Dynamic configuration computing resource management method and system based on power wireless terminal

Abstract

The invention provides a dynamic configuration computing resource management method and system based on a power wireless terminal, and the method comprises the steps: obtaining a real-time operation state data set and a power business demand data set of the power wireless terminal, and carrying out the multi-dimensional feature fusion processing of the real-time operation state data set and the power business demand data set; the method comprises the steps of generating a resource adaptation feature set, performing real-time strategy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base, generating a computing resource configuration strategy set, performing adjustment operation on computing resources of the power wireless terminal, and generating a resource allocation verification result and terminal operation efficiency feedback data. And continuously comparing the terminal operation efficiency feedback data with a preset efficiency optimization threshold value, and carrying out iterative updating processing on the resource adaptation rule base according to a comparison result. According to the invention, accurate matching between computing resource allocation and power business demand fluctuation can be realized.

Description

Technical Field

[0001] The present invention relates to the fields of resource management and data processing, and in particular to a method and system for dynamically configuring computing resource management based on power wireless terminals. Background Art

[0002] With the increasing intelligence level of power systems, the dynamic configuration of computing resources for power wireless terminals has become a key technology to ensure the efficient operation of power services. In existing technologies, resource management methods usually allocate resources based on static load monitoring of terminal devices or independent priority ranking of business needs. For example, resource adjustments are triggered by fixed thresholds or task requirements are matched using predefined rules. However, such methods are difficult to cope with the complex working conditions of instantaneous fluctuations in network load, sudden surges in business requests, and superposition of environmental interference in power scenarios. They can easily lead to a disconnect between resource allocation strategies and real-time demand, causing problems such as imbalanced computing core utilization, network bandwidth preemption conflicts, and delayed responses to high-priority tasks. In addition, traditional static rule bases lack a dynamic feedback mechanism and cannot continuously optimize strategies based on terminal operation performance data. In long-term operation, they are prone to defects such as resource fragmentation accumulation and failure of strategy coordination, which seriously restricts the resource scheduling efficiency and business stability of power wireless terminals in scenarios such as intelligent inspection and fault emergency response. Summary of the Invention

[0003] The present invention provides a method and system for dynamically configuring computing resources management based on power wireless terminals.

[0004] In a first aspect, an embodiment of the present invention provides a method for dynamically configuring computing resources based on a power wireless terminal, comprising: obtaining a real-time operating status data set and a power service demand data set of a power wireless terminal, the real-time operating status data set including operating parameters of the terminal device and network load fluctuation characteristics, and the power service demand data set including a service priority identifier and real-time task processing requirements; performing multi-dimensional feature fusion processing on the real-time operating status data set and the power service demand data set to generate a resource adaptation feature set, the resource adaptation feature set including terminal device load balancing characteristics, service demand conflict characteristics, and resource allocation efficiency evaluation indicators; performing real-time policy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base to generate a computing resource configuration policy set, the computing resource configuration policy set including a resource allocation priority adjustment policy, a task scheduling optimization policy, and a network bandwidth reallocation policy; performing adjustment operations on the computing resources of the power wireless terminal according to the computing resource configuration policy set to generate a resource allocation verification result and terminal operating efficiency feedback data; continuously comparing the terminal operating efficiency feedback data with a preset efficiency optimization threshold, and iteratively updating the resource adaptation rule base based on the comparison results.

[0005] In a second aspect, an embodiment of the present invention provides a computer system, comprising: a memory storing a computer program; and a processor configured to load the computer program to implement the above-described method for dynamically configuring computing resource management based on a power wireless terminal.

[0006] The present invention provides a method for dynamically configuring computing resource management based on power wireless terminals. By acquiring a dynamic data set of terminal device operating status and power service requirements in real time, a multi-dimensional feature fusion mechanism is established to generate a dynamic resource adaptation feature set comprising load balancing features, service conflict features, and resource efficiency evaluation indicators. Dynamic policy matching is then performed based on a preset rule base to generate a collaboratively optimized resource allocation policy set, achieving precise matching of computing resource allocation with fluctuating power service requirements. This method effectively addresses the issues of low resource utilization and task timeouts caused by sudden network load changes or service priority conflicts in traditional static resource allocation models through real-time coupled analysis of device status and service requirements. Furthermore, by combining terminal operating efficiency feedback data with a continuous iterative update mechanism of the rule base, dynamic adaptive resource management is implemented, significantly improving the flexibility and stability of resource allocation strategies in complex power scenarios. Furthermore, a multi-dimensional policy coordination verification mechanism is used to avoid allocation conflicts across different resource dimensions, ensuring consistency in the spatiotemporal allocation of computing cores, memory, and network bandwidth. This ensures real-time response to high-priority power services while optimizing resource fragmentation, thereby achieving both improved computing resource utilization efficiency and service operation reliability for power wireless terminals without human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a flowchart of a method for dynamically configuring computing resources management based on a power wireless terminal provided by an embodiment of the present invention.

[0008] Figure 2 It is a schematic diagram of the composition of a computer system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] See also Figure 1 , Figure 1 A flowchart of a method for dynamically configuring computing resources based on a power wireless terminal provided by an embodiment of the present invention is provided. The method for dynamically configuring computing resources based on a power wireless terminal can be executed by a computer system. The method includes the following steps:

[0010] Step S100: Acquire the real-time operation status data set and the power service demand data set of the power wireless terminal. The real-time operation status data set includes the working parameters of the terminal device and the network load fluctuation characteristics. The power service demand data set includes the service priority identifier and the real-time task processing requirements.

[0011] The real-time operating status data set is a collection of data reflecting the current operating status of the power wireless terminal. Terminal device operating parameters are various parameters related to the terminal's operation, such as CPU usage, memory utilization, and disk I / O rate. These parameters directly reflect the terminal's operating load. Network load fluctuation characteristics describe the characteristics of network load variations over different time periods, such as the time-varying curve of network bandwidth utilization and fluctuations in network latency. The power service demand data set is a collection of demand information related to power services. Service priority identifiers are used to distinguish the importance of different power services. For example, critical services that ensure the safe and stable operation of the power grid can have a higher priority, while auxiliary services can have a lower priority. Real-time task processing requirements specify the specific requirements for each power service task in terms of processing time and resource usage. For example, a task must be completed within a set time or have limits on computing cores and memory usage.

[0012] When acquiring real-time operational status data sets and power service demand data sets from power wireless terminals, a monitoring program can be installed on the terminal device to collect the terminal device's operating parameters at preset intervals and transmit them to the data processing center via the network. Network load fluctuations can be monitored and recorded in real time using network monitoring equipment, such as a network traffic analyzer. For power service demand data sets, interfaces can be established with various power service systems to obtain service priority identifiers and real-time task processing requirements from these systems. For example, the power dispatching system can transmit the priority and processing requirements of its service tasks to the data processing center via an interface, thereby completing the collection of power service demand data.

[0013] As an implementation manner, before obtaining the real-time operating status data set and the power service demand data set of the power wireless terminal in step S100, the method provided by the embodiment of the present invention may further include the following pre-steps S101 to S105:

[0014] Step S101: configuring a monitoring agent program of the power wireless terminal, where the monitoring agent program is used to collect operating parameters of the terminal device and network load data according to a preset period.

[0015] The monitoring agent is a software program installed on the power wireless terminal that regularly collects operating parameters and network load data. The preset period is a pre-set interval for data collection, for example, every minute, every five minutes, or every ten minutes. Operating parameters for the terminal device include the aforementioned CPU usage, memory utilization, and disk I / O rate, which reflect the terminal device's operating status. Network load data includes network bandwidth usage, network latency, and packet loss rate, which provide insights into the network's operational status.

[0016] When configuring a monitoring agent, for example, you can develop a monitoring agent using a programming language like Python or Java, leveraging the operating system's APIs to obtain terminal device operating parameters and network load data. The developed monitoring agent is then deployed to the power wireless terminal, either through a remote deployment tool or manual installation. Finally, you can set a preset period by modifying the monitoring agent's configuration file. For example, if you set a collection interval of 5 minutes, the monitoring agent will collect data based on this period. For example, in a power system with multiple power wireless terminals, deploying the monitoring agent to each terminal using a unified deployment tool and setting the same preset period will enable unified data collection for all terminal devices.

[0017] Step S102: establishing a standardized interface for business demand data to convert task requests output by different power business systems into power business demand data in a unified format.

[0018] The standardized interface for business demand data is used to uniformly process task requests output by various power business systems. These systems may use different data formats and protocols to output task requests, which makes unified data processing difficult. By establishing a standardized interface, these task requests in various formats can be converted into a unified format for power business demand data, facilitating subsequent processing and analysis.

[0019] The following technical approaches can be used to establish a standardized interface for business demand data. First, analyze the data formats and protocols of task requests output by different power business systems to identify the fields and data types that need to be standardized. Next, develop an interface program that uses Web services technologies, such as RESTful APIs, to communicate with these different power business systems. After receiving task requests from different business systems, the interface program parses and converts them into a unified format for power business demand data.

[0020] Step S103: Clean the historical resource allocation records, remove invalid data entries and complete the missing terminal device identification fields.

[0021] Historical resource allocation records are records of resource allocations to power wireless terminals over a period of time. These records may contain invalid data entries and missing terminal device identification fields. Invalid data entries may be caused by data collection errors, transmission failures, and other reasons, which can affect subsequent data analysis and processing results. Missing terminal device identification fields make it difficult to accurately identify the terminal device corresponding to each resource allocation record, thus affecting the formulation of resource allocation strategies.

[0022] Step S104: constructing an initial version of a resource adaptation rule base, which includes default load balancing rules and conflict avoidance rules generated based on historical data statistics.

[0023] The resource adaptation rule base is a collection of rules used to guide computing resource allocation. The initial version of the resource adaptation rule base, built during the initial phase, includes default load balancing rules and conflict avoidance rules. The default load balancing rules are generated based on historical data statistics and are used to balance the load across various end devices, preventing situations where some end devices are overloaded while others are underloaded. Conflict avoidance rules are used to prevent conflicts during resource allocation, such as when multiple tasks compete for the same resource.

[0024] When building the initial version of the resource adaptation rule base, we collected historical resource allocation data, including terminal device load and task allocation. We then conducted statistical analysis on this historical data to identify patterns in load balancing and the causes of conflicts. Based on these analysis results, we developed default load balancing rules and conflict avoidance rules. For conflict avoidance rules, for example, if we find that conflicts are likely to occur when multiple high-priority tasks simultaneously request the same network bandwidth, we can develop rules to allocate network bandwidth sequentially based on task priority and chronological order.

[0025] Step S105: completing the connectivity test of the monitoring agent program and the standardized interface to ensure the synchronous collection of the real-time operation status data set and the power business demand data set.

[0026] Connectivity testing verifies the proper connection between the monitoring agent and the standardized interface. Only by ensuring proper connection can the real-time operational status data and the power service demand data be collected simultaneously. This simultaneous collection of real-time operational status data and power service demand data ensures the accuracy of subsequent data analysis and processing results.

[0027] The following method can be used to complete connectivity testing of the monitoring agent and the standardized interface. First, write a connectivity test program that sends a test request to the monitoring agent. The monitoring agent then returns a response. If the standardized interface can properly receive the response from the monitoring agent, the connection is functioning properly. During the test, ensure that the timestamps of the collected real-time operating status data and the power service demand data are consistent to ensure synchronous collection.

[0028] Step S200: Perform multi-dimensional feature fusion processing on the real-time operation status data set and the power business demand data set to generate a resource adaptation feature set, which includes terminal device load balancing features, business demand conflict features and resource allocation efficiency evaluation indicators.

[0029] Multi-dimensional feature fusion processing is to comprehensively process multiple features in the real-time operation status data set and the power business demand data set to extract more valuable information. The real-time operation status data set and the power business demand data set contain information from different aspects. Through multi-dimensional feature fusion processing, this information can be combined to generate a more comprehensive resource adaptation feature set. The terminal device load balancing feature is used to describe the load balancing situation between terminal devices, such as the distribution of CPU usage, memory occupancy, etc. of each terminal device. The business demand conflict feature is used to identify conflicts that may arise in the resource allocation process, such as multiple tasks requesting the same resource at the same time. Resource allocation efficiency evaluation indicators are used to evaluate the effectiveness of resource allocation, such as task completion rate, resource utilization, etc.

[0030] The following steps can be used to perform multidimensional feature fusion processing on the real-time operating status data set and the power business demand data set. First, the real-time operating status data set and the power business demand data set are preprocessed, including data cleaning and normalization to ensure data quality and consistency. Then, an appropriate feature fusion algorithm, such as principal component analysis (PCA) or linear discriminant analysis (LDA), is selected to fuse the features of the two data sets. Finally, based on the fused features, terminal device load balancing features, business demand conflict features, and resource allocation efficiency evaluation indicators are generated.

