3048results about "Structural/machines measurement" patented technology

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention discloses an on-line monitoring and fault early warning system for the running state of a dynamic compression-shear testing machine, belongs to the technical field of fault diagnosis, and aims to solve the problems of poor adaptability to multiple motion modes, lagging fault early warning and fuzzy fault positioning in the prior art. According to the system, core collaborative link fault sensitive point monitoring parameters are matched according to a current motion mode, a dynamic threshold value is generated to construct a fault judgment threshold value system, real-time data subjected to cyclic division are collected and loaded to generate a time sequence data set, a reference candidate range state is judged based on the threshold value, and a fault judgment reference library is constructed; and executing deviation analysis prediction trend through the reference library, generating a compensation instruction, identifying potential faults in combination with a threshold value, an actual value and a prediction value, calculating a link fault probability, and completing diagnosis. According to the invention, monitoring adaptability and accuracy can be improved, compensation in advance and accurate fault early warning are realized, and stable operation and test accuracy of equipment are guaranteed.

The application provides a large-span bridge substructure vehicle-fire wind force real-time hybrid test platform and implementation method, the platform comprises a physical loading and transmission system, a T-shaped wind-fire coupling simulation system, a modularized prying integrated system, an intelligent control and calculation system and a multi-dimensional high-precision monitoring system. The method realizes real-time pre-test of the boundary displacement / force of the test piece through the construction of a numerical substructure fast calculation model based on LSTM and converts the boundary displacement / force into a loading instruction to drive the through core jack to realize thermal force coupling interactive loading; the T-shaped wind-fire coupling simulation system reproduces the real vehicle fire scene under the influence of the environmental wind. The test platform supports independent loading of any component of the cable system, can execute open space self-defined fire test and closed space standard fire test, and accurately analyzes the cable failure mode, critical temperature threshold and damage evolution law under fire through the multi-dimensional high-precision monitoring system, thereby providing a high-fidelity test platform and analysis method for fire resistance design of large-span bridges.

The invention relates to the technical field of artificial intelligence and robots, and discloses a robot health state authentication method and system based on multi-dimensional fusion, and the method comprises the steps: collecting multi-source data, and carrying out the standardization preprocessing; 17 health dimensions are estimated based on an algorithm model; fusing the health dimensions to generate a comprehensive health index; authenticating the health state, and generating a health authentication report; reporting and coding, and storing to a block chain; the system comprises a multi-source data acquisition module, a dimension calculation engine, a correlation analysis module, a dynamic authentication generator, a block chain evidence storage module and an application interface layer. According to the method, a 17-dimensional health index system is constructed, hardware multiplexing is realized by applying an algorithm model based on current, speed, vision, network data and the like, an expensive physical sensor is effectively replaced, and more comprehensive health data can be obtained by matching with a chemical risk inversion model based on vision and network data to generate an environmental chemical index.

The invention provides a bridge structure damage detection method based on image processing and CNN-LSTM, relates to the field of graphic image processing, and solves the problems that bridge vibration monitoring in the prior art is time-consuming, labor-consuming, high in cost and limited in precision, and adopts the technical scheme that the method comprises the steps of obtaining a recorded vibration video of a to-be-detected bridge structure; extracting the vibration video frame by frame to obtain bridge image data; detecting angular points by using a Harris algorithm, screening image data, and calculating vertical displacement of each feature point one by one; using a Lucas-Kanade optical flow algorithm to calculate the motion vectors of the feature angular points of the adjacent frame images; calculating the actual vibration displacement of the bridge; and carrying out standardization processing on the actual vibration displacement to obtain a two-dimensional matrix, inputting the two-dimensional matrix into the trained CNN-LSTM model, and outputting a final identification result of each type of damage. According to the scheme of the invention, high-precision bridge vibration displacement can be calculated, and efficient, accurate and high-precision bridge damage detection can be realized through the CNN-LSTM damage identification model.

