635results about How to "High risk" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent water conservancy inspection method, device and equipment and a storage medium. The method comprises the following steps: performing dynamic fusion processing on acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twinborn body; performing agent collaborative analysis on the digital twinborn body to obtain a self-adaptive inspection strategy; performing multi-modal sensing data acquisition on the target area according to the self-adaptive inspection strategy to obtain multi-modal data; performing federated learning processing on the multi-modal data to obtain holographic situation awareness data; performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; and inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report. According to the invention, the efficiency and accuracy of intelligent water conservancy inspection are improved.

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention provides a security risk assessment method and system for digital assets, and the method comprises the steps: obtaining a unique identifier and an ownership record of the digital assets, and determining key points of risk assessment, the key points comprising encrypted storage, transaction history and a circulation mechanism of the digital assets; for each key point, identifying identity authentication, access control and data encryption security factors in the key point, and performing security compliance verification on privacy protection and compliance requirements of the digital assets by adopting a probability statistical method; generating a plurality of digital asset cross-chain compatibility and value evaluation factor combinations through a Monte Carlo simulation method, simulating security events and consequences thereof in different scenes, and calculating the occurrence probability and loss influence of each scene; and according to the optimized risk assessment model, assessing the overall security risk of the digital assets, identifying the key link with the highest risk, and assessing the potential loss influence of the key link.

The invention relates to the technical field of supply chain risk analysis, in particular to a supply chain risk management and decision-making system based on a large model, and the system comprises a multi-layer supply chain data collection module which collects supply chain operation data from a multi-layer supply chain node; the multi-layer supply chain structure model construction module is used for constructing a multi-layer supply chain structure model among the supply chain nodes through the collected operation data of each supply chain node, and automatically identifying hidden risks in each supply chain node; the supply chain risk propagation path analysis module is used for analyzing a hidden risk propagation path of the hidden risk in each supply chain node after identifying the hidden risk, and analyzing a transmission mechanism of the risk between different nodes of the supply chain; and the decision support module supports the enterprise to adjust the supply chain strategy to reduce the risk. According to the method, an enterprise can grasp a conduction rule of risks in a supply chain from a global perspective, hidden risks of key paths and nodes are processed preferentially, and the early warning level and the anti-risk capability of the supply chain are remarkably improved.

The invention discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention relates to the technical field of traffic emergency evaluation, and discloses a comprehensive traffic network toughness evaluation method based on multiple indexes, which comprises the following steps of: in a pre-stage, selecting a plurality of key indexes for evaluating the traffic network toughness; an in-event stage: when an emergency occurs, acquiring real-time traffic data based on the evaluation model; and a post-event stage: after the emergency event is ended, analyzing the influence of the event on the traffic network, and evaluating the recovery capability of the traffic network based on the evaluation model in combination with the restoration time of the facility, the traffic flow recovery condition and the emergency resource allocation record. A topological structure index, a traffic flow index, a facility reliability index and an emergency response capability index are adopted, the robustness of the traffic network under different dimensions can be comprehensively evaluated, through multi-dimensional quantitative analysis, the impact resistance, the recovery capability and the emergency management capability of the network can be comprehensively evaluated, and the evaluation accuracy is improved.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The screen display glass film tearing equipment comprises a screen display glass loading unit, a film tearing unit and an adhesive supply unit, the screen display glass loading unit comprises a carrying table and a moving mechanism, the carrying table is used for positioning screen display glass downwards from a to-be-torn film, the film tearing unit is located below the to-be-torn film and comprises a film tearing head and a power device, and the film tearing head comprises a film tearing seat, a clamping jaw and a roller; the power device comprises a first power piece and a second power piece. On one hand, on the basis of movement cooperation of the clamping jaw and the rolling wheel, aligned gradual stripping of the rolling position and the stripping position of the to-be-torn film is kept, and on the other hand, the overturning angle is increased based on follow-up overturning of the extending section, so that the success rate of forming the stripping angle by inward overturning of the to-be-torn film is greatly increased; the probability that the glue is separated from the to-be-torn film or the corner of the to-be-torn film is torn is effectively reduced; on the other hand, diagonal film tearing is formed based on the relative movement of the screen display glass and the clamping jaw, the risk that the protective film is torn or falls off is reduced, and the whole film is torn off more efficiently in a more labor-saving mode.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention discloses an intelligent industrial chain construction method and system based on multi-dimensional qualification affirmation, and the method comprises the steps: constructing a multi-dimensional qualification evaluation model based on industrial characteristic data; obtaining the preprocessed multi-dimensional qualification data of the industry chain related enterprises; analyzing the multi-dimensional qualification data by using a multi-dimensional qualification evaluation model to obtain a multi-dimensional quantitative index of the qualification data of the industry chain related enterprises; based on the multi-dimensional quantitative indexes, a machine learning algorithm is adopted to carry out classification to obtain enterprise category distribution, and comprehensive scoring and sorting are carried out on industry chain related enterprises according to set weights to obtain a sorting result; interaction data, transaction intensity data and resource flow data among related enterprises of the industry chain are acquired; and constructing an intelligent industrial chain network diagram according to the data obtained in the steps. The method is helpful for understanding an industrial chain structure, identifying key enterprises and evaluating potential risks.

