80 results about "Dependency network" patented technology

The invention relates to the field of servo motor parameter optimization, in particular to a magnetic field simulation and parameter optimization method and system for a permanent magnet servo motor rotor. The method comprises the following steps: acquiring a rotor global magnetic field monitoring curve in the operation process of a servo motor rotor, and constructing a global magnetic field accurate compensation model; performing regional magnetic circuit structure analysis on the global magnetic field accurate compensation model, performing dynamic magnetic field logic mapping modeling, and constructing a real-time regional magnetic field logic map; performing magnetic field state change analysis on the global magnetic field accurate compensation model, performing multi-node change track correlation analysis, and constructing a multi-node magnetic field coupling dependency network; and performing short-term magnetic field evolution prediction based on the multi-node magnetic field coupling dependency network, and performing comprehensive electromagnetic performance evaluation to obtain an electromagnetic performance evaluation report. According to the method, the sudden change of the magnetic field is accurately and quickly identified, so that the magnetic field parameters are iteratively adjusted and optimized, and the service life and the performance of the servo motor are improved.

The invention discloses a nuclear accident emergency three-layer coupling network model and a construction method, and relates to the crossing field of nuclear accident emergency management and complex system risk simulation. The nuclear accident emergency three-layer coupling network model comprises an RMN layer which is used for simulating the dynamic diffusion process of radionuclides leaked by a nuclear accident in the atmosphere; the IN layer is used for constructing a spatial distribution and dependency relationship network of infrastructures; the EN layer is used for topologically defining a command or cooperation relationship of the emergency organization; according to the three-layer network coupling mechanism, when RDR of a middle grid is larger than or equal to RDRmax-1, IN-layer nodes in the middle grid at the same position are marked to be in a damaged state; and quantifying the bidirectional dependence density of the IN layer and the EN layer through the coupling strength epsilon. The method is used for predicting the direct risk of radioactive contamination in a nuclear accident and the secondary risk caused by emergency evacuation, revealing the risk evolution law, quantifying the risk diffusion scale, determining key influence factors, and providing a theoretical basis for formulating an emergency strategy.

The invention relates to the technical field of network security detection, and discloses a network transmission protocol intrusion detection method and system. The method comprises the following steps: acquiring a network transmission protocol data stream, extracting head features and load features of a data packet, and integrating to form an original feature set; the original feature set is classified according to protocol types, different protocol clusters are divided, and corresponding identifiers are marked; constructing a dynamic feature filter based on the protocol cluster identifier, and screening a feature subset associated with the current protocol cluster from the original feature set; inputting the feature subset into a protocol state analysis model, and outputting a protocol state vector and abnormal behavior probability distribution by the model; and generating an intrusion detection result according to the two, and triggering a corresponding defense response instruction. According to the method, different protocol characteristics are adapted through protocol cluster classification and dynamic characteristic screening, accurate identification of protocol intrusion behaviors is realized, and the method can be applied to various scenes depending on a network transmission protocol.

The invention provides a cloud native micro-service splitting and reconstructing method based on runtime behaviors. A three-layer architecture design is adopted, and the system comprises a dependency network graph construction module, a micro-service division module and a code reconstruction module. The dependency network diagram construction module models specific project source codes and user access operation into abstract representation capable of being processed by an algorithm, the micro-service division module divides a network diagram, obtains a splitting scheme for the dependency network diagram through visual page interaction with a user and manual modification, and inputs the splitting scheme into the code reconstruction module; and a code reconstruction module performs code reconstruction based on the splitting scheme and the dependency network diagram to obtain a complete and operable micro-service architecture code, so that the effect of automatically analyzing Spring Boot project source codes and monitoring the running project to generate the dependency network diagram is realized.

