199results about How to "Easy to classify" patented technology

The invention belongs to the technical field of motor fault diagnosis, and particularly relates to an asynchronous induction motor fault intelligent detection method based on a multi-modal time sequence. The method comprises the following steps: acquiring various signals of a motor, including three-phase current, vibration, equipment sound, environment sound and the like, and constructing a multi-modal data set; thirdly, constructing a Transform network classified by a single time sequence, and on the basis of the Transform network, connecting a plurality of the networks in parallel to obtain a lumped Transform network; and then, features of different modal data are dynamically integrated by using a self-attention mechanism, and accurate recognition of a motor fault is realized. In a network training process, a hyper-parameter of the lumped Transform network is optimized by adopting a chaos WOA algorithm; finally, different types of faults of the asynchronous motor can be accurately detected and judged, the situation that different fault features of the motor cannot be accurately represented by a single time sequence is avoided, and the accuracy and reliability of fault detection of the asynchronous motor are guaranteed.

The invention provides a data classification method, device and equipment and a computer storage medium, and the method relates to the technical field of computers, and is used for improving the efficiency and accuracy of data classification and improving the flexibility of data classification. The method comprises the following steps: acquiring data features corresponding to to-be-classified data; obtaining a corresponding query condition based on the data feature, and obtaining at least one target classification rule matched with the to-be-classified data from a preset classification rule set based on the query condition; inputting the to-be-classified data and at least one target classification rule into a trained label generation model, and generating at least one data classification label corresponding to the to-be-classified data based on description information included in the at least one target classification rule and the data features corresponding to the to-be-classified data; wherein each data classification label represents one data type; and based on the at least one data classification tag, determining a target classification result corresponding to the to-be-classified data.

The invention provides a forestry pest monitoring method and system based on machine learning, and the method comprises the steps: carrying out the principal component analysis of a forestry pest image, obtaining a plurality of recognition indexes of the pest of a target forestry region, determining an effective feature vector when the image recognition of the forestry pest is carried out according to all recognition indexes, and carrying out the recognition of the effective feature vector. Determining a first-level correlation feature of the pest image in the target forestry region through the effective feature vector; extracting a plurality of environmental influence factors from the monitoring image, and determining secondary correlation features of the pest image in the target forestry area according to all the environmental influence factors; carrying out interactive fusion on the first-level correlation features and the second-level correlation features to obtain multi-level distribution features for carrying out image recognition on the diseases and insect pests; and identifying the current pest and disease distribution of the target forestry area based on the multi-level distribution characteristics through a pest and disease identification model. By adopting the scheme of the application, diversified monitoring of the forestry diseases and insect pests can be realized under a complex background, so that the false detection rate of the forestry diseases and insect pests is reduced.

The invention discloses a PET waste plastic intelligent sorting method and system based on multi-feature fusion, and the method comprises the following steps: collecting an RGB-HSI original data set of PET waste plastic, carrying out the dimension reduction processing of the HSI data set based on a genetic algorithm to generate MSI data, and forming a feature fusion data set with RGB data; constructing a feature fusion network based on a YOLACT model, and training by adopting a feature fusion data set; the feature fusion network comprises an RGB branch, an MSI branch and a fusion prediction module, RGB is used for extracting features of RGB data, the MSI branch is used for extracting features of MSI data, and the fusion prediction module is used for combining output of the RGB branch and output of the MSI branch to make comprehensive prediction; and installing the trained feature fusion network to an industrial personal computer to realize intelligent sorting of the PET waste plastics. Compared with a traditional recognition algorithm, the method can improve the recognition precision.

The invention discloses a spoken English man-machine conversation interaction training method and system, and relates to the technical field of spoken English training, spoken English audio signals of a user are monitored in real time through a microphone, and the frequency spectrum information of the audio signals is refined into each frame of spectrogram and cepstrum coefficient feature vectors by using Fourier transform and feature extraction technologies; therefore, the voice signal characteristics of the user can be completely captured; three core dimensions of semantic similarity, grammatical structure similarity and fluency are incorporated into an evaluation model, a comprehensive oral English training evaluation system is formed, a text sequence is generated by analyzing a time sequence dependency relationship through a convolutional neural network, and the oral English training evaluation model of a deep learning technology is combined, so that the oral English training evaluation accuracy is improved. According to the method, the expressions of the user in the aspects of semantics, grammar and fluency can be accurately measured, the oral training level of the user can be comprehensively judged by the system by comparing the comprehensive evaluation coefficient with the preset qualified threshold value, and a personalized learning guide is provided according to a result.