The invention discloses an intelligent safety early warning method and system for excavation equipment operation based on environmental perception, and relates to the technical field of intelligent early warning, and the method comprises the steps: carrying out the initial detection of an operation environment based on an environment monitoring module, and carrying out the adjustment of equipment parameters according to an initial environment state, a matching target early warning mode and a reference target early warning mode, monitoring the working environment according to the adjusted environment monitoring module, obtaining real-time environment data, and performing safety risk identification in combination with a static safety boundary and a dynamic safety boundary; and according to the risk identification result, generating a safety early warning instruction, and performing path dynamic optimization of the mining equipment. The technical problems that in the prior art, a traditional excavation equipment operation early warning method is poor in risk sensing and early warning capacity and high in safety accident rate in a complex environment are solved, and the technical effects that the all-weather risk sensing and early warning capacity of excavation equipment in the complex environment is improved, and the safety accident rate is reduced are achieved.

The invention relates to the technical field of short message fraud recognition, and discloses a short message fraud recognition method and system based on machine learning and computer equipment, and the method comprises the steps: carrying out the preprocessing of collected fraud and non-fraud short message samples, and obtaining structured short message data; performing multi-dimensional feature extraction to obtain an initial feature vector; constructing a short message fraud multi-mode knowledge graph; performing feature enhancement to obtain a knowledge enhancement feature vector; inputting the knowledge enhancement feature vector into a plurality of machine learning models for training to obtain an integrated short message fraud recognition model; and performing real-time analysis on the newly received short message based on the integrated short message fraud identification model to obtain a fraud risk prediction result, so that the newly received short message can be quickly analyzed, the real-time requirement in practical application is met, and a risk threshold classification mechanism is adopted, so that the fraud risk prediction result is more interpretable and operable, and the fraud risk prediction accuracy is improved. And subsequent risk management and disposal are facilitated.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the testis size character of Hu sheep as well as a screening method and application of the SNP molecular marker, and belongs to the fields of genetic breeding and molecular biology. The marker is located in an intron region of a Hu sheep chromosome 3 LRRIQ1 gene and is remarkably related to the Hu sheep testis size character, and the accuracy and reliability of the SNP molecular marker are ensured through a scientific and rigorous screening method; according to the marker, Hu sheep individuals related to testis size characters can be rapidly and accurately screened out, the breeding efficiency and accuracy are improved, the breeding process is accelerated, the breeding cost is reduced, and the economic benefits are improved; the genetic diversity is protected, the accuracy of seminal fluid quality evaluation is improved, a powerful tool is provided for Hu sheep breeding work, and important basic data is also provided for researching a genetic mechanism of testis development and functions; the method can also be used as an auxiliary diagnosis tool to assist in identifying potential genetic defects or disease risks and promote progress and development of the breeding technology.

