the consistency of the feedback trend and the rolling decision is analyzed, and the circulating flow of the cooling coil and the heat exchange intensity of the condenser are adjusted. The problems that when power consumption of an existing two-phase immersion liquid cooling system changes dramatically, due to lag control of a single sensor and lack of multi-dimensional data prediction, evaporation and liquid supplementing actions are prone to being mismatched, and liquid level decline, pressure abnormity and chip dry burning are caused are solved.

The invention relates to the technical field of underground cavern construction, and discloses a layered rock mass cavern excavation parameter dynamic optimization decision system and method, and the system comprises a rock stratum database building module, an excavation simulation module and an excavation model optimization module. The method comprises the following steps: collecting basic data such as engineering geological conditions, rock mechanical properties and ground stress distribution; establishing an initial rock stratum parameter database through a three-dimensional ground stress test and rock mass permeability characteristic analysis; establishing a stratified rock mass excavation simulation model, simulating a failure mode of a stratified rock stratum under excavation disturbance, and analyzing stress redistribution and a potential failure area; determining a cavern axis direction, an excavation sequence and support parameters according to a simulation result; acquiring surrounding rock deformation, loosening circle expansion and seepage characteristic change; and utilizing a damage proximity index to predict the distribution of the loose circles, and optimizing the stratified rock mass excavation simulation model. The method can adapt to various complex working conditions from a sand shale interbed to an ultra-deep fractured stratum and the like.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention provides a potential information identification method and system in combination with a heterogeneous network environment, and the method comprises the steps: firstly obtaining a heterogeneous network environment data set of a target network environment, which comprises original information resources of various data formats, and carrying out the feature extraction of the heterogeneous network environment data set to obtain an associated feature set; performing hierarchical pattern matching processing on the associated feature set based on a pre-trained information pattern analysis model to generate a potential information identification result set containing significant value information resource attribute identifiers and distribution paths, and generating a dynamic resource integration strategy according to the potential information identification result set; and the resource management system is used for adjusting a resource scheduling rule and a storage path mapping relation, finally feeding back a dynamic resource integration strategy to the data management system, triggering a resource directory updating operation, and improving heterogeneous network resource utilization and management efficiency.

The invention discloses a traffic state monitoring method and system fusing video image recognition and charging data, and the method comprises the steps: obtaining multi-source traffic data, carrying out the preprocessing of the multi-source traffic data, and generating preprocessing data; identifying the pre-processed data to generate vehicle related information; fusing and matching the preprocessed data and the vehicle related information to generate fused traffic data; analyzing the fused traffic data to generate traffic state information; a traffic management and decision support result is generated, and real-time congestion early warning and traffic accident early warning are provided; and mining and analyzing the historical traffic data to generate a traffic flow rule and accident rule analysis result. According to the invention, comprehensive, accurate and real-time monitoring and analysis of the traffic state are realized, powerful support is provided for traffic management and decision making, and the problems of difficult integration of multi-source data, insufficient real-time performance and coverage of state monitoring and imbalance of decision support and cost efficiency are solved.

The invention discloses an AI-based digital advertisement material multi-modal adaptive generation method and system, and relates to the technical field of artificial intelligence and digital content, and the method comprises the steps: S1, obtaining historical operation records of a user in a material creation process, carrying out the classified storage of selection actions in each interaction, and obtaining a preliminary data set of the selection tendency of the user; s2, extracting style preference characteristics of the user in different creation scenes by adopting a behavior pattern analysis method according to the preliminary data set, and determining a dynamic change rule of the style preference of the user; s3, aiming at the dynamic change rule, constructing a dynamic capture mechanism, obtaining the latest selection tendency data, and judging the instant adjustment demand of the style preference of the user; according to the AI-based digital advertisement material multi-mode adaptive generation method and system, intelligence and individuation in the material creation process are realized, and a new technical scheme is provided for the field of creative design.

The invention relates to the technical field of heat energy recovery, in particular to an efficient multistage waste heat recovery and reutilization combined heat and power generation system which comprises a heat output calculation module, a temperature difference mode analysis module, a heat exchange efficiency analysis module, an optimal parameter identification module, a condenser management module and a condensate water recovery control module. According to the method, temperature, flow and pressure data of multiple positions are collected in real time, the heat fluctuation trend under multiple input conditions is accurately predicted, a basis is provided for follow-up optimization decision making, and through dynamic prediction of a temperature difference mode, the heat exchanger inlet and outlet temperature and the mutual relation between multiple equipment working parameters are combined, so that the heat exchange efficiency is improved. Real-time evaluation of the heat exchange efficiency is achieved, construction of a working model of a heat exchanger is combined, optimal working parameters under multiple temperature gradients are accurately recognized, optimization of working parameters of a condenser and condensate water recovery equipment is combined, the energy utilization rate of the combined heat and power generation system is increased, energy consumption is reduced, and resource waste is reduced.

