76 results about "Risk mapping" patented technology

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

The invention discloses a laboratory whole-process intelligent management and control system based on AI and Internet of Things fusion, which relates to the technical field of laboratory management and comprises an equipment access unit, a central decision unit and a business application unit. According to the system, a risk model library and a real-time risk map can be constructed through the risk assessment module, accident risks caused by human errors are reduced through the real-time risk map, automatic emergency disposal and personnel behavior compliance monitoring, and when the equipment operation and maintenance module detects that the equipment is abnormal, predictive maintenance can be automatically carried out, so that the downtime is shortened, and the maintenance efficiency is improved. The resource scheduling module can be used for optimizing the inventory and scheduling the equipment, and carrying out global allocation based on an inventory sharing model, so that resource idleness is reduced through cross-courtyard inventory sharing, resource sharing is facilitated, cross-courtyard collaboration is completed, and the situation that data cannot be synchronized due to independent inventory management of each courtyard is avoided; and further, global optimization can be realized.

The invention relates to the technical field of construction safety, in particular to a trestle construction hazard source assessment method and system, and the method comprises the steps: constructing a hazard source data set corresponding to a trestle construction process, carrying out the entry recording of various potential hazard sources, and setting impact factors for representing risks; collecting field data of the construction process, and associating the field data with the influence factors to obtain risk characterization forming a hazard source; fusing the risk characterization with the environmental parameters, and constructing a space-time risk mapping layer; based on the space-time risk mapping layer, performing quantitative analysis on the hazard source through a multi-factor comprehensive model to obtain risk distribution characteristics in different level intervals; and according to the risk distribution characteristics, generating a space-marked trestle risk map. According to the invention, the problems of strong subjectivity and lack of systematic analysis of existing trestle construction hazard source assessment are effectively solved, the objectivity of risk assessment is enhanced, and the capability of accurately judging the risk state in a complex construction environment is improved.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention relates to the technical field of coal mine fire prevention and control, and discloses a coal mine fire disaster prevention and control system and method based on artificial intelligence. A data preprocessing module of the system cleans and converts the data to obtain high-quality data; the fire prevention knowledge base construction module constructs a coal mine fire prevention ontology knowledge base in combination with the preprocessed data and a preset fire prevention knowledge text; the risk map construction module constructs a risk knowledge map containing risk nodes, attribute edges and relation edges based on the knowledge base and historical disaster data; a risk factor extraction module extracts fire risk factors from real-time monitoring data, and a risk mapping module maps the fire risk factors to a risk knowledge graph to obtain risk association nodes; and the risk reasoning module performs reasoning analysis on the risk association nodes according to the risk knowledge graph and a preset fireproof rule to form a preliminary risk assessment result, so that the comprehensiveness and effectiveness of coal mine fireproof disaster prevention and control can be improved.

The invention discloses a plateau mountain area avalanche susceptibility intelligent assessment method, and relates to the technical field of high mountain ice and snow disaster space early warning and risk mapping. According to the method, the influence of the microtopography is captured by adopting high resolution, the evaluation accuracy is improved, the control effect of the slope microtopography (the scale of 30-50 m) on the accumulated snow stress field can be accurately captured by adopting a factor map layer (based on data such as 12.5 mALOSPALSARDEM and Landsat-8OLI images) with the spatial resolution of 12.5 m, and compared with traditional 30mDEM and hectometer-level meteorological reanalysis data, the method has the advantages that the evaluation accuracy is greatly improved, and the evaluation efficiency is improved. Spatial differences of key factors such as terrains, earth surfaces and meteorology can be depicted more carefully, more accurate basic data support is provided for avalanche susceptibility assessment, and missing of key microtopography influence factors due to insufficient resolution is avoided.

