15371results about How to "Quick response" patented technology

The invention relates to the technical field of dispatching automation, and discloses a dispatching automation system application state management method which comprises the following steps: dynamic task state management: monitoring dynamic state indexes of various computing tasks in real time; according to different characteristics of task types, making differentiated priority management strategies; the task priority is dynamically adjusted, and it is ensured that key tasks are preferentially and efficiently executed; dynamic resource state management: continuously monitoring dynamic state indexes of various computing resources, wherein the dynamic state indexes comprise utilization rates and load conditions; dynamically adjusting a resource allocation scheme according to the resource state; dynamically optimizing resource allocation to ensure that the overall resource utilization rate of the system is continuously improved; and optimizing a self-adaptive scheduling strategy: introducing a self-adaptive scheduling algorithm based on reinforcement learning. According to the application state management method of the dispatching automation system, dynamic state management is adopted, a traditional static dispatching mode is abandoned, and the method can better adapt to a complex and changeable computing environment.

The invention provides an information network security self-defense method and system based on trusted computing, and the method comprises the steps: constructing a trusted computing platform which comprises the steps: deploying a trusted platform module TPM, building a trusted software stack TSS, and implementing a trusted network connection TNC; designing a self-defense architecture, wherein the self-defense architecture comprises deployment of a network layer, an application layer and a data layer defense system, integration of an intelligent sensing system and establishment of a central control and decision center; network traffic and user access behaviors are monitored, and embedded attack behaviors are preprocessed; performing anomaly detection on the network flow and the user access behavior by applying a machine learning algorithm; calculating a real-time risk score based on the anomaly detection result, and generating a security situation awareness report; and based on the security situation awareness report, defining a threat level and a response level, and executing automatic defense.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

The invention relates to a display screen backlight brightness adjusting method and system based on a Mini QLED technology. The method comprises the following steps: dividing a display image signal into a brightness signal, a chrominance signal and a saturation signal, and generating image feature data; performing sub-pixel-level boundary detection to obtain a plurality of initial backlight sub-regions, and calculating region brightness distribution data; executing region clustering operation and region merging to obtain a plurality of target backlight sub-regions and calculating backlight partition mapping data; carrying out nonlinear feature transformation, and calculating backlight unit control data of the Mini LED backlight unit; outputting the backlight compensation amount of each target backlight sub-region by adopting a deep Q network; and performing real-time feedback correction, calculating a deviation value between an actual display parameter and a target parameter of each target backlight sub-region, and outputting a backlight optimization control signal. According to the invention, the backlight brightness is accurately adjusted, and the consistency of the display effect is improved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses a wind driven generator state remote monitoring system based on the Internet of Things, and relates to the technical field of wind power generation equipment monitoring, and the system comprises a sensing module, a transmission module, a remote processing module and a user terminal module. The sensing module is used for accurately collecting various physical parameters such as vibration, temperature, pressure and rotating speed during operation of the wind driven generator by using various sensors, so that the operation condition of equipment is comprehensively mastered, and a data basis is provided for subsequent analysis and processing; according to the method, intelligent prediction of the future operation state of the wind driven generator is realized through deep learning and time sequence analysis, the fault type and time possibly occurring to the equipment can be predicted based on historical data and real-time data, powerful support is provided for operation and maintenance decision making, an operation and maintenance team can prepare in advance, and the operation and maintenance efficiency is improved. And targeted maintenance measures are taken, so that the maintenance cost is reduced, and the operation efficiency and the safety of the equipment are improved.

The invention relates to a financial question and answer method, system and device based on multi-agent interaction and a medium. The method comprises the steps that query understanding is conducted on user input through relevant entity word recall and similar question and answer pair recall, then knowledge recall and tool recall are conducted, and a prompt template of a planning agent is filled with recall results; the user input is answered according to the prompt template through the planning agent, the answer content is extracted to obtain an API execution link, and a directed acyclic graph is generated; calling a calling tool packaged by a related API through an execution agent, and executing a task corresponding to the directed acyclic graph in parallel; and generating an answer according to an output result of the execution agent through the summary agent, performing re-planning judgment through the reflection agent according to input and output information of the planning agent and the answer of the summary agent, and outputting a current answer. By adopting the method, more professional, accurate and efficient financial question and answer service can be provided for the user.

