1617 results about "State graph" patented technology

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention discloses an API-oriented dynamic fuzz testing and vulnerability detection method, and belongs to the technical field of software security testing. The method comprises the following steps: extracting a dependency relationship, an input parameter, an output response and context state data of API calling, generating an initial API dependency graph, dynamically updating by capturing API state change in real time, forming an API state graph, and executing boundary-oriented variation based on parameter constraint characteristics, so as to obtain an API state graph; generating a variation test parameter, calling an API (Application Program Interface) of the variation test parameter to monitor a process memory behavior and response metadata, and generating a multi-dimensional abnormal signal; and performing mode matching on the abnormal signal and the vulnerability feature knowledge base, outputting a vulnerability type label and generating a path tracing report. According to the method, a context state sensing dynamic graph modeling technology is adopted, and a boundary-oriented intelligent variation strategy and multi-source abnormal behavior collaborative analysis are combined, so that precise vulnerability triggering, intelligent vulnerability judgment and complex scene coverage can be realized.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

The invention discloses a space chain reasoning-based space intelligent multi-modal space understanding and planning method, relates to the field of space intelligence, solves the problem that the existing space intelligent multi-modal reliability and interpretability are insufficient, and comprises the following steps: S1, obtaining multi-modal space data; s2, constructing a spatial semantic state diagram; s3, task nodes are extracted, the spatial semantic state diagram is traversed, a logical reasoning list is constructed, and a reasoning track is obtained; s4, performing simulation operation on the motion subject model in the space world model based on the reasoning track, recording a simulation position and a simulation operation state, and optimizing the reasoning track to obtain an implementation track; s5, constructing a space cost function, and dynamically optimizing the implementation trajectory according to the space cost function; s6, the reasoning track is corrected; according to the method, the reliability and interpretability of space intelligence are effectively improved in a space chain reasoning mode.

The invention discloses an automatic compliance examination method and system based on graph retrieval enhancement. The method comprises the following steps of: obtaining a billing specification and a historical inquiry report as input documents; the input document is subjected to dual-mode document segmentation based on a large language model, logic blocks and physical blocks are generated, and each logic block is a continuous page unit and is attached with a content abstract generated by the model; constructing a precedent graph, extracting key legal elements in the historical inquiry report through multi-stage recursion, generating semantic network nodes carrying traceability identifiers, and establishing cross-document semantic association; and constructing a state graph, automatically identifying chapters and logic and semantic relationships among the chapters based on a document hierarchical structure, and generating a machine-readable structured index and the like. According to the method, the long text semantic understanding depth, the cross-section consistency and the legal reasoning accuracy are remarkably improved, the manual review cost is reduced, and the method is suitable for a listing compliance review scene under a registration system.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

The invention provides an interactive command line tool-oriented intelligent task planning and execution method and system, and the method comprises the steps: S1, generating a corresponding command sequence template for each operation of a target tool through a first large language model, and constructing an executable knowledge base; s2, calculating the semantic similarity between the task description and each command sequence template to obtain a candidate set, and selecting the most relevant template as a retrieval context by using a second large language model; s3, modeling a task execution process as a state diagram search problem, selecting candidate nodes from reachable nodes of a current node based on an algorithm, and selecting an optimal node according to a current state and a retrieval context by utilizing a second large language model; s4, executing an execution action corresponding to the optimal node, and analyzing tool output by using a second large language model to adjust a heuristic value of the corresponding node and execute a corresponding rollback strategy; and S5, repeating the steps S3 to S4 until the reasoning result shows that the task reaches the target node or the number of attempts exceeds a preset threshold.

The invention relates to the technical field of psychological health intelligent evaluation and collaborative intervention, and discloses a family-school psychological health collaborative method and system.The method comprises the steps that a multi-dimensional data model is built based on the psychological theory, and family-school double-end data collection is achieved through standardized indexes; constructing a multi-dimensional psychological state diagram structure, and realizing home-school data deep fusion by adopting a time sequence dynamic coding method; a time sequence modeling and multi-level risk assessment method is adopted, and a dynamic mental health risk assessment result and an intelligent early warning signal are output; a cross-platform collaborative intervention management method is adopted, and a personalized intervention strategy and a collaborative task allocation scheme are output; and a multi-dimensional effect evaluation and reinforcement learning optimization method is adopted to realize continuous optimization of an intervention strategy. Through artificial intelligence technologies such as the dynamic graph neural network and deep reinforcement learning, intelligent fusion and cooperative intervention of family-school psychological health data are realized, and the early recognition accuracy and intervention effect of psychological health problems are improved.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention relates to an intelligent state monitoring method and system for a high-voltage isolation switch operating mechanism, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: enabling a collected real-time sequence to update a standard operation response potential energy surface, obtaining a real-time operation response potential energy surface, generating a phase response difference tensor through data projection, and obtaining a real-time operation response potential energy surface; and depicting deviation characteristics of actual and standard operation states. And then, in the constructed graph attention network, on the basis of a real-time sequence, monitoring parameters and video motion characteristics, constructing a multi-modal sequence state graph, and determining a state typing result through fine-grained recognition and interpretable analysis of a fault. And finally, introducing a cyclic fingerprint residual model, performing clustering optimization based on a real-time sequence, determining an abnormal deviation index, and combining the abnormal deviation index with a state typing result to determine a fault diagnosis result. Therefore, the accuracy of intelligent state monitoring of the high-voltage isolation switch operating mechanism can be effectively improved, and the method can be suitable for non-ideal environments, low dominant anomalies and other conditions.

