Abstract: A multistage energy utilization system based on electricity-heat-hydrogen-methane coupling is connected with a power grid, a heat-supply network and a gas-supply network. The system includes a renewable energy resource module, a hydrogen energy storage module, a heat storage module, a gas fired-boiler module, a methane reactor module and a carbon capture and storage (CCS) module. The renewable energy resource module is connected with the power grid and the hydrogen energy storage module. The hydrogen energy storage module is connected with the power grid, the heat-supply network and the methane reactor module. The methane reactor module is connected with the hydrogen energy storage module, the CCS module and the gas-supply network. The gas fired-boiler module is connected with the gas-supply network. The hydrogen energy storage module, the CCS module and the methane reactor module are integrated to improve energy utilization and consumption level of renewable energy resources.

Abstract: Techniques are provided for implementing a unified object format. The unified object format is used to format data in a performance tier (e.g., infrequently accessed data, snapshot data, etc.) into objects that are stored into an object store for low cost, scalable, long term storage compared to storage of the performance tier. With the unified object format, compression of the data may be retained when the data is stored as the objects into the object store. Additional compression may also be provided for the data in the objects. The unified object format includes slot header metadata used to track the location of the data within the object notwithstanding the data being compressed and/or stored at non-fixed boundaries. The slot header metadata may be cached at the performance tier for improved read performance and may be repaired by a repair subsystem (a slot header repair subsystem).

Abstract: Techniques are provided for remote object store error handling. A storage system may store data within one or more tiers of storage, such as a local storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Because the remote object store may not provide the same data consistency and guarantees that the storage system provides for clients such as through the local storage tier, additional validation is provided by the storage system for the remote object store. For example, when data is put into an object of the remote object store, a verification get operation is performed to read and validate information within a header of the object. Other verifications and checks are performed such as using a locally stored metafile to detect corrupt or lost metadata and/or objects.

Abstract: A community question-answer (CQA) website answer sorting method and system combined with active learning. The sorting method comprises: step S1, performing question-answer data representation and modeling; and step S2, constructing a training set in combination with active learning, and predicting a sorting relationship of candidate question-answer pairs. Also provided is a community question-answer website answer sorting system combined with active learning. CQA website question-answer data is first represented and modeled, interference to answers sorting caused by long tail distribution of the community data is solved by means of a long tail factor, and an attention mechanism is introduced in a convolutional neural network to relieve a semantic gap problem among question-answer texts. Then, an unlabeled training set is also constructed, a sample is additionally selected from the unlabeled training set and labeled, and an answer sorting model is trained again after labeling results are merged.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Techniques are provided for object store mirroring. Data within a storage tier of a node may be determined as being data to tier out to a primary object store based upon a property of the data. A first object is generated to comprise the data. A second object is generated to comprise the data. The first object is transmitted to the primary data store for storage in parallel with the second object being transmitted to a mirror object store for storage. Tiering of the data is designated as successful once acknowledgements are received from both the primary object that the first object was stored and the mirror object store that the second object was stored.

Abstract: Techniques are provided for remote object store error handling. A storage system may store data within one or more tiers of storage, such as a local storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Because the remote object store may not provide the same data consistency and guarantees that the storage system provides for clients such as through the local storage tier, additional validation is provided by the storage system for the remote object store. For example, when data is put into an object of the remote object store, a verification get operation is performed to read and validate information within a header of the object. Other verifications and checks are performed such as using a locally stored metafile to detect corrupt or lost metadata and/or objects.

Abstract: A method and device for constructing a legal knowledge graph based on joint entity and relation extraction. The construction method comprises the following steps: constructing a triple data set; design of a model architecture and training of a model, wherein the model architecture comprises an encoding layer, a head entity extraction layer and a relation-tail entity extraction layer; determination of the relation between the sentences of the text; triple combination and graph visualization. The design of the model framework of the present disclosure adopts a Chinese Bert pre-training model as an encoder. In the entity extraction part, two BiLSTM binary classifiers are used to identify the start position and end position of an entity. The head entity is first extracted, and then the tail entity corresponding to the entity relation is extracted from the extracted head entity.

Abstract: Techniques are provided for implementing a unified object format. The unified object format is used to format data in a performance tier (e.g., infrequently accessed data, snapshot data, etc.) into objects that are stored into an object store for low cost, scalable, long term storage compared to storage of the performance tier. With the unified object format, compression of the data may be retained when the data is stored as the objects into the object store. Additional compression may also be provided for the data in the objects. The unified object format includes slot header metadata used to track the location of the data within the object notwithstanding the data being compressed and/or stored at non-fixed boundaries. The slot header metadata may be cached at the performance tier for improved read performance and may be repaired by a repair subsystem (a slot header repair subsystem).

Abstract: Techniques are provided for implementing a unified object format. The unified object format is used to format data in a performance tier (e.g., infrequently accessed data, snapshot data, etc.) into objects that are stored into an object store for low cost, scalable, long term storage compared to storage of the performance tier. With the unified object format, compression of the data may be retained when the data is stored as the objects into the object store. Additional compression may also be provided for the data in the objects. The unified object format includes slot header metadata used to track the location of the data within the object notwithstanding the data being compressed and/or stored at non-fixed boundaries. The slot header metadata may be cached at the performance tier for improved read performance and may be repaired by a repair subsystem (a slot header repair subsystem).