The invention discloses an estuary and gulf early warning management method and system based on digital twinning, and relates to the technical field of early warning management, and the method comprises the following steps: collecting multi-source data of an estuary and gulf region, and integrating the multi-source data into a unified digital twinning system; building a digital twinborn model of the estuary and gulf by utilizing the collected multi-source data, and analyzing dynamic trends of a physical water quality environment, a hydrological environment and an ecological environment of the estuary and gulf; the estuary bay environment is monitored in real time by deploying a sensor and monitoring equipment. By scientifically identifying the potential ecological problems of the ecological system in the estuary and gulf area, evaluating and predicting the performance of the ecological system, optimizing land-sea overall planning, river-sea linkage comprehensive treatment and preventive maintenance schemes, the ecological function of the gulf is improved, the decision risk is reduced, the response effect and management efficiency are improved, and the economic benefit is increased. And a new technical approach is explored and technical support is provided for sustainable high-quality development of an estuary and gulf ecological system.

The invention relates to the technical field of data management, in particular to an intelligent supervision method and system based on big data, and the method comprises the following steps: S1, multi-source data collection: collecting multi-source data in real time; s2, data preprocessing and spatio-temporal feature extraction: preprocessing the collected multi-source data, and extracting spatio-temporal features from the preprocessed multi-source data; s3, performing multi-dimensional risk assessment based on spatial-temporal characteristics: performing risk assessment on behaviors and states of the supervision objects by applying a multi-dimensional risk assessment algorithm; s4, dynamic early warning: in combination with a preset early warning rule, automatically triggering dynamic early warning, and providing a corresponding risk coping suggestion; and S5, supervision strategy optimization: optimizing and updating a supervision strategy through a self-learning mechanism based on a risk assessment result and feedback of dynamic early warning. According to the invention, the accuracy and response speed of risk identification are improved, so that the supervision work is more flexible and efficient.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention discloses a construction progress detection system and method based on three-dimensional model and field data fusion, and relates to the technical field of power grid construction engineering. The system comprises a data acquisition module, a data preprocessing module, a data matching module, a data fusion module, a progress detection module and a feedback module, the data matching module matches point cloud data with a panoramic image, a clonal selection algorithm is applied to optimize matching parameters, and on-site three-dimensional basic data is constructed; the data fusion module performs deep fusion on the BIM model and on-site three-dimensional basic data, and constructs a three-dimensional fusion model for construction progress detection by using a GeoBIM technology; and the progress detection module compares the real-time acquired panoramic image of the construction site with the three-dimensional fusion model frame by frame, and automatically detects the construction progress, so that efficient acquisition, accurate processing and real-time monitoring of construction site data are realized, and the efficiency and decision ability of construction management are remarkably improved through automatic progress detection and real-time feedback.

The invention discloses a Transform large model training method based on cloud edge collaboration. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, segmenting the preprocessed data set by using a data parallelism strategy, and performing distributed training by combining data parallelism and model parallelism technologies; s4, monitoring computing resources and network conditions of the edge equipment and the cloud server in real time, dynamically adjusting a data distribution strategy, and optimizing computing load distribution; s5, a multi-agent dominant actor-commentator algorithm is combined with a multi-arm tiger machine strategy, and the layered structure of the Transform model is adjusted in real time; s6, performing partial model reasoning tasks on the edge equipment in combination with the edge reasoning capability; s7, protecting data privacy by adopting a federal learning strategy and utilizing a differential privacy technology; and S8, performing hardware acceleration by utilizing heterogeneous computing resources. According to the method, a cloud edge cooperative computing and intelligent scheduling method is adopted, and large model training and reasoning with efficient privacy protection are realized.

The invention provides an unmanned ship autonomous navigation control method and system based on artificial intelligence, and relates to the technical field of control, and the method comprises the steps: collecting the real-time navigation data of an unmanned vehicle through a multi-source sensor, inputting the navigation environment feature data into a pre-trained reinforcement learning model, and enabling the reinforcement learning model to adopt a dual Q network structure, performing iterative optimization on the navigation strategy based on the environment reward function to generate a navigation strategy optimization parameter; constructing a multi-target route optimization model according to the navigation strategy optimization parameters, performing global optimization on a route by adopting an improved ant colony algorithm, and outputting optimal navigation path data; a hierarchical navigation control model is established according to the optimal navigation path data, the hierarchical navigation control model comprises a strategic layer, a tactical layer and an execution layer, a navigation control instruction is output through the hierarchical navigation control model, and autonomous navigation control of the unmanned vehicle is achieved.

