Abstract: The present disclosure discloses a high-temperature fast-curing starch-based adhesive for particleboards and a preparation method thereof, belonging to the technical field of adhesive preparation. The low viscosity of the starch-based adhesive is ensured by selecting a crosslinking monomer which does not self-crosslink in a reaction process in the present disclosure, and a binary crosslinking agent matched with the crosslinking monomer is added before use to be quickly crosslinked with the crosslinking monomer at high temperature so as to ensure better thermosetting property and water resistance as well as faster curing speed of the starch-based adhesive at the same time, which meets the requirements of the particleboards for the adhesive, solves the problem of long curing time of the existing starch-based adhesives at high temperatures, further shortens the curing time of the starch-based adhesives to about 60 s, and improves the production efficiency of the particleboards.

Abstract: The present disclosure discloses a high-temperature fast-curing starch-based adhesive for particleboards and a preparation method thereof, belonging to the technical field of adhesive preparation. The low viscosity of the starch-based adhesive is ensured by selecting a crosslinking monomer which does not self-crosslink in a reaction process in the present disclosure, and a binary crosslinking agent matched with the crosslinking monomer is added before use to be quickly crosslinked with the crosslinking monomer at high temperature so as to ensure better thermosetting property and water resistance as well as faster curing speed of the starch-based adhesive at the same time, which meets the requirements of the particleboards for the adhesive, solves the problem of long curing time of the existing starch-based adhesives at high temperatures, further shortens the curing time of the starch-based adhesives to about 60 s, and improves the production efficiency of the particleboards.

Abstract: A method and an apparatus for generating time series data based on multi-condition constraints, and a medium are provided. The method include the following: a data repair request from a client is received, the data repair request including to-be-repaired data and condition information; normalization processing is performed on the to-be-repaired data to obtain normalized data, and tensor processing is performed on the condition information to obtain a feature label; a trained data repair model is called to perform repair processing on the normalized data according to the feature label to obtain first repaired data, the data repair model being obtained by training according to sample data, a first sample condition, real sample data and a second sample condition, and the sample data being noise data; and the first repaired data is sent to the client.

Abstract: A method and an apparatus for generating time series data based on multi-condition constraints, and a medium are provided. The method include the following: a data repair request from a client is received, the data repair request including to-be-repaired data and condition information; normalization processing is performed on the to-be-repaired data to obtain normalized data, and tensor processing is performed on the condition information to obtain a feature label; a trained data repair model is called to perform repair processing on the normalized data according to the feature label to obtain first repaired data, the data repair model being obtained by training according to sample data, a first sample condition, real sample data and a second sample condition, and the sample data being noise data; and the first repaired data is sent to the client.