A discrete task-oriented resource scheduling optimization management system

Abstract

The application relates to the technical field of business dispatch automation in administration and business operation, and discloses a resource scheduling optimization management system for discrete tasks, which comprises the following: a task sensing module acquires discrete task flow arrival time sequence data under the distribution characteristics of business request; a logical processing module calculates task arrival jitter according to the time sequence data, determines target standard entry window step length, executes management decision damping logic, compares a sensitivity threshold value by maintaining a strategy stability index in real time, executes decision jump arbitration on the update action of the window step length, and generates a scheduling instruction; and a scheduling control module implements time sequence scheduling on a controlled management resource entity according to the scheduling instruction. Through the establishment of the management decision damping mechanism, the application can filter random disturbances in the task flow and suppress decision shock of the strategy layer, realizes the dynamic balance between resource occupation and business performance timeliness on the basis of maintaining the continuity of management decisions.

Description

Technical Field

[0001] This invention belongs to the field of business scheduling automation technology in administrative management and commercial operations, and particularly relates to a resource scheduling optimization management system for discrete tasks. Background Technology

[0002] Current resource management systems typically employ scheduling methods with pre-defined task flows exhibiting quasi-periodic or continuous characteristics. By setting specific administrative or commercial logic access conditions, they coordinate the arrival sequence of business requests with the execution rhythm of business management resources, thereby driving task distribution and resource recovery. However, when the system handles highly discrete tasks such as cross-regional non-standard business requests and sudden unstructured management needs, the long-tail distribution of task arrival times leads to a mismatch between the management decision-making level and the specific business execution end. Existing scheduling models often use triggering mechanisms based on fixed thresholds. This approach is prone to response oscillations when faced with situations where task intervals fluctuate greatly. During periods of dense task arrival, the management system may stagnate due to a sudden surge in load, while during periods of sparse task arrival, resources may be wasted due to the inability to release them in a timely manner.

[0003] Besides spatial resource allocation mismatch, control logic also suffers from stability bottlenecks in the temporal dimension. For example, Chinese invention patent CN117649080A discloses a resource scheduling optimization management method and system for task analysis. By constructing a dynamic resource distribution field sequence to address the coordinated scheduling of primary and secondary tasks and time window optimization, the real-time field matching optimization mechanism falls into an oversensitive control error when facing highly discrete random business flows. It lacks filtering of task arrival jitter features, and the control algorithm frequently triggers scheduling recalculation and field updates with discrete pulse fluctuations. This causes the management strategy to jump frequently at different step sizes, and the decision oscillation increases the metadata synchronization load. This causes the backend resource pool to receive instructions with uneven temporal distribution, resulting in logical disorder and weakening the continuity of scheduling decisions and execution steady state. To address these challenges, the industry usually tries to improve the system's response sensitivity by shortening the sampling period or lowering the trigger threshold. However, such linear improvement paths lead to frequent changes in management strategies under discrete conditions. This not only increases the synchronization overhead and computational load of management metadata, but also causes the backend resource pool to fall into a logical oscillation state due to receiving pulse-like instructions with uneven temporal distribution, weakening the consistency of scheduling decisions.

[0004] Therefore, how to dynamically adjust the management window step size based on the real-time jitter index of the task flow, and establish the logical steady state of scheduling decisions in combination with the physical response residual, is the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a resource scheduling optimization management system for discrete tasks, characterized in that it includes:

[0006] The task awareness module is used to acquire discrete task flow arrival time-series data that characterizes the distribution features of business requests;

[0007] The logic processing module is used to perform the following steps:

[0008] Step 101: Calculate the task arrival jitter rate based on the arrival time sequence data;

[0009] Step 102: Calculate the target input window step size based on the task arrival jitter rate, and generate the corresponding window step size adjustment instruction;

[0010] Step 103: Maintain in real time the strategy stability index that characterizes the cumulative change frequency of window step size adjustment commands within the preset observation period;

[0011] Step 104: When the absolute value of the deviation between the target input window step size and the currently executed window step size is less than the preset sensitivity threshold, maintain the logical continuity of the currently executed window step size.

[0012] Step 105: When the absolute value of the deviation is greater than or equal to the sensitivity threshold, if the strategy stability index is lower than the preset instability threshold, the current execution window step size is updated according to the target input window step size, and a scheduling instruction is generated.

[0013] The scheduling and control module is used to perform time-series scheduling of controlled and managed resource entities according to scheduling instructions.

[0014] Preferably, the real-time modeling process of the strategy stability index executed by the logic processing module includes: within a preset observation period, statistically analyzing the frequency of adjustment direction switching and the numerical variance of adjustment magnitude of the target input window step size, and then performing a weighted summation of the normalized frequency of adjustment direction switching and the numerical variance of adjustment magnitude to calculate the strategy stability index; when the strategy stability index exceeds the instability threshold, the logic processing module forcibly extends the duration of the currently executed window step size by mapping preset decision protection weights to offset the decision oscillations caused by fluctuations in the distribution characteristics of business requests.

