1282 results about "Accident risk" patented technology

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention discloses a highway situation awareness and accident risk early warning method fusing multi-source data. The method comprises the following steps: S1, constructing multi-source data fusion and multi-scale traffic flow data; s2, studying and judging an accident influence factor mechanism, and excavating factors influencing the occurrence of a traffic accident; s3, early warning of accident risks is carried out, and the safety risk level of the road section is evaluated; and S4, researching the beforehand management and control system, and constructing a beforehand management and control strategy system based on deep mining analysis of traffic accidents and actual operation experience. According to the invention, on the basis of the high-speed portal data and the charging flow data, the detection data of the thunder-vision integrated roadside equipment are fused for fusion processing, so that fine traffic operation parameters such as traffic flow and vehicle speed before and after a historical accident are obtained; traffic flow characteristics causing accidents are mined, and a traffic accident risk dynamic early warning management and control model and a post-accident evaluation mechanism are established based on the traffic flow characteristics, so that a management department is helped to recognize potential risks in advance.

The invention discloses an intelligent road alarm early warning patrol rescue linkage system and a method thereof, and relates to the technical field of road safety. An intelligent hectometer board, an unmanned aerial vehicle cluster and a mobile terminal are used for collecting accident alarm information and road multi-source data; performing accident target detection classification and abnormal condition classification based on the preprocessed data by utilizing edge calculation, and sending to the cloud; importing real-time road multi-source data into a digital twinborn model to obtain traffic situation spatial-temporal characteristics, and combining accident target detection classification and abnormal condition classification analysis to obtain an accident risk thermodynamic diagram for emergency decision making; and according to the accident risk thermodynamic diagram, generating alarm early warning information of different accident types and different risk levels, according to an emergency decision, generating a linkage mechanism of different departments, and sending the linkage mechanism to different department systems to realize automation of a disposal process. According to the invention, a full-chain linkage system of perception-analysis-early warning-linkage is constructed, rapid response and rescue are realized, the management cost is reduced, and the traffic efficiency is optimized.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses an intelligent traffic data processing method and system based on a 5G communication system, and relates to the technical field of traffic data processing, and the method comprises the steps: collecting data based on a 5G communication network; preprocessing the collected traffic data through an edge computing node; real-time prediction and risk assessment are carried out on traffic flow based on the cloud platform; sending real-time traffic information and optimization suggestions to a traffic management center and related vehicles; when an emergency occurs, a rapid response mechanism is triggered through a 5G network, and traffic resources are scheduled. The system collects, processes and analyzes traffic data in real time through the characteristics of high bandwidth and low delay of the 5G communication network and the distributed processing capability of edge calculation, achieves the real-time prediction and management of traffic flow and accident risks, and can improve the road utilization efficiency, reduce traffic accidents and improve the traffic safety through the intelligent decision and optimization control strategy. And the emergency response capability is improved, so that a more intelligent solution is provided for urban traffic management.

The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

The invention discloses an intelligent safety inspection system for a construction site, and relates to the technical field of construction inspection, multiple data sources of the construction site are obtained through a data acquisition module, and a data fusion and processing module dynamically optimizes heterogeneous data through time synchronization, format standardization and noise filtering in combination with a self-adaptive weighted fusion algorithm, so that the construction site safety is improved. The intelligent analysis and early warning module evaluates the data difference degree based on machine learning, adopts clustering analysis to mine an abnormal mode for high-difference data, carries out safety risk identification on low-difference data, and dynamically adjusts an early warning strategy. The terminal interaction module pushes the inspection result and the early warning information to the mobile terminal APP, the monitoring center display screen and the voice broadcast device, so that a manager can remotely check the construction condition and quickly make a decision, the intelligent level of construction site safety inspection can be remarkably improved, efficient and accurate potential safety hazard recognition and dynamic early warning are achieved, and the safety inspection efficiency is improved. And the safety accident risk is effectively reduced.

