Abstract: A lattice-based proxy signature method, apparatus and device, a lattice-based proxy signature verification method, apparatus and device, and a storage medium. Polynomials are randomly selected in rings to calculate public and private keys of nodes, and the magnitudes of proxy public and private keys are the same as the magnitudes of public and private keys of an original signer. Therefore, compared with existing proxy signature schemes, the present application has smaller lengths of public and private keys and higher storage efficiency. Proxy signature information generated in the present application shows a signature of the original signer and also shows a signature of a proxy signer. Once a proxy signature is created, the proxy signature cannot be repudiated by the proxy signer, and has strong non-repudiation and strong unforgeability. The proxy signature method has the advantage of resisting quantum computer attack.

Abstract: A trusted anonymous voting method includes: performing a hash operation on original voting content to obtain a hash output including a commitment value cvi; blinding the commitment value cvi based on information of other voters to obtain commitment value tuples {tilde over (c)} and {tilde over (x)}; signing the commitment value tuple {tilde over (c)} by using an ESDSA to obtain a signature tuple {tilde over (s)}; and establishing a vote tuple ({tilde over (c)}, {tilde over (s)}, {tilde over (d)}) based on the commitment value tuple {tilde over (c)}, the signature tuple {tilde over (s)} and a signature tuple {acute over (d)} of other voters, and uploading the vote tuple (?, ?, {acute over (d)}) and the commitment value tuple {tilde over (x)} to the blockchain, where signature tuple {tilde over (d)} is generated in a case that other voters verify the signature tuple {tilde over (s)} and the signature tuple {tilde over (s)} passes the verification, and the commitment value tuple {tilde over (x)} is used to decry

Abstract: A timestamp generation method and apparatus, and an electronic device and a storage medium, which are used for solving the technical problem of recorded information being easily leaked because a trusted third party is usually needed as a timestamp authority for recording in a traditional trusted timestamp generation method. In the present invention, the method comprises: acquiring a file record; calculating a hash value of the file record; sending the hash value to a random issuer, and receiving a signature result returned by the random issuer, wherein the signature result is generated after the random issuer signs the hash value and a receiving time, and the receiving time is the time when the random issuer receives the hash value; and storing the signature result in a blockchain as a timestamp of the file record.

Abstract: The invention discloses a method of photovoltaic cell parameter identification based on the improved equilibrium optimizer algorithm, which comprises: step 1, establishing PV cell model and fitness function; step 2, based on the measured output I-V data, predicting output data of PV cell by a BP neural network; step 3, identifying PV cell parameters by using IEO algorithm until convergence conditions of the IEO algorithm are reached, and finally outputting the optimal identified parameters. Solving technical problems of the existing technology such as, cannot reach the optimal parameter identification, and being easy to be trapped into the local optimal.

Abstract: A non-intrusive load identification method based on the Power Fingerprint characteristics of the load is provided. The method includes: S1, collecting Power Fingerprint characteristic data of several loads of the same type; S2, after preprocessing Power Fingerprint characteristic data of load, establishing convolution neural network based on attention mechanism to learn load characteristics; S3, using sliding time window algorithm to realize load switching event detection, In order to extract the change of electrical data of user bus before and after the switching event, the non-intrusive load identification problem is converted into the single load identification problem; S4, the load identification is realized, and the extracted electrical information features of single load are identified using the trained model.