430 results about "Life detection" patented technology

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

The invention discloses a chip life detection method and a life prolonging method, and the method comprises the following steps: 1, extracting a chip sample, constructing a chip life detection system, and obtaining a simulation data set of the chip sample through the chip life detection system, the simulation data set at least comprises a chip performance data set, a chip operation data set, a chip working environment data set and a chip failure data set, and 2, constructing an improved neural network model to train and learn detected chip data information parameters so as to improve the chip life detection fitting capability. The service life of the chip is detected by adopting the technical scheme of constructing the improved neural network model, so that the reliability and the performance stability of the chip under the conditions of long-time use and load can be evaluated conveniently, and the detection capability of the service life of the chip is improved.

The invention discloses a full-aviation life intelligent detection and emergency rescue decision-making method and system. The method comprises the following steps: dividing emergency rescue scene gridding partitions; identifying the disaster degree and the number of affected people; combining an identification result with the gridding partition map to form a red, yellow, blue and green post-disaster gridding partition map; a full-aviation transient electromagnetic method life detector, an optical personnel detection device and a microwave radar life detection device are sequentially adopted to carry out step-by-step detection on the potential affected area, and a final replacement result of red, yellow, blue and green gridding target marking is formed; determining the priority level of ground rescue entering search and rescue according to the gridding partition map and the final replacement result; searching and inputting rescue result information of ground rescue entering search and rescue, and forming box counting statistics of grid information; a rescue decision suggestion is automatically generated according to a box counting statistical result, and efficient cooperation with a ground rescue team is formed; the method can effectively overcome the limitation of a single detection method in complex terrains and extreme environments.

The invention relates to the technical field of target identification based on multi-modal data fusion, and aims to solve the problems that an existing life detection system is difficult to effectively distinguish spatial distribution of a human body target and a hot pseudo target and cannot realize real-time synchronous analysis of multi-source heterogeneous data, so that the target positioning reliability in an extreme environment is seriously restricted. The invention provides a multi-modal data fusion human body target recognition method based on automatic control, and the method comprises the steps: constructing a feature complementation and interference suppression model based on a deep neural network through fusing the cross-modal data of infrared thermal imaging and visible light imaging, and employing a multi-scale anti-interference network based on wavelet graph convolution and an attention mechanism to recognize a human body target. The environment noise broadband interference is suppressed through the time-frequency localization characteristic, high-robustness recognition of a human body target in severe environments such as dense smoke shielding and thermal radiation interference is achieved, and accurate distinguishing of the human body and a thermal pseudo target in a complex fire scene environment is achieved.

The invention discloses a petroleum pipeline service life prediction system based on a 3D technology, and relates to the technical field of pipeline service life detection, and the system comprises a contour reconstruction module S11 which is used for obtaining the surface three-dimensional point cloud image data of a petroleum pipeline housing in an actual operation environment, and carrying out the surface contour reconstruction processing based on the three-dimensional point cloud image data, extracting space geometric structure data containing a local deformation defect area; the force-heat coupling analysis module S12 is used for constructing a finite element analysis grid according to the space geometric structure data and loading operation condition data into corresponding grid nodes to generate a force-heat coupling stress field, and the operation condition data comprises a periodic internal working pressure value, along-line temperature field data and an axial disturbance stress tensor; the feature extraction module S13 is used for calculating an equivalent stress distribution value of a defect area node based on a force-heat coupling stress field; according to the invention, accurate quantification of fatigue damage evolution of the defect area is realized.

The utility model discloses a temperature control water nozzle service life testing machine, and relates to the technical field of temperature control water nozzle service life detection. The temperature control water nozzle service life testing machine comprises a testing stand and a constant temperature water nozzle body, a fixing frame is fixed to the rear side of the top of the testing stand, a water supply pipe is embedded in the fixing frame, a water outlet pipe is arranged on the front side of the water supply pipe, a threaded sleeve is rotationally connected to the front end of the water outlet pipe and is in threaded fit with the constant temperature water nozzle body, and a hose is connected between the water supply pipe and the water outlet pipe. A screw rod is obliquely arranged on the inner side of the fixing frame, a movable block is arranged on the screw rod in a threaded mode, an elastic assembly is connected to the bottom of the movable block, a U-shaped frame is connected to the lower end of the elastic assembly, a pressing wheel is rotationally installed on the inner side of the U-shaped frame through a rotating shaft, and the pressing wheel is matched with a hose. By combining the elastic effect of the elastic assembly, the pressing wheel extrudes the hose, the size of the drift diameter of the hose is changed, the purpose of adjusting the water flow speed is achieved, the structure is simple, and use is convenient.

