498 results about "Automated technique" patented technology

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention discloses an experimental process instruction arrangement method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining an operation description of a target object, and inputting the operation description into a target large language model; analyzing the operation description into a script instruction based on a target knowledge base by utilizing a target large language model; the target knowledge base comprises a professional knowledge base and a local knowledge base; performing parallel task scheduling on the script instruction to obtain a standardized instruction set; and controlling the experimental equipment to execute the target operation according to the standardized instruction set. According to the method, natural language operation description is analyzed into an executable instruction through the synergistic effect of a large model and a multi-dimensional knowledge base, and efficient semantic programming of an experimental process is achieved; through multi-device parallel task scheduling, the device utilization rate is effectively improved. Through deep fusion of a large model and an automation technology, experimental process instruction arrangement can be efficiently realized, and the method can be widely applied to the technical field of data processing.

The invention discloses a PCB component layout method, device and equipment based on a multi-modal large model, and relates to the technical field of electronic design automation, the method comprises the following steps: carrying out quality screening on a plurality of PCB design files which have been laid out to obtain a qualified design file set; constructing a serialized training sample set based on the qualified design file set; each training sample sequence comprises a plurality of training samples arranged according to a layout logic sequence; each training sample comprises multi-modal data representing a current layout state and a real position coordinate of a next component to be laid out; based on the serialized training sample set, training the open-source multi-modal large model to obtain a PCB component layout model; and iteratively generating a layout file of the target PCB based on the netlist file of the target PCB and the PCB component layout model. The method is high in generalization capability and low in cost based on the PCB component layout model trained by the layout data, and can realize rapid layout of components of a new PCB.

The invention relates to the technical field of Internet of Things and industrial automation, and discloses a multi-device collaborative management method and system based on the Internet of Things, and the method comprises the steps: obtaining industrial device data, and recognizing the industrial device data as an abnormal event marked with a priority; obtaining and classifying the abnormal events, extracting an approval process template and querying a resource schedule, and generating a conflict-free allocation scheme through conflict detection; a collaborative management instruction is obtained based on the conflict-free distribution scheme updating template and is distributed and executed; and obtaining an execution feedback record, and if an energy consumption deviation detection result in the record exceeds a threshold value, judging that the abnormal event is a new abnormal event and marking a priority. According to the method, dynamic analysis and scheduling are carried out on the edge side, so that the problem of insufficient real-time performance of multi-device collaborative management in the prior art is solved.

The invention relates to the technical field of mineral processing automation, and discloses a metal dissociation degree regulation-based grinding-flotation collaborative process optimization method which comprises the following steps: acquiring multi-dimensional sensor data in a grinding-flotation production process in real time; establishing a reference prediction model offline based on historical process data; in the online operation stage, the working condition drift state of the current process system is quantified and judged online; when it is judged that significant working condition drifting occurs, a residual compensation model is constructed online; and fusing the prediction result of the reference prediction model and the prediction result of the residual compensation model to generate a final process index prediction value. And based on the final process index predicted value, solving through an optimization algorithm, and generating a cooperative control set value. According to the method, the calculation cost and the data requirement of model maintenance are greatly reduced, and the response speed and the adaptive capacity of the system to the working condition change are remarkably improved, so that the long-term stability and reliability of the collaborative optimization effect are ensured.

The invention provides an intelligent ship decision-making system, and relates to the technical field of ship automation, and the system comprises the following modules: a multi-source data collection module which is provided with a plurality of sensors inside and outside a ship; the data fusion processing module is used for preprocessing, fusing and analyzing the original data acquired by the multi-source data acquisition module; the intelligent decision-making module is used for generating an optimal navigation path from the data input by the data fusion processing module through an A * algorithm, a Dijkstra algorithm, a deep learning algorithm and a collision avoidance algorithm; the execution module is used for transmitting the instruction to each corresponding equipment neutralization module; the man-machine interaction module and the communication module realize information interaction and sharing between the ship and an external related system; through the arrangement of the multi-source data acquisition module, the data fusion processing module and the intelligent decision-making module, various factors in ship operation can be fully considered, the optimal navigation route can be quickly and accurately planned, the collision risk is avoided, and the navigation time and cost are saved.