The invention discloses an agent-based security risk assessment and operation management integrated method and system, and relates to the technical field of industrial security production and artificial intelligence, and the method comprises the steps: obtaining enterprise security production operation data, carrying out the preprocessing of the data, obtaining a standardized data set, and carrying out the deep transfer learning through an agent, and extracting scene risk features; calculating a propagation entropy value of the risk mapping data and constructing a propagation path, and performing dynamic time sequence fusion to generate a risk assessment result; generating a scene control instruction based on group game operation and performing optimization to obtain a baseline check instruction; operating data are collected, the conduction probability is calculated, a safety response rule is generated, linkage optimization is carried out, and a cooperative defense strategy is formed. According to the invention, risk feature extraction, propagation path analysis and cooperative prevention and control are realized through the intelligent agent, and the intelligent management level of enterprise safety production is improved.

The invention relates to the technical field of food safety risk management, in particular to an aquatic product risk analysis system based on multi-factor analysis, which comprises a pollution factor response module, a path aggregation module, a channel decision module, a pollution risk simulation module and a risk mapping collection module. According to the method, by extracting the concentration change direction of the pollution factor in the specific water area in the continuous time period and establishing the sequence combination of the concentration rising section, the time sequence behavior characteristics of the pollution factor can be identified, and the overlapping relation between the risk factor and the biological class group exposure can be effectively judged; in a standard exposure period, contact frequencies are integrated, sensitive time periods are screened, and generality factor grouping is completed by analyzing frequency coincidence conditions of a plurality of aquatic products under the same factors, so that a risk influence map can reflect a factor interaction correlation and category common inductance response relationship; and the comprehensiveness, granularity and prediction capability of risk assessment are improved in a dynamic scene.

The invention belongs to the technical field of boiler maintenance, and particularly relates to a boiler four-pipe foreign matter blockage early warning method and system. The method comprises the steps of obtaining boiler four-tube foreign matter blockage historical data; collecting boiler four-tube real-time state data of a reheater, an induced draft fan and a flame detection cooling fan; a fault mode-influence-risk mapping relation is established by adopting FMEA based on historical data, and a risk assessment framework is formed; generating an initial risk level; training a machine learning algorithm model by adopting historical data and generating a blockage probability; and dynamically adjusting the initial risk level according to the blockage probability, generating a final risk level and outputting a corresponding maintenance strategy. The system comprises a data acquisition module, an equipment monitoring module, a risk division module, a risk assessment module, a jam prediction module and a decision module. According to the scheme, the FMEA and the machine learning algorithm are fused, early warning, accurate risk assessment and dynamic maintenance decision making of boiler foreign matter blockage are achieved, and boiler operation safety and maintenance efficiency are improved.

The invention relates to the technical field of hospital robot inspection, and discloses a hospital logistics robot security inspection path planning system. The system comprises a data acquisition module, a region partitioning module, a risk mapping module, a path combination module, a dynamic updating module and an anomaly detection module. The data acquisition module collects robot sensor data and environment data; the area partitioning module identifies a hospital area security risk intention based on the data, and divides a plurality of risk partitions; a risk mapping module obtains risk events corresponding to the risk partitions and constructs a mapping relationship between the risk events; a path combination module extracts key risk partitions and generates a path deviation combination; the dynamic updating module is combined with real-time monitoring data to update the mapping relation; and the anomaly detection module analyzes the behavior data of the robot based on the updated mapping relation to identify the abnormal activity. The system can adapt to the complex environment of a hospital, improves the polling pertinence and timeliness, and guarantees the security and protection safety of the hospital.

The invention discloses an intelligent ship navigation system based on a space-time prediction risk field and deep reinforcement learning, relates to the technical field of ship navigation, and aims to solve the problem that a conventional navigation system is insufficient in dynamic risk assessment and prospective planning in the prior art. The technical key points are that the system comprises a multi-source data acquisition module, a dynamic risk mapping module, an intelligent path planning module, a visual interaction module, a risk early warning module and a self-checking module. According to the system, multi-source data such as weather, ocean current and ship dynamics are collected in real time, a predictive risk field is generated by using a long-short-term memory network, a route is optimized in combination with an adaptive deep reinforcement learning algorithm, and risk distribution and a recommended route are visually presented in a multi-scale three-dimensional form; the system can early warn a high-risk area in real time, and the reliability is ensured through a self-checking function. According to the invention, the sailing safety and the intelligent level of the ship in a complex environment are obviously improved.

