3102 results about "Analysis method" patented technology

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention relates to the technical field of decoration construction management, and discloses a BIM-based decoration construction scene resource consumption simulation analysis method. The method comprises the following steps: firstly, creating a multi-dimensional BIM resource model, and integrating building information model data, real-time construction data flow and historical resource consumption records; identifying resource consumption characteristics through the model, and generating an initial resource allocation strategy set comprising material allocation, equipment scheduling and process priority schemes; receiving a user interaction instruction, and dynamically adjusting the initial strategy set; and finally, executing the adjusted strategy, and updating the multi-dimensional BIM resource model based on a result. Wherein the resource consumption feature comprises construction stage division, resource node dynamic weight calculation and space correlation correction operation. According to the method, by integrating multi-dimensional data and dynamically analyzing a resource consumption rule, real-time optimization of a resource allocation strategy is realized, and the accuracy and flexibility of decoration construction resource management can be improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an analysis method, device and equipment based on multi-modal information and a medium, and the method comprises the steps: obtaining visual information, sound information and physiological index information of a target object; the multi-modal sensing information is preprocessed, and standard multi-modal information is generated; extracting facial characterization features, behavior characterization features, voice characterization features and physiological characterization features; generating a state representation of the target object based on the multi-dimensional features; comparing the state characterization with a preset reference standard to generate a state difference result; and generating an analysis result according to the state difference result. According to the method, the multi-modal perception information is fused, the multi-dimensional features are extracted, the state representation is constructed, and the difference analysis is carried out, so that the analysis result has the characteristics of quantifiability, comparability and the like, and the objectivity and accuracy of evaluation are improved.

The invention relates to the technical field of electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

The invention discloses a data analysis method and device based on multi-agent cooperation and a storage medium. The method comprises the following steps: receiving a natural language request by a workflow arrangement agent, and generating an execution plan comprising a plurality of subtasks and corresponding task processing units by consulting a data exploration agent and a data engine understanding agent; in the execution stage, the professional agent matched with the subtask converts the subtask into an executable instruction, and the data engine dispatch agent calls and coordinates the corresponding task processing unit to execute the instruction. According to the method, through a layered collaborative architecture, the separation of planning, translation and execution is realized, and the automation level, modularization and expandability of data analysis are remarkably improved.

The invention discloses a clinical examination and detection item correlation analysis method based on multi-agent cooperation, and relates to the technical field of correlation analysis, and the method comprises the steps: obtaining the name and basic parameters of an examination item, and determining the technical field of the examination item through a classification system and an algorithm; related field parameters are extracted from the knowledge management module, and configuration parameters of the intelligent agent are set; generating a task instruction based on the basic parameters, transmitting the instruction through a structured message transmission mechanism, and obtaining an agent processing result; and performing verification analysis on the processing result by using a hypothesis production and verification engine to obtain a correlation analysis result of the project. According to the method, the hypothesis content can be evaluated from multiple angles, it is ensured that the obtained correlation analysis result has high scientificity and credibility, and powerful support and basis are provided for research and application of clinical examination and detection items.

The invention provides a fault inference analysis method and system based on a large language model, and the method comprises the steps: firstly obtaining a multi-source operation and maintenance data set of a to-be-diagnosed system, including a structured performance index, a semi-structured service log and unstructured text description data, carrying out the semantic enhancement of the multi-source operation and maintenance data set, and carrying out the semantic enhancement of the multi-source operation and maintenance data set; obtaining a semantic data unit containing entity, relation and attribute semantic tags, constructing a semantic association network based on the semantic data unit, calling a pre-trained large language model to carry out root cause reasoning analysis on the network, generating a candidate root cause set sorted according to confidence, and carrying out root cause reasoning analysis on the candidate root cause set; and finally, according to the candidate root cause set, generating an operation and maintenance decision instruction containing an entity operation sequence and a priority sequence, and sending the instruction to a system management terminal to trigger an automatic repair process. Therefore, the operation and maintenance efficiency and reliability of the system can be improved.

