19 results about "Petri net" patented technology

This invention belongs to the field of flexible manufacturing systems technology and discloses a path-execution cooperative control method for multiple mobile transport robots based on Petri nets. The method includes: acquiring current system state information; determining the effective starting point and first effective target based on the current task sequence information of each mobile transport robot; updating the current position index by combining historical path cache, and reusing or re-determining discrete path sequences according to preset conditions; injecting the path sequences as spatial constraints into the Petri net execution model, and constructing an executable transition binding instance set by combining the current system state, robot execution state, and resource enabling relationships; selecting and advancing compatible transition binding instances from the instance set to form execution fragments, and updating the system state accordingly, thereby achieving rolling closed-loop cooperative control of the multiple mobile transport robot system. This invention is beneficial for improving the consistency of path planning and execution processes and the efficiency of system resource coordination.

This invention discloses a single-arm combined device scheduling method based on hierarchical attention graph deep reinforcement learning. This method aims to address the problems of existing rule-based methods relying on artificial heuristics, and the complexity of Petri net models while neglecting topological properties. This invention proposes a State Extraction Graph (SEG) to simplify the modeling process for multi-variety wafer manufacturing, and its graph structure is simpler than Petri nets. Based on SEG, a graph deep reinforcement learning framework is developed, integrating a graph neural network based on a hierarchical attention mechanism (called A-GIN) as a feature extractor to better extract the topological properties of the graph. The single-arm robot agent interacts with the environment, autonomously learns and optimizes wafer release and task sequences, achieving end-to-end policy optimization.

A Petri net-based task scheduling method for heterogeneous environments relates to the field of collaborative decision-making and resource optimization in edge computing systems. During scheduling, the input tasks and their dependencies are first modeled as directed acyclic graphs (DAGs), and then converted into Petri nets to uniformly represent task states, resource constraints, and device collaboration relationships, in conjunction with device status information. Subsequently, graph attention networks are used to extract PN topological features, and a dynamic transition triggering strategy is learned using a dual-deep Q-network. The task unloading and resource allocation sequences are optimized through a reward function, and the sequence validity is further verified by a sequence detection module. During task execution, tasks are scheduled in real-time according to a reinforcement learning strategy, and the network state and decision model are dynamically updated based on environmental feedback, forming a closed-loop optimization. This invention significantly improves task throughput and resource utilization in heterogeneous edge environments, and is particularly suitable for device-to-device collaborative scenarios with complex task dependencies and dynamically changing resources.

This invention relates to the field of capacity measurement technology, specifically to a method and system for measuring the openable capacity of a novel distribution network under uncertain source load scenarios. It includes the following steps: real-time acquisition of operational data from distributed photovoltaic (PV), electric vehicle (EV), and novel loads in the new distribution network; generation of a typical operational scenario set using K-means clustering algorithm; identification of safe operating boundaries using fault tree analysis and Petri net modeling; construction of an objective function and constraints with the goal of maximizing openable capacity; and output of the optimal openable capacity sequence by solving the problem using a bibliometric optimization method; and realization of coordinated control of the distribution network and microgrid, and dynamic updating of the safe operating boundaries. This invention fully considers the complex scenario characteristics such as fluctuations in distributed PV output and random electric vehicle charging loads, solving the problems of traditional static capacity measurement methods ignoring source load uncertainties and large deviations between the measured results and actual operating states.

The application discloses a Petri-Net-GCN-based intelligent manufacturing system deadlock prediction method, which comprises the following steps: dividing an intelligent manufacturing system into a production equipment module, a production process module and a production resource module; performing formal modeling on the modules based on a Petri net; constructing a Petri-Net-GCN deadlock prediction model, which comprises a place-to-transition message transmission module and a transition-to-place message transmission module; bidirectionally aggregating and updating features between nodes in a Petri net graph by using a graph neural network structure; collecting initial token distribution, a correlation matrix and a deadlock label of the intelligent manufacturing system to construct a data set and input the data set into model training; and predicting a deadlock risk in an unknown system state by using the trained model; and the application can rapidly predict and judge the current state of the intelligent manufacturing system, thereby providing prior warning information for production scheduling and resource allocation.

The present application relates to a kind of precision grinding polishing machine tool of right angle gauge, including base, the top of base is equipped with X-Y axis moving module, the X-Y axis moving module is connected with slide, slide is equipped with clamp for clamping right angle gauge;The rear end of the top of base is fixed with gantry, Z-axis moving module is equipped on gantry, Z-axis moving module is connected with the electric spindle being arranged along vertical direction, and the output end of electric spindle is connected with precision grinding tool.The clamp in the present application adopts the upper and lower clamping surface of L type to jointly act and compress the right angle gauge to be processed, and clamping is reliable;The present application introduces the method of forward design-from top to bottom, simultaneously introduces Petri net theory to solve the resource scheduling problem in design process, in combination with modeling technology, a kind of design method of new L type right angle gauge polishing machine tool is proposed, improves the design efficiency of special processing equipment, reduces design cost, optimizes design scheme.

