Abstract: The present application provides a method and a system for calculating a stripe of a strip for a disk, a terminal and a storage medium. The method includes: calculating a pack offset of a parity block according to a given disk index; calculating an address of a strip where the parity block is located in the disk according to the pack offset; comparing an address of a to-be-checked strip with the address of the strip where the parity block is located in the disk to determine whether the parity block is on the to-be-checked strip; and calculating a stripe index of the to-be-checked strip by considering redundant elements caused by the parity block in response to determining that the parity block is on the to-be-checked strip; or calculating the stripe index of the to-be-checked strip directly in response to determining that the parity block is not on the to-be-checked strip.

Abstract: An electrostatic interference processing method, apparatus, and device, and a readable storage medium are provided. The method includes: receiving input data in real time, and determining whether an electrostatic interference signal is present in the input data; in response to determining that the electrostatic interference signal is present in the input data, interrupting reception of the input data; determining whether an interference frequency of the electrostatic interference signal is lower than a preset value; and in response to determining that the interference frequency of the electrostatic interference signal is lower than the preset value, continuing to receive the input data.

Abstract: Provided are a task allocation method and system for a solid state drive, an electronic device, and a storage medium. The task allocation method includes: dividing data managers into a plurality of data management groups; determining a service scenario according working states of all sibling master data managers; when the service scenario is a high-band-width scenario, controlling the sibling master data managers and sibling slave data managers to work in corresponding Central Processing Unit (CPU) cores, and allocating tasks to all the sibling master data managers and all the sibling slave data managers; and when the service scenario is a high-quality-of-service scenario, controlling the sibling master data managers to work in corresponding CPU cores, and allocating tasks to all the sibling master data managers.

Abstract: Provided are a bus exception handling method and apparatus, an electronic device and a computer-readable storage medium. The method includes: respectively obtaining, from multiple target buses, multiple pieces of target data corresponding to the multiple target buses, where the multiple target buses include a master bus and one or more candidate buses, and the target data corresponding to the master bus is referred to as first data; determining whether the first data satisfies a bus exception condition, where the bus exception condition is a data bus marker exception condition or a data content exception condition; and in response to determining that the first data satisfies the bus exception condition, selecting a target candidate bus in a healthy state as a new master bus, and updating local bus data.

Abstract: An IO path determination method and apparatus, and a device and a readable storage medium. The method comprises: determining a target volume of an IO request, and acquiring a path level of the target volume; when the path level is a host path level, sending the IO request to a preset preferable controller; when the path level is a back-end path level, selecting a target controller from controllers by using a consistent hashing algorithm, and sending the IO request to the target controller; and when the path level is a fault level, determining that a fault occurs in an IO path. An IO path is determined by means of setting different path levels for a volume and in conjunction with a consistent hashing algorithm, such that loads of controllers in a storage system can be effectively balanced.

Abstract: The present disclosure provides a method and system for storage management, a storage medium and a device. The method includes: making hard disks in a storage pool network with several controllers via network hard disk enclosures, respectively sending hard disk information to proxy drivers which are pre-configured in the controllers, and selecting one of the controllers as a main controller; respectively sending, the hard disk information to cluster drivers which are pre-configured in respective controllers; acquiring the hard disk information from each cluster driver via the main controller, and sending the total hard disk information sent to each cluster driver; acquiring information of a logical unit space corresponding to the request, and allocating an idle hard disk in the storage pool for the logical unit space according to the total hard disk information; processing, the read/write request in parallel in the idle hard disk.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: A method and apparatus for expanding cloud volume, and a device and a readable medium are provided. The method includes: determining whether there is an request of a host in a cloud volume and confirming a snapshot started during cloud backup (S1); in response to there being no IO request, expanding a snapshot volume of the snapshot started during the cloud backup and the cloud volume (S2); expanding a bitmap of the snapshot started during the cloud backup, and setting a bitmap obtained by expanding as a first preset value (S3); after completion of a cloud backup task, expanding a snapshot volume of an another snapshot that is not started in the cloud backup (S4); and expanding a bitmap of the another snapshot that is not started in the the present cloud backup, and setting a bitmap obtained by expanding as a second preset value (S5).

Abstract: Disclosed are a host discovery and addition method and apparatus in a data center, and a device and a medium. The method includes: scanning to discover computing nodes to be added; sending, through IPv6 multicast, a discovery message to the scanned computing nodes, and receiving discovery response messages that are sent by said computing nodes through IPv6 unicast after the discovery message passes verification; when the discovery response messages pass verification and no IP address is configured for said computing nodes, sorting BMC IPs in the discovery response messages, and allocating IP addresses according to the order of the BMC IPs, such that the IP addresses that correspond to adjacent BMC IPs are consecutive; and after the IP addresses are configured, sending an addition message to said computing nodes, receiving addition response messages, and adding said computing nodes to a data center that is managed by a management node.

Abstract: A method for handling large-scale host failures on a cloud platform includes: configuring a corresponding failed host queue for each host group; setting initial priority values and evacuation count thresholds for cloud hosts; arranging failed hosts in failed host queues according to priorities of the failed hosts, and arranging the cloud hosts in the failed hosts according to priority values of the cloud hosts; handling, by the host group, the failed hosts the cloud hosts and in the corresponding failed host queue according to an arrangement order; when evacuation of a cloud host fails, re-determining a priority value of the cloud host; detecting evacuation capability of the host group corresponding to each failed host queue, and disabling a failed host queue having poor evacuation capability; arranging failed hosts in the disabled failed host queue into remaining failed host queues; and enabling a failed host queue meeting a condition.

