Floor material identification method, system and device, and storage medium

A floor material identification method, applied in a cleaning robot, the floor material identification method comprising:

• acquiring a vibration signal generated by a cleaning robot when operating on a floor of a material among a plurality of materials;
• obtaining, based on a time domain signal formed by the vibration signal, a frequency signal of the floor of the material among the plurality of materials through frequency spectrum analysis;
• setting a predetermined low frequency band range and a predetermined high frequency band range for the frequency signal;
• determining low frequency band energy and high frequency band energy of the frequency signal based on the predetermined low frequency band range and the predetermined high frequency band range;
• determining a ratio of the low frequency band energy to the high frequency band energy of the frequency signal as an identification feature;
• obtaining the at least one identification parameter by inputting the identification feature into a classifier for training;
• and
• identifying a floor material type based on the at least one identification parameter.

A floor material identification system, comprising:

• a vibration signal acquisition device configured to acquire a vibration signal generated by a cleaning robot when operating on a floor of a material among a plurality of materials;
• an identification parameter determination device configured to determine at least one identification parameter for floor material identification based on the vibration signal;
• and
• a material identification device configured to identify a floor material type based on the at least one identification parameter;
• wherein the identification parameter determination device is further configured to:
• obtain, based on a time domain signal formed by the vibration signal, a frequency signal of the floor comprising the plurality of materials through frequency spectrum analysis;
• set a predetermined low frequency band range and a predetermined high frequency band range for the frequency signal;
• determine low frequency band energy and high frequency band energy of the frequency signal based on the predetermined low frequency band range and the predetermined high frequency band range;
• determine a ratio of the low frequency band energy to the high frequency band energy of the frequency signal as an identification feature;
• and
• input the identification feature into a classifier for training to obtain the at least one identification parameter.