180 results about "Traversal algorithms" patented technology

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention provides a project management method, system and device and a medium, and belongs to the field of project management. The method comprises the following steps: collecting project related data, and sorting and cleaning the project related data to obtain a structured data set; adopting an entity identification and relation extraction algorithm to carry out entity labeling and relation extraction processing on the structured data set so as to construct a knowledge graph related to the project; the method comprises the following steps: performing task decomposition by adopting a graph traversal algorithm on the basis of a knowledge graph, generating a task list and a priority sequence thereof, and monitoring task progress and resource utilization rate in real time on the basis of a dynamic optimization algorithm so as to dynamically adjust task priority and a resource configuration strategy. The task module is automatically decomposed based on the knowledge graph, the task decomposition efficiency and accuracy are improved, intelligent resource allocation and task priority adjustment are achieved in combination with dynamic planning and an optimization algorithm, the team cooperation efficiency is improved, and the flexibility and adaptability of a project are improved.

The invention provides a sensitive data identification protection method and system based on intelligent matching, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic analysis of sensitive data through a natural language processing model, and constructing a multi-level knowledge graph; performing node coding and relation intensity calculation by using a graph neural network and an attention mechanism; a graph structure importance degree evaluation algorithm is adopted to calculate node sensitivity weights and grade the node sensitivity weights; and applying a corresponding data protection scheme according to the sensitivity level, and identifying associated nodes through a graph traversal algorithm to perform hierarchical protection. According to the method, intelligent identification, accurate grading and comprehensive protection of sensitive data are realized, and the data security is improved.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention provides an intelligent supervision management method and platform based on multi-terminal interaction and data fusion, and the method comprises the following steps: obtaining a supervision standardized file and a to-be-used supervision resource of an electric power engineering project, extracting a key entity through a natural language processing technology, and recognizing an entity relationship; based on the entities and the incidence relation thereof, a multi-level project knowledge graph is constructed, and a heterogeneous graph neural network inference engine is formed in combination with a graph neural network model. Multi-dimensional supervision data are collected through an intelligent supervision terminal, the data are processed by using a self-adaptive cross-modal attention mechanism, and a project event cause representation is generated. Based on the representation, multi-level reasoning is carried out in a reasoning engine through an improved graph traversal algorithm, and an abnormal event result set caused by project event causes in a future time period is predicted. And in combination with the to-be-used supervision resources and the abnormal event result set, adopting a reinforcement learning optimization algorithm to generate an optimal supervision dispatching strategy. According to the invention, efficient supervision and resource optimization configuration can be realized.

The invention relates to the technical field of financial data analysis, in particular to a financial statement multi-dimensional incidence relation visual display method which comprises the following steps: extracting subject codes, amount values and business dimension labels, and generating a standardized financial data set with timestamps; generating an initial edge set based on a preset subject association rule base, endowing each edge with a weight value reflecting the sensitivity of a service scene through a dynamic weight calculation module, and forming a multi-dimensional association network with a weight label; identifying a cross-level association path through a path traversal algorithm, and generating a recessive relationship set including a path intensity index; and dynamically and visually displaying. According to the method, the fusion expression capability of the complex financial flow is enhanced, and a clear data basic support is provided for subsequent multi-dimensional modeling and analysis.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention discloses an unmanned aerial vehicle full-coverage path planning method fusing a greedy strategy and an A star algorithm. The method belongs to the technical field of full-coverage path planning, and comprises the following operation steps: firstly, initializing a map and obstacles, and defining a marker quantity; secondly, evaluating attributes of surrounding points, and selecting a next point by using a greedy strategy; then, judging whether a dead zone is entered or not, and if so, re-planning a path to get rid of the dead zone by using an A star algorithm; if not, continuing to perform the greedy strategy to find the next advancing point; then, the coverage completion condition is checked, and whether each point is covered or not is checked; and finally, verifying the effectiveness of the full-coverage traversal algorithm in a three-dimensional environment. According to the invention, the problems of low efficiency, low coverage rate, high repetition rate, limitation in a complex environment and the like when the current technology is used for full-coverage traversal inspection, detection and cleaning of chemical industrial parks and coal mines and agricultural plant protection are solved.

