1792 results about "Operational reliability" patented technology

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 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 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 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 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 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 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.

The invention relates to a switch cabinet operation reliability evaluation method, system and device and a medium, and belongs to the technical field of power distribution equipment monitoring, and the evaluation method comprises the steps: receiving real-time monitoring data of a to-be-detected switch cabinet; performing fast Fourier transform on the current waveform data, and calculating to obtain a harmonic distortion rate sequence; performing feature extraction on the vibration spectrum data to obtain a vibration dominant frequency amplitude sequence; correlation analysis is carried out on the harmonic distortion rate sequence and the vibration dominant frequency amplitude sequence, a sliding covariance matrix is constructed, and a dynamic coupling coefficient is calculated; generating a three-dimensional feature space distribution diagram according to the harmonic distortion rate sequence, the vibration dominant frequency amplitude sequence and the dynamic coupling coefficient sequence; and dividing the real-time monitoring data into a plurality of feature regions based on density clustering, determining a current fault state corresponding to the to-be-detected switch cabinet according to the feature regions mapped by the real-time monitoring data, determining a corresponding maintenance strategy, generating a reliability evaluation report, and sending the reliability evaluation report to the management terminal. The operation stability of the switch cabinet equipment can be improved.

The invention provides a driving station state monitoring system for a gravity energy storage transportation track, and relates to the technical field of intelligent control, and the system comprises a calculation module which is used for responding to a primary early warning signal, collecting the real-time oil pressure data of a gear box, a bearing temperature rise rate curve and a dynamic torque feedback value of a target standby driving station, and calculating an availability index; the correction module is used for determining the position of a gearbox input end flange and a driving motor output end coupler as a first monitoring point and the position of connection between a bearing seat and an output shaft as a second monitoring point on a transmission link of the target standby driving station, and respectively calculating the axial temperature gradient difference and the radial vibration energy ratio between the two monitoring points; and generating a dynamic correction value through a preset coupling coefficient, and compensating the availability index based on the dynamic correction value to obtain a corrected index. The operation reliability of the gravity energy storage system can be improved.

The invention discloses a scraper conveyor middle trough production line operation state monitoring method based on digital twinning, and belongs to the technical field of industrial intelligent manufacturing. The method comprises the following steps: constructing digital twin bodies in one-to-one correspondence with physical production line elements; establishing a real-time data driving channel between the digital twin and the physical elements; based on the channel, driving the digital twin to synchronously map the real-time operation parameters, and generating a virtual operation state of the production line; based on the virtual operation state, performing time sequence deduction on the processing flow in the digital twin according to the processing technology logic and the equipment performance parameters to obtain a production line pre-estimation state of a future time point; and comparing the parameters in the pre-estimated state with a preset threshold range to generate prediction information. According to the invention, real-time data driving and bidirectional interaction of the physical production line and the virtual model are realized, the problems of data isolation and feedback lag of a traditional monitoring mode are overcome, the prediction capability is provided, and the operation reliability and intelligent management of the production line are effectively improved.

The invention provides an inclined variable-angle aircraft interstage separation ground test device. The inclined variable-angle aircraft interstage separation ground test device comprises an aircraft separation device, an angle adjusting device, a temperature control and acquisition device, a vibration control and impact acquisition device, an ignition control device and a device base. The angle adjusting device comprises a device side plate, an arc groove, an angle ruler, a separation mechanism mounting plate, a supporting hinge, a supporting rod, an adjusting sliding rail, an adjusting base, a locking device, a device base gradienter, a rotating bearing and an auxiliary fixing block. The temperature control device comprises a quartz lamp array heating cage, an outer wall temperature control thermocouple and an inner wall temperature acquisition thermocouple, and can simulate a pneumatic heating environment at a separation moment, so that the thermal deformation matching capability of the separation mechanism in a high-temperature environment and the operation reliability of the separation mechanism are obtained. The vibration control and impact acquisition device is composed of a vibration table, a vibration control sensor and an impact sensor.

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.

The invention relates to the technical field of artificial intelligence and equipment control, and discloses an AI-driven commercial refrigerator dynamic energy-saving and load prediction system, which comprises the following steps: circularly multiplexing the working and shutdown of a compressor into an active detection process for the thermodynamic state of a refrigerator, and calculating the temperature of the refrigerator according to the temperature time sequence data generated in the process; according to the method, multi-scale dynamic characteristics capable of representing the total heat load and the heat exchange efficiency at the same time are extracted, a machine learning model is input for working condition classification, and then self-adaptive adjustment of a control strategy is achieved. The insight from the passive response to the single temperature to the core hidden variable of the dynamic thermal load is realized, so that the control system can identify the real working condition and make the optimal energy-saving decision, and the energy efficiency and the operation reliability of the equipment in the complex dynamic environment are improved.

The invention relates to the technical field of transformer coil detection, and discloses a detection device for a transformer coil, which comprises a base frame, the driving mechanism is installed on the base frame and used for providing linear motion in the X direction, the Y direction and the Z direction; according to the scheme, by means of the detection mode that spiral scanning motion is combined with self-adaptive contact and laser ranging, the fatal defects that manual detection is low in efficiency, high in subjectivity and prone to making mistakes and missing detection are overcome, complete three-dimensional shape data of the surface of the coil can be rapidly and accurately obtained, and the detection accuracy is improved. According to the method, local fine defects which are difficult to discover manually can be detected, the reliability and the consistency of detection results are improved, objective and quantitative data support is provided for evaluating the winding quality, the mechanical strength and the long-term operation reliability of a transformer coil, and the manufacturing quality of a transformer and the operation safety of a power grid are guaranteed.

The invention relates to the technical field of motors, and discloses a high-temperature-resistant direct-current coreless gear motor and a test system, and the system comprises a temperature monitoring module, a current regulation and control module, a heat dissipation optimization module, a load response module, a cloud analysis module and a virtual verification module. By arranging the temperature monitoring module, multi-dimensional temperature data during operation of the motor can be acquired in real time, and a temperature distribution diagram can be generated, so that the system can comprehensively sense the internal thermal state of the motor and accurately identify local overheating risk points, thereby improving the operation reliability and safety of the motor under a high-temperature working condition; through cooperative operation and closed-loop control of the current regulation and control module, the heat dissipation optimization module and the load response module, according to real-time temperature and load changes, driving current parameters and heat dissipation resource distribution can be dynamically adjusted, and the heat dissipation efficiency is improved. And the output performance and the torque stability of the motor under high-temperature and high-load conditions are ensured.