The invention relates to the technical field of infusion nursing, and discloses an intelligent monitoring method and system for nursing infusion in an operating room, and the method comprises the steps: collecting the pressure intensity, flow velocity and liquid level height of an infusion pipeline, and obtaining physiological parameters; in a non-transfusion state, collecting N frames of drip chamber images to establish a coordinate system and a background template; m frames of images are obtained in the infusion process, a binary foreground difference image is constructed, a connected domain is extracted, barycentric coordinates are calculated, and whether the images are the same liquid drop track or not is judged through the barycentric distance; counting the number of liquid drops and calculating the flow velocity by combining the drop ratio; constructing infusion indexes including a time sequence consistency score and a pressure diagnosis factor; risk vectors are constructed through patient parameters and infusion indexes, and normalization processing is carried out to generate dynamic risk mapping; and designing a risk scoring algorithm, setting grades according to risk values, and taking different treatment measures.

The invention provides a method and a system for generating a dynamic risk map of unmanned aerial vehicle operation on the ground, which provide detailed risk assessment for unmanned aerial vehicle flight, identify potential risks of a flight area, avoid a high-risk area, improve flight safety and provide a scientific basis for low-altitude flight path planning. And the damage to ground personnel and facilities caused by falling of the unmanned aerial vehicle or other accidents is reduced, so that the threat to the ground personnel and facilities caused by air crash accidents is reduced. According to the method and the system for generating the dynamic risk map of the unmanned aerial vehicle operation on the ground, the problems of insufficient multi-factor fusion, insufficient static data precision, poor dynamic adaptability and the like in an existing unmanned aerial vehicle risk assessment method can be efficiently solved.

The invention provides an allergy risk prediction method and device based on indoor physical environment simulation and electronic equipment, and relates to the technical field of medical health data analysis, and the method comprises the steps: constructing a parameterized three-dimensional building model based on spatial information; based on the three-dimensional building model and the external meteorological parameters, coupled physical field simulation is carried out on sunlight, natural lighting and natural ventilation conditions in the target building space, and quantitative environment field distribution data is obtained; matching and fusing the environment field distribution data with a preset risk mapping rule based on allergen breeding physical conditions, identifying and dividing different levels of environment risk areas in the target building space, and generating a visual risk distribution map with space coordinates; and generating a personalized report containing a targeted environment intervention strategy bound with the spatial position based on the specific risk area position and the grade thereof in the risk distribution map. According to the method, quantitative environment data support is provided, and health suggestions are upgraded from experience judgment to data decision.

This invention provides a method and system for constructing a negative obstacle risk map, relating to the field of robot environmental perception technology. The method includes: acquiring multimodal sensing data of a legged robot in the current operating environment to obtain the types of negative obstacles, multiple candidate regions, and the corresponding confidence and uncertainty of each candidate region; using a risk diffusion algorithm, performing ink blurring processing based on the type, confidence, and uncertainty to generate a continuous risk field corresponding to each candidate region; dividing the continuous risk field to generate a continuous risk gradient distribution corresponding to each candidate region; performing terrain modeling based on laser point clouds, updating the continuous risk gradient distribution using Bayesian fusion to obtain a multi-level grid map, and then performing tactile closed-loop correction on the multi-level grid map to generate a risk cost map. This invention improves the accuracy of constructing a negative obstacle risk map.

The application discloses a kind of based on chip drive automatic adaptation and performance optimization method, specifically related to chip performance adjustment technical field;First, construct operation characteristic tensor, extract and piecewise polynomial fitting by extreme value envelope to construct stability risk boundary function, establish risk mapping relationship and calculate risk gradient vector, in real-time operation process, input stability risk prediction model with real-time operation characteristic set and real-time phase characteristic vector, obtain risk coefficient and risk gradient vector, and under the condition that risk coefficient is less than preset risk threshold, construct multi-objective constraint optimization process, solve optimal feature adjustment vector, and core voltage, core frequency and load scheduling phase are optimized in coordination;The application can realize the prospective control of stability risk under complex transient load scene, improve unit power consumption performance under the premise of guaranteeing reliability, with higher adjustment precision and energy efficiency optimization effect.