The invention discloses an analysis method and device for screening a target customer group, equipment and a medium, and relates to the technical field of customer data analysis. The method comprises the following steps: constructing registered user behavior data into a triple graph structure comprising user nodes, behavior nodes and entity nodes, and injecting domain knowledge for semantic enhancement; 24 hours are divided into periodic time periods, time period embedding vectors are generated, and Transform modeling cross-time behavior dependence is sensed through the time periods. According to the method, a triple graph structure is constructed, domain knowledge is injected, periodic time period modeling and multi-modal feature fusion are combined, behavior semantic association and time sequence dependence are deeply mined, and the effect of capturing hidden demands of a user is achieved; customer layering is optimized through local multi-source data cross validation, four-level division is defined, reinforcement learning and dynamic threshold decision are associated, and the effect of improving layering precision is achieved.

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention belongs to the technical field of data analysis, and provides a multi-source operation and maintenance data anomaly analysis method and device, equipment, a medium and a product, the method comprises the steps that multi-source operation and maintenance data is acquired, and the multi-source operation and maintenance data comprises at least two of index time sequence data, application logs, call link tracking data, configuration change records, alarm events and work orders; carrying out joint anomaly modeling on the preprocessed multi-source operation and maintenance data based on a multi-model fusion architecture to identify an abnormal event in the multi-source operation and maintenance data; based on the operation and maintenance knowledge graph and the structured causal model, fault influence path tracing and root cause positioning are carried out on the abnormal event, a root cause analysis result is obtained, and the root cause analysis result is used for indicating a fault root cause node and a fault propagation path in the abnormal event. Therefore, the accuracy of anomaly analysis of the multi-source operation and maintenance data is remarkably improved.

The invention discloses a data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a target question proposed by a user; performing semantic analysis on the target problem, and determining a field corresponding to the target problem and semantic feature information of the target problem; determining a plurality of data processing tools related to the target problem based on the field and semantic feature information corresponding to the target problem; performing dynamic priority ranking on the plurality of data processing tools on the basis of correlation indexes of the plurality of data processing tools and the target problem and historical use conditions; and according to the dynamic priorities of the plurality of data processing tools, calling the corresponding data processing tools to analyze the target question, and generating and displaying an answer result corresponding to the target question. Compared with the prior art, the intelligent level and the execution efficiency of tool calling are enhanced, so that the overall service response capability and the user experience of the system are remarkably improved.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention relates to the technical field of power marketing, in particular to an AI large model-based power marketing data analysis method, which comprises the following steps of: acquiring structured data and unstructured data in a power marketing system, and generating unified coded data after space-time alignment and pre-training word embedding model processing; inputting the unified coding data into a pre-trained power field large model, and extracting static, dynamic and semantic features through multi-modal fusion, feature decoupling and semantic anchoring; constructing entity link feature pairs in combination with a power knowledge graph, and enhancing fusion feature expression through a graph attention mechanism; and finally, inputting a dynamic weight gating network, outputting an abnormal user identification tag, a demand response strategy and a customer loss early warning probability, and executing strategy optimization under specific conditions. The method can be widely applied to risk identification, strategy making and user behavior prediction tasks in power marketing.

The invention relates to the technical field of industrial internet data processing, in particular to a cross-platform multi-source data integration analysis method and system based on the industrial internet, and the system comprises a data collection layer, a data preprocessing layer, a data integration layer, a data storage layer, an intelligent analysis framework, an application layer and a data security layer. Through the data adapter and middleware technology, efficient collection and integration of multi-source heterogeneous data are achieved, multiple data sources and communication protocols are supported, the compatibility and expansibility of the system are improved, and the system can rapidly adapt to different industrial scenes and data source changes; according to the method, advanced big data and artificial intelligence technologies are applied, a specific analysis model for industrial production is combined, deep analysis can be performed on the data, potential values in the data are mined, an accurate analysis result is provided for equipment fault prediction and production process optimization, and the intelligent level of industrial production is improved.