[0031] As an implementation method, step S200 performs multi-dimensional feature fusion processing on the real-time operation status data set and the power business demand data set to generate a resource adaptation feature set, which may specifically include the following steps S210 to S250:

[0032] Step S210: extracting time series features of the terminal device operating parameters from the real-time operating status data set, where the time series features include parameter fluctuation period, peak duration, and abnormal parameter offset.

[0033] Time series features are characteristics of data that change over time within a certain period of time. Extracting time series features of terminal device operating parameters from real-time operating status data sets can better understand the changing patterns of terminal device operating status. Parameter fluctuation period is the length of the period during which terminal device operating parameters (such as CPU usage and memory utilization) exhibit periodic changes over a period of time. Peak duration is the duration after a parameter reaches its peak. Abnormal parameter offset is the degree to which a parameter deviates from the normal range at a certain point in time.

[0034] The following method can be used to extract time series features of terminal device operating parameters from real-time operating status data sets. First, perform time series analysis on the terminal device operating parameters in the real-time operating status data set, such as using an autoregressive integrated moving average (ARIMA) model or seasonal decomposition method. Then, based on the analysis results, calculate the parameter fluctuation period, peak duration, and abnormal parameter offset. For example, for CPU usage data, an ARIMA model is fitted to determine the CPU usage trend. Based on this trend, the parameter fluctuation period, peak duration, and abnormal parameter offset are then calculated.

[0035] Step S220: performing task decomposition processing on the real-time task processing requirements in the power business demand data set to obtain multiple subtask resource demand characteristics, wherein the subtask resource demand characteristics include processing delay constraints, computing core occupancy, and memory allocation thresholds.

[0036] Task decomposition involves breaking down a complex task into multiple simple subtasks to facilitate better resource allocation and management. Real-time task processing requirements in power business demand data sets typically include one or more tasks, each of which may require different resources to complete. Through task decomposition, these tasks can be broken down into multiple subtasks, and the resource requirement characteristics of each subtask can be determined. The processing latency constraint is the requirement that a subtask must be completed within a specified timeframe. The compute core occupancy rate is the proportion of compute cores that a subtask needs to occupy during execution. The memory allocation threshold is the maximum amount of memory a subtask is allowed to occupy during execution.

[0037] The following steps can be used to decompose the real-time task processing requirements within a power business demand data set. First, analyze the real-time task processing requirements to determine the task's logical structure and dependencies. Then, decompose the task into multiple subtasks based on the task's logical structure and dependencies. Finally, determine the processing latency constraints, core utilization, and memory allocation thresholds for each subtask. For example, a power business task involving power load forecasting can be decomposed into subtasks such as data collection, data preprocessing, model training, and forecast output.

[0038] Step S230: performing correlation matching processing on the time series features and the subtask resource requirement features to generate a task-device adaptability index, which is used to quantify the degree of matching between the terminal device processing capability and the subtask requirements.

[0039] Correlation matching involves comparing and analyzing time series characteristics with subtask resource requirements to identify correlations. The task-device compatibility index is a quantitative metric used to measure the degree of compatibility between the processing capabilities of a terminal device and the resource requirements of a subtask. Generating this index provides a basis for resource allocation, assigning subtasks to the most appropriate terminal device.

[0040] The following steps can be used to perform correlation matching between time series features and subtask resource requirement features. First, the time series features and subtask resource requirement features are standardized to eliminate the dimensional differences between different features. Then, a matching algorithm is selected, such as the Euclidean distance algorithm, the cosine similarity algorithm, etc., to calculate the similarity between the time series features and the subtask resource requirement features. Finally, a task-device adaptation index is generated based on the similarity. For example, the Euclidean distance algorithm is used to calculate the Euclidean distance between the CPU usage time series features of a terminal device and the computing core occupancy requirement features of a subtask. The smaller the distance, the higher the degree of matching. The task-device adaptation index is generated based on this distance.

[0041] As an implementation method, step S230 performs correlation matching processing on the time series characteristics and the subtask resource requirement characteristics to generate a task-device compatibility index, which may specifically include the following steps S231 to S236:

[0042] Step S231: extracting the duration distribution characteristics of the parameter fluctuation period and the equipment load state change curve corresponding to the peak duration from the time series characteristics.

[0043] The parameter fluctuation cycle duration distribution feature shows the distribution of parameter fluctuation cycle durations over a period of time, such as the frequency of parameter fluctuation cycles of varying durations. The device load state change curve corresponding to the peak duration shows how the device load changes over time when the parameter reaches its peak. By extracting these features, we can better understand the load variation patterns of terminal devices.

[0044] The following method can be used to extract the duration distribution characteristics of the parameter fluctuation period and the equipment load state change curve corresponding to the peak duration from the time series characteristics. First, perform a period analysis on the time series characteristics, for example, using methods such as Fourier transform and wavelet transform to determine the duration of the parameter fluctuation period. Then, count the frequency of parameter fluctuation periods of different durations to obtain the duration distribution characteristics of the parameter fluctuation period. For the equipment load state change curve corresponding to the peak duration, the load state change curve can be drawn by finding the time point when the parameter reaches the peak in the time series characteristics, and then extracting the equipment load state data for a period of time before and after this time point.

[0045] Step S232: performing task execution window division processing on the processing delay constraint in the resource requirement characteristics of the subtask, and generating a minimum time window requirement and a maximum time window tolerance threshold corresponding to each subtask.

[0046] Task execution window partitioning involves dividing the task execution time into different windows based on the processing latency constraints of the subtasks. The minimum time window requirement is the minimum time requirement within which a subtask must complete. The maximum time window tolerance threshold is the maximum execution time that a subtask can tolerate. By generating a minimum time window requirement and maximum time window tolerance threshold for each subtask, subtask execution times can be better scheduled.

[0047] The following steps can be used to partition task execution windows based on the processing latency constraints in the subtask resource requirements. First, analyze the subtask's processing latency constraints to determine the task's start and end times. Then, based on the task's importance and urgency, divide the task execution time into minimum and maximum time windows.

[0048] Step S233: performing time axis alignment matching on the device load state change curve and the minimum time window requirement, and identifying a set of available time periods in the device load state curve that meets the minimum time window requirement of the subtask.

[0049] Timeline alignment involves aligning the device load profile with the minimum time window requirement on the timeline, identifying the time periods in the device load profile that meet the minimum time window requirement. The available time period set is the set of all time periods in the device load profile that meet the minimum time window requirement for the subtask. By identifying the available time period set, it is possible to determine which time periods have sufficient resources for the terminal device to execute the subtask.

[0050] The following method can be used to align the device load state change curve with the minimum time window requirement on the time axis. First, align the device load state change curve and the minimum time window requirement on the time axis to ensure that their time starting point and time unit are consistent. Then, traverse the device load state curve to find the time period that meets the minimum time window requirement. For example, for the CPU usage load state change curve of a terminal device and the minimum time window requirement (30 minutes) of a subtask, after aligning them on the time axis, traverse the CPU usage load state curve to find the time period where the CPU usage is lower than a certain threshold (such as 70%) and the duration is not less than 30 minutes. These time periods constitute the available time period set.

[0051] Step S234: Based on the continuous distribution characteristics of the available time period set and the interval parameters of the peak duration, the dynamic matching coefficient of each subtask in the corresponding available time period is calculated. The dynamic matching coefficient is used to quantify the fit between the equipment load fluctuation and the subtask time window requirement.

[0052] The dynamic matching coefficient is a quantitative metric used to measure the degree to which device load fluctuations align with the subtask time window requirements. The continuous distribution characteristic of the available time slot set refers to the distribution of available time slots on the time axis, for example, whether the available time slots are continuous or discrete. The peak duration interval parameter refers to the time interval between two consecutive peak durations. By calculating the dynamic matching coefficient, we can better assess the suitability of a terminal device for executing a subtask within the available time slot.

[0053] Based on the continuous distribution characteristics of the available time period set and the interval parameters of the peak duration, the following steps can be used to calculate the dynamic matching coefficient of each subtask in the corresponding available time period. First, analyze the continuous distribution characteristics of the available time period set, such as calculating the average length of the available time period, the number of continuous available time periods, etc. Then, combine the interval parameters of the peak duration to determine the stability of the device load fluctuation. Finally, calculate the dynamic matching coefficient based on the stability of the device load fluctuation and the subtask time window requirements. For example, for a subtask and a terminal device's available time period set, if the available time period is continuous and has a long average length, and the interval parameter of the peak duration is large, it means that the device load fluctuation is relatively stable, and the dynamic matching coefficient is high.

[0054] Step S235: performing normalization correction processing on the dynamic matching coefficients according to the maximum time window tolerance threshold to generate a corrected dynamic matching coefficient set.

[0055] Normalization adjusts the dynamic matching coefficient to a reasonable range. The maximum time window tolerance threshold is the maximum tolerable execution time for a subtask. By normalizing the dynamic matching coefficient based on the maximum time window tolerance threshold, we ensure that the dynamic matching coefficient accurately reflects the fit between device load fluctuations and the subtask time window requirements, while also taking into account the subtask's time constraints.

[0056] The following method can be used to normalize and correct the dynamic matching coefficient based on the maximum time window tolerance threshold. First, determine the normalization range, for example, normalize the dynamic matching coefficient to the interval [0,1]. Then, adjust the dynamic matching coefficient based on the maximum time window tolerance threshold and the actual time window requirement of the subtask. For example, if the actual time window requirement of the subtask is close to the maximum time window tolerance threshold, then the dynamic matching coefficient may need to be appropriately reduced. Assuming that the dynamic matching coefficient is 0.8, the maximum time window tolerance threshold is 90 minutes, and the actual time window requirement of the subtask is 80 minutes, according to the preset correction rule, the dynamic matching coefficient is corrected to 0.7 to obtain the corrected dynamic matching coefficient set.

[0057] Step S236: The modified dynamic matching coefficient set is integrated with the computing core occupancy rate and memory allocation threshold in the subtask resource requirement characteristics, and a multi-dimensional weighted fusion process is performed to generate a task-device compatibility index associated with each subtask and terminal device.

[0058] Multi-dimensional weighted fusion processing combines the modified dynamic matching coefficient set, the computed core utilization, and the memory allocation threshold, taking into account their respective weights to generate a comprehensive metric. The task-device compatibility metric is associated with each subtask and terminal device, measuring the degree of compatibility between the terminal device and the subtask. Multi-dimensional weighted fusion processing allows for a more comprehensive consideration of subtask resource requirements and terminal device load.

[0059] The revised dynamic matching coefficient set is combined with the computing core occupancy rate and memory allocation threshold in the subtask resource requirement characteristics, and a multi-dimensional weighted fusion process is performed to generate a task-device adaptation index associated with each subtask and terminal device. The following steps can be used. First, weights are set for the revised dynamic matching coefficient set, computing core occupancy rate, and memory allocation threshold, for example, weights of 0.5, 0.3, and 0.2, respectively. Then, the revised dynamic matching coefficient set, computing core occupancy rate, and memory allocation threshold are weighted and summed to obtain the task-device adaptation index.

[0060] Step S240: Based on a preset resource conflict detection model, conflict analysis is performed on the network load fluctuation characteristics and the service priority identifier to generate a resource competition hotspot area identifier and conflict mitigation recommendation parameters.

[0061] The preset resource conflict detection model is a pre-established model used to detect conflicts that may arise during the resource allocation process. For example, the resource conflict detection model can be a decision tree model. The data source for its training can be an automated data acquisition tool that regularly extracts data from various data sources (such as network monitoring systems and power business systems) and stores it in a data warehouse. During training, general feature engineering techniques can be used to complete data cleaning, selection, transformation, and combination, and divide the training and test sets. The initial resource conflict detection model is then trained until the model converges.

[0062] The network load fluctuation characteristics describe how the network load varies over time, and the service priority identifier distinguishes the importance of different services. Conflict analysis analyzes the network load fluctuation characteristics and service priority identifiers to identify areas and situations where resource contention may occur. The resource contention hotspot identifier identifies areas where resource contention may occur, and the conflict mitigation recommendation parameters are used to mitigate resource contention conflicts.

[0063] Based on a preset resource conflict detection model, conflict analysis is performed on network load fluctuation characteristics and service priority identifiers to generate resource contention hotspot area identifiers and conflict mitigation recommendation parameters. The following steps can be used. First, the network load fluctuation characteristics and service priority identifiers are input into the preset resource conflict detection model. The model can use machine learning algorithms, such as decision trees or support vector machines, to analyze and process the input data. Based on the network load fluctuation characteristics and service priority identifiers, the model identifies time periods and areas where resource contention is likely to occur and generates resource contention hotspot area identifiers. Then, based on the conflict analysis results, conflict mitigation recommendation parameters are generated. For example, in a power grid, the preset resource conflict detection model analyzes network load fluctuation characteristics and service priority identifiers and identifies a time period in which multiple high-priority services simultaneously request the same network bandwidth. This model then identifies this time period and the corresponding network area as a resource contention hotspot area and generates conflict mitigation recommendation parameters for adjusting the network bandwidth allocation ratio.

