990 results about "Maglev" patented technology

The invention provides a maglev train multi-suspension system predictive control method based on event triggering, which comprises the following steps: establishing a multi-electromagnet suspension system continuous dynamic model based on electromagnet parameters of a maglev train; in combination with the input time delay of the multi-electromagnet suspension system, the continuous dynamic model of the multi-electromagnet suspension system is sequentially processed based on a fourth-order Runge-Kutta method, and a discrete coupling time delay dynamic model of the multi-electromagnet suspension system is obtained; establishing a model prediction controller of the multi-electromagnet suspension system by combining the discrete coupling time delay dynamical model of the multi-electromagnet suspension system and the optimization problem; and updating a control signal of the model prediction controller according to a preset composite event triggering mechanism to obtain an initial control quantity, designing a time delay compensation strategy to carry out input time delay compensation on the initial control quantity, and obtaining a standard control quantity to control the multi-suspension system of the maglev train. According to the method, the system energy consumption is remarkably reduced while the good operation performance of the suspension system in a complex environment is guaranteed.

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The invention discloses a high-temperature superconducting electric maglev vehicle multi-mode damping device based on an electromagnetic shunting technology. The high-temperature superconducting electric maglev vehicle multi-mode damping device comprises a coil system and a vehicle suspension frame. Wherein the coil system comprises a vehicle-mounted superconducting magnet, an 8-shaped suspension guide coil and a propulsion coil; the vehicle suspension frame carries a vehicle-mounted superconducting magnet, a low-temperature container, a damping coil and an external circuit module. Accurate design of electromagnetic parameters is achieved through a plurality of branch parallel circuits, dynamic responses of different frequencies can be effectively matched, and multi-mode vibration reduction is achieved in a passive mode. An optimized damping effect can be provided for various vibration modes in a rail system without depending on an active control system, and the vibration reduction performance of the system is remarkably improved. Aiming at different vibration frequencies, adjustment can be carried out by replacing components such as capacitors and inductors in the branches, the structure is simple, and the cost is low.

The invention discloses a maglev train suspension system electromagnet fault detection algorithm, which comprises the following steps: establishing a suspension system model of a maglev train, and designing a state observer; an electromagnet signal is collected and processed, and a residual value is obtained; the residual value is compared with a threshold value, and fault information of the electromagnet of the maglev train suspension system is obtained; and fault information of the electromagnet is reconstructed. The fault of the electromagnet can be effectively detected and reconstructed by constructing the third-order linear extended state observer (LSEO), system uncertainty, noise and the like can be dealt with through the self-adaption and self-calibration functions of the third-order linear extended state observer, robustness is enhanced, and detection reliability is improved. Fault information of the magnetic levitation system can be quantitatively estimated through LSEO for fault reconstruction, the fault type and position can be accurately identified, and false alarm and missing alarm can be avoided. Meanwhile, fault reconstruction provides an important basis for maintenance, and maintenance personnel can quickly position and take targeted measures, so that the maintenance efficiency is improved, and the cost is reduced.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention relates to the technical field of train cooperative control, in particular to a train cooperative control method based on a speed curve interval optimization setting strategy, and the method comprises the steps: building a train simple substance point dynamic feature model; according to the train simple substance point dynamic characteristic model, a train dynamic model is obtained and discretized; according to the discretized train dynamics model, optimizing a train tracking operation curve interval to obtain a train reference speed optimal interval; according to the train reference speed optimal interval, a leading train interval objective function and a following train interval objective function are established and optimized, and the optimal control acceleration is obtained to control the train. According to the maglev train operation curve interval optimization method provided by the invention, a continuous and limited optimal speed reference is provided for multi-train cooperation, and a layered cooperative control strategy is provided by adopting a set optimal speed interval, so that efficient, energy-saving and comfortable cooperative tracking operation of multiple maglev trains is realized.

