266 results about "Modelling analysis" patented technology

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention provides a whole-house health intervention lighting system and a lighting method thereof, and the system comprises a distributed multi-mode sensing layer which is used for non-inductively collecting the physiological, behavior and environment information of a user through a sensor module integrated in a lighting lamp; the edge calculation and model analysis layer is used for carrying out preprocessing and feature extraction on the data acquired by the distributed multi-mode perception layer and providing a model self-interpretation function; the decentralized federated artificial intelligence decision-making layer is used for constructing a personalized health model based on a federated learning framework, performing health state prediction and generating an intervention instruction; the cooperative multi-target intervention planning layer is used for converting the intervention instruction into a whole-house cooperative illumination control strategy; and the system health and fault-tolerant reconstruction layer is used for monitoring the state of the system and realizing function reconstruction when a fault occurs, and the organic cooperation among the layers realizes the capabilities of non-inductive monitoring, privacy protection, continuous learning and active intervention of the lighting system.

The invention relates to the technical field of geological informatization, in particular to an artificial intelligence modeling analysis method for a hydrate pilot production data set, which comprises the following steps of: acquiring logging data, lithology data, stratum physical property parameters and natural gas hydrate production dynamic data; screening, cleaning, complementing, de-noising and standardizing are carried out in sequence to obtain an artificial intelligence modeling data set; and establishing a stratum lithology machine learning recognition model, a stratum physical property machine learning recognition model and a natural gas hydrate artificial intelligence historical fitting model through a support vector machine SVM, a random forest RF and a neural network DNN. According to the method, a serial modeling architecture of lithology identification, physical property prediction and production history fitting is created, and the prediction output of the upstream model is used as the optimization input of the downstream model, so that the downstream production prediction model can learn physical property parameters which are recalculated based on machine learning and have higher precision; and the accuracy of final production prediction is improved from the data source.

The invention discloses a strabismus detection method based on causal feature gating, and the method comprises the steps: carrying out the modeling analysis of a causal relationship between a key point coordinate and a strabismus category through a causal discovery algorithm, obtaining a causal relationship model used for determining the causal relationship, screening the key point coordinate based on the causal relationship model, and obtaining a strabismus detection result. Extracting a plurality of causal variables from a plurality of samples of the training sample set according to the causal variable set; and introducing a causal feature gating module between adjacent network layers of the classification model, training the classification model by using a plurality of causal variables, and performing adaptive adjustment on gating parameters in the training process of the classification model until the classification model converges or reaches a preset training round number, thereby obtaining a final classification model. According to the embodiment of the invention, the accuracy of strabismus classification can be improved, and the interpretability and medical application value of the model can be remarkably enhanced.

The invention discloses a food safety risk assessment method and system based on model analysis, and relates to the technical field of food safety production monitoring, a production line virtual model synchronized with a physical production line in real time is constructed based on the structural parameters of a target production line, and the deviation of production line data is corrected through a model fusion algorithm, so that the risk assessment accuracy is improved. And identifying abnormal physical parameter inflection points strongly related to microbial pollution. According to the method, a production line virtual model is constructed through digital twinning, a model fusion algorithm is introduced, time-domain and frequency-domain multi-dimensional comparison and correction are performed on real-time misalignment data and historical steady-state data, instantaneous interference and systematic drifting are effectively filtered out, the corrected production line data are output, and the real-time misalignment data and the historical steady-state data are compared and corrected. And on the basis, abnormal physical parameter inflection points strongly related to microbial pollution are accurately identified, so that weak abnormal signals which are difficult to perceive by a traditional method can be captured in the initial stage of continuous production, and the early pollution risk perception capability for pathogenic bacteria is remarkably improved.

