116results about How to "Reduce imbalance" patented technology

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention provides an open earphone audio optimization method based on a self-adaptive compensation algorithm, and aims to improve the audio auditory experience of a user. The method comprises the following steps: firstly, acquiring an environmental noise signal through an earphone microphone, receiving a target audio signal, analyzing auditory perception of a user in a current environment by combining an ISO226 loudness curve and the like, and determining auditory sensitivity of the user to different frequency bands (including high frequency, intermediate frequency and low frequency); and then, enhancing the high-frequency part, deepening the low-frequency part and performing smooth transition compensation on the intermediate-frequency part by using a self-adaptive compensation algorithm. And then, dynamically adjusting the low-frequency part by using a hyperbolic filter, generating an adjusted audio signal, and generating a harmonic signal based on the amplitude and frequency characteristics to further optimize the tone quality. Finally, the optimized audio signal and the generated harmonic signal are provided for a user together, and the effects of automatically optimizing audio output in different environments and enhancing tone quality performance are achieved.

The invention discloses a balance control scheduling method and system based on distributed new energy, and the method comprises the steps: obtaining power grid parameters, and constructing a time-space characteristic model of multi-type adjustable resources and a distributed scheduling frame based on the obtained parameters; based on the space-time characteristic model and the distributed scheduling framework, establishing a distributed new energy participation power grid adjustable and controllable resource group internal cooperation strategy; establishing a distributed new energy participation power grid dispatching collaborative interaction and balance optimization control strategy according to the collaborative strategy; and realizing active / reactive coupling balance collaborative optimization of the high-permeability distributed new energy access power grid based on a collaborative strategy and an optimization control strategy. According to the invention, the utilization rate of new energy can be improved, cooperative work of multiple types of adjustable and controllable resources is realized, the flexibility and stability of a power grid are improved, dynamic adjustment of a power system is realized, and optimal matching of power supply and demand at different time and space scales is ensured.

The utility model relates to a crutch with adjustable height and angle, which comprises a crutch body and a stabilizing component, the stabilizing component is arranged in the crutch body, and the stabilizing component is used for stabilizing the crutch body; and the stabilizing assembly comprises a round cover, a round groove is formed in the round cover, a corrugated sleeve is fixedly connected to the bottom of the round cover, and a bottom plate is fixedly connected to the bottom of the corrugated sleeve. When the bottom of the walking stick body is in contact with the inclined ground, the bottom of the walking stick body can be always parallel to the ground under the running fit of the round ball and the round groove, and even if the walking stick is on the uneven or inclined ground, good contact can be kept, and more reliable support can be provided; according to the walking aid, shaking and unbalance caused by uneven ground when a user walks can be effectively reduced, meanwhile, the reset spring and the corrugated sleeve can provide certain buffering and damping effects in the using process, and the hand pressure of the user is relieved.

The invention discloses an automobile software defect prediction method based on dynamic feature selection and oversampling, which comprises the following steps: acquiring a data set of automobile software defects, and defining a full feature set of the data set; performing data preprocessing on the data set, and dividing the data set into a training set and a test set; dynamically evaluating the contribution of each feature in the full feature set to a decision feature through a granularity decision entropy algorithm, and reserving important features with high information gain; new samples are generated for the minority class samples by introducing an intelligent resampling algorithm of auxiliary loss and diversity loss, and the representativeness and diversity of the minority class samples are enhanced; training the defect prediction model through the training set and testing the test set; and outputting a corresponding defect prediction result based on the characteristic data of the automobile software through the trained defect prediction model. According to the method, the accuracy of automobile software defect prediction and the generalization ability of the model in new data or multi-class and mixed type data environments can be improved.

The invention provides an anti-unbalance lower limb rehabilitation training system and method. The method comprises the following steps: collecting simulated training data; extracting gait training tracks of all normal people in each gait cycle from the simulation training data, and determining an imbalance compensation coefficient of the target object during lower limb rehabilitation training through each gait training track; obtaining an initial gait track of the target object, and determining difference association features of gaits of the target object in each gait cycle according to the initial gait track; track correction is carried out on a dependence path when the target object carries out gait conversion between adjacent gait cycles through the unbalance compensation coefficient and each difference correlation feature, and a self-adaptive anti-unbalance track is obtained; and the lower limb rehabilitation training equipment performs personalized anti-unbalance adjustment according to the self-adaptive anti-unbalance trajectory. By adopting the scheme of the invention, personalized anti-unbalance rehabilitation training can be realized according to the difference of different patients on different rehabilitation training requirements, so that the effectiveness of the anti-unbalance lower limb rehabilitation training equipment can be improved.

