1971 results about "Operational reliability" patented technology

The invention provides a hydropower station auxiliary machine equipment real-time monitoring method and system based on the Internet of Things, and relates to the technical field of information, and the method comprises the steps: constructing a pressure oil pump fault diagnosis model for a temperature and vibration related feature subset, and training a temperature and vibration feature sample of a preset fault type, establishing a mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; on the basis of the pressure oil pump fault diagnosis model, typical faults of bearing abrasion and poor gear meshing of the pressure oil pump are classified and recognized, and the temperature and vibration fault diagnosis result of the pressure oil pump is determined through the mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; and according to the fault diagnosis results of the temperature and vibration of the pressure oil pump, carrying out comprehensive decision making on a plurality of diagnosis results, according to the preset confidence of different diagnosis results, giving corresponding weight coefficients, and generating a final health state evaluation result of the pressure oil pump. The operation reliability and the maintenance efficiency of the pressure oil pump are improved.

A transformer fault diagnosis method based on vibration mechanisms of an iron core and a winding. The method comprises: performing signal processing and feature extraction by means of calling simulation results of an iron core and a winding; and establishing a transformer fault diagnosis model. Thus, the accurate diagnosis of a transformer fault is realized. A conventional fault diagnosis method is improved, and chaotic mapping and an opposition-based learning strategy are introduced to optimize the diagnosis model, thereby improving the accuracy of diagnosis. Compared with the conventional method, a fault type and a fault degree of a transformer can be more accurately determined, so as to facilitate an engineering technician in maintenance, thereby improving the operation reliability of the transformer. In addition, a transformer fault diagnosis system based on vibration mechanisms of an iron core and a winding, and a computing device and a computer-readable storage medium are further provided.

The invention discloses a zero point correction method for an electromagnetic flowmeter based on pressure compensation, which relates to the technical field of industrial flow measurement, and is characterized in that abnormal data is corrected through multi-parameter data acquisition and preprocessing by using Kalman filtering denoising and an isolated forest algorithm, so that the accuracy of the data is ensured; a multi-parameter compensation model is further constructed, an LSTM network is combined with an attention mechanism to dynamically distribute weights, and the weights are lightly deployed to an embedded microcontroller through a TensorFlow Lite framework, so that rapid reasoning and real-time correction are realized; in the operation process, an embedded system reads preprocessed data in real time, a model is input to obtain a zero drift compensation value, compensation is carried out by comprehensively considering factors such as fluid density, pipeline vibration and electromagnetic interference, and a self-adaptive weight adjustment mechanism is introduced to optimize compensation precision; the measuring precision and the long-term operation reliability of the electromagnetic flowmeter are remarkably improved, and the electromagnetic flowmeter is suitable for flow measurement under complex working conditions.

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 relates to an equipment fault mode identification and diagnosis method based on deep learning, and aims to improve the accuracy and generalization ability of equipment fault diagnosis. A vibration signal, a temperature signal, an acoustic signal, a current signal and image data of equipment are synchronously acquired through a multi-modal data acquisition system, and weighted fusion is performed on different modal data by adopting a self-adaptive multi-head attention mechanism. And then, performing time sequence modeling by using a bidirectional long-short term memory network (BiLSTM), and finally outputting a fault type and a fault saliency map to help operation and maintenance personnel to position a fault area. And through a transfer learning technology, the adaptability and diagnosis precision of the model under different equipment and working conditions are further improved. The method can be widely applied to fault diagnosis and intelligent operation and maintenance of various devices, and the operation reliability and the maintenance efficiency of the devices are effectively improved.