The invention relates to a transformer defect early warning method based on time sequence fragment association evidence fusion and a dynamic risk map. The method comprises the following steps: acquiring a standardized time sequence data set with a state label; obtaining a time sequence evidence body; calculating a dynamic fault probability and generating a dynamic risk curve; constructing and continuously iterating a dynamic risk association graph; and outputting quantitative risk early warning and generating a self-adaptive optimization operation and maintenance strategy based on the cloud edge cooperative computing framework. By introducing preprocessing based on data fluctuation characteristics and time sequence evidence body construction, deep mining and uncertainty quantification of a multi-modal long-term evolution law are realized, and the early recognition capability and evaluation reliability of latent defects are remarkably improved; through cloud edge cooperation and multi-objective optimization strategy generation, the unification of efficient utilization of computing resources and decision scientificity is realized, the output quantitative early warning and priority strategy directly supports field lean operation and maintenance, and a complete intelligent operation and maintenance closed loop is formed.

The present application relates to the technical field of fire accident monitoring and early warning and fire rescue, in particular to a simulation deduction and auxiliary decision system for multi-point concurrent leakage accidents of hydrogen stations, which comprises an accident scene dynamic risk mapping module, which is used for receiving and gridding a rescue area, collecting real-time monitored hydrogen concentration, heat radiation flux and explosion overpressure values for each grid. In the present application, by receiving and gridding the rescue area, collecting real-time monitored hydrogen concentration, heat radiation flux and explosion overpressure values, and combining the path interruption probability caused by hydrogen storage and hydrogenation structure collapse, a unified grid risk value is obtained, and a time-varying three-dimensional risk field data covering the entire accident scene is established, which converts discrete and multidimensional on-site monitoring data into an intuitive global danger situation picture, enabling fire rescue commanders to master the evolution law of risk in space and time.

The invention discloses a public security digital certificate dynamic supervision and management method and system fused with big data analysis, and relates to the technical field of digital certificate management. All credible operation feature points and corrected feature points are integrated, a feature set of the operation is output, operation data of the feature set are input into a behavior-risk mapping model, and a behavior-risk mapping model is established; and deducing a corresponding risk quantized value, establishing a risk probability assessment function according to the risk quantized value, fusing prior knowledge provided by the macroscopic baseline scanning layer, performing inversion by utilizing a probabilistic reasoning technology to obtain posterior probability distribution of the operation on each risk dimension, and summarizing the comprehensive risk level of the digital certificate operation according to the posterior probability distribution. And the corresponding hierarchical management and control actions are automatically triggered. According to the management system, through fusion of big data analysis and a multi-level risk perception technology, full-process dynamic accurate supervision of public security digital certificate operation behaviors is realized.

The invention relates to a risk aggregation method based on improved conditional value-at-risk in an off-road scene, belongs to the technical field of vehicle control, and solves the problem that off-road environment traffic risks are difficult to accurately and reliably assess due to the fact that risk indexes are isolated analysis or simple fusion and uncertainty of data is not fully considered in the prior art. Comprising the following steps: establishing a conversion relation among a planning coordinate system, a vehicle body coordinate system and a sensor coordinate system by taking a world coordinate system as a reference; acquiring current pose information of the vehicle and current point cloud data of the sensor in real time; performing horizontal processing on the current point cloud data to obtain horizontal point cloud data, and constructing a 2.5 D grid elevation map by adopting a sparse Gaussian process regression method; obtaining the cost of each traffic risk index of each grid based on a 2.5 D grid elevation map; and converting the cost of each traffic risk index to a planning coordinate system, and generating a current comprehensive traffic risk map by adopting an improved conditional value-at-risk method.

