Thread pool configuration method and device

Abstract

The present invention discloses a method and device for configuring a thread pool, and relates to the field of computer technology. The method comprises the following steps: obtaining operating status parameters of a target thread pool; inputting the operating status parameters of the target thread pool into a thread pool monitoring index generation neural network model to obtain a target thread pool monitoring index of the target thread pool; matching the target thread pool monitoring index with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy of the target thread pool that match the target thread pool monitoring index; and performing real-time parameter adjustment on the target thread pool based on the index value of the target thread pool monitoring index obtained through real-time monitoring, using the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount, and the task allocation form corresponding to the index value. The present invention is used to realize the automation of thread configuration and improve the response speed and flexibility of thread pool configuration.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a configuration method and device for a thread pool. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] In modern software systems, thread pools, as an effective concurrent execution mechanism, are widely used to improve program efficiency and resource utilization. Traditional thread pool monitoring methods, such as Java's ThreadPoolExecutor and Spring's TaskExecutor, while providing basic management and monitoring capabilities, often focus on tracking common performance metrics such as thread pool size, task queue length, and the number of active threads. With increasing business complexity, traditional monitoring solutions are showing significant limitations, particularly in scenarios such as financial risk control intelligent decision-making engines, which place stringent requirements on performance and stability.

[0004] Especially in the context of intelligent risk control decision-making engines, the diverse and dynamic nature of tasks requires monitoring systems to not only monitor the operational status of thread pools in real time but also dynamically adjust resource allocation based on real-time load conditions. Existing monitoring tools, such as Prometheus and Grafana, while providing some performance analysis, fall short when it comes to meeting the customized requirements of specific scenarios.

[0005] 1. The standard performance indicators provided by existing thread pool monitoring solutions cannot meet the special business needs of risk control intelligent decision-making engines. They lack support for generating more specific monitoring indicators that are more responsive to the operation of different thread pools.

[0006] 2. The existing monitoring solution lacks intelligent tuning capabilities and cannot dynamically adjust the thread pool size and task scheduling strategy based on actual load, making it difficult to cope with the dynamically changing business needs of the risk control engine. Summary of the Invention

[0007] An embodiment of the present invention provides a thread pool configuration method for automatically executing resource adjustment and task allocation strategies based on real-time monitoring data, thereby automating thread configuration, improving the response speed and flexibility of thread pool configuration, and enhancing the intelligence level and operation and maintenance efficiency of thread pool configuration. The method includes:

[0008] Obtaining the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity, and thread pool volume;

[0009] Inputting the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results;

[0010] The target thread pool monitoring indicator is matched with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: an association relationship between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; the resource adjustment strategy includes different values ​​of the thread pool monitoring indicator, an associated thread pool capacity adjustment amount, and an associated thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring indicator, an associated task allocation amount, and a task allocation form;

[0011] Based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, the target thread pool is adjusted in real time according to the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0012] An embodiment of the present invention further provides a thread pool configuration device for automatically executing resource adjustment and task allocation strategies based on real-time monitoring data, thereby automating thread configuration, improving the response speed and flexibility of thread pool configuration, and enhancing the intelligence level and operation and maintenance efficiency of thread pool configuration. The device includes:

[0013] An operating status parameter acquisition module is used to obtain operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume;

[0014] A thread pool monitoring indicator determination module is configured to input the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator for the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks, and abnormal task processing results;

[0015] A resource adjustment strategy and task allocation strategy determination module is used to match the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different values ​​of the thread pool monitoring indicator, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring indicator, the associated task allocation amount and the task allocation form;

[0016] A real-time parameter adjustment module is used to adjust the parameters of the target thread pool in real time based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, using the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0017] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned thread pool configuration method when executing the computer program.

[0018] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned thread pool configuration method.

[0019] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned thread pool configuration method.