The invention relates to the technical field of capacitor life detection. The capacitor service life detection method comprises the steps of obtaining electrical performance response information of a capacitor under a multi-excitation condition, constructing a dynamic response behavior path of the capacitor in a state space based on the electrical performance response information, obtaining an evolution behavior sequence, and calculating the service life of the capacitor according to the evolution behavior sequence. And matching the evolution behavior sequence with the aging behavior path in the aging trajectory library, identifying a corresponding degradation trend type, obtaining current detection condition information, inputting the degradation trend type into a life evaluation model, and performing analysis in combination with the current detection condition information to obtain a life evaluation result. The method has the effect of improving the accuracy of service life evaluation of the capacitor.

The invention discloses a device for detecting the service life of a charging gun of a new energy charging pile. The device comprises a base, a charging interface and the charging gun, the device further comprises a plugging service life detection assembly, the plugging service life detection assembly comprises a lantern ring, the charging interface is fixedly arranged at the top end of one side of the base in a penetrating mode, the charging gun is arranged on the other side of the top end of the top side, the lantern ring is fixedly arranged on the peripheral side of the charging gun in a sleeving mode, and two first piston rods are oppositely arranged on the peripheral side of the charging interface. When the improved charging gun service life detection device is used, cooling processing of the head end of the charging gun and the charging interface can be synchronously completed in the process of completing connection of the charging gun and the charging interface so as to simulate that the charging gun and the charging interface can be reused after being charged for a long time after completing single time; therefore, the influence of an electric connection structure in the charging gun and the flashlight interface on the detection effect is avoided, and the plugging service life detection result of the charging gun is more accurate.

The invention discloses a button switch service life detection method, a detection device and a medium, button use parameters and physical response signals are collected in real time, instantaneous pressing force and accumulated fatigue damage amount are separated by adopting a signal decoupling technology, and a composite damage index is calculated based on a nonlinear rule. And the life prediction module dynamically selects an exponential decay model or a power law decay model in combination with the total damage amount, and outputs a residual life evaluation result. A calibration unit is arranged in the device, standard pressure is periodically applied, response signals are collected, the model sensitivity coefficient and fatigue parameters are dynamically adjusted through a recursive optimization algorithm, and long-term stability of prediction precision is guaranteed. According to the method, full-process closed-loop control from data acquisition, damage feature extraction to intelligent prediction is realized, the accuracy and real-time performance of button service life evaluation are remarkably improved, and the equipment shutdown risk and maintenance cost caused by button failure are effectively reduced.

The invention discloses a beta < + >-gamma-gamma coincidence positron annihilation lifetime-momentum correlation spectrometer, and belongs to the technical field of nuclear detection. The spectrometer comprises a positron annihilation life detection module, a positron annihilation gamma photon energy detection module, a signal acquisition module and a control and analysis module, and the positron annihilation life detection module is used for acquiring a life part in an AMOC spectrum; the positron annihilation gamma photon energy detection module is used for acquiring a momentum part in an AMOC spectrum by detecting gamma photons generated by positron annihilation; the signal acquisition module is used for acquiring signals generated by the positron annihilation life detection module and the positron annihilation gamma photon energy detection module; the control and analysis module is used for controlling the signal acquisition module and obtaining an AMOC spectrum based on signals acquired by the signal acquisition module. According to the method, the coincidence counting rate can be effectively improved, good coincidence time resolution and extremely low source components are achieved, and the construction cost of a spectrometer can be reduced.

