Laboratory chemical substance detection system
A chemical substance and detection system technology, applied in the field of chemical substance detection system in the laboratory, can solve the problem of prohibited use of chemicals
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Embodiment 1
[0081] In this example, if figure 1 The shown laboratory chemical substance detection system includes a terminal detection system and a data processing system. The terminal detection system includes chemical substance detection equipment and a data upload processing terminal. The chemical substance detection equipment passes the detected data through the data processing system. The upload processing terminal uploads to the data processing system, and the data processing system uses data classification algorithm, data encryption algorithm and similar data processing algorithm to process and transmit the processed data to the laboratory control terminal through the communication system.
[0082] The chemical substance detection equipment uses various chemical substance sensitive sensing systems and converts its concentration into electrical signals for detection; the data upload processing terminal can collect, store, retrieve, process, transform and transmit data by manual or au...
Embodiment 2
[0085] In the embodiment of the present invention, in the data classification algorithm, the data uploaded by the data upload processing terminal is expressed as the following mathematical programming function make 1≤i≤n; u ij ≥0,1≤i≤n,1≤j≤c; 1≤j≤c, also is a spatial s-dimensional data set, where n, i, j, m, and c are all natural numbers, n is the number of samples in the data set, c is the number of cluster centers, and m is the weight coefficient.
[0086] In the mathematical programming function, d ij =||x i -v j || is the sample point x i and cluster center v j distance
[0087]
[0088] away, and satisfy , u ij is the membership degree of the i-th sample belonging to class j, R s for a specific data set.
[0089] The data classification algorithm includes the following steps:
[0090]T1, initialization, select E>0, in the initial clustering, let K=1;
[0091] like but
[0092]
[0093] where r and E are natural numbers, if there is i, r makes d ...
Embodiment 3
[0100] In the similar data processing algorithm of this embodiment, data is defined, data set D=(O, A), object set O={o 1 ,o 2 ,...o m}, attribute set A = {A 1 ,A 2 ,...A d}, object o∈O, attribute A i The value is Then there is an object p∈O, so that there are at least K objects p′∈O satisfying dist(o,p′)≤dist(o,p), and there are at most k-1 objects p′∈O satisfying dist(o, p′)≤dist(o,p), then the k-distance of o k-dist(o)=dist(o,p).
[0101] Preferably, in the similar data processing algorithm, the data similarity is calculated, d is the similarity, and o∈O is set, A i ∈A, then o with respect to attribute A i A similar definition of is:
[0102]
[0103] in
[0104]
[0105] The similar data processing algorithm in this embodiment is used to query and delete the same redundant data.
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