Abstract: A data writing method includes that a client receives a first data block. The client generates a first temporary parity block based on the first data block. The client sends the first data block and the first temporary parity block to a server. The server stores the first data block and the first temporary parity block in the storage device. The server determines that the first data block is a final data block of the target stripe, and performs erasure code combination on the first temporary parity block and a second temporary parity block to obtain a combined target parity block. The second temporary parity block includes one or more temporary parity blocks generated based on another data block in the target stripe other than the first data block. The server stores the target parity block in the storage device.

Abstract: This disclosure discloses a thermal runaway management system for data centers and compute device clusters. The cluster comprises cabinets housing energy storage apparatuses, each containing energy storage units. The management system includes a storage apparatus filled with a cooling medium, a transmission assembly, and a control assembly. The transmission assembly comprises a main pipe connected to the storage apparatus, which branches into main and sub connecting pipes to link directly to each energy storage unit. The control assembly is mounted on the transmission assembly and partially disposed inside the energy storage unit. It automatically triggers the delivery of the cooling medium into the energy storage unit whenever the ambient temperature reaches or exceeds the unit's thermal runaway threshold.

Abstract: A data access method includes a target storage node that determines at least one data access policy input or selected by a user on a management platform. The target storage node receives a first write data stream from a request client, where the first write data stream indicates a first tape, first data to be written into the first tape, and first tape space required by the first data, the first tape space belongs to a same data partition. The target storage node writes the first data into the first tape according to the at least one data access policy using a first tape drive in at least one tape drive.

Abstract: A code generation method includes that a code development platform receives input information of a user in a first code file in a project, obtains an intra-file context and a first cross-file context based on a static structure of the project, obtains a second cross-file context based on a behavior feature of the user in development of the project, obtains a third cross-file context based on an evolutionary coupling degree between at least one second code file and the first code file in a code repository of the project, generates prompt information based on the input information, the intra-file context, the first cross-file context, the second cross-file context, and the third cross-file context, inputs the prompt information into a code generation model for inference, to obtain at least one group of generated code, and displays the at least one group of generated code to the user.

Abstract: A code testing method is applied to a code testing system. During generation of test code, context information of to-be-tested code is extracted, and the test code is generated based on the context information of the to-be-tested code.

Abstract: A data storage method includes receiving pieces of data, where each piece of data includes tag values. The tags include a first tag and a second tag that are specified as a sort key, and the first tag ranks before the second tag in the sort key. The method determines a tag value set to which a tag value of the first tag in each piece of data belongs, and determines a storage sequence of the pieces of data based on a sequence in the sort key, identification information of the tag value set to which the tag value of the first tag in each piece of data belongs, and a tag value of the second tag in each piece of data. The pieces of data are stored based on the storage sequence of the pieces of data.

Abstract: This disclosure provides a conference control method. The method is applied to a conference control system, and the conference control system includes a conference server and a conference controller. The method includes: The conference server receives a conference control request sent by the conference controller, where the conference control request is used to request to execute a target instruction related to a video conference; the conference server determines at least one hard terminal that has a pairing relationship with the conference controller, where the hard terminal is configured to implement a hardware video conference based on an embedded architecture; and the conference server processes the conference control request, to control the at least one hard terminal to execute the target instruction.

Abstract: A computing node management system includes a central offload node and a plurality of computing nodes. A riser card is inserted into each computing node. Each riser card is connected to the central offload node. Each riser card establishes a communication channel between the central offload node and the computing node into which the riser card is inserted. The central offload node is configured to provide resources for the plurality of computing nodes through the communication channels.

Abstract: A data compression method includes: obtaining second data corresponding to first data, and then compressing the second data to obtain compressed data corresponding to the first data. A storage structure of the second data is a tree structure, a bottom-layer node of the tree structure includes a root-child node and a leaf-child node that has an index relationship with the root-child node, and the compressed data includes data indicated by the root-child node and compressed data indicated by the leaf-child node. When data in the tree structure is queried, the query is sequentially performed from data indicated by a root node in the tree structure to data indicated by a child node. In this disclosure, the data indicated by the root-child node is not compressed.

Abstract: A zero-knowledge proof generation method and apparatus, a device, and a storage medium are disclosed. The method is applied to a management platform, the management platform is configured to manage an infrastructure, the infrastructure includes a plurality of compute resources, and each compute resource is used for generating ZKPs corresponding to a plurality of types of proof protocols. The method includes: receiving a proof generation request, where the proof generation request includes proof protocol information and a circuit file, the proof protocol information indicates a target proof protocol used for generating a target ZKP, the target proof protocol is one of the plurality of types of proof protocols, and the circuit file indicates a computing model for generating the target ZKP; determining a target compute resource in the plurality of compute resources based on the proof generation request; and generating the target ZKP by using the target compute resource.