The invention provides an application environment and version management method and system suitable for a low-code platform, and relates to the technical field of platform management.The application environment and version management method comprises the steps that a directed dependency graph is constructed by obtaining change records, and a dependency loop is detected and intelligently decomposed through a depth-first traversal algorithm; and packaging the node clusters into micro-services based on a function relevancy model to form an acyclic directed dependency graph, constructing a version backtracking index and a difference analysis matrix to determine a change influence range, executing incremental deployment according to a topological sequence, and recording deployment information. According to the method, the problem of version management and environment dependence in a low-code platform is solved, and the application deployment efficiency and the system stability are improved.

The invention relates to the technical field of ship manufacturing, and discloses a ship block assembly man-hour matching method based on multi-modal process feature recognition. Multi-modal process data including structured process parameters, unstructured process documents and the like are collected, each modal process feature vector is obtained through feature extraction, and then a fusion feature vector is generated through a multi-modal feature fusion algorithm. And constructing a man-hour matching knowledge base according to historical assembly order data, and determining an assembly process sequence and a man-hour allocation scheme by using a dynamic matching algorithm. According to the method, a process set can be optimized through a graph traversal algorithm, real-time sensor data are analyzed to deal with abnormal fluctuation, and process compliance is verified by means of a process rule knowledge graph. According to the method, multi-modal data can be effectively fused, the man-hour matching precision is improved, the assembly process is optimized, the ship block assembly efficiency and quality are improved, and the cost is reduced.

The invention discloses a graph theory-based industrial internet identification system full-process tracing method, which realizes full-process tracing in a supply chain by introducing identifier fields into metadata and constructing a directed graph structure. The identifier fields include fields with similar representations for representing associations of the product upstream and downstream of the supply chain. The system queries across enterprises through a graph traversal algorithm (such as depth-first search (DFS) or breadth-first search (BFS)), and tracks the full life cycle of a product. The method supports attribute pointing code management, is used for tracing multi-attribute information (such as allocation weight, time and the like) of a product, solves cross-enterprise and cross-system coding differences through an alias mechanism, and simplifies a tracing process. The method has a flexible traceability path setting capability, can monitor the product circulation process in real time, gives out an early warning when an abnormality is found, and improves the transparency and safety of a supply chain.

A content channel generation device comprises a resource unit assignment circuit, for assigning scheduled station(s) as node(s) of a full binary tree according to a search algorithm; a node computing circuit, for determining first node connection information of the full binary tree, and to determine second node connection information of a smallest full binary tree according to a smallest binary tree algorithm and the first node connection information; a load balance circuit, for determining user field numbers corresponding to content channels according to a load balance function and the second node connection information; a user field generation circuit, for generating a traversal result of the smallest full binary tree according to a traversal algorithm and the second node connection information, and for generating user fields corresponding to the content channels according to the traversal result, to generate the content channels.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

