160 results about "Risk scenario" patented technology

The embodiments of the present disclosure belong to the technical field of data processing. Provided is a multimodal scenario risk determination method based on a generative AI large language model. The method specifically comprises: step 1, acquiring multimodal data to form a target data set, wherein the multimodal data comprises visual data and text data; step 2, using an ALBEF algorithm to extract key features corresponding to the target data set, and fusing the key features into a comprehensive scenario representation; and step 3, on the basis of a preset safety index and a large language model, evaluating a risk degree corresponding to the comprehensive scenario representation, comparing the risk degree with a risk threshold, and determining whether the scenario corresponding to the comprehensive scenario representation is a high-risk scenario. By means of the solution in the present disclosure, a high-risk scenario can be rapidly recognized and identified, so as to provide a basis for taking emergency measures, thereby enhancing the real-time response capability.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention discloses an information physical security defense method, system and device based on a distribution network digital twin simulation platform and a medium, and belongs to the technical field of information security and control defense of the power distribution Internet of Things, and the method comprises the steps: constructing a three-dimensional channel for interaction of a physical power grid, an information model and simulation data; on the basis of a three-dimensional channel, a three-state dynamic conversion model among a steady state, a transient state and a recovery state is constructed, and multi-state automatic switching simulation is achieved through sub-region division and parallel computing; a multi-level risk modeling system oriented to various risks is constructed based on simulation results, complex attack scenes are identified, and mapping of risk types and response paths is achieved; security risk assessment and defense strategy generation verification are completed, and intelligent collaboration and continuous learning of cross-domain defense strategies are achieved through collaborative optimization. According to the method, the construction efficiency of a complex risk scene is improved, quantitative analysis of cross-domain risks such as circuit breaker mis-tripping caused by network attacks is realized, and the limitation of traditional single-domain risk analysis is broken through.

The invention discloses a metering equipment distribution decision simulation analysis method, and belongs to the technical field of computer data processing and simulation, and the method comprises the steps: fusing multi-source heterogeneous data to construct a space-time knowledge data set; performing causal inference based on the data set, and establishing a causal relationship graph; performing risk propagation deduction and causal link evolution simulation on the map to generate a risk situation set; generating an anti-fact intervention strategy for the risk situation; and performing quantitative analysis to output an optimal distribution strategy, and feeding back and updating the causal relationship graph by using an analysis result. According to the method, the data processing architecture of the dynamic knowledge graph is constructed by adopting causal inference, multi-step propagation inference and link evolution simulation can be carried out on risks in a complex system, and the accuracy and robustness of a data processing system for carrying out prospective decision making in an uncertain environment are remarkably improved.

The invention discloses a construction method of a vertical field large model, which comprises the following steps of: constructing a vertical field large model framework which takes a general large model as a base, takes data, knowledge and model multi-source driven knowledge enhancement engine as a core and is combined with technical application; constructing a data engine, a knowledge engine and a model engine in the vertical field large model framework; based on mapping association of a multi-risk scene and a business application scene in an urban lifeline, a vertical domain large model application system and a system platform are constructed through combined linkage of a general large model and a knowledge enhancement engine. Relates to the technical field of large models, and solves the problem that an existing general large model is difficult to realize reasonable analysis of professional data, accurate reply of problems, intelligent deduction of professional scenes and integrated application of professional businesses.

The invention relates to the technical field of voice interaction, in particular to a method and system for determining a voice reply strategy based on dynamic risk assessment, and the method comprises the steps: obtaining user input and calling a pre-trained large language model to output a risk score in response to a voice interaction request initiated by a user; grading the risk scores based on a preset risk threshold, wherein the risk scores comprise high risks, medium risks and low risks; if the risk is high, immediately entering a strategic refusal response process; if the risk is medium risk, entering a multi-round clarification dialogue process, and continuously recalculating a risk score based on the constructed clarification question and the answer of the user until the risk score is not in a medium risk interval; and if the risk is low, directly entering a normal interaction process. According to the invention, a strategy which can perform fine-grained evaluation and dynamic management and control on a multi-level risk scene and can adaptively switch rejection and clarification in the same closed-loop process can be constructed in a voice dialogue system, so as to give consideration to the interactive experience of safety compliance and natural smoothness.

