47 results about "Dynamic elasticity" patented technology

A method for managing log retention period of an application includes: inferring dependencies and connectivity among applications; generating an application chain (AC) based on the inferred dependencies and connectivity; analyzing distributed logs and AC to extract health parameters; deriving a failure score of the AC based on the health parameters and inferred dependencies; inferring a user access frequency (UAF) and remaining assigned storage space (RASS) for an application log (AL); analyzing the failure score, UAF, and RASS for the AL; making a first determination that the failure score is greater than a failure score; making a second determination that the AC comprises a high priority application (HPA); making a third determination that the RASS for the HPA's AL is less than a storage space limit; notifying an administrator to initiate obtaining of extra storage space; and extending the current AL retention period of the HPA's AL.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention discloses a dynamic elastic resource expansion hydropower station computing power server, and relates to the technical field of computing resource management. Comprising a distributed state perception acquisition module, a data preprocessing and structured construction module, a feature extraction and risk quantitative analysis module, a deep learning intelligent evaluation module and a dynamic risk regulation and rhythm management module. Through a distributed state sensing system deployed in a hydropower station computing power server central scheduling control platform, the operation state and link quality parameter data of each key node in a migration link are continuously collected in real time. Through a multi-module collaborative dynamic elastic resource expansion mechanism, risk identification and intelligent regulation and control in a hydropower scheduling task thermal migration process are realized, the perception capability of node states and link abnormity is improved, a migration loss risk is judged in combination with a depth model, a scheduling strategy is dynamically adjusted, and task continuity and operation safety are guaranteed.

A method for managing log retention period of an application includes: inferring dependencies and connectivity among applications; generating an application chain (AC) based on the inferred dependencies and connectivity; analyzing distributed logs and AC to extract health parameters; deriving a failure score of the AC based on the health parameters and inferred dependencies; inferring a user access frequency (UAF) and remaining assigned storage space (RASS) for an application log (AL); analyzing the failure score, UAF, and RASS for the AL; making a first determination that the failure score is greater than a failure score; making a second determination that the AC comprises a high priority application (HPA); making a third determination that the RASS for the HPA's AL is less than a storage space limit; notifying an administrator to initiate obtaining of extra storage space; and extending the current AL retention period of the HPA's AL.

The invention belongs to the field of security protection, and particularly relates to a cross-border traffic pool dynamic elastic scheduling system and method, and the method comprises the steps: collecting the real-time quality and traffic parameters of a multi-operator link, and generating a scheduling detection sequence in combination with historical data and the hidden intention of a service appeal text, generating state data through fusion calculation and sending the state data to a cross-border traffic pool dynamic scheduling platform; the flow pool dynamic scheduling model is used for decoupling a time sequence delay coupling relation between parameters, flow consumption fluctuation caused by cross-domain routing convergence is predicted, a feature vector containing a flow excess risk probability and link quality fluctuation is extracted, a target scheduling instruction is generated in response to a cross-domain linkage scheduling condition, and a flow distribution proportion is adjusted or a path is switched. According to the invention, elastic resource allocation and excess risk early warning are realized, and the utilization rate of cross-border traffic resources is improved.

The invention discloses an automatic test method and test platform based on cloud computing, and belongs to the technical field of software testing, and the method comprises the steps: firstly constructing a dynamic elastic resource pool containing a virtualized test node cluster, a tool chain and a distributed data warehouse; after obtaining a performance test request, analyzing data to screen an optimal node combination and tool configuration, and generating a test case set covering a boundary scene and an abnormal path in combination with historical data and reinforcement learning; using an improved genetic algorithm and a load prediction model to disassemble the subtasks and intelligently distributing the subtasks; the progress is tracked in real time through a digital twinborn model, abnormity is recognized, and a multi-stage processing mechanism is activated; and after the test is finished, normalizing the processed data, and generating a panoramic report by means of the analysis model. According to the method, the intelligent level, the execution efficiency, the resource elasticity and the exception handling capacity of automatic testing are improved, and the high-performance testing requirement in a complex scene can be met.

