162 results about "Resource contention" patented technology

The invention provides a multi-agent cooperation method, system and device and a storage medium, and relates to the technical field of multi-agent collaboration.The method comprises the steps that initial role allocation is conducted on multiple agents, one agent is an observer, the other agent is a coordinator, and the other agents are all executors; a strategy network based on deep reinforcement learning is introduced according to the running state of the multiple agents to dynamically adjust role allocation of the multiple agents, and a role allocation strategy is dynamically adjusted according to task completion rewards, role conflict punishment and resource conflict rewards; the coordinator constructs a task priority and a dependency relationship based on the task graph or the task dependency tree, and dynamically allocates tasks according to the state, the capability vector and the task adaptation degree of the executor; conflicts are recognized through resource contention detection, task overlapping detection and behavior conflict detection, and the conflicts are coordinated. According to the invention, multi-agent responsibilities are layered, and the task completion efficiency is improved through task allocation and conflict detection and coordination.

The invention discloses a cloud side-end cooperative task scheduling and efficiency optimization method and system for heterogeneous patrol resources, and relates to the technical field of intelligent scheduling and resource optimization. According to the method, accurate perception of a resource state is realized by constructing a digital twinborn and federated learning mechanism, resource contention conflicts are solved by adopting a space-time diagram attention network and multi-agent reinforcement learning, and multi-target optimization and trusted execution are realized in combination with a quantum genetic algorithm and a block chain smart contract. Finally, the stability of the system is verified through Lyapunov optimization, a complete scheduling system from resource perception and conflict resolution to steady state maintenance is formed, and the task scheduling efficiency and the system stability in the heterogeneous resource environment are remarkably improved.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention provides a containerized resource elastic scheduling method and system in a cloud computing environment, and relates to the technical field of containerized resource elastic scheduling, and the method comprises the steps: obtaining various resource contention events, and generating data which records a resource contention relationship between tenants in detail; identifying a preset high-frequency conflict mode by deeply analyzing the data, and determining a specific tenant group with a strong resource mutual exclusion effect; dynamically estimating a probability level of resource conflict between the new container and the specific tenant group in combination with historical contention information and a real-time resource load state; generating a set of dynamic isolation scheduling strategies according to the conflict risk indicated by the probability level; an independent conflict buffer resource pool is configured and adjusted for a specified key tenant, a tenant resource conflict mode can be identified, dynamic isolation scheduling and buffer resource configuration can be implemented, and the resource utilization efficiency and the system stability of a multi-tenant container in a cloud computing environment are improved.

The invention relates to the technical field of computing resource scheduling, in particular to a GPU server resource dynamic allocation management method, device and equipment and a medium, the GPU server resource dynamic allocation management method comprises the steps of extracting a jump path to divide task migration segments, matching path repeated tasks in a thread, screening overlapped tasks in combination with scheduling and residence time, and performing dynamic allocation management on GPU server resources. And analyzing a resource access trend to identify scrambling stable combinations, and associating address frequencies to generate a dynamic allocation list. According to the method, the behavior similarity among multiple tasks is identified by extracting the jump path among the tasks and performing repetition analysis, the dense overlapping interval of task execution is accurately delimited by means of cross mapping of task scheduling time and path residence time, and the task performance of the multiple tasks is determined in combination with the derivative judgment mode of the change trend of the access frequency of the shared resources. And identifying a task combination with stable resource scrambling behavior distribution, and establishing a dynamic binding strategy between the task and the GPU address resource based on the joint sorting of the address access frequency and the residence duration.

The invention discloses a management system for an integrated circuit test, and relates to the technical field of integrated circuit test management, and the system comprises a task scheduling module which is used for receiving a plurality of test task requests, and generating a test task scheduling sequence based on the priority parameter of each test task, the current state of a test resource, and a historical execution result; and the resource coordination module is used for acquiring the running states of the plurality of test devices, executing resource allocation operation according to the test task scheduling sequence, and generating a coordination abnormal event if resource conflict or scheduling failure is detected in the execution process. According to the invention, a dependency graph between test equipment and bottom hardware resources is constructed through the resource coordination module, and accurate modeling of a resource mutual exclusion relationship, concurrent capacity and conflict risk is supported, so that resource conflicts are pre-judged before scheduling, and the problems of resource scrambling and deadlock are effectively avoided.