The embodiment of the invention provides a voltage instability risk assessment and high-risk scene screening method and related equipment, and belongs to the field of power systems and automation thereof. The method comprises the following steps: extracting a voltage distribution balance degree according to a spatial distribution characteristic of a sample; constructing a voltage instability risk assessment index in combination with the voltage distribution balance degree, the new energy transmission margin ratio and the voltage out-of-limit risk factor; according to the voltage instability risk assessment index, abnormal samples in the sample set are screened through an isolation forest algorithm; performing K-means clustering analysis on the screened abnormal samples to subdivide the candidate scene into subclass clusters with clear risk characteristics; performing class cluster risk assessment according to a clustering result to obtain a class cluster comprehensive score, and dividing class clusters into different risk levels; through dispersion analysis, a centralized extreme scene and a discrete extreme scene are distinguished, and class cluster center and boundary scene screening is carried out respectively. According to the invention, efficient and comprehensive high-risk scene screening can be realized.

The invention provides an intelligent redundancy control method based on functional safety, and the method comprises the steps: 1, collecting key parameters in real time, and outputting a safety score S according to a fuzzy logic formula S = 0.3 XTnorm + 0.2 Vstable + 0.5 Cdelay; 2, preprocessing the key parameters, the safety score, the environmental data and the historical fault records, constructing a risk assessment model through the preprocessed data, and performing risk grade division; 3, triggering redundancy strategy adjustment according to the risk level and the safety score; and step 4, when the redundancy strategy is adjusted to a high-risk scene, triggering switching logic through hardware interruption, and completing redundancy control. According to the technical scheme provided by the invention, the problems that the existing scheme is high in cost, slow in response, incapable of adapting to a dynamic risk environment and easy to cause simultaneous failure of multiple nodes are effectively solved.

The invention discloses an electric power and electric quantity balance analysis method based on new energy grid connection, and relates to the technical field of electric power and electric quantity balance. Secondly, a multi-scene power prediction result is generated by fusing multi-source data through a space-time model, multi-scene uncertainty risks are quantified, then an electric power balance gap expected value is calculated based on scene probability weighting, a high-risk scene is positioned, and finally a collaborative optimization scheduling scheme is generated by solving a minimum value in combination with a time period electric quantity difference and a balance gap value. The power grid fluctuation adaptability is enhanced through dynamic time interval division, uncertainty is accurately described by means of multi-scene prediction and risk quantification, the response capacity of a new energy grid-connected system to output fluctuation is remarkably improved, and key technical support is provided for stability of a power system after large-scale new energy grid connection.

The invention discloses an automatic driving takeover method, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the external environment data of a running vehicle in real time, and obtaining a risk scene recognition result based on the external environment data; determining risk level information according to the risk scene identification result and historical risk scene data; evaluating the driving ability of the driver to obtain an ability evaluation result; monitoring the driver in real time to obtain physical state information of the driver; and according to the risk level information, the ability assessment result and the physical state information, whether the vehicle is taken over by automatic driving is judged. When the driver cannot cope with the driving environment due to the body state, the driving ability and other factors, the automatic driving actively activates and controls the vehicle to avoid the risk.

The invention provides a networked automobile risk scene analysis method based on a multi-modal corpus and related equipment, and the method comprises the steps: receiving the multi-modal corpus containing various types of test data, and carrying out the format standardization and time synchronization processing of the multi-modal corpus; performing risk scene analysis on the basis of association information among different types of test data in the multi-modal corpus, identifying abnormal behaviors occurring in the test process, and generating corresponding structured event description information; and extracting key data fragments and image frames associated with the abnormal behaviors, carrying out image-text association on the key data fragments, the image frames and the event description information, and generating a test report based on a preset template. According to the method, a multi-modal corpus analysis mechanism is introduced, risk scene analysis and abnormal behavior recognition are executed, and image-text matching and report generation are automatically completed. The manual arrangement burden is reduced, and the intelligent degree of risk scene identification and the automation level of test report generation are improved.

