206results about How to "High risk" patented technology

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 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 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 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 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 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 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 relates to the field of water treatment intelligent dosing control, and provides an intelligent dosing control method and system based on multi-source data fusion and time sequence prediction.The method comprises the steps that a raw water working condition, colloid electrical property balance and floc form attribute set are extracted, and the basic dosing matching amount is determined; obtaining a dynamic delay constant according to the volume of the sedimentation tank and an instantaneous flow value, and generating a causal mapping sample set by combining the three types of attribute sets and effluent turbidity signals of the sedimentation tank; generating a dynamic weight factor sequence according to the causal mapping sample set, and performing point-by-point inner product on the causal mapping sample set to generate a process condition sensitive feature set; and generating a predicted effluent turbidity value based on the process working condition sensitive feature set, performing reverse game optimization through the effluent target turbidity, generating an agent supplementing correction increment, and performing amplitude limiting truncation in combination with the basic agent adding proportioning amount to generate an agent adding control instruction. According to the invention, multi-source heterogeneous working condition sensing features and space-time causal alignment logic are fused, and an intelligent dosing decision and feedback compensation closed-loop mechanism is constructed.

The invention discloses a preparation method and application of a response type NO release anticoagulant hydrogel material. The preparation method comprises the following steps: providing methylacryloylated recombinant collagen; providing methacrylated hyaluronic acid; providing caffeic acid arginine amide modified hyaluronic acid; the response type NO release anticoagulant hydrogel material is obtained by taking methylacryloylated recombinant collagen, methylacryloylated hyaluronic acid and caffeic acid arginine amide modified hyaluronic acid as raw materials. The prepared hydrogel material has the advantages of resisting bacteria, promoting proliferation of endothelial cells, inhibiting proliferation of smooth muscle cells, resisting inflammation, promoting angiogenesis, inhibiting thrombosis and the like, arginine can serve as an endogenous NO donor in blood to release NO molecules, and therefore the effect of promoting vascular endothelial repair is achieved, and the hydrogel material has the advantages of being capable of resisting bacteria, promoting proliferation of endothelial cells, inhibiting proliferation of smooth muscle cells, resisting inflammation, promoting angiogenesis, inhibiting thrombosis and the like. The introduced NO molecules enable the hydrogel material to have good anticoagulant performance, and the hydrogel material is expected to be used for preparing an artificial blood vessel anticoagulant coating.

The invention relates to the technical field of financial management, and discloses a business and financial integrated financial centralized management system, which comprises an integrated data processing center for realizing real-time interaction and analysis of business data and financial data, and further comprises a multi-dimensional report generator in signal connection with the integrated data processing center, the generator can generate a multi-dimensional and dynamic report according to real-time interaction data of each independent financial department, and provides accurate data support for enterprise decision making. The financial centralized management system with business and financial integration is constructed, centralized management, real-time analysis and monitoring of financial data are achieved, the financial management level and the risk control capability are improved, and healthy and stable development of enterprises is ensured. On the basis, through deep learning and big data technologies, the system can provide more accurate decision support for enterprises, assist the enterprises to accurately grasp market pulses, optimize resource allocation and improve operation benefits.

The invention relates to an intelligent switching method and system for a bypass shelter and a main power line, and the method comprises the steps: deploying a plurality of sensors to monitor the state of the main power line, enabling the plurality of sensors to carry out the periodic collection through the regular transmission of data, and obtaining the detection data of the main power line in a plurality of periods; taking the main power line detection data of the adjacent three periods as a group of real-time detection data, comparing the detection data of the middle period with the detection data of the previous period and the detection data of the next period in the group of real-time detection data, and performing detection data change analysis to obtain a real-time detection change analysis result; based on a time sequence analysis method, predicting virtual detection data of a next period according to a real-time detection change analysis result; and evaluating the state of the main power line according to the virtual detection data, and judging whether to execute the switching action of the main power line and the bypass shelter. According to the method, prediction can be carried out in advance before the power line is abnormal, a bypass switching decision is made, and the stability and safety of a power system are improved.

