2472results about How to "Avoid accidents" patented technology

The invention provides a buried water delivery pressure steel pipe disease inspection method and system, and the method comprises the following steps: carrying out the time sequence alignment and quality evaluation of multi-source sensor data, such as ultrasonic waves, videos and positions, carrying out the feature extraction and optimization through adaptive filtering and frequency smoothing technologies, and carrying out the partitioning compression storage based on entropy and variability; carrying out decompression and feature reconstruction on the compressed data, carrying out state evolution prediction based on multi-scale state trajectory analysis, and carrying out anomaly detection and classification in combination with a dynamic reference model; health feature vectors are constructed, health state distribution is obtained through nonlinear feature fusion and probability density analysis, deterioration trend prediction is carried out based on environment coupling analysis, risk rating and early warning are carried out by adopting a multi-level dynamic threshold value, and finally a maintenance priority sequence and a maintenance scheme are generated. According to the invention, pipeline diseases can be accurately identified, and detection efficiency and reliability are improved.

The invention provides a bridge structure fatigue crack propagation prediction method and system, and relates to the technical field of artificial intelligence. According to the method, structural parameter information of a bridge is collected, a three-dimensional model of the bridge structure is established through finite element analysis, and a fatigue crack propagation prediction model is established based on the fracture mechanics theory and a fatigue damage accumulation model; performing expansion simulation on preset cracks in the three-dimensional model of the bridge structure, calculating a crack expansion rate and predicting a crack expansion path, correcting a crack expansion simulation result in combination with an actual working environment and a load condition of the bridge structure, and calculating a crack expansion simulation result according to the corrected crack expansion simulation result. And predicting crack propagation information of the bridge structure in the preset time period. The method can accurately predict the expansion path, rate and future state of the crack under a complex working condition, thereby providing a scientific basis for maintenance and repair of the bridge, realizing real-time feedback and dynamic adjustment, ensuring high efficiency and accuracy of a prediction result, and providing a scientific basis for maintenance management of the bridge.

The invention relates to the technical field of tin smelting, and discloses an AI intelligent tin smelting control system and method, and the system comprises a data collection and preprocessing module, an intelligent decision and control module, and a real-time monitoring and early warning module. The method comprises the following steps: collecting process data in a tin smelting production process in real time, and cleaning the collected process data; extracting and selecting characteristics of which the influence on the smelting quality and efficiency exceeds a preset threshold value, and carrying out standardization processing on the data; receiving the standardized process data, training the model, processing the data monitored in real time, and outputting the adjusted process parameters; various key parameters and data in the production process are displayed in real time, the real-time data are analyzed, abnormal conditions are detected, early warning is carried out on faults, and response measures are automatically triggered. Intelligent and automatic tin smelting is achieved, and the efficiency, quality and safety of the smelting process can be effectively improved.

The invention belongs to the technical field of safety monitoring of subway tunnels, and particularly relates to a subway tunnel water leakage monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-source sensor time sequence data in a subway tunnel, carrying out the feature extraction, and extracting feature parameters related to water leakage; carrying out fusion analysis on the characteristic parameters through a data fusion algorithm, and evaluating the position and severity of water leakage, including extracting time-space correlation characteristics of data by using a dynamic time warping method; after feature extraction is performed on the data set and the space-time correlation features by using a convolutional neural network and a recurrent neural network, feature fusion is performed; and performing anomaly detection and classification on the fused high-level features by using a neural network method to obtain the position and severity of the leakage water. The advanced data fusion technology and the feature extraction algorithm are adopted, various collected data can be analyzed in real time, abnormal conditions can be found in time, and early warning is carried out.