[0064] As an implementation method, step S240 performs conflict analysis on network load fluctuation characteristics and service priority identifiers based on a preset resource conflict detection model to generate resource contention hotspot area identifiers and conflict mitigation recommendation parameters. Specifically, the following steps S241 to S246 may be included:

[0065] Step S241: Determine the network resource saturation time period and idle time period according to the bandwidth occupancy change curve in the network load fluctuation characteristics, and associate them with the corresponding terminal device identifiers.

[0066] The bandwidth utilization curve in the network load fluctuation feature describes how network bandwidth utilization changes over time. The network resource saturation period is when network bandwidth utilization reaches or approaches its maximum. During this period, network resources are scarce and resource contention is likely to occur. The idle period is when network bandwidth utilization is low and network resources are relatively abundant. The terminal device identifier uniquely identifies each terminal device.

[0067] Based on the bandwidth occupancy rate change curve in the network load fluctuation characteristics, the network resource saturation time period and idle time period can be determined and associated with the corresponding terminal device identification using the following method. First, the bandwidth occupancy rate change curve is analyzed and a bandwidth occupancy rate threshold is set, such as 80%. When the bandwidth occupancy rate exceeds the threshold, the corresponding time period is the network resource saturation time period; when the bandwidth occupancy rate is lower than a lower threshold, such as 20%, the corresponding time period is the idle time period. Then, the terminal device identification using the network resources in each time period is recorded by the network monitoring device, and the network resource saturation time period and idle time period are associated with the corresponding terminal device identification.

[0068] Step S242: extract the urgent task mark and the task dependency chain in the business priority identifier to generate a task execution sequence constraint set.

[0069] Business priority identification includes an urgent task flag and a task dependency chain. The urgent task flag identifies which tasks are urgent and require priority. The task dependency chain describes the dependencies between tasks, meaning that the execution of one task may depend on the completion of other tasks. The task execution order constraint set specifies the order in which tasks are executed and is generated based on the urgent task flag and task dependency chain.

[0070] The following steps can be used to extract the urgent task tag and task dependency chain from the business priority identifier and generate a set of task execution order constraints. First, extract the urgent task tag and task dependency chain from the business priority identifier. Then, prioritize urgent tasks based on the urgent task tag. For tasks with dependencies, their execution order is determined based on the task dependency chain.

[0071] Step S243: performing a spatiotemporal overlap analysis on the network resource saturation time period and the task execution sequence constraint set to identify the resource contention conflict period and the affected task set.

[0072] Spatiotemporal overlap analysis compares network resource saturation periods and task execution order constraints in time and space to identify overlap. Resource contention periods are defined as periods within network resource saturation periods when multiple tasks simultaneously request the same resource. The set of affected tasks is the set of tasks affected by resource contention periods.

[0073] The following method can be used to perform spatiotemporal overlap analysis between the network resource saturation time period and the task execution order constraint set to identify resource contention conflict periods and the set of affected tasks. First, align the network resource saturation time period and the task execution order constraint set on the time axis. Then, traverse the task execution order constraint set to identify tasks that need to be executed during the network resource saturation time period. For these tasks, if they simultaneously request the same resource, the corresponding time period is the resource contention conflict period, and these tasks form the set of affected tasks. For example, the network resource saturation time period is 10:00 AM to 11:00 AM, and the task execution order constraint set is Task A (9:30 AM to 10:30 AM), Task B (10:00 AM to 11:00 AM), and Task C (10:30 AM to 11:30 AM). Task A and Task B both require the same network bandwidth. Therefore, the resource contention conflict period is 10:00 AM to 10:30 AM, and the set of affected tasks is Task A and Task B.

[0074] Step S244: Based on a preset conflict resolution algorithm, the network bandwidth allocation strategy within the resource competition conflict period is simulated to generate multiple candidate bandwidth allocation solutions.

[0075] The preset conflict resolution algorithm is a pre-designed algorithm used to resolve resource contention conflicts. The network bandwidth allocation strategy during resource contention conflicts is the strategy for allocating network bandwidth during resource contention conflicts. Simulations test and evaluate different network bandwidth allocation strategies through computer simulations. Candidate bandwidth allocation schemes are multiple possible network bandwidth allocation schemes derived through simulations.

[0076] Based on a preset conflict resolution algorithm, network bandwidth allocation strategies during periods of resource contention and conflict are simulated to generate multiple candidate bandwidth allocation schemes. The following steps can be used: First, determine a preset conflict resolution algorithm, such as a fair allocation algorithm or a priority allocation algorithm. Then, design different network bandwidth allocation strategies based on the network bandwidth resources and the requirements of the affected task sets during the resource contention and conflict period. These strategies are then fed into the preset conflict resolution algorithm for simulation. The execution results of each strategy, such as task completion rate and bandwidth utilization, are recorded. Based on these results, multiple candidate bandwidth allocation schemes are generated.

[0077] As an implementation method, step S244 simulates the network bandwidth allocation strategy during the resource contention conflict period based on a preset conflict resolution algorithm to generate multiple candidate bandwidth allocation schemes. Specifically, the following steps S2441 to S2446 may be included:

[0078] Step S2441: Decompose the tasks in the affected task set into independent execution units and dependent execution units according to the task execution order constraint set, and allocate an initial bandwidth occupancy ratio to each execution unit.

[0079] Tasks in the affected task set may contain multiple execution units, which can be categorized as independent and dependent. An independent execution unit is a task unit that can be executed independently and does not rely on the completion of other task units. A dependent execution unit is a task unit that can only be executed after the completion of other task units. The initial bandwidth usage ratio is the pre-allocated network bandwidth usage ratio for each execution unit.

[0080] The following steps can be used to decompose the tasks in the affected task set into independent execution units and dependent execution units based on the task execution order constraint set, and to assign an initial bandwidth usage ratio to each execution unit. First, based on the task execution order constraint set, the logical structure and dependency relationships of each task in the affected task set are analyzed, and the tasks are decomposed into independent execution units and dependent execution units. Then, an initial bandwidth usage ratio is assigned to each execution unit based on its resource requirements and importance. For example, the affected task set contains tasks A and B. Task A consists of independent execution unit A1 and dependent execution unit A2, while task B consists of independent execution unit B1. According to the task execution order constraint set, task A executes first, followed by task B. Task A has a greater resource requirement, so an initial bandwidth usage ratio of 40% is assigned to independent execution unit A1, 30% to dependent execution unit A2, and 30% to independent execution unit B1.

[0081] Step S2442: Based on the duration of the resource contention conflict period and the bandwidth occupancy rate change curve during the network resource saturation period, the initial bandwidth occupancy ratio is dynamically adjusted in the time domain to generate a first candidate bandwidth allocation scheme; wherein the time domain dynamic adjustment processing includes allocating an increasing bandwidth ratio to the dependent execution unit during the resource contention conflict period, and allocating a decreasing bandwidth ratio to the independent execution unit.

[0082] The duration of the resource contention conflict period is the length of the time period during which the resource contention conflict occurs. The bandwidth utilization change curve for the network resource saturation period describes how bandwidth utilization changes over time during this period. Time domain dynamic adjustment adjusts the initial bandwidth utilization ratio based on changes over time. The first candidate bandwidth allocation scheme is a candidate bandwidth allocation scheme obtained through the time domain dynamic adjustment process.

[0083] Based on the duration of the resource contention conflict period and the bandwidth occupancy rate variation curve during the network resource saturation period, the initial bandwidth occupancy ratio is dynamically adjusted in the time domain to generate the first candidate bandwidth allocation scheme. The following steps can be used: First, the duration of the resource contention conflict period and the bandwidth occupancy rate variation curve during the network resource saturation period are analyzed to determine the timing and magnitude of bandwidth adjustment. During the resource contention conflict period, the bandwidth ratio is allocated to the dependent execution unit in an increasing manner, while the bandwidth ratio is allocated to the independent execution unit in a decreasing manner over time.

[0084] Step S2443: Based on the task response timeliness requirements in the task dependency chain, priority weighting is performed on the bandwidth allocation ratios of the dependent execution units to generate a second candidate bandwidth allocation scheme; wherein the priority weighting includes allocating additional bandwidth redundancy to the dependent execution units with strict response timeliness requirements.

[0085] The task response timeliness requirement in the task dependency chain is the requirement that tasks be completed within a specified timeframe. Priority weighting assigns different weights to dependent execution units based on the importance and timeliness of the tasks. The second candidate bandwidth allocation scheme is obtained by applying priority weighting to the bandwidth allocation ratios of the dependent execution units.

[0086] According to the task response timeliness requirements in the task dependency chain, the bandwidth allocation ratio of the dependent execution units is prioritized and weighted, and the following steps can be used to generate the second candidate bandwidth allocation scheme. First, the task response timeliness requirements in the task dependency chain are analyzed to determine the importance and timeliness level of each dependent execution unit. For dependent execution units with strict response timeliness requirements, additional bandwidth redundancy is allocated to them. For example, the response timeliness requirements of dependent execution unit A2 in the task dependency chain are strict. In the first candidate bandwidth allocation scheme, the bandwidth ratio of dependent execution unit A2 is 40%. An additional 10% bandwidth redundancy is allocated to it, and its bandwidth ratio is adjusted to 50%. At the same time, the bandwidth ratios of other execution units are adjusted accordingly to obtain the second candidate bandwidth allocation scheme.

[0087] Step S2444: extract historical bandwidth adjustment records from the network bandwidth reallocation strategy, perform historical trend fitting on the initial bandwidth occupancy ratio, and generate a third candidate bandwidth allocation scheme; wherein the historical trend fitting includes matching bandwidth allocation patterns of similar conflict periods and copying them to the current resource contention conflict period.

[0088] The network bandwidth reallocation strategy uses historical bandwidth adjustment records for past periods of resource contention and conflict. Historical trend fitting analyzes these historical bandwidth adjustment records to identify bandwidth allocation patterns during similar conflict periods and applies these patterns to the current period of resource contention and conflict. The third candidate bandwidth allocation scheme is derived through historical trend fitting.

[0089] The following steps can be used to extract historical bandwidth adjustment records from the network bandwidth reallocation policy, perform historical trend matching on the initial bandwidth occupancy ratio, and generate a third candidate bandwidth allocation scheme. First, extract historical bandwidth adjustment records from the network bandwidth reallocation policy, including information such as the conflict period and bandwidth allocation ratio. Then, analyze the characteristics of the current resource contention conflict period, such as duration and network bandwidth resources, and match the bandwidth allocation patterns of similar conflict periods in the historical bandwidth adjustment records. Finally, copy the matched bandwidth allocation pattern to the current resource contention conflict period, adjust the initial bandwidth occupancy ratio, and generate a third candidate bandwidth allocation scheme. For example, by analyzing the historical bandwidth adjustment records, a conflict period similar to the current resource contention conflict period was identified, in which the bandwidth allocation pattern was 30% for independent execution unit A1, 50% for dependent execution unit A2, and 20% for independent execution unit B1. This bandwidth allocation pattern is applied to the current resource contention conflict period, and the initial bandwidth occupancy ratio is adjusted to generate the third candidate bandwidth allocation scheme.

[0090] Step S2445: Load the network load fluctuation data and the affected task set during the resource contention conflict period in the simulated execution environment, inject the first candidate bandwidth allocation scheme, the second candidate bandwidth allocation scheme and the third candidate bandwidth allocation scheme respectively, perform the simulated allocation operation and record the task processing delay change data.

[0091] The simulated execution environment is used to simulate the resource allocation process and can be implemented using computer software. Network load fluctuation data during resource contention conflicts describes changes in network load during these periods. The affected task set is the set of tasks affected during these periods. Simulated allocation operations involve allocating network bandwidth according to different candidate bandwidth allocation schemes within the simulated execution environment. Task processing latency variation data describes changes in task processing latency during these simulated allocation operations.

[0092] The following steps can be used to load the network load fluctuation data and the affected task set during the resource contention conflict period into the simulated execution environment, inject the first candidate bandwidth allocation scheme, the second candidate bandwidth allocation scheme and the third candidate bandwidth allocation scheme respectively, perform the simulated allocation operation and record the task processing delay change data. First, construct a simulated execution environment that can simulate network load fluctuations and task execution process. Then, load the network load fluctuation data and the affected task set during the resource contention conflict period into the simulated execution environment. Then, inject the first candidate bandwidth allocation scheme, the second candidate bandwidth allocation scheme and the third candidate bandwidth allocation scheme into the simulated execution environment respectively, and perform the simulated allocation operation. During the simulated allocation operation, record the processing delay change data of each task, such as task start time, task end time, etc.