The present disclosure relates to a maglev bogie, in particular to a maglev bogie with a centering function and a guiding method thereof. The maglev bogie comprises a maglev guide frame, crank arm brackets, guide wheels, first onboard magnet groups and cylindrical gears, wherein the crank arm brackets are hinged to the four corners of the top and bottom of the maglev bogie, the guide wheels are rotatably connected to the ends of the crank arm brackets, two first onboard magnet groups are slidably connected to each of the two inner sides of the upper part of the maglev guide frame, some of the cylindrical gears are connected to the top of the maglev guide frame where the upper crank arm brackets are hinged, two of the cylindrical gears are rotatably connected to each of the two sides of the top of the maglev guide frame, and every two adjacent cylindrical gears are meshed with each other. With the arrangement of the crank arm brackets, the guide wheels and the cylindrical gears, when the guide wheels on one side is squeezed by overhead track beam and displaced, the guide wheels on the other side can be synchronously driven to displace, so as to ensure a centered state of the maglev guide frame, the first onboard magnet groups, the overhead track beam and beam-borne magnetic track.

The invention provides a maglev traffic track structure and a construction method thereof. The maglev traffic track structure comprises a bridge deck, a maglev track assembly and an induction plate assembly. The magnetic levitation rail assembly comprises a magnetic levitation rail, a hexagon nut, a gasket, a thin nut and a screw. The induction plate assembly comprises an iron core, an aluminum plate, shear nails and screws. The iron core is vertically welded and fixedly connected with the shear nails, the shear nails and the bridge deck are cast and anchored into a whole, the iron core is arranged on the upper end face of the bridge deck, and the aluminum plate is installed on the upper end face of the iron core through screws; the construction method comprises the steps that the prefabricated bridge deck slab is modularly poured in a factory, the magnetic levitation rail is installed, adjusted and locked in the factory, the levelness and straightness of the magnetic levitation rail are measured again, and the bridge deck slab is installed on a construction site. According to the invention, high-precision, high-efficiency and low-cost installation of the maglev traffic track is realized.

The invention relates to the technical field of motor control, in particular to a speed sensorless control method of a long-stator linear motor for a maglev train in a full-speed domain range. According to the method, when the train runs at a low speed, the mechanical position sensor is adopted for control, and control precision is high; when the train runs at a high speed, speed sensorless control based on extended back electromotive force is adopted, so that control errors caused by data transmission delay can be avoided; and a weighted average switching algorithm is provided when switching is controlled by adopting a mechanical sensor at a low speed and adopting a speed-free sensor at a high speed, and the weighted average switching algorithm comprises motor rotating speed switching and motor angle switching, so that stable transition of the rotating speed and the angle of the motor during high and low speed switching is realized, and overcurrent of the motor is avoided. By adopting the method, stable operation of the long-stator linear motor in a full-speed domain range can be realized, and safe operation of a train is ensured.

The invention discloses a guide constraint control method for a maglev high-speed rail under network attacks, and belongs to the field of rail transit. The method adopts a dynamic air gap constraint and boundary layer sectional type continuous adjustment technology to perform anti-network attack control on the maglev high-speed rail guide system: establishing a dynamic mathematical model and a lateral centrifugal force model of the maglev high-speed rail guide system, and further obtaining a state-space equation and a centrifugal force suppression control rate of the maglev high-speed rail guide system; constructing a satellite navigation network attack model of the maglev high-speed rail track and driving information; converting a state-space equation of the magnetic levitation high-speed rail guiding system into a control equation of the magnetic levitation high-speed rail guiding system containing wind shear disturbance and guiding force mismatch matching interference; dynamic constraint control is adopted, a guiding controller is designed, and guiding constraint control over the magnetic levitation high-speed rail under network attacks is achieved. While the starting stability of the maglev high-speed rail is improved, mismatch of the guiding force is rapidly compensated, and stable operation of the maglev high-speed rail is guaranteed.

The invention discloses a suspension control fault detection and recovery method and device for a maglev train and a medium, and relates to the field of maglev trains, and the method comprises the following steps: S1, constructing a linearized suspension system model; s2, reconstructing a state vector, and generating a suspension gap estimation value; s3, constructing a health probe set, and executing a signal selection strategy; s4, detecting a slow change fault based on the statistical accumulation characteristic of the residual error, and outputting a slow change fault detection result; s5, monitoring the variation trend of the residual error, and triggering a reset mechanism to clear historical accumulated data when a reset condition is met; and S6, generating a final fault mark, and forcibly updating the fault mark to a normal state if a reset mechanism is triggered. According to the hierarchical collaborative decision-making architecture based on the maglev train lap joint structure dynamic model, the condition of performance degradation or failure does not occur in a real system by the algorithm, so that the engineering applicability and reliability of fault diagnosis are improved.