The invention discloses a government affair complaint dispatching system and method based on a dynamic knowledge graph and a decision engine, and relates to the technical field of government affair automation. The government affair complaint dispatching system and method based on the dynamic knowledge graph and the decision engine comprises the following steps: S1, collecting complaint work order monitoring data, and preprocessing the complaint work order monitoring data; s2, constructing an initial graph structure, calculating weight data of each semantic relation edge according to a period, updating the initial graph structure, and constructing a dynamic knowledge graph; s3, evaluating the risk emergency degree of each work order, and generating an emergency work order identifier based on an evaluation result; and S4, extracting all the emergency work orders, executing scheduling evaluation calculation on the current undertaking department, and judging whether the execution department distributes the work orders again or not based on an evaluation result so as to realize order distribution execution and dynamic scheduling closed loop. The problem that high-risk complaints cannot be recognized and scheduled in time due to the fact that an order dispatching strategy cannot be adjusted in real time according to work order complexity and department bearing capacity in the prior art is solved.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention discloses a mechanical arm simulation system supporting industrial quality inspection, which relates to the technical field of industrial automation and comprises a multi-physics field simulation engine module, a digital twin interface module, a defect sample library module and a cooperative control module. According to the method, traditional entity mechanical arm debugging is replaced by rigid body dynamics simulation based on a physical engine, loss and production line shutdown caused by repeated trial and error of equipment are avoided, rapid construction of the digital twin environment of the production line is achieved by means of an efficient CAD model conversion process and a lightweight loading technology, and high-fidelity optical simulation is achieved through a parameterized defect model library and high-fidelity optical simulation. Various common industrial defects and complex optical effects are covered, the modeling bottleneck of a quality inspection scene is broken through, real-time closed-loop interaction of visual inspection and motion control is realized by using thread separation and an efficient communication technology, and the recognition robustness of a visual algorithm to tiny defects is guaranteed; the reliability of algorithm verification is improved through the quantitative output of algorithm performance indexes and the comprehensive test of extreme working conditions by the simulation system.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention relates to the technical field of computer vision and industrial automation, and particularly discloses a MARK point high-precision PCB positioning and mechanical mapping method based on multi-coordinate system fusion, which is composed of PCB positioning and mechanical mapping in a template stage and PCB positioning and mechanical mapping in a production stage. Wherein the PCB positioning and mechanical mapping in the template stage comprises the steps of coordinate system establishment and image coordinate distortion correction; the PCB positioning and mechanical mapping in the production stage comprises Mark point real-time detection and offset calculation, real-time attitude solution and dynamic matrix calculation, and mechanical coordinate calculation of any PCB point. According to the method, a three-coordinate system collaborative modeling mechanism of an image coordinate system, a PCB coordinate system and a mechanical coordinate system is introduced, so that board attitude change and equipment fixing errors are separated, and the system robustness and the mapping precision are greatly improved; rigid body affine transformation is constructed by adopting double Mark points, the rotation angle, the proportion and the translation amount are solved in real time through two-point geometric constraint, and self-adaptive compensation of the posture change of the PCB is achieved.

