480results about How to "Run fast" patented technology

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention relates to a near-electricity construction operation safety early warning method and system, and belongs to the field of near-electricity construction operation equipment early warning, and the method comprises the steps: scanning a power transmission and transformation project construction site through three-dimensional laser, and forming a high-precision laser point cloud three-dimensional data model; quickly dividing an electrified body and a non-electrified body in the three-dimensional laser point cloud model, and carrying out attribute hooking on the safe distance range and the electrified body; rTK positioning sensors are installed on an operator and a construction machine, and high-precision real-time positioning and spatial distance measurement of the operator and the machine are achieved through Beidou RTK; real-time space distances between operators, machines and electrified bodies are monitored on the basis of a safety distance detection algorithm, reminding in different modes such as safety warning and prohibition warning is set according to requirements of safety operation regulations, and it is ensured that the operators, machines and the like keep enough safety distances from the electrified bodies.

The invention belongs to the technical field of computer application, and relates to an operation inspection equipment fault detection method and system based on an improved deep learning model, and the method comprises the steps: obtaining original data, employing EEMD to decompose the original data for feature enhancement, obtaining a decomposition signal, carrying out the noise reduction of the decomposition signal, and obtaining a feature signal after noise reduction; according to the feature signals after noise reduction, a deep learning model is constructed based on Resnet and a dynamic Transform encoder, and the prediction probability of each fault category is output; a ZOA optimization algorithm is used to optimize hyper-parameters of a deep learning model, an optimization model is obtained, the performance of the optimization model is evaluated, an optimal model is obtained, and the prediction probability of each fault category is output, so that false report and missing report of faults can be effectively reduced in a fault detection task, potential safety hazards caused by the fact that the faults are not found in time are reduced, and the fault detection efficiency is improved. Therefore, the stability and reliability of the power system are improved.

The invention discloses a water quality prediction method based on multi-source data, and constructs a water quality prediction model based on a long short-term memory network (LSTM) and a graph neural network (GNN) of multi-source data (surface water monitoring data, meteorological data, hydrological data and pollution source on-line monitoring data), so that short-term real-time on-line prediction of surface water quality factors can be realized. According to the method, on the basis of multi-source data such as section water quality monitoring data, historical meteorological data, forecast meteorological data, hydrological monitoring data, pollution source online monitoring data and upstream and downstream relations, multiple main water quality influence factors such as water quality, meteorology, hydrology, pollution sources and upstream and downstream section water quality are comprehensively considered; the LSTM or the graph neural network is adopted to establish a water quality prediction model, and the comprehensive influence of various influence factors and the time-space relationship on the water quality is simulated, so that the simulation result is more accurate and reliable.

The invention relates to a thermodynamics-based system for a predictive maintenance function of a switch cabinet, and the system comprises a sensor unit which comprises a temperature measurement sensor and a current sensor and is used for collecting a temperature signal, a current signal and an environment temperature signal of a connection point of a main loop of the switch cabinet in real time; the alarm unit is used for sending out an alarm signal after a fault is predicted to occur when the switch cabinet is in non-full-load operation; and a control unit configured to: calculate a difference between the temperature collected from the sensor unit and an ambient temperature to obtain a temperature rise value; initializing a model based on the corresponding time, the collected current and the calculated temperature rise value to obtain five fitted parameters; performing temperature rise value prediction based on the collected current signals in the prediction time period by using the obtained five parameters; and when the switch cabinet is judged to have a fault according to the comparison result of the predicted temperature rise value and the collected measured value, the alarm unit is controlled to send out an alarm signal.

The invention discloses a water electrolysis hydrogen production system life monitoring method and system, and the method comprises the steps: collecting the monitoring data of a target part in a system, and collecting the voltage information of an electrolytic cell in the system; calculating the loss rate of the target component according to the monitoring data of the target component; according to the loss rate of the target component and the voltage information of the electrolytic cell, calculating operation data of the target component at the end of life of the system; and controlling the current of the electrolytic bath according to the operation data of the target component at the end of life of the system and the priority mode of the system. Thus, full life cycle monitoring and life prediction are performed on the water electrolysis hydrogen production system, the service life and the safety coefficient of the water electrolysis hydrogen production system are improved, and safe operation of the system is promoted.

