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The invention relates to a method for calling RK3588NPU computing power by using DBus, and belongs to the technical field of edge computing and inter-process communication. According to the method, a standardized DBus server-side and client-side architecture is constructed, the underlying computing power calling logic of an RK3588 NPU is packaged into DBus service, the server-side runs in a daemon process form, core logics such as model loading, reasoning execution and resource management of an RKNN SDK are packaged, and the computing power calling capability of the NPU is exposed through a preset DBus interface; the client does not need to be in butt joint with RKNN SDK, and can call a server interface to complete NPU model reasoning, computing power state query, model resource release and other operations only through a DBus protocol. According to the method, decoupling and standardized calling of the NPU computing power are realized, the development cost of sharing the NPU computing power by multiple applications is reduced, the flexibility and efficiency of RK3588 NPU computing power calling are improved, and compared with an existing direct calling mode, the communication delay is reduced by 35%, and the computing power resource utilization rate is improved by 40%.

The invention discloses an output method based on 32-bit single-chip microcomputer firmware upgrade memory anti-overflow protection, and relates to the field of single-chip microcomputer memory anti-overflow protection, and the method specifically comprises the following operation steps: S1, environment variable configuration and management; s2, performing core execution of the Python automatic script; s3, performing anti-overflow protection on the memory; and S4, cleaning redundant files and environment variables. According to the output method based on the 32-bit single-chip microcomputer firmware upgrade memory anti-overflow protection, the file path is defined as the environment variable and stored in the independent configuration file in a centralized mode, complete separation of the path and script logic is achieved, the non-transplantable problem caused by a hard coding path is avoided, meanwhile, the adaptation cost in different environments is reduced, and the adaptation efficiency is improved. Operators do not need to modify script codes and only need to adjust configuration files to complete deployment.

This application discloses an intelligent selection method for manufacturing process templates based on field matching and directed graph judgment, comprising: acquiring key attribute information of the task to be produced; traversing the process template library, comparing the types and quantities of input fields provided by the task with the predefined required fields of each template, and filtering out a set of candidate templates with matching fields; when the number of candidate templates is greater than one, acquiring a preset directed graph corresponding to each candidate template, wherein the directed graph contains judgment nodes and directed paths terminating at template nodes; solving for the output values ​​of each judgment node in the directed graph based on the key attribute information, and determining the template activation status based on the output values ​​of all judgment nodes on the directed path; and finally determining and outputting a unique process template from the set of candidate templates. This invention achieves automatic and accurate selection of process templates through a dual filtering mechanism of field matching and directed graph logical judgment, improving the efficiency of process preparation and the flexibility of rule configuration.

The present application belongs to the field of electronic information, and relates to a data acquisition technology of a sensor, and specifically provides a self-adaptive front-end sensor data acquisition method based on AI detection, which is used to solve the problem of poor universality of an existing data acquisition system for low-resistance value sensors and high-resistance value sensors. In the self-adaptive front-end sensor data acquisition method based on AI detection provided by the present application, a random forest classifier with the advantage of light weight is creatively used as an AI model, and feature parameters are respectively proposed in combination with the working characteristics of low-resistance value sensors and high-resistance value sensors, and the prediction accuracy of the AI model is further improved through multi-feature extraction, so as to realize accurate prediction of the sensor type, and realize adaptive switching of the front end according to the prediction result, so as to complete universal and high-precision data acquisition of low-resistance value sensors and high-resistance value sensors based on a universal data acquisition board.

The invention relates to the technical field of virtualized communication, and discloses a hardware interrupt request-based Vsock rapid notification method. According to the method, a special physical hardware interrupt number is configured for a Vsock virtual queue, the interrupt is bound to an exclusive virtual CPU, interference of other interrupts is forbidden, the binding relation between the physical interrupt and ioeventfd / irqfd is established through ACRN drive, and a two-way mechanism that the hardware interrupt triggers ioeventfd and irqfd writing to generate the interrupt is achieved; instant scheduling is ensured by dynamically improving the priority of an exclusive virtual CPU, and front and rear end working threads are improved to a real-time level, so that a complete low-delay path from hardware interruption to thread processing is formed. According to the method, the problems of long path and high delay of a traditional work queue mechanism are effectively solved, and the real-time performance and the certainty of Vsock communication are remarkably improved while the code compatibility and the fallback capability are kept.