Provided in the embodiments of the present application are a chip resource elastic allocation method and apparatus based on an elastic microkernel. The method comprises: configuring a data structure of a partition scheduler of each adaptive partition and a budgeted CPU running time of each adaptive partition; on the basis of the data structure of each adaptive partition, scheduling a corresponding budgeted CPU running time for a task in each adaptive partition; and when the CPU running time actually used by any adaptive partition is less than the budgeted CPU running time thereof, allocating the remaining CPU running time of the adaptive partition to other adaptive partitions that have tasks waiting for scheduling. In the technical solution of the embodiments of the present application, chip resources are dynamically and elastically allocated by means of an elastic microkernel, and on the basis of demand changes of a computing task in each adaptive partition, computing resources are dynamically allocated and adjusted, thereby ensuring bursty CPU demands, and thus meeting computing power demands of different tasks.

The embodiment of the invention provides a chip resource elastic allocation method and device based on an elastic microkernel. The method comprises the steps that a data structure of a partition scheduler of each self-adaptive partition and CPU running time estimated by each self-adaptive partition are configured; according to the data structure of each self-adaptive partition, scheduling the corresponding budget CPU running time for the task of each self-adaptive partition; and when the CPU running time actually used by any self-adaptive partition is lower than the estimated CPU running time, the remaining CPU running time of the self-adaptive partition is allocated to other self-adaptive partitions with tasks waiting for scheduling. According to the technical scheme, the chip resources are dynamically and elastically allocated through the elastic microkernel, the computing resources are dynamically allocated and adjusted according to the demand change of the computing task of each self-adaptive partition, the sudden CPU demand is guaranteed, and the demand of different tasks for computing power is met.

The embodiment of the invention provides an optimal configuration method of a regional energy system. The method comprises the following steps: acquiring multi-dimensional basic data of the regional energy system; constructing a dynamic elastic layered optimization model; based on the multi-dimensional basic data, time scale elastic adjustment, space subarea energy mutual aid and dynamic weight adaptation of multi-main-body demands are carried out; determining collaborative optimization boundaries of energy storage and energy and collaborative optimization boundaries of energy storage and loads in a layered manner, and overall collaborative optimization targets of energy storage, energy and loads; a capacity configuration sub-model, a real-time scheduling sub-model and a game arbitration sub-model are introduced, decision bifurcation is made in combination with a multi-agent collaborative game mechanism, and parameters are dynamically adjusted to achieve Dapareto optimum; a multi-energy storage characteristic dynamic mapping mechanism is integrated into the hierarchical optimization model; and outputting a collaborative optimization configuration scheme of the stored energy, the energy and the load in the regional energy system according to a collaborative result. According to the embodiment of the invention, the dynamic adaptability of the optimal configuration scheme of the regional energy system is realized.

The application relates to the technical field of power system dispatching, and discloses an intra-day forward-looking dispatching fast solving method considering large-scale new energy cluster power generation fluctuation, which comprises the following steps: S1: new energy cluster space-time fluctuation scene generation based on neuron cellular automata, S2: power grid dynamic security domain definition and simplification based on physical information neural network, S3: dispatching fast optimization solving based on model prediction path integral, and S4: dispatching scheme dynamic elasticity and stability evaluation based on the theory of Koopman operator. The new energy cluster space-time fluctuation scene generation method based on neuron cellular automata can effectively generate a space-time scene reflecting the large-scale new energy cluster power generation fluctuation, support uncertainty analysis, has the advantages of high calculation efficiency and scene authenticity, and solves the problem that the traditional statistical model ignores space-time coupling, thereby causing inaccurate scene generation.

The application provides a threat detection method and system of a container cluster, a storage medium and a cloud platform. The system comprises an agent end. The agent end is used to acquire a traffic collection strategy and a threat detection rule, collect network card traffic of a business container according to the traffic collection strategy, and perform threat detection according to the threat detection rule and the network card traffic. The cloud native traffic is collected and detected locally, the traffic does not need to be sent out, a large amount of bandwidth resources of a host is saved, the threat detection system can be dynamically expanded and contracted according to business changes, and the threat detection performance is flexibly adapted to the dynamic elasticity of the cloud native.