[0020] In the embodiment of the present invention, the operating status parameters of the target thread pool are obtained; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume; the operating status parameters of the target thread pool are input into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on the thread monitoring indicator historical data; the thread monitoring indicator historical data includes the operating status parameters of different thread pools and the historical data of the thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, abnormal The number of tasks and the results of abnormal task processing; matching the target thread pool monitoring index with the preset thread pool load adjustment intelligent map to obtain the target resource adjustment strategy and target task allocation strategy of the target thread pool that matches the target thread pool monitoring index; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different values ​​of the thread pool monitoring index, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring index, the associated task allocation amount and task allocation form; the target thread pool monitoring index obtained based on real-time monitoring The target indicator value is used to adjust the target thread pool parameters in real time based on the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy. By building a neural network model based on historical data training, more accurate and targeted monitoring indicators can be generated. These indicators are more in line with the special business needs of the risk control intelligent decision-making engine; using the constructed thread pool load adjustment intelligent map, efficient mapping between monitoring indicators and resource adjustment strategies and task allocation strategies can be achieved, and the optimal adjustment strategy can be automatically matched according to real-time monitoring data, such as dynamic adjustment of thread pool capacity and core thread number, as well as intelligent allocation. tasks to ensure that the system can maintain the best operating state when facing different loads; in addition, based on real-time monitoring data, resource adjustment and task allocation strategies are automatically executed without human intervention, which greatly improves the response speed and flexibility of thread pool configuration, and improves the applicability of risk control intelligent decision-making engines that have extremely high requirements for performance and stability; it effectively solves the performance bottlenecks and resource waste problems that traditional solutions may encounter when facing high loads, improves the overall stability and processing efficiency of the system, thereby better supporting the efficient operation of the risk control intelligent decision-making engine, ensuring business continuity and risk controllability, solving the specific needs of the risk control intelligent decision-making engine in monitoring and resource management, and significantly improving the intelligence level and operation and maintenance efficiency of the thread pool configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 Schematic diagram of a thread pool configuration method according to an embodiment of the present invention;

[0023] Figure 2 This is a specific example diagram of a thread pool configuration method according to an embodiment of the present invention;

[0024] Figure 3 This is a specific example diagram of a thread pool configuration method according to an embodiment of the present invention;

[0025] Figure 4 This is a specific example diagram of a thread pool configuration method according to an embodiment of the present invention;

[0026] Figure 5 This is a structural diagram of a thread pool configuration device according to an embodiment of the present invention;

[0027] Figure 6 Schematic diagram of a computer device used for configuring a thread pool in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0029] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0030] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps therein is not limited and can be appropriately adjusted as needed.

[0031] The acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. The information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with the relevant laws, regulations and standards of relevant countries and regions, and necessary confidentiality measures are taken, which do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse. In addition, this application provides users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making. If the user chooses to refuse, they can enter the expert decision-making process.

[0032] In modern software systems, thread pools, as an effective concurrent execution mechanism, are widely used to improve program efficiency and resource utilization. Traditional thread pool monitoring methods, such as Java's ThreadPoolExecutor and Spring's TaskExecutor, while providing basic management and monitoring capabilities, often focus on tracking common performance metrics such as thread pool size, task queue length, and the number of active threads. With increasing business complexity, traditional monitoring solutions are showing significant limitations, particularly in scenarios such as financial risk control intelligent decision-making engines, which place stringent requirements on performance and stability.

[0033] Especially in the context of intelligent risk control decision-making engines, the diverse and dynamic nature of tasks requires monitoring systems to not only monitor the operational status of thread pools in real time but also dynamically adjust resource allocation based on real-time load conditions. Existing monitoring tools, such as Prometheus and Grafana, while providing some performance analysis, fall short when it comes to meeting the customized requirements of specific scenarios.

[0034] 1. The standard performance indicators provided by existing thread pool monitoring solutions cannot meet the special business needs of risk control intelligent decision-making engines. They lack support for generating more specific monitoring indicators that are more responsive to the operation of different thread pools.

[0035] 2. The existing monitoring solution lacks intelligent tuning capabilities and cannot dynamically adjust the thread pool size and task scheduling strategy based on actual load, making it difficult to cope with the dynamically changing business needs of the risk control engine.