The invention discloses a chip circuit state self-diagnosis method based on a graph neural network, and the method comprises the following steps: obtaining a chip netlist, layout and process information, collecting multi-source state data to construct a multi-domain coupling graph, and generating a multi-scale calculation tree based on a critical path; standardizing key node noise data, extracting noise fingerprints, constructing a noise consistency graph, and aligning the noise consistency graph with the multi-domain coupling graph to form node features; inputting the multi-scale calculation tree into an improved GFT model, and generating unified graph representation through structure token coding and feature fusion; constructing a multi-scale transition graph neural network, simulating multi-domain state propagation and outputting an abnormal score; anti-fact operation and reversible disturbance are applied to the high abnormal score nodes to generate an anti-fact state diagram, and a fault causal chain is constructed; and performing topological variation inversion based on the abnormal score and the causal chain to obtain a fault position, a fault type and a fault degradation trend. According to the invention, high-precision explainable chip fault self-diagnosis can be realized.

The invention discloses a surface defect detection system based on a microcrystalline glass panel finished product, and relates to the technical field of surface defect detection. The defect full-dimensional dynamic state map construction module is used for constructing and updating a defect full-dimensional dynamic state map containing defect three-dimensional coordinate information, multi-modal feature confidence information, virtual-entity parameter deviation information and feature definition-registration precision linkage threshold information in real time; the structured light three-dimensional contour acquisition module is used for acquiring three-dimensional contour data of the surface of the microcrystalline glass panel; the multi-modal feature fusion module is used for collecting two-dimensional image data of multi-light-source imaging and extracting multi-modal feature information; the digital twin virtual debugging module is used for constructing a virtual detection environment based on the defect full-dimension dynamic state graph; the problem of feature confusion caused by light source combinational logic solidification in an existing multi-light-source switching system is effectively solved.

The invention discloses an intelligent customer service question and answer optimization method and system based on large model driving, and relates to the technical field of data processing.The method comprises the steps that a semantic flow self-supervision state machine is used for generating a semantic attribution graph and extracting core semantic factors, reverse semantic generation and cross-factor compensation are executed, and a structured target semantic set is constructed; a multi-branch task state diagram is constructed through behavior path inversion, and after a main task state is recognized, two-dimensional semantic projection is carried out, and a strategy tensor is generated; generating a polymorphic answer and constructing a reply text based on the strategy tensor, the main task state and the semantic set; and calculating a deviation vector through a user feedback path and updating the large model. The technical problem that an existing intelligent customer service system is low in semantic understanding precision, single in answer and lack of individuation, and consequently the question and answer response speed and precision are low is solved, and the technical effect that the semantic understanding precision and the answer diversity are improved through a dynamic optimization mechanism driven by a large model, so that the question and answer response speed and precision are improved is achieved.

One or more embodiments of the invention provide a task processing method. The method comprises the following steps: receiving a to-be-executed task; the to-be-executed task is one of tasks which need to be executed and are determined by analyzing a question input by a user; creating a target node corresponding to the to-be-executed task in a state graph; the state atlas corresponds to the question and comprises a plurality of nodes, and edges among the nodes are used for representing dependency relationships among tasks corresponding to the nodes; determining a target tool or a target agent according to the to-be-executed task, and executing the to-be-executed task by using the target tool or the target agent; and under the condition that the execution of the to-be-executed task fails, recovering the to-be-executed task according to the state graph.

Simulation and validation of neural network systems is provided. In various embodiments, a description of an artificial neural network is read. A directed graph is constructed comprising a plurality of edges and a plurality of nodes, each of the plurality of edges corresponding to a queue and each of the plurality of nodes corresponding to a computing function of the neural network system. A graph state is updated over a plurality of time steps according to the description of the neural network, the graph state being defined by the contents of each of the plurality of queues. Each of a plurality of assertions is tested at each of the plurality of time steps, each of the plurality of assertions being a function of a subset of the graph state. Invalidity of the neural network system is indicated for each violation of one of the plurality of assertions.