[0093] Step S2446: Filter out a set of candidate bandwidth allocation solutions that meet preset conflict resolution conditions based on the task completion rate deviation and bandwidth utilization fluctuation range in the task processing delay variation data.

[0094] The task completion rate deviation in the task processing delay variation data is the difference between the actual task completion rate and the expected task completion rate. The bandwidth utilization fluctuation range is the range of bandwidth utilization fluctuation during the simulated allocation operation. The preset conflict resolution conditions are pre-set conditions for screening candidate bandwidth allocation solutions, such as the task completion rate deviation being less than a certain threshold, the bandwidth utilization fluctuation range being within a certain range, etc. The candidate bandwidth allocation solution set is the set of candidate bandwidth allocation solutions that meet the preset conflict resolution conditions.

[0095] The following steps can be used to select a set of candidate bandwidth allocation schemes that meet preset conflict resolution conditions based on the task completion rate deviation and bandwidth utilization fluctuation range in the task processing delay variation data. First, the task completion rate deviation and bandwidth utilization fluctuation range are extracted from the task processing delay variation data. Then, the first, second, and third candidate bandwidth allocation schemes are screened based on the preset conflict resolution conditions. For example, the preset conflict resolution conditions are that the task completion rate deviation is less than 5% and the bandwidth utilization fluctuation range is between 10% and 20%. Analysis of the task processing delay variation data reveals that the first candidate bandwidth allocation scheme has a task completion rate deviation of 3% and a bandwidth utilization fluctuation range of 15%. The second candidate bandwidth allocation scheme has a task completion rate deviation of 6% and a bandwidth utilization fluctuation range of 25%. The third candidate bandwidth allocation scheme has a task completion rate deviation of 2% and a bandwidth utilization fluctuation range of 12%. Therefore, the set of candidate bandwidth allocation schemes that meet the preset conflict resolution conditions is the first and third candidate bandwidth allocation schemes.

[0096] Step S245: based on the impact of the candidate bandwidth allocation schemes on the processing delay of the affected task set, a target bandwidth allocation scheme with the smallest delay increment is selected, and its bandwidth adjustment parameters are extracted as conflict mitigation recommendation parameters.

[0097] The impact of a candidate bandwidth allocation scheme on the processing latency of the affected task set is the change in processing latency when using different candidate bandwidth allocation schemes. The latency delta is the increase in task processing latency after adopting a candidate bandwidth allocation scheme. The target bandwidth allocation scheme is the candidate bandwidth allocation scheme with the smallest latency delta. The conflict mitigation recommendation parameter is the bandwidth adjustment parameter in the target bandwidth allocation scheme, used to mitigate resource contention conflicts.

[0098] Based on the impact of candidate bandwidth allocation schemes on the processing delay of the affected task set, the target bandwidth allocation scheme with the smallest delay increment is selected and its bandwidth adjustment parameters are extracted as recommended conflict mitigation parameters. The following steps can be used: First, analyze the impact of each candidate bandwidth allocation scheme on the processing delay of the affected task set and calculate the delay increment for each scheme. Then, compare the delay increments of each scheme and select the target bandwidth allocation scheme with the smallest delay increment. Finally, extract bandwidth adjustment parameters from the target bandwidth allocation scheme, such as the bandwidth allocation ratio of each execution unit, as recommended conflict mitigation parameters. For example, if the candidate bandwidth allocation schemes are the first and third candidate bandwidth allocation schemes, and analysis reveals that the delay increment of the first candidate bandwidth allocation scheme is 5 minutes and the delay increment of the third candidate bandwidth allocation scheme is 3 minutes, then the target bandwidth allocation scheme is the third candidate bandwidth allocation scheme. Its bandwidth adjustment parameters, such as 30% for independent execution unit A1, 50% for dependent execution unit A2, and 20% for independent execution unit B1, are extracted as recommended conflict mitigation parameters.

[0099] Step S246: Encapsulate the resource contention conflict period and the corresponding terminal device identifier into a resource contention hotspot area identifier.

[0100] The resource contention conflict period is a period of time during which multiple tasks simultaneously request the same resource within the network resource saturation period. The terminal device identifier is used to uniquely identify each terminal device. The resource contention hotspot area identifier is an identifier used to identify an area where resource contention may occur, and is obtained by encapsulating the resource contention conflict period and the corresponding terminal device identifier together. The following method can be used to encapsulate the resource contention conflict period and the corresponding terminal device identifier into a resource contention hotspot area identifier. First, determine the start time and end time of the resource contention conflict period, as well as the terminal device identifiers that use network resources during the period. Then, encapsulate this information in a set format, for example, in JSON format.

[0101] Step S250: Integrate the task-device adaptability index, resource competition hotspot area identifier, and conflict mitigation suggestion parameters to construct a resource adaptation feature set and associate it with the terminal device identifier and the business task identifier.

[0102] The task-device fit metric quantifies the degree of match between the terminal device's processing capabilities and the subtask requirements. The resource contention hotspot identifier identifies areas where resource contention may occur. Conflict mitigation recommended parameters are used to mitigate resource contention conflicts. The resource adaptation feature set, comprising the task-device fit metric, resource contention hotspot identifier, and conflict mitigation recommended parameters, guides resource allocation. The terminal device identifier uniquely identifies each terminal device, and the service task identifier uniquely identifies each service task.

[0103] The following steps can be used to construct a resource adaptation feature set by combining the task-device compatibility index, resource contention hotspot identifiers, and conflict mitigation recommendation parameters. This feature set is then associated with the terminal device identifier and the service task identifier. First, the task-device compatibility index, resource contention hotspot identifiers, and conflict mitigation recommendation parameters are combined to form a set containing various information. This set is then associated with the terminal device identifier and the service task identifier, ensuring that each terminal device and each service task has a corresponding resource adaptation feature. For example, for terminal device A and service task 1, the task-device compatibility index is 80%, the resource contention hotspot identifier is {"start_time":"10:00","end_time":"11:00","terminal_devices":["A"]}, and the conflict mitigation recommendation parameters are 30% for independent execution units and 50% for dependent execution units. This information is then combined into a resource adaptation feature set and associated with terminal device A and service task 1.

[0104] Step S300: Based on the preset resource adaptation rule library, perform real-time policy matching processing on the resource adaptation feature set to generate a computing resource configuration policy set, which includes a resource allocation priority adjustment policy, a task scheduling optimization policy, and a network bandwidth reallocation policy.

[0105] The preset resource adaptation rule base is a set of pre-established rules used to guide resource allocation. The resource adaptation feature set includes information such as task-device compatibility metrics, identification of resource contention hotspots, and recommended conflict mitigation parameters. Real-time policy matching involves analyzing and matching the resource adaptation feature set based on the preset resource adaptation rule base to identify appropriate resource allocation strategies. The computing resource configuration policy set, encompassing various strategies such as resource allocation priority adjustment, task scheduling optimization, and network bandwidth reallocation, is used to configure computing resources for power wireless terminals.

[0106] Based on a preset resource adaptation rule base, the resource adaptation feature set is matched in real time to generate a computing resource configuration policy set. The following steps can be used: Input the resource adaptation feature set into the preset resource adaptation rule base. The rules in the rule base can take the form of if-then statements, for example, "if the task-device adaptation index is greater than 80% and the resource competition hotspot area identifier is empty, then adopt resource allocation priority adjustment policy A." Then, match the resource adaptation feature set according to the rules in the rule base to find a suitable resource allocation policy. Finally, these resource allocation policies are integrated into a computing resource configuration policy set.

[0107] As an implementation method, step S300 performs real-time policy matching processing on a resource adaptation feature set based on a preset resource adaptation rule library to generate a computing resource configuration policy set, which may specifically include the following steps S310 to S360:

[0108] Step S310: extracting the historical change trend of the load balancing feature of the terminal device from the resource adaptation feature set, and matching it to the load balancing rule group in the resource adaptation rule library.

[0109] The historical change trend of the terminal device load balancing characteristics is the change in the terminal device load balancing characteristics over a period of time, such as the change trend of the terminal device's CPU usage, memory usage, etc. The load balancing rule group in the resource adaptation rule base is a set of rules used to balance the terminal device load.

[0110] The following steps can be used to extract the historical change trend of the load balancing characteristics of the terminal device from the resource adaptation feature set and match it to the load balancing rule group in the resource adaptation rule base. First, extract the historical data of the load balancing characteristics of the terminal device from the resource adaptation feature set, such as the CPU usage data of the terminal device in the past week. Then, analyze these historical data to find out their changing trends, such as whether they show an upward trend, a downward trend or periodic changes. Finally, match the changing trend with the load balancing rule group in the resource adaptation rule base to find the appropriate rule. For example, through analysis, it is found that the CPU usage of a certain terminal device has shown an upward trend in the past week. There is a rule in the load balancing rule group in the resource adaptation rule base that "if the CPU usage of the terminal device shows an upward trend, then allocate some tasks to other terminal devices with lower loads", and this rule is matched.

[0111] Step S320: activating the conflict avoidance rule group in the resource adaptation rule library according to the number of task processing conflicts and the resource preemption frequency in the business demand conflict characteristics.

[0112] The number of task processing conflicts in the business demand conflict feature is the number of task processing conflicts that occurred during the resource allocation process. The resource preemption frequency is the frequency with which resources were preempted during the resource allocation process. The conflict avoidance rule group in the resource adaptation rule base is a set of rules used to avoid resource allocation conflicts.

[0113] The following steps can be used to activate the conflict avoidance rule group in the resource adaptation rule base based on the number of task processing conflicts and resource preemption frequency in the business demand conflict characteristics. First, the number of task processing conflicts and resource preemption frequency in the business demand conflict characteristics are extracted from the resource adaptation feature set. Then, thresholds for the number of task processing conflicts and resource preemption frequency are set. When the number of task processing conflicts or resource preemption frequency exceeds the threshold, the conflict avoidance rule group in the resource adaptation rule base is activated.

[0114] Step S330: calling the resource utilization curve and task completion rate index in the resource allocation efficiency evaluation index, performing weight allocation processing on the load balancing rule group and the conflict avoidance rule group, and generating a rule combination priority ranking.

[0115] The resource utilization curve in the resource allocation effectiveness evaluation metric describes how resource utilization changes over time. The task completion rate metric is the ratio of the number of tasks completed within a preset time period to the total number of tasks. The load balancing rule group and the conflict avoidance rule group are two sets of rules in the resource adaptation rule base. Weight assignment involves assigning different weights to the load balancing rule group and the conflict avoidance rule group to determine their importance in resource allocation. Rule combination prioritization sorting sorts the load balancing rule group and the conflict avoidance rule group based on the weight assignment results to determine their execution order.

[0116] The resource utilization curve and task completion rate indicators used in the resource allocation effectiveness evaluation metrics are used to assign weights to the load balancing rule groups and conflict avoidance rule groups. The following steps can be used to generate a prioritized rule combination. First, analyze the resource utilization curve and task completion rate indicators to determine their importance. For example, if resource utilization has a significant impact on system performance, a higher weight can be assigned to resource utilization; if task completion rate has a significant impact on business objectives, a higher weight can be assigned to task completion rate. Then, based on the weights of resource utilization and task completion rate, weights are assigned to the load balancing rule groups and conflict avoidance rule groups. Finally, the load balancing rule groups and conflict avoidance rule groups are prioritized based on the weighted assignments, with the higher-weighted rule groups prioritized.

[0117] Step S340: Based on the rule combination priority sorting, a joint optimization calculation is performed on the terminal device load balancing characteristics and the service demand conflict characteristics to generate an initial version of the resource allocation priority adjustment strategy.

[0118] Rule combination priority ranking determines the execution order of the load balancing rule group and the conflict avoidance rule group. The terminal device load balancing characteristics reflect the load balancing status between terminal devices, while the service demand conflict characteristics reflect potential conflicts that may arise during resource allocation. Joint optimization calculation comprehensively considers the terminal device load balancing characteristics and service demand conflict characteristics, and performs optimization calculations based on the rule combination priority ranking to find a more reasonable resource allocation solution. The initial version of the resource allocation priority adjustment policy is a preliminary policy generated based on the joint optimization calculation and is used to adjust resource allocation priorities.

[0119] Based on the priority ranking of rule combinations, a joint optimization calculation is performed on the load balancing characteristics of terminal devices and the conflict characteristics of business demands. The following steps can be used to generate an initial version of the resource allocation priority adjustment strategy. First, the rule combinations are prioritized and the rule groups with higher priorities are applied first. Assuming the load balancing rule group has a higher priority, the load balancing rule group and the load balancing characteristics of the terminal devices are used to determine the terminal devices that require load adjustment and the direction of the adjustment, such as transferring some tasks from high-loaded terminal devices to low-loaded terminal devices. Then, considering the conflict characteristics of business demands and combining the conflict avoidance rule group, the initial adjustment plan is revised to avoid new conflicts during the adjustment process. For example, if adjusting task allocation may cause resources for a high-priority task to be preempted, the conflict avoidance rule group is used to make corresponding adjustments. Finally, based on the above calculation results, the initial version of the resource allocation priority adjustment strategy is generated.