The invention relates to the technical field of environmental protection, in particular to a big data monitoring and intelligent early warning platform and method for environmental protection. Comprising a multi-source data acquisition module, a data integration and preprocessing module, an environmental risk factor identification module, a real-time monitoring and anomaly detection module, a prediction model and early warning module, a self-adaptive early warning and feedback module and a decision support and visualization module. The data integration and preprocessing module preprocesses multi-source data, the environmental risk factor identification module identifies key risk factors by using an XGBoost algorithm, and carries out accurate prediction and early warning on risks through a random forest and long and short term memory network combination model, and an adaptive feedback mechanism dynamically adjusts an early warning threshold value, so that the early warning efficiency is improved. According to the invention, the intelligent level and the scientific level of environmental protection are obviously improved.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-load equipment energy consumption management method and device and computer equipment, and the method comprises the steps: obtaining electricity price data, and the energy consumption data, the operation state and the load demand of electric equipment, based on the electricity price data, the energy consumption data, the operation state and the load demand, the priorities of the electric equipment are classified; adopting a preset multi-task learning model to predict the load demand of the electric equipment in a future preset time period; generating a multi-target optimization model based on the priority, the load demand and the electricity price data of the electric equipment, and generating a dynamic power distribution scheme of the electric equipment based on the multi-target optimization model; and obtaining the load demand and the external environment of the electric equipment in real time, and adjusting the dynamic power distribution scheme of the electric equipment based on the load demand and the external environment. According to the method, the energy consumption optimization and the operation efficiency are always kept balanced, and the problem that the energy consumption optimization and the operation efficiency are not balanced is solved.

The invention, which relates to the technical field of data security monitoring, discloses a network data risk assessment system for a computer, comprising a data monitoring platform. The data monitoring platform is in communication connection with a data acquisition module, a data preprocessing module, an anomaly detection module, a threat evaluation module, an automatic alarm module, a response strategy generation module and a report generation module, and all the modules are in electric signal connection. According to the network data risk assessment system for the computer, the functions of anomaly detection, threat assessment, response strategy generation and automatic alarm are integrated, rapid detection and response to network threats are achieved, abnormal mode recognition is conducted through the machine learning technology, potential security threats are found, the recognition accuracy of abnormal behaviors is improved, and in addition, the network data risk assessment system for the computer has the advantages of being high in practicability and easy to popularize. And preset protection measures are automatically triggered, so that quick response is realized when high-risk threats are detected, and the damage of security incidents to the system is reduced.

The invention discloses an Internet of Things intelligent monitoring alarm method and system for water quality detection, and relates to the technical field of environmental protection monitoring, and the method comprises the steps: respectively sampling water quality electrochemical data for corresponding monitoring water area blocks based on each Internet of Things node; extracting electrochemical time sequence sensing characteristics respectively corresponding to each piece of water quality electrochemical data; extracting mode time sequence dependence features corresponding to the electrochemical time sequence sensing features based on a water quality anomaly identification model to predict water quality anomaly risks; identifying a corresponding water quality abnormity risk event type based on an abnormal event identification model; and according to the block identifier of each risk monitoring water area block and the corresponding water quality abnormity risk event type, executing water area water quality pollution alarm operation. Therefore, based on distributed water quality monitoring, intelligent risk identification and water quality pollution early warning operation of the Internet of Things nodes, water quality monitoring real-time performance, comprehensive coverage and intelligent management capability are remarkably improved.