The invention belongs to the field of data processing, and provides an equipment comfort measurement fault identification method, system and equipment, and the method comprises the steps: obtaining a plurality of modal sequences which are consistent with a sampling signal segment in length and are aligned in bit sequence through variational modal decomposition; traversing each sampling signal segment according to a bit sequence to form a signal segment extended wave; extracting the peak value of the signal segment extended wave and the characteristic of the bit sequence of the peak value to generate an extended wave ratio sequence; the difference between the wave ratio sequence of each sampling signal segment and the original sequence is used as the wave ratio fall; and screening each sampling signal segment according to the wave ratio fall by using the median of the wave ratio fall of all the sampling signal segments, and identifying a signal comfort abnormal segment. The method is not sensitive to amplitude fluctuation of random noise, but is more sensitive to envelope impact and bit sequence displacement of a specific frequency band, can prompt slight problems such as guide shoe clearance, guide rail joint impact and abnormal transmission of a traction machine in the early stage, and is beneficial to preventive maintenance.

The present invention relates to a collaborative robot system used in an industrial environment where workers collaborate. The collaborative robot system comprises: a collaborative robot body having a multi-joint structure; a control unit for controlling each joint or auxiliary shaft; one or more sensors for detecting the operational state of the robot in real time; and a status diagnosis and response module for diagnosing the state of the robot on the basis of detected data and controlling the operation of the robot accordingly. Specifically, the system is configured to enable the robot to autonomously perform actions such as deceleration, stopping, and recovery in response to various state changes occurring during work, and to perform self-learning and trajectory optimization on the basis of data accumulated through repetitive tasks. Furthermore, the system includes a graphical user interface (GUI) for intuitive user interaction, which is linked to a digital twin-based virtual simulation environment, enabling presetting and modification of work paths. When multiple collaborative robots are operated together, the system enables task synchronization, path collision avoidance, and sharing of status information among the robots, and may be connected to an external control server or a cloud-based control system to allow integrated management of the entire workflow.

To provide a method for predicting a strain distribution map and determining damage based on deep learning.SOLUTION: The present invention comprises: a step S1 of establishing an image dataset of finite element analysis results for strain distribution map prediction; a step S2 of building a deep learning model for strain distribution map prediction based on a DeepLabv3+ network and performing learning and validation; a step S3 of establishing a dataset for damage determination of a structural analysis model and performing preliminary processing and data enhancement operations; a step S4 of building a binary classification deep learning model for damage determination based on a convolutional neural network and performing learning and validation for transition learning; and a step S5 of performing an interpretability analysis on the learned binary classification structural analysis model and outputting a region in an image that more contributes to classification.SELECTED DRAWING: Figure 1

According to the equipment state recognition method and device, the equipment and the storage medium provided by the embodiment of the invention, the multi-modal data in the operation process of the target equipment is acquired, and the multi-modal data comprises the image data, the text data and the temperature data; performing feature extraction and embedded coding on the multi-modal data through a feature extraction model to obtain embedded vectors of all modals, and aligning the embedded vectors of all modals to a unified target semantic embedding space; through a modal attention mechanism, the fusion weight of each modal is adjusted according to the state feature of the target device, and the embedding vectors of each modal are fused according to the fusion weight of each modal to construct a multi-modal fusion vector; and recognizing the state of the target equipment and / or performing abnormal risk early warning through a recognition model according to the multi-modal fusion vector. By introducing a modal attention mechanism, dynamic weighted fusion of image, text and temperature modals is realized, the accuracy and generalization ability of state recognition are remarkably improved, and the accuracy and robustness of the model under complex working conditions are improved.

The invention relates to the field of thermal fault early warning of machine room inspection robots, in particular to a multi-modal sensing thermal fault early warning method of a machine room inspection robot, which comprises the following steps: acquiring infrared thermal image, acoustics, vibration and gas concentration data through a multi-modal sensor array, and generating voxelization temperature distribution; according to the method, temperature features are extracted through wavelet transform and a fractional Brownian motion algorithm and are fused into an equipment global temperature feature set; constructing noise, vibration and gas concentration tower extraction auxiliary features, and combining non-extensive entropy and a graph attention model to be fused into a high-dimensional fault feature pool; building a fiber bundle model by taking the machine room topology as a base space, and locating a thermal fault after optimizing parameters by a simulated annealing algorithm; and finally, a dynamic thermodynamic diagram and the like are generated through digital twinning, so that accurate early warning is realized.