The invention belongs to the technical field of data processing, and particularly discloses a multi-source heterogeneous data acquisition access method and system based on intelligent mapping and a dynamic interface, and the method comprises the steps: registering an input data source class, generating a data source identifier corresponding to the data type, and storing the data source identifier; respectively executing meta-model analysis operation to generate a standardized meta-model associated with the data source identifier and a container instance template; generating a mapping rule set through a twin neural network architecture with multi-modal feature fusion and a preset mapping rule; a middle layer API interface is constructed through interface logic, and real-time conversion and access between input and output data are achieved; and realizing containerized parallel access based on the container instance template and the containerized interface service instance. According to the method, data source change is dynamically adapted, the real-time performance and accuracy of the mapping rule are ensured, and meanwhile, multi-source heterogeneous data can be accessed efficiently in real time in combination with containerized parallel processing and a data source change monitoring mechanism.

The invention belongs to the field of big data processing, and particularly relates to an event analysis method, device and equipment and a storage medium, target event information is obtained, the target event information is converted into event feature vectors, the event feature vectors are input into multiple analysis models respectively, and the analysis models output corresponding event types and initial risk levels respectively. Performing similarity matching on the event feature vector and a historical event feature vector of a historical event in a preset knowledge base, screening one or more pieces of historical event information of which the similarity score is greater than a preset threshold, and determining the credibility of each analysis model based on the historical event information and the event type, performing weighted fusion on the initial analysis result based on the credibility of each analysis model, and determining a target risk level corresponding to the target event information; according to the method, by combining multi-model analysis, historical data similarity matching and credibility weighted fusion, the accuracy of risk level judgment of the target event is improved.

The invention proposes a power plant black-start unit monitoring and fault early warning method, and the method comprises the steps: deploying a sensor array at a key part of a unit through a high-precision sensor network, constructing a neural network model through an LSTM deep learning algorithm based on collected key state data, carrying out the modeling analysis of multi-dimensional time series data, and carrying out the early warning of a fault of the unit. The method comprises the following steps: acquiring a dynamic characteristic mode of a unit operation state, reconstructing a characteristic vector into a two-dimensional characteristic graph by combining a CNN convolutional neural network, performing fault mode identification, acquiring a potential fault type and risk level assessment, and establishing a dynamic threshold adjustment mechanism by adopting an adaptive threshold fault diagnosis algorithm based on the fault type and risk level assessment. And abnormal state determination is carried out, and accurate fault early warning signals and disposal suggestions are obtained. According to the method, the data processing capacity and the system expansibility are improved, the prediction model and the optimization model are constructed through the big data analysis technology, scientific selection and starting time sequence arrangement of the black-start unit are achieved, and the success rate and reliability of black-start are improved.

The invention discloses an open-web gravity dam deformation prediction and mechanism interpretation method, system, equipment and medium, and belongs to the technical field of dam and hydraulic structure health monitoring and state evaluation. Constructing the preprocessed monitoring data into corresponding feature vectors, dividing a time sequence data set, and performing XGBoost model training and hyper-parameter optimization to obtain a prediction model; calculating a corresponding SHAP value, and screening a key factor as an independent variable for model analysis; and selecting a control index to evaluate the performance of the model, and deploying the model to a dam safety monitoring system after verification so as to carry out real-time prediction and early warning. According to the method, while high-precision deformation prediction of the open-web gravity dam is realized, quantifiable mechanism explanation of the prediction result is provided, so that transparent and reliable decision support is provided for safety monitoring and early warning of the dam.

The invention discloses a shield butt joint section curtain grouting additional load determination method, and relates to the technical field of geotechnical engineering grouting, and the method comprises the steps: carrying out the preprocessing of obtained grouting measurement data, obtaining an envelope point set, and carrying out the fitting optimization based on an envelope line function model, and obtaining an optimized envelope line function; calculating an ultimate load, attenuating the ultimate load to a preset engineering allowable threshold value to obtain an ultimate load acting distance, and determining boundary conditions through an envelope line intercept analysis method; and correcting the limit load according to a preset grouting load safety coefficient to obtain a corrected limit load, and performing inversion correction in combination with the grouting measurement data to obtain a final limit load and a final boundary condition. According to the method, the significant action range and the most unfavorable load condition of the grouting load are determined by capturing the spatial distribution and evolution law of the additional soil pressure field caused by grouting, and accurate load boundary conditions are provided for model analysis or finite element calculation.