[0036] Specifically, in the current technological landscape, several thread pool monitoring solutions exist, such as Java's ThreadPoolExecutor and the Spring Framework's TaskExecutor. These solutions provide basic thread pool management and monitoring capabilities, meeting the needs of common business scenarios. Additionally, third-party monitoring tools, such as Prometheus and Grafana, can be used for thread pool monitoring and performance analysis.

[0037] Existing thread pool monitoring solutions usually provide some basic performance indicators, such as thread pool size, task queue length, and number of active threads. However, the special business needs of risk control intelligent decision-making engines may require more customized monitoring indicators, such as task execution time distribution and abnormal task statistics. Existing solutions are difficult to meet these customized requirements.

[0038] Existing thread pool monitoring solutions usually lack intelligent tuning mechanisms and are unable to adaptively adjust the thread pool size and task scheduling strategy according to real-time load conditions, and cannot effectively respond to the dynamically changing business needs of risk control intelligent decision-making engines.

[0039] The visualization capabilities and alarm mechanisms of existing monitoring tools are relatively simple, making it difficult to provide intuitive and comprehensive monitoring information display and timely and accurate alarm notifications, and the high reliability and stability requirements for risk control intelligent decision-making engines are insufficient.

[0040] To sum up, the existing thread pool monitoring solution has some obvious defects and shortcomings when facing the special needs of the risk control intelligent decision-making engine. These problems need to be improved and optimized to improve the applicability and effectiveness of thread pool monitoring.

[0041] In order to solve the above problems, the embodiment of the present invention provides a thread pool configuration method for automatically executing resource adjustment and task allocation strategies based on real-time monitoring data, realizing the automation of thread configuration, improving the response speed and flexibility of thread pool configuration, and enhancing the intelligence level and operation and maintenance efficiency of thread pool configuration. Figure 1 , the method may include:

[0042] Step 101: Obtaining the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity, and thread pool volume;

[0043] Step 102: Inputting the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator for the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results;

[0044] Step 103: Match the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy of the target thread pool that match the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: an association relationship between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; the resource adjustment strategy includes different values ​​of the thread pool monitoring indicator, an associated thread pool capacity adjustment amount, and an associated thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring indicator, an associated task allocation amount, and a task allocation form;

[0045] Step 104: Based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, the target thread pool is adjusted in real time according to the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0046] In the embodiment of the present invention, the operating status parameters of the target thread pool are obtained; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume; the operating status parameters of the target thread pool are input into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on the thread monitoring indicator historical data; the thread monitoring indicator historical data includes the operating status parameters of different thread pools and the historical data of the thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, abnormal The number of tasks and the results of abnormal task processing; matching the target thread pool monitoring index with the preset thread pool load adjustment intelligent map to obtain the target resource adjustment strategy and target task allocation strategy of the target thread pool that matches the target thread pool monitoring index; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different values ​​of the thread pool monitoring index, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring index, the associated task allocation amount and task allocation form; the target thread pool monitoring index obtained based on real-time monitoring The target indicator value is used to adjust the target thread pool parameters in real time based on the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy. By building a neural network model based on historical data training, more accurate and targeted monitoring indicators can be generated. These indicators are more in line with the special business needs of the risk control intelligent decision-making engine; using the constructed thread pool load adjustment intelligent map, efficient mapping between monitoring indicators and resource adjustment strategies and task allocation strategies can be achieved, and the optimal adjustment strategy can be automatically matched according to real-time monitoring data, such as dynamic adjustment of thread pool capacity and core thread number, as well as intelligent allocation. tasks to ensure that the system can maintain the best operating state when facing different loads; in addition, based on real-time monitoring data, resource adjustment and task allocation strategies are automatically executed without human intervention, which greatly improves the response speed and flexibility of thread pool configuration, and improves the applicability of risk control intelligent decision-making engines that have extremely high requirements for performance and stability; it effectively solves the performance bottlenecks and resource waste problems that traditional solutions may encounter when facing high loads, improves the overall stability and processing efficiency of the system, thereby better supporting the efficient operation of the risk control intelligent decision-making engine, ensuring business continuity and risk controllability, solving the specific needs of the risk control intelligent decision-making engine in monitoring and resource management, and significantly improving the intelligence level and operation and maintenance efficiency of the thread pool configuration.

