9results about How to "Improve quality management level" patented technology

The invention discloses an OQC shipment inspection intelligent monitoring system based on AI, which belongs to the technical field of shipment inspection and is mainly composed of an intelligent image acquisition system, an AI defect detection system, a size measurement system, a data management and analysis system and a human-computer interaction interface. The intelligent image acquisition system is provided with a high-resolution high-definition camera and an intelligent light source controller, so that high-quality images are obtained, and complex component structures and fine surface features are clearly presented. The AI defect detection system uses a deep learning technology, especially a convolutional neural network model trained by mass data, to accurately and efficiently identify various defects. The dimension measurement system accurately measures the dimension of the component by means of a precision algorithm and a calculation module, and quickly judges whether the component is qualified according to the standard, thereby improving the detection reliability. And the data management and analysis system mines a quality rule through statistical analysis and data mining technologies, and supports decision making. The man-machine interaction interface is simple and visual in design, convenient to operate and clear in result display.

The invention discloses a tunnel construction quality evaluation system and method. The evaluation system comprises a data acquisition module, a quality evaluation standard system, an early warning and notification module and a data management module. Wherein the data acquisition module is used for acquiring construction vehicle data, construction site sensors and site photographing; the quality evaluation standard system comprises drilling quality evaluation, back break evaluation, primary support thickness evaluation, anchor rod construction quality evaluation, waterproof and drainage pipe quality evaluation, steel bar binding quality evaluation, secondary lining quality evaluation and primary support flatness evaluation; the early warning and notification module integrates early warning signals from all the evaluation modules; and the data management module is responsible for storing data generated by each independent module and executing cross-module data analysis. According to the tunnel construction quality evaluation system, by integrating advanced monitoring equipment, an automatic algorithm and comprehensive data analysis, the quality control effect in the tunnel construction process is remarkably improved.

The invention relates to the technical field of refractory materials, in particular to a preparation method of a self-curing tundish dry material, which comprises the following steps of: mixing, forming, drying, sintering, cooling and grinding raw materials of the tundish dry material, and mixing the obtained tundish dry material bulk material and a gel material according to a preset proportion; the tundish mold is filled with the fireproof bulk materials obtained through mixing, layered filling is adopted, vibration and degassing treatment are conducted on each layer of fireproof bulk materials to obtain a degassed fireproof solid material, curing treatment is conducted on the degassed fireproof solid material to obtain a cured fireproof layer, and the curing strength of the cured fireproof layer is detected to obtain the actual curing strength; and for the condition that the actual curing strength cannot meet the standard curing strength requirement, the standard curing strength requirement is met by adjusting vibration parameters or adjusting grinding power. By adjusting the grinding power and the vibration parameters, the curing strength of the self-curing tundish dry material meets the curing requirement.

The present invention relates to the field of partition wall quality detection, and proposes a method and system for realizing the apparent quality detection of lightweight partition walls based on deep learning. The method comprises: obtaining an apparent image of an installed partition wall, marking an image label, constructing an apparent image set, performing image smoothing processing on the apparent image set to obtain a smoothed image set, identifying grayscale and querying the pixel quantity, calculating an apparent histogram, calculating the cumulative distribution value of each grayscale in the apparent histogram, and based on these cumulative distribution values, performing regional segmentation on the partition wall apparent image using a pre-constructed partition wall apparent quality inspection model to obtain an apparent segmented region, and calculating a texture feature value, performing clustering processing on the texture feature to obtain a partition wall apparent quality category, performing quality marking on the apparent segmented region according to the apparent quality category, and generating an apparent quality inspection report of the installed lightweight partition wall according to the regional quality marking. The present invention can improve the apparent quality detection efficiency of lightweight partition walls.

The utility model discloses a core box mould for typecasting of an arc lining plate, which comprises a base, a left mould block, a right mould block, a movable character code and a pin shaft, a mould cavity formed by the base, the left mould block, the right mould block and the movable character code can be used for manufacturing a loam core for typecasting of the arc lining plate, and the loam core is matched with a core head for typecasting of a mould for casting the arc lining plate for use. The left block and the right block are movably connected with the pin shaft, and the movable character code is installed on the inner surface of the right block. According to the core box mold for the arc lining plate typecasting, the quality management level of arc lining plate casting production is improved, the production date and the furnace number can be clearly marked, quality tracing is facilitated, the production efficiency and the scientificity of production management are improved, through application of the core box mold for the arc lining plate typecasting, the casting surface typecasting requirement is met, and the production cost is reduced. Production information recording is facilitated, installation and use of castings are not affected, and the overall quality and reliability of products are improved.