The invention relates to the technical field of machine tool calibration, and discloses a calibration method and device for precisely manufacturing a multi-axis linkage machine tool, and the method comprises the steps: collecting machine tool operation data and multi-modal sensor data of the multi-axis linkage machine tool, and carrying out the error prediction, and obtaining the error prediction data; the positions of all shafts of the machine tool are measured, and nanoscale resolution ratio position data are obtained; real-time state representation of the machine tool is obtained through the multi-branch deep neural network; constructing a multi-level hypergraph based on the real-time state representation of the machine tool, and carrying out feature processing to obtain a fault feature vector; reinforcement learning is carried out through a deep Q network to obtain an adaptive compensation strategy; linkage compensation values of all axes are generated on the basis of the self-adaptive compensation strategy, real-time adjustment and fault prevention analysis are conducted on the multi-axis linkage machine tool, a fault prevention control scheme is generated, the precision maintaining capacity and operation reliability of the multi-axis linkage machine tool are improved, and the method can be suitable for different types of multi-axis linkage machine tools.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention belongs to the technical field of distribution boxes, and discloses an intelligent fault early warning maintenance management system for a distribution box. Comprising the steps of obtaining multi-dimensional monitoring data of a full life cycle of a distribution box, and comprehensively describing a health state of the distribution box; dividing the whole life cycle into a plurality of relevance operation stages, identifying a hidden fault mode from an equipment operation event sequence by using a time sequence relevance mining algorithm, and calculating an abnormal evolution coefficient of each stage; based on the abnormal evolution coefficient and the hidden fault mode, constructing a dynamic risk entropy value of the current stage, and dynamically evaluating the fault risk; obtaining a gradient change trend of a risk entropy value, positioning a key degradation component set, generating a priority diagnosis map containing a fault root path, adaptively adjusting a strategy decision tree, and outputting an optimization maintenance scheme set; the operation reliability and the maintenance efficiency of the distribution box are obviously improved, and stable operation of a power system is guaranteed.

As described herein, various embodiments of the present invention improve computational efficiency of performing reliable persistent monitoring of a computer system. Persistent monitoring of the operations of a computer system has various operational reliability benefits for various computer systems and is an important part of service level objectives for highly maintenance-critical computer systems. However, doing the noted persistent monitoring operations in a reliable manner requires access to large amounts of labeled training data that are not always available for more customized computer systems with unique operational / behaviorial pattern signatures. In response, various embodiments of the present invention address the noted challenges by generating training data for an anomalous operational state detection machine learning model whose training data may be generated using a ground-truth validation criterion that is defined based at least in part on an inferred outlier score for a decomposed residual component of a given operational monitoring timeseries trend.

The invention relates to the technical field of power system automation, and discloses an automatic control method and system for a multifunctional ring main unit. The method comprises the following steps: acquiring voltage, current and temperature parameters of a ring main unit in real time, analyzing load characteristics based on a clustering algorithm in combination with historical operation data, and generating a classification result including a load type, a seasonal trend and a load distribution thermodynamic diagram; a load change trend is extracted based on time sequence regression analysis, a fault prediction model is constructed by using a long short-term memory network, and a dynamic alarm threshold value is set; a grading alarm mechanism is triggered through comparison of real-time data and a model, a fault type and a positioning result are extracted in combination with a Bayesian network algorithm, the fault type is positioned, and finally automatic control is executed according to the fault type. Through data-driven intelligent analysis, the problems that a traditional ring main unit is complex in debugging and lagged in fault response are solved, the operation reliability, the maintenance efficiency and the power grid stability are remarkably improved, and predictive maintenance and self-adaptive control are achieved.

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 provides a state self-checking system for an unhooking robot, and relates to the technical field of automatic machinery, and the system comprises a real-time collection module which collects a real-time working data set of the unhooking robot; the operation evaluation module is used for comprehensively evaluating the operation of the unhooking robot based on the real-time working data set; the fault detection module is used for carrying out point cloud matching and data comparison on the unhooking robot based on the operation state evaluation information, carrying out fault detection according to a comparison result and generating a fault self-detection report; the fault tracing module is used for synchronizing the fault self-checking report to a human-computer interface for fault tracing; and the fault processing module is used for formulating fault processing suggestions and sending the suggestions to the human-computer interface to perform remote control on the unhooking robot. The technical problem that in the prior art, due to insufficient fault detection precision, tiny or complex faults cannot be found in time, and the operation stability of the robot is affected is solved, the fault detection precision and the response speed are improved, and the operation reliability and safety are enhanced.