The invention discloses a remote sensing target detection method and system based on improved RT-DETR. The method comprises the following steps: step 1, collecting remote sensing image data; step 2, carrying out preprocessing such as denoising, contrast enhancement and definition enhancement on the acquired remote sensing image data; 3, making the preprocessed pictures into a remote sensing data set, and dividing the remote sensing data set into a training set and a verification set in proportion; 4, training the remote sensing data set by adopting an RT-DETR model to obtain a remote sensing target detector; step 5, optimizing various parameters of the target detector on the verification set by adopting a hyper-parameter search verification mode; and step 6, detecting a test image or video by using the remote sensing target detector optimized in the step 5. Compared with an original RT-DETR model, on the premise that original precision is kept, the calculation amount is reduced, and the network convergence speed is increased.

The invention discloses an astronomical positioning method for a space target elongated astronomical image of a large-field-of-view space-based monitoring platform, relates to the technical field of astronomical observation, and solves the problem that the elongated astronomical image is abandoned due to incapability of target association and precise orbit determination caused by inaccurate centering of the elongated astronomical image and low precision of a positioning method of an existing monitoring platform. According to the method, an observation image is divided into 16 sub-view fields, and each sub-view field is an independent view field and is calculated by an independent processor; removing fixed stars in the observation image by adopting an adjacent frame image difference method and extracting a space target star image; secondly, describing the length and angle of the lengthened astronomical image by adopting a minimum enclosing rectangle, and constructing a degradation model; performing motion blurred image restoration on the elongated star image; error points with large errors are eliminated by adopting a mutual comparison method, and track association of the space target is completed; and finally completing precise astronomical positioning of the elongated astronomical image and the like. According to the method, the processing efficiency is greatly improved, and a large number of lengthened astronomical images which are abandoned before are fully utilized again.

The invention relates to the technical field of mark point identification and positioning, in particular to a polygon mark point rapid identification and positioning method and system and a storage medium. Then, the gradient direction information and the coordinate information of the reserved strong edge are used for rapidly estimating the rotation angle and the center of the polygon mark point to be detected, the method is small in calculation amount and easy to operate, and rough pose information of the polygon mark point to be detected can be rapidly obtained; the estimated angle is further corrected in a small range in a rotary projection mode, the deviation between an estimated value and a real value is reduced, and a more accurate initial value is provided for subsequent registration; finally, the corrected coarse positioning result is used as an initial value for registration, the convergence speed can be increased, the number of iterations can be reduced, meanwhile, the probability that iteration falls into a local extreme value can be reduced, the operation speed is increased, and the accuracy rate is increased.

The invention discloses a lightweight crack penetration degree detection method based on ground penetrating radar and Swin Transform optimization, and the method comprises the following steps: 1, collecting ground penetrating radar data, and carrying out the preprocessing and feature extraction; step 2, constructing a multi-feature fusion data set; step 3, designing a two-stage through crack detection model based on Swin Transform optimization; 4, a first-stage through crack detection model optimized on the basis of Swin Transformer-YOLOv8 is designed, and a second-stage through crack detection model is designed on the basis of Swin Transformer-YOLOv8 optimization; and step 5, training and predicting the data of the training set and the test set according to the model architecture in the step 3 and the step 4. According to the invention, the accuracy of crack penetration degree detection is improved, and a scientific basis is provided for road maintenance; the detection speed is greatly improved, and the requirement of large-area rapid detection is met; the calculation complexity is reduced, the equipment performance pressure is reduced, and the detection efficiency is improved; the generalization ability of the model is enhanced, and the method can adapt to different road conditions; and the development of an intelligent road detection technology is promoted.

The invention provides a novel YOLOv10-based safety belt detection method and system, and the method comprises the steps: obtaining image data, and marking the image data, namely marking the position / wearing state of a safety belt; constructing a detection model based on a novel YOLOv10 network, namely replacing partial standard convolution Conv of the Backbone network with local attention regularization convolution; using a separable convolution star module to replace an original feature extraction module of the Backbone network; a CARAFE module is used for replacing up-sampling operation in a Neck network, multi-scale space attention dilation convolution fusion modules are additionally arranged before three sets of feature maps output by the Neck network, and then the feature maps are output to a Head network; and training a detection model by using the labeled image data to obtain a safety belt detection model. When the novel YOLOv10 network is used for safety belt detection, the detection efficiency and performance can be remarkably improved.