[0015] Preferably, the logic processing module is also used to identify the business source identifier of each business request and calculate the business diversity index, which represents the complementary characteristics of different business source tasks on the time axis. When the business diversity index exceeds the preset conflict threshold, the logic processing module performs a weight reduction correction on the task arrival jitter rate based on the business diversity index and divides the controlled management resource entity into multiple orthogonal logical sub-pools to eliminate resource preemption.

[0016] Preferably, the logic processing module executes the logic lane diversion strategy, including: obtaining the historical fulfillment priority of each business source, and dynamically adjusting the resource quota weight of each logic sub-pool according to the business diversity index; within the logic sub-pool, the logic processing module independently executes steps 103 to 105 to achieve parallel resource management under different business semantics.

[0017] Preferably, when updating the currently executing window step size, the logic processing module adjusts the slope of the change of the currently executing window step size using a nonlinear smoothing filtering algorithm based on the mapping value of the strategy stability index within a preset linear interval, so as to match the management decision output with the long-tail distribution characteristics of the discrete task flow.

[0018] Preferably, the scheduling control module executes a response residual hedging procedure, including the following steps: Step 601, obtaining the actual response time of the controlled managed resource entity to the historical task package, and calculating the response residual R between the actual response time and the baseline time: Where R is the response residual, This represents the actual response time. The preset baseline time is used; in step 602, the target input window step size is feedforward corrected according to the response residual R; when the response residual R shows an increasing trend, the scheduling control module provides logical buffer space for the controlled management resource entity by stretching the currently executed window step size.

[0019] Preferably, the scheduling control module further performs the following steps: calculating the rate of change of the response residual R, and performing a weighted mapping between the response residual R and its rate of change to obtain the resource response inertia index of the execution end; before issuing scheduling instructions, correcting the instruction issuance timing according to the resource response inertia index.

[0020] Preferably, the sensitivity threshold preset by the logic processing module has adaptive adjustment characteristics: the logic processing module monitors the arrival density characteristics of task packages in discrete task flows, and lowers the sensitivity threshold when the arrival density characteristics of task packages exceed the preset load threshold, so as to improve the sensitivity to the perception of peak business conditions.

[0021] Preferably, the scheduling control module further includes a task pooling and reconfiguration unit; during the logical waiting period before the scheduling instruction is issued, the task pooling and reconfiguration unit reorders the task packages in the discrete task flow based on business priority, and generates scheduling instructions within the constraint of the target input window step size.

[0022] Preferably, the system also includes a global feedback module, which is used to obtain the global resource saturation of the controlled managed resource entities, and when the global resource saturation exceeds the preset load warning line, the logic processing module executes the shrinking action of the currently executed window step size to limit the admission frequency of business requests.

[0023] Compared with existing technologies, the resource scheduling optimization management system for discrete tasks of this invention has the following advantages:

[0024] 1. In resource scheduling optimization management, by calculating the time interval sequence of pending task requests and extracting real-time jitter indicators reflecting task distribution characteristics based on variance analysis within a sliding window, a logical admission window with dynamically adjustable step size is constructed. This changes the passive triggering mode based on fixed thresholds in the traditional scheduling model, transforming the chaotic discrete random task flow at the surface level into a task aggregation package with overall flow stability at the logical management level. This change in management method enables the output frequency of scheduling instructions to adapt to the peaks and troughs of task arrival, avoiding processing stagnation caused by a surge in instantaneous load in physical resources within a very short time, eliminating idle losses caused by the inability to release reserved resources in time during task sparse periods, and achieving a deep match between management logic and physical resource execution capabilities.

[0025] 2. By executing the load state pre-projection procedure, the system utilizes the logical aggregation time of tasks within the admission window. When the task feature density within the window reaches a preset threshold and the running progress is more than halfway complete, the scheduling control module outputs a pre-projection pulse command without specific allocation data to the physical resource pool. This triggers the resource pool to perform pre-preparation actions such as metadata prefetching and logical link frequency increase, thereby transforming the original static waiting time into a dynamic warm-up gain. This asymmetric pre-triggering logic, without increasing the communication load, offsets the inherent lag when physical resources switch from a resting state to a high-load state, ensuring the certainty of fulfillment at the moment the scheduling command is issued.

[0026] 3. A business-dimensional correction procedure is introduced. By identifying the business source identifier of each task request and calculating the business diversity index, the system performs semantic correction on the single time axis jitter index. In the pseudo-stationary state where different business source tasks exhibit time complementary characteristics, the system can identify the resource ownership exclusivity risk behind the statistical feature stability. When the business diversity index exceeds the conflict threshold, the system stops the window shrinking action and starts the logical lane diversion strategy, dividing the physical resource pool into multiple orthogonal logical sub-pools. This realizes the management leap from the time dimension to the semantic dimension, effectively preventing high-cost resource preemption and management deadlock caused by multi-source business conflicts, and reducing the system operating entropy value in complex management environments. Attached Figure Description

[0027] Figure 1 This is a flowchart of the scheduling logic of the dynamic window step size and strategy damping mechanism of the present invention;