The present invention discloses an embedded maglev rail vehicle maintenance device and a rail system thereof, and relates to the technical field of rail vehicle maintenance. The technical solution provided by the present invention couples a rail and a jacking unit for specifically performing a jacking action. The jacking unit can move vertically and extend out of a section of a power supply rail, thereby jacking up an embedded maglev rail vehicle that falls on the power supply rail, or sinking into / leveling two adjacent sections for the embedded maglev rail vehicle to directly enter. The maglev rail vehicle can be jacked up after moving onto the rail, and the operation efficiency is higher. The present invention is particularly suitable for maintenance equipment.

The invention provides a maglev traffic track connecting structure, a maglev traffic track and a construction method of the maglev traffic track. The connecting structure comprises a penetrating type sleeve, a long bolt, an elastic cushion set and a fastening assembly. The penetrating type sleeve penetrates in the thickness direction of the concrete cover plate. The long bolt sequentially penetrates through the track, the penetrating type sleeve and the induction iron core. The elastic pad set comprises a first elastic pad arranged between the top end of the penetrating type sleeve and the induction iron core and a second elastic pad arranged between the bottom end of the penetrating type sleeve and the track. The fastening assembly comprises a nut in threaded fit with the end of the long bolt, and the rail and the induction iron core are fixed to the upper side and the lower side of the concrete cover plate by screwing the nut. The connecting structure is simple and reliable, good in stress condition and convenient to operate and maintain. On one hand, the track is tightly attached to the bottom end face of the concrete cover plate, the stress condition of the long bolts is good, and shear failure is not prone to occurring. And on the other hand, on the basis of realizing adjustable precision, the replacement difficulty and cost of the induction iron core and the track are relatively low.

The invention relates to a permanent magnet maglev train which comprises a train body, permanent magnet rails and sleepers, a linear motor primary is arranged in the middle of the bottom of a suspension frame, vehicle-mounted permanent magnets are arranged on the two sides of the bottom of the suspension frame respectively, and the vehicle-mounted permanent magnets on the two sides are located over the corresponding permanent magnet rails respectively; a linear motor secondary is laid in the middle of the line, and the linear motor secondary is located under the linear motor primary at the bottom of the suspension frame; steel rails are laid on the two sides of the line respectively, supporting wheels and guide wheels are arranged on the two sides of the bottom of the suspension frame respectively, the supporting wheels are connected to the top faces of the corresponding steel rails in a rolling mode, and the guide wheels are connected to the outer side faces of the corresponding steel rails in a rolling mode. The vehicle-mounted permanent magnets on the two sides and the corresponding lower permanent magnet rails are arranged in a staggered and offset mode. According to the invention, the vehicle-mounted permanent magnets on the vehicle body and the corresponding permanent magnet tracks below adopt a staggered bias design, so that the influence of the guiding force (transverse force) of the permanent magnet magnetic suspension system on the stability of the system can be reduced.

The invention discloses a probability density evolution analysis method, medium and equipment for a maglev train suspension system, and belongs to the technical field of suspension control. The method comprises the following steps: acquiring a probability density true value of a target quantity of the suspension system; performing supervised training on a target quantity probability density output value of the physical information neural network based on the probability density truth value to obtain a trained basic physical information neural network model; forming a correction matrix by using a set of target quantity output values, correcting a target quantity probability density value output by the trained basic physical information neural network model until an expected error is met, and obtaining a target quantity probability density physical information neural network based on residual enhancement; and predicting probability density evolution of a future time target quantity by using the target quantity probability density physical information neural network based on residual enhancement. The method effectively improves the calculation efficiency on the premise of ensuring the target quantity probability density calculation precision, and can be used for target quantity probability density evolution prediction.

The invention discloses a confidence embedded model predictive control method, a medium and equipment, and belongs to the technical field of suspension control. According to the method, a kinetic equation of a suspension system is established, a data-mechanism dual-drive uncertainty quantification method is adopted, original operation data of a maglev train and suspension state information measured on line are utilized, and dynamic characteristic learning modeling is conducted on the suspension system under the constraint of the kinetic equation; state quantity distribution of suspension air gaps, vertical speed, electromagnet current and the like at future moments is predicted, and prediction mean values and prediction standard deviations of corresponding states are obtained. In model prediction control optimization, a nominal trajectory of the suspension system is constructed by a predicted state mean value, and a penalty term proportional to prediction uncertainty is added into a cost function; meanwhile, confidence coefficient constraint is constructed based on the predicted standard deviation, and dynamic constraint tightening of key physical quantities such as suspension air gaps, vertical speed and coil current is achieved. According to the invention, real-time sensing and self-adaptive closed-loop control of the operation risk of the suspension system are realized.

