133 results about "Node level" patented technology

The invention discloses a cluster computing power energy efficiency perception scheduling and green computing system, which relates to the technical field of computers and comprises a multi-source energy efficiency perception and data acquisition module used for acquiring power consumption, utilization rate, temperature, cooling state, PUE index and environmental data of cluster nodes. According to the invention, through the multi-modal energy efficiency fusion sensing network and the multi-scale convolution and time sequence attention fusion network, multi-source heterogeneous energy efficiency data such as current, voltage, temperature, airflow and the like of a node level can be collected and fused in real time and with high precision, noise is effectively removed, abnormity self-correction is realized, the defect of energy efficiency sensing granularity in the prior art is made up, and the energy efficiency sensing precision is improved. And reliable input is provided for subsequent scheduling decisions. A cross-scale dynamic twinborn collaborative modeling mechanism is adopted, a physical information neural network and a computational fluid mechanics model are coupled, optimization is carried out through a generative adversarial network structure, and accurate prediction of a complex energy consumption evolution curve and a cooling flow field is achieved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses a space-time sequence interpolation method and device oriented to heterogeneous deletion, and belongs to the technical field of space-time data processing. Aiming at random or continuous loss of the sensor network caused by faults and communication interruption, the method comprises the following steps: setting static space experts, dynamic space experts, short-term experts and long-term period experts in parallel in the same framework, and respectively capturing fixed geographical adjacency, time-varying space correlation, local continuous trend and long-period rules; spatial features are extracted through high-order diffusion diagram convolution and bidirectional gating circulation, time features are extracted through multi-layer space-time attention, a memory attention gating network is introduced to dynamically weight and fuse output of experts according to reconstruction errors, and node-level and time-step-level self-adaptive interpolation is achieved. Experiments show that compared with the prior art, the method has the advantages that under various real data sets and heterogeneous missing scenes, the precision and robustness are remarkably improved, and the method can be widely applied to scenes needing high-integrity spatio-temporal data, such as intelligent transportation, air quality monitoring and energy internet of things.

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

The invention discloses an FPGA (Field Programmable Gate Array) netlist-level hardware Trojan horse detection method based on a large language model, and relates to the technical field of integrated circuit safety and hardware Trojan horse detection, and the method comprises the following steps: constructing an original FPGA netlist into a text attribute graph containing textualized attributes, generating a path text sequence through bidirectional random walk, and carrying out two-way random walk on the path text sequence; constructing a corpus to pre-train a large language model; then, delimiting a local neighborhood by taking each node as a center, generating a path text set, extracting semantic vector representation of the path text set by utilizing a pre-training model, and further constructing a node-level training sample set; on the basis, supervised fine tuning is carried out by combining a classifier and CB-Focal Loss, and a final model is obtained; in the reasoning stage, representation construction and discrimination are carried out on nodes to be detected, and node-level hardware Trojan horse detection is achieved. According to the method, circuit topology and semantic information can be reserved at the same time, node-level hardware Trojan positioning is achieved, the detection precision and generalization ability are improved, and the automation degree is improved.

The invention provides a high-performance virtual scrolling method and system supporting a large-scale tree structure, and the method comprises the steps: constructing a dual-mapping data table structure of the tree structure, which comprises a node mapping table and a sublevel relation mapping table; when it is detected that the node height changes, the virtual rolling rendering engine generates a flattened rendering list in real time based on the unfolding state of the corresponding node in the node mapping table; the virtual rolling rendering engine calculates rendering positions of nodes in the visual area through a dynamic position prediction algorithm; in response to the rendering location and the device type, the virtual scrolling rendering engine dynamically adjusts the virtual scrolling window and the tree structure node hierarchical indentation pitch, and renders in the virtual scrolling window according to the flattened rendering list. According to the application, the data relationship and the rendering position are decoupled through the double-mapping data table structure, the smooth rolling of ten thousand-level nodes can be realized, the memory occupancy rate is reduced, the interaction response speed and the rendering performance are improved, and the method has rolling stability, cross-device display consistency and deployment flexibility.

