Quality detection system based on cloud server
A cloud server and detection instrument technology, applied in the direction of instruments, measuring devices, scientific instruments, etc., can solve the problems of inability to monitor quality more effectively, waste of detection resources, etc., to improve the efficiency and coverage of quality detection, simplify networking, The effect of rapid quality inspection
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no. 1 example
[0035] In this embodiment, a multi-source spectrometer is used as a detection instrument to detect the sample to be tested and collect corresponding data. The multi-source spectrometer is composed of LED light sources including 275nm, 370nm, 675nm, 750nm, 830nm, 900nm, 1100nm, and 1500nm, and the LED light source is realized by a photodiode. As a sensor, the multi-source spectrometer is connected to the smart terminal with Bluetooth. The smart terminal is a mobile phone with an Android system. The mobile phone communicates with the cloud server through wifi. The cloud server uses Alibaba Cloud. The instrument database used is a database in mySQL format. The type of sample (Ginkgo biloba), the number of testing batches (10 batches), the testing time (2016.4.23), and the cumulative use time are in hours (10 hours). That is, the instrument with the serial number zlg1602030001 was used to detect 10 batches of ginkgo leaf samples on April 23, 2016, and the cumulative detection tim...
no. 2 example
[0043] Same as the first embodiment, the difference is that the detection instrument is composed of 275nm, 285nm, 295nm, 330nm, 375nm, 420nm, 450nm, 600nm, 675nm, 830nm ultraviolet and visible light LED light sources. The data records of the second embodiment in the instrument database are shown in Table 21.
[0044] The second embodiment data information recorded in the instrument database of table 21
[0045] serial number Test sample batch number Test date Instrument cumulative test time (h) zlg1604020012 Danshen 20 2016.5.7 3
[0046] The compound spectrogram of 20 batches of Danshen samples described in the second embodiment of the present invention is as follows image 3 shown. The correction of the instrument signal was realized with the neural network algorithm, and the prediction model of the sample properties was established with the PLS algorithm. The predicted contents of each component in 20 batches of Danshen are shown in Table 2...
no. 3 example
[0051] The same as the first embodiment, the difference is that the detection instrument is an ultraviolet spectrometer, and the smart terminal is a notebook computer. The data records of the third embodiment in the instrument database are shown in Table 31.
[0052] The data information of the third embodiment recorded in the instrument database of table 31
[0053] serial number Test sample batch number Test date Instrument cumulative detection time (h) zlg1604030001 honeysuckle 3 2016.6.7 2
[0054] The ultraviolet spectrum of 3 batches of honeysuckle samples described in the third embodiment of the present invention is as follows Figure 4 shown. The calibration of the instrument signal was simulated by the logistic regression algorithm, and the prediction model was established by the random forest number algorithm. The predicted contents of each component in the three batches of honeysuckle are shown in Table 32. The data in Table 32 is s...
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