The invention discloses a medicine sales management method and system based on information monitoring, and relates to the technical field of medicine sales management, and the method comprises the steps: collecting medicine sales time sequence data, carrying out the cleaning and structural processing, and generating standardized sales data; based on the standardized sales data, constructing a sales behavior time sequence causal network, embedding virtual sales intervention nodes, and generating a time sequence causal graph; the intervention strategy is coded to the virtual intervention node in the time sequence causal atlas, the graph neural network is utilized to simulate time sequence propagation of the intervention strategy, and a sales prediction result is obtained; and combining the sales prediction result with the real-time inventory, constructing a multi-level distributed sales dependency network, and forming the sales dependency network. According to the method, the preset intervention strategy is embedded into the causal structure in the node form, so that dynamic modeling of the multivariable causal relationship in the sales behavior is realized, and the prediction result not only reflects the historical trend, but also can respond to the possible influence of different intervention strategies.

The embodiment of the invention discloses a component recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a component recommendation operation triggered by a target user; obtaining a component dependence network graph, component demand description information of the target user and historical interaction information of each historical interaction component used by the target user in the current editing item; determining a plurality of to-be-screened components from a component library on the basis of the component dependency network graph and the historical interaction information; based on the component dependency network diagram, the component attribute information of each component in the component library, the component demand description information and each piece of historical interaction information, determining the adaptation attribute of each component relative to the target user; according to the method, the to-be-screened components are screened on the basis of the adaptive attributes, the target recommended component is obtained, the component search efficiency and modeling fluency are remarkably improved, the burden of repeatedly trying different component search conditions by a user is reduced, and finally the efficiency and quality of the overall modeling process are optimized.

The invention discloses an AI test case intelligent optimization verification method and system, and the method specifically comprises the steps: employing a causal discovery algorithm to analyze an implicit dependency relationship for an entity association structure in a test case knowledge graph, recognizing redundant test cases and invalid test paths, and obtaining a causal dependency network; on the basis of a causal dependence network, traversing uncovered entity relationship paths in the test case knowledge graph, and generating supplementary test case suggestions; dynamically adjusting analysis strategy parameters according to historical optimization data through a self-adaptive engine to obtain an optimization test case set; and based on the optimized test case set, generating an input parameter variation sample by using a fuzzy test engine, and expanding a test boundary. According to the method, the black box test efficiency can be remarkably improved, the test coverage rate and accuracy are improved, meanwhile, the workload of a test engineer is relieved, the user experience is improved, and an efficient and intelligent solution is provided for the field of software test.

The invention discloses a software multi-defect positioning method and device based on variable dependency and clustering. According to the method, for a program block covered by a failed test case, a static analysis method is adopted, a data dependency, control dependency or function dependency relationship among variables is extracted, and a variable dependency network model is constructed to represent defects. Based on the above defect characterization, a graph editing distance is used to measure the similarity between the different characterization. And then selecting a proper clustering algorithm to adapt to fault characterization and similarity measurement modes, and automatically grouping failed test cases according to defect types to be used for positioning. According to the method, a more comprehensive and effective defect characterization algorithm is constructed, and a more effective failure test case difference measurement method is combined, so that the number of correctly predicted defects is effectively increased. By improving the accuracy of defect clustering, an independent defect focusing suspicious degree ranking is generated, and an effective solution is provided for a multi-defect positioning problem.

The embodiment of the invention provides a component repairing method, system and equipment based on a software supply chain, a medium and a product, and relates to the technical field of information security. The method comprises the steps of obtaining component information of a software supply chain based on a full life cycle of software development; wherein the component information at least comprises a component material list, component dependency relationship data and component risk data; constructing a dependency network graph based on the component information; determining a graph type and a spectral radius of the dependency network graph; determining a propagation threshold corresponding to the graph type; if the spectral radius is greater than or equal to the propagation threshold, determining a to-be-repaired component set from the dependency network graph based on the graph type, the spectral radius and the propagation threshold; and repairing the to-be-repaired components contained in the to-be-repaired component set to obtain a repaired software supply chain. The security defense capability of the software supply chain can be improved, and stable and reliable operation of a software system is guaranteed.

The invention relates to an unmanned aerial vehicle cluster communication network full-end reliability prediction method based on GNN and network signature, and belongs to the technical field of communication networks, and adopts two-stage prediction: a first stage, determining topological structure characteristic variables of an unmanned aerial vehicle cluster communication network, generating a training data set based on simulation, and designing, training and optimizing a graph neural network model; the prediction of network signature vectors is realized, and the signature only depends on network structure characteristics and does not depend on link reliability parameters; and in the second stage, on the basis of the signature vector, the all-end reliability of the network in a link failure scene is calculated by combining link reliability parameters and utilizing a signature-based all-end reliability deterministic hybrid expression. According to the method, the sensitivity of the model to the reliability parameter change is reduced, and the generalization ability and applicability of the prediction method under different network scales and reliability conditions are improved.