The invention relates to the technical field of information and data processing, and discloses an intelligent recommendation method and system for a bone fracture rehabilitation training plan based on traditional Chinese medicine, and the method comprises the steps: dividing image information according to labels, and obtaining skeleton data sets with different functions; taking the first position information and the second position information as a special information set; deleting the special data set from the skeleton data set to obtain a target data set; obtaining position information of the bones to be subjected to bone fracture rehabilitation training from the target data set, and selecting matched contents such as shadowboxing, eight-section brocade or five-poultry play from a pre-stored traditional Chinese medicine rehabilitation training database; deleting related actions of the second position information from an action data set corresponding to the content; guiding the trainee to perform bone fracture rehabilitation training through the visual terminal; the system comprises a first data set construction module, a second data set construction module and a target action output module. According to the bone fracture rehabilitation training system and method, biological feature recognition and personalized, precise and systematic bone fracture rehabilitation training are achieved.

The invention discloses a vehicle-mounted multi-target classification and detection method based on multi-teacher distillation and dynamic reasoning, and the method comprises the steps: collecting an image through a vehicle-mounted image collection device, taking the image as the input through a dynamic neural network, and outputting the classification and bounding box regression of multiple targets. The dynamic neural network is composed of a multi-teacher network and a student network. The multi-teacher network is composed of a plurality of teacher models; a dynamic weight distribution system based on an attention mechanism is introduced into the multi-teacher network, and different weights are distributed to each teacher model according to the characteristics of the current input scene; a confidence coefficient threshold value is set in a multi-teacher network, and regional knowledge possibly containing a target is only taught to a student network. The student network is composed of a strategy network and a detection network, and the detection network is a two-stage target detection network obtained by integrating a feature extraction network and a target positioning network; the strategy network adaptively extracts task features according to knowledge provided by the multi-teacher network; and the detection network dynamically selects a route according to the policy network decision vector.

The invention provides a pipeline defect detection method and system and electronic equipment, and the method comprises the following steps: collecting and preprocessing a pipeline video, and obtaining a preprocessed pipeline image; detecting and positioning pipeline defects existing in the preprocessed pipeline image by adopting a pipeline real-time detection algorithm; studying and judging the type of the pipeline defect to obtain a pipeline defect detection result; and generating a pipeline defect detection report according to the pipeline defect detection result. According to the scheme provided by the invention, the pipeline defect is detected by using the pipeline real-time detection algorithm, so that the detection accuracy is improved and the manual operation is reduced; a ChatGLM3-6B technology and an RAG technology are applied to generate a more accurate defect detection report, and a pipeline CCTV detection technology is combined to realize one-button generation of the pipeline defect detection report, so that resource configuration can be optimized, pipelines can be managed and maintained in a targeted manner, and the management capability of an urban drainage system is effectively improved while the cost is reduced.

The invention belongs to the technical field of detection equipment, and particularly relates to an inductor tin slag defect detection machine which comprises a base, and the upper end of the base is fixedly provided with a bearing frame. The device further comprises multiple sets of overturning supporting mechanisms, an overturning power mechanism, an optical detection mechanism, a guiding feeding mechanism, a material collecting mechanism, an automatic statistics mechanism, a detection speed and frame rate self-regulation mechanism and a PLC. According to the invention, comprehensive and rapid detection work of the inductors can be realized, the detection is more complete, the detection accuracy is ensured, the detection speed and the detection frame rate of the optical camera can be automatically regulated and controlled based on the number of the inductors to be detected, and the detection efficiency is improved. While the detection efficiency is ensured, the problem that the use quality and the service life of the optical detection camera can be affected by using too high optical detection frame rate for a long time is solved, the inductors which are detected to be qualified and the inductors which are detected to be unqualified can be placed in a classified mode, and the automatic sorting function is achieved.

The invention discloses a waste residue recovery device capable of achieving multiple screening, and relates to the technical field of steel machining, the waste residue recovery device comprises a base, at least four supporting frames are fixedly installed at the top of the base, every two supporting frames form a group, and the two groups of supporting frames are symmetrically arranged on the base; a multi-screening assembly is arranged at the top of the supporting frame; through the arrangement of the sliding grooves and the sliding plates, one set of sliding plates slide in the sliding grooves, then the stop rods in the sliding plates fixedly connected with the first sliding blocks are moved away from the position over the screen plate, only the stop rods parallel to the first lead screws shield the screen plate, and therefore waste residues larger in size can pass through screen holes formed by the stop rods and the screen plate; and in addition, the mode that the blocking rods horizontally move above the screening plate to adjust the size of the screening holes is achieved, and the situation that the screening holes are blocked by the large waste residues can be effectively avoided.

