11885 results about "State diagram" patented technology

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention discloses a mine rescue training and danger dynamic simulation method based on virtual reality, and the method comprises the following steps: S1, modeling a rescue process into a directed graph structure, and outputting a task state diagram bound with a virtual reality scene; s2, collecting an operation behavior, a task state, an environmental parameter and a physiological response, and constructing a training state vector; s3, inputting the state vector into the deep Q network to calculate a jump node evaluation value, and outputting a jump node; s4, modeling the control rule as a rule node, constructing a directed acyclic graph, binding a weight, and outputting a rule graph; s5, performing self-evolution on the rule atlas according to training feedback, and outputting an evolution structure; s6, reasoning in the evolution rule map, fusing the rule recommendation node and the jump node, and outputting a final task node; and S7, executing jump control according to the final node, loading a virtual scene, and pushing environment parameters, task contents and risk information. According to the invention, intelligent path decision and training process adaptive optimization of the mine rescue task are realized.

Implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform task(s) associated with an entity and on behalf of the user. In various implementations, the unstructured free-form natural language input conveys details of the task(s) to be performed, but does not define any corresponding dialog state map (e.g., does not define any dialog states or any dialog state transitions). Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and / or prime a machine learning model that is already capable of being utilized in conducting generalized conversations. As a result, the chatbot can be generated and deployed in a quick and efficient manner for performance of the task(s) on behalf of the user.

The invention relates to the technical field of data processing, and discloses a data processing method and system based on liquid leakage detection. The method comprises the following steps: acquiring a sensor signal and processing to obtain leakage basic data; analyzing signal changes to determine leakage types and influence ranges; matching the risk threshold to determine a risk level; associating historical records to analyze leakage laws; calculating a failure probability to generate a maintenance strategy; and constructing a running state diagram to form a hierarchical management report. According to the invention, leakage management is converted from a traditional passive response mode to a data-driven active prevention mode, optimal configuration of maintenance resources is realized, and the leakage prevention and control efficiency is improved while the maintenance cost is reduced.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention provides a water heating pipeline pressure self-adaptive regulation and control method and system based on the Internet of Things, and belongs to the technical field of intelligent control of pipelines of the Internet of Things. According to the technical scheme, the method comprises the steps of building a pipeline acoustic sensing network, collecting pipeline external voiceprint signals to generate lightweight feature data, building a voiceprint-pressure mapping model based on the feature data, outputting a real-time pressure value, generating a pipe network state diagram based on the real-time pressure value, generating a pressure regulation and control instruction based on the pipe network state diagram, and achieving pressure self-adaptive regulation. Feedback data is collected according to the regulation and control result, and mapping model parameters are updated. The method has the advantages that the pipeline acoustic sensing network is constructed, edge calculation, depth feature extraction and the parallel neural network are combined, the pressure self-adaptive regulation and control capacity which does not need invasive sensing arrangement and has timeliness and robustness is achieved, meanwhile, a feedback closed loop is formed through a voiceprint-pressure mapping model, and the method is suitable for large-scale popularization and application. And the processing capacity of the system on complex dynamic working conditions is improved.

The invention relates to the field of vehicle-mounted equipment optimization, in particular to a vehicle-mounted equipment optimization method and system based on firmware updating. The method comprises the following steps: carrying out running state full monitoring on a vehicle-mounted firmware module, carrying out multi-behavior state sensing modeling, and constructing a firmware behavior state map; performing dynamic change trend analysis on the firmware behavior state graph, performing performance attenuation situation modeling, and constructing a performance attenuation situation curve; based on the performance attenuation situation curve, code-level program root cause analysis is carried out, firmware optimization target quantification is carried out, and firmware optimization demand data is generated; based on the firmware optimization demand data, multiple optimization direction decisions are made, multi-index optimization solution is carried out, and a firmware optimization task list is constructed; and performing firmware configuration update parameter simulation based on the firmware optimization task list to obtain a configuration update parameter scheme. According to the method, the optimal progressive equipment updating is realized, and the reliability and deployment safety of the vehicle-mounted equipment are improved.