The invention relates to the technical field of enterprise risk management and intelligent decision, and discloses an enterprise risk prediction and control method and system, and the method comprises the steps: collecting enterprise multi-source data, carrying out the preprocessing, constructing a risk prediction model of fusion modeling, and outputting a risk score; extracting an operation log behavior path based on the score, and generating a risk situation coding vector; similar events are matched in a historical event library, and the events are combined to re-calculate a response path; inputting the plurality of response paths into a strategy evolution sandbox for dynamic environment simulation, and outputting an evolution stability index; and performing path optimization through a multi-criterion decision method, updating an execution result and behavior codes to a historical mapping pool, and constructing a self-learning closed loop. The method has high interpretability, structural adaptability and strategy evolution capability, and improves the intelligent level of enterprise risk control.

The invention belongs to the technical field of power grid optimization, and particularly relates to a GAN driven learning-based power grid planning equalization optimization method and device, and the method comprises the steps: constructing a physical constraint-based space-time generative adversarial network PI-ST-GAN, building a composite risk index, and screening out a high-risk scene from a generated source-load probabilistic time sequence scene; constructing a risk-cost balanced double-layer optimization model; reconstructing the double-layer optimization model into a single-layer mixed integer programming (MIP) model based on a strong dual theory, and setting constraint conditions of the single-layer mixed integer programming (MIP) model; based on a single-layer mixed integer programming MIP model, a solver is utilized to carry out joint solution on a line investment scheme and operation scheduling variables, PI-ST-GAN is dynamically guided to enhance high-risk scene generation according to a solution result, an optimization-generation-feedback closed-loop collaborative iteration solution process is realized, and finally an optimal power grid planning scheme is output. The method can improve the toughness and economical efficiency of a power grid under the high permeation of renewable energy sources.

The invention relates to the technical field of automatic driving, in particular to an automatic driving risk scene generation method and system suitable for a strong interaction environment and a storage medium, and the method comprises the steps: firstly collecting the data of a real vehicle following or lane changing target scene, and then calculating the vehicle motion interaction feature F of the target scene; and a TimeGAN model is improved in combination with an attention mechanism to obtain an F-TimeGAN model, a target scene is input into the F-TimeGAN model for scene generation, and finally, a strong interaction risk scene is screened out from the generated scene for automatic driving test. The strong interaction risk scene data which is difficult to collect in reality can be efficiently generated based on the conventional target scene data, the interactivity and the risk of the generated scene are enhanced, the problem of rare key test scene data is effectively solved, and scene data support is provided for safety verification of automatic driving in a strong interaction environment.

The invention discloses a reservoir dispatching method for guaranteeing stability of a reservoir bank slope. The reservoir dispatching method comprises the following steps of collecting various data related to a small reservoir; evaluating the stability of the bank slope of the small reservoir; setting different risk scenes and carrying out risk prediction; formulating a reservoir scheduling scheme according to a risk analysis result; and dynamically adjusting the reservoir scheduling scheme according to the monitoring data. According to the method, the slope stability analysis model comprehensively considering various factors such as water level change and rainfall infiltration is established, the stability condition of the reservoir bank slope under different scheduling schemes can be evaluated more accurately, in the reservoir scheduling process, the maximization of a certain target is not pursued purely any more, and the stability of the reservoir bank slope is improved. Multiple targets such as slope stability, flood control benefits and power generation benefits are considered as a whole, an advanced multi-target optimization algorithm is applied, an optimal balance point among the targets is found, and a scientific and reasonable reservoir dispatching scheme which can guarantee stability of a reservoir bank slope and can also take the flood control benefits, the power generation benefits and the like into consideration is formulated.