[0120] Step S350: The initial version is subjected to policy effectiveness verification processing by simulating the execution environment. According to the resource utilization improvement and conflict resolution efficiency in the verification results, the resource allocation priority adjustment strategy is optimized and a final version is generated.

[0121] The simulation execution environment is a virtual environment used to simulate the resource allocation process. It can simulate the operating status of terminal devices, the execution process of tasks, and the allocation of resources. The policy effectiveness verification process is to run the initial version of the resource allocation priority adjustment policy in the simulation execution environment to observe and evaluate its effects on resource utilization and conflict resolution. The increase in resource utilization is the degree of improvement in resource utilization after the adoption of the policy compared to the original one, and the conflict resolution efficiency is the effectiveness of the policy in resolving resource allocation conflicts. The resource allocation priority adjustment policy is optimized based on the verification results. In order to improve the performance of the policy based on the increase in resource utilization and the conflict resolution efficiency, the parameters and rules in the policy are adjusted. Finally, the final version of the resource allocation priority adjustment policy is generated.

[0122] As an implementation method, step S350 is to perform policy effectiveness verification on the initial version through a simulated execution environment, which may specifically include the following steps S351 to S356:

[0123] Step S351: construct a simulation execution environment including the current terminal device state image and task queue, and load the initial version of the resource allocation priority adjustment policy.

[0124] The current terminal device state image is a replica of the actual terminal device's operating status, including parameters such as CPU usage, memory utilization, and network bandwidth usage. The task queue is a collection of tasks currently awaiting execution, containing information such as task priority and resource requirements. The simulated execution environment is a software simulation environment that simulates resource allocation and task execution based on the input terminal device state image and task queue. Loading the initial version of the resource allocation priority adjustment policy applies the generated initial version of the policy to the simulated execution environment to verify its effectiveness.

[0125] The following steps can be used to build a simulation execution environment that includes the current terminal device status image and task queue, and load the initial version of the resource allocation priority adjustment policy. First, the status data of the current terminal device is collected through the monitoring agent program, and the data is organized into an image file. For example, the terminal device status image can be created using virtual machine snapshot technology. At the same time, the current task queue information, including detailed task information, is obtained from the power business system. Then, using simulation execution environment software, such as a simulation system based on a cloud computing platform, the terminal device status image and task queue are loaded into the simulation environment. Finally, the initial version of the resource allocation priority adjustment policy is configured into the simulation execution environment so that it can run in the simulation environment. For example, the simulation execution environment is built using the OpenStack cloud platform, the collected terminal device status data and task queue information are imported into the OpenStack virtual machine instance, and the initial version of the resource allocation priority adjustment policy is configured.

[0126] Step S352: inject historical network load fluctuation data and sudden task request data into the simulated execution environment to trigger policy execution and record resource allocation process logs.

[0127] Historical network load fluctuation data is a record of changes in network load over time, including curves showing how parameters such as network bandwidth utilization and network latency change over time. Burst task request data simulates task requests that may suddenly appear during actual operation. These tasks have different priorities and resource requirements. Injecting historical network load fluctuation data and burst task request data is intended to make the simulated execution environment closer to the actual situation. Triggering policy execution is to start the resource allocation priority adjustment policy in the simulated environment. Recording the resource allocation process log is for subsequent analysis of policy execution. The log can contain information such as task allocation, resource usage, conflict occurrence and resolution.

[0128] The following steps can be used to inject historical network load fluctuation data and burst task request data into the simulation execution environment, trigger policy execution, and record resource allocation process logs. First, obtain historical network load fluctuation data from the network monitoring system and organize it into a suitable format, such as a CSV file. For burst task request data, simulated burst tasks can be generated based on the statistical laws of historical task requests. Then, inject the historical network load fluctuation data and burst task request data into the simulation execution environment, for example, by inputting the data into the system through the interface of the simulation execution environment. Next, start the execution of the resource allocation priority adjustment policy and let the policy allocate resources based on the injected data. During the policy execution process, use logging tools, such as Log4j, to record various information in the resource allocation process, including task allocation decisions, resource allocation amounts, task start and end times, etc.

[0129] Step S353: extracting the task processing delay variation, computing core occupancy fluctuation range, and memory leak abnormal events from the process log.

[0130] The process log records the execution process of the resource allocation priority adjustment policy in the simulated execution environment. The task processing delay variation is the difference between the time from the start to the completion of the task and the expected time, which reflects the impact of the policy on the task execution time. The computing core occupancy fluctuation range is the difference between the maximum and minimum occupancy of the computing core (such as the CPU core) during the policy execution process, which reflects the stability of computing resource utilization. Memory leak anomalies occur when memory cannot be released normally during task execution, which affects system performance and stability.

[0131] The following steps can be used to extract task processing latency variations, compute core utilization fluctuations, and memory leak anomalies from process logs. First, parse the process logs and categorize the information by task, time, and resource usage. Then, for each task, calculate the difference between the actual and expected processing times to obtain the task processing latency variation. For compute core utilization, find the maximum and minimum values ​​during policy execution and calculate their difference to obtain the compute core utilization fluctuation range. For memory leak anomalies, identify any instances of memory failure through memory usage information and error messages in the logs, and record the relevant task and time information. For example, use regular expressions to parse the process logs, extract task start and end times, and compute core utilization data, calculate the task processing latency variation and compute core utilization fluctuation range, and identify memory leak anomalies by searching for keywords such as "out of memory" and "memory not released" in the logs.

[0132] Step S354: Compare the task processing delay variation with a preset delay tolerance threshold to generate a delay compliance assessment result.

[0133] The preset latency tolerance threshold is the maximum permissible variation in task processing latency, determined based on business needs and system performance requirements. The latency compliance assessment results determine whether the variation in task processing latency falls within the preset latency tolerance threshold. These results are categorized as either compliant or non-compliant.

[0134] The following steps can be used to compare the change in task processing delay with the preset delay tolerance threshold and generate a delay compliance assessment result. First, determine the preset delay tolerance threshold. For example, according to business requirements, set the maximum allowable change in task processing delay to 10 minutes. Then, compare the change in processing delay of each task extracted from the process log with the preset delay tolerance threshold. If the change in task processing delay is less than or equal to the preset delay tolerance threshold, the delay of the task is considered to be compliant; if the change in task processing delay is greater than the preset delay tolerance threshold, the delay of the task is considered to be non-compliant. Finally, count the number of compliant tasks and non-compliant tasks to generate a delay compliance assessment result.

[0135] Step S355: Determine the resource allocation stability score based on the fluctuation range of the computing core occupancy rate, and generate a resource health index in combination with the frequency of occurrence of memory leak abnormal events.

[0136] The resource allocation stability score is a quantitative evaluation of the stability of computing resource allocation. It is determined based on the fluctuation range of computing core utilization. The smaller the fluctuation range, the more stable the resource allocation and the higher the score. The frequency of memory leak anomalies is the ratio of the number of memory leak anomalies occurring during policy execution to the total number of tasks. The resource health index is a comprehensive indicator derived from the resource allocation stability score and the frequency of memory leak anomalies, used to assess the overall health of system resources.

[0137] The following steps can be used to determine a resource allocation stability score based on the fluctuation range of the compute core utilization rate and generate a resource health index based on the frequency of memory leak anomalies. First, set a scoring standard based on the fluctuation range of the compute core utilization rate. For example, if the fluctuation range of the compute core utilization rate is less than 10%, the score is 10 points; if the fluctuation range is between 10% and 20%, the score is 7 points; and if the fluctuation range is greater than 20%, the score is 3 points. The resource allocation stability score is determined based on the fluctuation range of the compute core utilization rate. Then, the frequency of memory leak anomalies is calculated. For example, if 5 memory leak anomalies occur while processing 100 tasks, the frequency is 5%. Finally, the resource health index is generated by combining the resource allocation stability score and the frequency of memory leak anomalies. A weighted average method can be used, for example, with a weight of 0.7 for the resource allocation stability score and a weight of 0.3 for the frequency of memory leak anomalies. The resource health index = resource allocation stability score × 0.7 + (1 - frequency of memory leak anomalies) × 0.3 × 10. Assuming that the resource allocation stability score is 7 points and the frequency of memory leak abnormal events is 5%, the resource health index = 7×0.7+(1-0.05)×0.3×10=4.9+2.85=7.75.

[0138] Step S356: Calculate the comprehensive effectiveness score of the strategy based on the latency compliance assessment results, resource allocation stability score, and resource health index, and perform parameter tuning on the initial version based on the score results.

[0139] The latency compliance assessment reflects the policy's impact on task processing time. The resource allocation stability score reflects the stability of computing resource allocation. The resource health metric comprehensively considers resource allocation stability and memory leaks. The policy's comprehensive effectiveness score, derived from these three indicators, is used to evaluate the overall performance of the resource allocation priority adjustment policy. Parameter tuning involves adjusting the parameters of the initial version of the resource allocation priority adjustment policy based on the policy's comprehensive effectiveness score to improve policy performance.

[0140] The following steps can be used to calculate the policy's overall effectiveness score based on the latency compliance assessment results, resource allocation stability score, and resource health index. The initial version can then be optimized based on the score. First, weights are assigned to the latency compliance assessment results, resource allocation stability score, and resource health index, for example, 0.3, 0.3, and 0.4, respectively. The latency compliance assessment results are then converted into a score. For example, if the compliant task ratio is 80%, the score is 8. Next, the policy's overall effectiveness score is calculated based on the weights: Policy overall effectiveness score = latency compliance score × 0.3 + resource allocation stability score × 0.3 + resource health index × 0.4. Assuming the latency compliance score is 8, the resource allocation stability score is 7, and the resource health index is 7.75, the policy's overall effectiveness score = 8 × 0.3 + 7 × 0.3 + 7.75 × 0.4 = 2.4 + 2.1 + 3.1 = 7.6. Finally, the initial version's parameters are optimized based on the policy's overall effectiveness score. If the score is low, it indicates that the policy's performance needs improvement. Adjustment can be made to policy parameters, such as the task allocation priority rules and resource allocation ratios. For example, if analysis reveals that the task allocation priority rules are causing excessive processing delays for some high-priority tasks, the priority rules can be adjusted to increase their allocation priority. After multiple adjustments and verifications, the final version of the resource allocation priority adjustment policy is generated until the overall effectiveness score of the policy reaches a satisfactory level.

[0141] Step S360: Perform a policy coordination check on the final version, the task scheduling optimization policy, and the network bandwidth reallocation policy to generate a computing resource configuration policy set that passes the consistency check.

[0142] The task scheduling optimization policy is used to optimize the order and allocation of task execution, and the network bandwidth reallocation policy is used to adjust network bandwidth allocation. Policy compatibility verification checks whether the final version of the resource allocation priority adjustment policy is compatible with the task scheduling optimization policy and the network bandwidth reallocation policy, ensuring that they are consistent and conflict-free. A set of computing resource configuration policies that pass the consistency check has passed the policy compatibility verification, ensuring that the policies work together to form a unified and effective set of computing resource configuration policies.

[0143] The final version is verified for consistency with the task scheduling optimization policy and the network bandwidth reallocation policy. The following steps can be used to generate a set of computing resource configuration policies that pass the consistency check. First, a detailed analysis is performed on the final version of the resource allocation priority adjustment policy, the task scheduling optimization policy, and the network bandwidth reallocation policy to determine the objectives, rules, and execution processes of each policy. Then, the three policies are run simultaneously in a simulated execution environment to observe their interaction during resource allocation and task execution. Conflicts are checked, such as when the resource allocation priority adjustment policy assigns a task to one terminal device while the task scheduling optimization policy schedules the same task to another, or when the bandwidth allocated by the network bandwidth reallocation policy does not match the resource requirements of the tasks specified in the resource allocation priority adjustment policy. If a conflict is detected, the policies are adjusted, such as by modifying their rules or parameters to ensure consistency. After repeated adjustments and verifications, the three policies are synchronized and conflict-free in the simulated execution environment, resulting in a set of computing resource configuration policies that pass the consistency check. For example, in a simulated execution environment, it is found that there is a conflict between the resource allocation priority adjustment strategy and the task scheduling optimization strategy in task allocation. By adjusting the task sorting rules in the task scheduling optimization strategy to match the resource allocation priority adjustment strategy, a set of computing resource configuration strategies that pass the consistency check is finally generated.

[0144] Step S400: performing an adjustment operation on the computing resources of the power wireless terminal according to the computing resource configuration policy set, and generating a resource allocation verification result and terminal operation performance feedback data.