The invention relates to the technical field of automatic driving, in particular to a driving control method and system for an unmanned electric tractor, and the method comprises the following steps: obtaining humidity data to generate a state diagram, judging slip to trigger traction adjustment based on the state diagram, analyzing a slope recognition risk to generate a slope parameter, and reading a braking force to judge, coordinate and generate a distribution instruction. According to the method, the humidity difference is obtained through multi-point soil humidity monitoring, distribution characteristics are extracted, accurate sensing of the state of an operation area is achieved, the wheel rotating speed and traction force signals are combined, slipping is recognized in advance, the traction force is dynamically adjusted, and the driving force is kept stable; based on joint analysis of gradient change and vehicle state, slope risk is recognized in advance, optimization control is performed, braking force of front and rear wheels is dynamically distributed, slippage caused by unbalance is avoided, safety of complex terrains is improved, and vehicle stability, path control precision and energy utilization efficiency are improved through cooperation of multi-dimensional data perception and dynamic prediction.

The invention belongs to the field of traffic control systems, and relates to a vehicle driving track prediction system and method based on an improved graph neural network model, and the system comprises an FPGCN model and a Social LSTM model. The FPGCN model comprises a graph convolution layer, a Dropout layer, a differential pooling layer, a feature fusion module and a full connection layer; obtaining node feature vectors from graph structure data through a graph convolution layer, a Dropout layer and a differentiable pooling layer, then dividing and conquering node features, generating global features through a dense attention mechanism and principal component analysis, and generating local features through virtual filling and matrix calculation; after global features and local features are obtained, a feature fusion module is used for fusion, then the features are input into a full connection layer, and the influence score of a historical trajectory diagram on a current state diagram is calculated; integrating the preprocessed driving data and the data of which the score is calculated by the FPGCN model, and inputting the integrated data into a Social LSTM model; according to the system, while the model expression ability is maintained, the parameter quantity is remarkably reduced, the calculation logic is optimized, and the prediction accuracy is improved.

The invention discloses a vulnerability closed-loop disposal method and system based on intelligent collaboration, and belongs to the technical field of information security management. Vulnerability data and disposal information in a plurality of independent security systems are collected, and a multi-dimensional vulnerability semantic model is constructed; constructing a state perception graph based on a semantic model, extracting candidate state paths, and constructing a time sequence closed-loop prediction model in combination with a shortest closed-loop path and a historical track; according to a vulnerability influence range, a service dependency chain and a prediction path key node, adaptively dividing responsibility affiliation, generating a dynamic disposal responsibility graph and realizing automatic assignment; combining the node response capability and the task saturation, dynamically calculating the priority and scheduling the processing task; monitoring an actual disposal behavior, correcting an edge weight of the state diagram in real time and updating the model; according to the method, the whole-process intelligence, collaboration and dynamic evolution of vulnerability management are realized, and the processing efficiency and the prediction precision are improved.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

1. Name of the product of this design: Toothbrush (TR‑558). 2. Purpose of this design product: for brushing teeth. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: combination state diagram. 5. Other situations that require explanation Kit description: This design product is a complete set of products.

