3756results about How to "Improve operational safety" patented technology

The invention provides a digital factory operation virtual simulation teaching method and system, and relates to the technical field of smart factories, and the method comprises the steps: collecting factory operation data, carrying out the standardization processing and correlation analysis, constructing a multi-dimensional data model, carrying out the feature extraction and semantic mining of the hierarchical data of each dimension, and constructing a multi-level digital twin factory model. Carrying out dynamic modeling, constructing a personalized knowledge graph, generating a learning demand list, matching the learning demand list with a digital twin factory model, and generating a multi-dimensional simulation environment; in the multi-dimensional simulation environment, performing analysis and intention recognition on an operation instruction of the learner, converting the operation instruction into a virtual operation, calculating a normative score of the virtual operation, generating corresponding feedback information based on the normative score and the personalized knowledge graph, updating model parameters based on a response of the learner to the feedback information, and performing simulation on the learner according to the updated model parameters. And completing iteration of the model.

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

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

The invention discloses a battery life evaluation system and method based on different decline modes of a battery, and belongs to the technical field of battery management evaluation. Through combination of the data acquisition module and the information management module, battery manufacturing, operation and historical information is comprehensively acquired, accurate management of the whole life cycle of the battery is realized, the data analysis module and the performance evaluation module deeply analyze battery operation data, classify and identify recession modes and key influence factors, and the reliability of the battery is improved. The current performance and the remaining life of the battery are dynamically evaluated by combining a prediction model, decision making is simplified through health scoring, the battery management efficiency is improved, the adaptive performance optimization module adjusts battery parameters and temperature control strategies according to operation scenes, the performance and the life are balanced, active optimization and safety improvement are achieved, and the safety is improved. The carbon footprint evaluation module covers all stages of battery manufacturing, using and recycling, checks full-life-cycle carbon emission, traces the source of high emission links, provides low-carbon optimization suggestions, and assists enterprises in achieving the goal of green development.

The invention provides an instrument log recording and intelligent analysis method in a medical instrument software system, and the method comprises the steps: carrying out the preprocessing of operation log data of medical instrument equipment, carrying out the unified conversion of the log data of different sources and different formats, and obtaining the operation log data of the medical instrument equipment after the standardization processing; mining operation state change rules and abnormal modes of the medical equipment in different diagnosis and treatment stages through a time sequence mode according to the extracted log data semantic features in combination with the diagnosis and treatment workflow of the medical equipment; and analyzing the actual process of the equipment participating in the diagnosis and treatment activity from the equipment log data, and comparing and analyzing the actual process with a standard diagnosis and treatment work process to determine the information of the diagnosis and treatment work process, including a key path, a bottleneck link and an abnormal deviation.

The invention relates to the technical field of power grid fault repair, in particular to a power grid fault emergency rescue method. The method comprises the following steps: acquiring topological structure data, key equipment parameters and real-time operation state data of a power grid; constructing a power grid digital twin model according to the power grid topological structure data, the key equipment parameters and the real-time operation state data, and performing real-time state evaluation to obtain fault risk early warning data and real-time fault condition data; when the real-time fault condition data shows that a fault occurs, performing fault point positioning and fault type determination based on deep learning according to the fault risk early warning data and the real-time operation state data to obtain fault diagnosis data; and constructing a fault influence range model according to the fault diagnosis data and a preset FACTS and HVDC characteristic model. According to the method, deep learning, a graph theory and a multi-objective optimization technology are fused, and intelligent fault identification, influence range modeling and accurate emergency disposal scheme optimization are realized.

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 technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention provides a service life prediction method based on structural fatigue analysis of floating type offshore wind power equipment, and relates to the technical field of wind power generation. Equipment structure data of the floating type offshore wind power equipment is acquired, a sensor network is connected to acquire environmental load information, and a multi-body coupling simulation model is established based on the equipment structure data; environmental load information is input into the multi-body coupling simulation model for stress amplitude and load change, fatigue damage accumulation data is calculated based on the multi-body fatigue load spectrum, and the residual life of the floating type offshore wind power equipment is predicted through the fatigue damage accumulation data. The technical problem that in the prior art, unique stress characteristics and dynamic response of floating type offshore wind power equipment are ignored, and consequently the service life of the wind power equipment is predicted inaccurately is solved. The technical effects that the fatigue accumulation process of the floating type offshore wind power equipment is accurately calculated, and the operation safety and the service life prediction precision of the floating type wind power equipment are improved are achieved.

