Abstract: The present disclosure discloses Spark collaborative computing, job method and apparatus for multiple K8s clusters, and addresses the problem that most of the current multiple K8s clusters adopt the model of federated clusters, and Spark's own method of scheduling and optimization cannot be implemented across domains, by implementing a cross-domain job method, setting the multiple K8s clusters as a master cluster and a slave cluster, with the master cluster being responsible for creating Spark's The master cluster is responsible for creating Spark's Driver container and Pods, and the slave cluster is responsible for creating Spark's Executor container and Pods. After the containers are created, a direct tunnel is established between the master cluster and the slave cluster by aggregating address information and access credentials through the Collaboration Center, and the containers in the slave cluster register with Driver and continuously send heartbeat messages through the tunnel.

Abstract: The present disclosure belongs to the field of intelligent computing technologies, and relates to a multi-policy intelligent scheduling methods and apparatuses oriented to heterogeneous computing power. The method includes: step 1, setting an execution policy of a task based on heterogeneity of computing clusters, differences of computing tasks and a user requirement, and constructing a Markov decision process model by adopting a reinforcement learning method combined with the execution policy; step 2, adopting a proximal policy optimization to solve an optimal task scheduling policy of the task input by the user based on the constructed Markov decision process model; step 3, scheduling the task to a corresponding computing cluster for execution based on the optimal task scheduling policy.

Abstract: The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

Abstract: Disclosed is a method for adapting a deep learning framework to a hardware device based on a unified backend engine, which comprises the following steps: S1, adding the unified backend engine to the deep learning framework; S2, adding the unified backend engine to the hardware device; S3, converting a computational graph, wherein the computational graph compiled and generated by the deep learning framework is converted into an intermediate representation of the unified backend engine; S4, compiling the intermediate representation, wherein the unified backend engine compiles the intermediate representation on the hardware device to generate an executable object; S5, running the executable object, wherein the deep learning framework runs the executable object on the hardware device; S6: managing memory of the unified backend engine.

Abstract: Disclosed are a data flow method and apparatus for neural network computation. The data flow method for neural network computation includes initializing the lifecycle of a variable in a computational graph; and defining a propagation rule for a variable in use to flow through a node. A definition of the variable is produced at a precursor node of the node, such that an input set of valid variables flowing through the node contains the variable. The method may be used on neural network computation in a deep learning training system.

Abstract: The present application discloses a traffic light control method for an urban road network based on expected return estimation, which uses C-V2X wireless communication technology to obtain real-time information of all vehicles and traffic state in the road network from vehicle-mounted terminals, and adaptively and dynamically controls the phase transformation of the traffic light. According to the present application, the expected returns of keeping the current phase and executing phase switch are calculated by estimating the timely driving distance and the future driving distance of the passable vehicles in the next green light duration in combination with the proposed road priority traffic index. By comparing the expected returns of keeping the current phase or switching to other phases, the best phase is selected, so as to make as many passable vehicles travel farther as possible in the next green light duration. Therefore, the efficiency of traffic will be improved.

Abstract: The present invention discloses a two-dimensional photonic neural network convolutional acceleration chip based on series connection structure, which is integrated with a modulator, M microring delay weighting units, M?1 secondary delay waveguide, a wavelength-division multiplexer and a photodetector.

Abstract: The disclosure discloses a mimetic database-based network operating system design method, including: designing a mimetic data structure; designing a mimetic data object; designing a synchronization mechanism and a decision mechanism, designing a mimetic database safe storage command processing system, and designing a classification storage mechanism for interacting data between service modules and a master database in a network operating system. By means of vertical hierarchy and horizontal classification, the problem of compatibility of the database subjected to mimetic transformation and a network operating system is solved. By means of a memory random distribution storage mechanism and a memory hardware heterogeneous storage mechanism, the cost caused by mimetic transformation can be reduced, and the cost is controllable while the safety is improved.

Abstract: The present disclosure discloses a distributed model compilation system. A master node of the system determines the logic calculation graph of the model based on model information, divides the logic calculation graph into multiple logic calculation sub-graphs, generates a distributing message for each logic calculation sub-graph, and then transmits the distributing message to a slave node. Each of the slave nodes allocates a local computing resource to compile the logic calculation sub-graph based on the received distributing message, and transmits compilation completion information to the master node. The master node determines the completion of model compilation based on the compilation completion information returned by each slave node, and executes the target work based on the compiled model.

Abstract: A polymorphic network control system and a polymorphic network control system method supporting terminal mobile access. A network architecture is redesigned based on the idea of separation of an identifier from a locator. In this network mode, the three-layer protocol is no longer a traditional IP message, but a message carrying a locator and an identifier. The polymorphic SDN network controller is responsible for forwarding the data packet to a destination corresponding to a destination locator. When the network location of a mobile device is changed, the transmission connection established based on the identifier will suspend communication due to the disconnection of the physical link. When the mobile device is re-accessed and a new forwarding path is established, the transmission connection can recover communication.

