1227 results about "Mechanical devices" patented technology

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention relates to the technical field of mechanical equipment state monitoring, in particular to a method and sensor for detecting temperature and vibration data of dynamic equipment, and the method comprises the steps: collecting a temperature signal and a vibration signal of the dynamic equipment, and carrying out the fusion processing, thereby obtaining a fusion feature set; establishing a feature distribution baseline based on a Gaussian mixture model, calculating a relative entropy of the fusion feature set and the feature distribution baseline, and generating a dynamic threshold sequence; constructing a detection model according to the fusion feature set and the dynamic threshold sequence, and outputting to obtain an abnormal mode label; performing time serialization processing on the abnormal mode label, predicting a fault probability in a future time period according to the abnormal mode label subjected to time serialization, and generating a fault prediction result; and performing priority ranking on the fault types in the fault prediction result, and generating a maintenance report according to a priority ranking result. The reliability and practicability of temperature vibration data detection of the dynamic equipment are comprehensively improved, and an efficient solution is provided for health management of the dynamic equipment.

The invention discloses an auxiliary control system and method for abnormal intervention of a manufacturing and assembling production line, and relates to the technical field of intelligent manufacturing workshop auxiliary equipment and physical execution control of the intelligent manufacturing workshop auxiliary equipment, and the system comprises a multi-dimensional physical state collection unit which is used for generating a multi-dimensional physical state data set; the dynamic mapping and anomaly detection unit is used for detecting an abnormal state in real time; the multi-process linkage intervention unit is used for generating a flexible intervention control instruction; the auxiliary control unit is used for executing an intervention action; and the closed-loop optimization unit is used for constructing feedback comparison and residual attitude error judgment of an intervention action effect and forming a self-adaptive correction closed loop of the auxiliary device. The flexible adaptive capacity and the plug-in mounting stability of the workshop auxiliary mechanical device are enhanced, and the workshop auxiliary mechanical device has the characteristics of non-intrusive mounting, modular expansion, multi-process rapid adaptation and the like, and is particularly suitable for auxiliary control and accurate intervention of various and batch workpieces in a complex assembly scene.

The present invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory. The method of the present invention comprises: first, collecting multi-source information of a mechanical device, wherein the multi-source information includes vibration data, image data and sound data; respectively calculating corresponding feature indicators; then, using a PCA algorithm to perform dimensionality reduction processing on the indicators; using a Bayesian fault diagnosis model to perform fault diagnosis, so as to obtain a diagnosis result from each sensor; and then, using the DS evidence theory to perform fault fusion diagnosis. The present invention can comprehensively consider the characteristics of various types of data, extract more effective fault indicators, achieve more comprehensive fault identification, and fuse diagnosis results from a plurality of sensors, thereby ensuring the accuracy of final diagnosis. The present invention is suitable for fault diagnosis of various complex mechanical systems.

The invention discloses a gap-controllable retainer and a frictional resistance measuring method thereof, belongs to the technical field of mechanical equipment manufacturing, and solves the problem that an existing device can only test a retainer with a fixed gap and cannot simulate the influence of dynamic gap change on bearing performance. The retainer capable of controlling the gap comprises a retainer body capable of adjusting the gap between the retainer body and a rolling body, a retainer moving assembly and a gap adjusting part. The gap adjusting part comprises an adjusting motor, an eccentric wheel and a motor fixing frame. According to the retainer capable of controlling the gap, the gap between the retainer and a rolling body can be dynamically adjusted, the retainer can be matched with different types of ball-disc testing machines (rotary type and linear contact type) for friction resistance measurement, the gap between the rolling body and a pocket of the retainer can be flexibly adjusted by adjusting the motor and the eccentric wheel, and the friction resistance of the rolling body is measured. And therefore, the retainer main body can meet the retaining and limiting requirements of different working conditions and different types of bearings, and the performance, the service life and the reliability of the bearings are remarkably improved.