The invention discloses a slurry pump control method and related equipment, and relates to the technical field of seawater desalination, and the method comprises the following steps: obtaining sensor data of a slurry pump; data analysis is conducted on the sensor data, and the working condition abnormal index of the slurry pump is determined; determining a target control instruction corresponding to the working condition abnormal index based on the working condition abnormal index and a preset threshold interval; and based on the target control instruction, the slurry pump is controlled to execute corresponding target regulation and control operation. Real-time monitoring and intelligent judgment of the operation state of the slurry pump are achieved, early warning can be triggered at the initial stage of equipment abnormity, the operation parameters can be adjusted in a self-adaptive mode, the response delay problem of a traditional single control mode is effectively avoided, and the operation reliability and safety of the equipment are improved.

The invention discloses a cold plate-immersion liquid cooling cooperative heat dissipation method based on steam zone coupling modeling, and relates to the technical field of cooperative heat dissipation. Comprising the following steps of S1, collecting collaborative heat dissipation monitoring data of each key position in real time, and performing data preprocessing; s2, the comprehensive risk of the steam area is judged, and a risk cooling adjustment instruction is issued according to the comprehensive risk judgment result; s3, receiving a risk cooling adjustment instruction, carrying out collaborative heat dissipation, evaluating the collaborative heat dissipation capability, adjusting the rotating speed of a circulating pump and the opening degree of a valve, and requesting condensation backflow adjustment; s4, judging the adjusting quantity of condensation reflux, executing reflux adjustment and performing safety monitoring; and S5, continuously monitoring the data of each link, and carrying out anomaly detection on the state of each link to realize anomaly regulation and control. The problems that in an existing single-phase cold plate or two-phase immersion liquid cooling mode, steam accumulation is prone to occurring in a high-heat-flow scene, so that the pressure intensity is increased, the boiling point is increased, and the heat exchange efficiency is reduced, and then the operation reliability of equipment is affected are solved.

The invention relates to the technical field of agricultural robot motion control, in particular to a mechanical arm posture stable adjusting method based on vibration feedback, which comprises the following steps: S1, collecting a vibration signal of an end effector of a mechanical arm in real time; s2, frequency domain analysis and amplitude-phase decomposition are conducted on the vibration signals, and vibration characteristic parameters of the mechanical arm in the X / Y / Z axis direction are extracted; s3, generating a multi-axis vibration vector in combination with the terrain inclination angle data; s4, attitude compensation parameters of all the joints are calculated; s5, a mechanical arm joint angle adjusting instruction is generated according to the posture compensation parameters; and S6, circularly executing the steps S1 to S5 until the vibration amplitude is lower than a preset threshold value. According to the method, the multi-axis vibration characteristics are extracted in real time, the joint compensation angle is calculated through the anti-offset and anti-overturning matrix, rapid and stable attitude control of the mechanical arm in the high-frequency disturbance environment is achieved, and therefore the tail end positioning precision and the overall operation reliability are remarkably improved.

The invention discloses a method for predicting blast furnace TRT equipment maintenance time based on an LSTM model, and the method comprises the steps: constructing a sliding window adaptive algorithm based on vibration signal change characteristics, and dynamically adjusting the size of an input data sliding window of the LSTM model according to the fluctuation amplitude and change rate of a vibration signal; adopting an adaptive sliding window to obtain TRT equipment training data, and training the LSTM model; applying the trained LSTM model to the vibration signals acquired in real time, predicting the vibration trend of the TRT equipment in the future n days at the current point, and outputting a predicted value of the vibration signals in a future period of time; and according to the prediction result of the vibration signal, in combination with a preset threshold value of shutdown maintenance, judging the time when the TRT equipment reaches the threshold value, and further obtaining the remaining days of the TRT equipment away from the maintenance. According to the method, the future development trend of the TRT equipment vibration signal is accurately predicted, the maintenance plan is reasonably arranged, and the TRT equipment operation reliability and production efficiency are improved.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention relates to a power distribution network abnormal state intelligent identification method based on unmanned aerial vehicle routing inspection, and the method comprises the following steps: S1, obtaining power distribution network routing inspection related data, including asset main data, historical operation and maintenance data and environment constraints; s2, constructing the digital twinning of the power distribution network based on the power distribution network inspection related data, and obtaining the mapping from the equipment ID to the spatial pose and parameters; s3, generating a risk heat map based on historical defects and environmental risk scoring; s4, constructing an equipment-geography-working condition knowledge graph, and generating inspection targets and priorities according to line sections in combination with mapping from equipment IDs to spatial poses and parameters and a risk heat map to obtain a task package; and S5, performing multi-target route inspection according to the task package, the risk heat map and the mapping from the equipment ID to the spatial pose and the parameter, and intelligently identifying the abnormal state of the power distribution network. The operation reliability of the power distribution network is effectively improved, and the power supply quality is improved.