The invention relates to the technical field of industrial automation, and provides a master-slave distributed multi-machine asynchronous system sampling synchronization method and system, and the method comprises the steps: after a host device is powered on and started, a synchronous time synchronization message is sent to a slave device through a gigabit Ethernet optical port at the whole second moment; the slave devices are responsible for data sampling and receiving synchronous time synchronization messages from the host device, and the second starting time recorded by each slave device is synchronized with the second starting time of the host device after being subjected to delay compensation processing; the sampling CPU module of each slave device packs and frames the sampling data and the corresponding relative time mark to form a sampling data frame, and the sampling data frame is sent to the slave device of the previous node in real time through a gigabit Ethernet optical port; the CPU module of the host device receives the sampling data frames sent by the slave devices, and the sampling data of the slave devices are synchronously aligned through the relative time marks of the sampling data frames, so that the data sampling synchronization of the multi-machine asynchronous system is realized. The method is high in universality, and synchronization of sampling data of the slaves can be guaranteed.

The invention relates to the technical field of industrial control automation, and discloses an event-driven embedded software architecture implementation method and system, and the method comprises the steps: creating a resource object priority group, and initializing a data structure for managing a theme subscription state; allocating priorities for resource objects in the system, and registering each resource object into a priority group of the corresponding priority; subscribing the theme for the resource object, and recording subscription information in the data structure for managing the theme subscription state; in response to an event triggering condition, creating an event object containing a theme identifier, determining priority group information for subscribing the theme based on the data structure for managing the theme subscription state, and adding the event object into a queue to be processed; according to the method, each priority group is polled according to the priority sequence, the event object subscribed by each priority group is retrieved, the event processing function of the corresponding resource object is called for processing, and the framework realized through the method reduces the coupling among the modules.

The invention relates to the technical field of industrial automation, and provides a sucker carton unstacking anti-interference method and system based on dynamic position deviation. The method comprises the steps that original scene data are obtained, scene perception and initial grabbing pose calculation are carried out, and a carton pose list containing carton IDs, sizes and grabbing poses is calculated; a first carton is selected as a target, neighborhoods are separated, and a local coordinate system is constructed according to the target pose; generating a sucker rotation rectangle on an XY plane of the local coordinate system, and performing preliminary interference judgment by combining with the neighbor pose list to obtain an interference pose list; constructing an interference gradient field and preferably selecting a safety offset direction set; and direction-step length combined optimization is implemented in the safe direction to search the non-interference pose, and the final grabbing pose is output. Therefore, the sucker does not need to be transformed, the calculated amount is small, response is fast, mistaken suction of adjacent boxes can be reduced, and unstacking stability and takt are improved.

The invention relates to the technical field of data processing and industrial automation, in particular to an industrial process missing value filling method and system based on a time sequence. The method comprises the steps of obtaining original sequence data collected by various sensors in the industrial process and performing missing processing, constructing a generator by stacking LSTM layers of which the number of multiple layers of units is sequentially increased, establishing a time sequence characteristic model of a generative adversarial network, training the time sequence characteristic model by using the original sequence data subjected to missing processing, and constructing a time sequence characteristic model of the generative adversarial network. Calculating a loss function of the discriminator, setting a dynamic consistency loss function to constrain filling data generated by the generator in a sliding time window, obtaining a trained time sequence characteristic model through back propagation, inputting industrial sequence data of a to-be-filled missing value into the trained time sequence characteristic model, and obtaining a to-be-filled missing value; and evaluating the filling sequence data obtained through output. According to the method, data changes under different time scales can be adaptively processed, and the data processing efficiency and the generalization ability of the model are effectively improved.

The invention relates to the technical field of automation, in particular to a real-time multi-target visual positioning and mechanical arm cooperation system for intelligent material sorting, which comprises a visual perception and information acquisition module, a data processing and path planning module, a mechanical arm execution module and a man-machine interaction module. By combining the 2D camera and the 3D camera, 360-degree full-view-angle material sensing can be achieved, and the possibility of repeated grabbing errors of the mechanical arm can be reduced through the design of judging whether grabbing succeeds or not after the mechanical arm is controlled to execute the grabbing step every time.

