49results about How to "Avoid sudden failure" patented technology

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention relates to an equipment fault prediction method based on industrial Internet of Things and related equipment, and the method comprises the following steps: carrying out the physical signal collection of target equipment through a preset industrial Internet of Things, and obtaining an original sequence signal; identifying whether an abnormal signal exists in the original sequence signal to obtain an identification result; if the identification result is that the abnormal signal exists, performing fault type analysis on the target equipment based on the abnormal signal to obtain a fault position and a fault type corresponding to the fault position; performing failover prediction on the target equipment based on the abnormal signal, the fault position and the fault type corresponding to the fault position to obtain a predicted failover path; according to the method, the early warning instruction is generated on the basis of the predicted fault transfer path, and fault alarm is performed on the basis of the early warning instruction, so that the technical problems that a regular inspection or post-fault maintenance mode is mostly adopted in a traditional equipment maintenance mode, the cost is high, and sudden faults cannot be effectively prevented are solved.

The invention discloses a modular power supply efficiency intelligent management method, and aims to record the working state, power output and conversion efficiency of a power supply module in real time through a sensor and monitoring software, carry out system energy efficiency analysis regularly, and can significantly improve the overall performance of a power supply system. Firstly, according to the scheme, by optimizing power distribution and fault early warning, the energy efficiency can be effectively improved, energy waste is reduced, and the operation cost is reduced. Meanwhile, the system can dynamically adjust the load of each module, prolong the service life of equipment, reduce the equipment fault risk through preventive maintenance, and improve the stability and reliability of the system. In addition, an energy efficiency analysis report of the system provides a data-driven decision basis for a manager, helps to discover the potential of efficiency improvement, and guides the future optimization direction. And by reducing energy consumption, the power supply system can also reduce carbon emission, promote sustainable development of enterprises and improve environmental protection benefits.

The invention discloses a numerical control machine tool motor operation monitoring method and system based on data analysis, which are used in the field of motor monitoring and fault diagnosis, and the method comprises the steps: collecting working condition data corresponding to the operation process of a numerical control machine tool motor; establishing a temperature change trend model, and predicting temperature change conditions of the motor of the numerical control machine tool under different working conditions; combining the extracted frequency and amplitude with the prediction result of the temperature trend model, and judging the current operation state of the numerical control machine tool motor; and setting a threshold value of the working condition data, comparing a classification analysis result with a preset threshold value, and automatically triggering a multi-stage early warning mechanism when monitoring that the working condition data of the motor of the numerical control machine tool exceeds the threshold value. According to the method, preventative maintenance is realized, sudden faults are avoided, the motor is ensured to operate under efficient and stable conditions by determining the optimal electrical working range and real-time monitoring data, energy consumption is reduced, the service life of equipment is prolonged, and intelligent motor state analysis and prediction are realized.

The invention relates to the field of hoisting equipment, in particular to a crane monitoring system and a crane monitoring method, and the operation efficiency, the safety and the reliability of the system are remarkably improved through real-time monitoring analysis, trend analysis and fault analysis. Real-time monitoring and analysis can respond to abnormal conditions in time, operation parameters are optimized, and the equipment utilization rate is improved. Trend analysis supports predictive maintenance and resource optimization, and downtime is reduced. Fault reasons are accurately diagnosed through fault analysis, detailed reports and prevention measures are provided, and safety is guaranteed. Data quality and analysis accuracy are improved through data preprocessing, and long-term stable operation of the system is ensured through historical data analysis and trend prediction. And the comprehensive report and data visualization assist in management decision making, and the operation cost is reduced. On the whole, the data analysis module brings comprehensive optimization and remarkable benefit improvement to the crane monitoring system.