[0028] Figure 2 This is a system architecture diagram of the present invention, which integrates an intelligent scheduling center and a response residual feedback closed loop. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0030] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0031] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0032] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0033] This invention provides a resource scheduling optimization management system for discrete tasks, comprising a task awareness module, a logic processing module, and a scheduling control module. In its overall operation, the task awareness module captures the physical distribution of unstructured business requests along the time axis. The logic processing module quantifies the random fluctuations of discrete tasks into an admission window step size at the management level, and a decision damping mechanism maintains the logical steady state of scheduling decisions. Finally, the scheduling control module coordinates the execution sequence of controlled managed resource entities, achieving adaptation between resource occupancy and business fulfillment timeliness under discrete operating conditions. In administrative management or commercial operations, due to the existence of cross-regional non-standard business or sudden management needs, the arrival time of business requests is unpredictable, making it difficult for the scheduling system to preset a fixed processing cycle. The task awareness module acquires discrete task flow arrival time sequence data characterizing the distribution features of business requests. It monitors the admission time point of each pending task request through the access layer interface, constructs a timestamp sequence according to the arrival order, calculates the time difference between two adjacent task requests, and produces a time interval sequence reflecting the dynamics of task inflow. ,in, This is the difference between the arrival times of the nth task and the (n-1)th task.

[0034] Because the task intervals fluctuate significantly under discrete operating conditions, directly using a fixed threshold to trigger scheduling can easily cause oscillations in the response of backend resources. To address this issue, the logic processing module executes step 101, calculating the task arrival jitter rate D based on the arrival time sequence data. This module selects a time window containing N most recently arrived tasks and calculates the time interval sequence within that window. The sample variance is calculated, and the ratio of this variance to the average time interval within the window is determined as the task arrival jitter rate D; in step 102, the system calculates the target input window step size based on the task arrival jitter rate D. The system generates a corresponding window step size adjustment instruction. When the jitter rate D increases, the system increases the target input window step size. The numerical value of the target input window is used to mitigate the discreteness of the task through logical aggregation, and as the jitter rate D decreases, the system reduces the step size of the target input window. To improve the real-time performance of management response, in a random business environment, frequent adjustments to management parameters increase the synchronization overhead of metadata. Therefore, in step 103, the logic processing module maintains in real time a strategy stability index S that represents the cumulative frequency of changes in window step size adjustment instructions within a preset observation period. The modeling process of this index S includes: within the preset observation period, statistically analyzing the frequency of adjustment direction switching and the numerical variance of adjustment magnitude of the target input window step size, and then performing a weighted sum after normalization of the frequency of adjustment direction switching and the numerical variance of adjustment magnitude to calculate the strategy stability index S. When it exceeds a preset instability threshold, the logic processing module extends the currently executed window step size by mapping preset decision protection weights. The length of service offsets the decision-making oscillations caused by fluctuations in the distribution characteristics of business requests. This decision-making damping mechanism, by introducing logical inertia, avoids the management strategy from overfitting to instantaneous random noise and ensures the smoothness of scheduling instruction output.

[0035] To finely control the triggering timing of step size updates, the logic processing module, in step 104, updates the target input window step size. With the currently executing window step size When the absolute value of the deviation is less than the preset sensitivity threshold δ, the logical continuity of the currently executed window step size is maintained; in step 105, when the absolute value of the deviation is greater than or equal to the sensitivity threshold δ, if the strategy stability index S is lower than the preset instability threshold, then the target input window step size is used. Update the currently executing window step size The system generates scheduling instructions, where the sensitivity threshold δ has adaptive adjustment characteristics. The logic processing module monitors the arrival density characteristics of task packets in the discrete task flow, and lowers the sensitivity threshold δ when the arrival density of task packets exceeds the preset load threshold, thereby improving the sensitivity to peak business conditions and ensuring the logical stability of management decisions under discrete conditions. The logic processing module establishes the physical execution boundary of scheduling decisions by dividing the memory into an instruction temporary storage area and a current execution register, stores the calculated target admission window step size in the instruction temporary storage area, and polls the policy stability index in real time through decision jump arbitration. When the absolute value of the deviation is greater than or equal to the sensitivity threshold and the policy stability index is lower than the instability threshold, the current execution register is written to update the temporary value to the execution end register to generate scheduling instructions. When the policy stability index exceeds the instability threshold, the execution end write operation channel is blocked, so that the scheduling control module continues to read and use the step size value of the previous cycle stored in the current execution register in the current scheduling cycle, and filters random disturbances in the discrete task flow through the register-level read-write isolation mechanism.

[0036] The sensitivity threshold δ is determined by executing a gradient stress test program during the system application layer logic initialization phase. This involves inputting a simulated task flow with a known jitter gradient into the system and monitoring the resource saturation U of the controlled managed resource entities in real time. The baseline sensitivity is selected when the resource saturation U is within the operating window of 0.80 to 0.90, and the strategy stability index S remains below 0.50. The logic processing module adjusts the window frequency based on different task arrival jitter rates D, and uses the least squares method to fit the response curve between the task packet arrival density ρ and the window adjustment trigger frequency to determine the sensitivity coefficient k value, so that the sensitivity threshold δ satisfies the calculation formula. Where δ is the sensitivity threshold, Let e ​​be the base sensitivity, k be the sensitivity coefficient, and ρ be the arrival density of task packets. An exponential decay function is used to establish a nonlinear mapping relationship between the management system's sensitivity to dense task inflow conditions and the access frequency.