The invention discloses a superconducting magnetic levitation U-shaped track beam and a preparation method thereof, and belongs to the technical field of bridge engineering.The superconducting magnetic levitation U-shaped track beam is used for running of a magnetic levitation train with superconducting coils arranged on the two sides of a train body, and the track beam comprises a plurality of track beam sections which are sequentially spliced in the longitudinal direction; each track beam section comprises a main beam body and two side plates arranged on the two transverse sides of the main beam body respectively, the two side plates and the main beam body are integrally formed to form a U-shaped beam body structure, a maglev train can run in the U-shaped beam body, mounting grooves are formed in the inner side wall faces of the two side plates and used for mounting ground coils, and the invention further provides a preparation method of the superconducting maglev U-shaped track beam. The superconducting magnetic levitation U-shaped track beam is formed by splicing the multiple track beam sections, the main beam body and the two side plates in the track beam sections are integrally formed, the number of joints of the track beam is greatly reduced, the smooth performance of the track beam is guaranteed, and the overall stability of a magnetic levitation track system is improved.

The invention provides a wheel rail-permanent magnet electric fusion type maglev rail traffic system, which relates to the field of rail traffic and is characterized in that guide rails are arranged on sleepers, a frame moves along the guide rails, guide wheels are mounted at the bottom of the frame and are in contact with the guide rails, and the frame is guided to advance or retreat along a preset direction through the contact between the guide wheels and the guide rails. The stator is arranged on the side of the guide rail, and the rotor is arranged on the frame and parallel to the stator axis. When the stator is electrified, an alternating magnetic field can be generated, and the mover is subjected to the action of electromagnetic force under the action of the magnetic field, so that the frame is pushed to move along the guide rail, and the operation of the rail transit system is realized.

The invention discloses a suspension system sensor fault detection algorithm, which comprises the following steps: establishing a suspension system model according to the motion of a real part of a suspension system; designing a state observer based on the suspension system model; acquiring and processing a sensor signal to obtain a residual value; and comparing the residual value with a threshold value to obtain fault information of the suspension system sensor. According to the fault detection method based on the algorithm, the change of the system state can be sensed earlier, and an early warning signal is sent out at the initial stage of sensor fault occurrence. The magnetic levitation system is widely applied to scenes of rail transit, air transportation, medical care and the like, the algorithm provides guarantee for normal operation of the system, meanwhile, maintenance personnel provide sufficient time for checking and repairing a fault sensor, further deterioration of faults is effectively prevented, safe and stable operation of the scenes where the magnetic levitation system is applied is ensured, and the service life of the magnetic levitation system is prolonged. And the safety and the reliability are improved.

The invention discloses a method and a device for measuring the service life of magnetic poles in a normal-conducting high-speed magnetic levitation electromagnet, and relates to the field of magnetic levitation trains. And the service life evaluation coefficient is calculated by combining the initial magnetic pole parameters, so that the working condition of the suspension electromagnet can be evaluated more comprehensively and accurately, and the phenomenon of missed judgment caused by small damage area of an insulating layer is effectively avoided. The service life of the suspension electromagnet can be accurately evaluated, an operator can reasonably arrange maintenance and replacement plans of the suspension electromagnet by calculating the service life evaluation coefficient, maintenance or part replacement can be carried out at a proper time, excessive loss or sudden failure of the suspension electromagnet is avoided, the service life of the suspension electromagnet is prolonged, and the service life of the suspension electromagnet is prolonged. And the reliability and economical efficiency of the suspension electromagnet are improved.

The invention provides a train control method and device for a maglev train and the train, and can be applied to the technical field of vehicle control. The method comprises the following steps: acquiring an actual suspension gap between a suspension electromagnet of the magnetically levitated train and a bridge and an actual input voltage of the suspension electromagnet; according to the actual suspension gap and the actual input voltage, utilizing a pre-established state and disturbance observer to obtain disturbance active compensation voltage for resisting disturbance; according to the electromagnet acceleration of the suspension electromagnet, active vibration reduction voltage used for vibration reduction is obtained; obtaining a state feedback voltage according to the actual suspension gap and the reference suspension gap; and controlling the suspension gap between the suspension electromagnet and the bridge as a reference suspension gap according to the disturbance active compensation voltage, the active vibration reduction voltage and the state feedback voltage.