The invention relates to the technical field of seat cushion framework detection, in particular to an automobile seat cushion framework fatigue test method and system. The method comprises the following steps: acquiring information data of an automobile seat cushion framework sample; performing skeleton key part screening on the automobile cushion skeleton sample information data to obtain skeleton key part screening data; performing sensor data acquisition on the automobile seat cushion framework sample based on the framework key part screening data to obtain stress sensor acquisition data and deformation sensor acquisition data; and carrying out loading mode design on the information data of the automobile seat cushion framework sample to generate fatigue loading mode data of the automobile seat cushion framework. According to the invention, the accuracy and reliability of the fatigue test of the automobile seat cushion framework are improved by accurately screening key parts of the framework, designing a real loading mode, deeply detecting damage, predicting the service life and optimizing materials.

The invention relates to the technical field of textile, in particular to a digital textile equipment management method based on big data, and the method comprises the following steps: 1, an equipment connection and data initialization stage: checking the data interface type of equipment in each link of a textile process, and connecting the data interface type to a workshop data collection network, establishing an electronic file for each device, setting initial data acquisition parameters, and carrying out pre-operation test and calibration on the devices; 2, a production process monitoring and data recording stage; according to the scheme, the equipment performance evaluation model is established by using a big data analysis technology, multiple factors such as energy consumption efficiency, a production stability index and an equipment failure rate trend are comprehensively considered, and the equipment performance is comprehensively and accurately evaluated, so that sudden equipment failures are effectively avoided, the risk of production interruption is reduced, and the production efficiency is improved. The overall management efficiency and the operation stability of the equipment are improved, the continuity of the production process is guaranteed, and the cost increase caused by equipment faults is reduced.

The invention relates to the technical field of structure health monitoring and bearing capacity evaluation, and discloses a heat absorption tower hybrid structure dynamic detection bearing capacity evaluation method, which comprises the following steps: S1, arranging various sensors on a heat absorption tower structure, including an acceleration sensor, a strain gauge, a temperature sensor and a displacement sensor, collecting dynamic response data of the heat absorption tower under different working conditions; s2, on the basis of data collected by the sensors, a digital twin model of the heat absorption tower is built, and the model comprises finite element three-dimensional models of a tower footing, a tower body and a tower top. Real-time monitoring and high-precision prediction of the heat absorption tower are realized through a sensor network and a digital twinning technology, structural problems can be intelligently identified and early warning can be carried out in advance, meanwhile, a maintenance strategy is optimized based on fault prediction of AI, the maintenance cost is reduced, the equipment operation efficiency is improved, and the accuracy of structural analysis is further improved through introduction of the digital twinning technology. And the long-term safety and stability of the heat absorption tower are ensured.

The invention relates to the technical field of substation operation and maintenance, in particular to a substation communication power supply storage battery internal resistance detection method and system, electronic equipment and a storage medium, which can ensure the stable operation of a substation communication power supply system and improve the operation safety of a power network. The method comprises the following steps: collecting storage battery multi-element characteristic parameter information, storage battery multi-element external cause parameter information and a storage battery real-time internal resistance value of the substation communication power supply; performing data processing on the multivariate characteristic parameter information of the storage battery, and inputting the processed multivariate characteristic parameter information into a pre-constructed storage battery characteristic parameter analysis model to obtain a storage battery theoretical internal resistance value corresponding to the multivariate characteristic parameter information of the storage battery; performing external cause influence error compensation on the real-time internal resistance value of the storage battery based on the multi-element external cause parameter information of the storage battery to obtain a corrected internal resistance value of the storage battery; and calculating the internal resistance deviation amplitude between the storage battery correction internal resistance value and the storage battery theoretical internal resistance value.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

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 relates to the technical field of equipment state evaluation, in particular to an evaluation method and system for the health state of offshore operation equipment. The method comprises the following steps: acquiring component data of offshore operation equipment; performing initial life marking on the data of the offshore operation equipment component to generate initial life marking data of the offshore operation equipment component; performing real-time operation data acquisition on the data of the offshore operation equipment component to generate real-time operation data of the offshore operation equipment component; performing component division on the real-time operation data of the components of the offshore operation equipment based on the components of the initial life mark data to generate novel component operation data and old component operation data; performing environment change influence analysis on the operation data of the novel part to generate an external change influence factor of the novel part. According to the invention, through multi-dimensional data acquisition, environment and electrical influence analysis, fatigue damage assessment and intelligent prediction, the accuracy of health state assessment of the offshore operation equipment is improved.