Abstract: A source end obtains to-be-transmitted data, divides the to-be-transmitted data into a plurality of segments, determines a sending sequence number of each segment, generates verification information of each segment based on the segment and the sending sequence number of the segment, and then sends the plurality of segments and corresponding verification information to a destination end. In this way, the destination end may verify a received segment based on a receiving sequence number and the verification information of each segment, to determine whether a transmission exception exists in the segment in a transmission process.

Abstract: A data query method includes: providing a data query interface; obtaining an aggregation query task from the data query interface, where the aggregation query task is used to query for a total amount of data that meets a query condition; and displaying, in the data query interface, a first estimated query result of the aggregation query task, where the first estimated query result includes a first total amount of data that meets the query condition and that is obtained through approximate estimation based on a first partial exact query result.

Abstract: A computing acceleration method based on a cloud computing technology and a cloud management platform are provided. In the method, the cloud management platform may obtain a job task entered by a tenant, allocate, to at least one programmable compute device for processing, processing logic that is in job processing logic carried in the job task and that is applicable to a programmable compute device, and allocate, to at least one server for processing, other processing logic that is in the job processing logic and that is applicable to a server. This solution can be used to provide surplus cloud resources for the tenant to use, to obtain higher revenue and improve cloud resource utilization.

Abstract: An interface management method includes obtaining interface call records of a plurality of microservices in an application, where the interface call records include an interface call provided inside the application by the microservice and/or an interface call provided outside the application by the microservice; drawing an interface call relationship diagram of the application in a running state based on the interface call records of the plurality of microservices; drawing a boundary of the application in the interface call relationship diagram based on microservice registration information of the application; and when there is an interface call that breaks through the boundary of the application in the interface call records, displaying, to a user, an interface call that has a security risk, for example, the interface call that breaks through the boundary of the application.

Abstract: There is provided a method for compressing an Artificial Intelligence (AI) model comprising a neural network. The method involves running the model using calibration data, followed by evaluating the activation sparsity and the importance of each model layer. Based on this evaluation, each layer is more or less compressed, until a target compression ratio is achieved. In some embodiments, the type of compression (quantization, or low rank decomposition) is based on the rank deficiency of a layer. In some embodiments, the compression may be adjusted dynamically at inference time.

Abstract: A data analysis method includes receiving input content of a user, performing intent classification based on the input content, and determining a task type indicated by the input content. The method further includes, when the task type indicated by the input content is an instruction-based task, determining a subtask type of the instruction-based task through instruction keyword matching, and obtaining historical dialog–associated content of the input content. The method further includes processing the historical dialog–associated content based on the subtask type to obtain an adjusted data analysis result.

Abstract: A data processing method includes: obtaining a custom row-level expression, where the custom row-level expression is for specifying a condition for determining a field of a target attribute of each piece of row data that is in a first data table as cold data; obtaining the field of the target attribute of each piece of row data in the first data table using the custom row-level expression, where all pieces of row data in the first data table are stored in a first storage medium; determining cold data, where the cold data is row data whose field of the target attribute meets a specified field; and compressing or migrating the cold data to a second storage medium.

Abstract: An embodiment of this disclosure provides an internet of things technology-based message transmission configuration method applied to the field of internet of things technologies. The method is applied to an internet of things platform that is run on infrastructure. The internet of things platform obtains message transmission configuration information including a channel selection policy. The internet of things platform constructs a plurality of message transmission channels based on a channel identifier and a message transmission condition that are of each message transmission channel of the plurality of message transmission channels and that are included in the channel selection policy.

Abstract: A data sharding method includes obtaining a plurality of pieces of stored data, and inputting the plurality of pieces of data into a ranking model to obtain a score corresponding to each piece of data, where input information of the ranking model includes the plurality of pieces of data, output information of the ranking model includes the score corresponding to each piece of data, the ranking model is determined based on the plurality of pieces of data and at least one query command, and the query command is used to query target data from the plurality of pieces of data. The method further includes determining, based on the score corresponding to each piece of data, file blocks respectively corresponding to the plurality of pieces of data.

Abstract: A data processing method includes a data processing system that receives a first write request, where the data processing system includes a first compute cluster, a second compute cluster, and a shared storage, the shared storage is configured to store data of a first data table, the first write request is used to write first data into the first data table, and the first data table belongs to the first compute cluster. The second compute cluster writes, into the shared storage, the first data that belongs to the first data table. The second compute cluster sends, to the first compute cluster, first metadata corresponding to the first data. The first compute cluster stores the first metadata corresponding to the first data.