The invention discloses a service abnormity monitoring method and system based on dynamic graph calculation, and relates to the technical field of service monitoring data processing, and the method comprises the following steps: generating a service graph structure based on real-time service interaction data and service association attributes of an ERP report, performing node rationality dynamic evaluation based on the node basic stability parameter and the associated service parameter, and executing node dynamic adjustment based on the node update resource occupation condition and the report granularity mode according to a node rationality dynamic evaluation result; performing node weight dynamic processing based on the service interaction parameter of each associated service of the node and the node rationality dynamic evaluation result, and performing service influence dynamic evaluation based on the node weight dynamic processing result and the node level; and performing node risk dynamic assessment based on the service influence dynamic assessment result and the node dynamic stability parameter, and performing monitoring dynamic regulation and control processing based on the node risk dynamic assessment result.

The invention discloses a multi-mode local voltage control method and system based on a deep neural network, and relates to the technical field of power system operation optimization, and the method comprises the steps: constructing a multi-mode sample set comprising node voltage, photovoltaic output and load data, setting a voltage upper limit, a voltage lower limit and a hysteresis interval, and dividing operation modes; establishing a control mode selection unit by using a deep neural network, and establishing a reactive power output prediction model by using a convolutional neural network; and performing localized deployment based on the trained controller model, and performing node-level adaptive voltage control and real-time reactive power optimization according to the hysteresis interval and the residence time. According to the method, stable division and feature layering of voltage states are achieved, representativeness of model training samples and precision and stability of voltage mode recognition are improved, collaborative optimization of mode judgment and adjustment output is achieved, stable switching and dynamic optimization of the voltage states are achieved, and system immunity and stability of multi-node operation are improved.

The invention relates to a desensitization method, system, equipment and medium for industrial data, and the method comprises the steps: building and dynamically maintaining an industrial field standard data variable rule table, and defining a desensitization strategy and a sensitivity level matched with specific industrial variables such as temperature, rotating speed and the like in physical meanings, time scenes and node levels of the specific industrial variables; the method comprises the following steps: adding a multi-dimensional identifier to original industrial data, establishing a six-dimensional binding relation model of data variable type-time dimension-node hierarchy-cluster role-sensitive level-rule entry, driving the data to execute a time-coordinated stepped desensitization process in four node hierarchies of a workshop, an enterprise, a park and a cluster, according to the method, refined access control and data decryption fusing time, space and role dimensions are realized according to the model and a full-link tracing log, and the problems that rules and industrial scenes are disjointed, dynamic time adjustment is lacked, cross-node collaboration is insufficient and authority control coarse granularity is caused in the prior art are effectively solved.

The invention discloses an underwater Internet of Things destroy-resistant link optimization method combining self-guided graph representation and AMF. The method comprises the following steps: 1) generating a scale-free topology and a candidate link set according to node positions, residual energy and underwater acoustic channel quality; 2) inputting the topology into a self-guided graph representation learning model, and applying contrast constraint through unsupervised training to obtain robust node embedding representation; 3) on the basis of candidate link two-end embedding, combining node degree, betweenness centrality, bridge edge coefficient and link success rate to construct feature representation, and generating an existence probability matrix through a probability mapping function; 4) inputting the matrix into adaptive multi-layer filtering (AMF), reserving edges according to Top-k to form a connected skeleton, setting a hierarchical threshold according to node hierarchy and load, and maintaining approximate power law degree distribution; and 5) applying a global connectivity constraint, and outputting an optimized link structure. According to the method, link optimization can be realized without supervision labels, network connectivity and survivability are improved in an attack environment, and the method is suitable for ocean monitoring and security scenes.