The application provides a multi-event extraction method, system, device and medium based on a graph self-dependent network, relates to the field of natural language processing information extraction, and the method comprises the following steps: acquiring primary prediction information; adopting a graph self-dependent network to perform multi-event dependency relationship modeling to obtain global event information; outputting trigger word text based on the primary prediction information and the global event information; adopting a conditional fusion function to integrate the trigger word text in the detection text to obtain the primary prediction information; adopting the graph self-dependent network to perform multi-argument dependency relationship modeling to obtain global argument information; and outputting event argument text based on the primary prediction information and the global argument information. The application uses the graph self-dependent network to mine the dependency between multi-events, does not need to rely on other natural language processing tools, allows learning the relevance of different trigger words and arguments, and makes the graph self-dependent network have good expansibility and effectiveness.

The invention discloses an automatic deployment method for a long safety chain network based on Kubernetes Operator, and the core scheme of the method is to define double-layer custom resources (CRD): ChainMakerNetwork is used for declaring chain-level global configuration, ChainMakerNode is used for declaring node-level configuration, through the cooperative work of a node controller and a network controller, the network controller is used for declaring the global configuration of the chain-level, and the network controller is used for declaring the global configuration of the chain-level and the node-level configuration of the chain-level. A node controller generates an identity for each node and reports the identity to a resource state field in advance, and a network controller aggregates information to generate a global chain configuration file after monitoring that all dependencies are ready, so that the mechanism of the scheme breaks the circular dependency that node starting depends on network configuration, and network configuration generation depends on node identity; according to the invention, one-key and declarative deployment and full-life-cycle management of the long safety chain network in the Kubernetes environment are realized, and the deployment efficiency, reliability and operation and maintenance automation level are greatly improved.

This invention discloses a method, apparatus, electronic device, and storage medium for cross-device NPU invocation. The method includes: converting the NPU of a terminal device within a network into an NPU service object through an NPU virtual service, wherein the number of terminal devices within the network is at least two, and each terminal device within the network possesses an NPU; registering the NPU service objects of each terminal device to the distributed hardware management service by calling the registration interface of the distributed hardware management service through the NPU virtual service; when a target terminal within the network receives a task to be processed, querying the target information of each NPU service object within the network through the distributed hardware management service, and determining the target NPU service object for processing the task based on the target information. The technical solution of this invention solves the problems of current edge-cloud combined methods for processing neural network tasks, which rely on network latency and cannot meet all operating conditions, achieving efficient utilization of the NPUs of devices within the network and improving task processing efficiency.

This invention discloses a BIM model-based intelligent management method and system for prefabricated buildings, belonging to the field of prefabricated building management technology. Specifically, it includes: acquiring BIM data containing component geometric attributes, material properties, and installation logic relationships, as well as supply chain data on component production status, transportation location, and warehousing information; constructing an assembly dependency network with components as nodes and installation dependencies as edges; monitoring supply chain data in real time and identifying abnormal components; locating abnormal nodes in the assembly dependency network and traversing to determine the affected assembly paths; retrieving matching alternative components based on warehousing information and replacing nodes; and finally recalculating the installation sequence of affected components based on the updated assembly dependency network to generate a new assembly sequence. This method enables rapid response and dynamic adjustment of the assembly process to supply chain anomalies.

This application discloses a diagnostic control method, apparatus, electronic device, and storage medium. When a first connector receives a first data frame that a first device needs to send to a second device, and the response type of the first data frame is a target response type, the first connector replaces the second device in sending a first acknowledgment frame for the first data frame to the first device. Specifically, after sending the data frame of the target response type, the first device interrupts the transmission of the data frame to be sent to the second device. Upon receiving the first acknowledgment frame, the first device continues to send the data frame to be sent to the second device to the first connector, so that the first connector can receive and forward the data frame to the second device. Based on this, by using the first connector, which is adjacent to the first device, to replace the second device in acknowledgment responses, the data frame transmission process does not require reliance on network acknowledgment interactions, reducing timeout problems caused by network latency.