The invention relates to the technical field of electric digital data processing, and discloses a data processing system for message management, which comprises a look-ahead buffer unit for maintaining a sliding window with variable window size parameters and pre-reading a message sequence; the mapping logic circuit is used for converting the resource identifier into a resource request mask; the state register is used for storing a global occupancy bitmap for representing resource occupancy; the scheduling controller is used for distributing messages according to bit operation results, counting bitmap setting quantity based on the population counting instruction to generate a resource saturation index, and then adjusting window size parameters in a negative feedback mode. The resource contention entropy value is directly sensed by utilizing a hardware instruction, nanosecond-level self-adaptive breathing control of a scheduling window is realized, and the service life of the scheduling window is prolonged. The problem of physical computing power collapse caused by bitmap scanning jitter in a high-concurrency scene is solved.

The invention provides a complex scene-oriented multi-modal AI Agent integrated development method and system, and the method comprises the steps: generating a layout graph G in an integrated development environment, and carrying out the consistency verification to obtain a constraint set C; a graph neural network is adopted to represent the arrangement graph to obtain graph embedding Z, and an environment state s is formed by combining key performance indexes in a sliding window; and in an action space AC limited by the C, jointly deciding computing power node selection, concurrent resource quota adjustment and semantic caching or semantic routing parameter configuration according to a reinforcement learning strategy pi, and executing actions to optimize throughput, tail delay, failure rate and resource contention degree. Through a closed loop of verification-constraint-learning-execution-monitoring-updating, cross-modal consistency and computing power scheduling are integrally optimized, training convergence and online stability are improved, long-tail time delay and the failure rate are remarkably reduced, the resource utilization rate is improved, and the method is suitable for large-scale popularization and application. The method is suitable for the fields of medical diagnosis, industrial quality inspection and equipment maintenance, retail recommendation and shopping guide and the like.

The invention discloses a large model mixed load-oriented self-adaptive low-delay reasoning configuration generation method and device, computer equipment and a storage medium, and the method comprises the steps: determining a first token generation delay and an adjacent token delay interval which are historically configured on requests with different reasoning configurations, and obtaining tuples to form a configuration performance database, generating a delay prediction model by combining a least square method with the configuration performance database; dynamically dividing the historical request into a plurality of buckets according to the input length and the output length through a self-adaptive bucket dividing strategy; a configuration generator generates reasoning configuration for each bucket according to the input length and the output length of the historical request of each bucket; under the real mixed load, the problems of remarkable resource contention, queue head blockage, KV Cache switching overhead increase and the like are avoided in concurrent execution of long and short requests, and meanwhile, the problem of tail delay amplification is avoided, so that a reasoning system gives consideration to low delay and high throughput among different requests.

The invention provides a real-time monitoring and root cause positioning method and system for software defects. The method comprises the following steps: collecting real-time monitoring data of a software processing thread, and generating a thread competition thermodynamic diagram for identifying a shared resource contention point through time sequence association processing; triggering a server memory bus embedded hardware detection device based on a contention point to dynamically capture a locking state transition signal, and converting the locking state transition signal into a locking event sequence; constructing a locking contention track diagram according to timestamp sorting, and completely displaying a process path of a locking mechanism from obtaining to releasing; constructing a dependency topological structure of the thread and the lock based on the trajectory graph, and identifying a resource contention chain; and mapping the contention chain into an unsynchronized shared variable access path in the source code, and triggering a real-time alarm signal. According to the method, the access path of the unsynchronized shared variable is accurately positioned, the lock competition diagnosis efficiency is improved, and the system deadlock risk is zeroed.