[0145] The computing resource configuration policy set includes policies such as resource allocation priority adjustment, task scheduling optimization, and network bandwidth reallocation. These policies define how to configure the computing resources of power wireless terminals. Adjusting the computing resources of power wireless terminals involves reallocating and adjusting the terminal device's computing resources (such as CPU, memory, and network bandwidth) based on the rules and parameters in the policy set. Resource allocation verification results verify the resource allocation status after the adjustment operation, including whether resources are allocated according to the policy and whether the allocation is reasonable. Terminal operational performance feedback data reflects the operational performance of the terminal device after the adjustment operation, such as task processing speed and resource utilization.

[0146] As an embodiment, step S400, according to the computing resource configuration policy set, performs an adjustment operation on the computing resources of the power wireless terminal to generate resource allocation verification results and terminal operation performance feedback data, which may specifically include the following steps S410 to S450:

[0147] Step S410: parsing the resource allocation priority adjustment policy in the computing resource configuration policy set, and determining a list of terminal devices to be adjusted and corresponding computing core reallocation parameters.

[0148] The resource allocation priority adjustment policy within the computing resource configuration policy set specifies how to adjust resource allocation priorities. Interpreting this policy involves a detailed analysis of the policy's rules and parameters to understand its intent and specific operational methods. The list of terminal devices to be adjusted is the set of terminal devices that require computing resource adjustment, as determined by the policy. The compute core reallocation parameters include parameters such as the number or ratio of compute cores allocated to each terminal device to be adjusted.

[0149] The following steps are used to parse the resource allocation priority adjustment policies in the computing resource configuration policy set and determine the list of terminal devices to be adjusted and the corresponding computing core reallocation parameters. First, the resource allocation priority adjustment policy is parsed to extract its rules and conditions. For example, the policy may stipulate, "When the CPU utilization of terminal device A exceeds 80%, two computing cores are allocated to terminal device B." Then, based on the current operating status of the terminal devices, such as CPU utilization and memory usage, the terminal devices that need adjustment are determined and added to the list of terminal devices to be adjusted. Finally, based on the rules in the policy, computing core reallocation parameters are determined for each terminal device to be adjusted. For example, if monitoring reveals that the CPU utilization of terminal device A is 85%, and according to the policy rules, two computing cores are allocated from terminal device A to terminal device B, the computing core reallocation parameters for terminal device A are determined to be a reduction of two computing cores, and the computing core reallocation parameters for terminal device B are determined to be an increase of two computing cores.

[0150] Step S420: performing sequential reconstruction processing on the current task queue according to the task dependency chain in the task scheduling optimization strategy to generate an optimized task execution sequence table.

[0151] The task dependency chain in task scheduling optimization strategies describes the dependencies between tasks—that is, the execution of a task may depend on the completion of other tasks. The current task queue is a collection of tasks currently awaiting execution, and these tasks may be arranged in the order in which they were submitted. Sequence reconfiguration involves reordering the tasks in the current task queue based on the task dependency chain to improve the efficiency and rationality of task execution. The optimized task execution order table is a list of the task execution orders obtained after sequence reconfiguration.

[0152] As an implementation method, step S420, based on the task dependency chain in the task scheduling optimization strategy, sequentially reconstructs the current task queue to generate an optimized task execution sequence table, which may specifically include the following steps S421 to S425:

[0153] Step S421: identifying the critical path task nodes and non-critical path task nodes in the task dependency chain, and adding priority execution identifiers to the critical path task nodes.

[0154] A task dependency chain can be represented as a graph, where nodes represent tasks and edges represent dependencies between tasks. Critical path task nodes are those in the task dependency chain that play a critical role in the completion time of the entire task flow. Non-critical path task nodes are those that do not affect the completion time of the entire task flow. Priority execution flags are used to mark critical path task nodes so that they are prioritized during task scheduling.

[0155] The following steps can be used to identify critical and non-critical path task nodes in a task dependency chain and assign priority execution flags to the critical path task nodes. First, use a graph theory algorithm, such as the Critical Path Method (CPM), to analyze the task dependency chain. The critical path is identified by calculating the earliest start time, earliest finish time, latest start time, and latest finish time for each task node. Task nodes on the critical path are considered critical path task nodes, while the remaining task nodes are considered non-critical path task nodes. Next, assign priority execution flags to the critical path task nodes. For example, a "priority" field can be added to the task information and set to "high" for the critical path task nodes. For example, for a task dependency chain containing tasks A, B, C, and D, the CPM algorithm can be used to determine that tasks A->B->D are the critical path. Priority execution flags can then be assigned to tasks A, B, and D.

[0156] Step S422: According to the urgent task mark in the service priority identifier, tasks with the same priority are re-sorted according to the processing delay constraint.

[0157] The urgent task flag in the business priority identifier is used to distinguish the urgency of tasks. The processing delay constraint requires that tasks must be completed within a specified time. For tasks with the same priority, their execution order cannot be determined based solely on priority. A secondary sorting based on the processing delay constraint is required to ensure that urgent tasks with strict processing delay requirements are executed first.

[0158] Based on the urgent task mark in the business priority identifier, the following steps can be used to perform secondary sorting of tasks with the same priority according to the processing delay constraint. First, the urgent task mark and processing delay constraint information are extracted from the business priority identifier. Then, the tasks with the same priority are screened out and sorted according to the processing delay constraint. Ascending sorting can be used, that is, tasks with shorter processing delay constraints are placed in front. For example, there are tasks E, F, and G, which have the same priority. The processing delay constraint of task E is 30 minutes, the processing delay constraint of task F is 60 minutes, and the processing delay constraint of task G is 20 minutes. They are sorted in ascending order according to the processing delay constraint as task G, task E, and task F.

[0159] Step S423: Detect the circular dependency chain in the task queue and remove the circular dependency by inserting a virtual synchronization node.

[0160] A circular dependency chain occurs when tasks depend on each other, forming a loop. This circular dependency prevents tasks from executing properly because each task waits for the completion of other tasks. A virtual synchronization node is a virtual task node used to break the circular dependency. It does not actually execute tasks but serves as a synchronization point.

[0161] The following steps can be used to detect circular dependency chains in the task queue and resolve them by inserting virtual synchronization nodes. First, use a topological sorting algorithm in graph theory, such as the Kahn algorithm, to perform topological sorting on the task dependency chain. If the sorting cannot be completed during the sorting process, it means that a circular dependency chain exists. Then, find the task nodes in the circular dependency chain. For example, task H depends on task I, and task I depends on task H, which forms a circular dependency chain. Insert virtual synchronization nodes in the circular dependency chain, for example, insert virtual synchronization node J between task H and task I. Modify the dependency of task H to depend on virtual synchronization node J, and virtual synchronization node J depends on task I. This breaks the circular dependency and allows the task to execute normally.

[0162] Step S424: Based on the resource demand characteristics of task processing, tasks occupying the same computing resources are separated into time slices to generate a task time slice distribution diagram without resource conflicts.

[0163] The task processing resource requirement characteristics describe the computing resources (such as CPU and memory) required by each task during execution. Time slicing is the process of separating tasks that occupy the same computing resources in time to prevent them from occupying the same resources simultaneously and causing resource conflicts. The resource conflict-free task time slice distribution diagram is a graph that shows the time allocation of tasks, ensuring that each task does not conflict with other tasks during execution.

[0164] Based on the characteristics of task processing resource requirements, tasks occupying the same computing resources are time-sliced. The following steps can be used to generate a resource-conflicting task time-slice distribution map. First, analyze the task processing resource requirements to determine the type and quantity of computing resources required by each task. Then, group the tasks occupying the same computing resources. For each group, time-slice the tasks based on their processing latency constraints and resource requirements. A greedy algorithm can be used to prioritize tasks with shorter processing latency constraints. For example, suppose tasks K, L, and M all require CPU resources. Task K has a processing latency constraint of 20 minutes, task L has a processing latency constraint of 30 minutes, and task M has a processing latency constraint of 40 minutes. Task K can be scheduled first for execution between 0 and 20 minutes, followed by task L for execution between 20 and 50 minutes, and finally task M for execution between 50 and 90 minutes. Finally, plot the task time allocations on the time-slice distribution map to generate a resource-conflicting task time-slice distribution map.

[0165] Step S425: Reconstruct the task queue and generate an optimized task execution sequence table according to the priority execution identifier, the secondary sorting result and the time slice distribution map.

[0166] Priority execution flags mark critical path task nodes for priority execution. Secondary sorting determines the execution order of tasks with the same priority. The time slice distribution graph shows the time allocation of tasks, avoiding resource conflicts. Restructuring the task queue involves rearranging the current task queue based on this information to generate an optimized task execution order table.

[0167] According to the priority execution mark, the secondary sorting result and the time slice distribution map, the following steps can be adopted to reconstruct the task queue and generate the optimized task execution order table. First, according to the priority execution mark, the key path task node is placed in front of the task queue. Then, for tasks with the same priority, they are arranged according to the secondary sorting result. Finally, according to the time slice distribution map, the tasks are inserted into the task queue in chronological order. For example, the key path task nodes are tasks A, B, and D, and the secondary sorting results of tasks E, F, and G with the same priority are task G, task E, and task F. The time slice distribution map shows that task G is executed in 0-20 minutes, task A is executed in 20-30 minutes, task E is executed in 30-50 minutes, task B is executed in 50-60 minutes, task F is executed in 60-90 minutes, and task D is executed in 90-100 minutes. The optimized task execution order table obtained by reconstructing the task queue is task G, task A, task E, task B, task F, and task D.

[0168] Step S430: Based on the bandwidth adjustment ratio in the network bandwidth reallocation strategy, the network interface configuration of the target terminal device is modified in real time, and changes in network throughput are monitored.

[0169] The bandwidth adjustment ratio in the network bandwidth reallocation policy specifies how to adjust the network bandwidth allocation for target end devices. Target end devices are the devices for which network bandwidth adjustment is required. Modifying the network interface configuration of target end devices in real time involves modifying parameters such as the bandwidth limit of the end device's network interface based on the bandwidth adjustment ratio. Monitoring network throughput changes involves monitoring network throughput (i.e., the amount of data passing through the network per unit time) in real time after modifying the network interface configuration to assess the effectiveness of bandwidth adjustments.

[0170] The following steps can be used to modify the network interface configuration of the target terminal device in real time based on the bandwidth adjustment ratio in the network bandwidth reallocation policy and monitor changes in network throughput. First, determine the network interface information of the target terminal device, such as the name and IP address of the network interface. Then, calculate the new network bandwidth limit of the target terminal device based on the bandwidth adjustment ratio in the network bandwidth reallocation policy. Next, use network management tools, such as the tc command in Linux or the Netsh command in Windows, to modify the network interface configuration of the target terminal device in real time and set a new network bandwidth limit. Finally, use network monitoring tools, such as Wireshark or Ntopng, to monitor changes in network throughput in real time and record network throughput data before and after the modification in order to evaluate the effect of the bandwidth adjustment.

[0171] Step S440: After the adjustment operation is performed, the real-time resource occupancy rate, task processing completion rate, and network transmission error rate of the terminal device are collected to generate a resource allocation verification result.

[0172] Executing an adjustment operation involves adjusting the computing resources of the power wireless terminal based on a set of computing resource configuration policies. This includes reallocating computing cores, optimizing task scheduling, and reallocating network bandwidth. Real-time resource utilization is the real-time CPU utilization, memory utilization, and other resource usage of the terminal device after the adjustment operation. The task completion rate is the ratio of the number of tasks completed within a set timeframe to the total number of tasks. The network transmission error rate is the ratio of the number of data packets that experienced errors during network transmission to the total number of data packets. Resource allocation verification results are generated based on the collected real-time resource utilization, task completion rate, and network transmission error rate to verify whether the resource allocation adjustment operation achieved the desired effect.

[0173] After performing the adjustment, the real-time resource utilization, task completion rate, and network transmission error rate of the terminal devices are collected. The following steps can be used to generate resource allocation verification results. First, a monitoring agent is used to collect real-time resource utilization data from the terminal devices, such as CPU usage and memory usage. For the task completion rate, the task management system can be used to record the start and end times of tasks, count the number of completed tasks and the total number of tasks, and calculate the task completion rate. For the network transmission error rate, a network monitoring device, such as a network analyzer, can be used to count the number of error packets and the total number of packets during network transmission to calculate the network transmission error rate. The collected data is then organized and analyzed to determine whether the resource allocation adjustment was effective. For example, if the real-time resource utilization is more balanced, the task completion rate is improved, and the network transmission error rate is reduced, then the resource allocation adjustment has achieved the desired results. Otherwise, further analysis and adjustments are required. Finally, based on the analysis results, a resource allocation verification result is generated. The result can include information such as whether the resource allocation is reasonable and which areas need improvement.