The invention discloses a multi-target cooperative control method and device for a direct drive motor, and relates to the related technical field of motor control, and the method comprises the steps: obtaining a motor control scheme corresponding to a predetermined control scene; establishing a motor control expectation vector; obtaining a control expectation deviation evaluation result; if the control expectation deviation evaluation result does not meet the deviation evaluation multilateral constraint, generating a control optimization first guide factor; generating a first motor control strategy set; and performing multi-target joint optimization on the first motor control strategy set to generate a motor control optimization strategy, and controlling the direct drive motor in combination with a predetermined control scene. The technical problems that in the prior art, multiple targets of a direct-drive motor are difficult to control in a coordinated mode, complex working conditions lack an effective deviation evaluation and optimization mechanism, and the adaptive capacity is insufficient, so that the control accuracy of the direct-drive motor is insufficient, and the operation reliability is poor are solved. The technical effects of realizing multi-target cooperative control of the direct-drive motor and improving the control precision and the operation reliability are achieved.

The invention relates to a membrane stack (9) for an air humidifier (1) of a fuel cell for humidifying a dry supply air stream (2) of the fuel cell by means of a moist exhaust air stream (3) of the fuel cell, - wherein the membrane stack (9) is configured in a cuboid shape, has a stacking direction (S) and four stacking sides (10) perpendicular to the stacking direction (S), - wherein the membrane stack (9) has a vertical direction (Z) parallel to the stacking direction (S), a longitudinal direction (X) perpendicular to the vertical direction (Z) and a transverse direction (Y) perpendicular to the vertical direction (Z) and perpendicular to the longitudinal direction (X), - wherein the four stack sides (10) form an air supply inlet (ZE) and an air supply outlet (ZA) facing away from each other in the longitudinal direction (X), and an exhaust air inlet (AE) and an exhaust air outlet (AA) facing away from each other in the transverse direction (Y), - wherein the membrane stack (9) comprises several membranes (11) which are permeable to moisture and impermeable to air and which follow one another in the stacking direction (S) such that the membranes (11) within the membrane stack (9) each separate a supply air path (ZP) connecting the supply air inlet (ZE) to the supply air outlet (ZA) from an exhaust air path (AP) connecting the exhaust air inlet (AE) to the exhaust air outlet (AA). Improved functional reliability can be achieved by having the membrane stack (9) have several spacers (12) which are arranged in the stacking direction (S) between two immediately successive membranes (11) in one of the exhaust air paths (AP).

The invention provides a digital mechanical equipment data management system based on cloud computing. The digital mechanical equipment data management system comprises a data acquisition module for acquiring equipment state and environment information; the feature coding module is composed of a gating circulation unit and a multi-layer perceptron and is used for extracting high-dimensional features; the feature fusion module fuses the high-dimensional features and outputs sparse joint codes; the cloud computing model construction module constructs a model and introduces a trainable enhancement layer; the state interaction module enables the code to obtain an advanced interaction state based on a model; the state synchronization module synchronizes a device state and environment information through MechLayer; the implicit state generation module obtains an implicit state by combining normalization and enhanced connection; operating an environment perception modeling module to improve perception estimation modeling; the perception learning vector generation module obtains a learning vector; the state prediction module predicts an operation state; and the track decoding module processes the input to obtain an equipment running track. The equipment management efficiency and the operation reliability can be improved.