The invention relates to the field of unmanned aerial vehicle control, in particular to a multi-mode link switching method for a heterogeneous unmanned aerial vehicle cluster and related products, and the method comprises the steps: obtaining the current task mode information of the heterogeneous unmanned aerial vehicle cluster and the link state information of each node; dynamically selecting and activating a corresponding link switching strategy from a preset strategy set according to the task mode information; according to different task modes, different link switching strategy sets are dynamically selected, under the constraint condition that communication resources are strictly limited, the resource contention contradiction in the heterogeneous unmanned aerial vehicle cluster is solved, and the communication toughness and efficiency of a cluster system are improved.

The invention discloses a computing node power consumption monitoring and predicting method, and belongs to the technical field of computers. The problem that an existing power consumption prediction method is poor in accuracy is solved. According to the method, the dynamic operation states of the nodes, the hardware static configuration attributes and the coupling influence of the communication relation and power consumption between the nodes are comprehensively considered, a graph structure based on node communication is constructed, and a power consumption coupling path between the nodes is modeled and calculated, so that the dynamic energy consumption of local nodes can be captured; and cross-node resource contention, communication bottleneck and energy consumption conduction effect can be considered. Through a structure-guided graph attention mechanism, the model can realize information sharing and feature collaboration among a plurality of nodes. By introducing an energy efficiency attention fusion module, dynamic weighted modeling is performed on multi-channel input features, key factors having high influence on energy consumption fluctuation are automatically identified, and the accuracy and interpretability of the model in the aspects of power consumption prediction and optimization suggestion output are improved. The method can be applied to power consumption prediction of the computing node.

The invention discloses an advertisement project collaborative management method based on intelligent allocation, and particularly relates to the field of advertisements, which comprises the steps of system initialization, multi-dimensional basic data acquisition, resource dispersion and time pressure analysis, collaborative conflict coefficient synthesis analysis and dynamic priority allocation decision. According to the method, the resource utilization rate is remarkably improved, the resource tension type is accurately identified and the allocation strategy is dynamically optimized by calculating the standard deviation dispersion index of the resource occupancy rate in real time, and the phenomena of resource idleness and overload are eliminated; an exponential function time pressure model is innovatively constructed, dual dimensions of task residual workload and deadline are fused, nonlinear priority evaluation of emergency tasks is realized, and priority response of high-load short-period tasks is ensured; a cooperation conflict coefficient mechanism is originally created, a resource contention factor and a time window factor are integrated, a high-conflict task combination is actively early warned, and the risk of cooperation blocking is effectively reduced.

The invention provides a task scheduling method and device for a civil aviation industry-level data service platform, equipment and a medium, and relates to the technical field of task scheduling, and the method comprises the steps: determining the task type of each task in a to-be-scheduled task set; if it is determined that the resource demand of any calculation-intensive analysis task exceeds a preset resource threshold value and the calculation-intensive analysis task is decomposable, decomposing the calculation-intensive analysis task into a plurality of sub-tasks; constructing a to-be-scheduled queue comprising all routine data processing tasks, all sub-tasks and all undecomposed compute-intensive analysis tasks, and recording attributes of each task in the to-be-scheduled queue; based on the attribute of each task and the real-time available resource state and topological information of the computational nodes in the civil aviation industry-level data service platform, each task is dynamically allocated to a plurality of computational nodes, so that the problems of resource scrambling, routine data processing task performance damage, low computational intensive analysis task efficiency and the like are solved.

A GEMM load-oriented GPU modeling method is characterized in that through a multi-stage collaborative modeling mechanism, cache behaviors, instruction overhead and calculation intensity are deeply coupled, accurate performance prediction of GPU execution GEMM operators is realized, the method can be widely applied to scheduling optimization of GPU intensive scenes such as AI training and scientific calculation, firstly, a three-stage cache weight distribution mechanism is established, and then, a three-stage cache weight distribution mechanism is established; quantifying the contribution of the L1 / L2 cache hit rate and the DRAM bandwidth degradation factor to the effective bandwidth; secondly, an instruction-level memory access overhead correction mechanism is introduced, and the mixing precision and the real calculation strength of a sparse calculation scene are captured through dynamic parameter adjustment and optimization; then combining the calculation force peak value and the bandwidth upper limit to construct a double-boundary constraint model, and generating a theoretical performance critical value; further predicting a stream multiprocessor utilization rate based on a neural network, and quantifying efficiency loss caused by hardware resource contention through a multi-layer perceptron structure; and finally, the integration module outputs task execution time to realize end-to-end performance prediction.