The invention belongs to the technical field of battery detection, and particularly discloses a fuel cell performance evaluation method, system and device and a medium, and the method comprises the steps: collecting the multi-source feature data of a to-be-detected fuel cell in real time; carrying out preprocessing on the multi-source feature data; inputting the preprocessed multi-source characteristic data into an electrochemical-thermodynamic coupling model, and calculating to obtain an activation polarization voltage, an ohmic loss voltage and a concentration polarization voltage; and inputting the multi-modal input features into a pre-trained fuel cell performance health state evaluation model to evaluate the health state index and the remaining service life of the fuel cell to be tested. The accuracy of fuel cell health state and service life detection can be improved.

A sensor coupled to a computer can sense a pressure of a buffer tank. A computer can determine a start of dispensing a beverage from a beverage dispenser. The computer can determine that an elapsed time from the start of dispensing the beverage reaches a predefined duration threshold. The computer can measure a particular pressure of the buffer tank when determining that the elapsed time reaches the predefined duration threshold. The computer can determine that the particular pressure falls below a predefined pressure threshold. The computer can generate an order to replace a water filter of the beverage dispense in response to the particular pressure at the elapsed time falling below the predefined pressure threshold.

The invention discloses a vehicle windscreen wiper service life detection method and device, a vehicle and a program product, and the method comprises the steps: building a residual life prediction model of a vehicle windscreen wiper through an end-to-end deep neural network model, and enabling the model to take the state data of a windscreen wiper rubber strip and the parameter characteristics of the environment where the vehicle is located as the input; and outputting the predicted residual life of the windscreen wiper rubber strip. The input of the model comprises the multi-mode state data, obtained in the current detection period, of the windscreen wiper rubber strip of the vehicle and the parameter characteristics of the environment where the windscreen wiper rubber strip is located after the windscreen wiper rubber strip is replaced last time. When the vehicle is in different external environments, the aging speeds of the windscreen wiper rubber strip are different, the parameter characteristics of the environment where the vehicle is located serve as the input parameters of the model, and the model can be guided to dynamically predict the remaining life of the windscreen wiper rubber strip according to the parameter characteristics of the environment where the vehicle is located. Therefore, the accuracy of predicting the residual life of the windscreen wiper rubber strip is further improved.

The invention relates to the technical field of service life detection, and discloses a service life detection method and system for a direct-current brushless gear motor, and the method comprises the steps: determining the remaining service life of a to-be-detected motor according to a calibrated service life and accumulated service time; analyzing the parameter information, adjusting the residual service life based on an analysis result, and obtaining an initial residual service life prediction value of the to-be-detected motor; analyzing the historical motor operation record, calculating a historical abnormal operation index of the to-be-detected motor based on an analysis result, and judging whether the initial residual life prediction value is compensated or not according to the historical abnormal operation index; and determining a compensation coefficient of the initial residual life prediction value according to the historical abnormal operation index, and compensating the initial residual life prediction value according to the compensation coefficient to obtain a final residual life prediction value. The invention provides a new technical means for predicting the service life of the direct-current brushless gear motor, and is beneficial to improving the maintenance efficiency and prolonging the service life of the motor.

The invention provides an occlusion target stable tracking method and device based on trajectory prediction. According to the method, feature extraction and target detection are carried out on a single-frame image, then matching of detection frames is carried out according to target detection results of multiple-frame images, and when the detection frames are mismatched, it is determined that a target is shielded; the target shielding time is judged, and if the target shielding time is short shielding, a least square method is used for target trajectory prediction; and if the shielding is long, performing target trajectory prediction by using Kalman filtering. The invention further provides an association strategy, when the detection frames are mismatched, the mismatched detection frames are associated with the high-value detection frames and the prediction frames respectively, and the high-value detection frames can comprise non-deterministic state detection frames with association values, super-life detection frames and the like. By using the method, the problem of multi-target tracking identity switching after occurrence of shielding can be solved, the anti-shielding capability of a multi-target tracking algorithm is improved, and the stable tracking effect after multi-target shielding is ensured.