The invention relates to the field of tunnel and bridge monitoring, and particularly discloses a tunnel and bridge monitoring management system and method.The method comprises the steps that firstly, tunnel and bridge road surface monitoring images collected by cameras and road surface stress data, collected by fiber grating sensors, at multiple time points are obtained, and then the deep learning technology is utilized; and feature extraction and correlation analysis are carried out on the two images, and finally a classification result is obtained through a classifier so as to judge whether tunnel and bridge pavement early warning is sent out, so that abnormal conditions and potential problems of the tunnel and bridge pavement can be found in time, immediate response is realized, early warning and preventive maintenance are realized, and the accident risk and the maintenance cost are further reduced.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent road cone traffic early warning system. The problems that in the prior art, an intelligent road cone is single in function, and warning strategies cannot be intelligently adjusted according to real-time road conditions and construction environment changes are solved. The intelligent road cone is arranged and used for being deployed on a road construction or accident site to warn vehicles, collecting road environment information through a sensor and conducting risk assessment and early warning on the vehicles according to the detected vehicle information; the control center is used for integrating the monitoring information of the intelligent road cones and dynamically adjusting a placement scheme and an early warning strategy of the intelligent road cones; and the user terminal is used for receiving the early warning information from the intelligent road cone and the control center and sending an alarm to a user. Through cooperation of the intelligent road cone, the control center and the user terminal, effective safety early warning and guidance are ensured to be performed on vehicles passing through the construction area during construction, accident risks are reduced, and traffic management efficiency is improved.

The invention discloses an expressway dynamic speed limit method based on multi-source data, and belongs to the technical field of expressway speed limit, and the method comprises the steps: obtaining accident data, constructing an accident risk degree prediction model, and generating an accident risk degree trend chart; acquiring real-time traffic flow data, constructing a traffic flow prediction model, and generating a traffic flow tendency chart; obtaining meteorological data, and generating a local meteorological state distribution diagram; performing space alignment and data fusion on the traffic flow tendency chart and the local meteorological state distribution diagram to generate a road section comprehensive risk index; performing time alignment on the accident risk degree trend chart and the comprehensive risk degree index of the road section to generate a real-time risk degree score of the current road section; smoothly adjusting the speed limit value by adopting a linear interpolation algorithm on the basis of the real-time risk degree score in combination with a preset speed limit adjustment rule; and comparing the current vehicle speed with the adjusted speed limit value, and early warning over-speed. According to the invention, accurate speed limit control based on multi-dimensional data is realized, and the road safety is effectively improved.

The invention discloses a tunnel hydrogen energy vehicle leakage and explosion risk prediction and automatic ventilation emergency disposal method, and belongs to the technical field of hydrogen leakage accident risk prediction and emergency disposal. According to the method, the potential leakage point of each component is determined by obtaining hydrogen-related component information of the hydrogen fuel cell vehicle. An accident tree is used to identify a root cause that may cause hydrogen leakage in the system, and then different accident scenarios are defined. And according to the leakage scenes of different hydrogen-related parts, determining the leakage frequency and the complete fracture frequency, and determining the disaster category. And finally, according to the leakage probability, the ignition probability and the consequences of different accident situations, calculating the comprehensive probability of disaster occurrence. And in combination with a hydrogen concentration sensor array arranged in the tunnel, the longitudinal ventilation air speed and the transverse exhaust air rate are automatically analyzed and adjusted, and the optimal economical, safe and efficient combined ventilation array is obtained according to risk indexes. The method can effectively predict the disaster-pregnant risk of the hydrogen storage vehicle in different environments, provides an economic and safe emergency disposal scheme, and can provide a theoretical basis for the safety technology development of a hydrogen storage system.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention belongs to the field of automobile evaluation, and provides an automobile live risk evaluation system and method based on a large model, and the method comprises the steps: receiving voice input information of an evaluator in a vehicle on-site inspection process; extracting component state information of the target vehicle based on the structured check item text; acquiring historical maintenance record data of the target vehicle, and performing association fusion on the historical maintenance record data and the component state information; based on a large language model, performing joint reasoning on a currently input inspection label and the historical maintenance record, constructing a causal chain related to an accident risk, and generating at least one subsequent to-be-inspected item; generating voice guide content and feeding back the voice guide content to the evaluator so as to prompt the evaluator to continue to check the target item of the target vehicle; gradually perfecting the risk check path of the target vehicle until the risk chain closed loop is completed; and calling an estimation formula to calculate a residual estimation value of the target vehicle, and outputting a structured risk assessment report.