The invention relates to a hydroelectric main shaft all-condition simulation test system and method, and the system comprises a fixed rack, one side of the fixed rack is provided with a tripping starting detection device, the fixed rack is provided with a hollow three-axis platform, a three-axis excitation system and a rotation driving part, the moving end of the three-axis platform is connected with a hollow floating rack, and a bearing is disposed in the floating rack. The rotation driving part comprises a power part and a main shaft connected to the torque output end of the power part, the main shaft is in transmission connection with the rotating disc, a hydroelectric large shaft used for a simulation test is installed on the rotating disc, and the three-shaft excitation system is used for applying X-direction, Y-direction and Z-direction high-frequency sine excitation loads to the hydroelectric large shaft of the rotating disc. The device is used for overcoming the defects that an existing testing device mainly focuses on vibration loading of a static mechanical device, simulation and performance testing cannot be carried out on dynamic behaviors under the composite working conditions of rotation, vibration and impact of a hydroelectric large shaft, and safety testing and rating cannot be carried out on a hydroelectric generating set sensor and a tripper overspeed protection device.

The invention discloses a motor current fault diagnosis method based on a de-noising diffusion probability model, and belongs to the technical field of mechanical equipment state monitoring and fault diagnosis, and the method comprises the steps: obtaining an original motor current signal sample, carrying out the wavelet transformation, obtaining a time-frequency grayscale image, and obtaining a time-frequency grayscale image; dividing the sample into a training sample used for diffusion model training and a test sample of a fault diagnosis model; constructing a diffusion model DDPM, carrying out training by adopting the training sample, and generating a pseudo sample based on the trained diffusion model; constructing a fault diagnosis model MAF-Cnet, and training the MAF-Cnet based on the training sample and the pseudo sample to obtain the trained MAF-Cnet; and inputting a test sample into the trained MAF-Cnet for diagnosis to obtain a motor current fault diagnosis result. The method solves the core problem that the generalization ability of the diagnosis model is insufficient due to scarcity of motor fault samples, improves the diagnosis accuracy, and is wider in application scene.

The invention discloses a vibration signal feature extraction method and system based on signal processing, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a vibration signal of target mechanical equipment, and carrying out the preprocessing of the vibration signal; decomposing the preprocessed vibration signal by adopting multi-scale wavelet transform and frequency domain analysis to obtain time domain, frequency domain and time-frequency domain features; carrying out dimension reduction processing on the extracted feature data by using a deep learning model, and carrying out sequence feature processing on the data after dimension reduction to obtain dimension-reduced feature data; and training a deep learning mode, analyzing the dimension reduction feature data by using the trained deep learning model, identifying abnormality in the vibration signal, and outputting an abnormality monitoring result and fault early warning information. The method can provide reliable fault early warning under different working conditions, and is suitable for on-line monitoring and intelligent operation and maintenance of various mechanical devices.

The invention discloses a remote control crawler-type mower, and relates to the technical field of agriculture and forestry machinery, and the remote control crawler-type mower comprises a machine body, a crawler belt, a front cover plate, an obstacle avoidance protection structure, a modular integrated structure, a height adjustment structure and an operation area expansion structure; the situation that the blade assembly is damaged by low obstacles is avoided, a modular installation mode is adopted, replacement and installation of internal assemblies can be rapidly conducted, the machine adapts to different mechanical devices, the use flexibility of the machine body is expanded, the ground clearance of the working end of the bottom of the mechanical device can be adjusted, and different working requirements are met; meanwhile, the signal tower can be fixed at a remote control signal receiving critical position when needed, and a signal transfer effect is achieved through the signal tower, so that the remote control operation distance and range of the mower are greatly expanded.

The invention belongs to the technical field of aviation mechanical equipment, and provides aviation monitoring flight equipment and an implementation method thereof. The device comprises a framework, a floating ball arranged in the framework and restrained by the framework, spiral wings symmetrically arranged on the two sides of the framework, a control host arranged at the bottom of the lower end of the framework and a monitoring camera carried at the bottom of the control host, and a mounting table is arranged in the middle of the upper end of the framework; a wind speed and direction sensor and an adjustable mounting frame mechanism are arranged on the mounting table, and a flexible solar cell panel is mounted on the adjustable mounting frame mechanism. According to the unmanned aerial vehicle, the floating balls and the spiral wings adopted by the unmanned aerial vehicle are creatively combined, the floating balls are filled with helium, on one hand, the operation energy consumption of equipment can be reduced through buoyancy of the floating balls, the cruising ability of equipment operation is improved, on the other hand, based on the arrangement of the floating balls, the spiral wings can adopt small wing equipment, and the operation noise is lower.