The invention relates to the technical field of data processing, in particular to a material purchasing demand response system based on intelligent decision making, and the system comprises a data acquisition unit which collects environment physical data and purchasing business data, and outputs a cross-dimensional original data flow; the data processing unit fuses the heterogeneous data to generate multi-modal feature data; the prediction unit calculates a blocking risk probability and an optimal path, and generates a path optimization instruction; the reconstruction control unit allocates a high-blocking task to a low-delay optical channel, allocates a conventional task to a high-fault-tolerance channel, and outputs a task and path mapping table; the scheduling unit decomposes the mapping table into a neuromorphic scheduling instruction and a quantum optimization instruction and fuses the neuromorphic scheduling instruction and the quantum optimization instruction to generate a cross-scale scheduling command; and when resource contention is detected, rescheduling and acquisition strategy adjustment are triggered. Nanosecond instruction writing is realized through a photon and quantum direct connection architecture, and the problems of resource contention and delay accumulation of an electronic digital data processing system in a high-concurrency scene are solved in combination with a dynamic entropy reduction regulation and control and hardware resource isolation mechanism.

The invention discloses a power distribution automation terminal task scheduling method and device based on a power distribution network edge computing node, electronic equipment and a storage medium, and belongs to the field of power distribution network automation, and the method comprises the steps: obtaining the total amount of special and public containers of an edge node and the decomposition configuration and operation parameters of a target power distribution automation task; the method comprises the following steps of: decomposing a task into micro-applications, constructing a calling graph, counting the repetition degree of calling edges, combining the edges with high repetition degree into an undetachable micro-application chain, and calculating and distributing exclusive containers according to the repetition degree and the total number of the exclusive containers; and calculating weights according to task calling times, priorities and concurrency numbers, and calculating and distributing the public containers based on the weights and the total number of the public containers. Therefore, by implementing the method and the device, the problems that high-frequency fixed service flow resources are severely scrambled and the utilization rate of discrete task resources is low in the prior art can be solved.

The invention discloses a multi-disciplinary simulation task intelligent planning method based on a knowledge graph, and relates to the technical field of intelligent scheduling, and the method comprises the steps: constructing a preliminary knowledge graph comprising multi-disciplinary task description, resource limitation and execution dependence, analyzing historical task data through a pre-trained deep learning model, and obtaining a multi-disciplinary simulation task planning model; dynamically adjusting the preliminary knowledge graph through adaptive learning, and outputting an adaptive knowledge graph; carrying out resource redistribution optimization according to the updated task scheduling strategy, continuing to execute the task, starting monitoring, and collecting task progress and resource data as a new feedback data stream; the resource reallocation and the new feedback data stream are fed back to the adaptive knowledge graph, and multidisciplinary task description, resource limitation and execution dependency are updated through reasoning to generate a final optimization task execution sequence; the synchronous suppression of waiting time and resource contention is realized, the average utilization rate of resources and the time sequence stability are improved, and the adaptive adjustment capability of operation disturbance is formed.

The application discloses a kind of based on flow perception intelligent network interface card optimization system and method, it belongs to computer network technical field.The optimization system of the application includes flow perception module, flow prediction module, resource scheduling module, priority queue module and load balancing module.The application introduces resource monitoring, flow perception, resource prediction and dynamic scheduling module, real-time monitoring network flow and predicting its change trend, according to flow mode dynamically adjusts resource allocation.The application effectively solves the problem of resource contention, through priority ordering and load balancing, ensure the stable operation of high priority task, improve the throughput and stability of system, reduce resource waste.The application is suitable for resource management of intelligent network interface card in data center and cloud computing environment, improves network processing efficiency and resource utilization, guarantees service quality, reduces resource waste, and enhances the adaptive capacity of system to dynamic load and complex flow mode.