The invention discloses a rapid sequence visual presentation electroencephalogram classification method based on a spatio-temporal progressive attention model. The method comprises the following steps: acquiring electroencephalogram data to be classified; inputting the to-be-classified electroencephalogram data into the trained time-space progressive attention model for processing to obtain the category of the to-be-classified electroencephalogram data; wherein the trained spatio-temporal progressive attention model is obtained by training the initial spatio-temporal progressive attention model by taking collected and processed preset data as a training data set and aiming at extracting spatial features and time features of the electroencephalogram data. The electroencephalogram data is obtained by collecting an image sequence watched by a testee in a rapid sequence visual presentation mode, and the image sequence comprises an infrared small target image and a non-target image. Useful information of electrodes and time slices can be utilized to the maximum extent, and meanwhile irrelevant information is reduced as much as possible.

The invention discloses an adaptive gradient-based spiking neural network training method, which comprises the following steps of: firstly, constructing a to-be-trained spiking neural network adopting LIF model neurons, and inputting data set samples into the to-be-trained spiking neural network in batches; then obtaining pre-synaptic inputs of all neurons in each layer and membrane potentials of all neurons in each layer in the spiking neural network to be trained, associating the width of the proxy gradient with statistical characteristics of the membrane potentials through a correlation function, and adaptively adjusting the width of the proxy gradient in each layer; and finally, calculating cross entropy loss of an output layer of the pulse neural network and a sample label, performing gradient back propagation, and updating a synaptic weight. According to the method, the problem of gradient disappearance or mismatching caused by fixed proxy gradient function width is effectively relieved, the energy consumed by single reasoning of the network is less, and the trained network has excellent classification performance.

The invention discloses a double cropping rice classification method and system based on multi-source remote sensing data and improved indexes, and belongs to the technical field of remote sensing image recognition. The method comprises the following steps: firstly, acquiring multi-source remote sensing data in a whole double cropping rice planting time period, and based on the preprocessed multi-source remote sensing data, respectively extracting improved double cropping rice index features and spectral band features from each optical image, extracting texture features from a Sentinel-2 optical image, extracting topographic features from DEM data, and calculating the multi-source remote sensing data; the method comprises the following steps: extracting polarization characteristics from Sentinel-1 synthetic aperture radar data; and finally, inputting all the extracted features into a trained random forest model, and outputting a double cropping rice spatial distribution diagram. By combining the multi-source data, the problems that the observation density of a single data source is too low and the data quality is unstable are solved. The phenological index of the double cropping rice developed by the invention can accurately capture phenological characteristics of the double cropping rice, and the identification accuracy can be further improved in combination with other auxiliary characteristics.

The agricultural robot comprises a tracked robot, an adjusting structure is arranged on the front portion of the upper end of a bearing platform, a picking execution structure is installed at the upper end of the adjusting structure, and a fruit screening structure is arranged on the rear portion of the upper end of the bearing platform; a control module communicating with the inner side of the fruit screening structure is arranged on the front portion of the upper end of the bearing platform, and the control module and the picking execution structure are jointly and fixedly connected with a connecting hose. According to the agricultural robot for picking, mature fruits are recognized through cooperation of the picking execution structure and the control module, and fruits at different positions on a fruit tree are picked through cooperation of the picking execution structure and the adjusting structure; the picked fruits are fed into the fruit screening structure through cooperation of the connecting hose and the control module, sorting and storage are conducted through the fruit screening structure, the fruits with different diameters are stored separately, and subsequent classification is facilitated.

The invention discloses an intelligent suspension conveying device for building material production, which comprises a cable rod, a traversing shaft is arranged in the cable rod, a lifting cable rack is arranged at the bottom of the traversing shaft, a maintaining mechanism is arranged in the lifting cable rack, and an anti-collision mechanism is arranged at the bottom of the maintaining mechanism. The auxiliary rotating shaft is rotationally connected to the inner wall of the lifting cable frame, the maintaining cable is wound around the outer circumferential face of the auxiliary rotating shaft, the main holding cable is wound around the outer circumferential face of the main rotating shaft, and the lifting cable frame is slidably connected with the inner wall of the transverse moving shaft. Therefore, the stroke weight of the host cable is dispersed, shaking of the pipeline in the transportation process is limited, and the stability and safety in the transportation process are improved.