The invention relates to the technical field of Internet of Things monitoring, and particularly discloses a monitoring method and system based on an Internet of Things architecture, and the method comprises the steps: collecting environment data in real time through deploying a plurality of temperature and humidity sensors in a monitoring region, and transmitting the data to a central processing unit or a cloud end through a wireless network for storage and preliminary processing; the system establishes a prediction model by fitting historical data, evaluates the accuracy of sensor data, and calibrates or replaces an abnormal sensor to ensure the reliability of the data; the system comprehensively analyzes the calibrated sensor data, calculates a temperature abnormal coefficient and a humidity abnormal coefficient, and evaluates the overall environment condition of the monitoring area; when an abnormal condition is detected, the system immediately starts an early warning mechanism, sends alarm information to a monitoring center or related personnel, and records the detailed condition of the abnormal event for subsequent analysis and processing.

The invention discloses a casting product appearance defect detection method and system based on machine vision, and particularly relates to the technical field of casting product defect detection. According to the method, casting materials of a production line are divided into different categories, the casting materials with abnormal absorptivity are identified, terahertz wave scanning is carried out on the casting materials, scattering images are obtained, and the stability of scattering point density distribution is evaluated; scanning by using a terahertz time-domain spectrometer, analyzing an amplitude fluctuation state of a time-domain signal and evaluating a waveform distortion degree; after comprehensive analysis, the quality of images captured by the imaging system is evaluated, the images are divided into different grades, defect detection is directly carried out on high-quality images, the quality images are centered, the accuracy of the images is analyzed, and the transmitting power and the scanning speed of terahertz imaging equipment are dynamically adjusted, so that the imaging quality is improved, and the accuracy and the efficiency of detection are remarkably improved; and the quality and safety of casting products are ensured, and the product quality problem and potential equipment faults caused by untimely defect detection are effectively avoided.

The invention provides a real-time motion planning system based on dynamic environment perception, and aims to solve the limitation of the prior art in a dynamic environment. According to the system, environment data are captured and processed in real time through a highly integrated sensor fusion technology, and an accurate environment model is generated. And in combination with a deep learning algorithm, the system can predict environment change and realize quick response. In addition, the system adopts an advanced dynamic path adjustment algorithm to ensure path optimization and safe navigation of the machine in a complex environment. Particularly, a multi-machine collaborative planning mechanism is introduced, and efficient collaboration and avoidance among machine groups are realized through a distributed consistency algorithm and multi-agent reinforcement learning. On the whole, the system obviously improves the application potential of the machine in a variable environment, enhances the adaptability and stability of the machine, and opens up a new possibility for the development of the intelligent machine equipment technology.

The invention discloses an in-tunnel construction vehicle meeting scheduling system based on positioning equipment. The system comprises a basic service module which is used for storing basic data for business service analysis in an acquisition and collection mode; the business service module is used for carrying out detailed analysis of the corresponding service through the data acquired by the basic service, and providing an analysis result to the display end for display; and the display end module is used for displaying the analysis result of the business service module, wherein the display modes comprise a mobile phone APP, a webpage large screen, an LED panel, a tunnel loudspeaker and a tunnel traffic light. The system carries out weight grading on vehicle and personnel positioning labels by standardizing a tunnel construction process, and integrates data of avoidance points of a transverse tunnel. And in combination with an intelligent technology, efficient vehicle meeting scheduling, intelligent construction path planning and avoidance point path planning of the construction area are realized.

The invention relates to the technical field of shipping management, in particular to a shipping risk management method and system based on big data, and the method comprises the steps: obtaining ships in a target sea area, collecting the driving route and operation data of each target ship, and obtaining the operation abnormality evaluation index of each target ship through processing; obtaining the navigation speed of each target ship, dividing the driving route of each target ship, collecting the environment data of each navigation road section of each target ship, and obtaining the environment abnormal characteristic value of each target ship through processing; and carrying out risk assessment on the operation of each target ship according to the environment abnormity characteristic value and the operation abnormity assessment index of each target ship, obtaining an assessment result and carrying out feedback. According to the big data-based shipping risk management method and system provided by the invention, the environmental factors and the ship operation condition are comprehensively considered, and the composite factors possibly causing the risk can be more accurately identified, so that the accuracy of risk assessment is improved.