The invention provides a method for realizing an intelligent non-player character in an interactive game, which relates to the field of data processing and comprises the following steps of: packaging a decision intention into an immutable action package in a predefined decision period and placing the action package in a time domain trusteeship queue to realize time decoupling of a decision and a runtime load; the world state projection is divided into an opaque decision phase and a reconciliation phase, the decision phase only exposes the event category projection and the historical abstract, and the reconciliation phase reflows detailed trigger details in an independent window for posteriori maintenance but does not backtrack submitted actions; submission follows an atomization criterion and the submitted abstract does not contain time information; during resource contention, accumulating in response to a debt, and repaying through display shaping on the premise of not exposing a time sequence so as to keep the semantic consistency of main games; the posterior processing updates the action banks and event mappings offline based on the backflow details to improve subsequent decisions.

This disclosure provides embodiments of a method, apparatus, and electronic device for dispatching supplies for grassroots governance matters. One specific implementation of the method includes: in response to detecting a supply dispatch request for a grassroots governance matter characterizing a public emergency, acquiring corresponding matter information; performing information standardization processing on the matter information to obtain standard information; extracting target elements from the standard information to obtain a target element information set; performing resource contention conflict detection on the target element information set to obtain a resource contention index set; in response to the resource contention index set reaching a preset threshold, dynamically prioritizing the target element information set based on the resource contention index set to obtain a priority index set; generating a resource allocation instruction based on a hardware rule engine with pre-set resource arbitration rules and the priority index set; and performing supply dispatch operations according to the resource allocation instruction. This implementation achieves the technical effect of reducing the response delay of supply dispatch operations.

Systems, apparatuses, and methods are disclosed for arbitrating threads in a computing system. The computing system includes a processor having a plurality of cores that are each capable of concurrently processing instructions of a plurality of threads. When a thread throttling unit receives an indication that a shared cache has a resource contention, the throttling unit sets a cache miss threshold for the cache. If the number of cache misses exceeds the threshold, the throttling unit notifies a particular upstream computing unit to throttle the processing of instructions of the thread. After a time period elapses, if the cache continues to exceed the threshold, the throttling unit notifies the upstream computing unit to more restrictively throttle the thread by performing one or more of decreasing a selection rate and increasing the time period. Otherwise, the unit notifies the upstream computing unit to less restrictively throttle the thread.

The invention relates to a control method and device for a disk array, and belongs to the technical field of new generation information technology industries. In thread pool parallelization, a serial verification step is divided into a plurality of independent tasks, the independent tasks are executed in parallel by different threads, and parallel verification is performed through a thread pool, so that the total delay is reduced, and the requirements of a high-priority module are met; thread blockage and resource competition are avoided, and thread safety of task distribution and result processing is ensured; the DMA transmission mode and the verification type are adjusted according to the request priority, hardware changes such as disk faults and bandwidth fluctuation can be responded through dynamic selection of the DMA transmission mode and the verification type, the real-time performance and the resource utilization rate are balanced, the switching overhead is reduced through thread pre-binding, and the safety is improved through multi-dimensional verification.

This invention relates to a method and electronic device for obtaining blocking information of an uninterruptible sleep state of a process, belonging to the field of computer technology. The method includes: when a process triggers a lock contention start tracking point due to resource contention, determining the lock type by parsing the lock contention start tracking point parameters; in response to the lock type being a mutex lock, obtaining the lock address by parsing the lock contention start tracking point parameters; clearing the flag bits in the owner member of the kernel lock structure pointed to by the lock address by performing bitwise operations, obtaining a pointer to the process descriptor of the holder; obtaining the process identifier and process name of the mutex lock holder by accessing the pointer to the process descriptor of the holder; and storing the process identifier, process name, and lock address of the mutex lock holder in a task blocking context structure with the process identifier of the mutex lock holder as the key, thereby achieving accurate acquisition of the mutex lock holder information.