The invention provides a multi-mode dynamic elastic defense method for an industrial control system, and belongs to the field of industrial control information security, and the method comprises the steps: calculating an environment weight based on a traffic anomaly index and a key updating frequency, and triggering a heterogeneous execution body to dynamically switch to adapt to a system security state when the weight exceeds a threshold value; detecting the output deviation of the executor through a multi-mode resolver, and executing network parameter randomization and executor regrouping to block attack diffusion when the deviation exceeds a threshold value; a semantic model is adopted to compress a defense action space, and a high-score defense action is preferentially selected in combination with an elastic scoring mechanism; the relevance between a register instruction and sensor data is analyzed, the abnormal probability is calculated through Bayesian verification, when the abnormal probability exceeds a threshold value, a risk instruction is intercepted, a redundancy control channel is started, and dual protection is formed to cope with physical domain attacks. According to the technical scheme, the problems of protocol vulnerability sensing lag, attack chain defense splitting, physical layer response stiffness and the like in the prior art are solved.

The application discloses an elastic detection device based on research on elastic performance of a tensioner, relates to the technical field of elastic detection, and aims at the technical problem that the comprehensive elastic performance of the tensioner under complex working conditions cannot be comprehensively evaluated in the prior art. The elastic detection device is characterized in that a tension applying mechanism and a test body displacement mechanism are arranged based on a tension gauge body, dynamic elastic detection and static elastic detection of the tensioner are alternately performed, continuous testing function is realized in a cyclic feeding and discharging mode, and the comprehensive elastic performance of the tensioner is beneficial to be comprehensively evaluated. On the other hand, the intelligent detection system is combined with the auxiliary setting of the automatic control structure, so that the data of the tensioner in the elastic detection process is intelligently supervised and processed, the real level of data processing is fed back in the elastic performance detection mode combining static detection and dynamic detection, and whether the tensioner is qualified is judged by whether the elastic value is within the standard threshold.

The invention relates to the technical field of artificial intelligence security and cloud computing resource management, and discloses an AI reasoning service elastic scaling method and system for an edge cluster, and the method comprises the steps: constructing a dynamic risk file for each hardware computing unit in a computing cluster; distributing the reasoning service request to a target hardware calculation unit of which the risk state is matched with the security demand level; instantiating an independent logic calculation container for each allocated reasoning service request, and destroying the logic calculation container after the reasoning task is executed; and monitoring the resource configuration change of the computing cluster, and updating the risk state of the computing cluster in the dynamic risk file. According to the method, a mixed protection mechanism with logic isolation as a main part and physical reset as an auxiliary part is adopted, and hardware-level state pollution attacks triggered by antagonistic input are effectively defended in a dynamic elastic edge AI reasoning scene on the premise that the elastic scalability is not obviously sacrificed.

The invention discloses a new energy station service takeover device supporting elastic expansion and an implementation method thereof, through a modular architecture design and an edge fusion calculation model, the dynamic elastic expansion capability and multi-source heterogeneous data real-time fusion analysis of a new energy station are realized, a difference extraction model and a threshold value quantification decision mechanism are combined, and the new energy station service takeover device supporting elastic expansion is realized. On the premise of guaranteeing equipment operation safety, the system can respond to equipment state sudden change and scale expansion requirements in a second level, compared with a traditional scheme, data processing delay is reduced by 40%, hardware architecture expansion cost is reduced by 60%, and the problem of real-time service takeover in a dynamic capacity expansion scene of a new energy station is effectively solved.

The application relates to the technical field of smart power grids, and is a multi-dimensional dynamic elasticity identification method and system for a key coupling node of a power distribution information physical system. The system comprises a key node screening unit, a unified CPDS model is constructed, and a candidate key node set is preliminarily screened based on structural controllability and structural observability; a fuzzy scene generation unit is used to construct a dynamic fuzzy disaster scene set by using a Markov chain Monte Carlo method, and a set containing a large number of dynamic fuzzy disaster scenes is generated; an elasticity function construction unit; a node calculation output unit; the application proposes a new CPDS key coupling node accurate identification method which is fused with structural controllability, structural observability, dynamic fuzzy disaster scenes, reverse reinforcement learning and Shapley value comprehensive calculation, so that the accuracy, fairness and adaptability of system elasticity analysis and decision-making are improved, and the fast response and recovery capability of the power grid to disasters or attack disturbances is improved.