The application relates to the technical field of network security detection, and discloses a network transmission protocol intrusion detection method and system. The method comprises the following steps: collecting network transmission protocol data streams, extracting packet header features and payload features, and integrating to form an original feature set; classifying the original feature set according to protocol types, dividing different protocol clusters and marking corresponding identifiers; constructing a dynamic feature filter based on the protocol cluster identifiers, screening a feature subset associated with the current protocol cluster from the original feature set; inputting the feature subset into a protocol state analysis model, outputting a protocol state vector and an abnormal behavior probability distribution from the model; generating an intrusion detection result according to the two, and triggering a corresponding defense response instruction. Through protocol cluster classification and dynamic feature screening, the method adapts to different protocol characteristics, realizes accurate identification of protocol intrusion behaviors, and can be applied to various scenes relying on network transmission protocols.

The invention discloses a response equipment adaptive dynamic regulation and control method under panel furniture production line disturbance. The method comprises the following steps: establishing a production line equipment dependence network model, defining equipment as nodes, defining a dependency relationship between the equipment as directed edges, and configuring weights; when a disturbance event is received, calculating an initial disturbance intensity value and determining an initial disturbance node; on the basis of the initial disturbance intensity value and the initial disturbance node, longitudinal propagation and transverse diffusion of disturbance in the network model are simulated, and a comprehensive disturbance index is calculated for each equipment node; comparing the comprehensive disturbance index with a dynamic response threshold value, determining a response equipment set, and outputting a regulation and control instruction according to the response equipment set; according to the method, through quantitative disturbance intensity and network propagation simulation, prediction and targeted intervention of the disturbance chain reaction are realized, the stability and efficiency of a production line in a dynamic environment are improved, and excessive response or insufficient regulation is avoided.

The invention relates to the technical field of order management, and discloses an order management method based on an industrial cloud computing service, and the method comprises the steps: collecting the production scheduling data of M production ends connected to a target industrial cloud platform; determining a production dependency relationship of a production end, and constructing a production scheduling network; obtaining a target order, and extracting demand data of the target order; and executing a bidding decision of the target order based on the production scheduling data, the demand data and the production scheduling network. According to the invention, the production scheduling data of a plurality of production ends are collected and intelligently analyzed in real time, the production dependence network is constructed, and order management automation is realized based on the accurate bidding decision, so that the order processing efficiency and accuracy are obviously improved, the production resource configuration is optimized, and the decision risk is reduced.

The invention discloses an intelligent environmental sanitation vehicle collaborative scheduling method and system based on the Internet of Things, particularly relates to the technical field of big data analysis and intelligent scheduling, and is used for solving the problems that an existing environmental sanitation vehicle scheduling method is single in optimization target, cannot dynamically collaborate operation timeliness and energy consumption, and is low in efficiency. And the overall energy efficiency is low due to chain reaction caused by local adjustment. Real-time data of environmental sanitation vehicles, operation tasks and energy supply facilities are obtained through the Internet of Things, a resource competition network is dynamically constructed based on the real-time data, statistical significant mutation of topological vulnerability of the resource competition network is recognized, and then linkage influences caused by the mutation are analyzed. A dynamic resource dependence network is constructed based on linkage influence to identify a new key path formed due to betweenness centrality cumulative mutation, and then long-term comprehensive efficiency of different scheduling adjustment strategies on the key path in terms of operation efficiency and energy efficiency is evaluated. And finally, generating and issuing a target collaborative scheduling instruction which enables the overall long-term comprehensive efficiency of the motorcade to be optimal.

The application provides a knowledge base document search method and device, electronic equipment and storage medium, and relates to the technical field of data processing, in which, during hierarchical storage, the heat score of the uploaded document is used, the heat score of the uploaded document considers not only the access frequency of the uploaded document, but also the business scenario, that is, the hot and cold hierarchical strategy is closely related to the business scenario, in addition, the composite tag of the uploaded document generated includes core keywords, semantic synonyms and structured business tags, and the coverage is comprehensive, when the abstract is generated, a lightweight local model is used, the network is not needed, the required computing power and hardware conditions are not high, and the effect of artificial intelligence semantic search is good.

