  Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to
generate more than ten million events over 10\,s in Super-Kamokande (SK). At
such large event rates, the buffers of the SK analog-to-digital conversion
board (QBEE) will overflow, causing random loss of data that is critical for
understanding the dynamics of the supernova explosion mechanism. In order to
solve this problem, two new DAQ modules were developed to aid in the
observation of very nearby supernovae. The first of these, the SN module, is
designed to save only the number of hit PMTs during a supernova burst and the
second, the Veto module, prescales the high rate neutrino events to prevent the
QBEE from overflowing based on information from the SN module. In the event of
a very nearby supernova, these modules allow SK to reconstruct the time
evolution of the neutrino event rate from beginning to end using both QBEE and
SN module data. This paper presents the development and testing of these
modules together with an analysis of supernova-like data generated with a
flashing laser diode. We demonstrate that the Veto module successfully prevents
DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability
of the new modules. During normal running the Veto module is found to issue DAQ
vetos a few times per month resulting in a total dead time less than 1\,ms, and
does not influence ordinary operations. Additionally, using simulation data we
find that supernovae closer than 800~pc will trigger Veto module resulting in a
prescaling of the observed neutrino data.
