115 results about "Reasoning algorithm" patented technology

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention discloses a drilling and blasting method tunnel construction safety risk evolution analysis method based on a knowledge graph, and the method comprises the steps: obtaining and preprocessing the text data of a drilling and blasting method tunnel construction accident case, and obtaining a safety risk corpus; a mixed deep learning model is constructed, the safety risk corpus is trained through the mixed deep learning model, and the risk factors and the incidence relation of drilling and blasting method tunnel safety are obtained; constructing a drilling and blasting method tunnel construction safety risk coupling evolution analysis knowledge graph based on the risk factors and the incidence relation, and obtaining a key accident chain and a key edge in combination with a knowledge query and path reasoning algorithm; and quantitatively evaluating independent values and coupling effect intensity of the risk factors by using an interaction matrix algorithm, calculating comprehensive importance scores of the risk factors, and determining key risk points according to the comprehensive importance scores of the risk factors. The problems that in a traditional method, attention to the risk factor coupling effect is insufficient, and accident case data are not fully utilized are effectively solved.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention discloses a mesh belt furnace multi-equipment collaborative material tracking and graphical monitoring method, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: carrying out the dynamic feature extraction of material block data based on a dynamic twin database, carrying out the analysis through combining a causal reasoning algorithm, and generating an enhanced data set with a causal label; on the basis of the enhanced data set with the causal label, when material tracking abnormity is detected, constructing a dynamic causal graph, calculating the contribution degree of equipment operation parameters to the abnormity through a Bayesian network, and generating a repair instruction set; and the repair instruction set is issued to the PLC to adjust the equipment operation parameters, the state change of the material blocks is monitored in real time, and when the deviation exceeds the tolerance range, the causal graph is triggered to update and a regulation and control instruction is generated to regulate and control the equipment operation parameters. According to the method, the causal network node weight is updated in real time by adopting the dynamic time warping algorithm, and accurate positioning of an abnormal source and intelligent generation of a repair strategy are realized.

The invention belongs to the technical field of disaster information early warning, and particularly relates to a disaster information fusion and semantic reasoning algorithm based on a cross-modal diagram attention mechanism, which comprises a data preprocessing module, a cross-modal diagram construction module, a cross-modal diagram attention mechanism module, a semantic reasoning module and a result output module. According to the method, data sources from multiple modals can be effectively fused through a cross-modal graph attention mechanism, so that more comprehensive disaster information is provided, and meanwhile, the graph attention mechanism is realized through the graph attention mechanism, so that the model can dynamically and adaptively adjust the weight according to the correlation of different modal data, and the accuracy of information fusion is improved. The graph neural network is adopted for semantic reasoning of disaster events, the types and influences of disasters can be accurately predicted, decision support can be provided, disaster emergency management personnel can be helped to respond quickly, various different types of disaster data can be processed, and the method has high adaptability to data sources and formats.

The invention discloses a knowledge graph question-answer interaction method and system based on artificial intelligence, and the method comprises the steps: receiving a natural language question input by a user, carrying out the hierarchical analysis of entity words and intention words in the question through a dynamic weight distribution mechanism, and generating an enhanced semantic vector containing entity association strength and intention probability distribution; traversing the knowledge graph based on the enhanced semantic vector, and constructing a hierarchical query sub-graph comprising a core entity, an associated entity and an implicit relationship; performing reasoning calculation on the hierarchical query subgraph by adopting a multi-path collaborative reasoning algorithm to generate a candidate answer set; and performing semantic consistency verification and redundancy elimination on the candidate answer set, generating an adaptive answer and an association explanation path in combination with historical interaction preference of the user, and synchronously feeding back a newly discovered entity association relationship to the knowledge graph for incremental updating to form a question and answer interaction closed loop. By utilizing the embodiment of the invention, the accuracy and interpretability of questions and answers can be improved, and the user interaction experience and the information acquisition efficiency are improved.

The invention discloses a version knowledge graph reasoning method and system based on large language model enhancement, and relates to the technical field of dynamic knowledge graphs, and the method comprises the steps: employing a semantic drift detection and compensation mechanism, comparing context coding differences of same entities of different versions in an initial knowledge graph, recognizing drift, and dynamically adjusting entity embedding vectors, outputting a compensation update atlas; an LLM enhanced multi-hop reasoning algorithm is adopted, multi-hop reasoning is carried out on the compensation updating map, symbol reasoning, vector reasoning and context reasoning are carried out in parallel in each hop, and an entity relation reasoning result is obtained through dynamic weight fusion; and superposing an entity relationship reasoning result to the compensation updating graph through a cloud collaborative node, adding an entity edge and automatically maintaining a version history log, and obtaining a reasoning fusion version knowledge graph. Through a multi-hop reasoning algorithm enhanced by a large language model, the deep semantic mining capability and reasoning accuracy of a cross-version entity relationship are effectively improved.