The invention provides a split-phase cooperative asphalt mixture digital image multi-component segmentation method, and relates to the field of asphalt mixture image segmentation, and the method comprises the steps: obtaining a digital image of an asphalt mixture section, converting the digital image into a gray image, and carrying out the preprocessing of the gray image, and obtaining a to-be-segmented image; performing gap segmentation on the to-be-segmented image to obtain a gap phase image mask; segmenting the to-be-segmented image by using the RAN-UNet deep learning image segmentation model to obtain an aggregate image mask; performing three-dimensional voxel reconstruction to obtain a three-dimensional digital model, and performing connectivity analysis and edge detection to obtain a connected gap mask, an open gap mask and a closed gap mask; performing critical particle size screening and structural gap identification to obtain a structural gap mask and a mortar internal gap; and carrying out particle size screening on the aggregate image mask, extracting an asphalt mortar region, and slicing and converting the asphalt mortar region again to obtain a mortar phase image. According to the invention, the segmentation precision of the asphalt mixture digital image is improved.

The present invention discloses a fine-grained three-dimensional model classification method based on deep integration and detail perception. The method realizes fine-grained classification of three-dimensional model multi-views based on a three-dimensional model fine-classification network. The three-dimensional model fine-classification network consists of a backbone network and an auxiliary network. The backbone network consists of an initial feature extraction layer, a group of base learners and a deep integration learner, which is used to learn the global structural features of the three-dimensional model contained in the multi-views. The auxiliary network consists of a context detail perception module, a view maximum pooling layer and a fully connected layer, which is used to capture the detail features contained in the view; the classification decisions of the backbone network and the auxiliary network are combined to form a comprehensive classification decision of the overall network, and a joint loss function is constructed based on this, so that the three-dimensional model fine-classification network can accurately classify fine-grained three-dimensional models. The present invention can effectively capture fine-grained discriminative information and has good classification effect and universality in three data sets.

The present application relates to the field of chip package testing, in particular to a chip package testing system and a testing method thereof. The system comprises a testing box, the inside of which is divided into a testing room and a chip in-out room. The testing room is divided into a feeding room with adsorption equipment and detection equipment, a discharging room, and two groups of detection rooms. The rotating plate is divided into four groups of rotatable detection stations, each of which is provided with a positioning assembly. The testing box is also provided with a feeding box, a qualified product discharging box, an unqualified product discharging box, and a controller. Through the rotation of the detection stations, the feeding room, the two groups of detection rooms, and the discharging room are sequentially opposite, and the feeding, detection, and classified discharging are automatically performed. The structure is compact, and the items for simultaneous detection can be installed and replaced according to needs. The detection process is cyclic, continuous, efficient, and intelligent. After the detection is completed, the qualified products and unqualified products are classified and recycled, the detection results are intuitive, the classification and statistics are convenient, and the detection efficiency is improved.

The application relates to the field of optical fiber sensing, and more particularly to an event detection method, system and device for optical cable detection and a storage medium. The method comprises the following steps: inputting optical fiber data of an optical cable into a first processing unit for processing to obtain space-time features; inputting the optical fiber data into a second processing unit for processing to obtain frequency domain features; inputting the space-time features and the frequency domain features into an attention processing unit after splicing for feature enhancement to obtain enhanced features; inputting the enhanced features into a bottleneck layer processing unit for processing to obtain bottleneck features; wherein the bottleneck layer processing unit comprises at least two bottleneck layer processing subunits, and the bottleneck layer processing subunits can perform feature transformation processing on the enhanced features; and inputting the bottleneck features into a backbone network for optical fiber event detection to obtain an optical fiber event detection result corresponding to the optical fiber data. The application can improve the accuracy of optical fiber event detection in an optical cable.