1. Name of the Design Product: Extended Interface of the Intelligent Agent Graphical User Interface for Electronic Devices. 2. Use of the Design Product: For an electronic device. 3. Design Key Points of the Design Product: Lies in the interface content expressed by non-dotted lines in the graphical user interface. 4. Picture or Photo Most Indicating the Design Key Points: Design 1, Variation State Diagram 1. 5. Design 1 is designated as the basic design. 6. Use of the Graphical User Interface: The overall graphical user interface of this design is for the intelligent agent of an electronic device, and the locally protected part is for the extended interface of the intelligent agent graphical user interface of an electronic device. In Design 1, the user can input instructions in ways such as text or voice. After the intelligent agent, as the intelligent assistant of the electronic device, makes a response, the interface is as shown in the main view. At this time, the user can click the "Use" button in the window on the right side of the interface to expand the function extension area of the intelligent agent from one side of this window. The expanded interface is as shown in Variation State Diagram 1. In the interface shown in Variation State Diagram 1, the user can supplement the content of the input instructions or upload materials. When the user completes the relevant operations and clicks the "Generate Outline" button at the bottom of the interface, the intelligent agent will generate the corresponding content, and the generated interface is as shown in Variation State Diagram 2. In Design 2, the user can click on a certain application program card recommended by the intelligent agent, as the intelligent assistant of the electronic device, in the interface shown in the main view. After clicking, the application program window will expand, and the expanded interface is as shown in the variation state diagram. In this interface, the user can click the "Generate with One Click" button in the interface to generate relevant pictures and other content. In Design 3, the user can click on a certain application program card recommended by the intelligent agent, as the intelligent assistant of the electronic device, in the interface shown in the main view. After clicking, the application program window will expand, and the expanded interface is as shown in the variation state diagram. In this interface, the user can click on a certain file card in the interface to use the file management function. In Design 4, the user can click the "Generate Outline" button in the interface shown in the main view to open the outline content preview interface. In Design 5, the user can click the "Generate with One Click" button in the interface shown in the main view to generate pictures and other content. In Design 6, the user can click on a certain file card in the interface shown in the main view to use the file management function. 7. Other Situations Requiring Explanation: Other Explanation: The "X" in the interface represents text, numbers, and / or symbols. The claimed design does not include the interface content represented by dotted lines and the quantity of "X" in each design. Among them, the rounded rectangle frames indicated by relatively thick and dense dashed lines in the middle of the interfaces shown in the state change diagram of Design 1 and the front view of Design 4 are all inherent elements in the interfaces and are not the content that is not required to be protected as mentioned above.

The invention relates to a commercial concrete shared wagon balance site selection method, device and equipment and a storage medium, and belongs to the technical field of facility site selection, and the commercial concrete shared wagon balance site selection method comprises the steps: obtaining commercial concrete demand data and wagon balance state data of a commercial concrete distribution area, drawing a commercial concrete demand diagram and a wagon balance state diagram of the commercial concrete distribution area based on the commercial concrete demand data and the wagon balance state data; a preset double-branch neural network model is adopted to recognize the commercial concrete demand diagram and the wagon balance state diagram, an action value diagram is obtained, and the action value diagram is used for reflecting the action value corresponding to the site selection action of the commercial concrete shared wagon balance in the commercial concrete distribution area; and carrying out commercial concrete shared wagon balance site selection based on the commercial concrete shared wagon balance site selection action with the highest action value in the action value map. According to the method, complex commercial concrete site selection factors can be considered, changing commercial concrete requirements can be responded in real time, an efficient site selection strategy is provided, energy consumption and unreasonable consumption in commercial concrete transportation are reduced, and cost is saved.

The invention relates to the technical field of hydraulic support intelligent supporting scheme design, in particular to a hydraulic support intelligent supporting method and system and electronic equipment. The method comprises the following steps: collecting roof pressure, bracket attitude and geological structure parameters in real time through a multi-modal sensor array; performing spatio-temporal feature fusion by utilizing the edge computing nodes to generate a three-dimensional support state map; constructing a dynamic weight adjustment strategy based on a reinforcement learning algorithm, and generating multi-objective optimization support parameters; a control instruction is issued through a dual-mode communication network to realize self-adaptive adjustment of the supporting strength; and a closed-loop feedback verification mechanism is established in combination with the laser radar scanning roof sinkage. And a digital twin model and an energy consumption optimization function are integrated. The problems that a traditional method is single in data dimension, rigid in decision and insufficient in reliability are solved, dynamic optimization of support parameters, active prevention and control of risks and collaborative improvement of energy efficiency are achieved, and the support safety and the intelligent level under complex working conditions are remarkably improved.