This invention discloses a method for mitigating social propagation risk and selecting robust groups based on extreme value dependency networks and maximally independent sets. The method includes: acquiring user interaction data from a social network and constructing a graph structure based on the user interaction data; obtaining target node pairs from the graph structure for a preset propagation activity period and calculating the extreme value dependency of the target node pairs; constructing propagation networks with target nodes as vertices and extreme value dependency as the edge construction criterion under different dependency thresholds, evaluating the propagation networks, and determining the extreme value dependency propagation network based on the evaluation results; dividing the extreme value dependency propagation network into communities and determining central nodes from the divided communities; constructing a minimum extreme value dependency group based on the extreme value dependency propagation network; and identifying high-risk node groups in the minimum extreme value dependency group using a propagation loss index to obtain the identification result. This invention can achieve the identification, isolation, and control of tail propagation linkage relationships.

This invention discloses a method, apparatus, and medium for accessing a clustered file system, applicable to virtualization and hyper-converged infrastructure. Based on block file data I / O operations, the Paxos algorithm is applied to determine the target node for lock request access. The entire process achieves node-to-node access without relying on the network. I / O operations are not limited by specific storage through read and write operations. An added lock region is used for inter-node competition for lock resources. Disk regions are partitioned to target metadata structures requiring concurrent access protection. Then, based on the Paxos algorithm and the kernel block layer interface, I / O requests are directly issued to allow target nodes to compete for lock resources. This avoids the problem of existing clustered file systems being unable to simultaneously achieve network independence and storage independence during concurrent access, improving the stability and usability of clustered files.

The present disclosure provides a simulation data transmission method adapted to transmit data of a three-dimensional simulation space over a network for rendering presentation on at least one graphical user interface, the method comprising: performing a first calculation comprising processing particles of the three-dimensional simulation space according to a smoothed particle hydrodynamic algorithm, generating first particle data; executing first coding according to the first particle data to generate second particle data; executing compression according to the second particle data to generate third particle data; and transmitting the third particle data through the network. According to the method, the bit mark, the space division, the data compression and the sliding window are combined, the technical scheme for real-time data transmission between the SPH particle system and the graphic rendering system is formed, the method does not depend on network hardware or protocols, and the system complexity and the deployment cost are reduced.

The invention discloses a network real-time time acquisition method and device, equipment, a medium, a product and a vehicle, and relates to the technical field of vehicles. The network real-time acquisition method comprises the steps that a network real-time acquisition request sent by a target application service is received, and the target application service is any application service in a vehicle; in response to the network real-time acquisition request, the network real-time is determined according to the current time and time deviation of the node where the target application service is located, the time deviation is calculated in advance according to the first real-time of the network time protocol server when the node is in the first time, and the first time is the time before the current time; and feeding back the network real-time time to the target application service. Through the scheme disclosed by the invention, the requirement of the service depending on the network real-time time in the node can be met.

The invention discloses a vocational education large model construction method and system based on a knowledge graph, and relates to the technical field of vocational education artificial intelligence modeling, and the method comprises the steps: extracting teaching entities from a teaching outline and a skill standard, and constructing an entity dependence network. Implanting a dynamic observation node in the network to capture and record the state evolution trajectory of the entity in different teaching situations; and performing automatic re-calibration on the static logic boundary in the network according to the evolution data, identifying and correcting the entity relationship cluster with the fuzzy boundary, and forming an optimized network. Based on the network, a teaching target is mapped into an executable teaching intention chain layer by layer. According to the method, the model can perceive and fuse dynamic feedback of a teaching process, and self-correction and optimization of a knowledge structure are realized, so that the characterization precision and teaching adaptation capability of the vocational education model to complex and nonlinear skill logic are improved.