The invention relates to the technical field of computer machine vision, and discloses a plant growth condition monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining a target plant image, inputting the preprocessed target plant image into an improved YOLOv9 model (comprising a backbone network, a feature fusion network and a prediction classification network), and obtaining a target plant image; obtaining a target plant detection image; performing feature extraction on the preprocessed target plant image through a backbone network to obtain plant multi-scale features; performing feature conversion, branch processing and feature fusion on the plant multi-scale features through a feature fusion network to obtain plant fusion features; performing classification processing on the plant fusion features through a prediction classification network to obtain a target plant detection image; and inputting the target plant detection image into an image semantic segmentation model (including an image super-resolution network and an image segmentation network) to obtain a plant binary image. The method effectively improves the accuracy of plant growth condition monitoring.

The invention discloses an entity extraction method for mechanical fault diagnosis knowledge graph construction, which comprises the following steps: S1, constructing a mechanical fault diagnosis corpus database, and dividing the mechanical fault diagnosis corpus database into a training set and a test set; s2, constructing a mechanical fault diagnosis knowledge graph named entity recognition model EBBC-DAM-PATM fusing BERT-BiLSTM-CRF, data enhancement and adversarial training, and carrying out training test on the model to obtain a trained EBBC-DAM-PATM model; s3, the trained EBBC-DAM-PATM model is utilized to carry out named entity recognition; and S4, constructing the extracted entity words into a knowledge graph by using a neo4j graph database. The data enhancement module can be adaptively completed according to the data volume, the problem of sample imbalance is relieved, and overfitting of the model is relieved.

The utility model relates to an aviation starter generator and a connecting structure of a hollow shaft and a flexible shaft thereof, belongs to the technical field of aviation starter generators, and solves the problems of high processing time and cost and poor interchangeability in the prior art. According to the utility model, the flexible shaft comprises a first end and a second end, and the first end extends into a central through hole of the hollow shaft from the front end of the hollow shaft; a plurality of annular bosses are arranged on the peripheral surface of the flexible shaft; a central through hole of the hollow shaft is a stepped hole, and the annular bosses are respectively in clearance fit with one section of the stepped hole so as to ensure the coaxiality; a flat key is arranged between the flexible shaft and the hollow shaft to transmit torque; the hollow shaft and the flexible shaft are axially limited by adopting two step surfaces and are fastened by adopting bolts. The two annular bosses are adopted for guaranteeing coaxiality, the two step faces and the bolts are adopted for axial limiting, and the flat keys are adopted for transmitting torque, so that connection reliability is guaranteed, manufacturing difficulty and machining cost are reduced, and meanwhile rapid maintenance and replacement of the flexible shaft are achieved.

The invention discloses a semi-supervised multi-class medical image semantic segmentation method based on prototype comparative learning. The method comprises the following steps: dividing a marked image data set and an unmarked image data set into a training sample set and a test sample set; constructing a deep learning model of semi-supervised semantic segmentation, constructing a prototype-like feature library of a main and branch network, constructing a total loss function of the deep learning model, inputting the training sample set into the deep learning model for training to obtain a semantic segmentation model, wherein a data enhancement scheme of random rotation and random flipping is applied in each round of training; and testing the trained deep learning model by using the test sample, and outputting a test sample image segmentation result. According to the method, the workload that a doctor needs to label the data in a daily medical image processing task is reduced, and the problem that data labeling is scarce generally existing in the field of medical image processing is solved.

The invention provides a servo control system and method for a bulk cargo crown block, and belongs to the technical field of industrial automation control, and the system comprises two range finders which are used for collecting the distance information between the range finders and the crown block; the IMU sensor is used for collecting corner information of the crown block; the industrial personal computer is used for receiving speed information, distance information and corner information of a crown block and a bridge frame, determining a deviation angle of a crown block main beam based on the distance information and the corner information, carrying out cross coupling on the deviation angle and the speed information to obtain a motor target current value, and sending the motor target current value to the bridge frame; and inputting the motor target current value into the motor driver. According to the method, the deviation angle of the main beam can be obtained through fusion processing of the corner information and the distance measurement information, the deviation angle and the speed information are input into the cross coupling system together, the corrected motor target current value is obtained, and the technical problems that in the prior art, crown block operation efficiency is low, and bridge equipment is seriously abraded are solved.