The invention provides an index transaction attribution analysis method, electronic equipment, a storage medium and a product, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to an attribution analysis request for transaction of a target index, querying target data related to the target index; calling a plurality of agents, and analyzing the target data from a plurality of dimensions to obtain abnormal data under the plurality of dimensions; inputting the abnormal data under the plurality of dimensions into an attribution reasoning model, and outputting a plurality of candidate root causes causing the transaction of the target index; the multiple candidate root causes, the abnormal data under the multiple dimensions and data of a preset knowledge base are input into an attribution analysis model, an attribution analysis result of the target index transaction is output, and the preset knowledge base is used for storing description data of different indexes and dimensions. The method does not depend on a traditional reasoning algorithm, and the attribution analysis result reasoned by means of the model is more comprehensive and more accurate.

The invention relates to a method for improving the reasoning efficiency of a multi-machine algorithm based on cloud edge collaboration, and the method comprises the following steps: a cloud end issues a reasoning task instruction to an edge end, and the reasoning task instruction comprises at least one reasoning algorithm and at least one video stream address; the edge end responds to the reasoning task instruction, carries out stream pulling scheduling on the video stream address by adopting a time wheel mechanism, and decodes the video stream to obtain image data; the edge end writes the image data into a shared memory area through a shared memory mechanism so as to realize cross-process sharing; the edge end reads the image data from the shared memory area and executes a reasoning algorithm specified in the reasoning task instruction to generate a reasoning result; and the edge end reports the reasoning result to the cloud end. The method has the advantage of efficiently coordinating the strong computing power of the cloud end and the low delay characteristic of the edge end.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention relates to the field of power system automation, and discloses an intelligent power distribution network feed automation control system based on multi-source information fusion, which comprises a multi-source sensor module used for collecting current data in a power distribution network in real time, and an information fusion module used for receiving the preprocessed current data and sending the preprocessed current data to the power distribution network. The multi-source fusion module is used for carrying out multi-source fusion processing on the current data by adopting a Bayesian reasoning algorithm, the fault positioning module is used for receiving a fusion result, and the optimal control module is used for receiving a grounding fault occurrence position and adopting an optimal control theory and a dynamic planning algorithm; the invention further discloses an intelligent power distribution network feed automatic control method based on multi-source information fusion. The method comprises the following steps of an acquisition stage, a fusion stage, a positioning and decision-making stage and an execution and self-healing stage. According to the invention, by constructing the Bayesian fusion and dynamic programming control mechanism, the purposes of more accurate ground fault positioning, more efficient strategy removal and more adaptive response process are achieved.

The invention relates to the technical field of data processing and risk monitoring, and discloses a performance risk intelligent monitoring and collaborative response method and system, and the method comprises the steps: an edge sensing node calculates the compression characteristics of performance flow data through employing a mean aggregation algorithm according to a current sliding window parameter, and transmits the compression characteristics to a cloud end; the cloud decision-making platform executes data standardization management, calculates performance risk probability by using a model reasoning algorithm, and calculates feature uncertainty indexes by using a game theory interpretation method and a variance statistical method; a feedback control strategy module generates a self-adaptive sampling control instruction according to the index, drives an edge node to dynamically update a sliding window parameter, and constructs an acquisition feedback closed loop; and the collaborative response module calculates a response priority by utilizing multi-dimensional weighted logic, calculates desensitized data by utilizing a differential privacy algorithm and then executes response. According to the invention, adaptive adjustment of data acquisition granularity and safe cooperative processing of risk events are realized.

The invention belongs to the technical field of intelligent medical treatment, and discloses an ophthalmology department hospital guide knowledge graph construction system based on improved gradient lifting. Comprising the steps of collecting multi-source ophthalmology original data; performing word segmentation processing on the multi-source ophthalmology original data to obtain ophthalmology medical vocabularies, and dividing the ophthalmology medical vocabularies into disease vocabularies and disease vocabularies; identifying professional vocabularies in the disease vocabularies, and constructing a rough reasoning algorithm and a fine reasoning algorithm; according to the disease vocabularies, the specialized vocabularies and a predefined disease weight set, an ophthalmology entity relation network is constructed, a rough reasoning algorithm and a fine reasoning algorithm are fused, and an ophthalmology hospital guide knowledge graph is constructed; according to the invention, complex symptoms can be dynamically analyzed, the diagnosis accuracy is improved, the medical resource configuration is optimized, and the self-evaluation requirement of the patient is met, so that the rapid response and accurate recommendation of the user symptoms are realized, and the medical treatment efficiency and the medical service quality of the patient are improved.