[0037] In a multi-service parallel management environment, discrete tasks from different service sources may exhibit complementary characteristics on the time axis, leading to semantic conflicts in scheduling within a single time dimension. The logic processing module is also used to identify the service source identifiers of each service request and calculate the service diversity index H, which characterizes the complementary characteristics of tasks from different service sources on the time axis. This index H is determined based on the distribution entropy values ​​of different service identifiers within the current window. When the service diversity index H exceeds a preset conflict threshold, the logic processing module performs a weighted correction on the task arrival jitter rate D based on the service diversity index H, and divides the controlled management resource entities into multiple orthogonal logical sub-pools to eliminate resource preemption. The logic processing module executes a logical lane diversion strategy, obtains the historical fulfillment priority of each service source, and determines the service diversity index based on the service source's requirements. The performance index H adjusts the resource quota weights of each logical sub-pool to achieve parallel resource management under different business semantics. During the execution of logical steps, there is a startup lag when physical resources switch from a resting state to a high-load state. To offset this execution inertia, the scheduling control module executes a load state pre-projection program. During the operation of the admission window, it obtains the feature density of the suspended tasks in the current window. When it reaches the preset pre-sensing threshold and the running progress of the admission window exceeds 50%, the scheduling control module outputs a pre-projection pulse command to the physical resource pool in advance to instruct the physical resources to perform management metadata pre-fetching and logic link frequency increase. This pre-triggering logic converts the waiting time generated by window aggregation into physical preheating gain and eliminates the cold start load impact generated at the moment the scheduling command is issued.

[0038] Since the actual response time of the physical resource pool to the aggregated task package is affected by factors such as context switching, it may deviate from the expected latency. Therefore, the scheduling control module executes a response residual hedging procedure. In step 601, the actual response time of the controlled managed resource entity to the historical task package is obtained. And calculate the actual response time relative to the baseline time. The response residual R is calculated as follows: Where R is the response residual; This refers to the actual response time. The preset baseline time is used; in step 602, the system performs feedforward correction on the target input window step size based on the response residual R. When the response residual R shows an increasing trend, the scheduling control module stretches the currently executed window step size. To provide a logical buffer space for controlled resource entities, during the actual execution correction process, to avoid system oscillations caused by instantaneous jumps in the response residual R due to the randomness of discrete tasks, the scheduling control module performs first-order low-pass filtering on the calculated response residual R to extract the steady-state trend term of the residual. Specifically, the feedforward correction amount is dynamically weighted based on the task arrival jitter rate D: when the jitter rate D is high, the system increases the filtering time constant, and suppresses the gain of the feedforward loop by introducing the long-term mean of historical residuals, ensuring that the window step size is stretched based on the overall trend of the resource pool's digestion capacity rather than the random fluctuations of individual tasks. This correction mechanism is based on the lag characteristics of physical response and the proactive compensation of management decisions. By transforming the statistical residuals of historical responses into a prediction of the processing capacity for the next admission cycle, the logical loop from physical execution feedback to decision parameter correction is closed. The system employs a causal loop and calculates the rate of change of the response residual R. This rate of change is then weighted and mapped to the residual value to obtain a resource response inertia index at the execution end. Before issuing scheduling instructions, the timing of instruction issuance is corrected based on this inertia index to ensure that scheduling decisions align with the actual resource utilization capacity. To ensure system safety under extreme overload conditions, the system also includes a global feedback module. This module acquires the global resource saturation of the controlled resource entities. When this saturation exceeds a preset load warning line, the logic processing module performs a shrinking action on the current execution window step size to limit the frequency of business request admissions. Furthermore, the scheduling control module includes a task pooling and reconfiguration unit. During the logical waiting period before the issuance of scheduling instructions, it reorders task packages in the discrete task flow based on business priority and generates the final scheduling instruction within the constraints of the target entry window step size, achieving a balance between resource utilization and delivery timeliness.