[0174] Step S450: Compare and analyze the resource allocation verification result with the historical performance data before adjustment, extract the resource utilization improvement ratio and task processing acceleration ratio, and encapsulate them into terminal operation performance feedback data.

[0175] The resource allocation verification results reflect the resource allocation and operational performance of the terminal device after the adjustment operation. The historical performance data before the adjustment operation includes the terminal device's resource utilization rate, task processing time, and other data before the adjustment operation was performed. The resource utilization improvement ratio is the percentage increase in resource utilization after the adjustment operation compared to before the adjustment. The task processing acceleration ratio is the percentage reduction in task processing time after the adjustment operation compared to before the adjustment. Terminal operational performance feedback data encapsulates information such as the resource utilization improvement ratio and task processing acceleration ratio, and is used to provide feedback on changes in the terminal device's operational performance.

[0176] The resource allocation verification results are compared and analyzed with historical performance data before adjustment to extract the resource utilization improvement ratio and task processing speedup ratio, which are then packaged as terminal operation performance feedback data. The following steps can be used: First, extract the adjusted resource utilization and task processing time data from the resource allocation verification results, and extract the adjusted resource utilization and task processing time data from the historical performance data. Then, calculate the resource utilization improvement ratio: resource utilization improvement ratio = (adjusted resource utilization ratio - pre-adjusted resource utilization ratio) / pre-adjusted resource utilization ratio × 100%. Calculate the task processing speedup ratio: task processing speedup ratio = pre-adjusted task processing time / post-adjusted task processing time. For example, if the pre-adjusted resource utilization ratio is 60% and the post-adjusted resource utilization ratio is 70%, then the resource utilization improvement ratio = (70% - 60%) / 60% × 100% ≈ 16.7%. If the pre-adjusted task processing time is 100 minutes and the post-adjusted task processing time is 80 minutes, then the task processing speedup ratio = 100 / 80 = 1.25. Finally, the resource utilization improvement ratio and task processing speedup ratio are encapsulated as terminal operation performance feedback data. For example, the JSON format can be used: {"resource_utilization_improvement_rate":16.7,"task_processing_speedup_ratio":1.25}.

[0177] Step S500: continuously comparing the terminal operation performance feedback data with a preset performance optimization threshold, and iteratively updating the resource adaptation rule base according to the comparison result.

[0178] Terminal performance feedback data includes information such as resource utilization improvement ratios and task processing acceleration ratios, reflecting the terminal device's operational performance after resource allocation adjustments. The preset performance optimization thresholds are pre-set target values ​​for resource utilization improvement and task processing acceleration, used to measure whether the resource allocation adjustments have achieved the expected results. Continuous comparisons continuously compare terminal performance feedback data with the preset performance optimization thresholds. Iterative updates adjust and update the rules in the resource adaptation rule base based on the comparison results to improve resource allocation effectiveness.

[0179] As an implementation method, step S500 continuously compares the terminal operation performance feedback data with a preset performance optimization threshold, and iteratively updates the resource adaptation rule library based on the comparison result. Specifically, the following steps S510 to S550 may be included:

[0180] Step S510: extracting the resource utilization improvement ratio from the terminal operation performance feedback data, and comparing it with the lowest improvement threshold among the performance optimization thresholds.

[0181] Terminal performance feedback data includes information such as resource utilization improvement ratios and task processing speedup ratios. The minimum improvement threshold within the performance optimization threshold is the pre-set minimum target value for resource utilization improvement. The resource utilization improvement ratio is extracted from terminal performance feedback data and compared with the minimum improvement threshold to determine whether resource allocation adjustments meet the basic performance improvement requirements.

[0182] The following steps can be used to extract the resource utilization improvement ratio from the terminal operation performance feedback data and compare it with the minimum improvement threshold in the performance optimization threshold. First, parse the terminal operation performance feedback data and extract the resource utilization improvement ratio therein. For example, if the terminal operation performance feedback data is {"resource_utilization_improvement_rate":16.7,"task_processing_speedup_ratio":1.25}, the extracted resource utilization improvement ratio is 16.7%. Then, obtain the minimum improvement threshold in the performance optimization threshold, for example, the minimum improvement threshold is 15%. Finally, compare the extracted resource utilization improvement ratio with the minimum improvement threshold. If the resource utilization improvement ratio is greater than or equal to the minimum improvement threshold, it means that the resource allocation adjustment has met the basic performance improvement requirements; if the resource utilization improvement ratio is less than the minimum improvement threshold, it is necessary to further analyze the cause and make adjustments.

[0183] Step S520: If the resource utilization improvement ratio is lower than the minimum improvement threshold, the rule base diagnosis mode is activated to perform anomaly detection on the load balancing rule group in the resource adaptation rule base.

[0184] Rulebase diagnostic mode is used to check for anomalies in the resource adaptation rulebase. A load balancing rule group is a set of rules in the resource adaptation rulebase used to balance the load on terminal devices. If the resource utilization increase ratio falls below the minimum increase threshold, it indicates that resource allocation adjustments are not effective, possibly due to a problem with the load balancing rule group. Therefore, you should activate rulebase diagnostic mode to detect anomalies in the load balancing rule group.

[0185] As an implementation method, in step S520, anomaly detection is performed on the load balancing rule group in the resource adaptation rule base, which may specifically include the following steps S521 to S525:

[0186] Step S521: Count the number of times the load balancing rule group is triggered and successfully executed within a preset time period, and calculate the rule execution success rate.

[0187] The preset time period is a pre-defined time range used to calculate load balancing rule group execution statistics, such as the past week. The load balancing rule group trigger count is the number of times the load balancing rule group was triggered and executed during the preset time period. The successful execution count is the number of times the rule was successfully executed among the number of triggers. The rule execution success rate is the ratio of successful executions to triggers, which measures the effectiveness of the load balancing rule group.

[0188] To count the number of times a load balancing rule group was triggered and successfully executed within a preset time period and calculate the rule execution success rate, you can use the following steps. First, filter the execution records of the load balancing rule group within the preset time period from the rule execution log. The rule execution log records information such as the rule trigger time and execution results. Then, count the number of triggers and successful executions. For example, in the past week, the load balancing rule group was triggered 100 times, of which 80 were successfully executed. Finally, calculate the rule execution success rate: Rule execution success rate = number of successful executions / number of triggers × 100%, that is, rule execution success rate = 80 / 100 × 100% = 80%.

[0189] Step S522: If the rule execution success rate is lower than a preset success rate threshold, it is determined that there is an abnormality in the load balancing rule group.

[0190] The preset success rate threshold is the minimum requirement for rule execution success, used to determine whether the load balancing rule group is functioning properly. If the rule execution success rate is lower than the preset success rate threshold, it indicates that the load balancing rule group is experiencing frequent failures during execution, possibly indicating an anomaly that requires further analysis.

[0191] If the rule execution success rate falls below a preset success rate threshold, the following steps can be used to determine if an anomaly exists in the load balancing rule group. First, determine a preset success rate threshold, such as 85%. Then, compare the calculated rule execution success rate with the preset success rate threshold. If the rule execution success rate falls below the preset success rate threshold, such as 80% in the example above, then an anomaly exists in the load balancing rule group.

[0192] Step S523: extract the terminal device load data and resource allocation logs within the abnormal time period, and identify the unsuccessfully executed rule conditions and corresponding device status characteristics.

[0193] The abnormal time period is the period when the rule execution success rate is lower than the preset success rate threshold. Terminal device load data refers to the terminal device's CPU usage, memory usage, and other load conditions during the abnormal time period. The resource allocation log records the details of resource allocation during the abnormal time period, including task allocation, resource usage, and other information. Unsuccessfully executed rule conditions are the conditions in the load balancing rule group that caused the rule execution to fail. The corresponding device status characteristics are the terminal device status characteristics when the rule failed to execute successfully, such as excessive CPU usage or insufficient memory.

[0194] The following steps can be used to extract terminal device load data and resource allocation logs during the abnormal time period and identify unsuccessfully executed rule conditions and corresponding device status characteristics. First, obtain terminal device load data from the monitoring agent during the abnormal time period and organize it into a suitable format. Simultaneously, extract resource allocation logs from the rule execution logs during the abnormal time period. Then, analyze the resource allocation logs to identify unsuccessfully executed rule records. For each unsuccessfully executed rule record, extract the rule condition and corresponding terminal device status information.

[0195] Step S524: Divide the device status characteristics into multiple abnormal scenario categories through cluster analysis, and generate rule supplementary conditions for each abnormal scenario category.

[0196] Cluster analysis is used to group similar data points together. Device status features represent the status information of terminal devices when a rule fails to execute successfully, such as CPU usage and memory utilization. Abnormal scenario categories are categorized into different abnormal situations based on device status features. Rule supplementary conditions are added to enable the load balancing rule group to better handle abnormal situations. Cluster analysis can be used to classify device status features into multiple abnormal scenario categories and generate rule supplementary conditions for each abnormal scenario category. The following steps can be used to organize the extracted device status features into a dataset, where each data point contains the numerical values ​​of multiple device status features, such as (CPU usage, memory utilization). Then, cluster analysis is performed on the dataset using a cluster analysis algorithm, such as the K-Means algorithm. Based on the clustering results, the device status features are classified into multiple abnormal scenario categories. For example, the device status features can be divided into three abnormal scenario categories: high CPU usage and high memory utilization, high CPU usage and low memory utilization, and low CPU usage and high memory utilization. For each abnormal scenario category, its characteristics and potential causes of rule execution failure are analyzed to generate rule supplementary conditions. For example, for abnormal scenario categories with high CPU usage and high memory occupancy, the generated rule supplementary condition is "When the CPU usage of the terminal device exceeds 80% and the memory occupancy exceeds 90%, release some memory before assigning tasks."

[0197] Step S525: Add the rule supplementary condition to the original load balancing rule group to form an expanded load balancing rule group.

[0198] Supplementary conditions are created to handle exceptions. Adding them to an existing load balancing rule group improves the load balancing rule group and allows it to better handle various exceptions. An expanded load balancing rule group is an existing load balancing rule group with supplementary conditions added.

[0199] The following steps can be used to add supplementary rule conditions to the existing load balancing rule group to form an expanded load balancing rule group. First, analyze the existing load balancing rule group to determine the rule structure and format. Then, add the generated supplementary rule conditions to the existing load balancing rule group according to the rule structure and format. For example, the existing load balancing rule group contains a rule that states, "When the CPU usage of a terminal device exceeds 80%, allocate some tasks to other terminal devices." For the abnormal scenario category of high CPU usage and high memory usage, the supplementary rule condition generated is, "When the CPU usage of a terminal device exceeds 80% and the memory usage exceeds 90%, first release some memory before allocating tasks." Adding the supplementary rule condition to the existing rule creates the expanded rule, "When the CPU usage of a terminal device exceeds 80%, if the memory usage exceeds 90%, first release some memory before allocating some tasks to other terminal devices; otherwise, directly allocate some tasks to other terminal devices." Finally, update the load balancing rule group in the resource adaptation rule base and save the expanded load balancing rule group to the rule base.

[0200] Step S530: extract the historical records of rule matching failure events, and analyze the matching deviation between the rule triggering condition and the current terminal device state.

[0201] Rule match failure events are historical records of events during the resource allocation process where a rule failed to successfully match the terminal device status. The rule trigger condition is the prerequisite for rule execution, and the current terminal device status is the current operating status of the terminal device, such as CPU usage and memory utilization. The match deviation is the difference between the rule trigger condition and the current terminal device status, which is used to evaluate the applicability of the rule.

[0202] The following steps can be used to extract the historical records of rule matching failure events and analyze the matching deviation between the rule triggering conditions and the current terminal device status. First, extract the historical records of rule matching failure events from the rule execution log. The records contain the rule triggering conditions and the corresponding terminal device status information. Then, for each rule matching failure event record, compare the rule triggering conditions with the current terminal device status. For example, if the rule triggering condition is "the terminal device CPU usage exceeds 80%", and the current terminal device status is 75% CPU usage, there is a matching deviation of 5%. Numerical calculation methods can be used, such as calculating the difference or ratio between the rule triggering conditions and the current terminal device status, to quantify the matching deviation. Finally, perform statistical analysis on the matching deviation of all rule matching failure events to find out the rules with larger matching deviations and the corresponding device status characteristics so that the rules can be adjusted.

[0203] Step S540: Adaptively calibrate the condition thresholds in the load balancing rule group according to the matching deviation to generate an updated load balancing rule group.

[0204] The match deviation reflects the degree of discrepancy between the rule triggering conditions and the current terminal device status. The condition threshold is the threshold for triggering a rule in a load balancing rule group. For example, 80% in "terminal device CPU usage exceeds 80%" is the condition threshold. Adaptive calibration automatically adjusts the condition threshold based on the match deviation, allowing the rule to better adapt to the actual terminal device status. The updated load balancing rule group is the result of adaptive calibration.