The invention belongs to the technical field of battery monitoring, and discloses a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring. The method comprises the following steps: collecting multi-source battery parameters in real time, and marking the multi-source battery parameters as real-time multi-source battery parameters; the real-time multi-source battery parameters are processed, and battery characteristic parameters are extracted; performing fusion analysis on the real-time multi-source battery parameters and the extracted battery characteristic parameters, and predicting the thermal runaway probability of the lithium battery; judging whether to generate an evaluation instruction according to the thermal runaway probability; if the evaluation instruction is generated, evaluating the thermal runaway type of the lithium battery and starting active prevention and control measures; according to the invention, high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents are realized, the thermal runaway risk is reduced, and the safety and reliability of lithium battery operation are effectively improved, so that the life safety of new energy automobile users is protected.

The invention relates to the technical field of expressway unmanned aerial vehicle route planning, in particular to an intelligent route planning method and system for an expressway unmanned aerial vehicle, and the method specifically comprises the steps: planning unmanned aerial vehicle waypoints on an expressway based on a target three-dimensional model, and obtaining a waypoint set of the expressway; historical route data of the expressway are obtained, a deep learning algorithm is used for analyzing the historical route data, safe flight path features are extracted, a route safety evaluation model is constructed, and the route safety evaluation model is used for evaluating the route safety level of the route of the unmanned aerial vehicle; and determining route planning parameters according to the unmanned aerial vehicle task type, and determining an unmanned aerial vehicle route through the waypoint set, the route safety evaluation model and the waypoint planning parameters based on a multi-objective optimization algorithm. According to the invention, the safety and efficiency of the unmanned aerial vehicle in highway inspection and monitoring tasks are improved, and automatic planning and dynamic adjustment of the route of the unmanned aerial vehicle based on real-time data analysis are realized.

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention discloses a flood discharge gate opening degree real-time automatic distribution method for a centralized control center, and relates to the technical field of water conservancy gate control, and the method comprises the steps: setting a monitoring point in each sub-region, and collecting the mechanical state, the vibration stress parameter and the surrounding geological parameter of a flood discharge gate, so as to construct a gate wear index Msz; therefore, the state of the gate can be monitored in real time, and abrasion and fault problems can be found and early warned in time. And establishing a sub-region prediction model, extracting water flow characteristics and river volume characteristics, calculating an average water flow index Slzs and a regional river clogging index Ydzs of each sub-region, correlating the obtained predicted river load index Fzz, and performing evaluation. And setting a first difference threshold value C1 and a second difference threshold value C2, calculating and obtaining a load difference value FCz according to the predicted river load index Fzz and the load threshold value R, generating an opening instruction, and setting a priority distribution mechanism. The opening degree of the gate is optimized, and the accuracy and response speed of flood discharge control are ensured.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention provides a full-process monitoring and fault diagnosis method and system for a pneumatic conveying system. The method comprises the following steps: collecting multi-source monitoring data of the pneumatic conveying system; processing the multi-source monitoring data to obtain standardized monitoring data; constructing a training set based on the historical operation data, and training a pre-constructed deep learning model to obtain a system state evaluation model; based on the system state evaluation model and the multi-source monitoring data, adopting a system state vector algorithm to generate a differential flatness index; according to the differential flatness index, designing a k induction control algorithm, and generating control parameters for the rotating speed of an induced draft fan and the opening degree of a material distributing valve of the pneumatic conveying system; and constructing a disturbance observer with a self-adaptive safety boundary, and controlling the pneumatic conveying system based on the control parameters. According to the method, intelligent monitoring and safety control of the pneumatic conveying system are achieved in a mode of combining deep learning and intelligent control, and the operation efficiency and reliability of the system are improved.