The invention relates to the technical field of electrical equipment state monitoring, in particular to an intelligent circuit breaker electrical life detection method and system based on data analysis. The method comprises the following steps: synchronously acquiring opening coil voltage and current sequences, a main loop current sequence and environment temperature data when a circuit breaker acts, extracting actually measured action time based on the coil data, and calculating a mechanical health degree factor by combining the environment temperature and the actually measured action time; after decoupling correction is carried out on an electrodynamic force interference factor derived by a peak current of a main loop, a mechanism wear state index is obtained; a nonlinear damage model containing basic electric heat loss and mechanical degradation acceleration factors is constructed, a single-time electric life loss value is calculated, historical loss values are accumulated, a dynamic threshold value is combined to realize full-life-cycle prediction, and evaluation interference of environmental factors and large-current electrodynamic force is eliminated through deep data analysis. The nonlinear acceleration effect of mechanism hysteresis on contact ablation is quantified, and the detection precision is remarkably improved.

The invention relates to the technical field of battery detection, in particular to a battery life detection method and system, an intelligent terminal and a storage medium, and the method comprises the steps: collecting operation data in real time through a multi-source sensor array disposed in a charging pile; the multi-source sensor array comprises a multi-point temperature sensor, a current waveform sensor and a displacement sensor; performing dynamic feature extraction on the collected operation data to generate a dynamic feature set; inputting the dynamic feature set into a multi-parameter coupling health analysis model to generate a whole machine health state; the health state of the whole machine is recognized, and when it is recognized that the health state is attenuated, a maintenance instruction is output; the maintenance instruction includes a faulty component identifier and a remaining life predictor. The method has the effects of dynamically and quantitatively sensing the performance state of the key internal components of the charging pile, positioning the fault component and predicting the residual service life of the charging pile.

The embodiment of the invention discloses a living body detection method and related equipment, which are used for carrying out living body detection under the condition of improving the accuracy of living body detection. The method provided by the embodiment of the invention comprises the following steps: if a trigger signal of living body detection is detected, controlling a Bluetooth transmitting device to periodically transmit a first signal, and obtaining a signal strength indication of the first signal received by a Bluetooth receiving device, determining whether a living body exists in the target environment of the target environment factor based on the distribution condition of the signal intensity indication of the first signal, if so, controlling an emitter of the radar device to emit a second signal, and obtaining echo data of the second signal received by a receiver of the radar device, and inputting the echo data of the second signal into a pre-trained living body detection model, and performing living body detection of a target environment of the target environment factor on the echo data of the second signal by the living body detection model to obtain a living body detection result corresponding to the echo data of the second signal.

The invention relates to the technical field of life detection, and discloses a radar signal-based multi-target life detection method, which comprises the following steps of 1, collecting a multi-target mixed signal received by a radar, and preprocessing the signal; step 2, performing phase space reconstruction on the preprocessed signal to obtain a high-dimensional phase space trajectory; 3, constructing a self-adaptive dictionary, and carrying out sparse representation on the signals; and step 4, separating initial vital sign signals of each target from the mixed signals through a sparse optimization algorithm. According to the invention, the technical scheme of multi-band radar signal and deep learning fusion is adopted, and the technical effect of high-precision separation of multi-target vital sign signals in a complex electromagnetic environment is achieved. Compared with a technical scheme depending on single frequency band or fixed threshold classification in the prior art, the method solves the defects of serious signal aliasing and low separation precision in a dense obstacle or multi-target mixed scene.

The invention relates to the technical field of fault diagnosis and bearing life detection, in particular to a high-speed rail axle box bearing life cycle test bench and a test method.The test bench comprises a test bench body, a test head, a driving system, a loading system, a temperature control system, an electrical control system, a computer monitoring system and a data acquisition system; the speed spectrum and the load spectrum can be preset through program control, complex and strong load conditions and variable environment temperatures of a high-speed train can be comprehensively simulated, full-life-cycle tests can be carried out on high-speed rail axle box bearings of various models under high-speed, heavy-load and high-frequency vibration conditions, and the device has the advantages of being easy to operate, safe, reliable and efficient in testing and has a wide application prospect. The problems that in the prior art, the high-speed rail axle box bearing operation environment simulation is incomplete, and the service life evaluation credibility is low can be effectively solved.