The invention discloses a network information security access control system based on dynamic trust evaluation, and the system comprises a behavior collection module which collects original behavior log data, and builds a behavior feature sequence reflecting an entity security state through a preset streaming processing engine; the behavior analysis module is used for carrying out long and short term behavior modeling analysis on the behavior feature sequence to obtain long and short term behavior pattern representation, outputting potential causal chain data based on the long and short term behavior pattern representation and an abnormal causal inference model combined with context perception, and constructing a dynamic trust portrait according to the potential causal chain data; the long and short-term behavior modeling analysis comprises long behavior modeling analysis and short behavior modeling analysis; and the strategy generation module is used for driving a strategy engine in real time based on the dynamic trust portrait, carrying out attribute extraction and dynamic attribute coding based on a behavior sequence in a software defined boundary frame, and dynamically generating and issuing an optimal dynamic access strategy with attributes according to a minimum permission principle.

The invention relates to the technical field of satellite internet, and discloses a multi-satellite cooperative beam hopping resource allocation method and system based on interference avoidance, and the method comprises the steps: 1, constructing a multi-satellite beam hopping cooperative coverage interference analysis model; 2, modeling is carried out on a multi-satellite-hopping-beam multi-dimensional resource scheduling problem; 3, defining interference between spatial isolation measurement hopping beam patterns, constructing an interference constraint matrix, determining a shortest distance threshold value through a carrier-to-interference ratio threshold value, deducing an interference model in a same-rail scene and a different-rail scene, and bringing the carrier-to-interference ratio threshold value into optimization problem constraint; step 4, carrying out graph structure modeling based on GAT-PPO; 5, an Actor decision layer outputs a beam scheduling probability matrix and a power distribution vector in a double-branch mode, an action space is subjected to multiple constraints, and a reward function balances throughput and interference suppression; step 6, adopting a centralized training-distributed execution framework; according to the method, interference model analysis of multi-satellite cooperative coverage based on interference avoidance is constructed, and real-time analysis and scheduling of the interference condition are carried out.

The invention discloses a multi-algorithm collaborative optimization water turbine key component additive manufacturing method, and relates to the technical field of additive manufacturing. According to the method disclosed by the invention, a complete intelligent manufacturing system is constructed: in a data acquisition stage, a high-precision laser scanning technology (the precision reaches 0.01 mm) is adopted to realize accurate three-dimensional reconstruction of a damaged area; in the modeling analysis link, thermodynamic behaviors in the restoration process are accurately predicted through finite element simulation of multi-physics field coupling; in the aspect of process optimization, an adaptive differential evolution algorithm, an improved NSGA-II multi-objective optimization algorithm and deep reinforcement learning (DRL) are innovatively and organically combined to form a full-process intelligent decision-making system for coverage deviation analysis, path planning and parameter optimization. Particularly, due to introduction of a deep reinforcement learning algorithm, millisecond-level dynamic adjustment of key process parameters such as wire and powder feeding speed and laser power is achieved, and the stability and the material utilization rate of the manufacturing process are greatly improved.

The invention discloses a self-aligning roller bearing rotation flexibility test data and assembly parameter correlation analysis method and a self-aligning roller bearing rotation flexibility test data and assembly parameter correlation analysis system. According to the method, bearing assembly parameters and multi-dimensional time sequence data of a rotation test are acquired through a system, and a quantitative correlation model between the bearing assembly parameters and the multi-dimensional time sequence data is constructed by using a machine learning algorithm. On the basis of the model, reverse analysis can be carried out on abnormal test data, and an assembly deviation source which causes poor flexibility is accurately positioned; target parameters can be input in the process design stage, and the expected performance of the bearing can be predicted in the forward direction. The corresponding system integrates a data acquisition module, a model analysis module, an application output module and a process feedback control module, and is integrated with a production line control system. According to the method, traditional qualitative judgment depending on experience is converted into quantitative analysis driven by data and models, intelligent closed loop from quality detection and root diagnosis to process optimization is achieved, and the consistency of bearing assembly quality and the intelligent level of process control are remarkably improved.