The invention relates to a self-resetting three-dimensional damping rubber support based on SMA tows and a damping method, and belongs to the field of civil engineering structure damping. The support comprises an upper connecting plate, a lower connecting plate and a shock insulation rubber support, I-shaped sliding rails are fixed to the upper connecting plate and the lower connecting plate respectively, sliding pieces are connected to the sliding rails in a buckled mode, and multidirectional displacement is coordinated through sliding piece connecting rods; the SMA tow system is composed of a fixing rod, a push-pull rod and an annular tow, the two ends of the SMA tow system are anchored to the sliding rail and the connecting rod respectively, and energy consumption and self-resetting are achieved by stretching the tow through a sliding piece. When an earthquake causes horizontal deformation of the support, the SMA tows utilize the hyperelastic hysteretic characteristic to efficiently dissipate energy, automatically recover deformation after the earthquake and drive the sliding piece to reset. According to the invention, the vertical tensile function, the horizontal bidirectional free deformation function and the self-resetting function are integrated, the structure is simplified through the sliding rail-sliding piece module, a complex mechanical device is avoided, and the energy consumption efficiency and the engineering applicability are remarkably improved. And the problems of large residual deformation, poor multidirectional coordination and material fatigue are solved.

The invention relates to the field of mechanical equipment monitoring and intelligent diagnosis, in particular to an equipment real-time fault diagnosis method based on an FPGA and an SNN, and the method comprises the steps: obtaining an original vibration signal, and constructing a local training set; establishing an SNN diagnosis model based on an attention mechanism, and training the model by adopting a local training set; deploying the trained model to an FPGA (Field Programmable Gate Array) end; constructing a mechanical vibration signal real-time acquisition system based on the FPGA; the system obtains real-time vibration data and carries out cross point sliding window preprocessing on the real-time vibration data, the SNN model deployed to the FPGA end carries out reasoning on the preprocessed data, and a diagnosis result is obtained. The pulse neural network model and the FPGA technology are combined, low-energy-consumption and efficient calculation is achieved through edge calculation, the strict requirements for real-time performance and low delay in the industrial environment are met, and the intelligent level of equipment monitoring is effectively improved.

The invention relates to the field of agricultural machinery, in particular to multifunctional tea garden weeding, soil loosening and fertilizing integrated mechanical equipment which comprises a mounting frame, a driving module is mounted at the bottom of the mounting frame, a weeding module, a soil loosening module and a fertilizing module are mounted at the top of the mounting frame, and the weeding module comprises a power part which is fixedly connected with the top of the mounting frame; an output shaft of the power part is connected with a roller, the outer side wall of the roller is fixedly connected with a plurality of wave-shaped blades, the soil loosening module is installed in the roller, and an opening is formed in the side wall of the roller and used for loosening tea garden soil based on the centrifugal force effect; the driving module is in signal connection with a controller, the controller is in signal connection with the power piece and the fertilization module, and the controller is used for adjusting the power of the driving module and the rotating speed and the output power of the power piece according to preset terrain information. The method is used for improving the efficiency of weeding, soil loosening and fertilizing operation of the tea garden and enhancing the collaboration of all operation links.

The invention discloses an active compliance control method for a power transmission line live-line maintenance mechanical device, belongs to the technical field of power detection equipment, and aims to solve the problem that safety accidents are caused by the fact that an existing control method does not fully consider influence of strong wind interference working conditions on movement of the mechanical device. According to the method, the strong wind interference working conditions are accurately divided by establishing a multi-modal dynamic model of a normal working condition model and a plurality of strong wind interference working condition models, model parameters under each working condition are determined in combination with wind tunnel experimental data and kinematics and dynamic characteristics of the mechanical device, behaviors of the mechanical device under different working conditions are accurately described, and the working conditions of the mechanical device are accurately determined. When the mechanical device is interfered by complex and changeable strong wind, a control decision can be made, the accuracy and adaptability of control are greatly improved, the influence of the strong wind on maintenance operation is effectively reduced, and safe interaction between the mechanical device and power transmission line equipment in the maintenance process is guaranteed.