[0039] In scenarios involving priority refactoring of discrete task flows, the task pooling refactoring unit obtains the set of suspended tasks within the current admission window and extracts the deadline duration for each business request. The logic processing module combines the business weight factor v with the deadline duration. The product of the reciprocals is determined as the comprehensive priority index P. The formula for calculating the comprehensive priority index P is as follows: Where P is the overall priority index and v is the business weight factor; The deadline is specified; the task pooling refactoring unit executes a reordering procedure based on position adjustment, moving the task with the highest comprehensive priority index P to the head of the queue within the logical waiting period, and within the current execution window step. Within the constraints, scheduling instructions are generated; in feedback adjustment scenarios handling extreme overload conditions, the global feedback module monitors the global resource saturation U of the controlled managed resource entities. When the global resource saturation U exceeds the preset load warning line of 0.90, the logic processing module adjusts the target input window step size. When implementing a contraction intervention, the system determines the step size decay ratio based on the current load deviation, causing the admission frequency to converge towards the instantaneous throughput capacity of the controlled resource entity. If the global resource saturation U remains above the load warning line for three consecutive observation periods, and the task arrival jitter rate D is greater than 5.0, the logic processing module will adjust the current window step size. The shrinkage step size was set at 15% to mitigate the risk of management deadlock caused by excessive resource consumption. This 15% shrinkage step size was determined based on sensitivity analysis of the global resource saturation U around the load warning line (0.90). When the saturation exceeds 0.90, the instruction throughput and queuing frequency of the controlled managed resource entity are mismatched. The 15% shrinkage ratio can ensure that the queuing load is reduced to the lower limit of the physical processing capacity within three consecutive observation periods. If the step size is too small (e.g., below 5%), it will not be able to smooth out the instantaneous load surge in time, resulting in the deadlock risk not being resolved. If the step size is too large (e.g., above 30%), it will cause a drastic change in the management window, resulting in a significant decrease in the timeliness of business fulfillment. Therefore, 15% is set as the optimal step size threshold for balancing system security and decision stability.

[0040] Example 1: In a discrete working scenario of cross-regional non-standard business request processing, the arrival times of business requests are unevenly distributed and the time intervals are sequential. The drastic fluctuations, coupled with the traditional fixed-threshold trigger mechanism used in scheduling methods, cause oscillations in the response of controlled resource entities. This leads to processing stagnation during peak task periods and resource idleness during sparse task periods. The task awareness module monitors the admission time points of pending task requests in real time, generating a time interval sequence reflecting the dynamics of task inflow. The logic processing module selects a time window containing the 50 most recently visited tasks and calculates the time interval sequence within that window. The sample variance is calculated, and its ratio to the average time interval within the window is determined as the task arrival jitter rate D; the logic processing module calculates the target input window step size based on the task arrival jitter rate D. The strategy stability index S is the cumulative frequency of changes in the real-time monitoring window step size adjustment command within a preset observation period, when the target input window step size... With the currently executing window step size When the absolute value of the deviation is less than the preset sensitivity threshold δ, the system maintains the currently executing window step size. The logical continuity is maintained in this way, and the random disturbances that would normally be transmitted to the execution end are suppressed by the decision damping mechanism at the strategy layer, thus resolving the conflict between real-time adaptability and management consistency in the management system.

[0041] The scheduling and control module executes the load state pre-projection procedure within the currently executing window step. When the running progress exceeds 50% and the task feature density reaches the preset pre-sensing threshold, the scheduling control module outputs a pre-projection pulse command to the controlled managed resource entity. This command instructs the physical resource to perform management metadata prefetching and increase the logical link frequency. This asymmetric pre-triggering logic transforms the logical waiting time generated during the admission window aggregation process into physical preheating gain, eliminating the cold start load impact generated at the moment the scheduling command is issued. The scheduling control module executes a response residual hedging procedure to obtain the actual response time of the controlled managed resource entity to the historical task package. The formula for calculating the response residual R is as follows: Where R is the response residual; The actual response time for a controlled managed resource entity to process historical task packages; The preset standard processing time; when the response residual R shows an increasing trend, the scheduling control module adjusts the current execution window step size. This approach provides a logical buffer space for controlled management resource entities, changing the traditional approach of only adjusting at the execution end. By reconstructing the management time scale during the decision generation stage, it transforms the discrete task flow under long-tail distribution into a controlled resource allocation sequence, achieving a balance between resource occupancy and business performance timeliness.

[0042] Example 2: This experiment verifies the scheduling stability and physical resource execution efficiency of a resource scheduling optimization management system for discrete tasks when processing random business flows. The experimental platform is built in a discrete event simulation environment to simulate the access and processing of cross-regional non-standard business requests. The data source uses task flow samples generated by a simulated administrative management center. Gaussian white noise with a signal-to-noise ratio of 20dB is actively superimposed on the experimental signal to simulate data transmission jitter in a real environment. The sampling window N is set to 50. The determination of this parameter depends on the trade-off between the real-time performance of data acquisition and the system's computational load. When the characteristics of the task interval distribution are in the preset mid-frequency range, to ensure the statistical significance of the task arrival jitter rate D, the system selects 50 consecutive tasks as the benchmark for a single logical calculation. In the experimental startup phase, three groups of task flows with discrete gradients are set as the original input data. The task arrival jitter rate D of the low-discrete sample group is 1.05, D of the medium-discrete sample group is 3.42, and D of the high-discrete sample group is 6.18. Under the discrete condition of D of 6.18, due to the time interval sequence... Exhibiting a long-tail distribution characteristic, the control group showed a scheduling decision jump frequency of 35.6 times / min within a 60-second observation period, and an average idle rate of 24.5% for controlled managed resource entities. The sample group of this invention maintains the strategy stability index S through a logic processing module and utilizes a decision damping mechanism to adjust the current execution window step size. Maintaining the timeframe between 350ms and 380ms, the decision jump frequency decreased to 4.2 times / min, and the utilization rate of physical resources increased to over 92.3%.