The invention belongs to the technical field of battery changing cabinets, and discloses a multi-modal data fusion intelligent battery changing cabinet battery state monitoring system and method, and the method comprises the steps: collecting the electrical parameters, environmental parameters and use parameters of a battery; performing sliding window monitoring based on the electrical parameters, and generating a working condition time slice; performing multi-modal feature extraction on the working condition time slice to generate a node attribute vector; node attribute vectors of the latest N working condition time slices are extracted, and a battery state diagram is generated; performing feature learning on the battery state diagram by using a diagram neural network to generate a diagram embedding vector; and inputting the graph embedded vector into the health prediction model to obtain a battery health state value, and generating graded early warning information and maintenance suggestions. The battery state evolution characteristics are effectively captured through dynamic working condition segmentation and graph neural network time sequence modeling, preventive maintenance is achieved, and the problems that a traditional method is single in dimension and lacks time sequence characteristic consideration are solved.

The invention discloses a heterogeneous hardware computing power scheduling method and device, a program product and a medium, and relates to the technical field of computing power scheduling. The method comprises the following steps: collecting a current instruction execution stream and a current temperature of each heterogeneous hardware unit; predicting an expected load mode of each heterogeneous hardware unit based on the current instruction execution stream; determining a thermal expected state of each heterogeneous hardware unit based on the expected load mode and the current temperature; based on the thermal expectation state of each heterogeneous hardware unit, constructing a global thermal expectation state map; when a to-be-scheduled target task is received, determining a first hardware unit in each heterogeneous hardware unit based on the target task and the global thermal expected state graph; the target task is scheduled to the first hardware unit to be executed, and the task queue priority of at least one second hardware unit physically adjacent to the first hardware unit is adjusted according to the global hot expected state graph. The method has the effect of improving the accuracy of computing power scheduling.

The invention discloses a standardized work management system for a standard system revision project, and the system comprises a heterogeneous data fusion module which captures the multi-modal process data of each stage in real time, and converts the multi-modal process data into a process state vector sequence; a time sequence constraint graph rule base is built in the compliance graph calculation module, a process state graph with time sequence attributes is constructed, and the multi-dimensional compliance deviation degree is calculated through graph mode matching; the strategy generation and conflict resolution module is used for generating a candidate set with a weight adjustment instruction through a learnable strategy priority ranking model based on the deviation degree and resolving logic conflicts; and the trusted execution and feedback closed loop module atomizes an execution instruction and carries out uplink evidence storage, and collects project response data to update rule base parameters and model weights online. According to the method, automatic and accurate compliance evaluation is realized through graph calculation, self-evolution of the system is realized through intelligent strategy generation and credible execution feedback, and the management efficiency, compliance and intelligent level are comprehensively improved.

The specification discloses an agent evaluation processing method and device, a storage medium and an electronic equipment, wherein the method comprises: determining an agent service object driven by a transaction processing large model, performing graph structure evaluation benchmark generation processing on the agent service object to obtain a graph structured task, the graph structured task comprising a directed acyclic graph composed of multiple subtask nodes, controlling the agent service object to call the transaction processing large model to perform transaction task autonomous processing on the graph structured task, obtaining subtask node processing state information of the agent service object in the transaction task autonomous processing process, performing node state updating on the graph structured task based on the subtask node execution state information to obtain a structured task state graph, and performing task processing capability evaluation processing on the agent service object based on the structured task state graph.

1. The name of the design product: basketball game interface of mobile phone. 2. The use of the design product: for running programs. 3. The design points of the design product: the interface content and layout of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: front view. 5. The mobile phone is a conventional design, and other views are omitted. 6. The use of the graphical user interface: the interface of the present design is mainly used for the graphical user interface of basketball game activities. 7. The human-computer interaction mode of the graphical user interface: in the "main view" interface, by clicking the "latest game" button at the top of the interface, enter the "change state diagram 1" interface; in the "change state diagram 1" interface, by clicking the "bullfight" button at the bottom of the interface, enter the "change state diagram 2" interface; in the "change state diagram 2" interface, by clicking the "user avatar" in the already reserved basketball court (such as No. 1 court) in the interface, enter the "change state diagram 3" interface; after waiting for the bullfight time to end, the system automatically jumps to the "change state diagram 4" interface.