The invention discloses a power distribution network topology optimization method and system, and relates to the technical field of power distribution network topology optimization, and the system comprises a dynamic health degree obtaining module, an operation risk cost module, a topology optimization module and a maintenance scheme generation module. The dynamic health degree index and the operation risk cost of the key electrical equipment are incorporated into the topological optimization objective function, and the weight is dynamically adjusted according to the health state of the equipment, so that the problem that in the prior art, the actual health state of the equipment is ignored, so that the degradation of the equipment is possibly accelerated by the optimization scheme is effectively solved; therefore, the real-time health condition evaluation information of the key equipment can be effectively integrated, the short-term operation economic index and the long-term operation reliability of the equipment are dynamically balanced in the optimization process, the unexpected fault risk of the equipment is reduced, and the overall reliability and safety of operation of the power distribution network are improved.

The invention discloses a dynamic closed-loop management method for bridge-shaped contact performance, and relates to the technical field of power system protection. The method comprises the following steps: S1, a dynamic sensing process: acquiring multi-source dynamic parameters of a bridge-shaped contact in a non-intrusive mode; s2, a quantitative evaluation process: fusing the multi-source dynamic parameters and the static parameters, and calculating a health index or a health level of the contact through a preset model; s3, adaptively optimizing the process, dynamically adjusting the closing energy or protection parameters of the circuit breaker according to the health state, and compensating the performance change of the contact; and S4, a predictive maintenance process: performing trend extrapolation based on the historical data of the health index, predicting the residual life and generating a maintenance suggestion. The processes are sequentially connected and cyclically iterated to form dynamic closed-loop management, so that the problems of one-sided parameter sensing, inaccurate evaluation, fixed parameters and maintenance lag in the prior art are solved, the operation reliability of the bridge-shaped contact is improved, the service life of equipment is prolonged, and the operation and maintenance cost is reduced.

The invention relates to the technical field of logistics transportation management, and discloses a vehicle transportation management method based on a digital technology, and the method comprises the following steps: collecting the energy state data of a vehicle in real time through a vehicle monitoring module; transmitting the collected vehicle data to a big data platform by using an Internet of Things technology, analyzing the data, and generating an early warning signal of an energy state; when the early warning signal is triggered, determining an optimal supply path according to the current position of the vehicle and traffic information, and arranging an energy supply service in advance; and through an intelligent scheduling module, according to real-time traffic information, vehicle load and destination distance factors, a transportation route and task distribution of the vehicle are dynamically adjusted. Through real-time monitoring and analysis, the system can automatically trigger a supply early warning signal when the energy state is lower than a safety threshold value, so that non-planned parking caused by energy exhaustion is avoided, and the accuracy of energy management and the operation reliability are remarkably improved.

The invention discloses an elevator operation fault early warning method and system, relates to the technical field of intelligent elevator control, and aims to improve the operation safety and fault response capability of an elevator in a potential abnormal state. By constructing a load critical drift test working condition, a sudden non-vertical stress test working condition and a weak power supply fluctuation interference test working condition, abnormal behaviors which are difficult to appear but have potential safety hazards in the normal operation process of an elevator are induced; the method comprises the following steps: acquiring operation data under various working conditions, respectively calculating a drift overload response coefficient, a guide rail non-vertical interference coefficient and a power supply induced instability coefficient, respectively comparing the coefficients with preset thresholds, and judging whether operation faults such as brake response mismatching, asynchronous action of safety tongs and door lock control interference exist or not; and if the judgment result is abnormal, a targeted strategy is automatically generated and early warning is given out. According to the method, active recognition and classified response of elevator faults can be achieved, and the maintenance efficiency and the operation reliability are effectively improved.

The invention discloses a hydraulic engineering motor set fault early warning system, and relates to the technical field of hydraulic engineering equipment monitoring, and the hydraulic engineering motor set fault early warning system comprises the following steps: S1, collecting and integrating mechanical state data, including vibration, temperature and oil pollution monitoring; s2, electrical parameter monitoring and complementary analysis are carried out, and mechanical data are associated to identify faults such as short circuit; s3, hydraulic monitoring and cooperative early warning are carried out, pressure flow abnormity is detected, and mechanical and electrical data are linked; s4, multi-source data fusion and fault classification are carried out, and dynamic weight distribution is carried out to improve the diagnosis accuracy; s5, dynamic threshold optimization and cloud decision making are carried out, and the threshold is adjusted according to working conditions to reduce the false alarm rate; s6, closed-loop maintenance and intelligent pushing are carried out, faults are positioned, maintenance suggestions are pushed, and closed-loop verification is carried out; the method has the beneficial effects that accurate fault classification, cross-dimension collaborative early warning and closed-loop maintenance decision making are realized, and the operation reliability and the maintenance efficiency of the unit are remarkably improved.