The invention discloses a weather radar echo intensity online calibration method, and relates to the technical field of weather radar detection. The radar collects and processes an environment signal to obtain effective clutter data; determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and calibrating the weather radar based on the relative calibration offset. According to the method, the weather radar is calibrated online in real time by using the ground clutter creatively, and the dependence on external equipment is reduced. And the precision and timeliness of radar echo intensity measurement are obviously improved. The maintenance cost and the operation difficulty are reduced, and the calibration inconsistency caused by manual operation errors is reduced. The calibration cost of the weather radar is remarkably reduced, continuous high-quality output of data is ensured, the accuracy and reliability of data acquired by the weather radar are remarkably improved, higher requirements of modern meteorological observation and forecasting are met, and powerful support is provided for meteorological observation and early warning and forecasting. And the long-term consistency of the data is ensured. And the method has wide adaptability.

The embodiment of the invention provides a gas turbine combustion state real-time monitoring system and method, and solves the problems that when a plurality of combustion state parameters are integrated into a comprehensive parameter in an existing method, multi-source heterogeneous combustion state parameters cannot be effectively fused, so that the comprehensive parameter has deviation and is inaccurate in evaluation. The method comprises the following steps: acquiring combustion state parameters of a gas turbine, constructing and training a state monitoring model by combining category joint confrontation and a gating circulation unit, identifying and analyzing the combustion state parameters by the state monitoring model, and outputting a comprehensive evaluation value; according to the method, the state monitoring model is constructed and trained by combining the category joint confrontation and the gating circulation unit, so that the state monitoring model accurately identifies and analyzes the combustion state parameters, further outputs the comprehensive evaluation value, can accurately capture the change trend of the combustion state parameters, and improves the evaluation accuracy of the combustion state parameters. Therefore, the potential fatigue problem of the gas turbine can be conveniently found in time, the service life of the gas turbine is prolonged, and sudden faults of equipment are avoided.

The invention relates to the technical field of electronic components, in particular to an electronic component and a fault management method thereof. The method comprises the following steps that circuit board design data are analyzed through a system, and the reliability and performance of electronic components are improved; the method comprises the steps of obtaining circuit board design data, performing circuit wiring analysis, generating wiring data and cross wiring data, then detecting electromagnetic radiation enhancement conditions, calculating a circuit fault probability, and further detecting a short-circuit fault of an electronic component; performing comprehensive analysis on the short-circuit fault data and the fault probability, and estimating the aging trend of the component; circuit board design is optimized according to the aging trend and fault probability data, and availability performance improvement of the electronic components is calculated; according to the invention, the design of the electronic component and the circuit board is optimized, so that the fault identification of the electronic component is more accurate.

The invention discloses a method and system for simulating a latent fault of a medium-voltage distribution cable. The method comprises the following steps: establishing a target cable latent fault model based on an arc model; according to the target cable latent fault model, establishing a first simulation model, and obtaining a first fault simulation waveform; according to the first fault simulation waveform, obtaining a relationship between the target parameter and a fault feature in a target cable latent fault model; and obtaining an optimal target parameter to determine a final target cable latent fault model. By simulating a cable latent fault, the potential problem of the cable can be predicted and identified more accurately, so that a maintenance measure is taken in advance, and occurrence of a sudden fault is avoided. By optimizing the target parameters, it can be ensured that the simulation result is highly consistent with the waveform feature of the actual cable latent fault, and therefore the effectiveness of fault prevention and protection measures is improved.