[0043] To verify the synergistic effect, a control group was set up in the experiment, which removed the response residual hedging procedure. The sample group of this invention obtained the actual response time of the controlled management resource entity to the historical task package. And calculate the response residual R, the formula of which is as follows: Where R is the response residual; This refers to the actual response time. The preset standard processing time is 150ms; when R is detected to increase from 12.5ms to 45.2ms, the scheduling control module extends the current execution window step size. This provides a 60ms logic buffer time for the execution end, reducing the cold start time from 18.3ms to 2.4ms. The control group lacking this program experienced processing latency exceeding 450ms when the task density exceeded 0.8 tasks / ms. This demonstrates the cooperative relationship between the strategy layer damping logic and the execution layer residual feedforward mechanism in mitigating execution inertia bias. As the task arrival jitter rate D linearly increases from 1.05 to 8.12, the global resource saturation of this invention remains stable within a working window of 78% to 85%. When D exceeds 6... After reaching a performance point of 0.5, the response curve of the controlled managed resource entity tends to flatten, indicating that the logical aggregation effect smooths out the discreteness of the business flow and prevents the management strategy from fitting instantaneous noise. After D reaches 4.2, the strategy stability index S of the control group using the fixed threshold scheduling mode exceeds the instability threshold and falls into a response oscillation state. The experimental data confirms the technical path of mapping from data distribution characteristics to logical damping inertia, and proves the reliability of the system in maintaining the steady state of management decision logic under discrete working conditions, realizing the adaptation of resource occupation and business performance timeliness.

[0044] Example 3: This example combines Figures 1 to 2 This paper describes a resource scheduling optimization management system for discrete tasks, such as... Figure 1 As shown, step 101 is executed to calculate the task arrival jitter rate based on the arrival time sequence data of the discrete task flow; then step 102 is executed to calculate the target arrival window step size based on the task arrival jitter rate and generate the corresponding window step size adjustment instruction; in step 103, the system maintains in real time a policy stability index that characterizes the cumulative change frequency of the window step size adjustment instruction within a preset observation period; step 104 is executed according to the discrimination logic, and when the absolute value of the deviation between the target arrival window step size and the current execution step size is less than the sensitivity threshold, the logical continuity of the current window step size is maintained; finally, step 105 is executed, and when the absolute value of the deviation is greater than the threshold and the policy stability index is lower than the instability threshold, the current execution window step size is updated and a scheduling instruction is generated.

[0045] like Figure 2As shown, the system receives non-standard service request streams with high discreteness and long-tail distribution characteristics on the left. These request streams are input as time-series data streams to the core intelligent scheduling hub. This hub integrates policy damping and rectification functions, and internally deploys a service semantic correction module to output correction signals. It is also configured with a jitter perception engine to calculate the jitter rate and transmit it to the decision damping controller. The decision damping controller performs stability arbitration and receives residual correction signals from the response residual analyzer below. It then outputs step size instructions to drive the pre-projection pulse generator. Finally, the intelligent scheduling hub outputs steady-state scheduling instructions to the resource execution environment on the right. This environment consists of orthogonal sub-pools, specifically logical sub-pool A for handling high-priority tasks, logical sub-pool B for handling standard tasks, and logical sub-pool C for handling long-tail tasks.

[0046] Example 4: The administrative management system faces a sudden surge in business activity, with the task package arrival density ρ increasing exponentially. The logic processing module dynamically calibrates management decision parameters. The task awareness module detects that the task package arrival density ρ increases from 0.12 tasks / ms to 0.85 tasks / ms, triggering adaptive adjustment logic for the sensitivity threshold δ. The logic processing module determines the value of the sensitivity threshold δ based on the detected task package arrival density ρ. The formula for calculating the sensitivity threshold δ is as follows: Where δ is the sensitivity threshold; The baseline sensitivity is set at 100ms; k is the sensitivity coefficient, with a value of 1.5; ρ is the task packet arrival density. When ρ increases from 0.12 tasks / ms to 0.85 tasks / ms, the sensitivity threshold δ shrinks from 83.5ms to 27.8ms. The shrinkage mechanism based on the negative exponential function shortens the trigger interval of the window step size adjustment command, improving the management system's perception accuracy of the dense task inflow state. The logic processing module accumulates the time interval sequence within the sliding window. The system updates the task arrival jitter rate D based on the change in the target input window size N, sets the sampling window N to 50, and replaces the earliest sampling point within the window with the time interval between new arrival tasks when it receives a task request. The system then calculates the sample variance and the target input window step size. With the currently executing window step size When the absolute value of the deviation reaches 30ms and exceeds the current sensitivity threshold δ, the logic processing module initiates the arbitration process for the strategy stability index S.

