35results about How to "Facilitates automated implementation" patented technology

The application discloses automatic sorting equipment used for sorting express packages. The automatic sorting equipment comprises a helical conveyor belt and a plurality of second conveyor belts arranged outwards in the radial direction of the helical conveyor belt, wherein the helical conveyor belt comprises a plurality of first conveyor belts and a plurality of sorting parts, which are alternately arranged; and the sorting parts are connected with the second conveyor belts in one-to-one correspondence. The automatic sorting equipment further comprises a plurality of sorting mechanisms which are in contact with the sorting parts in one-to-one correspondence, and the sorting mechanisms are used for sorting the express packages from the sorting parts to the second conveyor belts correspondingly connected with the sorting parts. According to the automatic sorting equipment provided by the application, as the helical conveyor belt is adopted, the space is saved, and automatic implementation of small-scale sites or tail-end sorting is facilitated; and the express packages are dispatched to the second conveyor belts through the sorting mechanisms, the action is stable and quick and the express package sorting efficiency is remarkably improved.

The invention provides an automatic packaging production line for finished pens. The automatic packaging production line comprises a packaging device body. The packaging device body comprises a feeding body, a packaging box opening mechanism, a material combining conveying mechanism, a box closing device body, a stacking mechanism, a material combining mechanism and a detecting device. By means ofthe automatic packaging production line for the finished pens, automatic feeding, filling, packaging and stacking of the finished pens are achieved, the blank of an automatic production line of the pen industry is filled up, meanwhile, the finished pen boxing space is saved, the finished pen boxing automation degree is improved, and the overall production efficiency is improved. The automatic packaging production line for the finished pens is reasonable in design, ingenious in concept and capable of facilitating automatic implementation and facilitating pen industry application and popularization.

The invention discloses an industrial process fault classification method based on analytic hierarchy process and fuzzy fusion. The method comprises the steps that a training data set is used to carry out offline modeling on a number of classifier methods to acquire a number of models; the classification performance of the classifiers is presented in the form of a fusion matrix; and the analytic hierarchy process is used to score and evaluate a number of classifier models, so that a corresponding weight is given to each classifier; the classifier models are called; a discriminant matrix is calculated according to the classification result of each classifier; and the discriminant matrix and the scoring result of the analytic hierarchy process are used to integrate the classification results of a number of classifier through a fuzzy fusion method to acquire a final fault classification result. Compared with other methods, the method provided by the invention has the advantages that the diagnostic effect of an industrial process is improved; the process operator's confidence in grasping and operating a process is enhanced; and the limitation of a single fault classification method is greatly improved, which is conductive to automated implementation of industrial processes.

The invention discloses a soft measurement research method for the industrial production process under the condition that the number of available training samples is small, which is applied to carrying out soft measurement modeling under the condition that modeling data is small in amount and realizing prediction for product information. According to the invention, an effective linear prediction model is established by using a cooperative training based partial least squares learning method, a problem of low model precision under the condition that sampling data of the industrial production process is small in amount, and the predication accuracy and the performance of the model established in allusion to the process are improved, thereby enabling the industrial production process to be more reliable, and enabling the product quality to be more stable.

The invention relates to the field of lithium battery production detection, and discloses a lithium battery winding core tab detection device and a lithium battery production line, wherein an adjustment mechanism for bending a horizontal tab (5) is installed on a frame (1) ( 2), the adjustment mechanism (2) includes a bending plate (3) used to bend the tab (5), during detection, after the tab (5) collides with the bending plate (3) and the adjacent tab is bent (5) The gap between the outer ends of the sheets becomes larger, and the bent plate (3) includes a transparent plate (4). During detection, the tab (5) is in contact with the transparent plate (4). By bending the lugs to increase the distance between the ends, the vision system can be used on the detection lugs, which greatly improves the detection efficiency, saves manpower, reduces labor intensity, and facilitates automatic implementation.