The invention discloses a novel power system supply and demand risk assessment method based on multiple spatial-temporal scales, and relates to the technical field of power system supply and demand risk assessment, and the method comprises the following steps: obtaining a time-varying fault rate set and a dynamic transmission limit set of power grid equipment based on extreme weather prediction data; fusing the time-varying fault rate set, the dynamic transmission limit set and power supply, load and new energy power generation data to obtain a risk scene library; screening the risk scene library to obtain a typical risk scene set; analyzing the typical risk scene set, and outputting a supply and demand risk index under each time-space unit; and performing regional division on the power system based on the supply and demand risk indexes, and generating corresponding early warning and management and control suggestions. According to the method, the problem that the evaluation result is not accurate due to the fact that the time-varying characteristics of the equipment state are ignored in extreme weather is solved, and accurate decision support is provided for disaster prevention and supply protection of a power system.

The invention relates to an airport core risk whole-process management method based on a dual-prevention working mechanism, and the method comprises the following main steps: a data collection step: collecting multi-source data related to the operation of an airport in real time through a plurality of sensors and systems; a data fusion step of replacing a multi-head attention mechanism in a traditional Transform model with a plurality of expert large models, including feature extraction and analysis, information aggregation and data synchronization and standardization; the method comprises the steps of generating adversarial data, generating diversified virtual risk scenes and adversarial samples by utilizing a plurality of expert large models, automatically constructing a virtual environment, automatically constructing a high-fidelity virtual simulation environment based on the large models, and performing risk assessment and decision making. Accurate risk assessment and decision support are carried out by using a self-adaptive risk scoring system and an intelligent decision support mechanism; the comprehensive, accurate and intelligent management of the airport core risk is realized, and the safety and the emergency response capability of airport operation are obviously improved.

The invention relates to the technical field of pipeline safety monitoring, in particular to a long-distance pipeline risk event distinguishing monitoring system and method based on artificial intelligence. The artificial intelligence-based long-distance pipeline risk event distinguishing monitoring method comprises the following steps of S10, acquiring humidity time sequence data and anode output current time sequence data of soil in an area where a target pipe section is located, and respectively drawing a humidity time curve and a current time curve; acquiring a vibration signal acquired by the optical fiber sensing network; and S20, periodically fluctuating according to the current-time curve. According to the method, the relation between the regional self-adaptive humidity saturation interval and the corrosion rate is established, the huge cost for customizing a vibration analysis model for each region is avoided, the event recognition accuracy is improved under high-risk scenes such as coating damage by utilizing deep coupling of the corrosion state and vibration, and the event recognition efficiency is improved. And passive alarm is converted into active and accurate early warning based on humidity prediction.

The invention provides a management method for operation of a fire pump house. The method comprises the following steps: at least acquiring regional image information of a fire pump house; processing the fire-fighting water pump room area image information by adopting a fire-fighting water pump monitoring model to generate fire-fighting water pump room area monitoring information; and judging whether the fire-fighting water pump room area monitoring information accords with a preset condition, and generating a corresponding management strategy when the fire-fighting water pump room area monitoring information accords with the preset condition. Therefore, the management method ensures that the large model can correctly provide information through the professional knowledge base and the knowledge graph in the field of maintenance operation of the fire pump house. In addition, the detection rule is defined by using a natural language, so that the utilization rate of physical defense and civil defense can be improved to the greatest extent. And finally, multi-modal identification of a risk scene is realized by using computer vision and a large model, so that known risks can be effectively prevented, left risks can be found through anomaly identification, and a management system is continuously improved.

A system and method for operational and cyber risk assessment that utilizes a data-driven approach to evaluate the current security posture and identify areas for improvement based on the user's desired target profile. This process involves estimating the costs and benefits associated with various security program enhancements, increased, hiring, and control uplifts. The system and method then quantify these benefits in terms of reduction in tail value at risk, expected losses, cyber insurance premiums, and the amount of risk capital set aside. The system simulates attack paths associated with various risk scenarios and uses a risk scenario model to compute losses associated with each attack path for each risk scenario. The results of the simulation may be used to determine one or more business outcomes associated with the costs and benefits of implementing security enhancements.