The invention discloses an AI-based network security risk identification and management and control system, which is characterized by comprising network security equipment and a server, the network security equipment is used for monitoring flow data flowing into / out of the server, and the AI-based network security risk identification and management and control system also comprises an interface conversion module which is used for connecting different network security equipment, original operation logs of different network security devices are collected; data collection: collecting multi-source data from multiple channels of network traffic, server logs, application program logs and user behavior records; preprocessing the data; preprocessing the data, removing noise data, filling missing values, and extracting useful features from the original data; simulation training: training the model to identify normal and abnormal behaviors through machine learning by utilizing supervised learning, unsupervised learning or semi-supervised learning methods; the deep neural network is used for modeling a complex data mode, and the detection precision is improved.

The invention discloses an oil pipeline simulation analysis system, relates to the technical field of oil pipelines, and provides accurate data support for subsequent dynamic monitoring and pipeline cleaning decision by constructing a three-dimensional visual model and identifying wax precipitation temperature and deposition risk in time. Through the risk identification module, the system can analyze and calculate the wax precipitation temperature based on the oil product attribute data obtained in real time, and timely judge whether the deposition risk exists in the oil pipeline or not. After receiving a dynamic tracking instruction, the dynamic analysis module can obtain the deposition thickness of each position in a pipeline in real time and analyze the influence of oil temperature and flow velocity changes on deposition distribution, the analysis provides important input data for the pipe cleaning module, the deposition thickness and a heat loss coefficient are fitted through a deep learning technology, and the deposition thickness and the heat loss coefficient are matched with each other. The system may automatically calculate a delivery stability score. When the conveying stability score exceeds a set threshold value, the system can automatically start a pigging measure execution mechanism.

The invention discloses a financial examination and approval data monitoring system and method based on big data, and relates to the technical field of power supply data supervision. Historical financial examination and approval data is subjected to project type division to obtain data applied and submitted currently, and the data is matched with the divided historical data to accurately calculate the type risk value of the application data; the financial approval risk is calculated in a targeted manner, and the scientificity and rationality of risk management are ensured. An approval risk sliding window is set, real-time monitoring and dynamic adjustment of financial approval risks are achieved, fluctuation of approval data can be captured in time, abnormity can be recognized, approval requirements can be adjusted, and therefore the flexibility of an approval process and the effectiveness of a risk control strategy are improved; and comprehensive risk calculation is provided in combination with the type risk value and the department association risk value. The automatic decision not only improves the efficiency and consistency of financial examination and approval, but also reduces the workload of manual examination, and ensures the uniformity and fairness of financial examination and approval standards.

The invention discloses a high-precision contract document analysis method based on a large model, relates to the technical field of contract document analysis, and solves the problem that an existing intelligent contract management system analyzes contract documents by utilizing advanced technologies such as computer vision and natural language processing and provides a certain automation function. However, the systems still have many limitations in the aspects of contract document processing accuracy, contract content intelligent comparison, intelligent question and answer and the like. The method comprises the steps of obtaining input data of a user; performing fine tuning on the pre-training model to obtain a query question and answer generation model, and performing processing through the query question and answer generation model based on the input data to obtain a query result; carrying out difference content identification on the input data to obtain an identification result; the technical problem is solved by performing risk assessment on the input data to obtain the assessment result.

The invention provides a financial bill risk value calculation method and device, equipment and a storage medium, relates to the technical field of financial bill risk assessment, and can comprehensively and deeply mine potential risks in financial bills by integrating various anomaly detection models and classification models. The anomaly detection model can accurately identify the anomaly in the data, and provides a high-quality risk data sample for the classification model, thereby ensuring the accuracy and reliability of risk probability calculation. The risk probability is predicted by using the pre-trained risk classification model, so that the calculation efficiency is greatly improved. Compared with a traditional risk assessment method, the method does not need to manually carry out tedious data analysis and judgment, and the risk identification period is remarkably shortened. And the risk probability is converted into the corresponding risk value, so that the risk assessment result is more visual and easy to understand. Risk management and decision making of related financial departments are facilitated, and comprehensive control of financial risk conditions of enterprises is facilitated.