The invention relates to the technical field of heat dissipation performance testing, and discloses a heat dissipation performance testing system of a water-cooling radiator and a testing method of the heat dissipation performance testing system. The method comprises the steps that real-time temperature distribution data of a CPU, a DIMM and a GPU in a supercomputing server under a preset load are collected, and meanwhile the inlet temperature, the outlet temperature and flow parameters of cooling liquid of the integrated liquid cooling radiator are recorded. A three-dimensional heat conduction field model is constructed based on the data, and a local heat exchange coefficient matrix between the surface of each chip and the cooling liquid is obtained through calculation. And inputting the matrix into a fluid dynamic simulation network, and generating a turbulence intensity distribution diagram of the flowing state of the cooling liquid in the cold plate by combining a viscosity change curve of the cooling liquid and micro-channel structure parameters of the cold plate. Dividing a heat dissipation performance evaluation area according to the distribution diagram, and extracting cooling liquid flow velocity fluctuation characteristics for an area with turbulence intensity higher than a first threshold value; and marking the defect coordinates of the micro-channel structure for the region with the turbulence intensity lower than a second threshold value.

The utility model relates to the technical field of descent control devices, and particularly discloses a test device of a descent control device, which comprises a machine body; the descent control device is fixedly mounted at the upper end of the machine body; and the guide wheels are arranged on two sides of the lower end of the descent control device. According to the test equipment of the descent control device, in the process of testing the rope on the descent control device, through the arrangement of the test assembly, the height of the friction piece can be adjusted, the contact angle of the rope and the friction piece can be changed, the wear resistance test of the rope can be conveniently carried out at multiple angles, and the simulation of the wear resistance of the rope due to different installation heights of the descent control device by different users is realized. The contact angle between the rope and the windowsill is changed during use, so that the test effect of the descent control device is improved; meanwhile, the friction pieces are detachably mounted, so that the wear resistance of the rope can be conveniently tested according to the friction pieces made of different materials; in addition, through the arrangement of the swing assembly, the abrasion resistance test condition between the rope and the friction piece in the swing process is achieved, and the test effect of the descent control device is improved.

A measurement system includes a common port, the common port being connectable to a signal output of the device under test. The measurement system further includes a signal line being connected to the common port, wherein the signal line includes a first directional coupler portion and a second directional coupler portion. The measurement system further includes a signal processing circuit, wherein the signal processing circuit includes an IQ analysis circuit and an IQ synthesizer circuit. The IQ analysis circuit is connected with the common port via the first directional coupler portion so as to receive a forward-travelling signal from the common port. Further, a testing method of testing a device under test is described.

The invention relates to the technical field of radiator monitoring, and particularly discloses a radiator heat dissipation performance dynamic test system, which is characterized in that a heat source simulation body is arranged on a heat transfer interface of a radiator, and a high-fidelity dynamic electric power load is applied to the heat source simulation body to simulate an actual working condition; radiating a microwave detection signal to a space where the heat source simulator is located and receiving a multi-path scattering response signal; the three-dimensional dielectric constant dynamic distribution of the heat source simulation body is reconstructed by solving the inverse scattering problem fused with the space-time constraint; according to the material dielectric constant-temperature relation mapping, three-dimensional temperature field dynamic distribution is generated; and finally, intelligently identifying and extracting a migration track and a temperature change curve of the highest temperature point in the space from the continuous time sequence temperature field data, and taking the migration track and the temperature change curve as a direct evaluation basis for the dynamic heat dissipation performance of the radiator. According to the method, lossless, full-field, real-time visualization and quantitative analysis of the migration process of the dynamic hot spots in the hidden heat source are realized, and the authenticity of the dynamic heat test is improved.

The present application relates to a kind of heat shock flash evaporation loop lift membrane heat exchange test system, including heat exchange pipe, vapor-liquid separator, condenser, metering barrel, vacuum pump, liquid tank, heat shock heater, throttle valve, measurement and control system and high-speed camera, the upper outlet of heat exchange pipe is connected with the import of vapor-liquid separator, the secondary steam outlet of vapor-liquid separator is connected with the hot medium side import of condenser, the hot medium side outlet of condenser is connected with metering barrel and liquid tank respectively, the concentrated liquid outlet of vapor-liquid separator is connected with liquid tank, the outlet of metering barrel is connected with liquid tank, vacuum pump is connected with liquid tank, liquid tank is connected with heat shock heater, heat shock heater is connected with heat exchange pipe, throttle valve is arranged between heat shock heater and heat exchange pipe;High-speed camera is arranged in one side of heat exchange pipe.The present application can realize the experimental test to heat shock flash evaporation loop lift membrane heat exchange technology, and provide support for mechanism research, equipment optimization of heat shock flash evaporation loop lift membrane heat exchange technology.