The invention relates to the technical field of mechanical testing, and particularly discloses a method for testing the low temperature resistance of perfluoropolyether preparation equipment, which comprises the following steps of: counting a plurality of key components of the perfluoropolyether preparation equipment, and matching corresponding to-be-tested components based on characteristic values to obtain the layout number of adaptive surface monitoring points; performing low-temperature testing on each to-be-tested component, performing comprehensive analysis to obtain a structural safety evaluation index of each to-be-tested component, and performing comprehensive analysis to obtain a surface quality evaluation index of each to-be-tested component; and performing comprehensive analysis to obtain a low-temperature resistance evaluation value of the equipment, and obtaining and feeding back an evaluation result. The method for testing the low-temperature resistance of the perfluoropolyether preparation equipment provided by the invention can help to identify potential problems possibly occurring in the perfluoropolyether preparation equipment under the influence of a low-temperature environment in the production process, improves the production efficiency and the resource utilization rate, ensures that the produced perfluoropolyether product is qualified in quality, and reduces the production cost. And market requirements and customer requirements are met.

The invention discloses an equipment maintenance management method and device. The method comprises the following steps: collecting vibration data of equipment in different motion states; performing feature extraction and labeling on the vibration data; a training sample is generated according to the extracted feature values and corresponding labeling information, the labeling information is used for indicating equipment states corresponding to the vibration data, and the equipment states comprise a normal state and a specific fault state; training a fault diagnosis model by using the training sample; and performing fault diagnosis on the equipment by using the real-time monitoring data of the equipment and the fault diagnosis model, and determining the current state of the equipment. By means of the scheme, the real-time performance and intelligence of equipment vibration monitoring can be improved, and normal operation of equipment can be better guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to an efficiency evaluation method and device for intelligent operation and maintenance of a cluster system. According to the method, the efficiency of the cluster system in different operation stages can be scientifically and accurately evaluated by constructing the reasonable efficiency index system, the network average efficiency calculation model, the task execution balance degree evaluation model and the elasticity evaluation model. According to the method, the deep reinforcement learning algorithm is introduced, the multi-dimensional situation information is collected, analyzed and predicted in real time, and the intelligent operation and maintenance decision-making efficiency of the cluster system can be effectively improved.

The invention relates to the field of public facility management, in particular to a method for evaluating the running state of a highway drainage system. The method comprises the following steps: acquiring sensor data of different areas in real time, and calculating the displacement of the different areas; calculating the drainage efficiency of different areas based on the drainage amounts of different areas and the sensor data; based on the displacement, drainage efficiency and sensor data of the different areas, calculating the ponding depths of the different areas at the future moment; and based on the displacement, the drainage efficiency and the sensor data of the different areas and the ponding depths of the different areas at the future moment, calculating the operation state scores of the highway drainage system of the different areas, and further judging the operation state of the highway drainage system. The problems that the rainfall and drainage association of different areas is unclear, and the drainage capacity is difficult to evaluate are solved; the influence of rainfall intensity fluctuation on the stability of a drainage system is not considered, the drainage efficiency is difficult to accurately evaluate, and a traditional ponding prediction model may cause ponding depth prediction errors and decision delay.

The invention discloses a medical high-value consumable full life cycle management system, which relates to the technical field of medical management systems and comprises a data acquisition module, a central processing module, an X-ray tube life cycle evaluation module and a target material life cycle evaluation module. Potential problems of the equipment are found in time according to the health degree M, sudden failures caused by overheating or aging are avoided, the current state of the X-ray tube is comprehensively evaluated, it is ensured that all indexes of the equipment are within the normal range, the reliability and stability of the equipment are improved, a reasonable maintenance plan is made according to the five stages of the health degree M, and the maintenance efficiency is improved. Unnecessary downtime and maintenance cost are reduced, the service life of the equipment is prolonged, and the operation efficiency and safety of the equipment can be effectively improved by monitoring the change trend of the health degree M, predicting possible faults in advance, taking preventive measures, ensuring long-term stable operation of the equipment and calculating the health degree M of the X-ray tube.