The utility model relates to the technical field of garbage treatment, and provides a garbage classified collection ship which comprises a first ship body, a second ship body, a classification device and a baffle. The first hull and the second hull are arranged at intervals to form a garbage channel, the first hull and the second hull are connected on the rear side, and the baffle is arranged between the first hull and the second hull; the first hull is provided with a first garbage can, and the second hull is provided with a second garbage can. The classifying device is arranged on the rear side of the first ship body and the rear side of the second ship body and used for classifying the garbage blocked by the baffle in the garbage channel. According to the garbage classified collection ship, the collection efficiency is improved, the risk of secondary pollution caused by garbage omission is reduced, energy is effectively saved, the classification accuracy is high, quick cleaning of water area garbage and long-term maintenance of water area cleaning are achieved, and the collection accuracy and the classification reliability are improved.

The application relates to a wireless communication same-frequency interference detection method based on a CNN-SVM and a particle swarm optimization, which comprises the following steps: performing short-time Fourier transform on a received baseband signal to generate a log power spectrum time-frequency graph; automatically extracting a deep space feature vector through a convolutional neural network; performing global self-adaptive optimization on a penalty parameter and a kernel parameter of a support vector machine through a particle swarm optimization algorithm; and training the support vector machine with optimal parameters to perform binary classification judgment on a to-be-detected feature. Through a triple coordination mechanism, the application overcomes the problems of fixed parameters, poor robustness and the need for a large amount of labeled data in traditional methods, and realizes high detection probability and low false alarm rate under different signal-to-interference-and-noise ratios and bandwidth conditions.

The invention discloses an unmanned aerial vehicle remote sensing tree species classification method and system fusing a cascade canopy attention mechanism, and the method comprises the steps: obtaining unmanned aerial vehicle RGB image data of a campus scene, carrying out the preprocessing of the data, and obtaining a standardized input image; constructing a classification model fusing the cascaded canopy attention mechanism, wherein the classification model comprises a background suppression module, a fine-grained feature guide module, a channel alignment neck module and a global-local fusion classification head; inputting the standardized input image into a classification model, and obtaining an enhanced canopy image through a background suppression module; inputting the enhanced canopy image into a module carrying fine-grained feature guidance, and extracting fine-grained canopy features; performing channel calibration and feature smoothing on the fine-grained canopy features through a channel alignment neck module to obtain feature mapping of a unified dimension; and fusing the global canopy semantics and the local texture structure in the feature mapping by using a global-local fusion classification head, and outputting a tree species classification result.

The invention provides a quick drying processing line for cleaned and caked down feather in down feather processing, which comprises a plurality of groups of quick sorting equipment for cleaned down feather and caked down feather and a rear-end distributing and drying equipment connected with the quick sorting equipment for cleaned down feather and caked down feather, and each group of quick sorting equipment for cleaned down feather and caked down feather is connected with a corresponding feeding input table structure; a drying transmission line table structure is arranged at the rear end of each group of quick sorting equipment for cleaning down feather knotted materials, and each drying transmission line table structure is connected with one group or a plurality of groups of combined material transmission line table structures; the down feather drying device is reasonable in design, in the down feather manufacturing and processing process, washed dehydrated and caked down feathers can be dehydrated and dried, a drying device structure is arranged, when the down feathers are dried, down feather dredging operation can be conducted on water permeable holes of a bottom water permeable disc, and subsequent classified down feather discharging and packaging can be facilitated.

The utility model relates to the technical field of storage bag, disclose a kind of portable storage bag, including storage bag body, the inner side edge position of storage bag body is provided with first zipper, the inner side wall of storage bag body is provided with external closure layer, the inner side wall of storage bag body is fixedly connected with connecting layer, the inner side wall of storage bag body is fixedly connected with split layer, the outer surface of storage bag body is fixedly connected with support air bag column, the upper surface of storage bag body is fixedly connected with elastic air bag, the side of elastic air bag is provided with air inlet, the other side of elastic air bag is provided with check valve, the one end of check valve is fixedly connected with exhaust valve, the utility model is set to external closure layer, second zipper, connecting layer, third zipper and split layer, and by the setting of support air bag column, elastic air bag, air inlet, check valve and exhaust valve, effectively solve the problem that the internal article of existing storage bag is easy to be chaotic, and the structure is insufficiently supported.

The application provides a kind of foodborne microorganism identification method based on metal omics coupling XGBoost algorithm, belongs to microorganism identification technical field, the foodborne microorganism identification method includes the following steps: 1) with foodborne microorganism strain as research object, ICP-MS is used to analyze each standard strain metal group characteristics, combined with XGBoost algorithm to construct foodborne microorganism classification model;2) the ICP-MS full quantitative analysis is used to detect the bacterial species, and the detection data of the metal elements of the bacterial species to be detected are obtained;3) the obtained detection data is substituted into the obtained foodborne microorganism classification model for analysis, and the bacterial species to be detected is identified.The foodborne microorganism classification model constructed by the application can quickly and accurately identify 18 kinds of foodborne microorganisms, and lays a good foundation for the broad-spectrum and efficient classification and identification of more kinds of foodborne microorganisms in the future.