The invention relates to the technical field of design and manufacturing of automatic carrying mechanical devices for heavy-load parts on automobile production lines, in particular to an automatic carrying device for heavy-load parts on automobile production lines, which is used for automatically grabbing and transporting large-mass parts on the automobile production lines. Comprising a carrying mechanism and a control mechanism, and the carrying mechanism comprises a carrying machine translation track, a carrying machine lifting air cylinder, a carrying machine body, a rotating device, a clamping jaw telescopic track and a carrying machine clamping jaw; the upper end part of the transporter lifting cylinder is mounted on a rack of the production line, and a running track is arranged on the rack of the production line; the control mechanism is composed of a controller and a hydraulic pipe connecting base. The controller is mounted on the equipment mounting frame; a cable on the controller is connected with the hydraulic pipeline and hydraulic-electric pipeline connecting seat; according to the transporter clamping jaw structure designed by the device, grabbing and transporting of a full-automatic automobile chassis structure are achieved, the defects that in the current automobile manufacturing process, when heavy objects are hoisted by manpower at high frequency, the efficiency is low, and the load is high are overcome, and the production efficiency and the automation degree are greatly improved.

The invention discloses a bearing life prediction method based on data feature optimization and deep learning, and the method comprises the steps: collecting vibration signals of a bearing in different states, carrying out the processing of the signals through MEMD-DisEn, and decomposing the signals into an intrinsic mode function, so as to reveal the features of a bearing fault; expanding fault sample data by using a WGAN-GP strategy so as to improve the processing capability of the model on abnormal samples and enhance the prediction accuracy; a TimeKAN model is applied to analyze the enhanced data, large-scale time series data are processed, a long-term dependency relationship in the data is captured, and the remaining service life of the bearing is accurately predicted; an escape optimization algorithm is improved, and hyper-parameters of the TimeKAN model are optimized through the improved escape optimization algorithm, so that the distinguishing capability and the response speed of the model are improved. The method can effectively improve the prediction accuracy and robustness, and is of great significance to the maintenance and safety production of mechanical equipment.

The utility model discloses a full-automatic green prickleyash lower pile type threshing machine, belongs to the technical field of agricultural machinery, and relates to mechanical equipment for picking and threshing green prickleyash. The device mainly comprises a crawler, a mechanical arm, a multi-layer automatic threshing device and a collecting frame. The crawler is arranged below the thresher and is used for moving the whole thresher; the mechanical arm is arranged above the threshing machine and used for shearing pepper branches and conveying the pepper branches into the multi-layer automatic threshing device; the multi-layer automatic threshing device is used for automatically threshing pepper branches; the collecting frame is used for collecting Chinese prickly ash fruits; according to the green prickleyash harvester, full-automatic piling type picking and threshing integration of green prickleyash is achieved, full automation of the green prickleyash harvesting process is achieved, the labor intensity of green prickleyash picking is greatly reduced, and the green prickleyash picking efficiency is improved.

The invention relates to a numerical control machine tool spindle predictive maintenance system, and belongs to the technical field of mechanical equipment health management and intelligent manufacturing. The main shaft driving motor real-time power processing module is used for processing the main shaft driving motor real-time power to estimate and obtain a real-time cutting torque sequence and a real-time main shaft total power sequence, and processing the multiple paths of temperature signals to obtain multiple paths of real-time temperature sequences, and the basic degradation state evaluation module is used for calculating mechanical accumulated damage quantitative indexes based on the real-time cutting torque sequence. And calculating a cumulative thermally induced damage index based on the multi-path real-time temperature sequence, and fusing the mechanical cumulative damage quantitative index and the cumulative thermally induced damage index to generate a basic degradation index. And the robustness and operability of a prediction result in a real industrial environment are remarkably improved.

The invention provides a safety grating false alarm suppression method and system based on multispectral fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining a multiband detection data set, projecting signals of different bands to a unified amplitude scale interval, comparing the amplitude change directions and the change amplitudes of different bands on adjacent time slices, and determining the false alarm of the safety grating according to the amplitude change directions and the change amplitudes. Generating a wave band correlation map, combining wave bands with consistent amplitude change directions and similar change amplitudes into aggregation units, calculating the dynamic change amplitude of each aggregation unit, identifying the concentration degree of interference signals in each aggregation unit, obtaining an interference aggregation index, rejecting the interference signals, and generating a shielding characteristic matrix; and calculating the separation degree of the shielding signal according to the result, obtaining a shielding separation ratio, generating a judgment output result, and controlling the operation state of the mechanical equipment. According to the invention, a discrimination path which is insensitive to interference and sensitive to shielding is constructed, so that false alarm of the safety grating caused by strong light, dust and high-temperature radiation is inhibited.