The invention relates to the technical field of roadbed continuous compaction detection, and discloses a calibration method of roadbed continuous compaction detection equipment, which comprises the following steps: combining a plurality of rubber pads with different hardness and thickness to obtain a simulation test system; s2, testing the dynamic elasticity modulus of the plurality of rubber cushion layers to verify the rigidity uniformity and continuous variability of the rubber cushion layers; s3, performing in-situ single-point vibration test on the simulation test system, acquiring a steel wheel vibration acceleration signal by means of roadbed continuous compaction detection equipment, preprocessing the acquired signal, and calculating a continuous compaction detection index needing to be calibrated; and S4, taking the mean value of the dynamic elastic modulus of the rubber cushion layer as an equivalent stiffness reference value, and fitting the correlation between the index to be calibrated and the dynamic elastic modulus by adopting a regression analysis method so as to obtain a calibration curve. According to the method, a more accurate and universal calibration result can be provided for roadbed compaction quality detection equipment, the calibration efficiency is improved, and the calibration cost is reduced.

The present application belongs to the field of safety protection, and particularly relates to a cross-border traffic pool dynamic elastic scheduling system and method, comprising: collecting real-time quality and traffic parameters of multiple operator links, combining historical data and implicit intention of business appeal text to generate a scheduling detection sequence, generating state data through fusion calculation and sending to a cross-border traffic pool dynamic scheduling platform; using a traffic pool dynamic scheduling model to decouple the time sequence delay coupling relationship between parameters, predicting the traffic consumption fluctuation caused by cross-domain route convergence, extracting a feature vector containing traffic excess risk probability and link quality fluctuation, generating a target scheduling instruction in response to cross-domain linkage scheduling conditions, and adjusting the traffic distribution ratio or switching the path. The present application realizes elastic resource allocation and excess risk early warning, and improves the utilization rate of cross-border traffic resources.

The invention discloses an Elasticsearch paging query optimization method, system and device and a storage medium, whether the total amount of data needing to be queried exceeds the query upper limit or not is judged according to the query upper limit of Elasticsearch, the data volume of each page and the current page number, if yes, a data starting point and a data end point are calculated according to the data volume of each page and the current page number, and the data starting point and the data end point are sent to the Elasticsearch. The method comprises the following steps of: calculating elastic scrolling times and elastic scrolling data pieces, sending a scrolling request to Elasticsearch by using scrolling query, calculating an actual data starting point according to the actual size of a data set, assigning the value of the actual size to the actual data ending point if the actual data ending point is larger than the actual size, and extracting a data subset from the data set as a final query result. Depth paging query is decomposed into rolling requests with the minimum necessary number of times through a dynamic elastic rolling algorithm, and in combination with a data positioning and interception technology, the default strip data limitation of Elasticsearch can be broken through under the condition that the performance is ensured as much as possible, and the system resource consumption is reduced.

The invention relates to a network toughness evaluation and elasticity adjustment method based on digital twinning, and belongs to the technical field of communication network simulation and optimization. The method comprises three parts of digital twin network modeling, network toughness quantitative evaluation and an elastic adjustment method. A directed weighted graph is used for representing network topology, and three specific parameters of delay, bandwidth and packet loss probability are introduced to construct a digital twinborn body of an actual network; calculating a key and toughness comprehensive coefficient of the node for evaluating the key degree of the node and evaluating the network toughness; different fault conditions are classified and processed; in a normal condition, Dijkstra is used for rerouting; adjusting a trigger strategy of the directly connected nodes; and an alarm is given independently for abnormal communication loss. According to the method, the network toughness is systematically and quantitatively evaluated, a dynamic elastic adjustment strategy is triggered for a node destruction and paralysis failure scene, and optimization of the network toughness is realized.

The invention discloses a method for analyzing dynamic performance degradation of a protective coating-composite sheet in a thermal vibration environment, and belongs to the technical field of high-temperature vibration degradation. After a dynamic degradation model of a protective coating-composite sheet structure in the thermal environment is established, the dynamic elastic modulus of a protective coating and a composite at different temperatures and degradation time is determined; the dynamic response characteristics of the structure are solved based on the first-order shear deformation theory, the Ritz method and the minimum potential energy principle, the dynamic stiffness index for thermal shock degradation resistance quantitative evaluation is provided, and the method has the advantages that the method is suitable for optimizing the thermal shock degradation resistance design of the composite material structure and improving the service life prediction precision.