The invention relates to the field of resource management, and discloses a park resource scheduling method and device and a medium, and the method comprises the steps: constructing a park resource agent cluster based on park resource information; constructing a resource space-time state diagram based on a preset rule of the park resource agent cluster and the park resource information; based on a pre-constructed strategy cooperation rule and the resource space-time state diagram, determining an initial scheduling strategy of the resource scheduling event when the plurality of resource scheduling events are triggered; and optimizing the initial scheduling strategy through a multi-target optimization model to obtain a target scheduling strategy of the resource scheduling event. By constructing the agent cluster, when the resource scheduling event is triggered, the initial scheduling strategy can be quickly generated through the resource space-time state diagram of the park resource agents, the initial scheduling strategy is optimized when the resource scheduling conflict occurs, and the resource scheduling efficiency is improved while the current resource scheduling event is met. And optimizing resource scheduling strategies of the current resource scheduling event and other resource scheduling events.

The invention provides a network virus propagation prediction method based on a space-time diagram attention model, and mainly solves the problems of strong dependence on propagation parameters and low prediction precision in the prior art. The scheme comprises the following steps: 1) constructing a space-time infection state diagram based on a network topology structure and virus historical transmission data; 2) processing the historical virus transmission data by using a sliding window mechanism, and constructing a data set; 3) constructing a prediction model based on the space-time diagram attention network in combination with the diagram attention network and the long-short-term memory network; 4) training the prediction model by using the constructed data set, and evaluating the prediction performance of the model; and 5) predicting the propagation trend of the future network virus by adopting the trained model. According to the method, the spatial topological characteristics and the time evolution law of virus propagation are effectively combined, and meanwhile, a graph attention mechanism is introduced, so that the prediction accuracy and the calculation speed of virus propagation in a large-scale network environment can be remarkably improved.

The invention relates to the technical field of air tightness detection, in particular to a diaphragm type energy accumulator air tightness detection method which is used for solving the problems that in the prior art, equipment operation characteristics cannot be accurately described, typical defect modes cannot be recognized in combination with spectral clustering, and a quantitative basis cannot be provided for equipment fault trend analysis and intelligent maintenance. The method comprises the following steps: constructing a low-dimensional state map to describe equipment operation characteristics, identifying typical defect modes in combination with spectral clustering, establishing a nonlinear correlation model to reveal a defect evolution relationship, optimizing classification model parameters by adopting an evolutionary algorithm, improving the identification accuracy, mining a most probable defect evolution path based on a transition probability matrix, and improving the identification efficiency. And a quantitative basis is provided for equipment fault trend analysis and intelligent maintenance.

The invention relates to the technical field of data management, in particular to a real-time system synchronous data management system based on multi-thread processing, which comprises a thread data dependency sensing modeling module, a data processing module, a data processing module and a data processing module, and is characterized in that the thread data dependency sensing modeling module is used for constructing an implicit dependency graph among threads and generating a dependency node table and an access conflict weight matrix; the dynamic master-slave role switching module is used for outputting a main thread guide table and a synchronous window configuration table; according to the synchronous track reconstruction execution module, a master synchronous thread initiates a data consistency instruction according to the synchronous window configuration table, a slave thread executes data playback and state merging operation according to a track deviation correction strategy in a latest master thread guide table, and a unified synchronous data state diagram is output. According to the method, potential conflict paths can be dynamically found, the conflict identification precision and coverage capability in an asynchronous access mode are improved, and the method is particularly suitable for a real-time system which is high in concurrency, mixed in read-write and complex in access relation.