The invention discloses an industrial product quality prediction method based on an XGBoost algorithm. The method comprises the following steps: collecting product quality data of a target industrial product, preprocessing and extracting useful features as a training set; inputting the training set into an XGBoost algorithm for training, optimizing model performance by introducing a sparse matrix into the algorithm, optimizing model parameters of the algorithm, and then selecting an optimal parameter combination to complete training; and preprocessing the product quality data of the industrial product to be subjected to quality prediction, inputting the product quality data into an algorithm for processing, and outputting a quality prediction result of the industrial product to realize industrial product quality prediction. The method can solve the problems of low efficiency, difficulty in processing complex variation and nonlinear relationship, insufficient data utilization, strong subjectivity and the like in the traditional quality detection method, can remarkably improve the accuracy of product quality prediction, improve the production efficiency, reduce the production cost and enhance the data utilization rate, and is suitable for popularization and application. And the production targets of high quality and high efficiency are achieved.

The application discloses a method for quality prediction of tinplate cans based on iron ion migration amount, which comprises the following steps: sealing and soaking the tinplate cans filled with acetic acid, and performing temperature acceleration test; sampling on the third day of the temperature acceleration test, and detecting the iron ion migration amount; sampling on the tenth day of the temperature acceleration test, and detecting the iron ion migration amount; and predicting the quality of the tinplate cans based on the iron ion migration amounts on the third day and the tenth day. The application takes the migration amount on the third day as a short-term risk indicator, and takes the migration amount on the tenth day as a key quality determination standard of long-term stability indicator. Based on the detection results of the two time points, the iron ion migration trend of the tinplate cans within a six-month shelf period can be reliably predicted, the sensory quality and stability of beer products can be ensured, clear and quantitative delivery access conditions of suppliers can be set according to the detection results, the risk of beer quality deterioration caused by quality problems of packaging materials can be avoided from the source, early prediction and precise control of the quality of packaging materials are realized, and the overall quality management level is improved.

The utility model discloses an identification PCCP (prestressed concrete cylinder pipe), which relates to the technical field of concrete prefabricated parts and comprises a PCCP frame and an integrally formed concrete chip arranged on the PCCP frame. The PCCP frame comprises a peripheral steel cylinder, a pipe core outer wall concrete layer wrapping the peripheral steel cylinder, a prestressed steel wire wound around the periphery of the pipe core outer wall concrete layer, a mortar protection layer wrapping the prestressed steel wire and a pipe core inner wall concrete layer arranged on the inner side of the peripheral steel cylinder. The integrally-formed concrete chip is arranged on a peripheral steel cylinder at the socket end of the PCCP pipe frame and / or a concrete layer on the inner wall of the pipe core; the integrally-formed concrete chip is arranged in the identification PCCP pipe, so that identification and traceability management are facilitated. The PCCP information recording device is simple in principle and obvious in effect, can be effectively suitable for PCCPs with different information recording requirements, marks and traces the PCCPs at the same time, and remarkably improves the quality management level of concrete products or structures.

The invention belongs to the field of network information analysis, and specifically discloses an automatic network information evaluation method based on artificial intelligence. The invention realizes dynamic evaluation of network news quality by tracking the effects of horizontal and vertical dissemination after the target network news is released, and then dynamically assigning weights of multi-dimensional quality indicators based on the dissemination effect, so that the evaluation result is more in line with the actual dissemination situation, rather than relying on static and outdated data, and can achieve more comprehensive, accurate and timely network news quality evaluation, which is helpful to improve the overall quality management level of network news. At the same time, after the network news quality is evaluated based on the dissemination effect, a dissemination effect curve and a quality curve are constructed using the dissemination time period, and then the dissemination effect curve and the quality curve are correlated and compared, so that the relationship between the effect and quality of news in the dissemination process can be deeply understood, and the dynamic characteristics of news dissemination are revealed.