The invention relates to a method (50) and system (10) for assessing situations presenting an accidentology risk to a vehicle driving on a road network in a determined environment, the method comprising: - monitoring (51) the progress of the vehicle on the road network and in the environment into which the road network is integrated and through which the vehicle is moving; - detecting (52) a risky situation presenting a plurality of accidentology risk factors (13); -warning (53) the driver when a risky situation is recognized; - analysing (54) data recorded before detection (52) of the risky situation, in order to determine an origin of the risky situation; and - producing (55) driving advice depending on determined contextual parameters (16), the driving advice identifying the detected risky situation and recommending a driving behaviour in order to reduce the risks run by the vehicle when confronting the situation. The invention also relates to a vehicle in which the system (10) is embedded, and executing the method (50).

The invention discloses a wind generating set cabin crane safety control system, and relates to the technical field of wind power generation, the wind generating set cabin crane safety control system comprises a monitoring center, the monitoring center is in communication connection with a monitoring module, a data processing module, a judgment module, a crane motor control module and an alarm and prompt module, all the modules are in electric signal connection, and the monitoring module is in communication connection with the data processing module. The monitoring module is used for monitoring environment data and working state data of the crane in real time, and the environment data comprises wind speed, wind direction, temperature and humidity; the working state information of the crane comprises the speed, the lifting rope position, the load weight and the like. By integrating various sensors and a real-time monitoring technology, the operation state and the surrounding environment of the crane are accurately sensed, and when abnormal conditions are detected, the system immediately triggers an early warning or emergency shutdown mechanism, so that accidents are effectively avoided, and the safety accident risk caused by man-made misjudgment or equipment failure is reduced.

A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.

The invention discloses an electrical energy monitoring method and system, and particularly relates to the technical field of energy monitoring. The method comprises the following steps: monitoring target parameters of power equipment in real time, extracting load volatility characteristics and sampling interval deviation characteristics, calculating a synchronization accuracy value of equipment data in each time period, comparing and analyzing with a gradient standard threshold value, dividing synchronization accuracy into different grades, and implementing corresponding processing measures according to different conditions, so that the accuracy of the power equipment is improved. For accuracy synchronization, the system keeps normal operation; for inaccurate synchronization, the system triggers early warning and starts a self-diagnosis and calibration program; for incompletely accurate synchronization, fault diagnosis errors caused by data asynchronization are effectively avoided by predicting the severity of synchronization errors, adjusting an alarm threshold value in advance and taking corresponding maintenance measures, the safety of power equipment and the reliability of a monitoring system are improved, and the accident risk is reduced.

The invention provides a dam life dynamic probability prediction method and device. According to the dam life dynamic probability prediction method provided by the embodiment of the invention, the dynamic probability prediction of the residual life of the dam can be realized, the early warning timeliness and the scientificity of the maintenance decision are improved, and the full-life-cycle maintenance cost and the accident risk are effectively reduced.

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention provides an engineering safety prediction method and system based on the Internet of Things. An Internet of Things sensor and a camera are used for collecting multi-source data of an engineering structure, an environment, equipment and the like, and after cleaning and feature extraction, a multi-modal fusion prediction model combining a convolutional neural network and a long-short-term memory network is constructed. Through a dynamic weighted fusion method, image and time sequence feature fusion weights are adjusted according to engineering stages and environmental conditions, and an attention mechanism is used to reinforce a fusion effect. After a data set training model is divided, real-time data can be analyzed to predict security risks. Once the risk is found, the system can give an early warning in time and display response suggestions on the management platform. The method makes up the defects of a traditional monitoring mode, improves the accuracy and timeliness of prediction, provides an efficient solution for engineering safety management, can effectively reduce the accident risk, and has important application value in various engineering fields.

The invention discloses a road traffic information monitoring system and method, and the method comprises the steps: deploying a video monitor, a sensor, an unmanned plane and a satellite device to collect real-time road traffic data, and protecting the data safety through encryption transmission and multilayer identity authentication; the flow is monitored in real time by combining image recognition and sensor data, congestion is analyzed, traffic violation is recognized, and an alarm is given; future flow is predicted through machine learning and big data analysis, and the signal lamp period is intelligently adjusted; through combination of real-time and historical data, accident risk points are identified and early warning is carried out, and early warning is automatically issued and adjustment and control are carried out for severe weather. The AI technology automatically identifies accidents and notifies a management center, dispatches emergency resources, optimizes signal lamps and lane traffic, and dynamically adjusts surrounding flow; according to the road traffic information monitoring system and method, an automatic emergency processing system is established, accidents are automatically detected and alarms are triggered under the conditions of traffic accidents and emergencies, and emergency resources are quickly dispatched.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