[0047] During specific implementation, first perform step 101: obtain the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume. Obtain the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume.

[0048] In the embodiment, in the present invention, obtaining the operating status parameters of the target thread pool is the cornerstone of the entire automatic configuration and optimization process. This step is intended to fully understand the current operating status of the thread pool and ensure the accuracy and effectiveness of subsequent adjustment strategies. Specifically, the operating status parameters carefully select the following key indicators:

[0049] Core Thread Count: This parameter represents the number of threads in the thread pool that remain active at all times, even when idle. This parameter directly impacts system responsiveness and resource usage balance, making it crucial for evaluating the fundamental performance of the thread pool.

[0050] Thread Pool Activity Level: This is a comprehensive indicator of the thread pool's workload, typically measured by the ratio of active threads to total threads. High activity indicates that the thread pool is executing tasks efficiently, while low activity may indicate underutilized resources or a task scheduling bottleneck.

[0051] ThreadPool Queue Capacity: Specifies the size of the queue used to temporarily store pending tasks in the thread pool. This directly impacts the smoothness of task scheduling and system response time. A queue that is too small can easily lead to task overflow, while a queue that is too large can hide potential performance issues.

[0052] Thread Pool Volume: The term "volume" generally refers to the maximum workload a thread pool can handle, taking into account both immediate processing capacity and the buffer queue. It reflects the overall size of the thread pool and its upper limit on its processing capacity.

[0053] To accurately capture these parameters, the present invention utilizes technologies such as an event-driven architecture and real-time stream processing to ensure data timeliness and accuracy. Real-time monitoring and analysis of these parameters provides solid data support for subsequent intelligent decision-making, enabling the thread pool to be flexibly adjusted based on actual operational conditions, optimizing resource allocation and improving system efficiency.

[0054] In one embodiment, obtaining the operating status parameters of the target thread pool includes:

[0055] Use event-driven architecture and real-time stream processing technology to establish a real-time data pipeline for the thread pool;

[0056] Based on the events of thread pool state changes collected and analyzed in the real-time data pipeline, the operating status parameters of the target thread pool are obtained.

[0057] In the above embodiment, an event-driven architecture and real-time stream processing technologies (such as Apache Kafka integrated with Apache Flink or Spark Streaming) are used to establish a real-time data pipeline to collect and analyze events related to thread pool state changes. This real-time processing capability ensures immediate response to thread pool parameters, such as capturing thread pool state changes through event listeners and taking immediate action to adjust the configuration.

[0058] During specific implementation, step 101 is performed: obtaining the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume. After obtaining the operating status parameters of the target thread pool, step 102 is performed: inputting the operating status parameters of the target thread pool into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on the thread monitoring indicator historical data; the thread monitoring indicator historical data includes the operating status parameters of different thread pools and the historical data of the thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results.

[0059] In an embodiment, in order to more accurately evaluate the performance of the target thread pool and predict its future trend, the collected operating status parameters are input into a specially designed thread pool monitoring indicator generation neural network model to generate target thread pool monitoring indicators with high predictive value.

[0060] The neural network model is built on extensive and in-depth historical data on thread monitoring metrics. This data covers a wide range of thread pool operating parameters under different operating conditions, as well as monitoring records of corresponding thread pool metrics, including but not limited to key metrics such as thread pool utilization, thread pool capacity limit, task execution time, task queue time, number of abnormal tasks, and the results of abnormal task handling. This detailed historical data enables the model to learn and understand the complex patterns and correlations in thread pool behavior under different parameter configurations.

[0061] The neural network model training process is an iterative optimization journey. Machine learning algorithms such as backpropagation and gradient descent are used to fine-tune the pre-set neural network structure, ensuring that the model can extract effective patterns from historical data and predict future states. This model not only reflects the current thread pool status in real time, but more importantly, it can predict potential stress points in the thread pool based on historical trends and current parameters, such as potential overload risks, inefficiencies, or exception handling requirements, providing a scientific basis for dynamic strategy adjustments.