Abstract: A method for cloud-platform authentication based on blockchain includes: in response to a cloud platform being created, generating a random character string and performing hash operation to the character string to obtain a first hash value; in response to creation of a user in the cloud platform, performing operation to obtain a unique user ID; acquiring information of a hardware-authentication key, generating a unique authentication ID according to the information, and binding the unique user ID and the unique authentication ID; in response to the user being created in the cloud platform, generating a first password and a second password, performing operation to obtain a second hash value; and in response to the hardware-authentication key being inserted into the cloud platform, comparing the acquired information with the hash chain table, the unique authentication ID and the unique user ID stored in the cloud platform for the authentication.

Abstract: A password hardcoding checking method and apparatus based on PCA, and a medium. the checking method includes: step one, data collection, involving: collecting function code blocks in which data of password hardcoding that is subject to a false alarm is located; step two, extracting feature values in the function code blocks collected in step one, so as to obtain a feature set; step three, using the function code blocks collected in step one to serve as samples to construct a PCA model; and step four, on the basis of the PCA model constructed in step three and the feature set obtained in step two, detecting whether there is a false alarm in password hardcoding. by means of the method, the false alarm rate of hardcoding checking in code scanning is reduced, and the working efficiency of a developer and a code auditor is improved.

Abstract: A method, apparatus, and device for erasing a Solid State Disk (SSD), and a storage medium are provided. The method includes: receiving, from a Virtual Flash Translation Layer (VFTL) running in a preset virtual environment, an erasing request for erasing a target block; selecting a target spare block from preset spare blocks and feeding back the target spare block to the VFTL; collecting running information of the VFTL within a preset period of time, and determining whether a function of the VFTL is normal according to the running information; and in a case of determining that the function of the VFTL is normal, performing an erasing operation on the target block. The method may fully and comprehensively detect the abnormal condition of the VFTL, and may guarantee the integrity of data information in the solid state disk, thereby improving the reliability of the solid state disk.

Abstract: Provided are a Reduced Instruction Set Computer-Five (RISC-V)-based artificial intelligence inference method and system. The RISC-V-based artificial intelligence inference method includes the following steps: acquiring an instruction and data of artificial intelligence inference by means of a Direct Memory Access (DMA) interface, and writing the instruction and the data into a memory; acquiring the instruction from the memory and translating the instruction, and loading the data from the memory to a corresponding register on the basis of the instruction; in response to the instruction being a vector instruction, processing, by a convolution control unit, corresponding vector data in a vector processing unit on the basis of the vector instruction; and feeding back the processed vector data to complete inference.

Abstract: The present disclosure discloses a method for Huffman correction and encoding, a system and relevant components, wherein the method includes: obtaining a target data block in a target file; constructing a Huffman tree by using the target data block; determining whether a depth of the Huffman tree exceeds a preset value; and when the depth of the Huffman tree does not exceed the preset value, by using the Huffman tree, generating a first code table and encoding the target data block; or when the depth of the Huffman tree exceeds the preset value, by using a standby code table, encoding the target data block; wherein the standby code table is a code table of an encoded data block in the target file.

Abstract: The present application relates to an adaptive PAM4 decision feedback equalization circuit, including a decision feedback equalization main circuit and an adaptive circuit. The main circuit includes an adder, a first decision device, a second decision device, a third decision device, a first delay unit group, a second delay unit group, a third delay unit group, a decoder, and a DSP coefficient table; the adaptive circuit includes an eye pattern monitoring module and an adaptive module; and the adaptive module includes a comparison unit, a delay unit, and a coefficient regulation and control unit.

Abstract: A method, system and apparatus for monitoring a BIOS booting process of a server. The method includes: detecting whether a PCH in a server starts to transmit data to a BMC; when the PCH starts to transmit data to the BMC, acquiring data from an IO transmission line between the PCH and the BMC and parsing same, and determining whether the parsed data includes process data which represents a BIOS booting process of the server; and when the parsed data includes the process data, displaying the process data. It can be seen that a user may directly and quickly determine the current booting process of a BIOS by means of displayed information, such that quick trouble locating of a server during a BIOS booting process is facilitated.

Abstract: Disclosed is a JTAG-based burning device, including controllable switches arranged between a TDI terminal of a JTAG host and a first chip, and between two adjacent chips, and further including a master controllable switch module arranged between each chip and a TDO terminal of the JTAG host, wherein the JTAG host may, according to a received burning instruction, control corresponding input terminals of the controllable switches to be connected to corresponding output terminals and also control an output terminal of the master controllable switch module to be connected to the corresponding input terminal. Obviously, a JTAG chain can be automatically adjusted by controlling the connection relationship between input and output terminals of the corresponding switches by only building a circuit, so that firmware burning on different chips or chip combinations is realized without manual adjustment, thereby improving the test efficiency, and simplifying the circuit structure.

Abstract: A far-end data migration device and method based on a FPGA cloud platform. The device includes a server, a switch, and a plurality of FPGA acceleration cards. The server transmits data to be accelerated to the FPGA acceleration cards by means of the switch. The FPGA acceleration cards are configured to perform a primary and/or secondary acceleration on the data, and are configured to migrate the accelerated data. The method includes: transmitting data to be accelerated to a FPGA acceleration card from a server by means of a switch; performing, by the FPGA acceleration card, a primary and/or secondary acceleration on the data to be accelerated; and migrating, by the FPGA acceleration card, the accelerated data.

Abstract: An automatic driving simulation task scheduling method and apparatus, a computer device, and a readable storage medium. The method includes: constructing a network flow block diagram on the basis of the number of cluster racks, the number of nodes, and the number of processes, and coding information of the network flow block diagram (S01); setting information of a task to be simulated, and performing shortest path computation on a simulation task flow by means of the network flow block diagram (S02); and updating the state of a scheduling system in real time according to a fair policy and a preemptive scheduling policy, and executing scheduling on the basis of the state of the scheduling system (S03).