The invention discloses a robot joint module service life detection device, and relates to the technical field of joint module detection, the robot joint module service life detection device comprises a detection table, the top end of the detection table is provided with a connecting assembly, and the output end of the connecting assembly is provided with a torque applying and detecting assembly used for carrying out torque loading and detection on a joint module; a radial force applying and detecting assembly is arranged on one side of the outer wall of the connecting assembly and used for applying and detecting radial force to the joint module; the connecting assembly comprises a joint module body and a welding fixing frame base of the joint module body, an output flange is fixedly installed at the output end of the joint module body, a connecting flange is fixedly installed at one end of the output flange, and a torque sensor is fixedly installed at the other end of the connecting flange. A pressure sensor is mounted on one side of the outer wall of the output flange; the variable load test is carried out on the radial force and the rotating torque, the constant load can be realized, the variable load test can be realized at the same time, compared with the traditional constant load test, the stress state of the joint module in the actual work can be reflected more truly, and the service life detection result has higher reference value.

The present invention provides a life detection method and system based on radar signal processing, which relates to the technical field of radar signal processing. The method and system include collecting radar echo signals in a target area, controlling a phased array radar transmitting antenna array to transmit an orthogonal frequency division multiplexed dual-frequency continuous wave radar signal, inputting the processed radar echo signal into an orthogonal demodulation module for synchronous coherent demodulation to obtain a baseband signal; inputting a feature vector into a multi-task deep learning network with an integrated attention mechanism for feature fusion and target recognition to obtain a second processed signal; obtaining a target feature spectrum by calculating the time-frequency distribution of the second processed signal, judging whether there is a life form in the target area according to the time-varying characteristics of a respiratory frequency component and a heartbeat frequency component and the law of amplitude fluctuation; if it is determined that there is a life form in the target area, calculating the three-dimensional spatial coordinates of the life form through a multi-base station collaborative positioning algorithm, and outputting a detection result containing the three-dimensional spatial coordinates and the life form feature information.

The invention discloses a service life detection method, medium and equipment for a contact mechanism of a molded case circuit breaker, and the method comprises the steps: obtaining a high-resolution spectrogram of the contact mechanism through solid nuclear magnetic resonance and magic angle rotation technologies, and carrying out the preprocessing of the high-resolution spectrogram to obtain reconstruction representation data; performing multi-step iterative prediction on the reconstructed data by using a multi-contact life decline prediction model so as to accurately obtain the life prediction condition of the contact mechanism; according to the method, reconstruction data are mapped to a high-dimensional feature space, an optimal separation hyperplane is constructed, an elastic net contact life SOH model is established based on the optimal separation hyperplane, and continuous scaling prediction is carried out. The method comprises the following steps: sorting and screening intrinsic characteristic variables of reconstructed data; and generating echelon life prediction of the contact mechanism by using a conditional generative adversarial network in combination with the reconstruction data and the SOH prediction condition. According to the method, service life detection of the contact mechanism is updated in real time, the detection precision is remarkably improved, prediction errors caused by parameter degradation are avoided, and a more accurate method is provided for service life prediction of the contact mechanism.

The invention provides a sensitive component service life detection method and system, and relates to the field of sensitive component service life detection, and the method comprises the steps: recording the factory UTC time of a sensitive component and the initial performance data of the sensitive component during factory leaving; based on a preset timed monitoring period, obtaining current UTC time and average performance data of the sensitive component in the timed monitoring period when each timed monitoring period is ended; adjusting weight coefficients corresponding to different timing monitoring periods according to the environment data and the load parameters of the sensitive components; performing weighted summation on the average performance data according to the weight coefficient to obtain a total integral value; when it is detected that the total integral value is larger than or equal to a preset integral threshold value, or the variable value of the average performance data exceeds a preset amplitude threshold value, alarm information that the service life reaches the limit is triggered. By implementing the method, the performance change of the sensitive component under different environments and load conditions can be dynamically responded, so that the life end point of the sensitive component can be predicted more accurately.

