6results about How to "Improve data processing capabilities" patented technology

The application provides an AI computing power allocation method, system, device, storage medium and program product, and relates to the technical field of data processing. The AI computing power allocation method determines the corresponding target AI algorithm model based on the service demand of the target reasoning service in real time, and then dynamically matches the target computing power resource used to load the target AI algorithm model by combining the processing demand of multiple AI algorithm models, the preset adaptability rules of various heterogeneous computing power resources, and the current use state of the various heterogeneous computing power resources, so as to realize dynamic allocation of AI computing power resources, avoid computing power resource overload or idling, improve computing power resource utilization, reduce task reasoning delay, and thus improve data processing capability.

The application discloses a distributed storage time delay optimization method and device and medium, and relates to the field of distributed systems. The method is applied to a distributed cluster, data sent by a client is received, a log and a transaction are constructed in a master OSD according to the data, the master OSD is selected in advance in all OSDs of a PG, each OSD contains a shared memory, the log and the transaction are stored in the shared memory of the master OSD, and other OSDs in the PG acquire the log and the transaction from the shared memory of the master OSD through RDMA. The above scheme realizes the shared memory between the OSD set of the PG in the distributed cluster, and uses RDMA to replace the traditional TCP / IP mode to perform the synchronization logic of the data between the master and the replica of the cluster, thereby effectively optimizing the network time delay of data synchronization and improving the data processing capacity of the whole cluster.

The application relates to a project management method, device, computer equipment and storage medium. The project management method comprises the following steps: acquiring project data; splitting the project data, acquiring data segments, mapping the data segments, and acquiring mapping data; according to the type of the mapping data, merging the mapping data of the same type to acquire marking data; according to the type of the marking data, aggregating the marking data of the same type to acquire a first data unit; presetting a second data unit, comparing the first data unit with the second data unit, positioning problems, and acquiring a management result. Through acquisition of a large amount of project data, the project data is classified, aggregated and analyzed, automatic management of the project is realized, the data processing capacity of the system is enhanced, the data has more definite identification, the work efficiency is improved, and the problem positioning accuracy is improved.

The present application relates to the technical field of online car-hailing risk control, and particularly relates to a method for implementing an online car-hailing pick-up and drop-off abnormality risk control dynamic rule engine based on flink CEP. In the online car-hailing service, after a passenger issues an order on a mobile phone, the driver is affected by external environmental factors, vehicle condition factors, other human factors and complex environments during the process of picking up and dropping off the passenger, which may result in a variety of abnormal situations such as pick-up timeout, driver driving in the opposite direction, driver not moving and the like. A related risk control system is established in each service system. The present application uses mysql to dynamically load rules to ensure the dynamic scalability of the rules, and uses flink CEP to calculate the rules to ensure the timeliness and accuracy of the calculation results. The overall scheme supports the timeliness and dynamic scalability requirements of abnormal indicators in the risk control system under the premise of not stopping service when the rules are changed.

The application relates to the technical field of air conditioning systems, and discloses an intelligent air conditioning management and control and scene mode adjustment system, which comprises the following: a comprehensive monitoring module that monitors air quality parameters and user physiological data indoors and outdoors in real time; a strategy generation module that analyzes and processes the collected data to generate an intelligent air conditioning control strategy; a control execution module that adjusts the operation parameters of the air conditioning system in real time according to the generated control strategy; a scene mode identification module that analyzes the air quality parameters and user physiological data to identify the current scene; a scene mode adjustment module that adjusts the operation parameters of the air conditioning system to match the operation state of the current scene mode; and a user interaction interface that provides a user interaction mode and can realize manual switching of the scene mode and adjustment of the operation parameters of the air conditioning system. The application significantly improves the intelligent level of the air conditioning system, optimizes energy consumption management, and improves the comfort and use experience of users.

The application provides a strip product process parameter step-by-step recommendation method and system based on representation learning and a storage medium, and relates to the technical field of information. The method comprises the following steps: obtaining strip product production whole-process data and constructing a structured representation through a knowledge graph; embedding modeling entities and relationships in the knowledge graph based on a RotatE representation learning algorithm of complex space relationship rotation to generate vector representation of complex relationships; generating multi-process process parameter recommendation based on a step-by-step recommendation strategy of time sequence dependence, and realizing cross-process parameter constraint through dynamic updating of the knowledge graph. The application solves the complex relationship modeling problem based on the RotatE representation learning algorithm of complex space relationship rotation; improves the implicit knowledge capturing capability through the construction of a modular semantic aggregation knowledge representation model; in addition, a step-by-step recommendation strategy is proposed to process the time sequence dependence through a "link prediction-graph completion-link prediction" iterative process.