The invention discloses a time sequence root cause analysis method and system based on dynamic causal inference, and belongs to the technical field of industrial system fault diagnosis and intelligent operation and maintenance. The method comprises the following steps: collecting and preprocessing multivariable time series data in an industrial system; performing dynamic causal topology discovery by using an improved DAG-GNN algorithm, and generating a dynamic causal topology reflecting system state transition; performing spatio-temporal feature aggregation of causal guidance through GCN and LSTM, and extracting joint representation fusing node local abnormal information and a system-level global fault trend; node-level and system-level collaborative analysis is carried out through a multi-granularity root cause inference module, and a root cause candidate set with confidence ranking is generated; according to the method, the defects in the aspects of dynamic causal modeling, feature coupling and multi-granularity reasoning in the prior art are overcome, accurate tracing of a cross-level fault chain can be achieved, and reliable and explainable decision support is provided for intelligent operation and maintenance of an industrial system.

The invention discloses an AGV full-life-cycle intelligent operation and maintenance method based on a twin map and reinforcement learning, and belongs to the technical field of equipment operation and maintenance management, and the method comprises the steps: employing an edge-level dynamic attenuation function and a node-level economic weight operator for aggregation, and obtaining a health vector containing a whole vehicle risk score and a part first-level risk identifier; the method comprises the following steps: training a reinforcement learning agent according to a three-section reward function comprising production line instant income, health improvement income and future risk deduction, and determining an operation and maintenance strategy weight file by adopting a profit-oriented gradient update mode optimization strategy with risk suppression factors; and carrying out equipment operation and maintenance management on the AGV in workshop operation according to the strategy weight file. According to the method, dynamic risk propagation and economic quantitative evaluation are adaptively fused under the real-time working condition of a workshop, and a comprehensive optimal decision of maintenance opportunity and production line income is realized under the condition of non-stop observation through a reinforcement learning strategy closed-loop driving AGV fleet preventive maintenance and scheduling method.

The invention provides a full life cycle carbon emission evaluation method based on a transformer substation prediction model, and the method comprises the steps: building an operation structure tree based on the distribution characteristics and function relevance of a physical entity object of a transformer substation by taking a carbon emission entity object of each full life cycle stage as a tree node; mapping physical attributes and dynamic data of each carbon emission entity object to a digital twin platform based on node levels in the operation structure tree to obtain virtual nodes; associating the dynamic data of the virtual node with the static data of each full life cycle stage based on the node identifier in the running structure tree to obtain a full life cycle data chain; based on the associated data of each tree node in the full-life-cycle data chain, calculating a carbon emission result in each full-life-cycle stage; and according to a father-child node relationship of the running structure tree, upwards aggregating carbon emission results of lower-level nodes of each tree node to obtain a total carbon emission evaluation amount. According to the invention, the accuracy of a carbon emission evaluation result is improved.

Embodiments of the present disclosure provide a data processing method and cluster, a computing device, a computer readable storage medium, and a computer program product. The data processing method is applied to an application program interface service unit of the data processing cluster. The method comprises: receiving a data processing request sent by a client for a target storage unit, wherein the data processing request carries data to be processed and a unit path of the target storage unit; analyzing the data to be processed, and when it is determined on the basis of an analysis result that the data attribute of the data to be processed is a target data attribute, encrypting the data to be processed to obtain ciphertext data and an encryption key; and sending the ciphertext data and the encryption key to the target storage unit on the basis of the unit path, thereby minimizing an attack surface of a malicious user to a node level, and reducing the risk of sensitive data leakage.

The invention provides an attack detection and source tracing method and system for a multi-mode AI system, and relates to the technical field of artificial intelligence. The method comprises the steps that input data, model internal states, system logs and behavior data are collected, and time mark high-frequency records are unified to form a multi-modal data set; encoding each time window graph snapshot of the dynamic graph by applying a neural network, and generating a graph overall embedded vector and a node embedded vector to capture spatial structure and time sequence dependence; acquiring a graph embedding representation of a current time point according to the graph overall embedding vector and the node embedding vector, executing attack detection based on the graph embedding representation, and triggering an alarm when an anomaly is detected; attack traceability is carried out in an abnormal time point diagram snapshot, a node-level abnormal score is calculated through node embedding, and key nodes, edges and sub-graphs are identified by using attention weight of a neural network and a graph interpreter so as to locate attack entry points, propagation paths and influence ranges.