1. The name of the design product: electronic device with cartoon image dynamic online customer service consultation user interface. 2. The use of the design product: an electronic device. 3. The design points of the design product: the content of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The entity product is a common design, and other views are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: the design product is used for enterprise website platform consultation. When users visit the website, they can conduct online consultation by clicking different buttons of online customer service on the page. 8. The human-computer interaction mode of the graphical user interface: design 1 front view, design 2 front view, design 3 front view, and design 4 front view are all initial interface diagrams; design 1 interface change state diagram is the interface change state diagram based on design 1 front view, automatically scrolling complete subtitles and automatically playing IP image animation, wherein the enterprise IP image circulates to welcome users by opening hands, and the left side circulates to display "xxxx company welcomes you!" subtitles from bottom to top; design 2 interface change state diagram is the interface change state diagram based on design 2 front view, moving the mouse to the blue telephone icon, wherein the consultation hotline is displayed, and the telephone information can be copied through the mouse; design 3 interface change state diagram is the interface change state diagram based on design 3 front view, moving the mouse to the blue email icon, wherein the email information is displayed; design 4 interface change state diagram is the interface change state diagram based on design 4 front view, moving the mouse to the blue communication icon, wherein the scan code information and text are displayed.

1. The name of the design product: patient history operation graphical user interface for electronic device. 2. The use of the design product: the design is used for an electronic device. 3. The design points of the design product: the graphical user interface in the screen. 4. The picture or photo that best shows the design points: front view. 5. The appearance design contains colors. 6. The use of the graphical user interface: the interface of the design displays the patient's hospital records, key medical information and data changes of key indicators through graphical presentation, and understands the disease changes through the history of diagnosis and treatment. 7. The human-computer interaction mode of the graphical user interface: the main view is the operation home page of the patient's history of diagnosis and treatment; click the red line "July" on the history of diagnosis and treatment timeline of the interface change state diagram 1 to enter the interface change state diagram 2; click the green line "December" on the interface change state diagram 1 "history of diagnosis and treatment timeline" to enter the interface change state diagram 2; click the left upper corner of the interface change state diagram 2 "history of diagnosis and treatment" to enter the interface change state diagram 3; click the blue bar block above "May" of the interface change state diagram 3 to enter the interface change state diagram 4; click the red dot below "October" of the interface change state diagram 4 to enter the interface change state diagram 5.

1. Name of the designed product: gift box (garlic puff). 2. Use of the designed product: packaging for food. 3. Design points of the designed product: combination of shape and pattern. 4. Picture or photo that best shows the design points: combination of use state diagram.

1. The name of the design product: the real-time monitoring display of the graphical user interface of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: interface change state diagram. 5. The use of the graphical user interface: the graphical user interface for real-time monitoring during the microphone monitoring process. 6. The human-computer interaction mode of the graphical user interface: the main view is the system main interface, and the main view jumps to the interface change state diagram after the user clicks the up and down arrow keys on the outside of the microphone.

1.The name of the design product: service card of decoration service graphical user interface of electronic equipment. 2.The use of the design product: an electronic equipment. 3.The design points of the design product: in the local part of the graphical user interface. 4.The picture or photo that best indicates the design points: the front view. 5.The use of the graphical user interface: used as a whole as a decoration service graphical user interface, and the service card is used to show service information. 6.The change process of the graphical user interface: the service card in the form of a card in the decoration service graphical user interface of the front view is shown, and different cards can be viewed by sliding left and right, and in the case of sliding up and the like, the interface change state diagram is entered, and the service card is shown in the form of a control in the interface change state diagram, and the corresponding control can be clicked to further view information and the like. 7.The design is a local design, wherein the blue coated part is not claimed.

1. Name of the product in this design: Unlock control portion of the graphical user interface for screen lock transition of electronic device. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the parts of the graphical user interface that require protection. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of graphical user interfaces: screen locking to prevent accidental touches; unlocking control for areas requiring protection. 6. Human-computer interaction method of graphical user interface: As shown in the main view, the display screen is in locked mode, with a circular lock icon and prompt text displayed in the center of the interface; when the user long-presses the icon area in the center of the interface, the interface enters the dynamic change process of change state diagrams 1 to 5: the central filled circular background area instantly expands to cover the center of the screen, and then an unfilled ring pattern gradually expands and grows outward from the center in a concentric circle, forming a light and dark contrast with the filled background; as the operation is completed, the interface changes to the state of change state diagrams 6 to 7: the expanded filled background area and the unfilled pattern quickly shrink and disappear, and the central lock icon changes to the open state, indicating that the unlocking is successful. 7. Other situations requiring explanation: This design is a partial protection design, and the dotted line portion is not part of the content to be protected.