122 results about "Petri net" patented technology

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a network evidence obtaining and attack chain reconstruction method based on threat alarm, and belongs to the field of network security. Aiming at the problems of massive alarm fragmentation, strong attack chain concealment, low evidence obtaining efficiency and the like existing in a traditional network evidence obtaining method, the invention provides a dynamic reconstruction model of multi-dimensional alarm aggregation and probabilistic reasoning. Threat alarms are aggregated through an improved DBSCAN algorithm, an attack behavior graph is constructed, and attack chain probabilistic reasoning is carried out in combination with a time sequence Petr i network and an HMM. Experiments prove that the alarm aggregation compression rate reaches 92.3%, the attack chain reconstruction accuracy rate reaches 88.6%, the time consumed by single analysis is less than 3 seconds, and the network attack tracing and evidence obtaining efficiency is effectively improved.

The invention belongs to the technical field of equipment fault detection, and discloses a Petri network-based discrete manufacturing system toughness enhancement method and system. According to the method, redundancy design is carried out by determining a key area and reasonably configuring standby equipment and redundant personnel; petri net modeling is carried out, the intelligent agent carries out cyclic updating of a petri net model by using a deep reinforcement learning algorithm according to reward feedback, and an optimal scheduling strategy in different system states is learned; and optimizing the Petri network model according to the optimal scheduling strategy and the real-time state information obtained by learning to complete stable operation of the intelligent agent in a complex environment. Accurate execution of the instruction is ensured by comparing actual data through simulation. The intelligent agent gradually converges to an optimal strategy through an exploration rate attenuation mechanism to adapt to a complex production environment. The process waiting time is recorded through the timestamp, so that the overstock of products in process is reduced, and the process connection efficiency is improved.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The embodiment of the invention provides a Petri network-based digital twinborn model construction method, system and device and a medium, and belongs to the technical field of information. The method comprises the steps that a Petri network model for enterprise operation is constructed and used for describing concurrent, asynchronous and distributed behaviors in an enterprise business process, the concurrent, asynchronous and distributed behaviors in the enterprise business process comprise places and transitions, the places represent states or conditions in the process, and the transitions represent activities or events in the process; based on a Petri network model, a library and a changed physical carrier in the Petri network model are analyzed, and the digital twinborn model of the enterprise operation business is classified according to an analysis result to construct the digital twinborn model of the enterprise operation business. By introducing the Petri net, elements such as objects, activities, events, state transition and the like are fused into a digital twinborn modeling process, and the problem of digital modeling of a complex business process is effectively solved.

The invention discloses an intelligent manufacturing system deadlock prediction method based on Petri-Net-GCN. The method comprises the following steps: dividing an intelligent manufacturing system into a production equipment module, a production process module and a production resource module; carrying out formalized modeling on the module on the basis of a Petri network; a Petri-Net-GCN deadlock prediction model is constructed, and the Petri-Net-GCN deadlock prediction model comprises a house-to-house transition message transmission module and a transition-to-house message transmission module; performing bidirectional aggregation updating on features among nodes in the Petri net graph by using a graph neural network structure; collecting initial Tokenn distribution, an incidence matrix and a deadlock label of the intelligent manufacturing system to construct a data set, and inputting the data set into a model for training; predicting a deadlock risk in an unknown system state by using the trained model; according to the method, rapid prediction and judgment are carried out in the current state of the intelligent manufacturing system, and prior early warning information is provided for production scheduling and resource configuration.

The invention relates to the technical field of data processing, and provides a test case generation method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a Petri net model of a to-be-detected software system; based on a Petri network model, constructing an initialization environment for deep reinforcement learning; performing deep reinforcement learning training in the initialization environment to obtain a decision model for generating a test sequence; and generating a test sequence according to the decision model. By implementing the technical scheme disclosed by the invention, the generation efficiency of the test case can be improved.

The invention relates to the technical field of Internet of Things communication, and discloses a gateway management system and method based on multiple Internet of Things protocols, and the system comprises a processor architecture, a protocol conversion module, a collaborative management module, a unified security framework module and an edge computing module. The method is applied to the system. According to the application, efficient interconnection and dynamic collaborative management of multi-protocol equipment are realized through FPGA module hardware acceleration protocol conversion, a genetic algorithm and a Petr network hybrid task scheduling framework and a unified security mechanism irrelevant to a protocol, and various different mainstream Internet of Things protocols are compatible through a modular processor architecture and a dynamic expansion interface, so that the implementation is convenient, and the implementation is easy. According to the method, the hardware cost is reduced, rapid integration of a new protocol is supported, local and cloud resource allocation is further optimized through an intelligent switching mechanism of edge computing and cloud collaboration, the system stability and the data processing efficiency are improved, and the method is widely applied to scenes such as smart home, industrial Internet of Things and smart cities.