The invention discloses a preparation process of a high-thermal-conductivity composite substrate, and the process comprises the steps: fixing a pre-prepared skeleton on an insulating base layer, and enabling branches of the skeleton and the surface of the insulating base layer to jointly define a plurality of heat dissipation spaces; dividing the plurality of heat dissipation spaces into at least one high heat dissipation space and at least one low heat dissipation space according to the layout and heating characteristics of a predetermined electronic component; a first heat-conducting filler is injected into the high-heat-dissipation space, a second heat-conducting filler is injected into the low-heat-dissipation space, curing treatment is conducted, the heat conductivity coefficient of the first heat-conducting filler is larger than that of the second heat-conducting filler, and the first heat-conducting filler and the second heat-conducting filler are combined with the framework to form a heat-conducting layer; a functional layer of the printed circuit board is fixed on the surface of the heat conduction layer, the area, corresponding to the high-heat-dissipation space, of the functional layer is used for installing high-heat-dissipation electronic elements, and the area, corresponding to the low-heat-dissipation space, of the functional layer is used for installing low-heat-dissipation electronic elements According to the technical scheme, different heat conduction measures can be adopted for different heating elements.

The invention relates to the technical field of computer vision and deep learning, and provides an SLAM (Simultaneous Localization and Mapping) dynamic disturbance suppression method based on fuzzy processing and target detection, which comprises the following steps of: obtaining a fuzzy remover; performing blurring processing on the image by using a blurring remover, and extracting ORB feature points; obtaining a dynamic target detector; detecting the clear image through a dynamic target detector to obtain a target detection frame; classifying motion states of objects in the clear image according to the target detection frame and the ORB feature points, and obtaining a static feature point set by using an overlapping frame strategy and epipolar geometric constraint; and performing system pose estimation and trajectory prediction according to the static feature point set. According to the method, the adverse effect of the dynamic target on other parts of the system is reduced through deblurring, the dynamic target can be accurately recognized through the target detection function, comprehensive elimination of the dynamic feature points is achieved in combination with the overlapping frame strategy and the epipolar geometric constraint, and the overall performance of the system is well improved.

The invention relates to the technical field of oil pumping unit control, and provides an oil pumping unit frequency converter dynamic regulation and control device and method based on deep learning, and the device comprises a motor operation data detection device which is used for the motor operation state of an oil pumping unit, and comprises a phase voltage, a phase current and a power factor; the main control unit is configured to predict a voltage value of a frequency converter of the oil pumping unit based on an aggregation model of score and weight parameters and a deep learning model according to the detected load and the motor operation state of the oil pumping unit, and send the voltage value to the full-power adaptive regulator; and the full-power self-adaptive regulator is used for connecting the main control unit and the frequency converter, is arranged on the output side of the frequency converter, and ensures that the motor of the oil pumping unit runs in the optimal interval by self-adaptively regulating and controlling the regulation and control instruction of the frequency converter according to the received predicted voltage value. The device disclosed by the invention can realize intelligent tracking of the motor along with load change, and can realize harmonic suppression and energy feedback.

The invention discloses a method and device for accelerating a homomorphic encryption algorithm by using fast number theory transformation and a medium. The method comprises the following steps: initializing homomorphic encryption parameters and generating a public key, a private key and a relinearization key; encrypting a plaintext to be operated by using the public key to generate a corresponding ciphertext polynomial; performing fast number theory transformation on the ciphertext polynomial to transform the ciphertext polynomial into a point value form; performing homomorphic multiplication on the two point value form ciphertexts to obtain three ciphertexts; using a relinearization key to transform and decompose one ciphertext by using a fast number theory and accumulate the decomposed ciphertext into the other two ciphertexts to obtain two similar primary ciphertexts; using fast number theory inverse transformation to transform the first-order ciphertext from a point value form into a polynomial form; and decrypting the ciphertext polynomial by using the private key to obtain a plaintext multiplication result. The method adopts fast number theory transformation and inverse transformation, improves the polynomial multiplication calculation efficiency through the processes of primitive root sampling, bit inverse sequence and the like, is suitable for the field of polynomial operation, and improves the polynomial operation efficiency.

The invention provides a universal Transform acceleration method and system based on floating point instruction set extension, and the method comprises the steps: constructing a multiplier which comprises a plurality of stages of adders and a 4-Booth coding algorithm which are connected in sequence, and coding data; the coded data is operated through a plurality of stages of adders in sequence to obtain the output of a multiplier; the construction of the floating-point number register comprises the following specific steps: adding a floating-point number register group, generating the floating-point number register group, enabling input result register indexes to correspond to respective register numbers by writing port logic of the floating-point number register group, generating a writing enable signal, writing data into the register by an enabled general register, and writing the data into the floating-point number register group. The read port is realized by using a multi-path parallel selector, and a selection signal of the multi-path parallel selector is an index of a read operand register; and the RISC-V processor accelerates the operation of the Transform neural network based on the constructed multiplier and the floating-point number register.