The invention discloses a self-adaptive AI control method based on multi-modal sensing fusion heterogeneous data, and particularly relates to the technical field of multi-modal sensing, and the method comprises the following steps: collecting multi-modal data, constructing a unified time sequence state atlas, and extracting modal activity indexes and variation indexes to represent each modal response and change trend; constructing a multi-path control intention candidate atlas based on the two types of indexes, and dynamically selecting an optimal path to execute a control instruction; the control instruction comprises an action parameter and a modal feedback calibration parameter, and the modal feedback calibration parameter is used for adjusting a subsequent modal acquisition priority; according to the method, time alignment and state fusion of heterogeneous data are realized by constructing a multi-modal state transition map, the recognition capability of the system on environmental changes is improved by using modal activity indexes and variation indexes, a control path and feedback parameters are generated based on dynamic indexes, a closed-loop control mechanism of sensing, decision making and execution linkage is established, and the reliability of the system is improved. And the adaptability and the stability of the system are obviously enhanced.

The invention provides a distributed computing task office resource allocation method and device, equipment and a medium. The method comprises the following steps: in response to a received distributed computing task of an office scene, obtaining a type identifier of the distributed computing task, and associating the type identifier with a preset task stage template; monitoring a current stage identifier of the task process based on the constructed global state graph, and generating an execution stage migration event containing a target stage identifier when detecting that the logic state identifier is changed; matching a resource demand baseline of the target stage identifier according to the task stage template, and generating a target stage resource demand prediction value in combination with the global state map; and traversing the residual resource quantity of all the nodes in the global state map, screening a candidate node set meeting the resource demand predicted value of the target stage, and generating an optimal scheduling strategy based on the data locality parameter and the hardware suitability parameter of each candidate node in the candidate node set. By adopting the method, the execution efficiency of the distributed computing task can be improved.

1. Name of the product of this design: Graphical user interface for editing and debugging digital chip programs of electronic equipment. 2. Purpose of the product of this design: an electronic device. 3. The design highlights of this appearance design product are: the user program editing / debugging tool of the data chip tester and the content and layout of the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. Because they do not have design highlights, the top view, bottom view, left view, right view and rear view of Design 1 to Design 4 are omitted. 6. Designate Design 1 as the base design. 7.Purpose of the graphical user interface: The product's graphical user interface displays the various components of the user program as well as debugging information and edits / debugs the user program. 8. Areas of Graphical User Interface in the Product: Design 1 Main view displays the structured main interface: The interface is divided into menu bar, toolbar, file navigation bar, output bar, and module file preview area. 9. Human-computer interaction method of the graphical user interface: Click the ChannelMap submenu channelmap.chp in the file navigation bar of the Design 1 main view to enter the Design 1 change state diagram 1. The features displayed in the module file preview area include the number of sites, pin enable, channel type, and channel settings for the workstation. The number of sites can be modified directly through the Sites Count setting. Right-click in the Channel Mapping operation area of ​​Design 1 Change State Diagram 1 to display Design 1 Change State Diagram 2. Click Delete Site and Insert Site in the menu of Design 1 Change State Diagram 2. This operation will simultaneously reduce and add Site Count. Design 2 main view displays the structured main interface: the interface is divided into menu bar, toolbar, file navigation bar, output bar, and module file preview area. Click on the ChannelMap submenu channelmap.chp in the file navigation bar of the Design 2 main view to enter the Design 2 Change State Diagram 1. The setting features displayed in the module file preview area include the number of Sites, Pin enablement, Channel type, and Channel settings of the workstation. For the setting of the number of Sites, its modification can be directly set through Sites Count; right-click in the Channel Mapping operation area of ​​the Design 2 Change State Diagram 1 to display the Design 2 Change State Diagram 2, click Add Pin in the Design 2 Change State Diagram 2 menu to enter the Design 2 Change State Diagram 3, use Add Pin to complete the increase of Pins in ChannelMap, and use Delete Pin to complete the reduction of Pins in ChannelMap. The left area represents the PinList to be selected, and the right area represents the selected PinList to be added to the ChannelMap. The left and right areas are edited through the left and right arrows. Design 3 main view displays the structured main interface: the interface is divided into menu bar, toolbar, file navigation bar, output bar, and module file preview area. : Click the ChannelMap submenu channelmap.chp in the file navigation bar of the Design 3 main view to enter the Design 3 Change State Diagram 1. The setting features displayed in the module file preview area include the number of Sites, Pin enablement, Channel type, and Channel settings of the workstation; for the setting of the number of Sites, its modification can be directly set through Sites Count; right-click in the Channel Mapping operation area of ​​the Design 3 Change State Diagram 1 to display the Design 3 Change State Diagram 2, click on the Design 3 Change State Diagram 2 menu and select Import CSV to enter the Design 3 Change State Diagram 3; select the target CSV file to enter the Design 3 Change State Diagram 4, and it also handles exceptions (the occurrence of duplicate Pins), and a prompt box pops up to let the user choose the processing method: Skip import of these items: skip duplicate items, the default option; Replace existing items with imported items: replace duplicate items. Design 4 main view displays the structured main interface: the interface is divided into menu bar, toolbar, file navigation bar, output bar, and module file preview area. Click the ChannelMap submenu channelmap.chp in the file navigation bar of the Design 4 main view to enter the Design 4 Change State Diagram 1. The setting features displayed in the module file preview area include the number of Sites, Pin enablement, Channel type, and Channel settings of the workstation. For the setting of the number of Sites, its modification can be directly set through Sites Count; right-click in the Channel Mapping operation area of ​​the Design 4 Change State Diagram 1 to display the Design 4 Change State Diagram 2, click the Design 4 Change State Diagram 2 menu and select Change Slot Number for Selected Pins, All Sites to enter the Design 4 Change State Diagram 3, and batch modify the slot number of the Channel of all Sites for the selected Pins.