The invention discloses a multi-scale target detection method based on a YOLOv8-SAHI model, which optimizes and improves the network structure of the YOLOv8 based on the YOLOv8. Firstly, in order to cope with target scale and category diversity challenges, variable kernel convolution is adopted to replace a common convolution in a backbone network; then, a C2f-TA module is designed, so that the model is focused on a target area, and interference of irrelevant information is effectively suppressed. Finally, in order to solve the problem of low detection precision of the small target, a slice-assisted super reasoning algorithm is introduced, and more detailed feature information is obtained by enhancing pixel representation of the small target. According to the method, the adaptability of the model to targets with different scales in complex backgrounds such as a construction site is improved, the small target detection capability is particularly enhanced, the phenomena of false detection and missing detection are effectively reduced, the detection precision is improved, and on-site monitoring and safety management are enhanced.

The invention discloses a traditional Chinese medicine whole industry chain big data construction method and system, and relates to the field of data links, and the method comprises the steps: generating a standardized metadata template for seven links of a traditional Chinese medicine industry chain; block chain nodes are deployed in each link of an industrial chain, timestamp fingerprints are generated for key process data, and the key process data are automatically chained for evidence storage; constructing a data fusion model based on a graph neural network, and generating cross-link associated data; constructing a traditional Chinese medicine ontology knowledge graph, and mapping the association relationship between the cross-link associated data in the graph; full-chain digital tracing from implantation to clinic is realized through a path reasoning algorithm; based on the hybrid collaborative storage architecture, access management is carried out through dynamic fragmentation strategy data points; and constructing a process prediction model, detecting process parameter deviation, and carrying out risk early warning. The method has the advantages that through block chain evidence storage and combination of the graph neural network and the knowledge graph, full-chain digital tracing and risk early warning from implantation to clinic are realized.

The invention relates to the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle target identification and threat assessment method and application, and the method comprises the steps: obtaining an original data stream of an unmanned aerial vehicle target from a multi-source sensor, and carrying out the processing, and obtaining a unified multi-source data set; generating a static feature set by adopting a feature extraction algorithm; generating a dynamic feature set by adopting a time sequence analysis algorithm; performing feature fusion by adopting a neural network model to obtain a fused feature set; generating an unmanned aerial vehicle target knowledge graph by adopting a relation extraction algorithm; extracting association strength from the unmanned aerial vehicle target knowledge graph, and mining threat modes by adopting a graph neural network algorithm to obtain a threat mode set; generating a threat evaluation vector set by adopting a probabilistic reasoning algorithm; generating a countering strategy set by adopting a classification algorithm; and updating the unmanned aerial vehicle target knowledge graph by adopting a feedback learning algorithm to obtain an updated knowledge graph. By adopting the method provided by the invention, the unmanned aerial vehicle target can be quickly and accurately identified, a targeted countering strategy is formulated, and accurate countering is realized.

The invention provides a man-machine co-driving decision generation method and device. The method comprises the following steps: acquiring multi-source data in a driving process of a vehicle; based on the multi-source data, a fuzzy reasoning algorithm and a multi-modal model are used for identifying the driving group type of the driver, and obtained identification results are fused through a linear weighted fusion algorithm to obtain the driving group type; obtaining a driving condition based on the multi-source data, determining a corresponding look-up table based on the driving condition, and determining a man-machine cooperation factor estimation value by using the look-up table and the driving group type of the driver; determining a man-machine cooperation factor of the decision through a cooperation equation; and the decision of the intelligent driving system and the decision of the driver are fused to obtain a comprehensive driving decision. Dynamic cooperation of the intelligent driving system and human drivers is achieved, the scientificity and adaptability of driving decisions are improved, and the safety and reliability of vehicle driving are enhanced.

