Abstract: A method for cyclic biochemical conversion of carbon dioxide and hot gas cogeneration in depleted oil and gas reservoirs includes: S1: selecting a target depleted oil and gas reservoir; S2: adjusting a temperature of the target depleted oil and gas reservoir to 30° C. to 70° C. and detecting whether formation water of the target depleted oil and gas reservoir contains methanogenic archaea, in which if no methanogenic archaeon is contained, then methanogenic archaea is injected and step S3 is proceeded, and if methanogenic archaea are contained, then step S3 is proceeded directly; S3: injecting a mixture of carbon dioxide and hydrogen into the target depleted oil and gas reservoir through a gas injection well; and S4: shutting down the gas injection well to wait for the methanogenic archaea to convert carbon dioxide and hydrogen into methane and exploiting the methane and heat energy in the target depleted oil and gas reservoir.

Abstract: Disclosed in the embodiments of the present disclosure are a data processing method and apparatus, and an electronic device. A specific implementation of the method comprises: determining a target first storage region from among a preset number of first storage regions; on the basis of a interaction process with a second device, determining identical target identification information comprised in the target first storage region and a target second storage region, and determining whether the interaction process meets a target requirement; and in response to the interaction process meeting the target requirement, storing target first data identified by the target identification information, so that the second device stores target second data identified by the target identification information. Thus, the target first data and the target second data identified by the same target identification information in the target first storage region and the target second storage region are aligned.

Abstract: A network connection method and device for a training participant of a joint training model are provided, the training participant operates in a master-worker mode. The method includes: acquiring communication state information of a worker, the communication state information indicating a communication connection phase that the worker is in; acquiring communication state information of a target worker as target communication state information, where the target worker includes a peer node corresponding to the worker, and the peer node belongs to a different training participant of the joint training model; and resetting, in response to determining that the target communication state information does not match the communication state information of the worker, a communication connection phase that the worker is in.

Abstract: Provided are a method for processing model parameters, and an apparatus. The method comprises: a model parameter set to be sharded is obtained, wherein the model parameter set comprises a multi-dimensional array corresponding to a feature embedding; attribute information for a storage system used for storing the model parameter set to be sharded is obtained, wherein the storage system used for storing the model parameter set to be sharded differs from a system on which a model corresponding to the model parameter set to be sharded is located when operating; the model parameter set to be sharded is stored in the storage system according to the attribute information.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

Abstract: Provided are a method for processing model parameters, and an apparatus. The method comprises: a model parameter set to be sharded is obtained, wherein the model parameter set comprises a multi-dimensional array corresponding to a feature embedding; attribute information for a storage system used for storing the model parameter set to be sharded is obtained, wherein the storage system used for storing the model parameter set to be sharded differs from a system on which a model corresponding to the model parameter set to be sharded is located when operating; the model parameter set to be sharded is stored in the storage system according to the attribute information.

Abstract: Provided are a method and apparatus for evaluating a joint training model. A specific implementation of the method for evaluating a joint training model comprises: receiving a model evaluation data request sent by a target device, wherein the target device comprises a participant of a joint training model; acquiring a sample set matching the model evaluation data request, wherein the matching sample set is labeled data associated with the joint training model; and generating model evaluation data of the joint training model according to the matching sample set. By means of the implementation, an effect index of a joint training model can be shared on the premise of not exposing original sample data. Accordingly, a timely and effective data reference basis is provided for the optimization and improvement of the joint training model.

Abstract: A network connection method and device for a training participant of a joint training model are provided, the training participant operates in a master-worker mode. The method includes: acquiring communication state information of a worker, the communication state information indicating a communication connection phase that the worker is in; acquiring communication state information of a target worker as target communication state information, where the target worker includes a peer node corresponding to the worker, and the peer node belongs to a different training participant of the joint training model; and resetting, in response to determining that the target communication state information does not match the communication state information of the worker, a communication connection phase that the worker is in.

Abstract: Provided are a method and an apparatus for generating prediction information, an electronic device, and a computer readable medium. The method includes: generating, based on first user characteristic information of a target user, anonymous user information of the target user (201); sending the anonymous user information to a second processing end to enable the second processing end to generate prediction information based on the anonymous user information and second user characteristic information (202) of the target user. Data interaction and sharing are realized while ensuring data privacy, thereby improving accuracy of the prediction information.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.