The invention relates to the technical field of medical point cloud data processing and application, in particular to a medical point cloud segmentation system and method based on boundary point supervision, and the system comprises an orientation and position correction module, a boundary point search module and a loss function based on sample equalization. The orientation and position correction module unifies the position and orientation of the original point cloud, so that the orientations of different dental cast point clouds are unified; the boundary point searching module is fused with a k-nearest neighbor algorithm and a farthest point sampling algorithm to identify boundary points in a data set, and the boundary points are subjected to pointnet + + down-sampling and up-sampling; and carrying out loss calculation on the boundary points based on a loss function of sample equalization. According to the method, the oral medicine point cloud can be accurately and efficiently segmented, so that the problems that an existing segmentation algorithm cannot predict the large-angle rotating point cloud, boundary points in a three-dimensional point cloud segmentation result are poor, and the point cloud segmentation effect is poor due to the serious unbalanced class number are solved.

The invention discloses an aero-engine vibration signal generation method under extreme imbalance, and belongs to the technical field of aero-engine fault signal generation. The method comprises the following steps: acquiring an original vibration signal at a target position of the aero-engine, and performing preprocessing operation; taking the preprocessed real vibration signal as a training sample, constructing a forward noise adding process, and training the improved one-dimensional diffusion generation network to learn a reverse denoising mapping relation; and inputting random Gaussian noise into the one-dimensional diffusion generation network after training convergence, and generating a target vibration signal matched with a real vibration signal feature through a reverse diffusion denoising process. Through targeted improvement of the diffusion generation network, the problem of data imbalance is effectively relieved, the model deployment cost is reduced, the signal generation capability of the model for minority types of faults is improved, and the method is suitable for fault signal generation scenes of aero-engines under complex working conditions.

The invention relates to the technical field of circuit breaker control, and discloses a high-voltage circuit breaker intelligent control system based on a positive and negative zero sequence control algorithm, and the system comprises a cascaded H-bridge static synchronous compensator which is in star connection, and each phase of the cascaded H-bridge static synchronous compensator is composed of a plurality of H-bridge units; the data acquisition module is used for acquiring voltage and current signals of the three-phase system in real time; the control module is used for operating a positive and negative zero sequence control algorithm and realizing voltage fluctuation optimization and unbalanced load compensation through independent control of three-sequence components; the communication module is used for data interaction between the control module and an upper computer; and the connecting inductor is used for connecting the static synchronous compensator and a power grid so as to reduce harmonic interference and provide necessary impedance. By independently controlling the positive sequence component, the negative sequence component and the zero sequence component, the technical effect of reducing the unbalance degree of the power grid is achieved, the problems of response lag and high unbalance degree under the condition of rapid load change in the traditional technology are solved, and the electric energy quality of the system is improved.

The embodiment of the invention relates to the technical field of electromagnetic wave sensors, and discloses a target detection method, an integrated circuit, a sensor and equipment. The invention discloses a target detection method, and the method comprises the steps: carrying out the independent constant false alarm processing of at least two receiving and transmitting channels based on a distance-Doppler spectrum, so as to obtain the candidate target data of the at least two receiving and transmitting channels; and processing the candidate target data based on the at least two transceiving channels to obtain final target data. At least constant false alarm processing is independently carried out and then combined processing is carried out through a receiving and transmitting channel, a scheme of target detection in space areas such as an automobile cabin, a room and a factory building is realized, the detection rate can be effectively improved, the false alarm target number is reduced, the angle estimation accuracy can be greatly improved, and the method is suitable for large-scale popularization and application. Therefore, special targets or weak targets such as infants and the like in the cabin can be accurately detected, and applications such as CPD and SBR are realized.