The invention relates to the technical field of data analysis, in particular to a data and knowledge combined driven intelligent decision-making method and system.The method includes the steps that multi-source heterogeneous data and knowledge base concepts are unified into a structured graph structure through multi-dimensional feature extraction and multi-head attention fusion context prompt, and a structured semantic basis is provided for intelligent decision-making; by combining data-driven mining and knowledge prior constraints, the accuracy and robustness of causal reasoning are improved, and the reliability of root cause diagnosis is ensured; a root cause path is searched and enumerated in a reverse breadth-first manner by taking an abnormal entity as a starting point, so that rapid and traceable abnormality positioning is realized, and a user is assisted to focus a high-probability root cause; through modular assembly and full-link element traceability, while the analysis threshold is reduced, decision full-link transparency is realized, and the auditing requirement of a high-risk scene is met; by automatically converting natural language requirements into executable scripts, the decision-making efficiency of non-technical users is improved.

The invention relates to the technical field of intelligent financial accounting, and discloses an intelligent financial accounting system based on multi-dimensional data verification, comprising: S1, a multi-dimensional data verification module for performing real-time multi-dimensional data verification on a financial accounting system, comprising a business rule dimension verification unit, a time sequence continuity dimension verification unit, a multi-source association dimension verification unit and a risk scanning dimension verification unit. According to the intelligent financial accounting system based on multi-dimensional data verification, real-time risk scanning is realized through the multi-dimensional data verification module, threats can be rapidly isolated and resource utilization can be optimized in combination with hierarchical response control and a dynamic resource allocation mechanism when risks occur, and meanwhile, through trap data generation and a multi-dimensional two-way verification technology, the risk can be effectively eliminated. The method effectively improves the emergency disposal capability and data recovery reliability of the system in a complex risk scene, and has the advantage of dual improvement of financial data safety protection and system operation efficiency.

The invention provides a ship network security risk assessment method, system and device based on a dynamic risk model, and a medium, and relates to the technical field of ship network security. Comprising the following steps: collecting a ship asset attribute data set and total factor data required by ship network security risk assessment, and determining vulnerability items; identifying potential network security risk scenes according to the types of historical ship network security events, and estimating the event influence degree and threat occurrence possibility of each risk scene; calculating a unit risk initial value and a unit average risk value corresponding to each risk scene according to the vulnerability severity degree, the event influence degree and the threat occurrence possibility corresponding to the vulnerability item in each risk scene; constructing a dynamic risk model based on time factor and risk evolution double-factor weighting; and calculating the risk value of each evaluation unit based on the dynamic risk model, and taking the maximum risk value as the network security risk evaluation value of the whole current ship. According to the invention, optimized management of ship network security risks is realized.

The invention discloses an electric vehicle transportation risk scene construction method and system based on multi-source data clustering, and the method comprises the following steps: collecting a multi-source heterogeneous data source, and obtaining original data in the transportation process of an electric vehicle; preprocessing the original data to obtain a standardized data set; performing feature extraction on the standardized data set to form a feature vector set; performing clustering analysis on the feature vector set by using a clustering algorithm to obtain a plurality of data clusters; and constructing an electric vehicle transportation risk scene model based on the data clustering cluster. According to the invention, through multi-source data fusion and clustering analysis, a potential risk mode in electric vehicle transportation can be automatically identified, a fine-grained risk scene is constructed, and the accuracy and efficiency of risk management are improved. In the embodiment, data quality is ensured by data preprocessing, dynamic risk factors are captured by feature extraction, natural data grouping is realized by clustering analysis, and interpretable risk classification is provided by a risk scene model.

The invention discloses an industrial big data visual management system based on big data analysis, and relates to the technical field of industrial data management, and the system comprises a data collection module, a data analysis module, a visual early warning module and a strategy optimization module. The data analysis module integrates the three-dimensional data to form a single-main-body full-process and single-element full-access double-track chain, the single-main-body full-process and single-element full-access double-track chain is fused to obtain a behavior track chain, and abnormal nodes are identified through double verification; the visual early warning module visually presents data in a layered manner through a statistical layer and a risk layer, and triggers real-time early warning; the strategy optimization module extracts risk scenes based on the visual data, and adopts different optimization strategies respectively. According to the method, complete tracing of the operation track, accurate identification of abnormal nodes and dynamic optimization of strategies are realized, and the industrial data security control and operation and maintenance efficiency is improved.