A five-degree-of-freedom zero-gravity simulation suspension device for a space complex unfolding track belongs to the technical field of space mechanism zero-gravity simulation unfolding and mainly comprises a rocker arm frame mounting plate, a rocker arm frame assembly, a spring suspension assembly, a guide rail fine adjustment assembly, a movable constant-force suspension assembly and a mass center point connecting assembly. Wherein the guide rail fine adjustment assembly is used for adjusting the levelness of a guide rail in the movable constant-force hanging assembly. During installation, the rocker arm frame assembly is installed on the rocker arm frame installation plate through screws, the three spring hanging assemblies are installed on the rocker arm frame through the rocker arm frame embracing rings, the movable constant-force hanging assembly is installed on the rocker arm frame through the three guide rail fine adjustment assemblies, and the mass center point connecting assembly is installed on the movable constant-force hanging assembly through the spring buckles. According to the invention, the movable constant-force hanging assembly is added to realize gravity follow-up unloading in the vertical direction, and the mass center connecting assembly containing the spherical hinge assembly is superposed for use, so that the zero-gravity unfolding test requirement of a complex mechanism with five-degree-of-freedom unfolding in space can be met.

The invention relates to the technical field of server heat dissipation monitoring, and discloses an intelligent monitoring system for energy consumption of a server cluster radiator. An energy consumption acquisition unit of the system periodically acquires radiator energy consumption parameters and sends the radiator energy consumption parameters to a threshold value judgment unit; the threshold value judgment unit generates temperature, flow or pressure abnormal signals through threshold value comparison; the heat dissipation efficiency analysis unit calculates a deviation value between the energy consumption parameter and a preset threshold value based on the abnormal signal, generates a heat dissipation regulation and control lag proportion, and counts regulation and control time consumption to obtain heat dissipation regulation efficiency; the regulation and control correction unit regulates the collection period according to the lag proportion and regulates the regulation and control intensity according to the regulation efficiency; the system stability monitoring unit counts the total amount of abnormal signals, calculates out-of-control frequency in combination with operation duration, and generates system failure signals when the out-of-control frequency exceeds a threshold value. According to the system, dynamic adjustment and stability early warning of energy consumption monitoring are realized, and the reliability and adaptability of operation of the server cluster radiator are improved.

The invention discloses a stability monitoring method and system for an improved mobile bridge fabrication machine, and relates to the technical field related to bridge fabrication equipment, and the method comprises the steps: obtaining a shaft mechanism of a target bridge fabrication machine, and excavating an instability matrix in a multi-scene mode based on the force line transmission of a power source; constructing a stability monitor and embedding the stability monitor in a central control system deployed in the target bridge fabrication machine, wherein the stability monitor comprises a first alarm monitoring branch and a second early warning monitoring branch; triggering monitoring execution and activating a stability monitor along with driving of a power source of the target bridge fabrication machine, triggering three-thread parallel monitoring of an alarm dimension and an early warning dimension and a data return decision, and determining a steady state; and executing dynamic instability control of the target bridge fabrication machine according to the steady state. The technical problems that in the prior art, multi-mode instability feature capture is insufficient, and stability monitoring real-time performance and accuracy are poor are solved, and the technical effect of achieving high-precision dynamic stability monitoring and multi-thread parallel real-time early warning management and control is achieved.

The invention discloses a test method for multi-parameter synchronous excitation of a water electrolysis hydrogen production system, and belongs to the technical field of water electrolysis hydrogen production, and the test method comprises the following steps: obtaining key operation parameters of the water electrolysis hydrogen production system to construct a dynamic coupling response model, and generating a multi-parameter synchronous excitation signal sequence; the multi-parameter synchronous excitation signal sequence is applied to the water electrolysis hydrogen production system, system response data are synchronously collected, and a system response data set with a time sequence mark is generated; generating an adaptive test path planning instruction based on the system response data set with the time sequence mark and the dynamic coupling response model; and updating the multi-parameter synchronous excitation signal sequence according to the self-adaptive test path planning instruction, and executing an iterative test. According to the method, the dynamic coupling response model is constructed, multi-parameter synchronous excitation is generated and applied, and a closed-loop strategy based on response data adaptive iterative testing is adopted, so that the dynamic characteristics and the performance boundary of the system can be efficiently and accurately identified, and a comprehensive global performance map is constructed.