The invention discloses an attention mechanism driven belief rule base aviation component performance evaluation method, which comprises the following steps: acquiring observation data of an aviation component inertial navigation system, and inputting the observation data into an aviation component performance evaluation model to obtain a performance evaluation result; the aviation component performance evaluation model is trained according to the following steps: acquiring various monitoring data of an aviation component inertial navigation system to construct a confidence rule base, and setting default weights of precondition attributes and rules; using a feature attention module and monitoring data to optimize default weights of the premise attributes; based on the optimization weight of the premise attribute, determining an activated rule in a confidence rule base by using a rule attention module; and determining a final evaluation result after the various monitoring data are input into the activated rule by using an evidence reasoning algorithm, and optimizing parameters of the confidence rule base by using the final evaluation result and a preset objective function until the final evaluation result reaches preset precision, thereby obtaining the aviation component performance evaluation model, and improving the evaluation precision.

The invention discloses an automatic testing method, system and equipment based on a protocol configuration table and a medium. The method specifically comprises the following steps: capturing a communication data packet between a game client and a game server; analyzing the communication data packet by using a protocol format reasoning algorithm based on a hidden Markov model to generate a protocol configuration table; constructing an automatic test robot, and simulating player behaviors based on the protocol configuration table to execute a protocol-level test; based on the protocol configuration table and the execution state of the test robot, preferentially exploring a high-risk branch path, and dynamically adjusting the test sequence; and constructing a defect root cause analysis engine according to a protocol level test result and a multi-modal detection result, and automatically positioning and outputting a problem root in combination with protocol data, game state data and a system log. According to the method, the whole process of game automatic testing is realized, the game problem is accurately and efficiently detected, the source is positioned, the manual testing cost and error are reduced, and the game testing quality and efficiency are improved.

The invention provides a weaving machine networking data acquisition and analysis method and system based on an intelligent algorithm, and relates to the technical field of data processing, and the method comprises the steps: constructing a multi-source sensor network, and then collecting vibration data, temperature data and rotation speed data; based on a quantum noise auxiliary data acquisition algorithm, microcosmic features of vibration data, temperature data and rotating speed data are enhanced; establishing an edge computing node, and carrying out preliminary cleaning and feature extraction on the enhanced data to obtain a preprocessed feature vector; constructing a weaving machine physical knowledge graph; integrating the weaving machine physical knowledge graph based on a quantum-classical data fusion algorithm according to the preprocessed feature vector to obtain a multi-modal feature library; according to the multi-modal feature library and the vibration data, the temperature data and the rotating speed data which are collected in real time, the state of the loom is evaluated, an evaluation result is obtained, a fault prediction type is obtained based on a neural symbol reasoning algorithm, and then maintenance suggestions and optimization strategies of corresponding faults are executed.

The invention discloses a multi-modal reasoning algorithm based on bidirectional chain type thinking enhancement. The method comprises the following steps: firstly, performing bidirectional thinking chain generation and quality verification on original multi-modal question and answer data, constructing a multi-modal thinking chain data set (MCD), and providing standardized reasoning process data for a model; secondly, three types of annotation labels of knowledge types, question types and reasoning types are added on the basis of the MCD, and a knowledge-enhanced multi-modal thinking chain dataset (KMD) is obtained and used for model supervision fine tuning (SFT) to constrain model reasoning; and finally, generating multiple candidate outputs by using KMCD in combination with the structured cue word, and obtaining a strong model with complex reasoning ability through reinforcement learning training of two-dimensional adaptive reward strategy optimization (Di-GRPO). According to the method, the problems of low data quality and insufficient training focusing of the existing multi-modal reasoning task thinking chain are effectively relieved, and the reliability and the accuracy of multi-modal reasoning are improved.

The invention relates to the technical field of computers, and discloses a health degree monitoring method for a server storage system, and the method comprises the steps: collecting a multi-source heterogeneous operation index; constructing a dynamic causal inference graph based on system topology and driving specifications; an anti-factual reasoning algorithm is adopted to trace an abnormal propagation path so as to identify a root cause; performing mode matching in combination with a historical fault case library, and calculating a comprehensive health degree score of the storage unit; and triggering grading early warning according to the score. According to the invention, through explicit modeling of the physical and logic dependency relationship and fusion of causal reasoning and historical experience, the fault positioning accuracy and early warning effectiveness are significantly improved, and the operation and maintenance noise is reduced.

The invention relates to the technical field of agricultural remote sensing and ecological monitoring, in particular to a dendrocalamus brandisii biomass region estimation method combining remote sensing data and a heterosexual model. The method mainly solves the problem of insufficient estimation precision of dendrocalamus brandisii biomass caused by terrain interference and phenological period change in the prior art. According to the method, a multi-scale feature data set is generated through collaborative preprocessing of multi-source remote sensing data; training a random forest biomass prediction model based on ground measured data; performing phenological phase identification by using a time sequence satellite image, and establishing a phenological period self-adaptive parameter lookup table; performing terrain invariant feature conversion by adopting a conditional generative adversarial network to eliminate terrain interference; the phenological period parameters and the standardized features are fused, and a high-precision biomass spatial distribution diagram is output through a dynamic weighted fusion reasoning algorithm; and finally, automatic conversion and visual monitoring from biomass to carbon reserves are realized through a carbon sink accounting module.