The invention relates to the technical field of network security, and discloses a security vulnerability AI positioning method and system based on log files. The method comprises the following steps: firstly, analyzing user feedback describing open source component security vulnerabilities to extract fault key entity information, screening related log entries based on the fault key entity information, and constructing a vulnerability propagation graph; identifying a vulnerability triggering initial point set by using a graph traversal algorithm and a graph neural network, calling a component vulnerability case library to deduce a vulnerability utilization propagation chain and calculating a matching degree with a known open source component vulnerability utilization chain feature, and obtaining a maximum probability vulnerability utilization propagation chain and an associated vulnerability triggering initial point set; and generating a root cause positioning report, and updating the knowledge base or model training data according to a user verification result. According to the method, the initial vulnerability point and the complete vulnerability propagation chain of the security vulnerability of the open source component can be accurately and efficiently positioned, and the positioning accuracy and timeliness are improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention discloses a regression testing method and device, equipment and a medium, and relates to the technical field of software testing, and the method comprises the steps: collecting current code change data between a previous version code and a current version code of to-be-tested software, and generating current change details; performing static code analysis and dynamic code analysis on the current version code to obtain an initial dependency relationship graph and a runtime dependency relationship, and obtaining a target dependency relationship graph based on the initial dependency relationship graph, the runtime dependency relationship and the current change details; traversing each dependency relationship in the target dependency relationship graph by using a graph traversal algorithm to obtain a propagation path influenced by the current code change data, and determining a regression test range of the current version code by using the target pre-training model and the propagation path; and continuously integrating and deploying the assembly line to execute the test case set generated based on the regression test range, and completing the regression test to obtain a current regression test result. And the regression test efficiency and timeliness rate are improved.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention discloses a quick and automatic layout method for a warehouse logistics conveying system based on Unity 3D and CAD. The method comprises the following steps: extracting detail data of warehouse logistics conveying equipment; exporting and optimizing warehouse logistics conveying equipment detail data, and adjusting data in exported equipment detail table grids and a list arrangement sequence; the optimized details of the warehouse logistics conveying equipment are imported into Unity; and querying and analyzing data in the equipment details on the basis of a linear traversal algorithm in combination with matrix transformation, and automatically substituting corresponding equipment position information, rotation parameters and scaling into matrix transformation to complete spatial layout and attribute assignment of the warehouse logistics conveying system. Compared with an existing traditional conveying system layout technology, the data conversion step can be reduced, and data interaction of Unity3D and CAD is improved; the modeling cost is reduced, and the project economic benefits are improved; errors of spatial layout and attributes are reduced, and the model precision is improved; compared with the prior art, the layout efficiency of the conveying system can be improved by 70%-85%.

The invention provides a two-hop incremental interface consistency verification method and system based on a knowledge graph, and the method comprises the steps: 1, carrying out the language analysis of a modified source file, and extracting an interface entity; 2, mapping the interface entity into a language-independent meta-model, and writing the language-independent meta-model into a knowledge graph; 3, executing a two-hop breadth-first traversal algorithm by taking a modified node in the knowledge graph as a starting point to obtain an affected sub-graph; 4, a hot-pluggable rule set is operated on the affected subgraph for verification, the rule set comprises a parameter number matching rule, a type compatibility rule, a version number increasing rule and a field deletion forbidding rule, and if verification is illegal, an error report is generated, and a blocking instruction is output; and if no violation exists in the verification, merging the affected sub-graphs back to the knowledge graph cache. According to the method and the device, online faults caused by field missing during operation are avoided, and the influence of back-end change on the front end is automatically detected and blocked.

The invention relates to a red-black image recognition and extraction method and system based on a power distribution network line, and belongs to the technical field of data processing. The method comprises the following steps: acquiring a red image and a black image of a power distribution network line from a GIS system; traversing the red image and the black image based on a set traversal algorithm, identifying switch equipment nodes in the power distribution network, and constructing a topological structure of the power distribution network; wherein a traversal algorithm is set to express the power distribution network as a graph, then node traversal is carried out, and verification is carried out through a preset rule; and based on the power distribution network topological structure, automatically constructing a power distribution network sketch. According to the embodiment of the invention, a complex and disordered power distribution network red-black diagram is converted into a power distribution network sketch which is matched with the switch equipment nodes more conveniently and visually, a solid foundation can be laid for subsequent intelligent monitoring of the power distribution network, and the data processing precision is improved.

The invention belongs to the related technical field of distributed system optimization and scheduling, and particularly relates to a distributed parallel training method of a deep neural network in a dynamic computing environment, which comprises the following steps of: determining a model segmentation scheme by minimizing the actual convergence time of an asynchronous pipeline, the model slices corresponding to the segmentation scheme are distributed to corresponding equipment nodes, and the actual convergence time is the product of the upper limit of the number of iterations corresponding to a preset convergence error and the optimal running time of the assembly line required by single iteration; each device node is configured with a trained lightweight random forest model which is used for evaluating the interference influence of the additional load located at the device node on the training efficiency of the model slice located at the device node. After training is started, interference coefficients corresponding to interference influences obtained by the random forest models on all equipment nodes are obtained in real time, a new segmentation scheme is obtained through model search space pruning and a traversal algorithm, and efficient training is achieved through layer migration.

