10 results about "Function block diagram" patented technology

The application discloses an evaluation method and device of a motor controller, electronic equipment and a storage medium. The method comprises the following steps: determining a scene working condition and a safety target of the motor controller; determining parameter values of related factors influencing the safety target; constructing a function block diagram of the motor controller under the scene working condition based on a task profile; constructing a fault tree model with a safety target violation as a top event based on the function block diagram; and performing quantitative analysis on the top event according to the parameter values and the fault tree model to obtain an analysis result of the top event, so as to optimize the motor controller according to the analysis result. Thus, the safety target is modeled by a fault tree, the weak link of the motor controller system is found out according to the quantitative analysis result of the fault tree, and design optimization and improvement are performed, so that the safety and reliability of the motor controller are improved.

Various systems and methods are presented regarding generating executable computer code / instructions from input files, whereby the input files may be an image file (e.g., JPEG, PDF, etc.). The image file can be digital capture of a sequence of instructions such as a graphical representation comprising a ladder diagram, a function block diagram, a sequential function chart, etc. P&ID and suchlike can also be submitted to the system. Code generated from the input files can be enhanced by application of historical data comprising pertinent subroutines, and suchlike. Further, an entity can be prompted to provide further information in the event of the input file does not provide all of the content.

The invention provides a function block diagram processing method, system and device, a medium and a program product. The processing method comprises the steps of obtaining a function block diagram; wherein the function block diagram comprises a plurality of graphical function modules, and the function modules are used for realizing preset functions based on the data; compiling the function block diagram based on the execution sequence of the function modules to obtain an instruction stream; wherein the instruction stream comprises a plurality of instruction codes which are arranged in sequence and correspond to the function modules and / or data addresses which correspond to the data. Through the design of the graphical function block diagram, the development threshold is reduced, and the development efficiency is improved; by compiling the function block diagram into the instruction stream based on the function module, the flexibility, expandability and real-time performance of FPGA hardware design are remarkably improved, and efficient and dynamic hardware configuration and execution are achieved; under the condition that FPGA codes do not need to be modified, function change can be achieved, and the development efficiency and the expandability of the system are further improved.

A method of model checking a PLC algorithm inscribed in a graphical programming language and a method for automatic test case generation and model-based testing of the PLC implementing these PLC algorithms are provided. The model checking method includes converting the functional block diagram algorithm into a functionally-equivalent formalized functional block diagram algorithm. The method includes model checking the functionally-equivalent formalized functional block diagram algorithm. The verified PLC algorithm is a functional block diagram algorithm including discrete-time deterministic function blocks. The verified PLC algorithm can be then downloaded into a target PLC and periodically executed. The test case generation method includes predefined testable behaviors (unit tests) of each function block and combination of compatible unit tests to valid test cases according the assume-guarantee principle. The resulting test scenarios and test cases are used for model-based grey-box testing in MIL, SIL and HIL testing of the PLC implementing these PLC algorithms.

The application discloses an industrial equipment motion control PLC code automatic generation method and device based on AutomationML, and comprises the following steps: an industrial simulation platform performs simulation motion simulation on a digital twin of field industrial equipment. After determining equipment motion behavior, equipment driving information, initial state information, target state information and complete motion process information are modeled and represented to obtain an equipment behavior model; a PLC (Programmable Logic Controller) programming platform imports the equipment behavior model, performs ontology semantic description on the equipment behavior model, and performs knowledge fusion according to a constructed function block knowledge base to obtain a knowledge graph; the knowledge graph is analyzed by reasoning according to specific rules and algorithms to generate FBD (Function Block Diagram) code based on the IEC 61131-3 standard, which is consistent with the motion control logic described by the equipment behavior model. The application realizes code automatic generation from the industrial simulation platform to the PLC programming platform, solves lossless exchange of cross-platform data, and improves industrial automation engineering development efficiency.

The application discloses a self-defined function block development system and a running and debugging method, and the system comprises a function block library, a self-defined function block development tool, a function block diagram configuration tool and a function block diagram executor. The self-defined function block development tool uses basic function blocks and existing self-defined function blocks to form user self-defined function blocks which are stored in the function block library; the function block diagram configuration tool comprises a function block diagram editor and a function block diagram debugger; the function block diagram executor comprises a debugging manager and a function block diagram execution engine; the debugging manager receives a debugging request of the function block diagram debugger, informs the function block diagram execution engine to obtain a latest debugging subscription queue, the function block diagram execution engine receives a function block diagram created by the function block diagram editor, performs processing and operation, generates debugging data, and the debugging manager responds to the debugging request.

An industrial controller supports deterministic execution of control programs (e.g., ladder logic, function block diagrams, structured text, or other such control code) and is also capable of executing non-deterministic execution cycles, including mathematical optimization algorithms—in which a systematic search in a solution space is performed to identify a desired solution—or machine learning algorithms, either of which can be used by the controller to dynamically update the deterministic control program or code based on current or predicted states of the automation system being controlled by the controller.

The embodiment of the invention relates to the technical field of information processing, and discloses a software failure mode and influence analysis method and device, a storage medium and a product, and the method comprises the steps that a software function block diagram is constructed according to software demand analysis, a software parameter diagram is constructed based on the software function block diagram, and the software parameter diagram is used for defining a function boundary of software; generating a software failure analysis chart through the software function block diagram and the software parameter diagram, and performing failure mode and influence analysis on the software; in the software failure analysis chart, software is analyzed through the use frequency, failure reasons and failure consequences of software components, and the failure and the failure reasons comprise the severity of the failure consequences and the failure controllability; based on the severity of the failure consequence, the use frequency of the software component and the failure controllability, the measure priority level of the software component is calculated and determined, the measure priority level is used for software failure mode and influence analysis, and potential risk points and weak links of software can be found.

A method, apparatus, and storage medium for generating function diagrams (FDM) for a DCS system include: converting a Point of Unit (POU) configured in a controller project into an XML file; obtaining function block symbols created and labeled with pins in a graphical editing software interface; wherein the function block symbols correspond one-to-one with the function blocks in the XML file; after the function diagram is generated, real-time data of the pins on the corresponding function blocks is obtained from the DCS system using the graphical editing software and the pin labels, and displayed on the pins of the corresponding function block symbols; obtaining the input and output connection relationships of each component from the obtained XML file; and based on the obtained function block symbols, the input and output connection relationships of each component, performing logical connections between the components in the POU to generate the function diagram corresponding to the POU. This embodiment enables intuitive visualization of control logic, significantly improving the efficiency of system status understanding and fault location speed.

An industrial controller supports deterministic execution of control programs (e.g., ladder logic, function block diagrams, structured text, or other such control code) and is also capable of executing non-deterministic execution cycles, including mathematical optimization algorithms—in which a systematic search in a solution space is performed to identify a desired solution—or machine learning algorithms, either of which can be used by the controller to dynamically update the deterministic control program or code based on current or predicted states of the automation system being controlled by the controller.