The invention discloses a forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention discloses an intelligent finance and tax question answering method based on an artificial intelligence question answering system, and the method comprises the steps: S1, generating a structured semantic representation through a natural language processing technology, and generating an intention tag through a machine learning algorithm; s2, constructing and maintaining a knowledge graph containing finance and tax field related information, storing the information in the knowledge graph in a structured manner, and reasoning by using a logical reasoning algorithm to generate a preliminary answer; s3, designing a document retrieval and indexing mechanism, and verifying and supplementing the preliminary answer; s4, training the system based on the finance and tax related data and question and answer pairs, and continuously updating the model; s5, identifying common finance and taxation question modes by using a mode identification technology, and providing standardized answers; and S6, supporting multiple rounds of dialogues with the user, and generating more detailed and accurate answers by gradually clarifying the questions of the user. The method has the advantages of high accuracy, high response speed and capability of continuously learning and adapting to new problems.

The invention discloses an internet data security protection method and system based on an intelligent algorithm, and relates to the technical field of security protection. During system operation, user behavior data are collected in real time through log recording, a sensor and an API interface, the real-time operation behavior of a user is monitored, the interaction mode and transaction behavior of the user on a platform are analyzed, and the security of the user is improved. Calculating and acquiring a user behavior safety coefficient Yhxw, a data transmission safety coefficient Sjcs and a risk protection response coefficient Fxfh, identifying potential fraudulent behaviors, performing dynamic risk assessment by combining user historical behaviors, a transaction amount, a geographic position and data of payment equipment, performing threshold comparison to obtain an assessment scheme, and during login and transaction, combining multiple identity verification means to obtain a risk protection result; according to the method, user identity verification is carried out, security of login behaviors is dynamically monitored, network traffic and platform logs are analyzed, abnormal access and malicious requests or DDOS attacks are detected in real time, and abnormal behaviors or attacks are automatically identified and prevented.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention provides a method for generating an active immune defense strategy under cloud-side cooperation, and the method comprises the steps: obtaining the state information of edge nodes in a cloud-side cooperation environment, carrying out the analysis of the node resource occupancy rate, network flow, and connection number key indexes, obtaining a node feature vector, and uploading the node feature vector to a cloud end; on the edge nodes, carrying out propagation speed evaluation and influence range analysis on potential malicious nodes, judging the diffusion trend of a high-risk threat, if the threat is in a rapid diffusion situation, executing complete isolation and network cut-off operations, and reporting the disposal situation to the cloud; in a cloud-side collaborative environment, recording historical behaviors and threat response conditions of each node, and identifying potential novel variant threats by calculating variant threat similarity; and updating the active immune rule base and the threat feature classification system of the cloud according to the result of the threat response and the newly discovered threat features by adopting a rule base dynamic updating mechanism, and synchronizing the updating to the edge node.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention discloses a hanging basket transportation safety management method and system, and relates to the field of damage detection. Collecting weight data of the hanging basket; establishing a steel wire rope health state prediction model by using a multi-layer perceptron (MLP) algorithm, predicting the damage degree of the steel wire rope, and obtaining a health state prediction result of the steel wire rope; according to the health state prediction result of the steel wire rope, a bidirectional long-short-term memory neural network BiLSTM is used for prediction, and a damage trend prediction result of the steel wire rope in a future period of time is obtained; comparing the collected weight data of the hanging basket with a preset safety weight range to obtain a judgment result of whether the weight of the hanging basket exceeds the limit or not; according to the health state prediction result of the steel wire rope, the damage trend prediction result and the judgment result of whether the weight of the hanging basket exceeds the limit or not, a decision tree algorithm is adopted to generate a hanging basket operation control scheme; aiming at low safety of a hanging basket in the prior art, the safety of the hanging basket is improved.

The invention relates to the technical field of user behavior analysis, and discloses a method for analyzing abnormal user behaviors based on logs and flow data, and the method comprises the steps: collecting user behavior data, including historical user behavior data and real-time user behavior data; based on historical user behavior data, constructing a group behavior baseline and an individual behavior baseline, and obtaining key behavior characteristics of the user; the deviation degree DEV of the key behavior characteristics of the user deviating from the group behavior baseline and the matching degree SimScore of the real-time behavior sequence of the user and the personal behavior baseline are calculated; according to the deviation degree DEV and the matching degree SimScore, whether the user behavior is abnormal or not is judged, and the risk level of the abnormal behavior is determined; triggering corresponding abnormal alarm and response measures according to the risk level; according to the method, the abnormal behavior of the user can be identified more accurately by fusing the log and the flow data and combining group and individual behavior baselines, and the accuracy and efficiency of anomaly detection are improved.