The invention belongs to the technical field of heat supply equipment diagnosis, and discloses an equipment diagnosis analysis system based on a performance attenuation characteristic curve. The method comprises the following steps: a sensor network acquisition layer, a data governance layer, a curve construction module layer, a dynamic calibration layer and a prediction output layer are included; the sensor network acquisition layer is responsible for comprehensively acquiring operation state data of heat supply equipment and constructing an initial data set by using the data in a stable operation period of the first 7-10 days after the equipment is put into production; and the data governance layer is used for processing and standardizing the collected state data and the original data set to construct a high-dimensional original feature set. According to the method, the equipment performance attenuation characteristic curve is generated through the health score and the curve generation model, the time-varying trend of the performance, the attenuation rate and possibly existing stage characteristics are clearly displayed, the problems of threshold comparison and statistical analysis are solved, and meanwhile, the remaining service life of the equipment can be dynamically predicted.

The invention discloses a boat hanging frame mechanical system fault diagnosis method and system, a program product and a computer readable storage medium. The method comprises the following steps: acquiring vibration data of a boat hanging frame mechanical system in a normal-temperature environment and a low-temperature environment; performing noise reduction processing on mechanical vibration data in a normal-temperature environment and a low-temperature environment by a multi-domain fusion self-adaptive fault feature enhancement noise reduction method; dividing the denoised data into a training set, a verification set and a test set; training set data is transmitted to a fault diagnosis model, and normal temperature environment mechanical vibration data features after noise reduction and low temperature environment mechanical vibration data features after noise reduction are extracted; respectively aligning the extracted normal-temperature environment mechanical vibration data characteristics and the extracted low-temperature environment mechanical vibration data characteristics on a plurality of scales by using a multi-layer category perception multi-core adaptive maximum mean difference method; data features are transmitted into a classifier for fault classification, model parameters with the best performance on a verification set are taken, and the effect and performance of the model are checked on a test set.

The invention discloses a real-time monitoring and safety early warning method for an anti-seismic structure of a fuel oil unit, belongs to the technical field of structural health monitoring and safety control, and aims to solve the problems that an existing monitoring method is passive and lagged, depends on artificial experience and cannot automatically, quickly and objectively evaluate the health condition of an anti-seismic system after an earthquake. A three-axis acceleration sensor is adopted to monitor vibration acceleration in real time, an edge computing gateway triggers an earthquake event when a vibration peak value exceeds a first preset threshold value, and a sensor array is controlled to be switched to a high-frequency recording mode; uploading the collected high-frequency data to a data processing platform, carrying out seismic design threshold comparison and machine learning model analysis, and fusing and outputting a part damage probability grade and a health assessment result; and based on the evaluation result, a targeted check list is generated through a preset mapping relation library and is pushed to the mobile terminal of the operation and maintenance personnel. The method is used for monitoring and early warning of an anti-seismic structure of a fuel unit, can realize rapid and reliable evaluation and accurate operation and maintenance guidance after an earthquake, and guarantees power supply recovery of key facilities.

The invention provides a spacecraft thermal test thermal response automatic test method and system. The method comprises the steps that S1, preset configuration information is acquired, and a resistance interpretation test is carried out on a current power output loop; s2, initial test current is applied to the power output loop passing the test, and the temperatures of all test points are obtained regularly; and S3, judging whether the temperature change of the measuring point meets the response condition or not, if so, judging that the power output loop is matched with the measuring point meeting the response condition, and testing the next power output loop, and if not, returning to the step S2 and increasing the test current step by step until the measuring point meeting the matching condition exists. The testing process is high in safety, it is initially determined that the configuration of the power output loop is not abnormal, then the matching performance of the temperature measuring point and the power output loop is determined step by step in a stepped power-up mode, and the problems that due to the fact that the current applied to the power output loop is too large for a long time, a heating piece is burnt or the temperature of a product is abnormal are completely eradicated.