The present application provides an automatic control system and method for a coating machine, which relates to the field of intelligent control. The system first obtains the slurry coating state image and the slurry characteristic text description of the target detection area, and then uses machine learning and natural language analysis technology to perform image encoding on the slurry coating state image, and semantic encoding on the slurry characteristic text description, and generates the detection surface density corresponding to the slurry in the target detection area based on the cross-modal significant interaction of the core clues of the two, so as to adjust the coating machine in combination with the target surface density corresponding to the slurry. In this way, by fusing multimodal data, it is possible to reduce the dependence on environmental factors of a single sensor or parameter estimation method, better understand and process complex coating processes, and make adaptive adjustments according to the characteristics of different slurries, with high flexibility.

The invention relates to the technical field of intelligent manufacturing systems, and discloses an optimal scheduling search method for a multi-robot coordinated cooperation system, which comprises the following steps of: constructing a library timing Petri net model of the multi-robot coordinated cooperation system according to a robot resource use condition and a production process procedure of the multi-robot coordinated cooperation system; constructing a state diagram under a steepest excitation strategy based on a library time-assigned Petri net model, wherein the state diagram retains an optimal state sequence corresponding to the shortest processing time; designing a state merging rule in combination with the structure information of the Petri network, removing redundant states which do not belong to the optimal state sequence, and reducing the scale of the state diagram; the optimal scheduling scheme of the system can be obtained by searching the state diagram through global search. According to the method, a timing Petri net model which effectively represents a multi-robot resource conflict relationship and a processing event logic and time relationship is constructed, and a state space containing an optimal processing event sequence is constructed and searched on the basis, so that an optimal scheduling strategy is solved.

1. The name of this design product: User interface with surgical equipment installation tutorial for electronic equipment. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this appearance design product lies in the content of the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: main view. 5. The key point of this appearance design lies in the content of the graphical user interface in the display screen panel. The display screen panel is of conventional design, omitting the rear view, left view, right view, top view, bottom view and stereoscopic view. 6.Purpose of graphical user interface: used for installation display and control interaction of surgical robots. 7. Human-computer interaction mode of the graphical user interface: Main view installation tutorial step 1 interface, click “>” on this interface to enter the interface change state diagram 1, and click “>” on each interface change state diagram in turn to enter the next interface change state diagram. In the above-mentioned GUI images, the “X” is used to represent text such as numbers, Chinese characters or letters.