According to the dynamic stress analysis system and method for the fabricated beam string structure, the strain, vibration, displacement, temperature, soil pressure and construction load of the structure are comprehensively monitored by integrating various sensors, the safety of data is ensured through encryption transmission, sensitive data are protected by adopting a role-based access control mechanism, and the reliability of the system is improved. In the data processing process, a stress prediction model is established by cleaning, calibrating and synchronizing data, extracting key vibration characteristics, considering temperature influence and using finite element analysis to simulate structural response, and is used for identifying a potential damage area and predicting the residual service life of the structure, so that maintenance measures can be taken in time, the safety of the structure is ensured, and the service life of the structure is prolonged. And the health management efficiency and accuracy of the fabricated beam string structure are improved.

The invention discloses a sensor anomaly detection method based on a conditional diffusion probability model and a related system, the system comprises a data acquisition processing unit, a detection model unit and an anomaly detection unit, anomaly is detected by using the conditional probability diffusion model, noise is predicted by adding and removing noise and using a U-Net model, and data is reconstructed. Completing the training of a detection model, and obtaining a trained detection model; and taking the distance between the to-be-detected data and the noise reconstruction data obtained by the trained detection model corresponding to the to-be-detected data as an abnormal score, and if the abnormal score exceeds a threshold value, determining that the data is abnormal. According to the invention, the monitoring accuracy is improved, the false alarm is reduced, the anti-noise capability is enhanced, the noise is effectively identified, and the pertinence is high.

According to the 20KV electric brake switch multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition, all internal faults of a 20KV generator brake switch are monitored online in real time through the voiceprint recognition technology, and the industry blank of real-time online monitoring of the 20KV generator brake switch is filled up; a voiceprint collector, a temperature sensor, a vibration sensor, an SF6 gas monitoring device, a position node relay and the like are installed in a proper area, and multi-modal data are integrated into the same system, so that comprehensive analysis of the working condition of the 20KV generator brake switch is realized; and deep learning and fault judgment process logic design are carried out on each data parameter, so that the multi-mode working condition abnormity comprehensive diagnosis of the 20KV generator brake switch is realized, and the timeliness of working condition abnormity or fault judgment of the 20KV generator brake switch equipment is greatly improved.

The invention relates to the technical field of fuel gas safety, and discloses an intelligent fuel gas safety valve and a method for intelligently opening and closing the safety valve, and the intelligent fuel gas safety valve comprises a sensor unit which is configured to be installed near a fuel gas pipeline or fuel gas equipment; the control unit is in communication connection with the sensor unit; the execution unit is in communication connection with the control unit; the communication unit is in communication connection with the control unit; and the standby power supply is used for providing emergency power for the intelligent gas safety valve when the main power supply fails or is powered off. The concentration, the pressure, the temperature and the flow of the fuel gas are monitored in real time through the sensor unit, the system can quickly send out an instruction for closing the valve when detecting abnormal conditions (such as gas leakage and too high or too low pressure), and accidents such as fire disasters or explosions caused by fuel gas leakage are effectively prevented. The system monitors a plurality of key parameters of the gas system through a plurality of sensors in a combined mode, and a multi-level safety guarantee mechanism is formed.

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 provides a multi-mode anomaly detection system based on digital twinning, and relates to the technical field of information technologies. Comprising seven main subsystems including a digital twin modeling system, a real-time data acquisition and sensor network, a multi-modal data fusion and processing system, a real-time data analysis and anomaly detection algorithm, a decision support and feedback system, a virtual simulation and visualization system and a safety and compliance management system. The multi-mode anomaly detection system based on digital twinning further comprises six additional subsystems, namely an automatic response and adjustment system, an extensible model updating and management system, an intelligent prediction and early warning system, a deep learning and mode recognition system, a real-time optimization and resource allocation system and an intelligent feedback and user interface optimization system. Through application of a real-time data analysis and anomaly detection algorithm and in combination with a decision support and feedback system, the system can quickly generate decision suggestions or responses, and a decision result is visually displayed through a virtual simulation and visualization system.