The invention discloses a building fire-fighting monitoring system based on the Internet of Things. The building fire-fighting monitoring system comprises a data acquisition module, a building fire-fighting evaluation model establishment module and a building fire-fighting monitoring module. The invention belongs to the field of data processing, and particularly relates to a building fire-fighting monitoring system based on the Internet of Things, and the scheme is based on a multi-scale time sequence feature extraction and self-adaptive time step scaling mechanism, and achieves the synchronous detection of an instantaneous fire signal and periodic equipment abnormality; based on time domain feature focusing and a nonlinear sensor intensity change and dynamic trend adjustment mechanism, high-precision positioning of fire precursor and equipment fault is realized; a secondary screening mechanism and a dynamic threshold adjustment strategy are introduced, so that the accuracy of a fire signal and equipment anomaly detection result is remarkably improved; through construction of early detection reward loss, the penalty of early exposure of fire precursor signals or equipment faults is reduced, the early warning requirements of fire and abnormal equipment faults are met, and then the early warning accuracy and response efficiency of the building fire-fighting monitoring system are comprehensively improved.

The invention discloses a logistics warehouse management system based on big data, and belongs to the technical field of logistics management. The method is used for solving the technical problems that the warehousing states of different warehousing positions cannot be supervised and analyzed according to each batch of logistics warehousing and data expansion and utilization of different dimensions cannot be implemented in the existing scheme. The warehousing states of different warehousing positions are supervised and analyzed according to each batch of logistics warehousing storage, so that periodic local warehousing state supervision can be implemented on different warehousing positions in a logistics warehouse; reliable local data support can be provided for subsequent adjustment management analysis of different storage positions and overall saturation condition analysis of the logistics warehouse; according to the method, the periodic local storage state supervision results of different storage positions in the early stage are expanded and utilized, and reliable data support can be provided for subsequent dynamic management prompts of different storage positions while the overall filling stability conditions of the different storage positions can be analyzed and classified.

The invention relates to the technical field of metal welding, and discloses a welding process of high-strength medium alloy carburizing steel, which comprises the following steps: preheating the whole weldment to 200-250 DEG C before welding; the overall temperature of a weldment is kept at 200-250 DEG C in the welding process; and after welding is completed, a weldment is integrally heated to 250-300 DEG C, and heat preservation is conducted for preset time. According to the scheme, a new preheating technology in the welding technology is provided for the high-strength medium alloy carburizing steel, overall preheating is adopted before welding, during welding and after welding instead of local preheating, the temperature ranges before welding and during welding are kept consistent, the temperature range after welding is slightly higher than the temperature ranges of the first two stages, it is advantageously kept that the temperature of a mother part in the whole welding stage is stable and uniform, and the welding quality is improved. The temperature gradient in the whole welding process is reduced to the minimum, and through appropriate process steps and welding parameters, it is guaranteed that weld joints do not have welding defects, and meanwhile the mechanical property requirements of products are met.

The invention provides a submarine pipeline corrosion rate prediction method, system and device and a medium, and the method comprises the steps: obtaining monitoring data along a target submarine pipeline and pipeline body data; constructing a microbial corrosion random kinetic model based on the monitoring data, and simulating and generating a random microbial corrosion current component; calculating a pipe wall temperature field which is non-uniformly distributed along the axial direction of the pipeline based on the monitoring data and the pipeline body data; calculating an electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipeline material; performing multi-physics field coupling calculation on the random microbial corrosion current component and the electrochemical corrosion current component to generate a corrosion space-time distribution field; and performing corrosion risk analysis based on the corrosion space-time distribution field to obtain a corrosion risk prediction result. The method can improve the corrosion prediction accuracy of the submarine pipeline.

The invention discloses an intelligent assessment method and system for regional geological disaster risk, and relates to the technical field of intelligent assessment. During operation of the system, various real-time data related to debris flow are acquired by integrating real-time sensors and satellite remote sensing data, original data are preprocessed and stored in a data storage system; based on the collected data, a physical model and a machine learning model are constructed, a debris flow disaster risk index MRI, a rainfall intensity coefficient RIC, a geological vulnerability coefficient GVC and a hydrodynamic pressure coefficient HPC are obtained after calculation, the probability of debris flow occurrence is predicted, and a result is output according to the model. The debris flow disaster risk index MRI is compared with a preset first safety threshold value M and a preset second safety threshold value N, the risk level of debris flow occurrence is evaluated, different areas are managed in a classified mode, debris flow early warning is given out according to real-time monitoring data and risk evaluation results, and automatic emergency response measures are supported.