The invention discloses a self-service terminal large model task processing system and method, and the system comprises a multi-mode perception fusion unit which is integrated with an infrared multi-point touch screen, an MEMS microphone array, and an industrial grade code scanning module. A medical diagnosis knowledge graph and a financial transaction ontology industry exclusive graph are built in the multi-field intention classification model; the dynamic demand matching engine is used for analyzing a cross-domain task link by adopting a Transform-XL long sequence modeling technology; a Petri net simulator and a conflict detection algorithm are built in the task arrangement middleware, and the task arrangement middleware is used for guaranteeing the consistency of execution time sequences of multiple sub-tasks; the self-adaptive response generation module is used for supporting automatic conversion from a natural language instruction to a PLC control code; according to the method, the task identification accuracy can be improved based on multi-modal fusion; the dynamic demand matching engine supports processing of a large number of concurrent requests per second, and response delay is low.

The invention relates to the technical field of coal mine intelligent grouting control and scheduling, and discloses a method and system for generating a slurry leakage blocking strategy based on coal mine separation layer grouting, and the method comprises the steps: collecting grouting state parameters through a multi-source sensing device, constructing a state representation vector, and building a weighted Petri net model; based on the historical state representation vector, extracting a long-term trend component and a short-term residual component, and fusing to generate a prediction state representation vector; the system fuses the current state information and the predicted state information, calculates the scheduling score of each transfer position, and selects the transfer position with the highest score to execute plugging operation; and dynamically correcting a state vector and updating a weighting function based on grouting feedback information to realize adaptive evolution of scheduling logic. The accuracy and execution efficiency of plugging decision making are improved, and the problems of resource coordination and dynamic control under the complex grouting working condition are effectively solved.

The invention discloses a part flexible dynamic production optimization method and device based on a low-carbon target, and relates to the field of intelligent machining. The method comprises the following steps: firstly, aiming at an autonomous production station process and an outsourcing production station process of part production, constructing a multi-objective optimized Petri net model; secondly, respectively calculating the production cost, the carbon emission and the production cycle of each path in the Petri network model based on the decision variable group; and finally, performing multi-objective optimization through a marine predator algorithm according to the priorities of the three objectives of the production cost, the carbon emission and the production cycle. On the basis of a traditional part production and manufacturing organization mode, multiple targets such as the product production cycle, the production cost and the carbon emission in the production process are considered, a part flexible dynamic production organization optimization model is constructed, and dynamic flexible distribution of autonomous production and outsourcing production is achieved. The carbon emission in the production process can be estimated in the product design stage, and effective measures are taken to reduce the carbon emission when production tasks are distributed.

The invention provides a multi-AGV path planning method in a flexible manufacturing workshop complex environment, and the method comprises the steps: firstly carrying out the modeling of a logistics process of a flexible manufacturing workshop based on a Petri net theory, and then designing a single-AGV path planning algorithm considering the congestion degree based on a constructed Petri net model of the logistics process of the flexible manufacturing workshop, a multi-AGV collision deadlock prevention strategy based on a Petri net is designed, finally, a multi-AGV path optimization algorithm considering an AGV motion state uncertainty scene is designed, a collision conflict prevention method is expanded, and the AGV is constrained by constructing a probability motion model according to a station arrival prediction probability. According to the method, the stability and the transportation efficiency of the multi-AGV system in a complex dynamic environment are remarkably improved, and an innovative solution is provided for realizing intelligent and efficient logistics in a flexible manufacturing workshop.