[0047] The strategy stability index S uses the following weighting logic: the system counts the execution records of the most recent 10 window step size adjustment commands, sets the weight α for the adjustment direction switching frequency to 0.6, and sets the weight β for the numerical variance of the adjustment magnitude to 0.4. When the strategy stability index S exceeds the instability threshold of 0.75, the current business environment is determined to be in a period of severe disturbance, and the logic processing module maintains the currently executed window step size. The duration of the incumbent is maintained until the strategy stability index S decays to below 0.45. The weight allocation scheme, based on the direction switching weight, suppresses the oscillation of scheduling decisions between contraction and expansion, maintaining the coherence of administrative resource allocation logic. The scheduling control module calibrates the load status pre-projection program. When the progress of the admission window reaches 65% and the backlog of pending tasks in the logical sub-pool exceeds the load threshold, the scheduling control module outputs a pre-projection pulse command to the controlled management resource entity. This pre-projection signal instructs the physical resource to prefetch management metadata and increases the logical link frequency of the controlled management resource entity from 800MHz to 2.4GHz. In terms of specific hardware implementation, the logical link frequency adjustment of the controlled management resource entity is achieved through the operating system's Dynamic Voltage Frequency Adjustment (DVFS) driver interface. When the intelligent scheduling center determines that the current business is at its peak and is expected to be in a peak state based on the task packet arrival density ρ, the system will continue to implement the pre-projection signal. As the entry window is about to open, the pre-projection pulse generator sends a frequency modulation command to the kernel-mode frequency controller. This command triggers the adjustment of the multiplication factor of the underlying hardware phase-locked loop (PLL), thereby enabling the processor's ring bus or memory controller frequency to quickly jump from the base frequency in the low-power state to the turbo frequency level in the high-performance state. This cross-level frequency scheduling, through the pre-projection pulse, preemptively offsets the several milliseconds of physical locking time required for hardware frequency switching, ensuring that when the scheduling command is issued and the task officially enters the execution end, the underlying logic link is already in a stable high-frequency working state. Under high-load conditions with ρ of 0.85 tasks / ms, through this parameter calibration procedure, the scheduling decision jump frequency of the controlled management resource entity is maintained at 5.0 times / min, and the average business fulfillment time is improved by 32.5%, which confirms the value of adaptive sensitivity adjustment and stability weight calibration in processing discrete task flows.

[0048] Example 5: When the system is deployed in an environment containing heterogeneous physical resources, the scheduling control module performs a benchmark calibration procedure on the controlled and managed resource entities, and determines the standard processing time by obtaining historical service response data of the target resource pool within a 24-hour continuous operating cycle. The logic processing module statistically analyzes the sequence distribution of business request response times and determines the sampled value at the 95th percentile of the sequence as the standard processing time. During the calibration process, the task packet density ρ is kept constant at 0.05 tasks / ms.

[0049] In scenarios involving the initialization of system application layer logic, the logic processing module determines the sensitivity coefficient k and the baseline sensitivity by executing a gradient stress test program. The system receives multiple discrete task flows, with the input task arrival jitter rate D increasing from 1.0 to 10.0 in increments of 1.0. It also monitors the resource saturation of controlled resource entities in real time. When the resource saturation is between 80% and 90% and the strategy stability index S remains below 0.5, the system selects the current deviation sample value as the baseline sensitivity. Based on this, the logic processing module adjusts the window frequency according to different task arrival jitter rates D, and uses the least squares method to fit the response curve between the task packet arrival density ρ and the window adjustment trigger frequency to obtain the sensitivity coefficient. The value.

[0050] Example 6: In the pre-verification scenario of multi-tenant service isolation deployment, the logic processing module performs a benchmark quantification procedure on the service diversity index H, and the task awareness module extracts the total inflow of various service traceability identifiers within an initial observation period of 600 seconds, and determines the proportion of the i-th type of service in the total inflow as the probability distribution operator. In calculating probability distribution operators In this invention, a sliding observation window is used, with the current admission time as the endpoint. The window length is set to cover the arrival cycles of the most recent N task packets. Each time the system receives a new task, it identifies its business traceability identifier and re-counts the frequency of occurrence of various identifiers within the sliding window. The normalization process is achieved by dividing the frequency of the i-th type of service by the total number of tasks within the window, ensuring that all... The sum of all values ​​is always equal to 1. This dynamic sampling based on a sliding window can sensitively capture the instantaneous distribution shift of business source categories, thus enabling the subsequently calculated business diversity index H to reflect the latest semantic features of the current discrete task flow, rather than being interfered with by historical outdated data. Based on this, the logic processing module constructs a calculation model for the business diversity index H according to information theory principles. The calculation formula for the business diversity index H is as follows: Where H is the business diversity index; M is the total number of business source categories in the current window; Let H be the probability distribution of the i-th type of business. The system determines the conflict threshold by simulating the instruction conflict rate of the controlled managed resource entity under different business diversity index H values. When the calculation result corresponding to the business diversity index H is in the range of 0.65 to 0.85, and the proportion of resource preemption time to the total processing time exceeds 15%, the system determines the conflict threshold to be 0.70, providing a quantitative admission judgment basis for the orthogonal partitioning of logical sub-pools.