The invention relates to the technical field of aeronautical manufacturing, in particular to a method, a system and equipment for rapidly compiling a wire harness process file based on a graph theory and a medium. The method comprises the following steps: firstly, according to an aircraft assembly mode, constructing part assembly feature knowledge, and defining a multi-proportion part, a small-proportion part and a part type; secondly, according to the obtained MBD data, part assembly relation information is extracted, and a part assembly relation graph is constructed; then, calling a graph traversal algorithm, and matching nodes of the part assembly relation graph with part assembly feature knowledge to obtain a part assembly relation; finally, a process file is compiled to express the assembly relation of parts; by planning the assembly method of different types of parts of the aircraft wire harness, the standard description of wire harness assembly is standardized, and the assembly efficiency of the aircraft wire harness is improved.

The embodiment of the invention provides an authority control method and device based on fine granularity, and the method comprises the steps: carrying out the node relation extraction of a mapping database, obtaining a role node and a product node, and introducing a user node and a context node; the method comprises the following steps: constructing a hyperedge structure, performing hyperedge linking on role nodes, product nodes, user nodes and service context nodes, determining a corresponding hyperedge relationship, performing dynamic weight endowing on the hyperedge relationship according to a time decay factor and a context risk coefficient, determining a dynamic graph structure, and constructing a directed acyclic graph based on the role inheritance relationship. Fusing the dynamic graph structure and the directed acyclic graph according to a graph algorithm, and determining a corresponding product permission layer graph database; the method comprises the steps of receiving a user operation instruction, performing context-aware query based on the operation instruction according to a graph traversal algorithm, determining a user permission, and performing permission control on a user according to the user permission. The method can improve the efficiency and the refinement degree of permission allocation.

The invention discloses a multi-source property right data verification method, and relates to the technical field of data processing. The method comprises the following steps: acquiring property right data from different data sources, and converting the property right data into a unified field according to semantics of the property right data and a preset mapping rule; after the knowledge graph is constructed based on the uniform field, the business rule vectors are coded into node attributes of the knowledge graph; and traversing nodes and edges in the knowledge graph through a preset graph traversal algorithm, and verifying the property right data according to the business rule vector to obtain a verification result. According to the method, the association relationship between the knowledge graph integration entities is constructed, and the service rule is encoded into the knowledge graph for verification, so that verification is not limited to single node data any more, cross-node verification can be performed in combination with the association information, and the verification accuracy is improved.

The invention relates to the technical field of industrial data management, in particular to a supply chain data real-time integration processing method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining an external compliance data document, calling a document layout analysis model based on a multi-modal converter, extracting a text semantic vector, fusing the text semantic vector with a two-dimensional space coordinate feature to generate a composite index key, and carrying out the real-time integration processing of the supply chain data. Executing clustering analysis to recognize the text blocks and generating structured metadata; carrying out discretization processing on the business transaction data flow, and constructing an associated database with production environment and logistics data as attributes; executing cross-modal entity alignment by using the structured metadata, and establishing a bidirectional pointer link between the unstructured document index and the associated database; receiving a query vector, executing a recursive traversal algorithm based on a bidirectional pointer link, calculating a cascade influence probability as a correlation sorting score, and outputting a retrieval result; through multi-modal feature fusion and cross-modal entity alignment, deep integration and risk linkage retrieval of supply chain heterogeneous data are realized.

The invention provides a digital main line processing method based on an industrial data atlas, a medium and a system, and belongs to the technical field of the industrial data atlas. Unified access of cross-system data is realized by constructing a multi-source data acquisition adapter, cleaning and standardization processing are performed on heterogeneous data, an integrated data set is constructed, and the data collection efficiency is improved. Data entities are abstracted as nodes, cross-system association relationships are abstracted as edges to construct an industrial data graph, a graph index optimization strategy and a hierarchical storage architecture are adopted to improve cross-system query performance, a graph adaptive evolution model is utilized to realize dynamic adjustment of a graph structure, and a cross-system deep association analysis function is realized through a graph traversal algorithm. And an access control and data security mechanism is established to complete system protection, so that the technical problem that the industrial data processing system is difficult to realize cross-system deep correlation analysis is solved.