The invention relates to the field of computer vision, in particular to a bovine embryo quality evaluation method and system based on a time sequence microscopic image, and the method comprises the steps: collecting a microscopic image and a timestamp, carrying out the embryo segmentation and inter-frame registration of the image, and obtaining the frame quality weight, morphological characteristics, image block embedding and topological data; respectively calculating a topological distance and an optimal transmission distance, determining a change point generation event sequence in combination with a frame quality weight, and aligning and outputting a penalty term based on a time Petri network; inferring a stage sequence and a quality posteriori based on the event sequence and a penalty term, and constructing a multi-instance set based on an event time window to obtain a multi-instance learning score; and fusing quality posteriori and multi-instance learning score to obtain an evaluation result, calculating a conflict coefficient, and relocating an event time window when a threshold value is exceeded to update the evaluation result. According to the method, rating consistency, noise immunity and traceability are improved.

The invention relates to the technical field of business process modeling, in particular to a meta-model-driven interactive business process intelligent modeling method and system. The method comprises the following steps: constructing a meta-model framework containing core elements such as activities, events and gateways, and establishing a meta-model constraint rule base to define a structure legality rule of a BPMN2.0 specification; based on a domain ontology Schema and the domain exclusive large language model after instruction fine tuning, extracting a business entity and a process relationship thereof from the unstructured text; carrying out information integrity verification and logic consistency detection on the extracted BPMN elements by utilizing a constraint satisfaction algorithm and Petri net reachability analysis; displaying a difference label of the process sketch through an interactive verification interface, and realizing semantic correction and model iteration of a user on the process elements based on a natural language feedback mechanism; an XML code is generated by adopting a template-driven code generation strategy, and a flow chart is generated through a BPMN parser. According to the method, the business process modeling efficiency can be remarkably improved, and the manual intervention proportion in the modeling process is reduced.

The invention discloses a train control on-board subsystem reliability analysis method, system, equipment and medium, and relates to the technical field of reliability analysis, and the method comprises the steps: carrying out the modeling of the service life of an on-board equipment unit based on Weibull distribution; for a cold standby structure of the vehicle-mounted equipment, an active switching strategy is adopted to switch a main system and a standby system, and the influence of the active switching strategy on unit reliability is quantified through a virtual age; considering the influence of the common cause failure on the redundant structure, and modeling the common cause failure by using a beta factor model; the influence of personnel operation on the system reliability is considered, the success probability of personnel operation is analyzed, and the unit reliability is calculated; and establishing a random Petri net model of the vehicle-mounted system, performing simulation by adopting a Monte Carlo method, and generating system life data and system reliability parameters. According to the method, the action mechanism of active switching for improving the reliability of the system is analyzed, common cause failure and human cause are considered, and the reliability of the system can be improved by improving the reliability of the cold standby unit.

The present invention belongs to the field of intelligent transportation, and specifically relates to a deadlock avoidance method for an unsignaled intersection that takes into account multiple vehicle travel directions. The method comprises the following steps: S1, dividing the right-of-way points in the unsignaled intersection and establishing a Petri net model; S2, encoding the vehicles to be entered into the intersection and randomly generating a scheduling code sequence, decoding the scheduling code sequence into a scheduling transition sequence; S3, judging whether the order of vehicles in the same lane is satisfied by the scheduling code sequence, and judging whether deadlock occurs between vehicles in different lanes by the scheduling transition sequence. If the order of vehicles in the same lane is not satisfied or deadlock occurs between vehicles in different lanes, the codes and transitions that meet the conditions after the current position are replaced with those at the current position. In order to solve the problem of vehicle passage at an unsignaled intersection, the vehicles are encoded, decoded and repaired, so that the scheduling sequence satisfies the unchanged order of vehicle passage in each lane and avoids deadlock between vehicles in different lanes.

The present invention discloses a method for optimizing FMS system scheduling and resource allocation based on Petri nets, belonging to the technical field of manufacturing system optimization and intelligent control. The method comprises the following steps: allocating resources of a flexible manufacturing system according to production requirements of the flexible manufacturing system, obtaining current processing information of the flexible manufacturing system, and establishing a Petri net model of the flexible manufacturing system based on the current processing information of the flexible manufacturing system; dividing the transition set of the Petri net into base partitions, and constructing a base reachable graph based on the base partitions; using an improved simulated annealing algorithm combined with a generative filter beam search algorithm to perform integrated optimization of resource allocation and scheduling in the base reachable graph to obtain an optimized resource allocation scheme and machine processing time; and, based on the optimized resource allocation scheme and machine processing time, sorting the operations of all workpieces on a time axis according to the process start time to generate a final scheduling result.