The invention discloses a new quality productivity space analysis method based on machine learning, relates to the technical field of urban planning, and constructs a new quality productivity space index system, constructs a plurality of machine learning models for optimization, and provides a new quality productivity space analysis method based on machine learning. A machine learning model with the best prediction and generalization effect is selected to analyze the influence of each element on the new quality productivity space, an SHAP model is used for explaining and analyzing, and an SHAP value explaining mechanism for calculating the marginal contribution degree of each feature based on the Shapley value theory is introduced; the contribution degree of key indexes such as enterprise facility density and night light intensity to new quality productivity space distribution is quantitatively analyzed, and collaborative optimization of a machine learning model in prediction precision and interpretability is achieved; and in combination with spatial visualization analysis, a priority intervention basis is provided for urban planning, and finally, accurate quantitative analysis of a nonlinear effect and an element interaction effect is realized.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The invention discloses a resume analysis method and system based on multiple large language models, and belongs to the technical field of large language models.The resume analysis method and system based on the multiple large language models.The resume analysis method and system based on the multiple large language models comprise the following specific steps that firstly, model parallel analysis is conducted, simultaneously inputting the data into at least two heterogeneous large language models through an application program interface; and each large language model independently performs information extraction and analysis according to the model structure and the training data of the large language model, and outputs a structured data result containing a plurality of preset fields. Through the multi-model parallel analysis and conflict re-judgment mechanism, the misjudgment risk of a single model is effectively reduced, the robustness of the whole system is improved, the resume analysis accuracy is remarkably improved, the model pool is automatically optimized and updated through the dynamic scoring mechanism, and the problem that the model is difficult to select and update is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an anomaly analysis method, device and equipment based on multi-agent collaboration and a medium. The method comprises the following steps: generating fusion data through processing of a data fusion agent, extracting abnormal feature information through a data analysis agent, generating an abnormal analysis result and a decision instruction through a decision agent, collecting decision feedback through a feedback optimization agent, and analyzing and generating an optimization instruction for optimizing the data analysis agent and the decision agent. According to the method, closed-loop processing of data acquisition, fusion, analysis, decision and feedback optimization is realized through cooperation of multiple agents, so that multi-source heterogeneous data is efficiently integrated and deeply analyzed, the accuracy and adaptability of analysis and decision are continuously improved in combination with feedback optimization, and the timeliness and reliability of anomaly recognition and response are improved.

The invention relates to the technical field of artificial intelligence and data analysis processing, and provides a smart home complex scene object analysis method based on multi-modal fusion. The method specifically comprises the steps of performing standardization processing on input data, and extracting multi-scale features of local textures and global semantics; generating a candidate box and a category score by a YOLO model, and adaptively generating a box prompt, a point prompt and a category / text prompt according to the confidence coefficient and the target scale; and inputting the prompt information and the multi-scale features into a mask decoder together, generating a high-precision instance mask in the candidate region, and checking to obtain a final output. According to the method, repeated coding is avoided, the performance of a small object and the performance of a complex boundary target are effectively distinguished, the real-time performance and the resource efficiency are considered at the same time, and the method is suitable for scenes of smart home, indoor navigation, security monitoring, augmented reality and the like.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-agent traceable analysis method, device, equipment and medium, and the method comprises the steps: receiving a target theme and a data source list, collecting a multi-source document, and carrying out the preprocessing of the multi-source document to generate a preprocessing document set; configuring an analysis agent based on a semantic clustering result, and setting an analysis direction to form an analysis agent set; generating a structured note and index data table, and executing cross-document comparison to form an analysis output set; and receiving a feedback instruction to adjust the analysis agent set, triggering incremental processing to update the analysis output set, generating a theme research and judgment report, and keeping mapping consistency. According to the method, the multi-source document is fused through semantic clustering and a multi-agent cooperation mechanism, semantic association and traceable analysis are achieved, agent configuration is optimized in combination with interactive feedback, and the accuracy and the intelligent level of report generation are improved.

The invention discloses a steam pipeline pressure control method, which comprises the following steps of: acquiring operation information of each key position of a pipe network in real time by constructing a data acquisition system and deploying an execution unit; a multivariable analysis method is utilized, topological structure characteristics of a steam pipeline are combined, a load change model of a steam consumption unit is constructed, the pressure change process caused by operation of different devices is recognized in advance, and a long-term pressure control strategy is made; meanwhile, an accumulative deviation curve is constructed based on a prediction and actual measurement residual evolution sequence, the potential fault risk of the system is identified in an auxiliary manner, and structured alarm output is formed, so that the safety guarantee capability of pipe network operation is enhanced. The steam pipeline pressure control method is suitable for the application scene that the overall steam use requirement is stable, but the local high-frequency disturbance behavior exists, the tiny pressure fluctuation trend can be accurately predicted under the high-disturbance working conditions such as frequent starting and stopping, and it is guaranteed that the steam pressure is stable.