This invention belongs to the field of virtual reality technology, specifically relating to an XR task planning and instruction issuance method for brewing automation, comprising the following steps: Step 1: Collecting operator demonstration action sequences and voice process descriptions of the brewing production line in an extended reality environment to obtain a hierarchical task graph consisting of process layers and action layers; Step 2: Marking the execution devices corresponding to each action layer node in the hierarchical task graph as game participants, and the global coordination strategy includes the coordinated movement path scheme and rhythm allocation scheme of all execution devices; Step 3: Constructing a flexible Petri net model of the brewing production line based on the global coordination strategy, replacing the original strategy corresponding to the local subnet in the flexible Petri net model with the local replanning scheme, and continuing to issue instructions. This invention achieves formalized automatic mapping from process semantics to equipment actions, improving the programming efficiency, coordination quality, and anomaly response capability of the brewing production line.

The invention relates to the technical field of software and artificial intelligence, in particular to a software development method based on natural language orchestration, which comprises the following steps of: S1, in response to a natural language demand, identifying a business entity and an action, mapping the entity into a library of a colored Pearl network, defining a color set, mapping the action into transition, and establishing a color set; constructing an incidence matrix according to the input consumption and the output of the Tokenn; and S2, iteratively constructing a branch process based on the incidence matrix, calculating a state identifier of a local configuration of a new event, and marking a cut-off event when the state identifier is repeated with a historical event so as to generate an acyclic finite complete prefix structure. According to the method, verification and supervision control synthesis are carried out through the Pearl network, and then a mathematical-level anti-deadlock section is automatically woven into a business code, so that the problems that traditional generative development mostly depends on direct generation and concurrency control of a large model, the model lacks global logical reasoning ability, and the development efficiency is low are solved. And therefore, the problem of frequent random deadlock of software in a high-concurrency environment is solved.

The application discloses a partial observable Petri net fault diagnosis and optimal sensor configuration method of a high-speed rail signal system, and comprises the following steps: step 1, establishing a label Petri net model of the high-speed rail communication signal system; step S2, constructing a corresponding proof net model of the label Petri net system; step S3, constructing basic bad paths of the label Petri net system; the partial observable Petri net fault diagnosis and optimal sensor configuration method of the high-speed rail signal system; step S4, converting each basic bad path into corresponding inequality constraint conditions; step S5, applying a related program package to a corresponding integer linear programming problem to obtain an optimal solution of transition re-marking. The application fuses the proof net and system structure analysis technology, and the analysis process does not need to enumerate the entire state space, so that the calculation complexity of the algorithm is reduced.

The application discloses a kind of manufacturing unit deployment methods of fusing single point incremental forming and drive-by-wire equipment, comprising: determining manufacturing unit deployment as storage area, single point incremental forming processing unit (processing input area, processing center, processing output area), drive-by-wire equipment assembly unit (assembly input area, assembly center, assembly output area), assembly completion area, establish three kinds of assembly resource model including drive-by-wire equipment, assembly center and assembly completion area Petri net model, determine that drive-by-wire equipment exists waiting to carry, carry workpiece, reset, maintenance state information, assembly center exists waiting to assemble, check assembly, assembly workpiece, assembly maintenance state information, assembly completion area exists two kinds of state information of available storage space and occupied storage space.The application divides manufacturing unit into different functional areas, improves product quality and processing and assembly efficiency, utilizes Petri technology to realize the state perception of dynamic and efficient manufacturing resources, and optimizes resource allocation efficiency.

This invention belongs to the field of production scheduling technology for flexible manufacturing systems, specifically relating to a Petri net-based method for rescheduling urgent orders in flexible manufacturing systems. Based on the Petri net model of the flexible manufacturing system, this invention proposes a novel rescheduling strategy; when urgent orders arrive, it proposes a method for reconstructing the Petri net model and classifying and rearranging the processing sequence. In the design of the scheduling strategy, this invention optimizes and improves the genetic algorithm. First, it uses the Tent chaotic mapping method to generate the initial population, making the population distribution more uniform; second, in the selection operation of the inheritance operation, it adopts an elite retention strategy and a roulette wheel method, selecting the better individuals based on their fitness values; in the crossover operation, it uses the POX crossover method, which can effectively expand the range of neighborhood solutions; and in the mutation operation, it uses a non-uniform mutation rate to enhance the algorithm's spatial search capability, enabling it to perform a more comprehensive spatial search.

The application discloses a business process prediction method based on Petri net reachable graph interpretation, and belongs to the technical field of business process management. First, a technical expert generates a reachable graph and a transition sequence set by constructing a Petri net. Second, a recurrent neural network is trained by using the transition sequence set, and a hidden state of the recurrent neural network is obtained, and then a hidden state decoding class is obtained. A mapping relationship is established, and a reachable state set of the Petri net reachable graph is mapped onto the hidden state decoding class of the recurrent neural network. Finally, the predictive business process monitoring is completed by using an interpretable recurrent neural network model and adding a fully connected neural network. The application proposes a new method of recurrent neural network internal state interpretation based on the Petri net reachable graph, effectively improves the interpretability of the recurrent neural network internal state, and realizes the future state prediction of the business process transition sequence.