The embodiment of the application belongs to the field of artificial intelligence and financial technology, is applied to the field of insurance risk map construction, and relates to a business risk map construction method and device, computer equipment and a storage medium, which comprises risk factor normalization processing; an area map to be subjected to risk map construction is acquired and subjected to rasterization processing; property type business data is subjected to normalization processing; the correlation between different risk factors and the property type business data is analyzed by using a typical correlation analysis method to obtain business weight coefficients of different risk factors; multiplication calculation is performed according to the business weight coefficients and the property type business data in each grid map block to obtain corresponding business data of different risk factors in each grid map block, and the business data is marked in the area map corresponding to a specific map area to complete business risk map construction. The application combines actual business data to perform risk map construction, thereby guaranteeing the scientificity of business risk map construction.

The invention relates to the technical field of fire accident monitoring and early warning and fire rescue, in particular to a hydrogen refueling station multi-point concurrent leakage accident simulation deduction and auxiliary decision making system which comprises an accident site dynamic risk composition module, an accident site dynamic risk composition module, an accident site dynamic risk composition module and an accident site dynamic risk composition module, and the hydrogen concentration, the heat radiation flux and the explosion overpressure value monitored in real time are integrated. According to the method, a rescue area is received and gridded, hydrogen concentration, thermal radiation flux and explosion overpressure numerical values monitored in real time are integrated, and path interruption probabilities caused by collapse of hydrogen storage and hydrogenation structures are combined, so that a unified grid risk numerical value is obtained through calculation, and then time-varying three-dimensional risk field data covering a whole accident scene is established; discrete and multi-dimensional field monitoring data are converted into a visual global danger situation picture, so that fire rescue commanders can master evolution rules of risks in space and time.

The invention discloses a mine robot emergency risk avoiding intelligent decision-making system, and the system comprises a disaster risk prediction module which is used for carrying out the time sequence prediction based on the data of a multi-source sensor, and outputting a disaster occurrence probability and a risk comprehensive index; the dynamic risk avoiding path planning module is used for constructing a dynamic risk map and performing path planning by adopting an improved A * algorithm; according to the system, advanced early warning, dynamic path planning, hierarchical response and multi-agent coordination in a mine catastrophe environment are realized by constructing an intelligent closed-loop architecture of perception, prediction, decision making, execution and coordination. Multi-source sensor data are fused, an LSTM time sequence prediction model is adopted to carry out disaster probability prediction, an A * algorithm is improved to realize risk sensitive path planning, a hierarchical response strategy is realized based on a finite-state machine, and robot group collaborative risk avoiding is realized through a publishing and subscribing mechanism. According to the method, the survivability, the decision-making efficiency and the collaborative operation level of the mine robot in the catastrophe environment are improved.

The invention discloses a real-time video anti-theft monitoring method and system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: obtaining monitoring data in a historical preset time period, and calculating a behavior purposiveness index of each dynamic target in the monitoring data; based on the behavior purposiveness index, determining a suspicious target in each dynamic target, and calculating a risk mapping index of the motion behavior of the suspicious target to a risk area; based on the risk mapping index and the behavior purposiveness index, determining a theft risk assessment value of the suspicious target for the risk area; and performing anti-theft monitoring on the suspicious target based on the theft risk assessment value. According to the invention, the false alarm rate and the missing report rate of the monitoring system for risks are reduced.

The invention provides an intelligent early warning and protection method and system for falling of an inpatient based on multi-modal data fusion. According to the method, individual risk data, behavior posture data and equipment state data of an inpatient are collected, a tumble risk index of the patient is calculated from the three aspects of potential risk, behavior risk and environmental risk by using a tumble risk prediction model, the tumble risk level of the patient is comprehensively evaluated, and then corresponding protection measures are taken. Specifically, each risk factor is quantified through a weight coefficient and a risk mapping function, and finally, an S-type normalization function is utilized to obtain a comprehensive risk index, so that accurate and personalized early warning is realized. The system comprises a sensing layer, a data processing layer, an intelligent analysis layer and an intervention execution layer, and can realize full-process automation and intellectualization. According to the invention, the problems of inaccurate evaluation, incapability of personalized early warning, lack of active intervention and the like in the prior art are effectively solved, and the occurrence rate of tumble of the inpatient is remarkably reduced.