The invention discloses a multi-source data fusion-based air volume dynamic adjustment method of a fume hood for a clean environment, particularly relates to the technical field of clean environment control, and aims at solving the problem that air volume adjustment of an existing fume hood cannot give consideration to front air suction safety, pressure difference stability and overall energy conservation. A basic operation data set is formed by collecting and filtering window opening and cabinet air speed, an air volume demand value and an initial priority parameter are calculated according to the data set and a room use state mark to form a demand data set, and a deviation memory parameter and a fluctuation influence parameter are calculated by fusing room pressure difference data and the availability of an exhaust fan. Obtaining a stable control priority adjustment coefficient through a time decay score learning model, correcting the priority, distributing the target exhaust air rate and the exhaust fan output, forming a target distribution result, comparing the target distribution result with the actual air valve opening degree and the exhaust fan output state, calculating the adjustment amount, and issuing a control instruction according to the priority; and the deviation record feedback updating coefficient and the demand value are verified in the control period to realize self-adaptive adjustment.

The invention discloses a few-sample fault diagnosis method based on TFCD fusing time sequence characteristics, and the method comprises the steps: inputting a preprocessing state signal into a trained fault diagnosis model, so as to enable the fault diagnosis model to carry out the fault diagnosis of a core part. The fault diagnosis model is obtained by training a plurality of training samples generated by a trained TFCD model, and the TFCD model extracts discriminative time sequence features by introducing a long short term memory network, and embeds the discriminative time sequence features as static condition vectors into a DDPM model. According to the method, the model can effectively capture the long-range dependency relationship and the dynamic mode characteristics in the vibration signal, so that the generated sample keeps good coherence in the time domain, meanwhile, the specific periodic impact mode of the fault is reserved, and the physical rationality of the generated sample is remarkably improved. Therefore, accurate diagnosis under cross-working conditions can be realized.

The present application belongs to the technical field of installation detection of clothes processing equipment, and discloses an installation detection method, an installation detection device and clothes processing equipment. The installation detection method comprises the following steps: after the installation of the clothes processing equipment is completed, a detection operation process of the clothes processing equipment is performed; during the detection operation process of the clothes processing equipment, the balance state of the clothes processing equipment is judged according to the pressure values of a plurality of detection positions at the bottom of the clothes processing equipment, and balance adjustment prompt information is reported; and installation prompt information is reported according to the noise value and the vibration acceleration value of the clothes processing equipment. The installation detection method can detect the clothes processing equipment after the installation is completed, analyze the causes of the eccentricity problem of the equipment, and facilitate quick adjustment.

The invention discloses a lactobacillus delbrueckii subsp. Bulgaricus CCFM1520 for transforming curcumin to produce tetrahydrocurcumin to relieve nervous anxiety, and belongs to the technical field of microorganisms. The lactobacillus delbrueckii subsp. Bulgaricus CCFM1520 screened by the invention has the capability of converting curcumin, and can utilize curcumin and generate tetrahydrocurcumin. The invention further provides a synbiotic preparation prepared from the lactobacillus gasseri CCFM1481 and curcumin, the synbiotic preparation can be used as an anti-fatigue functional medicine, the individual behavioral memory level can be improved, the imbalance of body neurotransmitters (DA, 5-HT and NE) can be slowed down, the expression of body neuroprotection related genes (BDNF, TrkB, DRD1 and TH) can be improved, and the synbiotic preparation has a huge application prospect.

The present invention discloses a rolling bearing fault diagnosis method based on L-ICGAN, which belongs to the field of fault diagnosis technology, and includes the following steps: step S1, data preprocessing, collecting vibration acceleration signals under different states, converting the one-dimensional time domain signal into a two-dimensional logarithmic spectrum diagram through logarithmic spectrum transformation, and dividing the preprocessed data into a training set and a test set; step S2, network training, initializing the L-ICGAN model through the training set, and training the L-ICGAN model; step S3, fault diagnosis, introducing the divided test set into the trained L-ICGAN model. The present invention adopts the above-mentioned rolling bearing fault diagnosis method based on L-ICGAN, which can not only effectively remove redundant information and show stronger frequency characteristics, but also increase the generation probability of minority category samples and alleviate the problem of data imbalance.

The invention belongs to the technical field of peanut planting, and particularly relates to a peanut strip rotary tillage stubble cleaning seeder and a seeding method.The peanut strip rotary tillage stubble cleaning seeder is characterized in that one end of a seeding frame is fixedly connected with a fixing frame, a transmission shaft set is arranged on the inner side of the fixing frame, and the end, away from the fixing frame, of the seeding frame is rotationally connected with a plow column; a plurality of soil ploughing blades are fixedly connected to the annular side of the plow column, a stirring and dispersing column is rotationally connected to the center of the seeding frame and close to one end of the plow column, a plurality of linkage columns are fixedly connected to the annular side of the stirring and dispersing column, and the transmission end of the transmission shaft set is in meshed connection with the linkage columns. Soil scattered by the ploughing blade is further vibrated and scattered through the stubble cleaning assembly, rhizomes of crops in the previous batch in the soil are picked out, the cultivated soil is finer, the number of the rhizomes in cultivated land is reduced, decomposition and consumption of nutrients in the soil by the residual rhizomes are reduced, and unbalance of the soil nutrients is reduced.

