Abstract: Various embodiments of the teachings herein include industrial network-based codeless tracking analytics methods for industrial software. Some methods include: identifying an industrial intranet topology through detection, and generating a configuration map of the industrial field according to the topology and/or an application program running list of the software; injecting a sidecar agent for codeless runtime tracking to an edge device of a target field device in the industrial field according to the configuration map, and performing data synchronization for the edge device and the sidecar agent corresponding to the edge device, at least one tracking point existing between the target edge device and the sidecar agent corresponding to the target edge device, and the target edge device running at least one application program of the industrial software; and obtaining analysis data from the tracking point when the target edge device runs an application program therein, to analyze the industrial software.

Abstract: Various embodiments of the teachings herein include a system for PLC connection. An example system may include: an automatic converter set on a third-party platform; a local adapter set on a local computer to access locally stored PLC information; and a set of intelligent communication interface adapters set on respective edge devices to access a PLC through a communication protocol connector compatible with multiple industrial networks. The automatic converter obtains information of each respective edge device through the intelligent communication interface adapter, and obtains information of each PLC through the local adapter and/or corresponding intelligent communication interface adapter. Based on a preset voting mechanism, the converter triggers the edge devices and the PLC for connectivity detection and voting, and select an individual edge device and a PLC to be connected according to the number of votes of each respective edge device and each PLC.

Abstract: Various embodiments of the teachings herein include a graph transformation method of a function block chain. The method may include: extracting a plurality of function block pairs from a function block chain, wherein the function block chain comprises a plurality of function blocks connected in sequence; respectively transforming the function block pairs into nodes; transforming the nodes into directed acyclic graphs or undirected acyclic graphs; and building a graph of the function block chain based on the directed acyclic graphs/undirected acyclic graphs and a weight served by the count.

Abstract: Various embodiments include a semantic-based causal event probability analysis method. The method may include: generating a cause event instance and corresponding effect event instances thereof according to user requirements and based on a cause event template and an effect event template; assigning each cause event or effect event instance to a parent node, wherein the event instance comprises a plurality of entities and a mutual relationship between the entities; and calculating a probability of a cause event instance or an effect event instance having a common parent node. A probability that a first cause event instance causes a first effect event instance equals: P R E 1 R C E 1 C = P C E 1 R R E 1 C ? P R E 1 R P C E 1 C P(CE1(R) |RE1(C)) represents a probability of the first cause event occurring when the first effect event occurs. P(RE1(R)) represents a probability of the first effect event occurring among all effect events.

Abstract: Various embodiments of the teachings herein include a method for building a graph-based industrial flow model. The method may comprise: importing a set of entities based on an industrial flow including a user-defined entity and a system-defined entity; querying the model for the user-defined entity, and if found, obtaining a connection of the user-defined entity, otherwise generating a new implementation layer; querying the model for an implementation layer of the user-defined entity, and if found, obtaining a connection of the implementation layer of the user-defined entity, otherwise generating a new implementation layer; querying the model for the system-defined entity, and if the system-defined entity is found, obtaining a connection of the system-defined entity, otherwise generating a new implementation layer; and iteratively performing the above queries until a final graph-based model for the industrial flow results.

Abstract: A point cloud model reconstruction method, apparatus, and system are disclosed. In an embodiment, the method includes randomly selecting four non-coplanar reconstruction points in a point cloud model; iteratively selecting other reconstruction points successively until a reconstruction condition is met, and reconstructing the point cloud model based on all reconstruction points. A reduction degree of a reconstructed point cloud model is: Reward=?k·(PointNum?4)+g·VolRate, where PointNum represents a number of current selected reconstruction points, VolRate represents a ratio of a volume of a solid shape, k represents a proportion of the number of the selected points, and g represents a proportion of the volume ratio. The reconstruction method further includes adjusting a ratio of g to k based on user requirements to adjust the reconstruction condition.

Abstract: The present invention provides a point cloud model reconstruction method, apparatus, and system. The method includes the following steps: randomly selecting four non-coplanar reconstruction points in the point cloud model; keeping iteratively selecting other reconstruction points successively until a reconstruction condition is met, and reconstructing the point cloud model based on all reconstruction points, where a reduction degree of the reconstructed point cloud model is: Reward=?k·(PointNum?4)+g·VolRate, where PointNum represents a number of current selected reconstruction points, VolRate represents a volume ratio of a volume of a solid shape formed by all of the current reconstruction points to an original volume of the point cloud model, k represents a proportion of the number of the selected points in the reduction degree, and g represents a proportion of the volume ratio in the reduction degree.