The invention discloses a sealing structure of a steering bearing of an automobile shock absorber, which belongs to the technical field of steering bearings, and comprises an upper ring and a lower ring, wherein the upper ring is arranged above the lower ring; an annular plate, wherein the annular plate is fixedly connected to the upper surface of the lower ring, and the upper end of the annular plate extends to the inside of the upper ring; balls, wherein the balls are rollingly installed between the annular plate and the upper ring, wherein the balls are arranged at the upper end of the annular plate, wherein the balls are arranged in multiple groups, and the multiple groups of balls are distributed in an annular shape; and a sealing component, wherein the sealing component is arranged between the outer wall of the annular plate and the inner wall of the upper ring. The invention realizes the primary sealing of the steering bearing by a rubber ring, realizes the secondary sealing of the steering bearing by a magnetic fluid, realizes the multi-stage sealing of the steering bearing, can effectively prevent muddy water, dust, etc. from entering the inside of the steering bearing, and avoids the problem of external impurities contaminating the lubricating oil inside the steering bearing.

The invention relates to a socket type energy dissipation swing pier-cover beam joint structure and a construction method thereof, and belongs to the technical field of bridge engineering. Comprising a pier prefabricated and assembled upper section, a cover beam, a swinging device, a limiting type energy dissipation device, an amplification type energy dissipation device and unbonded prestressed tendons. The prefabricated and assembled upper section of the bridge pier is embedded into the cover beam in a socket manner, and a swinging device is arranged between the top and the bottom of the cover beam; limiting type energy dissipation devices are arranged on the portion, embedded into the bent cap, of the prefabricated and assembled upper segment of the bridge pier and the side wall of the bent cap. An amplification type energy consumption device is arranged at the connection position of the side wall of the prefabricated and assembled upper segment of the pier and the bottom face pier-cover beam of the cover beam; unbonded prestressed tendons are arranged among the prefabricated and assembled upper section of the bridge pier, the cover beam and the swinging device in a penetrating manner. The problems that a traditional swing bridge pier and bent cap joint is insufficient in shear resistance and concentrated in stress are solved, the shear resistance, the energy dissipation capacity and the function restorability of the bridge pier-bent cap joint are improved, and the requirements for post-earthquake maintenance and reinforcement are reduced.

The present application belongs to the technical field of photovoltaic equipment monitoring fault early warning, and relates to a photovoltaic equipment cluster benchmarking analysis and implicit fault early warning method and system. The system collects basic parameters and implicit associated data of photovoltaic equipment through a data acquisition module to ensure data comprehensiveness and timeliness. Through a data fusion module, the collected data is processed and fused to generate a standardized data sequence. Through a cluster benchmarking module, the photovoltaic equipment is divided into different clusters, a group health benchmark is constructed, and the model accuracy is improved through individual difference calibration. Through a dynamic early warning module, early warning information is generated, and a basis is provided for subsequent fault troubleshooting. Through an intelligent operation and maintenance module, early warning is prioritized and an operation and maintenance scheme is generated, which is pushed to operation and maintenance personnel for optimization guidance. The present application can realize accurate benchmarking analysis and implicit fault early warning of photovoltaic equipment clusters, and reduce the failure rate.

The present invention relates to the field of seat cushion frame detection technology, and in particular to a method and system for automobile seat cushion frame fatigue testing. The method comprises the following steps: obtaining automobile seat cushion frame sample information data; screening key parts of the frame for the automobile seat cushion frame sample information data to obtain key part screening data for the frame; collecting sensor data for the automobile seat cushion frame sample based on the key part screening data for the frame to obtain stress sensor collection data and deformation sensor collection data; designing a loading mode for the automobile seat cushion frame sample information data to generate automobile seat cushion frame fatigue loading mode data. The present invention improves the accuracy and reliability of automobile seat cushion frame fatigue testing by accurately screening key parts of the frame, designing a true loading mode, conducting in-depth damage detection, life prediction, and material optimization.