The invention discloses a multivariate progressive rainstorm-mountain torrent chain disaster comprehensive risk evaluation method and system, and the method comprises the steps: constructing a multiband weather radar networking multi-target optimization model, and coupling a radar rain measurement simulation model to obtain an optimization networking model; taking the collected scheme set as input to solve and obtain an optimal networking scheme and rainfall forecast; constructing a flood forecast model, extracting rainfall forecast as model input to obtain initial flood forecast, correcting and optimizing to obtain an optimized flood forecast model, and simulating to obtain flood forecast; screening initial mountain torrent risk factors to construct a mountain torrent risk factor set, carrying out dimension reduction on the mountain torrent risk factors to obtain a rainstorm-mountain torrent chain type comprehensive risk index, calculating a comprehensive risk index value based on flood forecasting, inputting the comprehensive risk index value into a mountain torrent risk evaluation model constructed by adopting an XGBoost model, and solving the model to obtain comprehensive risk forecasting. According to the invention, the scientificity and timeliness of disaster emergency decision making are enhanced, and the early warning capability and risk management level of rainstorm-mountain torrent disasters are improved.

The invention relates to a multi-mode heart and cerebral hemodynamics monitoring method and device, and belongs to the technical field of medical equipment. According to the method, brain absorbance information and pulse wave information at the middle artery of the brain are obtained based on a near infrared spectrum technology by utilizing the relevance among the cardiac output, blood pressure, intracranial pressure and cerebral perfusion pressure of a human body, cardiac output information and cuff-free blood pressure information are obtained based on a thoracic impedance method, and physiological signals are obtained based on the two methods; and carrying out fusion processing to obtain a multi-modal signal, and further constructing a non-invasive monitoring model of non-invasive intracranial pressure, cardiac output, blood pressure and cerebral perfusion pressure, so as to realize continuous non-invasive monitoring reflecting human heart and cerebral hemodynamic parameters. According to the invention, through fusion application of multi-mode signals, the mechanism of cardio-cerebral hemodynamics interaction can be effectively disclosed, and a more effective, safer and noninvasive monitoring means is provided for clinic.

The invention discloses a power transmission line corona discharge risk assessment method based on bird nest polarization characteristics, and relates to the field of power transmission line safe operation. The method specifically comprises the following steps: establishing a bird nest polarization characteristic mathematical model, considering bird nest material and shape characteristics, and calculating an electric field enhancement coefficient; introducing critical polarization intensity as a criterion of corona discharge; establishing a kinetic equation of polarization intensity changing along with time, and revealing dynamic characteristics of the polarization process; and defining a corona discharge probability function to realize quantitative evaluation of the discharge probability. According to the method, a complete physical model from bird nest polarization to corona discharge is established, key links such as electric field enhancement, polarization dynamics, critical polarization intensity and discharge probability are covered, a systematic theoretical framework is provided for corona discharge caused by bird nests, and the method has remarkable practical value for guiding design, operation and maintenance of power transmission lines and is worthy of popularization and application. And an important contribution is made for ensuring safe and stable operation of a power system.

The present invention belongs to the technical field of construction risk assessment, and specifically discloses a subway construction safety risk intelligent assessment system, which includes a field data acquisition module, an equipment data import module, a construction data analysis module, a construction risk assessment module, a risk sorting and display module, a database, and an early warning execution and feedback terminal. The present invention solves the current deficiencies in data collection scope and depth by setting up key areas, and performing real-time construction site data acquisition and importing the accumulated construction time and real-time equipment monitoring data of key areas. At the same time, risk assessment is performed through three key aspects of personnel, equipment and structure, forming an independent and complete risk indicator analysis system, and by constructing risk indication maps in each key area, it is convenient for construction management personnel to conduct comprehensive risk monitoring and management, and improves the effectiveness of risk control and feedback links.

The invention belongs to the technical field of construction risk assessment, and particularly discloses and provides a subway construction safety risk intelligent assessment system, which comprises a field data acquisition module, an equipment data import module, a construction data analysis module, a construction risk assessment module, a risk arrangement display module, a database and an early warning execution and feedback terminal. According to the method, the key area is set, real-time construction site data acquisition is carried out, and accumulated construction duration and real-time equipment monitoring data of the key area are imported, so that the problem of deficiency in the aspects of data acquisition range and depth at present is solved, and risk assessment is carried out through three key aspects of personnel, equipment and structure, so that the risk assessment accuracy is improved. An independent and complete risk index analysis system is formed, and the risk indication diagrams in the key areas are constructed, so that the construction management personnel can conveniently carry out comprehensive risk monitoring and management, and the effectiveness of risk management and control and feedback links is improved.