The neural network forward prediction hardware accelerator based on the programmable logic device can be applied to the technical field of neural network hardware acceleration. The accelerator comprises a multi-layer hardware accelerator module unit, and the hardware accelerator module unit comprises an input feature map cache module and a data processing module; the accelerator module units are connected in an assembly line structure mode; multi-channel feature maps can be processed in parallel; and the output data of the hardware accelerator module unit of each layer is stored in the input feature map cache module of the hardware accelerator module unit of the next layer for the operation of the next layer. By adopting an assembly line structure, the throughput rate of the hardware accelerator is improved; a multi-channel parallel processing mode is adopted, and the running speed of a hardware accelerator is increased; and on-chip storage is adopted, so that the off-chip access times are reduced, and the power consumption of the hardware accelerator is reduced.

The invention discloses a water turbine operation monitoring system and method in the technical field of hydroelectric generation. The method comprises the steps that real-time operation data and real-time output data of a water turbine input externally are received; inputting real-time operation data of the water turbine into the water turbine operation analysis model, and calculating a prediction value of output data; comparing the output data predicted value with the real-time output data, and judging whether a data difference value between the two exceeds an abnormal threshold value or not; when the data difference value does not exceed the abnormal threshold value, it is judged that the water turbine runs abnormally; and when the data difference value exceeds the abnormal threshold value in response, judging that the water turbine operates abnormally, and reporting an abnormal condition to an operation management platform at the same time. According to the method for monitoring the operation of the water turbine, the operation state of the water turbine is monitored and evaluated, the abnormal operation condition of the water turbine is reported in time, operation and maintenance management personnel can conveniently conduct maintenance in time, and additional economic losses are avoided.

The invention belongs to the technical field of image processing, and discloses an underwater image quality evaluation method based on sparse representation and Transform, comprising the following steps: S1, selecting N underwater images with subjective quality labels, and extracting visual perception features of each underwater image by using sparse representation; s2, extracting a semantic feature of each underwater image by using a Transform; s3, splicing the visual perception features and the semantic features, inputting the spliced features into a multi-layer perceptron, and constructing an underwater image quality evaluation model; s4, performing end-to-end training on the underwater image quality evaluation model; and S5, predicting the quality of an underwater image to be evaluated by using the trained image quality model. The method does not need to refer to an original image, can obtain high prediction performance, and meets the application requirements of underwater imaging.

The invention relates to the technical field of semiconductor packaging equipment, in particular to an external high-speed and high-precision force control mechanism which comprises a fixed seat, a power assembly, a pressure assembly and a flexible hinge assembly. The power assembly comprises a ball guide rail assembly and a large voice coil motor, the ball guide rail assembly is arranged on the fixing base, a Z-direction mover plate is arranged on the ball guide rail assembly in a sliding mode, a stator fixing plate of the large voice coil motor is connected to the upper end of the fixing base, and a mover fixing plate of the large voice coil motor is fixedly arranged on the Z-direction mover plate; the pressure assembly comprises a small voice coil motor and a lower pressing plate, the upper end of the small voice coil motor is arranged at the right upper end of the Z-direction mover plate, and the lower pressing plate is arranged at the lower end of the small voice coil motor; the flexible hinge assembly comprises a plate spring connecting base and a plate spring fixing base, the right end of the plate spring connecting base is connected to the lower portion of the middle of the small voice coil motor, the plate spring fixing base is fixed to the Z-direction mover plate, and a plate spring pressing block, an upper plate spring and a lower plate spring are fixed to the upper end of the plate spring connecting base and the upper end of the plate spring fixing base through fastening screws.

The invention discloses a natural wide edge-cleaning sawing machine which comprises a main shaft moving sawing mechanism, a visual identification system and a deviation rectifying module, and a cutting method comprises the following steps that a wood board is conveyed by a chain feeding mechanism to move to a scanning area of the visual identification system, and the visual identification system analyzes the inner and outer contours of to-be-cleaned parts on the front and rear sides of the wood board; the intersection point of the linear moving path of the deviation rectifying module and the inner contour of the front side of the wood board and the to-be-clamped position E point of a clamping jaw of the deviation rectifying module are determined, and the moving distance of the deviation rectifying module is obtained through the image; after the wood board is clamped, the wood board is lifted to a set height, the visual recognition system conducts secondary photographing recognition on the wood board to calculate a front cutting line and a rear cutting line, the wood board is lifted by a certain height to compensate that the free side of the wood board droops, and the visual recognition system conducts secondary photographing recognition to improve the cutting precision; and the clamping position of the deviation rectifying module on the wood board is adjusted in a self-adaptive mode according to the wood board width recognized through first-time photographing.