The invention relates to the technical field of artificial intelligence model interpretation, in particular to a multivariable time sequence prediction interpretation method and system based on an information theory and causal reasoning. The method comprises the following steps: acquiring original multivariable time sequence data; performing data preprocessing on the obtained original multivariable time sequence data; performing causal information reasoning on the preprocessed multivariable time sequence data; based on causal information reasoning, calculating a dynamic feature weight of the multivariable time sequence data; performing local fitting and interpretation generation based on the dynamic feature weight; and a time-feature two-dimensional thermodynamic diagram is obtained. Through multi-dimensional feature relationship mining, an optimized causal relationship reasoning algorithm, dynamic weight calculation and an intuitive time-feature two-dimensional thermodynamic diagram generation mechanism, time continuity is effectively considered, prediction interpretation is presented in an intuitive and understandable mode, and a user can quickly understand an artificial intelligence model decision process.

The present invention discloses a method for identifying weak links in a combined heat and power system based on a Bayesian network, which belongs to the technical field of energy systems. The method comprises the following steps: establishing a multi-state-coupling logical relationship model; a temporal simulation process of a Bayesian network of a combined heat and power system taking into account the multi-states of coupling elements; and identifying reliability weak links in a multi-state combined heat and power system of coupling elements based on the Bayesian network temporal simulation. The present invention establishes a "multi-state-coupling" logical relationship model that is closer to actual engineering scenarios. A comparative analysis is conducted on the Bayesian network temporal simulation processes of the distribution subsystem and the thermal subsystem with and without considering the multi-states of coupling elements, which more finely depicts the degree of complex coupling correlation between various energy forms and the states of electrical and thermal load points. The improved Bayesian network temporal simulation reasoning algorithm is used to accurately evaluate the multi-state reliability level of the combined heat and power system and precisely identify the weak links in the system reliability.

The invention relates to the field of sentiment analysis, in particular to an aspect sentiment triple extraction method based on double-feature enhancement of a graph neural network, which comprises the following steps of: inputting a text X for encoding and splicing, and outputting features through dimension conversion; inputting the features into a graph convolution decorrelation module to perform feature enhancement of a graph structure, and expanding the features of nodes by using an orthogonalization method; a syntactic structure and topological distance information of the graph are fused into a Transform module to serve as a graph neighbor enhancement module, and enhanced term features are obtained; and constructing mark information of text words, and decoding term features by using a span reasoning algorithm to obtain a final aspect emotion triple. The method has the advantages that a double-feature enhancement mechanism is introduced into a graph neural network framework, the problem of balance between insufficient syntactic information utilization and weak semantic modeling ability of a traditional aspect emotion triple extraction method is effectively solved, the over-smoothing problem is relieved, and the information extraction ability is remarkably enhanced.

The invention relates to the technical field of information, in particular to a knowledge graph-based grassroots governance law enforcement scene intelligent research and judgment method and a storage medium. A technical scheme capable of dynamically studying and judging, predicting risks and optimizing decision support is provided by combining a knowledge graph, a graph neural network GNN and a plurality of reasoning algorithms, so that the intelligence and informatization level in a grassroots governance law enforcement process is improved, the analysis, studying and judging and decision-making capabilities of law enforcement departments are enhanced, complex law enforcement challenges are dealt with, and the law enforcement risk prediction and risk prediction are realized. And effective implementation of social public safety and environmental protection is ensured.

An automated functional fault localization method for software product lines integrates techniques such as Spectrum-Based Fault Localization (SBFL), machine learning algorithms, data mining, and information theory. By utilizing code blocks as the detection granularity, it performs probability allocation from three perspectives: prediction, actual execution, and correlation. The suspiciousness values of code blocks are then computed using uncertainty reasoning algorithms, enabling the rapid identification of suspicious statements. Building on this, a more precise assessment of these statements is achieved by evaluating at three levels of granularity: global, local, and code block. This ultimately results in more efficient and accurate fault localization. The code blocks directly correspond to internal feature interactions, significantly enhancing the efficiency of searching for these interactions. For program statements with a higher likelihood of causing software errors, this method allows for quick identification and localization without manual intervention, thereby facilitating the maintenance of software product line systems.