The invention discloses a formal description and decomposition method for a metamorphic relation. The method includes the following steps: (1) according to targeted tested software, extracting and designing the metamorphic relation that the software mush satisfy to create the metamorphic relation; (2) performing formal modeling on the obtained metamorphic relation on the basis of symbolic logic, determining constraint conditions under which the metamorphic relation is established, performing formal description, then describing input parameters and output parameters of the metamorphic relation on the basis of the symbolic logic, and establishing a formal description model of the metamorphic relation; (3) on the basis of the formal model, respectively extracting subrelations forming the metamorphic relation and a set description form of a metamorphic relation composition model, and establishing a metamorphic relation decomposition model. The method is simple and clear, description capacity and application range of the formal model are guaranteed through symbolic logic description, the accurate, standard and effective method is provided for description of the metamorphic relation, and the method is more conducive to automated implementation of subsequent metamorphic testing.

The invention discloses a control method of a penicillin production process of cooperative training and Local Weighted Partial Least Squares (LWPLS), and the control method is used for soft measurement modeling under the condition that the quantity modeling data is relatively small and realizing prediction of product information of a penicillin production process. According to the control method, an effective linear prediction model is established by using a cooperative training-based local weighted partial least squares learning method, the problem of low model precision under the condition that the quantity of sampling data of the penicillin production process is too small is overcome, and the predication accuracy and the performance of the model established directing at the process are improved, thereby enabling the penicillin production process to be more reliable, and enabling the product quality to be more stable.

The invention discloses an industrial process fault diagnosis method based on a switching linear dynamic system model. The method comprises the steps of firstly carrying out independent repeated sampling on normal operation data and various known fault data of the industrial process, and then establishing the switching linear dynamic system model through a learning algorithm of the switching linear dynamic system model; and then acquiring a diagnosis result of the current monitoring data by using a Gaussian sum filtering method, that is, judging whether the current data is located at a normal operating condition, and if not, judging which fault the current data is located. Compared with other method at present, the industrial process fault diagnosis method not only improves a fault diagnosis effect of the industrial process, enhances mastering of a process operator for the process state, enables industrial production to be safer, and enables the product quality to be more stable. In addition, the industrial process fault diagnosis method improves the dependency of a fault diagnosis method for process knowledge to a great extent, thereby being more conducive to automation implementation of the industrial process.

The invention discloses a semiconductor process monitoring method based on independent component analysis and Bayesian inference, comprising the following step of: firstly, dividing working conditions according to the mixed data of the semiconductor process, conducting the independent component analysis for each working condition data, and establishing a corresponding independent component analysis model; and then integrating and combining the monitoring information under the different working conditions by a Bayesian inference method to obtain a final monitoring result. In addition, the invention can also acquire the working condition information of current monitoring data by a posterior probability analysis method, that is to say, the invention can judge that the current monitoring data is in what process operation working condition; compared with the present other methods, the invention can not only greatly enhance the monitoring effect of the semiconductor process, but also largely improve the dependence of the monitoring method on process knowledge and enhance the comprehensive ability and the operating confidence of process operators on the process, thereby being more beneficial to the implementation of the automation of the semiconductor process.

The invention discloses an industrial process fault classification method based on self-adaption integrated semi-supervision Fisher discrimination. The method comprises the steps of when off-line modeling is conducted, firstly conducting off-line modeling on unlabeled data, and constituting a semi-supervision random training subset by combining labeled data with the unlabeled data; when iteration training is conducted on a sub classifier each time, conducting semi-supervision Fisher dimensionality reduction to obtain a Fisher discrimination matrix, and obtaining a posterior probability matrix, a combined weight of the sub classifier and a sample weight of the labeled data during next time iteration with the labeled sample data after dimensionality reduction according to a Bayesian statistics method; adopting the posterior probability matrix of the labeled data and a label of the matrix as a training set of a fusion algorithm K near neighbor; during online classification, calling each sub classifier to obtain the posterior probability matrix of an online sample to be detected, and inputting the posterior probability matrix into a fusion K near neighbor classifier with the weight to obtain a final result. Compared with an existing method, the industrial process fault classification method based on the self-adaption integrated semi-supervision Fisher discrimination improves the fault classification result of an industrial process, and more facilitates automated implementation of the industrial process.