The invention provides a task processing method and device based on an industrial large model, a medium and a product, and relates to the technical field of industrial automation. The method comprises the following steps: constructing a multi-source data set based on an industrial scene, and training a plurality of adapters corresponding to the multi-source data set by adopting an adaptive low-rank adaptation algorithm; according to the learnable weight, fusing the plurality of adapters to obtain a fine-tuned industrial large model; and inputting an operation and maintenance task of the intelligent equipment in the industrial scene into the fine-tuned industrial large model, outputting an operation and maintenance strategy of the intelligent equipment, and controlling the intelligent equipment in the industrial scene to execute the operation and maintenance strategy. According to the method, efficient fine tuning and capability integration of an industrial large model in a multi-source heterogeneous data scene are achieved by introducing a self-adaptive low-rank adaptation algorithm and a multi-task adapter fusion framework, the fusion conflict problem of multi-source heterogeneous data is solved, efficient reasoning of full-process intelligent interaction of the air compressor is achieved, and the intelligent interaction of the air compressor is achieved. And the stability and the precision of the large industrial model in a complex industrial task are improved.

The invention discloses a paper product counting method based on machine vision, and relates to the technical field of image processing and industrial automation. The method is used for solving the problem of low technical accuracy of paper products in complex industrial environments. The method comprises the following steps of: quantitatively recording inherent interference characteristics of a counting area by constructing an interference baseline library capable of being dynamically updated; performing real-time anomaly detection and positioning, acquiring images through a multispectral camera, comparing baseline features, and generating a difference thermodynamic diagram to position a paper candidate area; performing multi-modal counting, adopting track coding counting logic for a dynamic transmission scene, and adopting thickness gradient counting logic for a static stacking scene; counting results are subjected to cross check, dynamic and static counting results are compared, and accuracy is ensured through confidence evaluation; and the accuracy and the reliability of a paper product technology are obviously improved.

The invention belongs to the technical field of industrial automation, and particularly relates to a snake-shaped pipe grabbing point positioning method based on 2D vision and 3D vision fusion, and the method comprises the steps: collecting an original image set of a snake-shaped pipe, the original image set comprises original images of the snake-shaped pipe at a plurality of different visual angles, and the original images comprise depth images and texture images; based on each texture image in the original image set and the trained target detection model, determining a planar clip-free area; based on the original image set and the planar clamping piece-free area, determining a complete point cloud of the coiled pipe and a point cloud of the area to be positioned; based on the complete point cloud of the coiled pipe and the point cloud of the to-be-positioned area, determining a target positioning area; and determining a grabbing point of the coiled pipe based on the target positioning area. According to the technical scheme provided by the invention, the grabbing point is positioned in combination with the 2D image and the 3D image of the serpentine pipe, so that the accuracy and efficiency of positioning the grabbing point of the serpentine pipe are improved.

The invention discloses an automatic testing method and device based on a front-end UI and a terminal, and belongs to the technical field of front-end development testing. Comprising the following steps: detecting a first test instruction of each set of front-end UI program, controlling to carry out demand analysis, determining a function module which needs to be subjected to UI automatic test according to a demand analysis result, and determining a test target; performing automatic technology type selection, and selecting a specified test tool and a continuous integration tool; generating a compiled test case according to the determined function module needing to be tested and the test target; running the test case, and generating a test script according to the test case and an automation technology type selection result; when the test script is verified to be correct, integrating the test script into a continuous integration tool, and automatically running a test through the test script when the code of the subsequent version of the front-end UI program is submitted; and generating a test report, positioning problems and giving repair suggestions. According to the invention, the testing efficiency is improved, and the labor cost of testing is reduced.

The invention relates to the technical field of industrial automation, in particular to a weftless tape machine process parameter self-adaptive acquisition system fused with machine vision. The system comprises a data acquisition module which is arranged on a production path of the weftless tape machine and is used for capturing a digital image of a weftless tape flowing through a view field of the weftless tape machine in real time; the data processing and parameter extraction module is used for receiving and processing the digital image and extracting process parameters from the digital image; the control module is used for receiving the process parameters, comparing the process parameters with preset target parameter values to generate deviation signals, generating control instructions according to the deviation signals and sending the control instructions to a controllable execution part of the weftless tape machine so as to drive the controllable execution part to carry out adjustment. By constructing an intelligent system from high-precision visual perception to real-time closed-loop control, the product quality consistency is improved, the rejection rate is reduced, and high automation and intelligence of the production process are achieved.