The invention relates to the technical field of data processing, in particular to a double-layer hybrid decision satellite constellation distributed task allocation method, which comprises the following steps of: acquiring satellite operation state parameters and task requests through inter-satellite and satellite-ground links, and generating a virtual task queue with a conflict detection mark by adopting modeling based on a time Petri network; executing distributed bidding calculation which introduces a regret value mechanism based on the virtual task queue, and outputting a temporary allocation scheme; inputting the temporary allocation scheme into a multi-agent deep reinforcement learning model, calculating an influence weight between nodes through an attention mechanism, and outputting a collaborative optimization decision; model parameters are updated online through federated learning during task execution; and establishing a data interaction channel between the auction decision-making layer and the reinforcement learning layer to realize a cross-layer feedback closed loop. According to the invention, real-time response and long-term optimization collaboration of constellation task allocation are realized through a double-layer hybrid decision architecture.

The application provides a Petri net supervisory control method and a controller applied to a complex manufacturing system, and the method comprises the following steps: obtaining system information of the complex manufacturing system, and constructing a Petri net model of the complex manufacturing system according to the system information; constructing a reachable graph of the Petri net model according to the Petri net model; taking the current state and resources of each device as input, and predicting transition enabling values of each device in all reachable states through a trained neural network model; and performing supervisory control on the complex manufacturing system according to the transition enabling values of each device in all reachable states. For the complex manufacturing system, the application can avoid the state explosion problem caused by the calculation of the reachable graph of the complex manufacturing system, greatly reduce the time complexity and calculation complexity caused by the calculation of the reachable graph, and directly realize the deadlock control of the complex Petri net and has high behavior permissibility.

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.

The invention discloses a novel VPSA method biogas decarbonization purification equipment intelligent control joint control system, belongs to the industrial automation and artificial intelligence fusion field, and comprises a global perception and data acquisition layer, an edge intelligent decision and control layer, an accurate execution and linkage control layer, and a cloud edge collaborative management and digital twinborn layer four-layer architecture. The global sensing layer is provided with a 48-node sensor network and a 10kHz acquisition card to complete data acquisition preprocessing; the edge decision-making layer carries a reinforcement learning algorithm and an LSTM-CNN model through an ARM + FPGA architecture; the precise execution layer realizes millisecond-level response through a high-speed execution mechanism and a Petri net linkage matrix; and the cloud side management layer realizes remote management and virtual debugging by relying on a cloud platform and a digital twin system, solves the problems of rigid control, weak fault early warning and the like of traditional equipment, and is suitable for the fields of biomass energy utilization and environmental protection engineering.

The application provides a procurement document generation and evaluation method based on process mining analysis, relates to the technical field of enterprise management, and comprises the following steps: collecting event log data in an enterprise procurement system, constructing a procurement process hypergraph model through a process hypergraph discovery algorithm, and generating a variation correlation topological structure; constructing a self-adaptive rule mapping network according to the procurement process hypergraph model and the variation correlation topological structure, inputting specific procurement demand data into the evolved template by utilizing the network to dynamically evolve the procurement document template, filling the file content by applying a semantic intervention filling mechanism, and generating a complete procurement document; the process hypergraph discovery algorithm is introduced to construct the procurement process hypergraph model, so that complex correlations such as multi-party interaction, group decision and high-order dependence in the procurement activity can be effectively captured, and the expression capacity of the process model is significantly improved compared with the traditional Petri net or simple directed graph model.

The invention discloses a power grid fault deduction method and device based on scene evolution, equipment and a medium, and the method comprises the steps: constructing a risk evolution scene library of a target power grid according to the pre-obtained multi-source heterogeneous data of the target power grid; wherein the multi-source heterogeneous data comprises historical accident data, equipment parameter data, power grid operation data and external environment data; the risk evolution scene library comprises a plurality of scene triples; the scene triad is composed of states, reasons and judgment; mapping the risk evolution scene into a corresponding Petri network dynamic model; and calculating and setting a trigger probability for each transition in the Petri network dynamic model, and performing random cascading failure evolution simulation on the Petri network dynamic model for a plurality of times to obtain a cascading failure deduction result of the target power grid. According to the method, cascading failure dynamic deduction can be carried out on brand new risk scenes which are lack of historical data support or are caused by multi-factor complex coupling, and the generalization ability of power grid cascading failure deduction is improved.