The invention discloses a pet health state monitor based on multiple sensors, comprising: a multi-sensor data acquisition module, which comprises an inertial measurement unit, a physiological parameter acquisition unit and an environment monitoring unit and is used for acquiring motion data, physiological parameters and environment data of a pet; and the edge calculation module is connected to the multi-sensor data acquisition module and is used for carrying out real-time preprocessing and feature extraction on the acquired data. Multi-dimensional data acquisition of the pet health state is realized by integrating inertial measurement, physiological parameter acquisition, environment monitoring and other sensors, compared with a single sensor scheme, the health risk assessment accuracy is improved, a collaborative architecture combining edge calculation and cloud analysis is adopted, and the risk assessment efficiency is improved. The edge end is responsible for real-time preprocessing and simple anomaly detection, and the cloud carries out complex model analysis, so that the real-time performance is ensured, and the powerful computing power of the cloud is fully utilized.

The invention relates to the technical field of helicopter structure and function integrated anti-strike structure design, and particularly provides an integrated anti-strike plate parameter optimization method based on model analysis. According to the method, materials of the ceramic plate and the composite material plate are selected, iterative analysis and structural bearing performance analysis of the integrated anti-strike plate are carried out, static analysis software is adopted to carry out structural bearing performance analysis on the structure of the integrated anti-strike plate, and whether the design requirements of rigidity and strength are met or not is judged. The integrated anti-strike plate based on the titanium alloy honeycomb lattice structure form of the embedded ceramic plate and the composite material plate is reasonable and compact in structure, and the space efficiency can be effectively improved.

The invention relates to a lightweight reasoning acceleration architecture based on an instruction set architecture, and the architecture comprises the steps: initializing hardware level configuration, building a software level environment, and constructing a basic operation environment of the lightweight reasoning acceleration architecture; a model analysis mode is adopted to reduce the memory demand and carry out allocation optimization on the memory, and a reasoning execution basis is constructed; completing calculation parameter configuration by adopting modes of accelerator parameter configuration, weight data preloading and output data loading; based on the calculation task and monitoring information of the calculation process, calculation resources are dynamically scheduled, and efficient scheduling and execution of the calculation task are achieved. The energy efficiency is improved based on a data reuse strategy, memory access optimization and power consumption management; a comprehensive anomaly detection system is constructed, and anomaly conditions are detected and reported; the collaborative optimization of reducing the power consumption and improving the calculation efficiency is achieved, the real-time performance and the stability are both considered, and the efficient and reliable lightweight reasoning effect is achieved in the edge calculation scene.

The invention discloses a stay cable tensioning system and method. The stay cable tensioning system comprises a data acquisition module, a modeling and simulation module, a multi-source data fusion module, a cable force value prediction module, a disease diagnosis and maintenance module and a tensioning control module. According to the stay cable tensioning system, through full-process digitization of data acquisition, modeling analysis, intelligent control and maintenance rehearsal, the change from experience driving to data driving is achieved. The method has the core advantages that the deep fusion of multi-source data, the precise simulation of the digital twin model and the efficient decision of the intelligent algorithm significantly improve the construction precision of the stay cable, the safety of the structure and the maintenance efficiency. The method has the advantages that the prediction error of the cable force value in the construction stage is reduced from + / -5% to + / -1.2%, the stability of the cable force value after bridge forming is improved by 60%, and the vibration amplitude is reduced by 85%.