The invention discloses a rice cultivation method capable of effectively preventing low-temperature cold damage, and relates to the technical field of agricultural planting. The method aims at solving the problems that the low-temperature-resistant potential of seeds cannot be brought into full play, plough layer conditioning measures are not meticulous enough, and personalized schemes for different soil types and low-temperature environments cannot be achieved. Through technical measures of rice germplasm treatment, cultivation soil treatment, field management and the like, the cold resistance of the rice is effectively improved, the rice can still keep a good growth condition in low-temperature cold damage weather, meanwhile, the soil quality is improved, a foundation is laid for sustainable agricultural development, and the method is worthy of popularization and application. Through low-temperature climate data analysis, early warning system establishment and emergency response mechanism matching, the stability and risk resistance of agricultural production are improved, innovation and development of a rice planting technology are promoted, the rice quality is improved, the requirement of the market for high-quality rice is met, and higher economic benefits are brought to farmers.

The invention discloses a two-stage space-time diagram network and multi-source information fusion time sequence probability prediction method and device, and relates to the technical field of artificial intelligence and big data analysis. The method comprises the following steps: acquiring historical wind speed sequence data and corresponding target variable sequence data; preprocessing the data, and constructing a training sample according to a preset time sliding window; constructing a time sequence probability prediction framework of the two-stage space-time diagram network and multi-source information fusion; based on the fluctuation correlation of the historical sequence data, constructing an adjacent matrix between nodes; inputting the training sample and the adjacent matrix between the nodes into a wind speed prediction model, training the model through a designed threshold perception loss function of a wind speed-power nonlinear relationship, and outputting a multi-node wind speed prediction probability of a first stage; and inputting the multi-node wind speed prediction probability and the multi-source environmental factor data into the gradient boosting tree model for second-stage prediction, and outputting a multi-node wind power prediction result. According to the invention, prediction errors can be reduced.

The invention discloses an on-chain enterprise risk identification method and system based on a credit chain large model. The system comprises a data preprocessing module, a model matching module, a weight parameter loading module, a data operation reasoning module, a risk prediction output module and the like. Large model training and optimization are carried out by integrating industry, region, type and other multi-dimensional data sets, a credit chain vertical type general model and a special model are formed, and the method can adapt to credit and risk prediction evaluation, monitoring and early warning of a single enterprise or an integrated enterprise in different supply chain and industry chain scenes. A more comprehensive, accurate and real-time solution is provided for enterprise management and industry supervision, and remarkable beneficial effects and advantages are achieved.

The invention provides an intelligent electronic contract auditing and risk early warning method based on AI, and belongs to the field of contract auditing, and the method comprises the following steps: building a standard database for contract auditing, carrying out the pre-classification of contract types, selecting one contract from contracts of the same type as a standard template, and carrying out the risk early warning of the contracts; the standard templates are imported into a standard database to serve as templates, and each input standard template is classified and marked; a general auditing process and an independent auditing process are set for each type of contract, the general auditing process is used for comparing and verifying each preset item in the standard template with the contract signed by adopting the standard template, and target marking is carried out on distinguishing points in the general auditing process; wherein in the independent auditing process, terms added in a standard template are subjected to cross auditing, similar contracts of the same kind are selected through classification marking, and the whole contract is traversed to select similar terms for comparison auditing.

The invention relates to a multi-factor dynamic correction investment benefit intelligent evaluation system, in particular to the field of investment evaluation, and aims to capture macroscopic event and project risk index impact in real time and dynamically generate factor impact coefficients through an event-driven factor correction module, so that the response speed and sensitivity of a model to project sudden change are remarkably improved, and the evaluation efficiency is improved. The self-adaptive weight optimization module utilizes a deep reinforcement learning framework to continuously optimize factor weight distribution based on revenue after risk adjustment in a rolling time window so as to realize self-adaptive adjustment of an investment strategy and optimization of a revenue-risk ratio, and the abnormal conduction early warning module constructs a dynamic directed graph and combines a graph neural network technology to realize early warning of the abnormal conduction. According to the method, abnormal factor conduction paths can be perspectively recognized, systematic engineering risks can be effectively warned, a strategy execution module fuses weight optimization results and risk warning signals, rebalance is executed under the condition that investment cost and risk control constraints are strictly met, and finally a project configuration scheme giving consideration to revenue, robustness and real-time performance is generated.

The invention relates to the technical field of medical instruments, in particular to an intravascular stent. According to the specific technical scheme, the intravascular stent comprises a stent skeleton, after the stent skeleton is subjected to surface treatment, at least one layer of film covering section is arranged outside the stent skeleton, and medicine release sections are arranged at the two ends of the stent skeleton. The problems that in the drug release process of an existing stent, the drug release rate is uneven, biocompatibility is poor, and thrombus and restenosis are caused by a metal stent are solved.