The invention discloses a sealing structure of a steering bearing of an automobile shock absorber, and belongs to the technical field of steering bearings, the sealing structure comprises an upper ring and a lower ring, and the upper ring is arranged above the lower ring; the annular plate is fixedly connected to the upper surface of the lower ring, and the upper end of the annular plate extends into the upper ring; the balls are installed between the annular plate and the upper ring in a rolling mode, the balls are arranged at the upper end of the annular plate, the balls are arranged in multiple sets, and the multiple sets of balls are distributed in an annular mode; and the sealing assembly is arranged between the outer wall of the annular plate and the inner wall of the upper ring. First-stage sealing of the steering bearing is achieved through the rubber ring, second-stage sealing of the steering bearing is achieved through the magnetic fluid, multi-stage sealing of the steering bearing is achieved, external muddy water, dust and the like can be effectively prevented from entering the steering bearing, and the problem that external impurities pollute lubricating oil in the steering bearing is solved.

The present invention relates to the technical field of state monitoring and analysis of a twenty-high rolling mill, and relates to a state degradation modeling method for a twenty-high rolling mill under the action of vibration-wear coupling. By performing data analysis on vibration data of each key part of a target twenty-high rolling mill corresponding to each target monitoring time point, the vibration characteristics of each key part of the target twenty-high rolling mill corresponding to the target monitoring time period are obtained through analysis. Meanwhile, based on the wear data of each key part of the target twenty-high rolling mill corresponding to each target monitoring time point, the wear characteristics of each key part of the target twenty-high rolling mill corresponding to the target monitoring time period are obtained through analysis. Then, a reference vibration-wear coupling model of each key part of the target twenty-high rolling mill corresponding to the target twenty-high rolling mill is established. Finally, the state degradation evaluation of each key part of the target twenty-high rolling mill corresponding to the target twenty-high rolling mill is performed, which effectively ensures that the degradation and failure of the equipment are discovered in time, thereby taking preventive maintenance measures and effectively avoiding sudden failure of the equipment.

The utility model discloses a kind of gate sealing devices of shield machine, belong to shield machine gate technical field.Aiming at the problem that shield machine gate sealing effect in prior art needs to be improved, the utility model greatly improves sealing reliability by bilateral main and auxiliary sealing elements synergistic effect, slag ring active interception impurity, compression spring provides thrust to continue to compensate the wear of dynamic sealing lip, ensure the close fit under high pressure;Pressure cavity introduces medium pressure to form self-tight type sealing, significantly reduce sealing lip load, prolong service life;Leakage chamber design combines pressure detector and indicator light, realize real-time alarm and early intervention of leakage, avoid sudden failure;Grease hole supports maintenance without stopping, inject sealant can temporarily repair microleakage.The overall structure has self-adapting sealing ability under high pressure, complex working condition of slag, maintenance cost is significantly reduced, safety redundancy is comprehensively enhanced.

The invention relates to the technical field of equipment operation and maintenance, and discloses a fire-fighting equipment intelligent management system and method based on the Internet of Things, and the system comprises a sensing layer which comprises a sensor and is used for collecting the fire-fighting state information of fire-fighting equipment in real time; the Internet of Things communication layer is used for transmitting the fire-fighting state information collected by the sensing layer to the application layer; the application layer is provided with a data processing module, a storage module and a user interaction module, the data processing module is used for analyzing and processing the received fire-fighting state information, and the storage module is used for storing historical data and real-time data of fire-fighting equipment. The system can accurately collect the state information of the fire-fighting equipment in real time, and grasp the physical parameter state of the fire-fighting equipment at any time; the Internet of Things communication layer realizes stable and efficient data transmission with the application layer, and the system can quickly and accurately transmit information acquired in real time to the application layer for processing, thereby providing data support for timely decision making.