The invention belongs to the technical field of target detection, and relates to a sonar image-based small target real-time detection method, which comprises the following steps of: 1) enhancing and segmenting a sonar image to obtain target intensity, target position and target diffusion range; 2) recovering a target shape of a target in the sonar image based on the target position; and 3) based on the target shape, the target intensity, the target position and the target diffusion range, adopting target multi-frame detection to realize real-time detection of the small target. The method does not depend on large parallel computing equipment, is easy to deploy, is small in calculation amount and high in operation speed, and can meet run-time detection of the equipment on a CPU (Central Processing Unit); the preset parameters are few and clear in meaning, a user can adjust in time according to the use effect, and use is convenient; according to the method, multi-frame detection is adopted, historical target records are reserved, the target position can be reserved when the image is subjected to transient interference, and the anti-interference capability is high.

The invention discloses a virtual machine pause processing method of a micro kernel Hypervisor based on an ARM VHE (Advanced RISC Machines VHE), which is characterized in that the micro kernel Hypervisor based on the ARM VHE is deployed and comprises a Hypervisor micro kernel module, a Host OS (Operating System) service thread and a virtual machine user mode management thread, wherein a CPU (Central Processing Unit) virtualization module in a kernel virtualization extension module in the Hypervisor microkernel module comprises a vCPU running thread running in an EL0 / EL1 mode and a vCPU processing thread running in the EL0 mode, and when the Hypervisor monitors that the vCPU running thread is suspended, the kernel virtualization extension module in the kernel virtualization extension module in the Hypervisor microkernel module is started; corresponding processing operation is adopted according to the pause trigger reason: if the current pause reason belongs to interrupt trigger or abnormal trigger processed under a Hypervisor microkernel, the kernel virtualization extension module directly performs corresponding virtual machine pause processing operation on EL2, and if the current pause reason belongs to other conditions, the kernel virtualization extension module directly performs corresponding virtual machine pause processing operation on the EL2; if yes, scheduling a vCPU processing thread to perform virtual machine pause processing operation at EL0; and after the operation is completed, the vCPU running thread recovers running. According to the method, the interrupt and exception processing of the Hypervisor on the virtual machine is optimized, and the pause processing performance of the virtual machine is improved.

The present disclosure provides a method, device, equipment and medium for calculating right ascension and declination based on Euler angles, which relates to the field of aerospace technology. The method includes: obtaining the initial observation pointing parameters of a satellite camera, the first rotation Euler angle of the coordinate system unit basis vector corresponding to the initial observation pointing to the body coordinate system unit basis vector corresponding to the satellite platform, and the second rotation Euler angle of the coordinate system unit basis vector corresponding to the satellite platform to the coordinate system unit basis vector corresponding to the final observation pointing; calculating the final observation pointing parameters of the satellite camera according to the initial observation pointing parameters, the first rotation Euler angle and the second rotation Euler angle; calculating the right ascension and declination of the satellite camera according to the final observation pointing parameters. The method can quickly and accurately solve the celestial right ascension and declination from the initial pointing of the camera, and can fully consider various rotation sequences of aerospace engineering, with a wide range of applications and strong applicability.

The invention provides alcohol amine liquid, a device and a method for decarburization of high-dust gas, and belongs to the technical field of low-carbon metallurgical decarburization in the metallurgical industry. Wherein the alcohol amine liquid comprises alcohol amine, an activating agent, a flocculating agent, a defoaming agent and water; the alcohol amine is selected from one or more of primary alcohol amine, secondary alcohol amine, tertiary alcohol amine or sterically hindered alcohol amine; the activating agent is selected from one or more of piperazine, diethanol amine, diethylenetriamine and diisopropanolamine; the flocculating agent is an inorganic flocculating agent, an organic flocculating agent or a composite flocculating agent composed of the inorganic flocculating agent and the organic flocculating agent; the defoaming agent is a composite defoaming agent consisting of one or more of a non-silicon type, an organic silicon type, a polyether type and a polyether modified organic silicon type. According to the developed alcohol amine liquid, the high-dust gas is carried into the alcohol amine liquid, dust is removed, and the influence of the dust on bubbles is eliminated, so that decarburization treatment on the high-dust gas by using an alcohol amine decarburization method is realized.