The invention discloses a network fault prediction method, and relates to the technical field of fault prediction. The method comprises the following steps: constructing a cross-level semantic topology model; screening a key link by using a node importance algorithm and extracting a structural fingerprint vector; calculating the deviation degree between the current link and the structure fingerprint vector, and judging that the structure is abnormal if the deviation degree is smaller than a threshold value; constructing an anomaly propagation rule base according to historical faults and operation and maintenance data; when a plurality of structure anomalies appear at the same time, the conflict resolution algorithm determines an anomaly dominant factor; potential signals are collected according to the abnormal dominant factors, weights are given according to a rule base, and weighting is carried out to obtain comprehensive potential signals; based on this, a fault propagation path is constructed, and a path weight is calculated by using a Pearch ranking algorithm; a fault chain is rehearsed through a depth-first search graph traversal algorithm; and verifying the rehearsal chain by using a preset test statistic circumstantial evidence to obtain a fault prediction result. According to the invention, on the basis of the fault prediction result, cross-level network fault prediction in network propagation is realized.

The invention provides an anchor position detection method based on a traversal algorithm and a data set optimization algorithm and application thereof, and the method comprises the steps: carrying out the successive operation of an anchor ground obstacle coordinate set and an anchor position point coordinate set which is generated through a Monte Carlo random algorithm and is used for simulating a to-be-moored ship through an anchoring region detection model of an anchored ship; the method comprises the following steps: acquiring a to-be-moored ship anchoring position point two-dimensional coordinate set meeting a ship safety spacing value, performing traversal operation on points in an anchoring position circle in combination with a data set optimization algorithm, and checking a traversal result with a to-be-moored ship draught water depth value, so as to obtain to-be-moored ship anchoring position points meeting two-dimensional plane and three-dimensional water depth requirements. Three-dimensional detection of the ship anchor position can be realized through water depth limitation on the basis of meeting two-dimensional safety spacing limitation, anchor position point data meeting the water depth limitation are provided for a driver while plane safety spacing is met, and the accuracy and safety of ship anchor position detection and selection in actual anchoring operation are greatly improved.

The invention discloses a software supply chain risk detection method and system, a medium and a product, and relates to the technical field of risk detection. The method comprises the following steps: acquiring multi-source heterogeneous data of a software supply chain, and constructing the multi-source heterogeneous data into a multi-dimensional software supply chain relation graph; querying nodes and attributes associated with the target asset node based on the multi-dimensional software supply chain relation graph to extract risk features; obtaining an initial risk score of a vulnerability node associated with the target asset node from the public vulnerability library, calculating the shortest propagation path depth between the vulnerability node and the target asset node by using a graph traversal algorithm, and adjusting the initial risk score in combination with code reachability analysis to obtain a corrected technical risk feature; and calculating a comprehensive risk score based on the corrected technical risk characteristics, and executing automatic safety examination and approval, interception or manual reexamination operation. By implementing the technical scheme, the software supply chain risk can be comprehensively and accurately detected.

The invention relates to the technical field of aircraft manufacturing, and discloses a part model cross-BOM tracing method and system based on a global identifier and a medium, which are used for recording and tracing data formed in a process procedure planning and compiling process, and comprise the following steps: constructing part model metadata for each part model, defining a normal form of part model metadata; storing data formed in the process of programming the technological procedure into part model metadata; constructing a graph network of the part model metadata according to the logical relationship between the part model metadata; and adopting a graph traversal algorithm to trace the full life cycle process data of the part model in the graph network. According to the method, part model metadata is constructed for each part model, data formed in the process of planning and compiling the technological procedure is recorded according to the part model metadata, a graph network is established based on the part model metadata, and tracing of the whole life cycle of the data is achieved.