[0051] In an engineering debugging scenario aimed at optimizing the smoothness of administrative management strategy execution, the logic processing module calibrates the decision protection weights in the management decision damping procedure. The system obtains the strategy stability index S sequence when the task arrival jitter rate D fluctuates, and sets the current execution window step size. The forced continuation duration is limited to the nonlinear mapping relationship between the strategy stability index S and the baseline step size. When the strategy stability index S is at the instability threshold of 0.75, and the target input window step size is... With the currently executing window step size When the absolute value of the deviation exceeds the sensitivity threshold δ, the logic processing module extracts a protection coefficient of 1.5 times to ensure that the minimum interval between the issuance of scheduling instructions is not less than twice the context switching overhead of the controlled managed resource entity. At the same time, the scheduling control module compares the warm-up benefits of the pre-projection pulse instruction under different running progresses in the admission window, determines the synchronous response curve of the pre-sensing threshold and the task feature density, and enables physical resources to enter the management metadata prefetching state when the progress of the admission window reaches 55% to 70%.

[0052] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A resource scheduling and optimization management system for discrete tasks, characterized in that, include: The task awareness module is used to acquire discrete task flow arrival time-series data that characterizes the distribution features of business requests; The logic processing module is used to perform the following steps: Step 101: Calculate the task arrival jitter rate based on the arrival time sequence data; Step 102: Calculate the target input window step size based on the task arrival jitter rate, and generate the corresponding window step size adjustment instruction; Step 103: Maintain in real time the strategy stability index that characterizes the cumulative change frequency of window step size adjustment commands within the preset observation period; Step 104: When the absolute value of the deviation between the target input window step size and the currently executed window step size is less than the preset sensitivity threshold, maintain the logical continuity of the currently executed window step size. Step 105: When the absolute value of the deviation is greater than or equal to the sensitivity threshold, if the strategy stability index is lower than the preset instability threshold, the current execution window step size is updated according to the target input window step size, and a scheduling instruction is generated. The scheduling and control module is used to perform time-series scheduling of controlled and managed resource entities according to scheduling instructions.

2. The resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The real-time modeling process of the strategy stability index executed by the logic processing module includes: within a preset observation period, statistically analyzing the frequency of adjustment direction switching and the numerical variance of adjustment magnitude of the target input window step size, and then performing a weighted summation of the normalized adjustment direction switching frequency and adjustment magnitude variances to calculate the strategy stability index; when the strategy stability index exceeds the instability threshold, the logic processing module forcibly extends the duration of the currently executed window step size by mapping preset decision protection weights to offset the decision oscillations caused by fluctuations in the distribution characteristics of business requests.

3. The resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The logic processing module is also used to identify the business source identifier of each business request and calculate the business diversity index, which represents the complementary characteristics of different business source tasks on the time axis. When the business diversity index exceeds the preset conflict threshold, the logic processing module performs weight reduction correction on the task arrival jitter rate based on the business diversity index and divides the controlled management resource entity into multiple orthogonal logical sub-pools.

4. A resource scheduling and optimization management system for discrete tasks according to claim 3, characterized in that, The logic processing module executes the logic lane diversion strategy, including: obtaining the historical fulfillment priority of each business source and dynamically adjusting the resource quota weight of each logic sub-pool according to the business diversity index; within the logic sub-pool, the logic processing module independently executes steps 103 to 105.

5. A resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, When updating the currently executing window step size, the logic processing module uses a non-linear smoothing filtering algorithm to adjust the slope of the change in the currently executing window step size based on the mapping value of the strategy stability index within a preset linear interval, so as to match the management decision output with the long-tail distribution characteristics of the discrete task flow.

6. A resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The scheduling control module executes a response residual hedging procedure, including the following steps: Step 601, obtaining the actual response time of the controlled managed resource entity to the historical task package, and calculating the response residual between the actual response time and the baseline time. : Where R is the response residual, This represents the actual response time. The preset baseline time is used; in step 602, the target input window step size is feedforward corrected according to the response residual R; when the response residual R shows an increasing trend, the scheduling control module provides logical buffer space for the controlled management resource entity by stretching the currently executed window step size.

7. A resource scheduling and optimization management system for discrete tasks according to claim 6, characterized in that, The scheduling control module also performs the following steps: calculates the rate of change of the response residual R, and performs a weighted mapping between the response residual R and its rate of change to obtain the resource response inertia index of the execution end; before issuing scheduling instructions, it corrects the instruction issuance timing according to the resource response inertia index.

8. A resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The sensitivity threshold preset by the logic processing module has adaptive adjustment characteristics: the logic processing module monitors the arrival density characteristics of task packets in discrete task flows, and lowers the sensitivity threshold when the arrival density characteristics of task packets exceed the preset load threshold.

9. A resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The scheduling control module also includes a task pooling and reconfiguration unit. During the logical waiting period before the scheduling instruction is issued, the task pooling and reconfiguration unit reorders the task packages in the discrete task flow based on business priority and generates scheduling instructions within the constraints of the target input window step size.

10. A resource scheduling and optimization management system for discrete tasks according to claim 1, characterized in that, The system also includes a global feedback module, which is used to obtain the global resource saturation of the controlled managed resource entities, and when the global resource saturation exceeds the preset load warning line, the logic processing module executes the shrinking action of the currently executing window step size.

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