The invention relates to the technical field of artificial intelligence and computer vision, in particular to a shooting target paper bullet hole recognition and behavior analysis method based on a large model, which comprises the following steps: step 1, image acquisition and preprocessing; step 2, bullet hole identification and classification; 3, large model feature extraction and fusion; step 4, behavior agent analysis; step 5, behavior classification and scoring; and step 6, generating intelligent feedback. Key technologies such as target detection, image processing, deep learning, intelligent analysis and man-machine interaction feedback are fused, and the method is oriented to automatic identification of target paper bullet holes and modeling analysis of shooting behaviors in shooting training; according to the method, target detection algorithms such as YOLO and the like and an image enhancement mechanism are comprehensively applied, accurate recognition and classification of bullet holes are achieved, behavior feature modeling and trend analysis are carried out in combination with a large-scale pre-training model, personalized training feedback is generated in an intelligent interaction mode, and the method has high robustness, cross-scene adaptive capacity and closed-loop intelligent evaluation capacity.

The invention discloses a novel data exit abnormal behavior analysis system and method, and relates to the technical field of network security, and the method comprises the steps: obtaining the network asset attribute information of a data exit subject, and carrying out the classification marking of the network asset attribute information according to an exit mode, and obtaining a key asset subject library; acquiring communication logs of data outbound, and screening corresponding communication logs from the communication logs based on the key asset subject library to obtain preliminarily screened logs; screening corresponding communication logs from the preliminary screening logs based on the key application ports to obtain risk communication logs; and matching a corresponding analysis model according to the exit mode, and performing abnormal behavior analysis on the risk communication log based on the analysis model to obtain a risk assessment result. Differentiated model analysis is carried out according to different departure modes of the data departure subjects, the problems that a single analysis model is high in false alarm rate and difficult to study and judge are solved, and the accuracy of abnormal behavior analysis is improved.

The application discloses a single-side fulcrum cantilever special-shaped steel truss lifting method and a cantilever lifting support, wherein the method comprises the following steps: setting two cantilever lifting supports with the same structure; establishing an overall model and simulating and analyzing the conditions of each stage; dividing the steel truss into a pre-installed section, a lifting section and a post-installed section, so that the temporary center of gravity of the lifting section corresponds to the centroid position of the lifting point; installing the pre-installed section and assembling the lifting section; lifting the lifting section and connecting the lifting section with the pre-installed section; monitoring the deformation and stress conditions and installing the post-installed section. According to the application, two cantilever lifting supports are symmetrically arranged on the building, the steel truss is divided into the pre-installed section, the lifting section and the post-installed section through modeling analysis, the lifting section can be assembled on the ground, then is lifted through the cantilever lifting support, and the temporary center of gravity and lifting parameters are controlled according to the modeling analysis data in the process, so that the workload of high-altitude assembly and welding of components is reduced, the high-altitude operation environment and other factor limitations are avoided, and the building quality is improved and the construction safety risk is reduced.

The application relates to the technical field of modeling, in particular to a modeling analysis method for construction site dust, which comprises the following steps: construction site dust modeling analysis; multi-source data acquisition and grid pretreatment; lightweight flow field reconstruction and dust diffusion potential calculation; spatiotemporal clustering analysis and dust control priority evaluation; adaptive time step optimization and dynamic simulation control. The application aims to ensure the simulation accuracy while greatly improving the calculation efficiency, and supports the rapid response and multi-scenario analysis requirements of the construction site dust.

Disclosed are various embodiments for analyzing machine learning models. A selection is obtained of a first tuple comprising a first feature vector and a first result generated by a machine learning model and a second tuple comprising a second feature vector and a second result generated by the machine learning model. Then, a plurality of emulated feature vectors are generated. Next, a plurality of emulated results are generated. Subsequently, a plurality of emulated decision instances are generated. Next, a decision tree is built based at least in part on the first tuple, the second tuple, and the plurality of emulated decision instances. Finally, an importance of each feature on the decision tree is computed.