The invention relates to the field of laser radar signal processing, in particular to a laser radar cloud layer detection algorithm based on deep learning, a storage medium and an electronic device. The laser radar cloud layer detection algorithm based on deep learning comprises the following steps: preprocessing a laser radar backscattering signal; establishing a laser radar cloud layer detection data set; building a laser radar cloud layer detection model; training the laser radar cloud layer detection model and storing a training result; the laser radar signals collected in real time are preprocessed; and inputting the real-time preprocessing signal into the trained laser radar cloud layer detection model for cloud layer detection. The cloud layer detection method has the advantages that efficient and accurate cloud layer detection can be achieved by using the deep learning model, the cloud layer detection method can adapt to environment changes under different meteorological conditions, and missing detection and false detection are effectively reduced.

The invention relates to the technical field of remote sensing image fine-grained target detection, in particular to a two-way guided remote sensing image fine-grained target detection method. Comprising the steps of 1, acquiring image data, and dividing the image data into a training set and a test set; 2, constructing a target detection network, and obtaining image multi-scale features from the preprocessed image through a feature extraction network; then, a coarse-grained detector screens out a target candidate area and judges whether the target candidate area is a foreground or a background; then, a fine-grained detector carries out finer classification and positioning on the candidate areas; and step 3, carrying out performance evaluation on the test set so as to test the detection capability of the model on the fine-grained target in an actual scene. According to the method, for the problems of class sample imbalance and easy confusion of fine-grained classes in fine-grained target detection, the remote-sensing image fine-grained target detection capability is effectively improved by constructing a layered detection network architecture and combining an instantaneous confidence coefficient signal and class accumulation learning quality.

The application relates to a video instance segmentation method based on a dynamic convolution decomposition of a lightweight attention mechanism; the method first inputs a video frame, and a backbone network independently extracts a feature map of each frame in the video; then the feature map extracted by the backbone network enters an HQT encoding and decoding module, the position change of an instance in each frame is accurately positioned by combining an output head, and three prediction branches are used to supervise model training; in the application, a convolution layer of an encoding network adopts dynamic convolution decomposition, a more compact model is obtained, model training is easier, the required parameter quantity is greatly reduced, the training speed is improved, and the training time is shortened; a lightweight HQT encoding and decoding module is provided, which helps to reduce model parameters and improve efficiency; the loss function is improved, model training is more stable, the convergence speed and convergence precision are improved, and the problems of imbalance between foreground and background in samples and imbalance between foreground categories under a long tail condition are relieved.

The present invention belongs to the field of power systems, and specifically relates to a power grid reliability assessment method based on fine-grained data drive; the method includes randomly extracting power grid system status data using the Monte Carlo method; calculating the power difference of the system DC power flow before and after the power grid system is disconnected; inputting the power difference and other power data into the power flow module as the input feature vector of the sample to be tested; calculating the regression result and classification result of the sample to be tested, and determining the problem sample; inputting the input feature vector of the problem sample into the optimal power flow module, calculating the classification result of the problem sample, and determining the fault sample; performing minimum load shedding analysis on the fault sample, and according to the analysis result, combining the variance coefficient to calculate the uncertainty of the fault sample, and outputting the reliability assessment result of the fault sample. The present invention can be widely used in the online calculation of the operation reliability of large power grids, and is particularly suitable for the scene after large-scale access of high-proportion new energy.

The invention discloses a hybrid intrusion detection method suitable for incomplete data of the Internet of Things. The method comprises the following steps: acquiring an open source data set and preprocessing the open source data set; configuration of a training environment; constructing and training a multi-classification-stage algorithm model, wherein the multi-classification-stage algorithm model is used for carrying out weighted aggregation on four integrated learning algorithms, namely an RF algorithm, a category type feature lifting algorithm CatBoost, an XGBoost algorithm and a lightweight gradient lifting machine algorithm LightGBM; a binary classification stage algorithm model is constructed and trained, according to the binary classification stage algorithm model, weighted summation is carried out on output abnormal scores of an auto-encoder, a single-class support vector machine and an isolated forest algorithm to obtain an aggregation abnormal score, and the aggregation abnormal score is compared with a threshold value to obtain a final detection result. The method can better adapt to a data set with incomplete classes, further provides the self-adaptability of the system, and can effectively aggregate the training results of various algorithms.