An artificial intelligence-based system and method facilitate enhanced interaction between users and mechanical devices such as vehicles. One system uses an application equipped with AI algorithms to process user inputs concerning specific vehicle information and issues, providing tailored recommendations and feedback. This includes automated troubleshooting, parts recommendations, and maintenance advice, aiming to improve user experience and vehicle upkeep efficiency.

The utility model discloses a deep soft soil solidifying device which comprises pulping equipment used for preparing first slurry; the first storage tank and the second storage tank are respectively used for storing an exciting agent and a water absorbent; the feeding device is installed on an excavating arm of mechanical equipment, the feeding device is communicated with the pulping equipment through a mortar conveying hose, and a first mortar conveying opening and a second mortar conveying opening are formed in the feeding device; the first material storage tank and the second material storage tank are communicated with the first ash feeding port and the second ash feeding port through a first hose and a second hose respectively, and the first slurry is conveyed into deep soft soil through a feeding device and is sequentially mixed with an exciting agent and a water absorbent in the conveying process. According to the utility model, the soft soil curing agent can be smoothly injected into the deep soft soil.

The invention relates to the technical field of fault diagnosis, in particular to a fault prediction method and system based on observable data, and the method comprises the following steps: collecting real-time energy efficiency data, including power consumption, temperature and vibration data, of a gear and a bearing of mechanical equipment, calculating the deviation between the real-time energy efficiency data and a set energy efficiency reference, and calculating the energy efficiency of the gear and the bearing; and generating real-time energy efficiency deviation data. According to the method, the energy efficiency data of the key parts are monitored in real time, the abnormal state of the equipment is recognized in real time, the maintenance cost and downtime caused by fault diffusion are remarkably reduced, the deviation mode is analyzed through the random forest model, the abnormity judgment precision is improved, and the reliability of the equipment is improved. According to the method, fault propagation is analyzed by constructing the dependency graph and utilizing a graph theory method, the influence range and key nodes of potential faults are effectively predicted, the pertinence and efficiency of a maintenance strategy are greatly enhanced, and on the whole, the method improves the safety and reliability of a system, and meanwhile efficient operation of equipment is guaranteed.

The invention discloses an online health monitoring and remote control method and system for hoisting mechanical equipment, and relates to the technical field of industrial equipment health monitoring and intelligent control, and the method comprises the steps: constructing health grade judgment, credibility calculation and abnormal trend intensity analysis results into a real number BPA, carrying out the complex field expansion of the real number BPA, and carrying out the remote monitoring of the health of the hoisting mechanical equipment; obtaining a plurality of evidence sets; calculating the credibility of the plurality of evidences in the plurality of evidence set, and classifying the plurality of evidences, including high-credibility evidences and low-credibility evidences; and performing weight discount correction on the low-credibility evidence, fusing the corrected plurality of evidences by using a plurality of evidence combination rule, and generating a final health evidence value by adopting a conservative decision strategy. Therefore, a stable and credible health assessment conclusion can still be formed when various state assessment results have uncertainty or conflicts, and the conclusion is used as a remote control decision basis, so that the reliability of equipment health state judgment and the safety of a remote control process are improved.

The invention discloses a dual-mode orchard gas explosion subsoiler, and belongs to the technical field of agricultural mechanical devices. Comprising a moving platform, an electric telescopic arm device, an air generating device, an air conveying device, a handheld operating device, a pedal device, an angle adjusting device, a self-closed double-layer pneumatic deep scarification device and a self-closed soil penetrating device. The electric telescopic arm can move up and down and stretch out and draw back to adapt to plant spacing, and automatic deep scarification operation is achieved at a proper position; in an area where the terrain is complex and the platform is difficult to enter, the handheld operating device can be detached from the mobile platform and manually operated by an operator, and the subsoiling operation position is flexibly selected; the self-closed double-layer pneumatic deep scarification device enables the air holes to coincide by adjusting the angle of the inner-layer air rod, soil can be prevented from entering the air channels in the soil penetrating process, and blockage caused by the fact that the soil enters the air channels is effectively prevented; the fruit tree deep scarification device has portability and high efficiency, can cover the deep scarification requirement around fruit trees, and prevents soil from entering an air passage.