The invention relates to a manufacturing system node ALCR strengthening strategy method of a double-layer dynamic Bayesian network, belongs to the field of reliability analysis, and aims to solve the problem that redundant resource allocation is unreasonable due to the fact that a traditional key node equipment strengthening strategy occupies limited resources. A critical value of node equipment capacity redundancy and an inhibitory penalty function of resource overload are considered, and an improved reinforcement strategy (ALCR) based on an optimization theory is provided. The dynamic elasticity evaluation method of the double-layer dynamic Bayesian network is combined to compare the elasticity strengthening effects after different strengthening strategies are strengthened, and the accuracy and effectiveness of the ALCR strengthening strategy are verified. When the equipment average degree is large, compared with other reinforcement strategies, the improved reinforcement strategy based on the optimization theory can ensure that the system has better elastic performance, provides a theoretical basis for further formulating a reliable production strategy and reducing the maintenance cost, and is suitable for popularization and application. And an improvement direction is indicated for further improving the reliability of the node strengthening strategy of the manufacturing system.

The invention discloses an electric power data quality checking method and system based on dynamic elastic scheduling, and aims to solve the problems that in the prior art, centralized scheduling cannot process massive electric power data, the checking time consumption is long, the upper limit of parallel tasks is low, and a traditional database is slow in query. The method comprises the following steps of: segmenting data into a plurality of blocks through data volume estimation, single server processing capacity and maximum allowable check duration, and scheduling a plurality of execution servers to perform parallel processing by a container cloud; a distributed storage architecture is adopted, an LSM tree structure is adopted to store check results, and the storage and query efficiency is optimized in combination with an exclusive check result table, LZ4 and ZSTD dual-mode compression, timestamp column value and other fragmentation strategies. According to the electric power data quality checking method and system, the time consumption of data checking is low, the checking result query speed is high, and the problems of timeliness and result query of data checking in the electric power data are effectively solved.

The invention belongs to the technical field of data processing, and particularly relates to a GIS flange bolt tightening quality judgment method based on torque corner curve analysis, which comprises the following steps of: resampling torque and corner data based on a preset angle step length, and calculating instantaneous tangent rigidity by adopting a Gaussian kernel weighted difference operator, the high-frequency friction noise in the aluminum alloy tightening process is smoothly inhibited; constructing a dynamic elastic reference value based on the linear elastic region characteristics and the hardening coefficient; and calculating a damage accumulation value based on the deviation between the instantaneous stiffness and the reference value, and when the accumulation value exceeds a threshold value, judging that micro-yield occurs and stopping. According to the method, transient viscous sliding interference and real plastic deformation are effectively distinguished through an energy integral idea, the problem of misjudgment caused by no obvious yield point and individual difference of the cast aluminum alloy is solved, and high-precision lossless judgment of the bolt tightening quality is achieved.

The invention discloses a distributed optimization method and device for a multi-agent system triggered by a dynamic elastic event under a DoS attack and a storage medium wherein the distributed optimization method for the multi-agent system triggered by the dynamic elastic event under the DoS attack comprises the following steps: obtaining elastic event triggering data, the elastic event triggering data triggers the heterogeneous linear multi-agent system model to carry out distributed optimization of the multi-agent system, and an optimal solution of the distributed optimization method is obtained; and auxiliary variables are introduced to solve the imbalance problem caused by the directed imbalance topological graph. The invention discloses a distributed optimization method of a multi-agent system triggered by a dynamic elastic event under a DoS attack, equipment and a storage medium. On the basis of a heterogeneous linear multi-agent system, a dynamic elastic event triggering mechanism is introduced to save communication resources, and the efficiency of solving a distributed optimization problem is improved on the premise of ensuring the system security.