The invention discloses a multi-source reactive power compensation control method, system, program, medium and device, and relates to the technical field of power grid systems, and the method comprises the following steps: extracting the dominant influence characteristics of the reactive power of a power grid as the input of a CNN-LSTM network, predicting the vacancy interval of the reactive power of the power grid, and taking the vacancy interval as the power distribution target of the multi-source reactive power compensation device; a power distribution model of the multi-source heterogeneous reactive compensation integrated system is constructed by taking minimization of system network loss and voltage drop and stability of the multi-source heterogeneous reactive compensation integrated system as optimization targets. The influence factors of the reactive power of the power grid are researched, and the influence characteristics are used as neural network input to predict the vacancy interval of the reactive power of the power grid, so that the spatial-temporal distribution characteristics of the reactive power of the power grid can be reflected, the change trend of reactive requirements is provided for power grid operators, and corresponding reactive compensation measures are taken in time.

The invention provides a wind power plant fault analysis method and system based on multi-dimensional information association, and relates to the technical field of fault analysis. Field domain configuration information of a target wind power plant is read, a secondary loop topology is established, topology node identification is performed based on data homology and data correlation, a network time protocol is constructed, and the wind power plant fault is analyzed based on the secondary loop topology. Constructing a fault prediction module by combining a fault knowledge graph, determining a transmission timestamp, carrying out data return constraint on the multi-dimensional source data by combining a network time protocol, transmitting the multi-dimensional source data to the fault prediction module, carrying out wind power plant fault prediction, and carrying out fault operation and maintenance management on a target wind power plant based on a fault prediction result. The technical problem that in the prior art, comprehensive analysis on multi-dimensional information of a wind power plant is lacked, and consequently fault prediction accuracy and real-time performance are insufficient is solved. The technical effects of comprehensively and accurately analyzing the multi-dimensional information of the wind power plant and improving the accuracy and real-time performance of fault analysis and prediction are achieved.

The invention discloses an integral type automatic directional drilling machine suitable for underground directional construction and a control method of the integral type automatic directional drilling machine. The integral type automatic directional drilling machine comprises a walking mechanism, a drilling main machine, a rod loading and unloading mechanical arm, a rod supplementing device, a suspension arm and a power pump station, in the drill rod loading and unloading construction process, the rod loading and unloading mechanical arm can achieve the rod loading and unloading function at different drilling angles, the rod loading and unloading action within the range of 0-90 degrees can be achieved in the mode that linear motion and rotary motion are combined, accurate positioning at multiple angles is achieved in the mode that mechanical limiting and an angle sensor are combined, and the drill rod loading and unloading efficiency and reliability are improved; during rod supplementing operation, the drilling machine has the rod bin capacity meeting the single-shift footage average value in actual construction, single-shift one-time rod supplementing can be achieved, and auxiliary personnel do not need to wait for rod supplementing basically. The drilling machine is suitable for underground coal mine automatic directional drilling machine construction, can achieve integral layout, is compact in structure, is provided with a power system, and does not need to be matched with a special power pump truck for use.

The invention discloses a charging pile output power control method, and particularly relates to the technical field of charging pile management, and the method comprises the following steps: collecting the real-time operation information of a charging station, analyzing the overall load fluctuation condition, and judging whether an early sign that the operation fluctuation intensity is too high exists or not; and if abnormity is identified, further collecting operation information of each charging pile, extracting feature data, and performing stability and adaptability evaluation. And an evaluation result is imported into a fuzzy reasoning model, and the charging pile is divided into a high-quality operation state and a low-quality operation state. In a single abnormal scene, the charging piles in a high-quality operation state maintain a constant-current and constant-voltage strategy, and the charging piles in a low-quality operation state are switched to an adaptive power adjustment strategy; in an overall abnormal scene, all the charging piles are switched to an adaptive power adjustment strategy, so that the operation efficiency and stability are improved; the strategy can be dynamically adjusted according to different operation scenes, and the requirements under different operation conditions are met.

The embodiment of the invention discloses a boiler anti-wear and anti-explosion online early warning system based on a sensor and big data, and relates to the technical field of boiler safety monitoring, and the system comprises a sensor module which collects operation parameters in a boiler in real time; the data acquisition module is used for summarizing various data acquired by the sensor module; the data processing module is used for carrying out processing, analysis and trend prediction on the collected real-time data through a big data analysis technology; the early warning module is used for evaluating the anti-abrasion and anti-explosion risk of the boiler according to the analysis result output by the data processing module and generating an alarm when a risk threshold value is exceeded; and the display and interaction module is used for displaying the boiler operation state, the risk assessment result and the early warning information to an operator. According to the invention, intelligent monitoring and risk early warning of boiler equipment can be realized, and through accurate data analysis and prediction, the operation safety of the boiler can be improved, the occurrence of safety accidents can be reduced, and the maintenance strategy of the boiler can be optimized.