The invention provides a marine intelligent test device, which comprises a heave measurement unit, a tension detection device, a winch, a telescopic mechanism and a control unit, and is characterized in that the heave measurement unit is used for measuring a heave signal of a ship body and transmitting the heave signal of the ship body to the control unit; the winch is used for winding and unwinding a cable, the telescopic mechanism comprises a pulley block used for guiding the cable, the pulley block is installed on a telescopic beam, the telescopic beam is connected with a telescopic driving device used for driving the telescopic beam to horizontally move, and the cable of the winch penetrates through the pulley block on the telescopic mechanism to be connected with a tested object; the tension detection device is used for detecting the tension on the cable and transmitting a tension signal to the control unit; and the control unit is used for receiving the heave signal and the tension signal, outputting a first control signal to the winch according to the heave signal and the tension signal so as to realize winding and unwinding of the cable, and outputting a second control signal to the telescopic driving device so as to realize expansion or retraction of the telescopic beam.

The invention discloses a self-adaptive regulation and control method for environmental stress in a thermal vacuum test process, relates to the technical field of self-adaptive regulation and control, and is used for solving the problem that the internal junction temperature risk of a single machine is increased under a high-load operation condition. Judging the operation state of a single machine example according to the instantaneous ripple voltage, judging whether a thermal lag analysis mechanism is entered or not by combining clock cycle deviation, collecting operator execution time delay and shell strain intensity of a test cabin, calculating a thermal accumulation index through the operator execution time delay, and obtaining thermal extrinsic characteristics based on the shell strain intensity; generating an invisible junction temperature identifier by integrating the heat accumulation index and the heat extrinsic characteristic, selecting a continuous operation test or executing an environmental stress regulation strategy according to the invisible junction temperature identifier, calling a current temperature change rate when the environmental stress regulation strategy is executed, setting a regulation analysis period, counting an edge trigger frequency and a logic flip density, correcting the temperature change rate, and calculating the temperature change rate. And the test safety and the data reliability are improved.

The invention belongs to the technical field of unmanned aerial vehicle testing, and discloses an air-ground unmanned aerial vehicle wind resistance performance testing platform based on a physical information neural network. The air-ground unmanned aerial vehicle wind resistance performance test platform is composed of a wind field simulation system, a physical information neural network control system and an unmanned platform indoor positioning system. The wind field generated by the wind field simulation system is accurately controlled based on the physical information neural network control system, the complex wind field required by the air-ground unmanned aerial vehicle in the wind resistance test process of the air and ground subsystems is simulated, and the uniformity of each index of the wind field is ensured.

The present application relates to the technical field of kitchen air conditioner detection, and particularly relates to a cooking oil fume generating device and a generating method based on multi-modal fusion, which comprises: a multi-component oil fume generation simulation module, a multi-modal data acquisition module and an AI decision engine module. The present application can precisely reproduce the oil fume components generated by five cooking methods by means of accurate simulation and reproduction, and can precisely reproduce the transient oil fume characteristics of five Chinese cooking methods, namely, frying, frying, frying, stewing and stir-frying, to provide reliable oil fume gas for oil fume purification equipment such as kitchen air conditioners, range hoods, kitchen air purifiers and the like; a multi-modal fusion mechanism is adopted, an AI model of an improved CNN convolutional neural network model is used to monitor parameters such as the temperature, humidity and particle size distribution of oil fume gas in real time, has the ability to respond to environmental disturbances in real time, can automatically adjust PID control parameters according to external environmental changes, ensures the stability of the generated oil fume gas, and improves the identification precision of small smoke particles.

The invention discloses a central air conditioner pollution degree autonomous evaluation robot based on multi-modal perception, and relates to the technical field of intelligent detection, the robot comprises a voxel space division module, a data acquisition module, a pollution characteristic mapping modeling module, a physical pollution detection module, a chemical pollution detection module and a biological pollution detection module; through three-dimensional point cloud data scanning and real-time data acquisition, the air pipe wall surface roughness, the temperature delay time, the gas fluctuation, the heat retention degree and the microbial metabolism gas concentration are monitored in real time, and a three-dimensional pollution characteristic mapping model is generated according to the pollution characteristic data set; by calculating a physical pollution index PPI, a chemical pollution index CPI and a biological pollution index BPI and comparing and analyzing with corresponding threshold values, whether dust retention, gas concentration and microorganism quantity in an air pipe exceed standards or not is judged in real time, cleaning, filtering and sterilizing strategies are automatically triggered, and the air quality and operation safety of a central air conditioning system are effectively guaranteed.