The utility model discloses a cable transportation device for wire and cable processing, which comprises a bottom plate, the upper surface of the bottom plate is fixedly connected with a push frame, the upper surface of the bottom plate is fixedly connected with a base, the upper surface of the bottom plate is fixedly connected with a supporting seat, the upper surface of the base is fixedly connected with a motor, and the upper surface of the motor is fixedly connected with a transmission shaft. The output end of the motor is fixedly connected with a rotating shaft, and the side surface of the rotating shaft is fixedly connected with a gear; the device comprises a placing cylinder, a toothed belt is fixedly connected to the side surface of the placing cylinder, a placing bin is arranged in the placing cylinder, a limiting supporting rod is fixedly connected to the rear inner wall of the placing bin, and a sealing cover is movably connected to the front surface of the placing cylinder. And the taking convenience of the cable drum and the safety in the transportation process can be improved.

This invention relates to the field of chip inspection technology, and more particularly to a chip non-destructive testing system based on embedded current scanning. The system includes a current scanning module, an information acquisition module, a data analysis module, an abnormal scanning region generation module, and a scanning path generation module. Instead of simply performing a higher-density secondary scan of the entire chip, this invention intelligently filters out abnormal scanning regions requiring special attention based on the initial scan results and calculates their scanning priority. This allows valuable inspection time and computing resources to be concentrated on the most suspicious and severe areas, avoiding unnecessary repeated scanning in normal areas. The system dynamically selects the optimal scanning path based on the regularity of the region shape; for example, parallel lines are used for regular regions, and radial lines are used for irregular regions. This makes the scanning path more closely match the geometric features of the defect, obtaining a more complete region characterization with fewer scanning points, resulting in higher path planning efficiency.

The invention discloses background layer updating during coding. A method performed by an image processing apparatus for updating a background layer during encoding of a scene is provided. A scene is encoded based on classifying objects depicted in the scene as foreground or background. The background is divided into ranked background layers, wherein each background layer is associated with a respective depth model. The method comprises detecting (S102) a change in an image portion of a background layer. The method comprises calculating (S104) a difference between the image portion and a corresponding image portion of a background layer ranked behind the one background layer. The method comprises selecting (S106a) the background layer ranked behind the one background layer to represent the image portion when the difference is less than a threshold.

The invention belongs to the technical field of wind power generation, and particularly relates to a wind turbine generator state evaluation method and a related device, and the method comprises the steps: obtaining an electromagnetic transient response signal of a to-be-evaluated wind turbine generator; performing empirical mode decomposition on the electromagnetic transient response signal of the wind turbine generator to be evaluated to obtain a plurality of image forming model components; performing feature extraction on each image forming model component, and performing normalization processing to obtain a feature vector; classifying the feature vectors by using a pre-constructed state classification model to obtain a state evaluation result of the to-be-evaluated wind turbine generator; wherein the pre-constructed state classification model is a pre-trained support vector machine; according to the method, the fault features in the electromagnetic transient response signals of the wind turbine generator can be effectively extracted, and the accuracy and robustness of state evaluation are improved; meanwhile, non-linear and non-stationary signals can be decomposed in a self-adaptive mode, the time domain features of the signals are reserved, and the information amount and accuracy of state evaluation are improved.

The utility model belongs to the technical field of table tennis ball quality inspection, and provides a discharging mechanism of table tennis ball quality inspection equipment, which comprises a discharging driving device, a discharging sucker mechanism and a discharging collecting mechanism, and the discharging driving device and the discharging collecting mechanism are arranged on an equipment frame body of the table tennis ball quality inspection equipment. The driving end of the discharging driving device is connected with the discharging suction cup mechanism, the discharging driving device drives the discharging suction cup mechanism to move relative to the equipment frame body, and the discharging collecting mechanism is oppositely arranged below the discharging suction cup mechanism. The table tennis ball quality inspection device is reasonable in structural design, table tennis balls in a quality inspection placement area can be automatically transferred and collected, manual participation is reduced, and the automation degree is higher.