The invention relates to the technical field of rehabilitation monitoring, in particular to an old people rehabilitation monitoring method and system based on big data. An old people rehabilitation monitoring method based on big data comprises the following steps: S1, constructing a three-level edge computing architecture according to a rehabilitation monitoring task of old people, and obtaining rehabilitation monitoring related data; s2, according to the rehabilitation monitoring related data, using an architecture grading calculation formula to obtain an architecture grading value of an old people grading rehabilitation task; and S3, dynamically selecting a main edge computing node level for executing the rehabilitation monitoring task of the old people according to the architecture level value, and correspondingly adjusting a data transmission path and task processing of monitoring equipment. According to the invention, by constructing a three-level edge computing architecture and combining real-time positions of old people, personalized working peaks and comfort feedback, dynamic self-adaptive scheduling of computing node levels and data acquisition frequencies can be realized.

The invention provides a power grid node equivalent inertia analysis method, and relates to the technical field of power grids. According to the invention, after the disturbance event occurs, the operation data of each node is identified and classified, the disturbance type, the disturbance position and the disturbance duration of the disturbance event are determined, scene adaptation analysis is carried out in combination with the initial frequency change rate and the power unbalance amount of each node, accurate adaptation of the disturbance type of each node is realized, and the accuracy of the disturbance type of each node is improved. And analyzing the equivalent inertia from the node level, determining an equivalent inertia analysis result of each node, generating a space-time distribution map, and identifying the inertia weak node in each node. Through power grid node-level equivalent inertia analysis, the problem that the traditional global average inertia is difficult to describe frequency response conditions of different regions and nodes is solved, and the evaluation precision of the frequency stability of a power system is improved.

Current data center deployments, particularly layer 2 (L2) deployments that use multi-chassis link aggregation group (MC-LAG) implementations, have limitations. Such deployments use a link, known as an inter-node link (INL) / inter-chassis link (ICL), to connect peer information handling system nodes. Because the peer nodes are coupled together via one or more INL connections, there are fewer ports available for each peer node to connect to end nodes. This configuration creates limitations to bandwidth and scaling, and also increases costs. Embodiments herein allow for the elimination of INLs by moving forwarding decisions to the spine node level. In one or more embodiments, a spine node determines ports to reach dual-homed, single-homed, and orphaned nodes that are connected to leaf node(s) based on information learned from the leaf nodes. For a leaf node, a sub-LAG (link aggregation group) may be created to reach single-homed / orphaned nodes connected to the leaf node.

The invention relates to the technical field of computer cluster communication, in particular to a set communication method suitable for a core particle equipment cluster with unified bus interconnection, which comprises the following steps of: acquiring hierarchical topology and link information of the core particle equipment cluster, and constructing a global topology information base according to the acquired hierarchical topology and link information; optimizing the core size level primitive, the node level primitive and the super node level primitive; a link monitoring unit is deployed to monitor a link state index in real time, an alarm is triggered when a link fault is detected, an optimal backup link is screened based on a global topology information base, a communication path is updated, and a dynamic resource scheduling algorithm is adopted to allocate computing power and communication resources; and dynamically adjusting a communication path and bandwidth allocation according to a set communication task requirement and a link dynamic parameter. The method is suitable for a three-level link architecture of a core particle equipment cluster, can improve the communication efficiency and the bandwidth utilization rate, enhances the cluster reliability and the resource utilization rate, and effectively meets the requirements of high computing power and high communication intensity of large model training.

The invention belongs to the technical field of medical scene consensus, provides a medical alliance block chain grouping consensus method and system based on leader group decision, and aims to solve the practical problems of high communication complexity, power concentration and main node failure of a traditional PBFT in a large-scale node scene. Through hierarchical design of leader group decision, a grouping strategy is adopted to prevent a power concentration phenomenon, and through leader group parallel decision, post quantum security is guaranteed, communication overhead is reduced, consensus efficiency is improved, and the power concentration phenomenon is avoided.