The invention discloses a building construction mechanical arm, and relates to the technical field of construction mechanical equipment, the building construction mechanical arm comprises a rotating base, a movable joint and a connecting joint, a main frame is fixedly mounted on the connecting joint, a center shaft is fixedly mounted on the main frame, two main clamping jaws are rotatably mounted on the center shaft, and the main clamping jaws are symmetrically arranged on the main frame; the two main clamping jaws are rotatably connected, the main clamping jaws are used for supporting a main body of the device, each main clamping jaw is a quarter arc, an auxiliary clamping jaw is installed on the outer side arc surface of each main clamping jaw in a sliding mode, and the radian of each auxiliary clamping jaw is the same as that of the corresponding main clamping jaw. And therefore, the single-time clamping capacity of the device is increased, some objects of this kind can be rapidly clamped, and the working efficiency of the device is improved.

The invention relates to the technical field of rolling bearing state monitoring, and discloses a rolling bearing performance degradation evaluation method based on kernel optimal global locality preserving projection and Sinkhorn distance, which comprises the following steps: acquiring an original vibration signal of a rolling bearing, performing feature extraction on the original vibration signal, and constructing a high-dimensional feature set; calculating and screening features in the high-dimensional feature set, and constructing a sensitive feature set; performing dimension reduction processing on the sensitive feature set by adopting a kernel optimal global locality preserving projection method to obtain a low-dimensional feature matrix; in the low-dimensional feature matrix, the difference between samples is calculated based on the optimal transmission distance of entropy regularization, and a Sinkhorn distance matrix is constructed; on the basis of the Sinkhorn distance matrix, constructing a degradation index; a health threshold is set, an early degradation point is judged through the degradation index and the health threshold, rolling bearing fault recognition and performance evaluation are achieved in combination with the change trend of the degradation index value, and technical support can be provided for health monitoring and intelligent maintenance of rotary mechanical equipment.

1. Name of the Design Product: Electric Lifting Column (Nine). 2. Use of the Design Product: It is used to drive the lifting of various mechanical equipment in a linear reciprocating motion. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective view.

The invention relates to the field of intelligent equipment, and discloses a cargo handling method and system based on spatial relationship reasoning, and the method comprises the steps: obtaining a color image and a depth image containing cargos, screening effective cargos, and obtaining the spatial relationship information of the effective cargos, the spatial relationship information comprises depth information, height information, depth difference, vertical overlapping degree and horizontal overlapping degree; according to the depth difference, the vertical overlapping degree and the horizontal overlapping degree of the effective cargos, an unconstrained cargo set is identified and obtained; according to the unconstrained cargo set and based on the depth information and the height information of the unconstrained cargos, priority ranking is carried out, and an optimal carrying sequence is generated; and according to the priority sequence of the optimal carrying sequence, mechanical equipment is driven to execute actual grabbing operation, and carrying operation of the goods is completed. According to the method, the constraint graph model based on the spatial dependency relationship is innovatively constructed, and the unconstrained target which can be safely grabbed is intelligently identified.

The invention relates to the technical field of mechanical equipment fault diagnosis, and particularly discloses a press machine fault early warning method and system, and the method comprises the steps: collecting operation state data; constructing a plurality of dynamic thresholds; automatically generating a conflict logic set according to the dynamic threshold, and calculating a priority score for the conflict logic set to generate an optimal logic result; generating press machine fault early warning information in combination with the fault prediction model; in the prior art, conflicts are easy to occur and misjudgment is caused under a multi-logic condition, and especially the technical problem that logic conflicts cannot be effectively processed and clear diagnosis and explanation cannot be provided under the condition that multiple data sources dynamically change in the operation process of the press machine is solved. According to the method, accurate fault diagnosis and clear explanation under the condition of multiple logic conflicts are realized through a digestion method combining logic conflict detection and priority scoring, so that the problem of misjudgment caused by complex decision logic is avoided, and the accuracy and interpretability of press fault early warning are improved.