The invention relates to the technical field of optical storage charging stations, and discloses an optical storage charging station optimized operation method considering stepped carbon transaction and multi-agent collaboration, which comprises the steps of collecting carbon emission data of each agent in a charging station, considering interaction between the charging station and a power grid, and establishing a stepped carbon transaction mechanism; comprehensively considering the interest appeal and operation safety of each main body, and establishing a comprehensive optimization objective function by taking the maximization of the comprehensive benefit of the charging station as an objective; schedulable resources in the charging station are abstracted into intelligent agents, the intelligent agents cooperate with a carbon transaction mechanism, a multi-agent reinforcement learning framework is constructed to solve a comprehensive optimization objective function, and a multi-agent cooperative optimal operation scheduling strategy is obtained. According to the method, the nonlinear stepped carbon transaction mechanism close to the real carbon transaction market is established, the optimal scheduling strategy obtained based on the carbon transaction mechanism has perspectiveness, the benefits of the power grid and the optical storage charging station can be balanced, and the overall market competitiveness and risk resistance of a power system are effectively improved.

The invention provides a pre-disaster and post-disaster two-stage power grid toughness optimization method and system for extreme weather, and aims to improve the stability and self-healing capability of a power grid in an extreme weather event. The method comprises the following steps: firstly, identifying potential risk factors in advance by monitoring the operation state of a power distribution network and combining weather forecast information; in the pre-disaster stage, a mixed integer quadratic programming (MIQP) optimization method is adopted to carry out optimal distribution on power grid resources so as to minimize load loss and power interruption risks. When an extreme weather event occurs, the system dynamically adjusts resource allocation according to real-time data through a model predictive control (MPC) optimization method. The system integrates a monitoring and data collection module, a pre-disaster planning module, a post-disaster recovery module, a scheduling and execution module and a user interface module, and through cooperative work of the modules, reliability and stability of a power system under extreme weather conditions are ensured.

The invention relates to the technical field of new-generation information, and discloses a distribution-based network security automatic intelligent compliance system, which is characterized in that a distributed node deployment module adopts a distributed algorithm to distribute compliance detection tasks to each node; the data acquisition and processing module is used for automatically acquiring network logs, user behaviors and traffic data; the compliance rule base and updating module performs compliance judgment according to a multi-dimensional dynamic rule matching method; the intelligent compliance judgment module performs multi-level evaluation on network compliance through a distributed deep learning model; and the dynamic risk response module calculates a real-time response priority, triggers a corresponding response mechanism based on the priority, and supports automatic emergency strategy generation. According to the invention, efficient detection and dynamic response of network compliance are realized through an intelligent technology, and an efficient, intelligent and reliable solution is provided for network security management.

The invention discloses an agricultural insurance claim risk assessment method and system, and the method comprises the steps: obtaining agricultural insurance contract data, extracting key information, such as an insured crop type, a growth cycle and an insurance amount, carrying out the matching of a risk assessment result with the contract information through a natural language processing technology, and automatically calculating the claim risk of each contract; according to the level of the claim settlement risk, performing level-to-level management on the contract, and taking corresponding risk control measures for the high-risk contract, such as adjustment of underwriting conditions, improvement of insurance premium and starting of a manual checking process for the contract with the claim settlement risk exceeding a preset threshold value; obtaining claim settlement case data, adopting a decision tree algorithm, analyzing a relationship between case characteristics and claim settlement results, summarizing a claim settlement law, optimizing a risk assessment model and a claim settlement process, and improving risk prevention and control and claim settlement efficiency.

The utility model discloses a manual operating mechanism with protection for an isolating switch, which comprises the isolating switch, a fixed block is fixed on the right side of the isolating switch, a transmission rod is rotatably connected inside the fixed block, an operating rod is rotatably connected inside the fixed block and located on the right side of the transmission rod, and the operating rod is connected with the isolating switch. The operating rod and the transmission rod are in transmission connection through a gear, and the top of the transmission rod is fixedly connected with a transmission mechanism of the disconnecting switch. According to the manual operation mechanism with protection for the disconnecting switch, the outer protection mechanism is arranged, a handle is held, the handle is rotated, the handle drives a rotating plate to rotate, the rotating plate pulls a first pull rope, the first pull rope pulls a clamping block, then the handle is pulled, so that a protection cylinder is separated from a fixing ring, then an operation rod is rotated, and the operation rod drives a transmission rod to rotate through a gear; the transmission rod rotates to enable the transmission mechanism of the disconnecting switch to work, the disconnecting switch is turned on and turned off, and after operation is completed, the protective cylinder is inserted outside the fixing ring.