The application discloses a digital twin park real-time data acquisition and processing method based on fusion edge computing, belongs to the technical field of data processing, and comprises the following steps: step 1, deploying a P4 programmable switching device between a data acquisition terminal and an edge computing node of a digital twin park, and forwarding a de-duplicated message to a corresponding edge computing node through a multi-stage series P4 flow table; step 2, deploying a hybrid expert processing architecture comprising a route distributor and a plurality of expert processing units in each edge computing node; and step 3, constructing a Petri net synchronization coordination model. The application effectively solves the problems in the prior art that a network forwarding layer cannot perform content-level de-duplication classification, an edge processing layer lacks a multi-expert routing mechanism that is deployed on demand, abnormal detection relies on fixed thresholds and cannot adapt to fluctuations in operating states, and when a plurality of edge nodes concurrently push data to a digital twin model, there is a lack of global timing consistency coordination.

This application discloses a discrete simulation method for process flow, electronic equipment, storage medium, and products, relating to the field of industrial manufacturing technology. This application constructs workpiece entities and process information components through a physical component system, achieving efficient mapping between data and simulation tokens. Through Petri net state space decomposition, key process states are mapped to locations, and actions are abstracted into transitions, forming a logical topology and decoupling process states from control flow. Combining equipment constraints, general resources, and dynamic and fixed-time components, transition execution is divided into four standard types, and asynchronous event simulation is driven by standardized lifecycle templates, achieving accurate workpiece state updates and rational resource scheduling under high concurrency. This application can provide real-time response, process flow analysis, and bottleneck identification in large-scale simulations, providing efficient and flexible technical support for process optimization and digital twin systems.

This invention belongs to the field of construction information technology and smart construction site technology, specifically involving a method and system for real-time analysis of construction progress and compliance based on edge-cloud collaboration. The method first constructs a process flow map based on a time-delayed color Petri net, generating initial process states and an evidence list. Then, based on the process flow map and initial process states, it determines forward transitions, forming attention instructions. Subsequently, an edge computing terminal receives the attention instructions and on-site video streams, generates scene events based on an event relationship parsing network, calculates the necessity of evidence, and increases the necessity of evidence for matching scene events according to the evidence list, forming event objects. Then, based on the event objects and the evidence necessity threshold, it performs minimum preservation, generating key fragments and structured event packages, which are reported to the cloud. The cloud verifies transition triggers based on the structured event packages and key fragments, forming a progress compliance adjudication result. This invention achieves real-time and verifiable linkage between construction site progress and compliance.

This invention discloses a method and system for modeling discrete manufacturing workshops based on a hierarchical fusion of Petri nets and static knowledge graphs. By constructing a hierarchical structure that integrates time-varying and logical Petri nets, the upper layer models workshop-level resource flow, while the lower layer characterizes equipment-level state transitions, achieving a dynamic formal description of the concurrency, asynchronicity, and dependencies of the manufacturing system. Simultaneously, a specialized workshop knowledge graph is constructed, and a joint extraction model combining a RoBERTa-BiLSTM-CRF model and a focus loss function is employed to automatically extract entities and relationships from unstructured text, effectively improving knowledge construction efficiency and robustness. By defining a dual fusion mechanism of modeling and knowledge, deep coupling and collaborative reasoning between dynamic Petri nets and static knowledge graphs are achieved. This method supports multi-level modeling from system to equipment, simultaneously describing production logic, resource states, and process knowledge. Through continuous learning, the model can continuously accumulate and solidify production knowledge, ensuring long-term collaborative reliability.

This invention relates to the field of railway transportation scheduling technology, specifically to a method, device, and storage medium for verifying the feasibility of railway station signal control based on time-delay Petri nets. The method involves: simplifying the target railway station map to extract track sections and turnout elements and defining route sequences to obtain a simplified station map; combining a pre-set route arrangement module and a decision module to construct a time-delay Petri net model of the station; calculating the time taken for trains to pass through each track section based on the simplified map and train timetable, and arranging them in time sequence to generate a train operation plan within the station; assigning the time parameters in the plan to the corresponding transitions in the Petri net model and performing simulation analysis to obtain verification results indicating the feasibility of the plan. This invention achieves automated, high-precision verification and accurate fault location of operation plans at the track section + time point granularity, effectively overcoming the problems of low simulation accuracy, poor verification efficiency, and ambiguous fault location in traditional methods.

The application discloses a kind of robot assembly process simulation methods based on digital twinning, including the following steps: extracting the assembly feature pair of moving parts and fixed parts from digital twinning model;Generation assembly phase sequence and construct phase invariant;Phase invariant synchronous time Petri net is constructed;Current assembly state is generated and written into phase token;According to next phase invariant, drive simulation and obtain virtual assembly phase;Compare real assembly phase with virtual assembly phase, correct phase split and form assembly phase evidence chain.The application utilizes digital twinning and phase invariant synchronous time Petri net, realizes robot assembly phase drive simulation, with the advantages of high phase consistency, accurate exception correction and high simulation credibility.