The embodiment of the invention provides a fuzzy test method and device for an underwater control system communication protocol, and the method comprises the steps: defining each protocol field and an invariant segment according to a protocol description file, and generating input data meeting the protocol requirements; modeling a protocol interaction process based on the state diagram, and performing multi-path traversal in the state diagram based on input data; monitoring abnormity of network communication and a target process in operation of the fuzzy test program, obtaining feedback information, and adjusting the test case based on the feedback information; and according to the anomaly detection information in the test process, generating a standardized test report and a test case script capable of collapsing and reproducing.

1. Name of the design product: Graphical user interface for operating data of electronic devices. 2. Use of the design product: For electronic devices. 3. Design key points of the design product: Lies in the interface content of the graphical user interface. 4. Picture or photo that best shows the design key points: Front view. 5. Use of the graphical user interface: For displaying operating data. 6. Description of the change states of the graphical user interface: Clicking "This Week" in the front view enters the interface change state diagram 1; Clicking on the time number in the interface change state diagram 1 enters the interface change state diagram 2.

The invention belongs to the field of electric power, and provides a method for performing operation and maintenance on power grid assets based on a large reasoning model, comprising the following steps: collecting multi-source operation data of the power grid assets; constructing a power grid topological structure, and forming a power grid operation state diagram; performing targeted training and optimization on the pre-trained reasoning model to ensure that the running state prediction output by the reasoning model meets the electrical feasible region; enabling the reasoning model to automatically avoid maintenance schemes which do not accord with the load curve, the reserve capacity and the scheduling approval in the reasoning process; optimizing the inference model through a chain type inference loss function, so that the inference model can predict the linkage effect of the equipment fault; and based on an output result of the reasoning model, generating a maintenance action sequence which accords with a feasible region of the power grid maintenance period and can minimize risk conduction. According to the method, the operation and maintenance efficiency of the power grid assets is improved.

1. The name of the product of this design: Graphical operating interface with floor thickness detection for electronic equipment. 2. Purpose of the product of this design: an electronic device. 3. The design highlights of this appearance design product are the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. Designate Design 1 as the base design. 6. Purpose of the graphical user interface: This electronic device is a floor thickness detector. The interface displayed in the main view of this appearance product is the interface of the detector APP. The main display interface includes four modules: thickness detection button, measurement and verification button, data management button, and system setting button, as shown in the main view of Design 1. Click the thickness detection button in the main view of Design 1 to enter the floor thickness measurement interface, as shown in the Design 1 change state diagram. Click the Metrology Verification button in the main view of Design 2 to enter the Metrology Verification interface, as shown in the Design 2 change state diagram.

1. Name of the product of this design: Dynamically activated graphical user interface with voice keyboard for electronic devices. 2. Purpose of this design product: an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: Design 3 interface change state diagram 5. 5. Designate Design 3 as the base design. 6. Purpose of the graphical user interface: The purpose of the interface is to select and start the dynamic change process of the voice keyboard according to user operations, collect the user's voice and convert it into text. 7. Human-computer interaction method of graphical user interface: In Design 1, the main view is the main interface displayed by the electronic device. When the user clicks the input box in the main view, the interface jumps to interface change state diagram 1, interface change state diagram 2, interface change state diagram 3 and interface change state diagram 4 in sequence. In Design 2, the main view is the main interface displayed by the electronic device. When the user long presses the circular microphone icon in the main view, the interface jumps to Interface Change State Diagram 1, Interface Change State Diagram 2, and Interface Change State Diagram 3 in sequence. In Design 3 and Design 4, the main view is the main interface displayed by the electronic device. When the user clicks the input box in the main view, the interface jumps to Interface Change State Diagram 1 and Interface Change State Diagram 2 in sequence; when the user long presses the circular microphone icon in Interface Change State Diagram 2, the interface jumps to Interface Change State Diagram 3, Interface Change State Diagram 4 and Interface Change State Diagram 5 in sequence.