The invention discloses an attribution analysis method based on a BI platform, a medium and equipment, and the method comprises the steps: collecting multi-dimensional business index data and an associated index data set, detecting abnormal fluctuation through employing a self-adaptive sliding window algorithm, and calculating an absolute growth contribution degree in combination with an improved contribution rate decomposition model; and fusing to generate a composite influence matrix. According to the technical scheme, chart type limitation can be broken through, and attribution adaptation of most mainstream charts is achieved; eliminating the risk that a denominator is zero through an improved contribution rate model, and precisely recognizing a proportion mutation dimension in combination with a JS divergence algorithm; through an automatic attribution map model and a rule engine, the manual operation time is shortened, and the hidden danger of data leakage is eliminated; the constructed unified data query structural body significantly improves the expansibility of the system, and meets the real-time analysis requirements of complex service scenes.

The invention relates to a field information analysis method and system based on RAG and a knowledge graph, and belongs to the technical field of artificial intelligence, and the system comprises a multi-source heterogeneous data collection module which is used for collecting field-related original data; the intelligent document processing engine is used for carrying out cleaning and structured processing on the data and extracting entities, relationships and events; the multi-source knowledge graph construction and management module is used for constructing and fusing multi-sub-field knowledge graphs to form a unified knowledge graph; the RAG retrieval enhancement generation dynamic fusion sequencing module is used for receiving user query, performing multi-channel information retrieval by querying a text vector index and a knowledge graph, performing fusion resequencing on a result, and generating an analysis result by utilizing a large language model; and the agent workflow engine is used for decomposing query into subtasks and scheduling professional agents to cooperatively complete analysis and report generation. Deep semantic understanding, intelligent retrieval and automatic analysis of domain information are realized, and the accuracy and efficiency of information processing are improved.

The invention discloses a big data equipment fault prediction method based on machine learning, and the method comprises the steps: S1, collecting data in the operation process of equipment, and carrying out the preprocessing of the collected data; s2, multi-dimensional features in the preprocessed equipment data are extracted, and statistical characteristics are calculated; s3, processing the equipment data by adopting a time sequence analysis method, and generating a time sequence feature matrix; s4, processing the input time sequence feature matrix by using the improved Transform model, and constructing a fault prediction model; s5, monitoring the equipment in real time by using the trained fault prediction model; s6, fault early warning information is sent to operation and maintenance personnel according to a prediction result; and S7, continuously optimizing and updating the fault prediction model according to real-time monitoring and early warning feedback. According to the method, an efficient and scientific optimization scheme can be provided in big data equipment fault prediction, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses an electric power system monitoring data analysis method, equipment and a medium. The method comprises the following steps: S1, acquiring a historical monitoring data set and a real-time monitoring data stream of an electric power system; s2, constructing a power equipment operation reference mode library based on the historical monitoring data set; s3, mapping the real-time monitoring data stream to the power equipment operation reference mode library for mode matching processing, and generating a matching degree sequence; s4, performing difference evolution analysis processing on the matching degree sequence to generate an abnormal evolution track; s5, determining starting equipment, a propagation path and an influence range of abnormity according to the abnormity evolution track, and generating an early warning instruction; according to the data analysis method, the accuracy and timeliness of monitoring of the power system are greatly improved on the whole, faults of the power system can be effectively prevented and reduced, the stability and reliability of power supply are guaranteed, and economic losses and social influences caused by the power faults are reduced.

The invention discloses a financial traceability risk measurement and analysis method and system based on dynamic entropy transfer, and the method comprises the steps: constructing a dynamic entropy transfer graph through combining with various types of structure information, such as local guiding entropy, path exploration entropy and behavior collaborative entropy; the method can effectively describe the propagation path and the structure level of the risk factor in the complex financial network while keeping the calculation controllable. According to the method, the edge behavior sequence is constructed through the Gaussian kernel function on the basis of the node behavior time sequence, and the multi-scale permutation entropy is combined to model the inter-edge time sequence collaborative change, so that the expression ability of the system to the microscopic interaction behavior is remarkably improved, and the method has good adaptability and interpretability. The method can achieve the positioning of a potential high-influence risk source and the reconstruction of a propagation chain, is suitable for a plurality of scenes, such as financial supervision, risk early warning and abnormal path tracking, is high in processing efficiency, and is stable and reliable in result.