The invention provides a centrifugal pump operation monitoring method and system, and the method comprises the following steps: collecting the operation parameters of a centrifugal pump in real time through a plurality of sensors, including inlet flow, inlet and outlet pressure and pressure difference, a main shaft vibration signal, a main shaft rotation speed, and the temperature of a pump body and a medium; denoising, abnormal value elimination and normalization preprocessing are carried out on the collected data; establishing a centrifugal pump spindle radial hydraulic exciting force model; calculating the alternating stress of the main shaft according to the hydraulic exciting force and predicting the fatigue life; and a fault early warning mechanism is designed based on the fatigue life prediction result, and real-time monitoring and fault early warning of the running state of the centrifugal pump are achieved. Through combination of data acquisition and processing, life prediction and a fault early warning mechanism, real-time monitoring of the running state of the centrifugal pump spindle and residual life prediction are achieved, and the running reliability and safety of equipment are improved.

The invention discloses an unattended intelligent control method and system for a heat exchange station, and relates to the technical field of heat energy automatic control, and the method comprises the steps: collecting multi-source environment parameter data, constructing an environment parameter sequence set, constructing a heat load prediction model, and outputting a predicted heat load value through extracting a change relation between local temperature trend characteristics and a time sequence. Carrying out residual comparison on an actual thermal load value and the predicted thermal load value, constructing a residual fluctuation measurement sequence, carrying out multi-dimensional state judgment in combination with a preset threshold range, outputting an operation state identifier, carrying out linkage adjustment on a water pump frequency parameter and a water mixing valve opening parameter, calling an optimization control strategy set, and carrying out optimization control on the water pump frequency parameter and the water mixing valve opening parameter. And generating an updated water pump adjusting instruction and an updated valve adjusting instruction. The operation reliability and the energy utilization efficiency of the heat exchange station are improved, unattended intelligent control is achieved, and the technical problems that in a traditional control method, prediction precision is low, abnormal recognition is not accurate, and control parameter coordination is poor are solved.

The invention provides an old wind power plant fan foundation reconstruction structure and method, and relates to the technical field of new energy wind power generation, the old wind power plant fan foundation reconstruction structure comprises an existing fan foundation, a first section of tower drum is arranged in the center of the top surface of the existing fan foundation, a reinforcing member is arranged on the first section of tower drum, and a supporting column is radially arranged around the side surface of the reinforcing member; pile foundations are arranged at the ends, away from the reinforcing components, of the supporting columns. The bottom of the first tower drum is tightly matched with the original foundation, the top of the first tower drum is connected with the new tower drum, the first tower drum, the supporting columns and the existing fan foundation are cooperatively stressed to form a new bearing structure, the load of the original foundation can be reduced, and the overall stability of the fan structure is achieved; the bearing capacity is further improved by arranging the reinforcing components and the stiffening ribs, the stability is enhanced, the transformation effect and the operation reliability are comprehensively improved, and a new thought is provided for efficient, economical and safe transformation of the old wind power plant.

According to the technical scheme, the power transmission line comprises a plurality of lines, and the method comprises the following steps: calculating the ice thickness prediction value of each ice melting section based on the real-time ice thickness prediction value of each ice melting section, the bearing capacity of a tower, the tension and crossing distance of a ground wire, and the load of a hardware fitting and an insulator; calculating the real-time icing safety degree of each ice melting section; sequencing all the ice melting sections according to the real-time icing safety degree of each ice melting section to form an ice melting sequence set, and selecting the ice melting section with the highest ice melting urgency degree according to the ice melting sequence set to execute ice melting operation; in the ice melting process, ice melting current is intelligently regulated and controlled according to real-time monitoring data, a fastest ice melting mode or a common ice melting mode is selected according to an external instruction, and three strategies of aggressive, balanced and conservative are further subdivided in different modes; according to the invention, efficient and safe ice melting operation can be realized under complex working conditions, and the operation reliability and the ice melting efficiency of the power transmission line are remarkably improved.