The invention discloses a feeder automation test method and system, and relates to the technical field of distribution network automation, and the method comprises the steps: building a qualitative simulation model according to the physical parameters of a power distribution network; performing fault simulation on the power distribution network through the qualitative simulation model to obtain operation states and parameters at different operation moments, and taking the operation states and parameters as typical section sequences; performing fault transient simulation on the power distribution network according to the typical section sequence to obtain a transient simulation result; matching the transient simulation result with a preset strategy library to obtain an optimal processing strategy; and collecting actual action data of feeder automation in a fault scene in real time, and performing consistency comparison on the actual action and the optimal processing strategy to obtain a feeder automation test result. By adopting the embodiment of the invention, the limitation of the traditional simulation step length can be broken through, efficient technical support is provided for logic verification of a feeder automation system in a millisecond response scene, and the test capability of power distribution network fault processing is remarkably improved.

The invention relates to a robot intelligent control method integrating visual guidance and digital twinning technologies, and belongs to the technical field of industrial automation. The method comprises the following steps: acquiring multi-source attribute data of a robot to construct a robot control digital twin architecture, and acquiring a point cloud of a to-be-operated workpiece to position a key point location; self-adaptive noise reduction and completion are carried out on the point cloud, a to-be-operated workpiece model is constructed, features are extracted, and a feature change trend pre-judgment model is established; transmitting data by utilizing an edge computing-cloud collaborative architecture, carrying out multi-dimensional comparison in a robot control digital twin architecture, constructing a dynamic weight path planning function, solving an optimal path by adopting a genetic algorithm, and then carrying out virtual-real interaction fault simulation to optimize the path; a double-end state comparison matrix is constructed for real-time monitoring, a multi-source fusion strategy is adopted for adjustment when deviation occurs, and a linkage mechanism is triggered to suspend operation and give an alarm when the deviation exceeds a threshold value. According to the method, high-precision, self-adaptive and reliable intelligent control of the robot is realized.

The invention relates to a unified PDK interface specification-based conversion method, and relates to the technical field of electronic design automation, and the method comprises the following steps: obtaining commercial PDK data; performing semantic analysis and structure reconstruction on the model file based on a machine learning transfer learning algorithm to generate a standardized intermediate model; according to a unified PDK interface specification, converting the intermediate model into target PDK data conforming to an independent research and development simulation platform format; and automatically importing the target PDK data to the simulation platform through the templated script, and verifying the integrity and consistency of the target PDK data. The method can be applied to a core particle system design process, unified access and co-simulation of the commercial PDK and the open-source PDK are realized, and the design efficiency of an integrated circuit is improved.

The invention relates to the technical field of industrial automation, in particular to an inspection path generation method and device, a storage medium and computer equipment, when an inspection path is generated, time-space synchronization can be carried out on data collected by different sensors, geometric and physical consistency of a motion track is ensured, the dynamic positioning precision is further improved, and the accuracy of the inspection path is improved. Moreover, when part of the sensors fail, the robustness of the system can be effectively enhanced through a synchronized redundant data maintenance function, and the data processing delay can be reduced through time-space synchronization, so that the real-time control requirement of a dynamic scene is met, and a map can be updated in real time according to the current environment to improve the accuracy of navigation and positioning. The method adapts to complex environments; in addition, the current positioning error can be corrected according to the target point cloud data and the target image data so as to further improve the positioning accuracy, and the relatively accurate inspection path and control instruction can be generated based on the corrected positioning result and the target map.