The present application belongs to the technical field of wind tunnel test, and discloses a method and device for verifying the effect of hot deformation prediction of a flexible nozzle panel. The present application builds a complete verification closed loop of data acquisition-surface reconstruction-node level comparison-statistical determination, quantitatively evaluates the hot deformation prediction accuracy based on the maximum absolute error and statistical distribution characteristics, and clearly distinguishes between valid and invalid states, solving the problem of lack of standardized verification means in the prior art and providing a reliable accuracy evaluation basis for flexible wall nozzle surface control.

This application discloses a protocol migration method, apparatus, electronic device, and medium, relating to the field of database technology. The method includes: generating a flow control rule set based on a preset migration order and node information of cluster nodes in a target database that have enabled a dual-protocol stack listening mechanism; the target protocol address is generated by the cluster nodes based on a stateless address auto-configuration mechanism; distributing the flow control rule set to network devices connected to the cluster nodes, enabling the network devices to perform flow control on the cluster nodes according to the flow control rule set; and controlling the target database to update the address field of the cluster nodes in the cluster topology table from the original protocol address to the target protocol address, so that the cluster nodes switch node communication from the original protocol to the target protocol based on the updated cluster topology table. This application achieves full-process automation of protocol migration, precise flow control at the node level, and seamless and smooth decommissioning of old protocols without any service interruption.

The utility model provides a storage and calculation integrated server and a data center, and relates to the technical field of server architecture design, and the storage and calculation integrated server comprises a calculation resource module and at least one storage resource module; the storage resource module comprises a first data processor, a second data processor, a backboard assembly and a plurality of storage units. The first data processor and the second data processor are both connected with the computing resource module, the first data processor and the second data processor are further connected with the backboard assembly, and the backboard assembly is further connected with the multiple storage units. And the first data processor accesses part or all of the storage units according to a storage instruction sent by the computing resource module. And the second data processor accesses part or all of the storage units according to a storage instruction sent by the computing resource module. The first data processor and the second data processor are arranged to form a double-control double-active high-availability architecture, so that the problem of single-point failure of storage resources in the server is solved, and the reliability of storage node levels is improved.

The invention relates to the technical field of communication networks, and particularly discloses a communication network performance evaluation method based on artificial intelligence, which comprises the following steps: acquiring network historical operation data, setting a performance critical judgment condition, judging a current network critical balance state and marking a performance avalanche warning time period; time delay jitter and ping-pong switching are analyzed at the starting moment of the warning time period, an evaluation value is calculated, and whether performance avalanche early warning is triggered or not is judged; after early warning is triggered, time delay abnormal network points and ping-pong switching network points are identified, a new abnormal node identification model is constructed in combination with a convolutional neural network, and a structured result is output; through directional causal analysis, a performance coupling causal graph is constructed, effective new abnormal network points are screened, an avalanche evaluation value quantification risk is calculated, accurate positioning of network performance abnormity from an index level to a node level is realized, avalanche precursor characteristics are captured, early warning accuracy is improved, and an avalanche risk of communication network performance is effectively evaluated.

This disclosure relates to a method, apparatus, electronic device, and storage medium for generating a knowledge graph, belonging to the field of data processing technology. The method includes: first, acquiring information to be displayed, which represents the number and content of knowledge nodes to be displayed; then, dividing the information to be displayed into node levels based on preset generation rules, obtaining the number of nodes to be displayed at each node level and the content to be displayed for each node, with each node level corresponding to a target template; the preset generation rules include node level division rules and node input rules; further, determining the target template corresponding to each node level based on the number of nodes to be displayed; and finally, inputting the content to be displayed into the corresponding node position of the target template to generate the knowledge graph corresponding to the information to be displayed. By applying the technical solution of this disclosure, the controllability of the graph node positions can be improved, the complexity of node connection lines can be reduced, and thus the display effect of the knowledge graph can be improved.