This invention discloses a dynamic task scheduling system for space control simulation model computation, comprising a scheduling center, several scheduler nodes, and several executor nodes. The scheduler nodes and executor nodes are deployed on different server clusters. The scheduling center dynamically adjusts task scheduling based on dynamic load balancing technology according to the task scheduling requirements of the space control simulation service, allocating tasks to registered scheduler nodes. The scheduler nodes are used for task production and caching, and trigger the executor center using Remote Procedure Call (RPC) technology. They are also used for dynamic elastic scaling of executor nodes for tasks that cannot be fulfilled. The executor center logs into the scheduling center via heartbeat registration to achieve remote communication between the two. It is also used to decompose tasks and trigger the corresponding executor nodes. The executor nodes execute the corresponding space control simulation model computation tasks.

The application discloses a simultaneous homogenous loading module, and the loading module is used for exciting homogenous shear waves propagating in at least two directions in the X-Z plane in the medium at one time and inducing the shear waves on the surface or in the medium. The loading module is suitable for dynamic elastic imaging, and can excite homogenous shear waves propagating in multiple directions by using dynamic load to excite the medium, has strong adaptability to the shape and structure of the medium, can in-situ characterize the properties of the medium, is suitable for non-uniform medium, and can improve the signal-to-noise ratio of the measurement result to some extent, thereby improving the accuracy and stability of the measurement result. The application further provides a material mechanics property measuring device and method.

The application discloses a deep coal bed gas reservoir high brittleness sweet spot area prediction method, system and equipment, relates to the oil and gas and coal bed gas geophysical exploration field, and the method comprises the steps of: taking dynamic elastic modulus as the independent variable, taking coal bed fracture pressure as the dependent variable, adopting a multiple linear regression method to perform fitting, determining a partial regression coefficient, and thereby constructing a brittleness index model; based on original data of a target area deep coal bed gas reservoir, adopting prestack simultaneous inversion technology and rock physical formula to perform inversion and calculation, obtaining a three-dimensional dynamic Young's modulus data body, a three-dimensional dynamic Poisson's ratio data body and a three-dimensional dynamic shear modulus data body, and substituting into the brittleness index model to obtain a three-dimensional brittleness index data body; according to a brittleness index threshold, dividing a region corresponding to the three-dimensional brittleness index data body to determine a high brittleness sweet spot area. The application can improve the accuracy of predicting the high brittleness sweet spot area of the deep coal bed gas reservoir.

The application discloses a deep coal bed gas reservoir high brittleness sweet spot area prediction method, system and equipment, relates to the oil and gas and coal bed gas geophysical exploration field, and the method comprises the steps of: taking dynamic elastic modulus as the independent variable, taking coal bed fracture pressure as the dependent variable, adopting a multiple linear regression method to perform fitting, determining a partial regression coefficient, and thereby constructing a brittleness index model; based on original data of a target area deep coal bed gas reservoir, adopting prestack simultaneous inversion technology and rock physical formula to perform inversion and calculation, obtaining a three-dimensional dynamic Young's modulus data body, a three-dimensional dynamic Poisson's ratio data body and a three-dimensional dynamic shear modulus data body, and substituting into the brittleness index model to obtain a three-dimensional brittleness index data body; according to a brittleness index threshold, dividing a region corresponding to the three-dimensional brittleness index data body to determine a high brittleness sweet spot area. The application can improve the accuracy of predicting the high brittleness sweet spot area of the deep coal bed gas reservoir.

The present invention discloses a distributed optimization method, device, and storage medium for a multi-agent system triggered by dynamic elastic events under DoS attacks. The distributed optimization method for a multi-agent system triggered by dynamic elastic events under DoS attacks includes: obtaining elastic event triggering data, using the elastic event triggering data to trigger a heterogeneous linear multi-agent system model to perform distributed optimization of the multi-agent system, and obtaining an optimal solution for the distributed optimization method; and introducing auxiliary variables to resolve imbalance problems caused by directed unbalanced topological graphs. The present invention discloses a distributed optimization method, device, and storage medium for a multi-agent system triggered by dynamic elastic events under DoS attacks. Based on a heterogeneous linear multi-agent system, the method introduces a dynamic elastic event triggering mechanism to save communication resources, thereby improving the efficiency of solving distributed optimization problems while ensuring system security.