Abstract: The disclosure discloses a graph optimization method and apparatus for neural network computation. The graph optimization method includes the following steps: S1: converting a computation graph; S2: allocating a register; S3: defining a route selector for a redefined variable; S4: solving the route selector for the redefined variable; S5: defining a criterion of inserting the route selector for the redefined variable into a node; S6: analyzing a dominating edge set of the node for the redefined variable; S7: inserting the route selector for the redefined variable; and S8: renaming the redefined variable. The disclosure solves the problem of the corresponding route selection on a correct definition of the redefined variable when a node including the redefined variable in a computation graph in the compiling period flows through multiple paths of computation flow, reduces the memory cost and promotes the development of implementation application of a deep neural network model.

Abstract: A job decomposition processing method for distributed computing, which comprises: analyzing a source program to be run by program static analysis to determine a function call graph contained in the source program; determining feature information of functions contained in the source program by program dynamics analysis or/and a program intelligent decomposition algorithm, wherein the feature information of the functions is used to characterize relevant information when each function is being running; decomposing the source program based on the feature information of the functions, a function relationship and available resource information of a computing platform to form an execution recommendation for each function on the computing platform, i.e., which hardware resources are used for computing each function; finally inserting a modifier in the source program and starting computation on the computing platform.

Abstract: Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.

Abstract: Disclosed is a method and system for identifying a protein domain based on a protein three-dimensional structure image. According to the present application, the protein domain is identified based on a structure similarity, the identification errors and omissions of the protein domain caused by protein multi-sequence alignment errors when sequence consistency is not high can be effectively solved. According to the present application, the point cloud segmentation model based on the dynamic graph convolutional neural network is constructed, and by integrating global structural features and local structural features, segmentation of the protein domain and acquisition of semantic labels of the protein domain can be completed at the same time.

Abstract: A system and a method for detecting a moving target based on multi-frame point clouds. The system comprises a voxel feature extraction module; a transformer module used for matching and fusing the feature tensor sequence, fusing a first feature tensor with a second feature tensor, fusing the fused result with a third feature tensor, fusing the fused result with a fourth feature tensor, and repeating the fusing steps with a next feature tensor to obtain a final fused feature tensor; and an identification module used for extracting features from the final fused feature tensor and outputting detection information of a target. The method comprises the following steps: S1, constructing each system module; S2, training the model by the data in a training set; S3, predicting by the trained model.

Abstract: A wafer-level heterogeneous dies integration structure and method are provided. The integration structure includes a wafer substrate, a silicon interposer, heterogeneous dies, and a configuration substrate. A standard integration module is defined by the heterogeneous dies connected to the silicon interposer. The standard integration module is connected to an upper surface of the wafer substrate, and the configuration substrate is connected to a lower surface of the wafer substrate. The wafer substrate is connected to the configuration substrate via Through Silicon Vias on lower surface of the wafer substrate. And the upper surface of the wafer substrate is provided with Re-distributed Layers and a standardized micro bump array to form standard integration zone connected to the standard integration module.

Abstract: The present invention discloses a two-step anti-fraud vehicle insurance image collecting and quality testing method, system and device, the method comprises: step 1, collecting vehicle insurance scene images and marking vehicle orientation; step 2, performing object detection on the collected vehicle insurance scene images and screening to obtain object coordinates; step 3, according to the vehicle orientation and the object coordinates, obtaining the specific position of the object coordinates located in the whole vehicle; step 4, according to the object coordinates screened in step 2, performing vehicle component detection on the vehicle insurance scene images, obtaining the component coordinates of the vehicle components, and screening to obtain the vehicle component closest to the object coordinates; step 5, according to the specific position of the object coordinates located in the whole vehicle and the vehicle components closest to the object coordinates, obtaining the position of the vehicle components

Abstract: The present disclosure relates to deep learning image classification oriented to heterogeneous computing devices. According to embodiments of the present disclosure, the deep learning model can be modeled as an original directed acyclic graph, with nodes representing operators of the deep learning model and directed edges representing data transmission between the operators. Then, a new directed acyclic graph is generated by replacing the directed edges in the original directed acyclic graph with new nodes and adding two directed edges to maintain a topological structure.

Abstract: The present disclosure discloses a UDF-based traffic offloading method and a system, the method includes: step 1, performing P4 language programming: performing P4 language programming description on a protocol packet format of the target traffic in a network and a corresponding flow table description on a protocol packet format of the target packet and parsing, matching and offloading rules of the target packet; step 2: compiling and mapping the P4 program by UDF-oriented compilation and mapping method, to an ASIC chip; step 3: in the ASIC chip, matching a traffic offloading rule according to the UDF rule, and executing a traffic offloading action according to the traffic offloading rule, where the traffic offloading action includes offloading the target packet to a corresponding processing node or discarding the target packet.

Abstract: A distributed acoustic sensing (DAS) system based on random fiber lasing amplification (RFLA) and a Rayleigh scattering enhanced fiber (RSEF), and relates to the field of distributed optical fiber sensing. The system comprises a DAS demodulation unit, a high-order RFLA unit and a RSEF. The present disclosure adopt the high-order RFLA technology to replace the traditional high-order distributed Raman amplification technology, and it does not need to use a plurality of pumps with different wavelength, and only needs a high-order random fiber laser pump and a broadband reflector to provide feedback for cascaded random fiber lasings to perform distributed amplification on signal light. By using high-order RFLA combined with the RSEF, high-efficiency and low-threshold RFLA can be achieved, and the signal-to-noise ratio and performances of a DAS system can be further improved, which enables realization of long-distance and high-performance distributed acoustic sensing.