The invention discloses a soil heavy
metal monitoring device based on spectrum
deep learning and an evaluation method, the soil heavy
metal monitoring device comprises a plurality of column leaching containers, the plurality of column leaching containers are sequentially connected in series through hoses to form a
percolation passage, the front end of the
percolation passage is connected with a water outlet bucket through a
peristaltic pump, and the rear end of the
percolation passage is connected with a water inlet bucket; a
branch pipe is led out of the hose between every two adjacent column leaching containers through a three-way
pipe, the multiple
branch pipes are connected with the multiple collection bottles respectively, and
electrochemical detection assemblies are arranged on the collection bottles; the plurality of
cuvette brackets are respectively arranged on the plurality of hoses, each
cuvette bracket is connected with the
optical fiber spectrometers through optical fibers, and the plurality of
optical fiber spectrometers are electrically connected with the computer; the scheme aims at providing an effective evaluation method aiming at the soil
pollution problem of a heavy
metal high background area, the concentration of harmful heavy metal elements in the soil is remarkably reduced, the
soil quality is improved, and important
technical support and theoretical basis are provided for
soil remediation.