4results about How to "Avoid Quality Accidents" patented technology

The invention provides an underground diaphragm wall joint pipe pulling time monitoring system and method, and the method comprises the steps: arranging a joint pipe and a reinforcement cage before the pouring of an underground diaphragm wall, and arranging the underground diaphragm wall joint pipe pulling time monitoring system based on an optical fiber sensing technology on the joint pipe. The underground diaphragm wall joint pipe pulling time monitoring system comprises an optical fiber sensor for monitoring the temperature and strain of concrete on the outer side of a joint pipe, and the optical fiber sensor achieves synchronous measurement of temperature and sound waves based on a Brillouin-Rayleigh mixed framework and comprises a BOFDA optical fiber and a DAS optical fiber; and the DAS optical fiber is wound by taking the BOFDA optical fiber as an axis. The method has the advantages of high real-time performance and high precision. The distributed optical fiber sensing technology is adopted, temperature and strain changes in the concrete solidification process can be continuously monitored in real time, temperature and sound waves are synchronously obtained through single measurement, equipment does not need to be switched, and compared with a traditional empirical formula method, the method has higher precision and reliability.

The utility model provides a kind of bottle body precision positioning tool device, it is related to the technical field of automobile air conditioner, including bottom plate, fixed limiting mechanism and movable pressing mechanism;The fixed limiting mechanism is set in the first side of the bottom plate, movable pressing mechanism is set in the second side of the bottom plate;It further includes product supporting plate, is set between the fixed limiting mechanism and the movable pressing mechanism, the upper surface of the product supporting plate is formed with the positioning arc surface matched with the appearance of liquid storage drying bottle;The fixed limiting mechanism includes L-shaped locating plate, the bottom of the L-shaped locating plate is fixed on the bottom plate, the L-shaped locating plate has horizontally extended first locating plane and second locating plane, which is vertically upward from the first locating plane.The utility model has the advantages of simple structure, convenient operation and good stability, which can effectively improve the accuracy of liquid storage drying bottle positioning installation, thereby avoiding quality accidents caused by misplacing liquid storage drying bottle.

The invention relates to the technical field of big data management and data quality control, and discloses a big data platform model change influence quantitative evaluation and data verification method, which comprises the following steps of: firstly, constructing a full-amount blood relationship graph containing dominant and implicit dependencies by combining dynamic job logs and static code abstract syntax tree analysis; performing statistics on change influence indexes, and calculating a total influence score representing a risk level by using a multiple linear regression model; generating a dynamic check threshold based on the score, and selecting a sentinel node according to betweenness centrality; and calculating node data similarity and introducing a distance attenuation factor to obtain a global consistency index so as to judge a verification result. And finally, iteratively correcting the regression model coefficient by using the verification residual error. According to the method, the problem of dependency deficiency of complex links is effectively solved, accurate quantification of alteration risks and adaptive adjustment of verification strategies are realized, and the accuracy and efficiency of data management are improved.

This invention belongs to the field of production control technology and provides a green production optimization management method and system for jacquard fabrics. The method includes: based on the weaving parameters and the preset process parameters, using a pre-trained Transformer energy consumption prediction model to predict the energy consumption per unit fabric length; in response to the energy consumption per unit fabric length exceeding a preset threshold, using a reinforcement learning model to adjust the process parameters to obtain first optimized process parameters that meet the energy consumption threshold; obtaining the first correlation degree between the optimized parameters in the first optimized process parameters and surface defects of the jacquard fabric; conducting a preset number of trial productions based on the first optimized process parameters; obtaining second optimized process parameters by dynamically inputting surface defects during the trial production process into the reinforcement learning model; and producing the jacquard fabric based on the second optimized process parameters, thereby achieving a balance between energy consumption reduction and quality assurance in the jacquard fabric process parameters.