[0205] The following steps can be used to adaptively calibrate the condition thresholds in the load balancing rule group based on the matching deviation and generate an updated load balancing rule group. First, analyze the statistical results of the matching deviation to determine the condition thresholds that need to be adjusted. For example, if it is found that the matching deviation of the rule "The CPU usage of the terminal device exceeds 80%" is large, and in most cases the terminal device needs to be assigned tasks when the CPU usage reaches 75%, you can consider adjusting the condition threshold to 75%. Then, modify the corresponding condition thresholds in the load balancing rule group. For each rule that needs to be adjusted, replace the condition threshold with a new value. Finally, generate an updated load balancing rule group to ensure that the rules can more accurately match the status of the terminal device and improve the effect of resource allocation.

[0206] Step S550: Re-inject the updated load balancing rule group into the resource adaptation rule base, and close the rule base diagnostic mode.

[0207] The updated load balancing rule group is the result of anomaly detection, rule supplementation, and condition threshold calibration. Re-injecting it into the resource adaptation rule base improves the load balancing rule group within the resource adaptation rule base and better guides resource allocation. Disabling rule base diagnostic mode stops the rule base diagnostic check after the rule update is complete and resumes normal resource allocation operations.

[0208] The following steps can be used to reinject the updated load balancing rule group into the resource adaptation rule base and disable the rule base diagnostic mode. First, save the updated load balancing rule group to the corresponding location in the rule base, replacing the original load balancing rule group. Rule group updates can be accomplished using database operations or file read and write operations. Then, update the rule base version information, recording the update time and content of the rule group. Finally, disable the rule base diagnostic mode and restore the normal operation of the resource adaptation rule base, allowing it to continue matching and generating resource allocation strategies based on the new rule group. By continuously comparing terminal operational performance feedback data with preset performance optimization thresholds and iteratively updating the resource adaptation rule base, resource allocation strategies can be continuously optimized, improving the computing resource utilization efficiency and operational performance of power wireless terminals.

[0209] It is understandable that the various algorithms involved in the above-mentioned introductions of the embodiments of the present invention, such as Euclidean distance, cosine distance, conflict resolution algorithm, etc., can all be learned from the relevant content in the prior art. In order to save space, they will not be expanded too much in the embodiments of this application. In addition, when implementing the scheme of this application, those skilled in the art can supplement the details according to the common knowledge in this field. For example, according to the common knowledge in this field, normalization can be used to eliminate dimensional conflicts before feature fusion, interpolation can be used to eliminate dimensional differences, thresholds can be reasonably set in combination with historical data scores or business needs, and models can be trained based on a general model training method, etc. This application will no longer provide redundant introductions to the overly detailed implementation process.

[0210] See below Figure 2 , Figure 2This is a schematic diagram of the structure of a computer system provided in an embodiment of the present invention. The computer system includes at least a processor 101, a communication interface 102, and a memory 103. The processor 101, communication interface 102, and memory 103 may be connected via a bus or other means. The processor 101 (also known as the Central Processing Unit (CPU)) is the computing and control core of the computer system, capable of parsing various instructions within the computer system and processing various data within the computer system. The communication interface 102 may optionally include a standard wired interface or a wireless interface (such as Wi-Fi, a mobile communication interface, etc.), which can be used to send and receive data under the control of the processor 101. The communication interface 102 may also be used for data transmission and interaction within the computer system. The memory 103 is a storage device in the computer system for storing programs and data. It is understood that the memory 103 herein may include both the built-in memory of the computer system and, of course, the extended memory supported by the computer system. The memory 103 provides storage space, which stores the computer system's operating system, but this is not limited to this in the present invention.

[0211] In one embodiment, the processor 101 executes the method for dynamically configuring computing resources management based on a power wireless terminal provided in the above embodiment of the present invention by running the computer program in the memory 103 .

Claims

1. A method for dynamically configuring computing resources management based on power wireless terminals, characterized in that: include: Acquire a real-time operating status data set and a power service demand data set of the power wireless terminal, wherein the real-time operating status data set includes operating parameters of the terminal device and network load fluctuation characteristics, and the power service demand data set includes a service priority identifier and real-time task processing requirements; Performing multi-dimensional feature fusion processing on the real-time operation status data set and the power business demand data set to generate a resource adaptation feature set, wherein the resource adaptation feature set includes terminal device load balancing features, business demand conflict features, and resource allocation efficiency evaluation indicators; Based on a preset resource adaptation rule library, real-time policy matching processing is performed on the resource adaptation feature set to generate a computing resource configuration policy set, which includes a resource allocation priority adjustment policy, a task scheduling optimization policy, and a network bandwidth reallocation policy; According to the computing resource configuration strategy set, adjusting the computing resources of the power wireless terminal, generating resource allocation verification results and terminal operation performance feedback data; The terminal operation performance feedback data is continuously compared with a preset performance optimization threshold, and the resource adaptation rule library is iteratively updated according to the comparison result.

2. The method according to claim 1, characterized in that The performing multi-dimensional feature fusion processing on the real-time operation status data set and the power service demand data set to generate a resource adaptation feature set includes: Extracting time series features of terminal device operating parameters from the real-time operating status data set, wherein the time series features include parameter fluctuation period, peak duration, and abnormal parameter offset; Decomposing the real-time task processing requirements in the power business demand data set to obtain a plurality of subtask resource demand characteristics, wherein the subtask resource demand characteristics include processing delay constraints, computing core occupancy rates, and memory allocation thresholds; Performing correlation matching processing on the time series characteristics and the subtask resource requirement characteristics to generate a task-device adaptability index, wherein the task-device adaptability index is used to quantify the degree of match between the terminal device processing capability and the subtask requirement; Based on a preset resource conflict detection model, conflict analysis is performed on the network load fluctuation characteristics and the service priority identifier to generate a resource competition hotspot area identifier and conflict mitigation recommendation parameters; The resource adaptation feature set is constructed by integrating the task-device adaptability index, resource competition hotspot area identifier, and conflict mitigation suggestion parameters, and is associated with the terminal device identifier and the business task identifier.

3. The method according to claim 2, characterized in that The conflict analysis and processing based on the preset resource conflict detection model on the network load fluctuation characteristics and the service priority identifier to generate a resource competition hotspot area identifier and conflict mitigation suggestion parameters includes: Determine the network resource saturation time period and idle time period according to the bandwidth occupancy change curve in the network load fluctuation characteristics, and associate them with the corresponding terminal device identifiers; Extracting the urgent task mark and the task dependency chain in the business priority identifier to generate a task execution sequence constraint set; Performing a spatiotemporal overlap analysis on the network resource saturation time period and the task execution sequence constraint set to identify resource contention conflict periods and affected task sets; Based on a preset conflict resolution algorithm, the network bandwidth allocation strategy during the resource competition conflict period is simulated to generate multiple candidate bandwidth allocation schemes; Based on the impact of the candidate bandwidth allocation schemes on the processing delay of the affected task set, a target bandwidth allocation scheme with the smallest delay increment is selected, and its bandwidth adjustment parameters are extracted as conflict mitigation recommendation parameters; The resource competition conflict period and the corresponding terminal device identifier are encapsulated as a resource competition hotspot area identifier.

4. The method according to claim 1, wherein The method of performing real-time policy matching processing on the resource adaptation feature set based on a preset resource adaptation rule library to generate a computing resource configuration policy set includes: Extracting historical change trends of load balancing features of terminal devices from the resource adaptation feature set and matching them to the load balancing rule group in the resource adaptation rule base; activating a conflict avoidance rule group in the resource adaptation rule library according to the number of task processing conflicts and the resource preemption frequency in the business demand conflict characteristics; Calling the resource utilization curve and task completion rate index in the resource allocation efficiency evaluation index, performing weight allocation processing on the load balancing rule group and the conflict avoidance rule group, and generating a rule combination priority ranking; Based on the rule combination priority sorting, a joint optimization calculation is performed on the load balancing characteristics of the terminal device and the service demand conflict characteristics to generate an initial version of the resource allocation priority adjustment strategy; Performing policy effectiveness verification on the initial version through a simulated execution environment, optimizing the resource allocation priority adjustment policy and generating a final version based on the resource utilization improvement and conflict resolution efficiency in the verification results; The final version is subjected to a policy coordination check with the task scheduling optimization policy and the network bandwidth reallocation policy to generate a computing resource configuration policy set that passes the consistency check.

5. The method according to claim 4, characterized in that The performing of policy effectiveness verification on the initial version through a simulated execution environment includes: Constructing a simulation execution environment including a current terminal device state image and a task queue, and loading an initial version of the resource allocation priority adjustment strategy; Injecting historical network load fluctuation data and sudden task request data into the simulated execution environment to trigger policy execution and record resource allocation process logs; Extracting task processing delay variation, computing core occupancy fluctuation range, and memory leak anomalies from the process log; Comparing the task processing delay variation with a preset delay tolerance threshold to generate a delay compliance evaluation result; Determining a resource allocation stability score based on the fluctuation range of the computing core occupancy rate, and generating a resource health index in combination with the frequency of occurrence of the memory leak anomaly event; The delay compliance assessment results, resource allocation stability score and resource health index are comprehensively considered to calculate the comprehensive effectiveness score of the strategy, and the parameters of the initial version are tuned according to the score results.

6. The method according to claim 1, wherein The step of performing an adjustment operation on the computing resources of the power wireless terminal according to the computing resource configuration strategy set to generate a resource allocation verification result and terminal operation performance feedback data includes: Parsing the resource allocation priority adjustment policy in the computing resource configuration policy set to determine a list of terminal devices to be adjusted and corresponding computing core reallocation parameters; According to the task dependency chain in the task scheduling optimization strategy, the current task queue is sequentially reconstructed to generate an optimized task execution sequence table; Based on the bandwidth adjustment ratio in the network bandwidth reallocation strategy, the network interface configuration of the target terminal device is modified in real time, and changes in network throughput are monitored; After performing the adjustment operation, the real-time resource utilization rate, task processing completion rate and network transmission error rate of the terminal device are collected to generate the resource allocation verification result; The resource allocation verification result is compared and analyzed with the historical performance data before adjustment, and the resource utilization improvement ratio and task processing acceleration ratio are extracted and packaged into terminal operation performance feedback data.

7. The method according to claim 6, characterized in that The step of sequentially reconstructing the current task queue according to the task dependency chain in the task scheduling optimization strategy to generate an optimized task execution sequence table includes: Identifying critical path task nodes and non-critical path task nodes in the task dependency chain, and adding priority execution identifiers to the critical path task nodes; According to the urgent task mark in the business priority identifier, tasks with the same priority are secondary sorted according to the processing delay constraint; Detect circular dependency chains in the task queue and break them by inserting virtual synchronization nodes; Based on the resource demand characteristics of task processing, tasks occupying the same computing resources are separated into time slices to generate a task time slice distribution map without resource conflicts. According to the priority execution identifier, the secondary sorting result and the time slice distribution map, the task queue is reconstructed and an optimized task execution sequence table is generated.

8. The method according to claim 1, characterized in that The continuously comparing the terminal operation performance feedback data with a preset performance optimization threshold, and iteratively updating the resource adaptation rule base according to the comparison result, includes: Extracting a resource utilization improvement ratio from the terminal operation performance feedback data, and comparing it with the lowest improvement threshold among the performance optimization thresholds; If the resource utilization improvement ratio is lower than the minimum improvement threshold, the rule base diagnosis mode is activated to perform anomaly detection on the load balancing rule group in the resource adaptation rule base; Extract historical records of rule matching failure events and analyze the matching deviation between the rule triggering conditions and the current terminal device status; Adaptively calibrating the condition threshold in the load balancing rule group according to the matching deviation to generate an updated load balancing rule group; The updated load balancing rule group is re-injected into the resource adaptation rule base, and the rule base diagnostic mode is closed.

9. The method according to claim 8, characterized in that The performing anomaly detection on the load balancing rule group in the resource adaptation rule base includes: Count the number of times the load balancing rule group is triggered and successfully executed within the preset time period, and calculate the rule execution success rate; If the rule execution success rate is lower than a preset success rate threshold, it is determined that there is an abnormality in the load balancing rule group; Extract terminal device load data and resource allocation logs during the abnormal time period to identify unsuccessfully executed rule conditions and corresponding device status characteristics; Dividing the device status characteristics into multiple abnormal scenario categories through cluster analysis, and generating rule supplementary conditions for each abnormal scenario category; The rule supplementary condition is added to the original load balancing rule group to form an expanded load balancing rule group.

10. A computer system, characterized in that: include: a memory storing a computer program; A processor, configured to load the computer program to implement the method for dynamically configuring computing resources management based on a power wireless terminal according to any one of claims 1 to 9.

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