The invention relates to the technical field of microcosmic damage mechanism recognition of refractory materials, and discloses a refractory material damage mode recognition method based on an acoustic emission technology, which comprises the following steps of: acquiring acoustic emission signals of a refractory material under the action of static load through a three-point bending experiment, determining the number and characteristic parameters of data set categories of the acoustic emission signals, and determining the number of the data set categories of the acoustic emission signals; and performing clustering analysis on the acoustic emission signals by adopting a K-means clustering algorithm, dividing the acoustic emission signals into three classes, analyzing by combining an acoustic emission parameter association diagram, determining acoustic emission characteristics corresponding to different damage mechanisms, and determining a damage mode corresponding to each class of signals. According to the method for identifying the damage mode of the refractory material based on the acoustic emission technology, a method combining clustering analysis and acoustic emission parameter association diagram analysis is adopted, accurate judgment on the microscopic damage process of the refractory material can be effectively achieved, and theoretical support is provided for further research on mechanical behaviors and the microscopic damage mechanism of the refractory material.

The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.

The invention discloses a community fuel gas system management method and system based on fuel gas safety, and relates to the technical field of fuel gas system management.The method comprises the following steps that basic data of an old community fuel gas system are collected, field inspection is conducted on fuel gas pipelines, valves and combustion appliances in a community, and current situation data are recorded; risk assessment is carried out based on the on-site inspection result and basic data of the gas system, and potential safety hazards and weak links are identified; and potential safety hazards and weak links are identified according to risk assessment. Sensors are installed on gas pipelines and key equipment, key data of pressure, flow and leakage detection are collected and processed in real time, early warning of different levels is triggered once abnormal data are monitored, a corresponding early warning mechanism is generated, and therefore the safety monitoring level of a gas system is improved, and the safety of the gas system is guaranteed. Potential safety hazards can be found and early warned in time, and accidents are effectively avoided.

The invention provides a crane hoisting monitoring method and system based on multi-normal-form visual fusion and a medium, and relates to the technical field of computer vision. Acquiring an image of the target acquisition area through an image acquisition device, and obtaining a real-time image acquisition video stream; preprocessing the real-time image acquisition video stream to obtain real-time image frame data; performing real-time image frame data identification based on the visual detection model to generate detection information; inputting the detection information into a visual tracking module, and tracking the identification object to obtain tracking information; acquiring a fixed dangerous area or a dynamic dangerous area based on the detection information and the tracking information; and identifying a dangerous condition in the video stream according to the dynamic dangerous area or the fixed dangerous area, obtaining dangerous condition accumulated data and triggering an alarm. The technical problems that in the prior art, the monitoring means is single, and dynamic dangers are difficult to accurately recognize are solved, and the technical effect of accurately recognizing potential dangers of hoisting operation in real time is achieved.

The invention provides a deep well roadway surrounding rock area stress optimization control method, and belongs to the technical field of surrounding rock area stress optimization, and the method comprises the following steps: obtaining key data of deep well roadway surrounding rock; identifying a potential stress concentration area; performing grading evaluation on the identified potential stress concentration area, and determining the grade of the surrounding rock according to an evaluation result; according to the grade of the surrounding rock, a corresponding supporting strategy is formulated; the advanced presplitting blasting technology is applied to all grades of surrounding rocks; arranging a plurality of sensor nodes in the stress concentration area; the sensor nodes send monitoring data to the cloud processing center in a wireless mode, and the cloud processing center predicts the stability of surrounding rock and sends a prediction result to the expert terminal; and an expert formulates a new support strategy or adjusts an existing support strategy according to the prediction result of the expert terminal. According to the stress optimization control method for the surrounding rock area of the deep well roadway, the stability of the surrounding rock is improved through comprehensive technical means, and safe and efficient mining of a mine is guaranteed.

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.