The invention belongs to the technical field of power distribution network leakage current detection, and particularly relates to a CNN and FiLM-based leakage current type intelligent identification method. Comprising the following steps: S1, collecting real-time operation data of a typical power supply area, obtaining leakage current waveforms and related environment characteristic parameters under different working conditions, and constructing an original leakage current sample data set; s2, a CNN-based leakage current classification model is constructed, a FiLM condition modulation module is introduced, a multi-task learning framework is used at the tail of the network, a main task is leakage current type identification, and an auxiliary task is grounding system discrimination; s3, using a weighted cross entropy loss function to alleviate a class sample imbalance problem; an OneCycleLR dynamic learning rate scheduling strategy is introduced; s4, evaluating the performance of the model on the test set, and using the accuracy and the confusion matrix as evaluation indexes; according to the method, high-precision identification of multiple types of faults such as single-phase grounding, arc type electric leakage and direct current system electric leakage is realized, different grounding systems can be adapted, and the accuracy and robustness of system diagnosis are improved.

The invention provides a wind-light-fire-storage integrated collaborative planning method and system, and relates to the field of wind-light-fire-storage integration, and the method comprises the steps: generating a first quantification result according to the site selection related information of a to-be-planned region and the influence of regional quantification meteorological factors on the wind-light output of the to-be-planned region, determining a resource evaluation result of the to-be-planned region, and carrying out the resource evaluation of the to-be-planned region. Determining a target position; according to the building related information of the load area, quantifying the load influence of the meteorological factors on the load area, and generating a second quantification result; predicting power load information of the load area according to the second quantification result; the method comprises the steps of generating an initial planning scheme, performing source load unbalance degree evaluation on the initial planning scheme according to power load information of a load area and historical meteorological information of a target position and a first time scale, generating a source load unbalance degree evaluation result, and generating a target planning scheme according to the source load unbalance degree evaluation result. And the resource utilization rate and the power supply reliability of wind-solar-thermal storage integration are improved.

The invention discloses a pixel arrangement structure, a virtual multiplexing method and a display, belongs to the technical field of displays, and solves the problems that the existing arrangement design generally follows the principle of equal primary color balance symmetry, but the arrangement design does not necessarily meet the actual requirements under certain specific requirements. The pixel arrangement structure is sequentially arranged in the form of pixel units in the horizontal direction, each pixel unit comprises multiple rows and multiple columns of sub-pixels, the multiple rows and multiple columns of sub-pixels are arranged in a staggered mode, in the column direction, the sub-pixels in each pixel unit are sequentially arranged according to i, i + 1,..., i + n, in the row direction, the sub-pixels in each pixel unit are sequentially arranged according to j, j + 1,..., i + n, and in the row direction, the sub-pixels in each pixel unit are sequentially arranged according to i, i + 1,..., i + n. The (i + 1) th column of sub-pixels of each pixel unit, the (j + 1) th column of sub-pixels of the adjacent pixel units, the (i + 2) th column of (j + 1) th row of sub-pixels of the adjacent pixel units, and the (i + 2) th column of (j + 1) th row of sub-pixels of the adjacent pixel units, and the (i + 2) th column of (j + 2) th row of sub-pixels of the adjacent pixel units are arranged in the same primary color.

The invention provides a blade mounting auxiliary tool for a large stirring shaft, and relates to the technical field of stirring equipment. The blade mounting auxiliary tool for the large stirring shaft comprises a supporting assembly and a reference assembly, and the supporting assembly is at least detachably and rotationally connected with one end of the stirring shaft, so that the stirring shaft can rotate around the axis of the stirring shaft; the reference assembly comprises a reference part, the surface of the reference part is provided with a reference line, the reference line extends along the axis of the stirring shaft, and the distance between the reference line and the axis of the stirring shaft is not smaller than the maximum outer diameter of the stirring shaft. The installation difficulty of the blade can be simplified, the installation precision of the blade is improved, and time and labor are saved.