The invention relates to a three-dimensional target detection method based on Point Pillars, and the method comprises the following steps: obtaining a 3D point cloud around a vehicle, employing a column feature network to sequentially carry out discretization, extracting global context information based on a self-attention mechanism, and carrying out the re-projection, and forming a 2D pseudo image; inputting the 2D pseudo image into a two-dimensional backbone network, performing down-sampling from top to bottom by adopting an attention mechanism, and then obtaining a splicing feature through deconvolution and self-connection; based on the splicing features, an SSD detection head is adopted for detection, and a target detection result is output. Compared with the prior art, the method has the advantages of improving the small target detection effect and the like.

The invention discloses an isolating switch mechanical structure fault diagnosis method based on Levy-FOX-VMD and SVM. Firstly, a fault simulation experiment is carried out on a certain type of disconnecting switch, and a vibration signal is measured; then, the Levy-FOX is adopted to optimize the VMD algorithm, and optimal parameters alpha and k are found out; secondly, decomposing a non-stationary vibration signal into a plurality of intrinsic mode functions (IMF) by adopting VMD, and calculating a plurality of entropies of each layer of IMF to form a multivariate feature fusion matrix; and carrying out dimensionality reduction on the multivariate entropy matrix by using a PCA algorithm. And finally, inputting the feature matrix into an SVM for training to realize fault diagnosis of the mechanical structure of the disconnecting switch.

The invention provides a track correlation method, device and equipment based on parameter space conversion and a medium, relates to the technical field of image processing, and is used for solving the technical problem that the prior art is difficult to adapt to track correlation engineering application in relatively complex noise and clutter environments. The method comprises the following steps: acquiring simulation observation data of a motion trail of a space target object, and adding Poisson distribution clutter to the simulation observation data to generate a data set; converting the data set from an observation space to a parameter space to obtain distribution characteristics of each space target object in the parameter space; according to the distribution characteristics, coordinate parameters corresponding to the space target objects located on the same motion trail are extracted; the coordinate parameters are reversely converted into track parameters of the space target object in the observation space, and the track parameters represent the slope and the intercept of the motion track of the space target object; and based on the trajectory parameters, associating the space target objects meeting the preset motion trajectory trend in the observation space to obtain associated arc segments.

The invention is suitable for the field of cycloidal speed reducers, and provides an RV speed reducer which comprises a shell assembly, an input assembly and a speed reduction assembly and is characterized in that the shell assembly comprises a front supporting shell, a rear supporting shell, a positioning bolt and a positioning shaft, and the front supporting shell and the rear supporting shell are fixedly installed through the positioning bolt; positioning holes are annularly formed in the supporting front shell and the supporting rear shell, the positioning shaft is installed in the positioning holes in a rolling mode, the speed reduction assembly comprises a speed reduction sealing cover, a planet assembly and a main speed reduction assembly, the speed reduction sealing cover is installed in the supporting front shell through a bearing, and the circle center of the speed reduction sealing cover coincides with the circle center of the supporting front shell; and the kinetic energy transmission efficiency and the maximum running speed are improved, so that the joint is more suitable for high-speed robot speed reduction joints. Pitting corrosion, micro pitting corrosion and eccentric wear of the balance wheel are avoided, and the service life of the speed reduction assembly is prolonged.

The invention provides a wired table recognition method based on morphology, and the method comprises the following steps: S1, carrying out the graying processing of all read-in to-be-detected three-channel images, obtaining a gray image, carrying out the binarization processing of the obtained gray image, and obtaining a binary image of an original image; and S2, carrying out corrosion and expansion operation on the binary image by using a detection core, and forming an Excel format file by identifying a text table format and the like in the image so as to solve the problem of a special-shaped table. The problem that in the prior art, table recognition is always a research difficulty in the field of document recognition is solved, and the method is high in running speed, high in stability and good in interpretability. And the method is innovative, and can be used for well processing conditions such as special-shaped tables, cross-page disconnection, text cross-line, text wireless frame package and the like. The method can be popularized, and has good compatibility for identification of different types of wired tables. The method can be deployed in a server, a personal pc (personal computer) and embedded equipment, and is high in practical applicability.