The invention discloses a hoisting tool for a steam turbine rotor, and belongs to the technical field of hoisting equipment, the hoisting tool for the steam turbine rotor comprises a cross beam and two groups of hoisting frames, the two groups of hoisting frames are symmetrically arranged on two sides of the cross beam, the hoisting frames are slidably connected to the cross beam, and hoisting parts are connected below the hoisting frames. Longitudinal beams are symmetrically and fixedly connected to the two ends of the cross beam, adjusting components are slidably connected to the tops of the longitudinal beams, auxiliary mooring ropes are hung at the bottoms of the longitudinal beams, and traction ropes are arranged at the tops of the longitudinal beams. The lifting belt is used for lifting the steam turbine rotor, compared with cable lifting, the lifting belt is more stable, stress of the lifting belt can be adjusted, accidents caused by uneven stress are avoided, meanwhile, the cable is assisted to lift the steam turbine rotor together, it is guaranteed that the steam turbine rotor is stably lifted, the steam turbine rotor is prevented from falling off when the lifting belt breaks down, and serious accidents are avoided.

The invention relates to the technical field of data processing, in particular to a hoisting state intelligent sensing and early warning system, method and equipment, and the system comprises an environment interference early warning module, a hoisting equipment abnormity early warning module, a hoisting operation abnormity early warning module and a hoisting system database, according to the intelligent sensing and early warning system, method and equipment for the hoisting state, the influence of environment interference, hoisting equipment abnormity and hoisting state abnormity on hoisting operation is comprehensively analyzed, and a comprehensive hoisting operation abnormity evaluation system is formed; the evaluation system not only can monitor the environmental interference degree of a hoisting operation site in real time, but also can perform analysis and early warning on hoisting equipment abnormity and hoisting state abnormity, and through comprehensive evaluation and early warning on hoisting operation, the emergency response capability of hoisting operation can be improved, and the safety of hoisting operation is improved. And therefore, the hoisting operation can still be kept stable and safe under the emergency condition.

The invention, which relates to the technical field of building construction monitoring, discloses a tall template construction safety monitoring system comprising a template data acquisition module, a dynamic load analysis module, an interpolation module, a time window analysis module, an adjacent region comprehensive analysis module and an early warning module. The template data acquisition module collects stress data of a basic area in real time through a sensor network, the dynamic load analysis module dynamically adjusts division of the stress area according to the stress data, the interpolation module performs smoothing processing on the adjusted data, the time window analysis module calculates area stress fluctuation, and the time window analysis module analyzes the area stress fluctuation. The adjacent region comprehensive analysis module evaluates the stress influence of adjacent regions, the early warning module generates risk levels through fuzzy logic, and the construction report generation module is used for recording and generating a safety report; according to the invention, a local dangerous area can be better controlled and accurately positioned, and large-range blind troubleshooting is avoided, so that the safety management efficiency of a construction site is improved.

The invention discloses a hydropower station operation risk early warning system and method based on digital twinborn and AI image recognition. The system comprises a data acquisition module, a digital twinborn model construction module, an AI image recognition module, an early warning management module and an effect evaluation and optimization module. According to the system and the method, comprehensive monitoring of the working environment of the hydropower station is realized, and the efficiency and the accuracy of safety management are improved. Potential safety risks are warned in advance, accidents are effectively prevented, and losses are reduced. Moreover, the management and display of the work ticket are supported, thereby guaranteeing the smooth operation of the maintenance work, and preventing the violation operation. In addition, generation and guidance of an emergency response plan are provided, and the emergency response speed and effect are improved.