The invention relates to the technical field of staged consumption of home decoration, in particular to a dynamic risk monitoring method for staged consumption of home decoration, comprising the following steps: S1, acquiring multi-stage consumption data and construction stage information formed in the process of staged consumption of home decoration; s2, based on a time sequence neural network, extracting a time sequence risk feature vector used for representing time evolution of a consuming behavior; s3, inputting the construction site image data into a convolutional neural network (CNN), and generating an image risk feature vector for representing the consistency of the construction progress and the declaration plan; s4, performing calculation through a risk mapping function to obtain a staging risk score of the staging node; s5, the comprehensive feature vector and the staging risk score are updated, and continuous dynamic risk monitoring of the whole home decoration staging consumption process is achieved; according to the invention, automatic and dynamic analysis and monitoring of the consistency of the fund flow and the real flow are realized.

The invention provides a three-level environmental risk assessment linkage method and system for an industrial park, relates to the technical field of environmental risk management, and aims to realize a unified semantic framework of enterprise-level, special-level and park-level data by constructing an environmental risk assessment ontology. According to the method, an event-driven dynamic updating mechanism is adopted, data recalculation and risk map adjustment are automatically triggered, and the timeliness and accuracy of environmental risk response are improved. The whole system effectively solves the problems of data islands, updating lagging, verification missing and the like, and provides real-time and accurate decision support for environment management of industrial parks.

The invention discloses a risk control method and system for real-time transfer transaction, and relates to the technical field of finance. The method comprises the steps of collecting transaction related information, and establishing an initial transaction intention fingerprint; obtaining transfer execution behavior information, updating the initial transaction intention fingerprint, and generating a transaction intention evolution track; performing consistency analysis on the transfer execution behavior information and the transaction intention evolution trajectory to obtain a transaction intention deviation result; generating a corresponding risk energy variation according to a preset risk mapping rule, and constructing a risk energy change track; judging the risk state of the transfer transaction request, and determining a risk control level; setting a risk control permission condition, judging whether the risk control permission condition corresponding to the risk control level is met or not, and executing a risk control action matched with the risk control level according to a judgment result. According to the method and the device, the accuracy of transfer transaction risk identification and the performability of risk control are remarkably improved.

The application discloses a kind of based on user behavior's right management method and system, belong to artificial intelligence field, including following steps: S1, the behavior data of electric user is obtained, and data is preliminarily cleaned;S2, the multi-dimensional feature extraction of behavior data after cleaning is carried out, and user behavior portrait is generated in combination with user identity label;S3, user behavior portrait is input into behavior scoring model, and behavior score and risk coefficient are output;S4, according to the risk mapping rule of scoring, and the right level is dynamically configured by right control engine.The application can monitor and analyze the behavior mode of user in real time by introducing the dynamic right adjustment mechanism based on user behavior score and risk assessment, and dynamically adjust the right level of user according to the real-time demand and risk level of user, can judge the right demand of user according to actual operation behavior, avoid excessive or insufficient right authorization, improve the intelligence and dynamic of right management.

The invention relates to the technical field of image analysis, in particular to a building crack detection system based on image recognition, which comprises the following steps: a perspective distortion repairing module, a crack contour complementing module, a boundary fuzzy recovery module, a structure risk mapping module and a component affiliation recognition module. According to the method, geometric restoration is carried out on the images collected at multiple angles, errors caused by perspective differences can be eliminated while the real component space relation is kept, missing part complementation is completed in the crack path inference process by combining edge spacing changes and direction differences, and the accuracy of crack path inference is improved. The crack trend and the overall boundary have continuity and integrity, a fuzzy region is retrieved by using gray fluctuation and direction consistency, accurate extension and fitting are realized under the condition of boundary fuzziness, an interference intensity relationship between a crack region and a peripheral area is established, and refined assessment of crack risks is realized. And in combination with the coordinate point distribution proportion and the path direction deviation, the component to which the crack belongs is clearer.