The invention discloses an automatic long drilling tool loading and unloading system of a directional drilling machine and a control method. The automatic long drilling tool loading and unloading system comprises a drilling main machine, a rod loading and unloading mechanical arm and a rod supplementing device. In the drill rod loading and unloading construction process, the rod loading and unloading mechanical arm can achieve the rod loading and unloading function at different drilling angles, the rod loading and unloading action within the range of 0-90 degrees can be achieved in the mode that linear motion and rotary motion are combined, accurate positioning at multiple angles is achieved in the mode that mechanical limiting and an angle sensor are combined, and the drill rod loading and unloading efficiency and reliability are improved; during rod supplementing operation, the drilling machine has the rod bin capacity meeting the single-shift footage average value in actual construction, single-shift one-time rod supplementing can be achieved, and auxiliary personnel do not need to wait for rod supplementing basically. The drilling machine is suitable for underground coal mine automatic directional drilling machine construction, can achieve integral layout, is compact in structure, is provided with a power system, and does not need to be matched with a special power pump truck for use.

The invention discloses an electric power system climbing demand evaluation and optimization method considering load side resources, and the method comprises the steps: providing a calculation method for maximum upward and downward climbing demands in different time periods, including a calculation method for deterministic and uncertain climbing demands, in order to effectively cope with the fluctuation and uncertainty of loads or new energy; a climbing demand optimization model which takes minimization of the uncertainty climbing demand as an objective function and takes constraint conditions such as load adjusting quantity and demand response into consideration is provided; a gradeability optimization model which takes minimization of comprehensive consumables of the power system as an optimization target and considers constraints such as node balance and line power flow is provided; modeling is carried out on AMPL software, a solver GUROBI is called for solving, and an electric power system climbing demand evaluation and optimization scheme is obtained. The comprehensive operation consumables of the power system can be effectively reduced, and the net load peak load shifting effect on the power system is achieved, so that the operation reliability of the power system is improved, and the dispatching pressure of the power system is relieved.

The invention discloses a power grid anomaly detection and diagnosis method based on artificial intelligence and topological data analysis. The method comprises the steps of S1, forming power operation data; s2, constructing a topological network model of the power grid based on the physical structure of the power grid; s3, using a first generator to generate abnormal data in the simulation power grid based on the power operation data; s4, optimization abnormal data generation is carried out; s5, embedding the power operation data and topological structure features of the power grid into the anomaly detection model through a graph convolutional network; s6, power grid anomaly detection parameters are dynamically adjusted through the adaptive learning model, so that the anomaly detection model is continuously optimized along with the change of the power grid operation condition; and S7, based on the topological information and abnormal data generated by the generative adversarial network, combining topological data analysis, and positioning a fault node or an abnormal line in the power grid. According to the invention, the accuracy of fault prediction and positioning is improved.

The invention discloses a method for calculating the insulation field intensity of a transformer under the influence of moisture and temperature distribution, and relates to the technical field of electric power engineering, and the method comprises the steps: constructing a multi-physical field coupling simulation platform for monitoring and analyzing the temperature, humidity and electric field distribution in the transformer; setting a humidity sensitive element array, collecting humidity data, and transmitting the humidity data to a central processing unit for processing; constructing an insulation prediction model through a deep neural network algorithm, inputting the insulation prediction model based on historical humidity, historical temperature and insulation state data to complete training, and predicting a change trend of insulation field intensity under a specific working condition; processing the real-time data, inputting the processed real-time data into the insulation prediction model to obtain a real-time insulation state prediction value, and dynamically adjusting parameters of the insulation prediction model; an abnormity early warning mechanism is established based on a real-time insulation state prediction value, and once an insulation state abnormity is detected, the system immediately starts early warning to inform operation and maintenance personnel to check and maintain.