Abstract: Techniques are provided for implementing a unified object format. The unified object format is used to format data in a performance tier (e.g., infrequently accessed data, snapshot data, etc.) into objects that are stored into an object store for low cost, scalable, long term storage compared to storage of the performance tier. With the unified object format, compression of the data may be retained when the data is stored as the objects into the object store. Additional compression may also be provided for the data in the objects. The unified object format includes slot header metadata used to track the location of the data within the object notwithstanding the data being compressed and/or stored at non-fixed boundaries. The slot header metadata may be cached at the performance tier for improved read performance and may be repaired by a repair subsystem (a slot header repair subsystem).

Abstract: Techniques are provided for object store mirroring. Data within a storage tier of a node may be determined as being data to tier out to a primary object store based upon a property of the data. A first object is generated to comprise the data. A second object is generated to comprise the data. The first object is transmitted to the primary data store for storage in parallel with the second object being transmitted to a mirror object store for storage. Tiering of the data is designated as successful once acknowledgements are received from both the primary object that the first object was stored and the mirror object store that the second object was stored.

Abstract: Techniques are provided for object store mirroring. Data within a storage tier of a node may be determined as being data to tier out to a primary object store based upon a property of the data. A first object is generated to comprise the data. A second object is generated to comprise the data. The first object is transmitted to the primary data store for storage in parallel with the second object being transmitted to a mirror object store for storage. Tiering of the data is designated as successful once acknowledgements are received from both the primary object that the first object was stored and the mirror object store that the second object was stored.

Abstract: Techniques are provided for orphan object detection, invalid sequence number detection, and asynchronous object cleanup. A storage system may store data within one or more tiers of storage, such as a storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Orphan objects, within the remote object store, that are no longer used by the storage system may be detected and/or deleted. When an aggregate of volumes is deleted, corresponding objects, within the remote object store, may be identified and/or deleted. Invalid sequence numbers (e.g., lost or corrupt sequence numbers locally maintained in a metafile) assigned to objects within the remote object store may be identified, deleted, and/or fixed.

Abstract: Techniques are provided for remote object store error handling. A storage system may store data within one or more tiers of storage, such as a local storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Because the remote object store may not provide the same data consistency and guarantees that the storage system provides for clients such as through the local storage tier, additional validation is provided by the storage system for the remote object store. For example, when data is put into an object of the remote object store, a verification get operation is performed to read and validate information within a header of the object. Other verifications and checks are performed such as using a locally stored metafile to detect corrupt or lost metadata and/or objects.

Abstract: A community question-answer (CQA) web site answer sorting method and system combined with active learning. The sorting method comprises: step S1, performing question-answer data representation and modeling; and step S2, constructing a training set in combination with active learning, and predicting a sorting relationship of candidate question-answer pairs. Also provided is a community question-answer website answer sorting system combined with active learning. CQA website question-answer data is first represented and modeled, interference to answers sorting caused by long tail distribution of the community data is solved by means of a long tail factor, and an attention mechanism is introduced in a convolutional neural network to relieve a semantic gap problem among question-answer texts. Then, an unlabeled training set is also constructed, a sample is additionally selected from the unlabeled training set and labeled, and an answer sorting model is trained again after labeling results are merged.

Abstract: Disclosed is a taxpayer industry classification method based on label-noise learning, which comprises the following steps: extracting text information to be mined from taxpayer industry information for text embedding, and performing feature processing on the embedded information; extracting, non-text information from the taxpayer industry information for encoding; constructing a BERT-CNN deep network structure, a number of neurons and the dimensionality of input and output in each layer and the number of target categories; pre-training the network constructed in the previous step through contrastive learning, nearest neighbor semantic clustering and self-labeling learning in turn; adding, a noise modeling layer on the basis of the constructed deep network, modeling label noise distribution through network self-trust and noisy label information, and performing model training; taking the deep network before the noise modeling layer as a classification model, and classifying taxpayer industries.

Abstract: Techniques are provided for orphan object detection, invalid sequence number detection, and asynchronous object cleanup. A storage system may store data within one or more tiers of storage, such as a storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Orphan objects, within the remote object store, that are no longer used by the storage system may be detected and/or deleted. When an aggregate of volumes is deleted, corresponding objects, within the remote object store, may be identified and/or deleted. Invalid sequence numbers (e.g., lost or corrupt sequence numbers locally maintained in a metafile) assigned to objects within the remote object store may be identified, deleted, and/or fixed.

Abstract: A copy-free transition tool converts storage objects from a source format associated with a source storage system to a destination format associated with a destination storage system. The transition tool exports configuration information associated with the source storage system to the destination storage system and brings the storage objects offline. Once the transition tool determines that the storage devices containing the storage objects are physically connected to the destination storage system, the tool can convert file systems from the source format to the destination format while leaving data and file layout unchanged. The tool can also modify metadata associated with each of the storage objects to conform to the destination format and store the modified metadata with the destination storage system.

Abstract: Techniques are provided for remote object store error handling. A storage system may store data within one or more tiers of storage, such as a local storage tier (e.g., solid state storage and disks maintained by the storage system), a remote object store (e.g., storage provided by a third party storage provider), and/or other storage tiers. Because the remote object store may not provide the same data consistency and guarantees that the storage system provides for clients such as through the local storage tier, additional validation is provided by the storage system for the remote object store. For example, when data is put into an object of the remote object store, a verification get operation is performed to read and validate information within a header of the object. Other verifications and checks are performed such as using a locally stored metafile to detect corrupt or lost metadata and/or objects.