The invention discloses an intelligent monitoring control method and system for conveying equipment based on chain tension, and relates to the technical field of conveying equipment.The intelligent monitoring control method for the conveying equipment based on the chain tension comprises the following steps that chain tension characteristic data are obtained; predicting the change of the chain tension at the future moment by using the tension prediction model to obtain the change trend of the chain tension; identifying a chain tension change mode; and operating parameters of the conveying equipment are adjusted based on the recognized key factors. By monitoring the tensioning force of the chain in real time and predicting the future change trend of the tensioning force, the tensioning state of the chain can be adjusted more accurately, it is ensured that the chain runs in the optimal state all the time, excessive abrasion and damage of the chain and a chain wheel are prevented, operation parameters can be automatically adjusted according to the recognized tensioning force change mode, and the working efficiency is improved. And therefore, the performance and the safety of the conveying equipment are improved.

The invention discloses a new energy automobile rapid charging diagnosis and test system and method. The system comprises an industrial personal computer serving as a control unit, an electrical safety detection module, a charging safety detection module, a man-machine interaction module, a power module, a direct current charging module and a channel switching module. The scheme aims at the intelligent diagnosis technology of the new energy automobile, the function and performance detection of key parts such as power electronic equipment of the new energy automobile can be realized, the performance and the safety condition of the automobile are evaluated, and the safety and the reliability of core components of the automobile are ensured.

The invention discloses a blockchain-based intelligent video monitoring data storage security sharing method for an iron and steel plant. The method comprises the following steps of: generating a public key PKS and a master key TMK based on an attribute encryption algorithm; distributing shares of the shared key S by constructing a matrix and defining a function, wherein the shares are distributed in different rows of the matrix; re-encrypting the data by using a hybrid proxy re-encryption algorithm based on a ciphertext strategy; secure data storage and access control are realized based on a secure storage mode of revocable attribute-based encryption RABE and a block chain; and in combination with a block chain technology, a consensus algorithm is adopted to verify transactions, so that the security and robustness of the system are enhanced. According to the invention, the utilization rate of the data is improved, the cooperation efficiency is enhanced, the security and transparency of the video monitoring data are improved, and the cost of information transmission and storage is reduced. The distributed storage and access control of the data are realized, the confidentiality and integrity of the cloud center data are ensured, and the security and efficiency of the steel plant video monitoring system in the storage and transmission process are enhanced.

The invention relates to the technical field of digital factories, in particular to a smart factory real-time safety monitoring method and system based on edge computing. Firstly, an edge computing device, a camera and a sensor group are deployed in each production area; then, acquiring image data through a camera; acquiring sensor data through the sensor group; and finally, constructing a safety monitoring model, and operating the safety monitoring model on the edge computing device. Inputting image data and sensor data into the safety monitoring model, and obtaining a safety monitoring result through a data preprocessing layer, a feature analysis layer, a feature fusion layer and a monitoring result prediction layer in sequence; the image data and the sensor data are finely analyzed through the safety equipment wearing feature analysis network and the dangerous behavior feature analysis network, the finally obtained safety monitoring result can accurately reflect the real-time safety state of the smart factory, and efficient and dynamic safety management is ensured.

The invention discloses an improvement method of an improved dry quenching coke tank transverse movement traction device, and relates to the technical field of coke tank transverse movement traction. The improvement method of the improved dry quenching coke tank transverse movement traction device comprises the following specific operation steps: carrying out improvement work as follows: transversely moving a coke tank to the lower part of a lifting derrick without a'coke tank stop signal 2 'signal; the position of the ejector pin limiting signal detection piece at the bottom of the coke tank trolley is adjusted and lifted upwards by 2 mm in the vertical direction, and it is guaranteed that the traction hook is evenly stressed. According to the improvement method of the improved dry quenching coke pot transverse movement traction device, a motor single-side brake of an originally designed transverse movement traction device is transformed into a double-side brake working mode, the phenomena of sliding, displacement and the like of the coke pot on a transverse movement traction device track are prevented, the operation safety coefficient is ensured, and the service life of the coke pot is prolonged. A swallow-tail rod with a tilted tail part of an originally designed transverse moving traction device rod is disengaged and transformed into a straight and horizontal pushing mode, so that the coke tank trolley is prevented from sliding due to gravity inertia, and a group of head-tail stopping limiters are added on the existing limiting opposite side.