The invention relates to the technical field of docking station remote control, in particular to a cloud management-based micro host docking station remote control system, which comprises a multi-tenant resource slice configuration module for receiving a multi-tenant configuration instruction according to a cloud management platform and establishing a virtual docking station resource allocation table. According to the method and the device, the specific USB port, the specific HDMI port, the specific power budget and the specific network bandwidth limit value are bound with the unique tenant voucher by establishing the virtual docking station resource allocation table, so that a shared physical device is converted into a plurality of logically independent virtual devices, the problems of resource scrambling and security interference in a multi-tenant environment are solved, and the service life of the virtual docking station is prolonged. And when the behavior of one tenant, such as connection with high-power consumption equipment or malicious USB equipment, is strictly limited in the allocated resource quota, and does not affect other tenants, so that the stability and the safety of the platform are ensured.

The invention discloses a method and system for deploying a multi-client operating system for preventing resource contention, and the method comprises the steps: generating a grouping information table and a resource distribution table based on the business demands and the total number of hardware resources of a multi-core processor system, the number of the processing groups is configured to be smaller than or equal to the total number of hardware resources which can be independently divided in the multi-core processor system, and the resource allocation table is used for storing a plurality of pieces of hardware resource allocation information corresponding to the processing groups; dividing the plurality of processing cores into each processing group based on the service requirements of the multi-core processor system and the grouping information table; and based on the resource allocation table, performing hardware resource configuration on each processing group so as to obtain a plurality of hardware resource isolated client operating systems. Based on the deployment method, isolation of hardware resources among multi-client operating systems can be realized, and the performance and resource occupation of a multi-processor system are optimized.

This invention provides a method, storage medium, and device for collecting database statistical information. The method for collecting database statistical information includes: initiating statistical information collection; obtaining log information since the last statistical information update; filtering modified data information related to the statistical information from the log information; and updating the statistical information based on the modified data information. By collecting statistical information through accessing log information, there is no need to access the tables, thus avoiding resource contention with user operations, reducing the burden of table access, and consequently minimizing the impact on database processing performance.

The invention relates to a task scheduling method, system and device based on a multi-core processor and a medium. In the application, cores of a processor are divided into different core pools, and when a target task sent by a user side is executed, a target core can be selected from the corresponding core pool to execute the target task based on priority information of the target task; through the mode, core resources in different core pools can be provided for tasks with different priorities, so that core resource competition is reduced, core scheduling distribution is planned, mutual interference among the tasks in the core pools is reduced, and the real-time task response capability is improved; when the target task is executed, if it is detected that the user side is in the inactive state, the target core is automatically released, core resources are prevented from being occupied, and the resource utilization rate is increased; moreover, when the user side is reconnected, the target task can be continuously executed according to the task operation record, the working state of the uncompleted task is recovered in time, and resource waste caused by repeated execution of the task is avoided.

This invention relates to the field of fault diagnosis technology, and in particular to an intelligent operation and maintenance method for the entire IT system chain for precise fault location. It constructs a dual dependency topology including explicit logical call edges and implicit resource contention edges, simultaneously covering business call associations and resource contention associations within the same host machine. This eliminates the blind spots in traditional solutions and is adaptable to complex fault scenarios involving business call anomalies, resource contention anomalies, and both. A micro-level queuing entropy increase rate quantification model is constructed. Through integral calculations of system scheduling saturation and effective instruction execution rate, it achieves refined quantification of micro-level anomalies at the kernel scheduling level, enabling early detection of fault precursors and accurate differentiation between native node anomalies and propagated anomalies. Starting from the alarm node, it traverses all associated nodes in reverse, and adjusts the weights of nodes with different anomaly types based on the node's micro-level queuing entropy increase rate, accurately quantifying the true fault contribution of each node.