A high-frequency resistant ground fault circuit interrupter (HF-GFCI) with self-test and end-of-life detection is disclosed. The invention incorporates a noise-resistant fault detection circuit, an auto-test module, and an end-of-life shutdown mechanism to ensure reliable protection. The device minimizes false tripping due to high-frequency noise and enhances safety through continuous self-monitoring. Experimental validation confirms improved immunity to leakage currents and transient voltage disturbances. The HF-GFCI also provides an LED circuit having dual functionality: an LED indicator circuit and neutral to ground detection on the load side of the HF-GFCI.

The invention provides a lithium battery residual life detection method and apparatus, an electronic device and a storage medium. The method comprises the steps of performing correlation analysis on a voltage battery capacity curve of each charging interval and a battery health state to obtain a correlation coefficient; when the absolute value of the correlation coefficient is greater than a preset value, constructing a battery voltage characteristic sample set through the battery voltage and the battery capacity of the charging interval, and taking impedance spectrum data corresponding to each charging interval in the battery voltage capacity characteristic sample set as an impedance spectrum characteristic sample set; inputting the battery voltage characteristic sample set into a deep learning model to obtain a predicted impedance spectrum, and training the deep learning model based on the predicted impedance spectrum and the impedance spectrum characteristic sample set; and inputting the charging data of a to-be-predicted lithium battery into the trained deep learning model to obtain a target predicted impedance spectrum, and determining the predicted residual life of the lithium battery based on the equivalent circuit model and the target predicted impedance spectrum. The detection efficiency is improved by detecting the battery voltage characteristics of the lithium battery.

The invention discloses a lithium ion battery life prediction method based on a life prediction combination model, and the method specifically comprises the following steps: S1, carrying out the fitting analysis according to the data of a battery capacity recession experiment, and relates to the technical field of lithium ion battery life detection. According to the lithium ion battery life prediction method based on the life prediction combination model, the prediction precision of the battery life is improved, the life prediction model is established on the basis of a capacity attenuation curve fitting formula and an empirical model, and model parameters are updated on the basis of the improved empirical model; the established prediction model not only can accurately reflect the trend of battery capacity decline, but also can effectively combine the actual use state of the battery, so that the high-precision prediction of the residual service life of the lithium ion battery in different health states can be realized, and the prediction accuracy of the lithium ion battery in different health states is improved. And the development of the battery echelon use industry is facilitated.

The invention relates to the technical field of mine rescue detection, and discloses a mine rescue multivariate information life detection system and method.The mine rescue multivariate information life detection method comprises the steps that a heterogeneous sensor network is deployed, a belief propagation algorithm is applied to construct a self-organizing network, and dynamic topological optimization is achieved; an environment adaptability calibration system is constructed, and dynamic calibration of the sensor in complex environment changes is achieved; the sensor performance is quantified through a functional space model, degradation evaluation is carried out, and intelligent redistribution of sensing tasks is achieved; constructing a cooperative detection framework by using a belief propagation algorithm, and fusing multi-sensor data through a distributed decision algorithm; performing multi-dimensional feature extraction and analysis on the life signal, performing reliability evaluation on a result, and outputting a high-reliability detection result; according to the invention, the technical problems of sensor performance drift, function degradation and insufficient topological structure adaptability of a traditional mine rescue detection system in a complex environment are solved.

The invention discloses a die-casting die thermal fatigue life detection device, and relates to the technical field of die-casting die detection, the die-casting die thermal fatigue life detection device comprises a detection machine body and a control platform, and the top of the detection machine body is provided with a material thermal fatigue detection assembly and a mechanical load mechanism. When the device is used, the electric push rod pushes the heat insulation sleeve to penetrate through the penetrating hole and continue to move, and the movable turning cover is jacked upwards. Under the combination of the vacuum cavity, the honeycomb-shaped porous heat insulation plate and the metal foil reflecting screen, the heat insulation sleeve forms a triple barrier of reflection, vacuum and porous heat insulation, heat transfer is blocked, and it is guaranteed that cold air is not affected by heat of an air cooling opening when blown. When the material thermal fatigue detection assembly works, the cold air pipe and the heat insulation sleeve are located outside the detection box, the heat absorption temperature of the cold air pipe is prevented from increasing in the heating process, and the mold thermal fatigue detection precision and efficiency can be improved.