The invention discloses a security risk evaluation system for cloud printing data transmission, and relates to the field of cloud printing data transmission security, and the system comprises the following components: a data acquisition module which establishes connection with a server or an interface related to cloud printing data transmission according to configuration information, and performs data acquisition according to a set acquisition frequency; according to the invention, analysis and risk assessment are carried out on a real-time data set through an anomaly detection algorithm and a risk assessment model adopted by the data analysis module, multiple risk factors and indexes are comprehensively considered, quantitative assessment is carried out on abnormal conditions, and the risk assessment accuracy is improved. Therefore, the risk level and possible loss can be judged more accurately, so that the system can discover potential safety risks in time, and the accuracy and efficiency of risk identification are greatly improved.

The invention discloses a real-name system information management method supporting multi-level authentication, and the method comprises the following steps: collecting basic identity information of a user, and storing the basic identity information through an asymmetric encryption algorithm; according to the use demand and the access authority requirement of the user, generating a multi-level authentication hierarchy; dividing the real-name system information into a basic information area, an authority information area and a sensitive information area according to the sensitivity of the real-name system information; carrying out distributed storage on real-name system information, and carrying out hierarchical encryption on data of different information areas by using a hierarchical encryption strategy; based on a multi-level authentication result and in combination with an access control strategy, authority distribution is carried out on the user, and controlled access of real-name system information is carried out under multi-dimensional verification; user information operation behaviors are monitored in real time, user behavior characteristics are analyzed based on an anomaly detection model, and when risk behaviors are recognized, an additional verification process is triggered or user operation is directly limited. According to the invention, safer, more flexible and more efficient real-name system information management is realized.

The invention discloses a bridge safety evaluation method and system based on data analysis, and relates to the technical field of bridge safety monitoring, and the method comprises the steps: collecting short-term dynamic data and long-term static data of a bridge; based on the short-term dynamic data and the long-term static data, short-term state features and long-term state features of the bridge are extracted; predicting a short-term state score of the bridge based on the short-term state characteristics of the bridge, and predicting a long-term state score of the bridge based on the long-term state characteristics of the bridge; the short-term state score and the long-term state score are fused, and bridge safety is comprehensively evaluated; and according to an evaluation result, dividing bridge safety levels and carrying out dynamic early warning. According to the method, the short-term state score and the long-term state score are dynamically fused through the adaptive weight adjustment rule, the influences of short-term emergencies and long-term aging effects are fully considered, and the accuracy and dynamic early warning capability of bridge safety assessment are remarkably enhanced.

The utility model relates to a long-spiral gauge-protection negative-pressure forward-inclined-tooth PDC (polycrystalline diamond compact) drill bit which comprises a drill bit handle. The drill bit head part is supported on the drill bit handle part; the drill bit head comprises a drill bit body, a plurality of long blades, a plurality of short blades, a plurality of nozzles and forward-inclined PDC cutting teeth. The long blades and the short blades are arranged on the drill bit body at intervals in the circumferential direction of the drill bit body. One end of each long blade is connected with the end part of the drill bit body, and each long blade is spirally arranged on the outer wall of the drill bit body; a blade chip groove is formed between the long blade and the short blade; gauge protection chip grooves are formed among the long blades; and the nozzles are respectively arranged in the chip grooves of the cutter wings. When the long-spiral gauge protection negative-pressure forward-inclined-tooth PDC drill bit works, local negative pressure is generated through rotation, the forward-inclined PDC cutting teeth are in a plowing mode, upward returning of rock debris is accelerated, the well bottom cleaning effect is improved, bit balling is prevented, and the well bottom hold-down effect is reduced; and meanwhile, the plow PDC cutting teeth enable the rock debris to rapidly enter the bottom of the well, so that the mechanical drilling speed is increased.