The invention relates to a normal-conducting high-speed maglev traffic energy fusion system which comprises a high-speed maglev traction power supply system, a renewable energy power generation system, a composite energy storage system, an energy management system, a cloud computing platform system and a maglev train. Compared with the prior art, efficient fusion of a renewable energy source system and a high-speed maglev traffic system is achieved, and the high-speed maglev traffic system has the advantages of being efficient and safe in high-speed maglev traffic, energy-saving and low-carbon in operation and the like.

The invention discloses a selection algorithm for a residual threshold of a levitation system of a maglev train, and the algorithm comprises the steps: collecting and processing a normal working signal of the levitation system, and obtaining a residual value; selecting and setting an adaptive threshold based on the characteristics of the residual value; and the residual value is compared with a set threshold value, and fault information of the electromagnet of the maglev train suspension system is obtained. According to the algorithm, the dynamic self-adaptive threshold value model is constructed, and the dynamic adjustment of the threshold value is realized according to the real-time state and the operation condition of the system, so that whether a fault occurs or not is more accurately judged according to the real-time condition of the system. The Bayesian decision theory can consider the change of historical information and system states, so that the self-adaptive threshold method based on the theory can better adapt to fault detection requirements under different working conditions, and on the basis, the downtime and maintenance cost of the maglev train caused by faults can be further reduced, and the fault detection efficiency is improved. And the safety and the reliability of the maglev train are further improved.

The invention provides a truss type steel structure magnetic levitation track panel, a track and a manufacturing method. The track panel is of a steel bolting modular truss track structure and is provided with at least three sleepers which are transversely distributed at intervals in parallel at equal intervals; the bottoms of the two ends of the sleeper are longitudinally bolted with n-shaped rails respectively; induction plate iron cores and skid rails are longitudinally mounted at the tops of the two ends of the sleepers respectively; a plurality of supporting pieces are longitudinally and uniformly distributed between adjacent sleepers, and the top ends of the supporting pieces are bolted with induction plate iron cores and skid rails; the induction plate iron core is modularly and detachably provided with a driving surface induction plate, and the skid rail is modularly and detachably provided with a skid plate; two magnetic pole faces are integrated at the bottom of the n-shaped rail, and a detection face is arranged between the magnetic pole faces. The device is stable and reliable in structure, convenient to maintain, excellent in adaptability, 100% in-plant prefabrication rate, reduced in installation linear control difficulty, improved in construction efficiency, reduced in maintenance cost and replacement cost, and suitable for popularization.

The invention provides a maglev train bottom bolt looseness identification method based on deep learning. The method comprises the following steps: acquiring a bottom image of a maglev train by adopting imaging equipment, and carrying out bolt target detection on an image in a bottom image data set through an improved YOLOv8 network to obtain a bolt image; performing classification processing on the bolt image through a lightweight network RepViT fused with a lightweight ViT design principle; color space conversion, image binaryzation, morphological processing and connected domain screening processing are carried out on the bolt image to extract an anti-loosening line, and whether the vehicle bottom bolt in the bolt image is loosened or not is judged based on the bolt anti-loosening line according to loosening characteristics of different types of bolts under multiple visual angles. According to the method, the detection precision is improved by means of improving a target detection network structure, refining bolt loosening characteristics, providing adaptive detection methods for different types of bolts and the like; by designing a lightweight classification network mode, the calculation amount and time consumption are reduced, and the detection efficiency is improved.

The present application discloses a maglev air compressor, which includes a compressor housing, a motor stator, a rotor, a first impeller assembly and a second impeller assembly subjectable to an axial magnetic force and a control module. The compressor housing, the motor stator and the rotor are sleeved sequentially, both ends of the rotor are rotatably connected to an inside of the compressor housing through a maglev bearing group, the first impeller assembly and the second impeller assembly are fixedly oppositely installed on the both ends of the rotor. The maglev bearing group includes: a first thrust bearing piece generating a first axial magnetic force to the first impeller assembly, and, a second thrust bearing piece generating a second axial magnetic force to the second impeller assembly.

