5 results about "Rule based inference" patented technology

The application relates to a fault diagnosis method of an aviation equipment system based on rule reasoning and a knowledge base, which comprises the following steps: S1, constructing a fault diagnosis data model of the aviation equipment system, acquiring fault diagnosis information, and establishing a fault knowledge base; S2, determining associated fault diagnosis information of the aviation equipment system based on rule reasoning of a rule base, and acquiring a similarity result through the knowledge base; S3, analyzing the similarity result of the fault diagnosis information of the aviation equipment system, sorting, and outputting a fault diagnosis result. The application extracts and identifies fault features based on a keyword base; obtains matched associated LRUs based on rule reasoning, and records corresponding trace information; searches and matches fault diagnosis information based on the knowledge base, calculates information matching similarity, completes similarity sorting of a final fault result, and determines the fault diagnosis result of the aviation equipment system. The application improves the fault diagnosis efficiency of the aviation equipment system, reduces the fault diagnosis time, and prolongs the service cycle of the aviation equipment.

The invention provides a dental implant osseointegration prediction method and system based on dynamic data, and the method comprises the steps: carrying out the feature extraction of preoperative risk assessment data through a time sequence analysis method according to an initial data set, and obtaining a preoperative osseointegration risk feature set; according to the synostosis state change trend, clinical decision support information is generated by adopting a rule-based reasoning method, and the treatment priority of the current stage is determined; if the treatment priority is higher than a preset threshold value, extracting prediction information related to the priority from the dynamic data set through a real-time information matching algorithm to support decision making of doctors; through a real-time matching result of prediction information, a decision tree algorithm is adopted to update supply rule design, and optimized clinical decision support information is obtained; and according to the optimized clinical decision support information, continuously updating postoperative healing monitoring data by adopting a feedback circulation mechanism to obtain a latest osseointegration quality evaluation result.

The application discloses a kind of based on multi-agent reinforcement learning's car body clamp clamping unit generation method. Among them, the method includes: generating the standardization input data of car body clamp clamping unit, wherein the standardization input data includes RPS grouping prediction result, RPS distance margin parameter and car body part structure feature;Based on the standardization input data, the standardization input data is inferred by graph neural network or rule-based reasoning engine, and parameter constraint subgraph containing part node, constraint relationship and assembly logic is generated;Based on the parameter constraint subgraph, the parameters of the car body clamp clamping unit are optimized by multi-agent reinforcement learning model, and the optimization parameter vector is obtained, and the car body clamp clamping unit is generated based on the optimization parameter vector.The application solves the technical problems that the design of existing clamp clamping unit is based on static library, lacks dynamic relationship inference and cannot adaptively assemble.

The invention discloses a vehicle body clamp clamping unit generation method based on multi-agent reinforcement learning. The method comprises the steps that standardized input data of a vehicle body clamp clamping unit is generated, and the standardized input data comprises an RPS grouping prediction result, an RPS distance edge distance parameter and vehicle body part structure characteristics; based on the standardized input data, reasoning the standardized input data through a graph neural network or a rule-based reasoning engine, and generating a parameter constraint sub-graph comprising part nodes, constraint relationships and assembly logic; and on the basis of the parameter constraint subgraph, parameters of the vehicle body clamp clamping unit are optimized through a multi-agent reinforcement learning model, an optimized parameter vector is obtained, and the vehicle body clamp clamping unit is generated on the basis of the optimized parameter vector. The technical problems that the design of an existing clamp clamping unit is established on the basis of a static library, dynamic relation reasoning is lacked, and self-adaptive assembly cannot be achieved are solved.

An edge-optimized retail analytics system for autonomous in-store decision support, consisting of: an edge computing unit housed in a robust, thermally conductive enclosure and configured for use in a retail store environment; System-on-Module (SoM) mounted on a multilayer printed circuit board (PCB), wherein the SoM comprises a multi-core central processing unit (CPU) configured to manage sensor data orchestration and rule-based inference processing, and a neural processing unit (NPU) configured to perform deep neural network inference operations in real time; a high-bandwidth volatile memory module electrically connected to the SoM to buffer time-aligned multimodal sensor streams; a non-volatile solid-state drive (SSD) configured to persistently store AI model weights, inference outputs, and business-specific event logs; a power management circuit integrated on the printed circuit board, configured to regulate the input voltage of a Power-over-Ethernet (PoE) line; and a sensor interface bus that is connected to a variety of sensor modules, including visible spectrum cameras, thermal imaging sensors, passive infrared motion sensors, RFID readers, and load cell-based shelf weight sensors.