The application discloses a kind of topological optimization method, device, medium and equipment based on CutFEM and SIMP, method includes: definition design domain, discrete design domain into background grid unit, with node density as design variable;Node density is converted into node level value;According to node level value, unit is divided into entity, blank and cutting unit;Subgrid division is carried out to cutting unit;Boundary is identified as inner boundary and outer boundary;Stiffness matrix assembly is carried out to the material area unit of entity unit and cutting unit, obtains node displacement;Solving topological optimization formula, calculating sensitivity updates design variable, until optimal structure is obtained.The application combines CutFEM and SIMP, can solve the problem of insufficient calculation accuracy and efficiency of existing SIMP method under complex geometry and boundary conditions.

The invention discloses a graph structure data layout optimization method for a Qt tree layering and dynamic force oriented graph, which comprises the following steps of: loading graph structure data and carrying out data preprocessing, converting original data into a structured data table containing node attributes and connection relationships, dividing node levels through a depth-first traversal algorithm, and carrying out layout optimization on the structured data table; and dynamically generating a fan-shaped initial layout according to the degrees of the nodes, carrying out layout iterative optimization calculation by adopting a force-oriented layout algorithm, determining the position information and topological connection relationship of each primitive after optimization, and carrying out visual implementation and interaction on the optimized graph structure data layout. According to the scheme of the invention, through cooperation of hierarchical and elastic constraint mechanisms, the readability of a graph containing circulation / multiple father nodes is improved, a dynamic updating strategy enables layout to maintain overall stability during interaction, the structure confusion degree is reduced, and pre-layout processing and a parameter dynamic attenuation strategy effectively improve the convergence speed of a force steering algorithm.

The invention relates to the technical field of computers, in particular to a cluster performance analysis system, method and device, a storage medium and electronic equipment.The system comprises a server side and a front end, and the front end is used for sending a monitoring data obtaining request to the server side to request monitoring data; the monitoring data comprises hardware monitoring data and module performance data, the hardware monitoring data represents the use condition of hardware resources in a cluster, and the module performance data represents the performance during module execution; the server side is used for responding to a monitoring data acquisition request and sending monitoring data to the front end; the front end is used for receiving the monitoring data and displaying the monitoring data in multiple levels according to multiple display levels, and the multiple levels comprise overall data of a cluster level and refined data of a node level. According to the embodiment of the invention, the user can identify the performance bottleneck more accurately.

The invention relates to the technical field of public transport stations and crowd behavior simulation, and discloses a pedestrian node selection behavior simulation method based on an unreal engine. The method comprises the following steps: firstly, constructing a dynamic three-dimensional scene containing interactive equipment logic and environment elements by utilizing an unreal engine; defining pedestrian node alternative schemes and dividing queue groups to construct a nested structure; then objective attributes and finite rationality factors influencing node selection are determined; constructing a queue group and a utility function of a specific node level based on a nested structure and finite rationality factors; calculating a pedestrian selection probability through a hybrid nested Logit model; and finally running simulation in the unreal engine and outputting a result. The method solves the problems that an existing simulation method is difficult to reflect pedestrian heterogeneity and limited cognition and is poor in applicability, has wide applicability, comprehensive influence factor consideration and high visualization ability, and can be used for scenes such as station layout optimization, emergency evacuation and intelligent scheduling.

The invention discloses a supply chain node toughness dynamic evaluation method based on information entropy, and belongs to the field of supply chain network toughness research. The method comprises the steps of calculating efficiency and redundancy of enterprise nodes in supply and demand directions based on an information entropy theory; according to the roles of the enterprise nodes in the supply chain network, performing weighted fusion to obtain the total efficiency and the total redundancy of the enterprise nodes; by comprehensively considering efficiency and redundancy, dynamic quantification of enterprise node toughness is realized. In addition, node toughness driving mechanism diagnosis based on an internal structure is further introduced, and internal structure factors for driving node toughness fluctuation are accurately recognized through exponential decomposition and sensitivity analysis. According to the method disclosed by the invention, dynamic quantification and attribution analysis of the fine-grained toughness of the enterprise node level based on the supply and demand bidirectional dependency relationship are realized, and powerful decision support is provided for accurately positioning the fragile link of the supply chain, optimizing the structure of the supply chain and improving the overall anti-risk capability of the supply chain.