The invention discloses a bridge pier stud crack automatic detection system based on a wall-climbing robot, and relates to the technical field of crack detection, and the system realizes closed-loop operation from accurate crack detection to intelligent repair through a crack detection module and a repair material intelligent spraying detection module. The crack detection module uses a multispectral imaging device, a thermal imaging camera and a laser scanning technology, can collect a multispectral difference value G (x, y), surface temperature distribution T (x, y) and a depth matrix D (x, y) of a crack area, extracts a crack boundary B (x, y) through a high-precision algorithm, and calculates a crack volume Vc. And the repairing material intelligent spraying detection module dynamically calculates the volume Vm of the repairing material according to the volume of the crack, and the SLAM technology is utilized to optimize the spraying path and parameters, so that uniform coverage and efficient utilization of the repairing material are ensured. The system greatly improves the precision of crack detection and repair, and avoids material waste and repair omission.

The method comprises the following steps: constructing a thermal shock test environment, carrying out a thermal shock cycle test on the refractory material, and collecting thermal shock cycle data; monitoring and recording the performance change of the material in the thermal shock cycle process; based on the thermal shock cycle data, a thermal shock historical memory model of the refractory material is established, and the change rule of the performance of the refractory material along with thermal shock cycle is analyzed; constructing a life prediction model, and predicting the service life of the material under a specific working condition; comprehensively evaluating the heat resistance and earthquake resistance of the material according to a model analysis result, and outputting an evaluation result. By monitoring the temperature gradient, the residual stress and the crack propagation in the thermal shock cycle in real time, the cumulative damage model and the service life prediction model are established, and accurate quantitative evaluation of the heat resistance and shock resistance of the refractory material is realized. The scientificity and dynamic adaptability of material performance evaluation are remarkably improved, and technical support is provided for material optimization design and high-temperature environment application.

The invention relates to the technical field of mine electromechanical equipment management and maintenance, in particular to a mine electromechanical equipment full-life-cycle management method and system, and the method comprises the steps: carrying out the equipment debugging according to the production characteristics and environmental conditions of a mine; the operation state and performance indexes of the equipment are monitored in real time, the monitoring content is analyzed through an intelligent diagnosis algorithm of the mine electromechanical equipment, and fault judgment is conducted; formulating an intelligent repair scheme and a maintenance strategy according to a fault judgment result; carrying out full life cycle management and optimization on the mine motor equipment; by combining technical means of equipment debugging, real-time monitoring, intelligent diagnosis, intelligent repair and maintenance, full-life-cycle management and the like, the management and maintenance level of the mine electromechanical equipment is improved, the comprehensive benefits of the equipment are improved, and the service life of the equipment is prolonged.

The invention discloses a public vehicle operation management method based on big data analysis and related equipment, and the method comprises the steps: receiving a battery alarm signal of an electric public vehicle, and obtaining a battery parameter and vehicle operation data; inputting the current environment data and the real-time road condition data into a big data model to obtain a current operation scene of the vehicle; inquiring to obtain a battery parameter interval corresponding to the current operation scene of the vehicle; under the condition that the battery parameters are located in the battery parameter interval, vehicle energy consumption data in the preset kilometer interval are obtained, and according to the battery parameters, the vehicle energy consumption data and the current operation scene of the vehicle, the energy consumption trend of the electric public vehicle in the future preset time period is predicted; generating a power output mode of the electric public vehicle according to the energy consumption trend, the vehicle operation data and the driving behavior data; generating a brake adjusting instruction according to the power output mode; and the brake adjusting instruction is sent to the vehicle terminal, so that the vehicle terminal executes a brake strategy.

The invention discloses a power battery management method and system based on data driving, particularly relates to the technical field of battery degradation monitoring, and is used for solving the problem that hidden degradation of a battery in a microcirculation state is difficult to monitor in real time in the prior art. According to the invention, microcirculation energy spectrum is analyzed through a transient spectrum clustering algorithm, and risks of spectrum concentration abnormity and structure degradation are identified; the internal resistance deviation trend of the battery is analyzed, and the influence of internal resistance fluctuation on active material granulation is evaluated; judging whether microcirculation abnormity risks exist or not based on spectrum abnormity and internal resistance fluctuation conditions; when an abnormal risk exists, analyzing a stress accumulation condition through tensor decomposition and singular value decomposition, and evaluating an attenuation gradient of an electrode polarization signal in combination with variational mode decomposition and a recurrent neural network so as to determine an overall degradation trend of the battery; management of the power battery can be dynamically optimized, the service life is prolonged, and safety and reliability are improved.