The invention relates to a polar region ship navigation risk management and control method and system, and the method comprises the following steps: obtaining polar region natural environment original data, predicting missing data points through a random forest, and obtaining a high-density environment risk data sample in a space-time dimension; a Bayesian network model of a polar region ship navigation process is constructed based on accident risk factors, model parameters are calculated based on environment observation data, and navigation safety assessment in a multi-risk mode is realized by constructing a risk assessment model; and for the most important risk type after navigation safety assessment, comprehensive risk management and control of polar region ship navigation are realized in combination with ship seaworthiness, ship navigation window period, air route and risk management and control measures. Compared with the prior art, the method has the advantages that environment and climate real-time characteristics are fully considered, risk assessment and comprehensive risk management and control are realized through subjective and objective combination, and the like.

The invention relates to the technical field of chemical safety and emergency management, and discloses an emergency team informatization management system and method.The emergency team informatization management method comprises the steps that a multi-level Bayesian network model is constructed, and chemical accident evolution chain event node condition dependence is shown; establishing an uncertainty propagation analysis model, and quantifying an accident risk transmission rule; multi-dimensional node influence evaluation is carried out, and structural importance, propagation characteristics and intervention feasibility are comprehensively considered; constructing an intervention scheme by combining resource constraints according to an optimal intervention point identified by evaluation; through intelligent simulation evaluation, the intervention effect is optimized, and an emergency team iteration strategy is assisted; according to the method, systematic and dynamic analysis of complex chemical accidents is realized, key intervention nodes of'taking small as big 'can be found under the condition of limited resources, and the emergency disposal efficiency and the resource utilization rate are improved.

The invention provides a chemical ship cabin intelligent coating leakage positioning and early warning control method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting data through a multi-class sensor array, constructing a parameter distribution map, extracting features through a convolutional neural network, carrying out the time sequence analysis through a long and short term memory network, and carrying out the early warning. The variational auto-encoder constructs a leakage feature fingerprint database, collects data in real time, then carries out feature extraction and fusion processing, matches the data with the fingerprint database, determines a leakage position and a leakage rate, divides a monitoring area, and executes prevention and control. The leakage monitoring precision and the response speed can be improved, and the chemical accident risk is reduced.

The invention provides a maritime traffic accident severity prediction method based on feature engineering, and the method comprises the steps: constructing a maritime accident risk influence factor data set according to risk influence factors and accident severity in historical data, and carrying out the preprocessing of the data; coupling the influence factors through a feature fusion algorithm, and mining association rules; carrying out data balance analysis by using an SVM-SMOTE oversampling method, and balancing the sample proportion of serious accidents and non-serious accidents; the original influence factors are trained by using multiple machine learning models, and key influence factor subsets are screened out by calculating the importance degrees of the influence factors; and S4, evaluating an optimal model in the machine learning model through a UAR evaluation index, and predicting the severity of the marine traffic accident by using the optimal model and combining the key influence factor subset in the S4. By reasonably matching the combination of the prediction model and the oversampling method, the model performance is remarkably improved, and a comprehensive and profound insight is provided for predicting the severity of the marine accident.

The invention relates to the technical field of hoisting construction dynamic safety assessment, and particularly discloses a hoisting construction dynamic safety assessment method, which comprises the following steps of: acquiring operation parameters of a hoisting system in real time through a multi-modal sensor array, and generating a hoisting dynamic parameter set; the lifting dynamic parameter set comprises pressure distribution data of each lifting point, lifting appliance attitude angle data and load space position data; based on the hoisting dynamic parameter set, force system dynamic balance analysis is executed, and the force system unbalance degree of the crane is obtained through analysis; and when the force system unbalance degree of the crane is larger than a set threshold value, an unbalance active correction mechanism is triggered, and unbalance correction is conducted on the crane through adjustment of the electro-hydraulic proportional valve. And the safety and the efficiency of hoisting operation can be obviously improved. The automatic unbalance correction mechanism not only reduces the accident risk, but also improves the operation flexibility and response speed, and provides powerful technical support for modern hoisting operation.