The invention discloses a correlation analysis-based magnetic suspension rotor system on-line time-lag identification method, which comprises the following steps of: generating a sine excitation signal through an excitation signal generator to excite a magnetic suspension rotor system, collecting a control voltage and a displacement signal, and introducing the control voltage and the displacement signal into a cross-correlation time-lag identifier to carry out time-lag identification. The time-delay identifier is composed of a band-pass filter, a conversion transfer function, a data storage module and a cross-correlation time-delay identification operation module. Voltage and displacement signals which are introduced into the cross-correlation time-lag identifier are firstly filtered through a band-pass filter and then converted into homologous signals through a conversion transfer function, then data storage and iteration are carried out by utilizing a data storage module, and finally the required time-lag amount is identified by utilizing a cross-correlation operation module. According to the method, the advantage that the magnetic suspension rotor system is active and controllable is utilized, extra testing equipment does not need to be used, the structure is simple, few computing resources are occupied, time delay information in the system can be obtained online, and time delay identification of the magnetic suspension rotor system is achieved.

The invention relates to the technical field of high-speed maglev traffic, in particular to an ultrahigh-speed low-vacuum pipeline dust removal device. The technical problem to be solved by the invention is that the resistance is increased and the vacuum degree of the pipeline is reduced due to dust accumulation in the ultra-high-speed low-vacuum pipeline. Comprising a vacuum pipeline and a cleaning mechanism, the cleaning mechanism comprises prefabricated frames, the prefabricated frames are symmetrically and fixedly connected to the inner wall of the vacuum pipeline, sliding boxes are slidably connected into the prefabricated frames, and first compression springs are arranged between the sliding boxes and the inner walls of the prefabricated frames. Through the design of the cleaning mechanism, dust removal is conducted on the vacuum pipeline, the situation that dust exists in the vacuum pipeline, consequently, the dust occupies the space in the pipeline, the flowing resistance of gas or fluid is increased, and the vacuum degree of the vacuum pipeline is prevented from being reduced is avoided; and through the design of a scraping plate, dust falling into the first U-shaped frame is scraped away, the dust is prevented from being accumulated in the first U-shaped frame, and the dust in the sealed space can be purged through a purging pipe.

The invention provides a beam end large-displacement stretching and retracting and rail temperature adjusting integrated device of a maglev traffic bridge. The beam end large-displacement stretching and retracting and rail temperature adjusting integrated device comprises a first fixed F rail section, a second fixed F rail section, an F rail length adjusting section and a steel sleeper distance adjusting device. The second fixed F rail section and the F rail length adjusting section are sequentially connected to the second beam body, the first fixed F rail section is arranged on the first beam body and extends to the second beam body across the beam seam to be connected with the F rail length adjusting section, and the F rail length adjusting section can adjust the length. The steel sleeper distance adjusting device is arranged at a beam seam between the first beam body and the second beam body and located below the first fixed F rail section, the steel sleeper distance adjusting device comprises a plurality of sliding steel sleepers arranged at intervals in the longitudinal bridge direction, and the sliding steel sleepers are in sliding connection with the first fixed F rail section above the sliding steel sleepers. And the distance between the adjacent sliding steel sleepers can be adjusted along with the change of the beam seam. The problem of telescopic displacement of the F rail and the steel sleeper caused by large displacement of the beam body is solved, and the system is simpler and more stable.

The invention discloses a scaling active and semi-active suspension device test platform suitable for an electric maglev train, the scaling active and semi-active suspension device test platform comprises a plurality of scaling train bodies, scaling suspension frames, vibration simulation devices and workshop connecting devices, the adjacent scaling train bodies are connected through the workshop connecting devices, and each scaling train body comprises a base plate, a longitudinal beam and a transverse actuating device mounting seat; the scaling suspension frame comprises a framework, magnets, a primary suspension, a secondary suspension, a transverse actuating device, a vertical actuating device and a mounting adapter, the magnets are arranged on two sides of the framework, the primary suspension is arranged at the bottom of the framework, the secondary suspension is arranged on the upper portion of the framework, the transverse actuating device is arranged at the end of the framework and connected with a mounting seat of the transverse actuating device, and the vertical actuating device is connected with the mounting adapter. The vertical actuating device is arranged at the upper part of the framework and is connected with the base plate; the vibration simulation device comprises a six-degree-of-freedom vibration simulation platform and a ground mounting plate, the six-degree-of-freedom vibration simulation platform is arranged on the ground mounting plate, and the primary suspension is connected with the six-degree-of-freedom vibration simulation platform through a mounting adapter.