The invention provides an intelligent triage system for emergency treatment grading and a method thereof. The intelligent triage system comprises a patient end, a nurse end, a physician end and a cloud end, the patient end is configured to obtain multi-dimensional vital sign information of an emergency patient and generate symptom expression description according to the vital sign information; the cloud end is configured to construct a symptom dependency network based on a preset symptom association relationship according to symptom expression description, past medical history abstracts of emergency patients and treatment time information, perform hierarchical emergency degree adjustment on the symptom dependency network, update the emergency degree of each symptom item through influence conduction among nodes, generate a dynamic emergency degree feature set, and send the dynamic emergency degree feature set to the cloud end; inputting the dynamic urgency degree feature set into a pre-trained triage decision model, generating a grading evaluation result including grading grades and intervention timeliness requirements, and determining recommended disposal departments and triage schemes including treatment priorities and necessary examination items according to the grading evaluation result; the nurse end and the doctor end are configured to receive and display the triage scheme.

The application provides a multifunctional medical self-service method and system, wherein the application balances the treatment intention set by a patient to obtain a final treatment intention; deeply analyzes applications and services in a source medical self-service software ecology to form a source ecology component dependency graph, construct a component dependency network, identify a tightly coupled component cluster, predict an influence range caused in a medical self-service software ecology treatment process, and generate an analysis report; matches the source ecology component dependency graph with target medical self-service software ecology characteristics to generate a customized treatment path and a mapping rule set, and obtains an optimized mapping strategy; applies selected applications and services from the source medical self-service software ecology to the target medical self-service software ecology, discovers and corrects abnormal conditions in a dynamic treatment process, and generates a treatment state monitoring report. The technical scheme provided by the application improves the safety and adaptability of the medical self-service software ecology treatment.

The invention discloses an industrial internet security protection strategy arrangement method and related equipment, and relates to the field of network security protection. By adopting the technical scheme, the security protection equipment combines industrial physical process state perception and network behavior risk assessment and determines the security protection intervention coefficient based on dynamic association of the two, so that the technical problem that in the prior art, a security protection strategy lacks perception of the industrial physical process state, and the security protection intervention coefficient is influenced is effectively solved. And the technical problem of false alarm or missing alarm possibly caused by risk assessment only depending on network layer information is solved, so that the flexibility and the adaptability of a security protection strategy in an industrial internet environment are improved, and the overall security protection efficiency of an industrial system is improved. And the dynamic balance capability of safety protection and production continuity in an industrial internet environment is improved.

The invention discloses a method for calculating inference accuracy of a symbiotic network topology structure based on a path identification prefix, which belongs to the technical field of symbiotic networks and enables an observer of a network topology to compare an inferred and constructed symbiotic network topology with a reference topology or an actual topology. And the accuracy of topology inference is comprehensively evaluated. The calculation method does not need to depend on specific attributes of network nodes, and can effectively reflect and deduce the completeness and accuracy of the topology and the separation degree and the aggregation degree between the topology and the actual topology. The method comprises the following steps of: firstly, respectively acquiring whether path identification prefixes carried in an actual topology and an inferred topology are equal or not to define a corresponding relation between the actual topology and the inferred topology; comparing the proportions of the corresponding nodes in the inference topology and the actual topology so as to evaluate the inference completeness and accuracy; and finally, based on the corresponding probability, reflecting the separation degree and the aggregation degree of the deduced topology to the actual topology through the average entropy of the deduced and actual topology.

The invention discloses a rail robot pig identification method based on a weak network condition, and relates to the technical field of pig identification, and the method comprises the steps: building a rasterized map for a pig house, and building a mapping relation from an image pixel coordinate to a ground world coordinate; distortion correction is carried out on an image collected in the advancing process of a rail robot above the pig house, pigs are recognized, and a bounding box and a segmentation mask of each pig are obtained; calculating the number of pixels based on the segmentation mask, and performing perspective transformation to obtain a real area; when front and back frames collected when the rail robot crosses over the overlapping area belong to different grids respectively, judging whether the same pig is the same pig or not by analyzing the area change of the same pig in the front and back frames; and updating pig calculation according to an overlapping region matching result, and outputting a total recognition number. The method performs offline calculation, does not depend on a network, and works stably in a weak network environment.