This invention provides a method for generating city-level real-scene 3D geographic scene data, relating to the field of 3D modeling and analysis technology. The method includes: collecting multiple sets of historical defect sample records; identifying multiple modeling defect areas; extracting multiple structural deviation features of the modeling defect areas related to different model construction stages to construct a defect structure association model for each modeling defect type; extracting scene stage association data and constructing a scene stage association list; after determining the target defect area of ​​the target scene model, identifying the target defect type and target scene type of the target defect area; constructing scene association tracing paths and stage association tracing paths for the target defect area; fusing the scene association tracing paths and stage association tracing paths to perform source tracing optimization of the target scene model, generating real-scene 3D geographic scene data corresponding to the target scene model. This invention improves the construction quality and efficiency of city-level real-scene 3D data.

The application discloses a new energy power generation intelligent control method and system based on big data, and concretely relates to the field of new energy power generation control, and comprises the steps of multi-dimensional data acquisition, data preprocessing, model analysis, control decision generation and execution and feedback correction: through the collection of environment, equipment, power grid and historical data by Internet of Things sensors, after the elimination of outliers, space-time alignment and normalization processing, a multi-parameter coupling output model and an optimization objective function are constructed, the photovoltaic, wind power and energy storage control instructions are generated in combination with equipment safety and power grid constraints, and the weight coefficient is corrected when the deviation exceeds the threshold. The system comprises data acquisition, processing, mathematical modeling analysis, control execution and storage modules, the scheme improves the control precision and system stability, prolongs the equipment life, has strong compatibility, can be directly applied to various scale new energy power stations, does not need to be re-adjusted, and effectively supports the efficient and stable operation of new energy power generation.

The invention relates to the technical field of network security, and discloses a network advanced threat detection and situation awareness system based on a multi-modal large model, an acquisition unit performs multi-modal data acquisition, and a processing unit processes the data acquired by the acquisition unit and transmits the processed data to a processor. The model unit receives the processed data and generates a security situation representation, the scoring unit is used for calculating an abnormal score in the model unit, the center unit judges the calculated abnormal score, the traceability unit identifies a candidate attack chain based on the security situation representation, and the data processing unit processes the candidate attack chain. According to the system, various modal data such as texts, codes, network diagrams and byte features are collected, it is guaranteed that data acquisition is sufficient and comprehensive, the collected data are cleaned, standardized, sliced and uniformly represented, and the security of the attack chain is improved. And high-quality and structured input is provided for subsequent model analysis.

The application discloses a charged water body equivalent impedance model construction and parameter acquisition method and related devices, and innovatively establishes a calculation system containing resistance and inductance correction factors, and realizes high-precision characterization of water body impedance characteristics by introducing an electrode spacing function. Specifically, a scalable grid technology is used to construct an adaptive calculation framework, and the calculation accuracy of resistance and inductance parameters is significantly improved through a power function correction algorithm. A reliable numerical calculation is provided for water body impedance network model analysis in complex hydrological environments. The application solves the problems of the prior art, such as complex modeling process, difficulty in accurately describing nonlinear behavior, insufficient precision, and inability to accurately capture the real behavior of water body.

The invention provides a hair detection and hair density calculation method based on image input and multi-model analysis. The method comprises the following steps: acquiring a head image by using common imaging equipment; preprocessing the image; inputting the image into a plurality of analysis models for independent analysis; hair detection and hair quantity density calculation precision is optimized through a self-research fusion algorithm and high-order multi-task analysis; outputting the hair quantity estimation, the partitioned hair density, the scalp coverage rate, the health score, the thermodynamic diagram and the trend curve, and generating personalized nursing suggestions. Further, according to the method, images and related data are periodically acquired and stored as historical data, and the historical data are periodically analyzed to generate a hair health change trend; the system can access an additional analysis model or external sensor data through an expansion interface to realize long-term tracking and comprehensive evaluation, so that the evaluation precision and the use value of a user are improved.