This application relates to a method and system for intelligent policy analysis and risk monitoring based on a family graph. The method includes: acquiring multi-source policy data and performing structured processing; constructing a family policy relationship graph based on the relationships between policyholders and insured persons in the dataset; acquiring a predefined risk scenario library, containing multiple preset risk scenarios and corresponding coverage types for each scenario; calculating the total coverage amount based on the family graph by identifying related policies for each risk scenario; acquiring a risk exposure benchmark value, comparing the total coverage amount with the benchmark value, and generating a quantitative result of the coverage status; combining a pre-built insurance product knowledge graph, generating coverage optimization suggestions based on the quantitative results; and re-executing the calculation and generation operations in response to user lifecycle events or periodic assessment instructions, and generating risk warning notifications based on the analysis of result changes. This application achieves intelligent analysis of family policies, dynamic risk monitoring, and coverage optimization, improving risk management efficiency.

The application discloses a kind of based on risk scene's electric power market parallel clearing method and system, it is characterized by: S1, multi-dimensional risk scene synchronization generation;S2, scene space-time feature extraction;S3, scene clustering group;S4, distributed parallel clearing calculation;S5. Risk index aggregation analysis.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.

This invention discloses a method for improving the grid-load interaction capability of high-energy-consuming industrial users under carbon emission constraints. The method includes: real-time acquisition of dynamic power output characteristic data from new energy sources, capturing key dynamic features such as instantaneous power fluctuations, ramp rate changes, and spatiotemporal correlations of wind farms and photovoltaic power plants; constructing an adaptive generation model for multimodal risk scenarios, and using cluster analysis and stochastic process theory to summarize the complex stochastic fluctuations of new energy power output into several representative typical risk modes. The robustness-first rolling decision optimization mechanism of this invention prioritizes ensuring system power balance and safety under all generated risk scenarios in its core objective function and constraints. This fundamentally overcomes the decision failure risk caused by prediction bias in traditional methods, thus providing the power grid with excellent resilience and anti-interference capability under complex and uncertain operating conditions.

The invention provides a business and enterprise business management system and method based on artificial intelligence, and relates to the technical field of enterprise planning management, and the management system comprises an inventory management system, an intelligent data center, a core business module and an AI-driven supply chain center. According to the inventory management system, a plurality of storage and transportation devices are set up in a warehouse and used for carrying out statistical management on warehouse-in and warehouse-out of goods, and each storage and transportation device comprises a storage management recording platform, a driving mechanism, a conveying mechanism and a bottom surface recording assembly. Compared with a traditional afterward remedy mode, operation risks can be effectively reduced, weight information statistics and collection can be conducted on goods in and out of a warehouse, by means of a quick disassembly structure between the driving mechanism and the conveying mechanism, the transfer process of the goods can be facilitated, the content of image data in and out of the warehouse can be expanded, and more comprehensive clues are provided for subsequent goods traceability.

This invention discloses an accelerated safety assessment method and system for low-altitude navigation systems based on alternating cross-entropy and importance sampling, relating to the fields of computer simulation and safety assessment technology. The method includes: constructing a probabilistic risk assessment model and initializing a proposal distribution; entering an alternating iterative loop: first, performing importance sampling assessment, calculating accident rate estimates and statistical errors; if the error does not meet accuracy requirements, initiating cross-entropy optimization, updating the proposal distribution parameters using simulation data; the new distribution is fed back to the next round of assessment. This loop continues until the results converge, and an assessment report is output. This invention, through a closed-loop feedback mechanism of alternating assessment and optimization, enables the sampling distribution to adaptively approximate the optimal distribution, intelligently focusing simulation resources on high-risk scenarios, solving the computational efficiency bottleneck of assessing extremely low probability events, achieving an order-of-magnitude improvement in assessment efficiency, and possessing high adaptability and engineering practical value.