The invention discloses a user security feature recognition method based on behavior pattern analysis, and aims to solve the problems of inaccurate recognition of power utilization security features of power consumers and insufficient robustness in the prior art. The method comprises the following steps: preprocessing and segmenting original power consumption time sequence data; then, a self-supervised learning model based on an expert hybrid architecture is constructed, the architecture integrates five neural networks to construct an expert model, expert weights are dynamically distributed through a gating network, and a power utilization mode deep embedding vector is output through self-supervised training; clustering the embedded vectors by using a clustering algorithm, determining an optimal clustering number in combination with an elbow method and a contour coefficient method, generating a user portrait, and performing visualization and feature analysis; and finally, according to the user portrait data, carrying out transaction behavior pattern recognition on the input to-be-recognized user data, and outputting a security feature recognition result. The method can comprehensively and accurately identify the power utilization safety characteristics of the user, and is suitable for scenes such as intelligent power grid safety monitoring.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a transformer substation risk detection method and system based on multi-modal decision-level fusion, and relates to the technical field of data fusion, and the method comprises the steps: obtaining a real-time image data stream of a transformer substation dangerous area, synchronously obtaining time sequence operation parameters collected by a power equipment sensor, carrying out the target detection analysis of the image data stream, and obtaining a real-time image data stream; and identifying personnel targets in the dangerous area and generating a first early warning signal, performing abnormal mode analysis on the time sequence operation parameters, identifying equipment operation state abnormity and generating a second early warning signal, performing decision-level fusion on the first early warning signal and the second early warning signal, and outputting a comprehensive risk assessment result through a weighted voting mechanism. According to the invention, by combining the image data and the sensor data, the perception capability of potential risks in the transformer substation is enhanced, the monitoring accuracy and the early warning reliability are improved, and the safety and stability of the whole power grid are further improved.

The invention relates to the technical field of data analysis, and discloses a dormitory noise monitoring data analysis method and system, and the method comprises the steps: carrying out the noise signal feature extraction of a dormitory space, and obtaining a noise type recognition result and noise parameter data; analyzing the distribution characteristics of the noise in time and space to obtain a noise space-time distribution characteristic spectrum and a noise standard exceeding judgment result; determining a noise source area and a noise type attribute based on the noise space-time distribution characteristic spectrum and a noise standard exceeding judgment result, and obtaining dormitory noise traceability data; performing mode analysis and trend prediction on the dormitory noise traceability data to obtain dormitory noise early warning information and high-incidence time period prediction data; and creating a dormitory noise environment optimization scheme according to the dormitory noise early warning information and the high-incidence time period prediction data. Therefore, context perception evaluation of noise interference is realized, the limitation that a traditional method neglects the environment context is overcome, and the accuracy of dormitory noise monitoring data analysis is improved.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention provides a drainage basin ecological environment evolution analysis method and system combined with a remote sensing image, and relates to the technical field of remote sensing. Firstly, a multi-temporal remote sensing image data set covering a target drainage basin and provided with time stamps is obtained, and then ecological feature extraction is carried out on the multi-temporal remote sensing image data set; spatial distribution features and time sequence features of drainage basin ecological elements are obtained, then ecological evolution mode analysis is executed based on the spatial distribution features and the time sequence features, and ecological state transition features of different time nodes are generated; the method comprises the following steps of: determining a drainage basin ecological environment evolution trend including stability, degeneration and restorability directions according to an ecological state transition characteristic, and finally generating a drainage basin ecological environment evolution analysis result containing a time node corresponding relation, so as to obtain a drainage basin ecological environment evolution analysis result. The method is used for indicating a historical evolution process and current state positioning information of a watershed ecological environment.

The invention relates to the technical field of elevator fault diagnosis, and discloses an elevator fault diagnosis method and system.The elevator fault diagnosis method comprises the steps that elevator operation data are collected, operation stages are divided for the elevator operation process according to the elevator operation data, and the residual error of the elevator operation data is calculated in the operation stages; based on an elevator structure, a causal chain graph reflecting the relation between components is established through fault mode analysis, and a direct upstream node set of downstream nodes in the causal chain graph serves as a parent set of the downstream nodes; in the operation stage, performing block replacement processing for keeping time sequence characteristics on an upstream node residual sequence of each edge in the causal chain graph to generate a contrast residual sequence, and calculating the propagation intensity of the edge according to the contrast residual sequence; effective propagation paths are screened according to the propagation intensity of the edges, the effective propagation paths are sorted to obtain candidate nodes, residual errors of the candidate nodes are set to be zero through virtual pinch-off, forward propagation is carried out through a semi-physical model, and fault nodes are determined in the candidate nodes based on forward propagation results.

The invention discloses a graph isomorphic network model-based power distribution network wiring mode identification method, which comprises the following steps of: according to a wiring mode analysis requirement, defining a sub-graph generation method, a rapid feature rule and a node mode category, and constructing and training a GIN model to realize analysis and identification of a power distribution network wiring mode. The specific process comprises the following steps: acquiring power grid data and simplifying subgraph extraction; performing preliminary classification analysis based on simple rules; marking equipment nodes according to the wiring mode, and generating a training data set; constructing a GIN model and carrying out model training; and according to the training model, applying all the simplified subgraph node classification data of the power distribution network to generate a wiring mode identification result. According to the method, a traditional graph search algorithm and a GIN graph neural network model are integrated, the defect that a traditional method depends on a manual rule arrangement process and a complex method for rule implementation is overcome, and a data annotation and model training method is adopted to achieve the recognition target of the wiring mode in the power distribution network.