The application discloses a kind of based on distribution Petri net's intelligent substation protection operation risk's modeling method and device, it includes the association matrix of the Petri net transition and library of each protection is constructed;The relevant pressboard is added to the input library of the relevant transition of each protection of Petri net that has been constructed as library;Coupling relationship library, coupling state library are constructed to the information transmission between each protection;The input library state of protection of Petri net is set, respectively deduces and analyzes each stage of protection of Petri net, according to coupling relationship library, using the state value of the output library of protection of Petri net updates coupling state library, to assign value to the state value of the relevant input library of subsequent protection of Petri net, start subsequent protection of Petri net;If the protection action corresponding to primary equipment failure is not completed, then the Petri net of this protection occurs blockage.The application can obtain the specific reason of protection operation in a certain link occurs blockage, obtains the risk point position of protection.

The present invention belongs to the technical field of equipment fault detection and discloses a method and system for enhancing the resilience of discrete manufacturing systems based on Petri nets. The method performs redundant design by determining key areas and rationally configuring spare equipment and redundant personnel; performs Petri net modeling, and the intelligent agent uses a deep reinforcement learning algorithm to cyclically update the Petri net model based on reward feedback to learn the optimal scheduling strategy under different system states; based on the learned optimal scheduling strategy and real-time status information, the Petri net model is optimized to achieve stable operation of the intelligent agent in a complex environment. The present invention ensures the precise execution of instructions by comparing actual data through simulation. The intelligent agent gradually converges to the optimal strategy through an exploration rate decay mechanism and adapts to complex production environments. The waiting time of the process is recorded by timestamp to reduce the backlog of work-in-progress and improve the efficiency of process connection.

The application relates to a system reliability analysis method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: constructing an operator operation process model according to basic elements of a system operation process, the basic elements comprising operation resources, the operation resources comprising personnel resources and equipment resources, and the basic elements being obtained by defining the system operation process based on a Petri net model; determining a reliability quantization result of the personnel resources based on the operator operation process model; determining a failure probability of the equipment resources according to the failure rate and working time length of each equipment in the system; and determining the success probability of an operation task of the system according to the reliability quantization result and the failure probability. According to the scheme, the personnel resources and the equipment resources involved in the operation process of the system for completing the operation task are quantized into a reliability analysis model, and then the reliability of the system is analyzed, so that the influence of elements related to personnel participation on system reliability analysis is solved.

The invention discloses a real-time task WCET optimization method based on instruction and data cache static locking, and relates to the field of task WCET optimization in a real-time system, and the method comprises the following steps: carrying out offline program control flow diagram construction and WCET analysis on a to-be-operated program task; constructing a Petri net graph-based semantic flow model at an instruction-data level; extracting a feasible path of a program task by utilizing loop detection based on a depth-first search algorithm according to the Petri net graph semantic flow model; performing memory arrangement and cache mapping according to instructions and data information in the feasible path; constructing a WCET optimization problem of a single executable path; and solving the optimization problem, and comparing the WCET of all the paths to obtain the WCET of the program task. According to the method, the program running path is obtained through the instruction-data level control flow diagram, static locking is carried out on the memory block where the hotspot instruction and data are located, the search scale of the large-scale program WCET problem is reduced, the solving feasibility and speed are increased, and the algorithm practicability is improved.

A multi-perspective network transaction risk identification method based on data Petri nets includes the following steps: S100: Based on the e-commerce transaction process flow and transaction data, data Petri nets are used to perform data interaction analysis and formalize modeling of the transaction process; S200: Process mining is used to perform multi-perspective analysis of each activity in the constructed model and identify outliers; S300: A logistic regression model is used to train these outliers to generate a system model capable of identifying transaction risks. This method can dynamically simulate the entire transaction process, clearly observing the trends of user behavior and the status of each activity point. Combining monitoring results from multiple perspectives can better identify potential risks in the transaction process.

Provided are a method and a system for detecting data loss errors in a cyber-physical system based on a Petri net. 【Solution means】The method first includes steps of mapping a cyber-physical system to a Petri net with data to generate a merging process, optimizing events and conditions in the merging process based on deletion rules to obtain an optimized merging process, determining whether each event in the optimized merging process is a lost event, and outputting a set of events of data loss errors. By optimizing the merging process based on deletion rules, the complexity of the model can be reduced, a detection algorithm for data loss errors can be designed, data loss errors in the cyber-physical system data stream can be detected, and the design accuracy of the cyber-physical system can be verified.