[0062] In one embodiment, Figure 2 As shown, the thread pool monitoring indicator generation neural network model is trained and verified as follows, including:

[0063] Step 201: Clean, standardize, and normalize the collected thread monitoring indicator historical data to obtain processed data;

[0064] Step 202: Based on feature engineering technology, extract features from the processed data to obtain a feature data set;

[0065] Step 203: Using a deep learning framework, based on the feature data set, a preset variational autoencoder neural network model is trained to obtain a trained thread pool monitoring indicator generation neural network model;

[0066] Step 204: Using the feature data set as a verification data set, the trained thread pool monitoring indicator generation neural network model is verified; and a verified thread pool monitoring indicator generation neural network model is obtained.

[0067] In the above embodiment, to build an intelligent model that accurately predicts thread pool monitoring indicators, the present invention adopts a rigorous training and validation process, aiming to improve the model's generalization ability and prediction accuracy through deep learning technology. This process specifically includes the following key steps:

[0068] 1. Data Preprocessing: First, a large amount of historical thread monitoring indicator data collected from the actual operating environment is meticulously cleaned to remove outliers and fill in missing items to ensure data purity and integrity. Subsequently, standardization and normalization are implemented to adjust the data ranges of various indicators and eliminate dimensionality effects, enabling effective comparison and analysis of data from different dimensions on a unified scale. This series of operations ultimately generates high-quality processed data, laying the foundation for subsequent model training.

[0069] 2. Feature Engineering and Dataset Construction: Based on data processing, feature engineering techniques are used to deeply explore the internal structure and potential correlations of the data. Scientific methods are used to screen and construct a set of features that can highly characterize thread pool behavior patterns. These features are integrated into a feature dataset, which not only significantly reduces data dimensionality but also enhances the relevance and efficiency of model learning.

[0070] 3. Neural Network Model Training: Using an advanced deep learning framework, a pre-set Variational Autoencoder (VAE) neural network model is meticulously trained based on a constructed feature dataset. VAEs, due to their unique structural design, can simultaneously learn from data distributions and generate new samples, making them ideally suited to the needs of this invention. During training, model parameters are continuously adjusted through an iterative optimization algorithm, striving to ensure that the model accurately captures the complex variations in thread pool monitoring metrics and is capable of generating future metric trends.

[0071] 4. Model Validation and Optimization: To evaluate the model's predictive performance and stability, the aforementioned feature dataset was split into a training set and a validation set. After training, the trained neural network model generated by the thread pool monitoring metrics was rigorously validated using the validation dataset. This step includes, but is not limited to, calculating prediction errors, evaluating the model's generalization capabilities, and performing necessary model tuning based on the validation results. Only when the model demonstrates good predictive accuracy and stability in the validation set is it considered validated and officially qualified for use in actual monitoring metric predictions.

[0072] Through the above steps, the present invention ensures that the generated thread pool monitoring indicator neural network model can not only accurately reflect the characteristics of historical data, but also effectively predict future trends.

[0073] During specific implementation, after performing step 102: inputting the operating status parameters of the target thread pool into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool, perform step 103: matching the target thread pool monitoring indicator with the preset thread pool load adjustment intelligent map to obtain the target resource adjustment strategy and target task allocation strategy of the target thread pool that matches the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different numerical values ​​of the thread pool monitoring indicator, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different numerical values ​​of the thread pool monitoring indicator, the associated task allocation amount and the task allocation form.

[0074] In this embodiment, to achieve efficient management and optimization of a specific target thread pool, the acquired target thread pool monitoring indicators are precisely matched with a constructed thread pool load adjustment intelligent graph, thereby automatically generating a corresponding target resource adjustment strategy and target task allocation strategy. This intelligent graph, as a high-level knowledge framework, systematically summarizes the inherent connections between different thread pool monitoring indicators and their corresponding optimal resource allocation and task deployment solutions, ensuring the accuracy and practicality of policy recommendations.

[0075] The core of the thread pool load adjustment intelligent graph lies in mapping the complex relationships between various thread pool monitoring metrics (such as thread pool utilization, capacity limits, task execution and queuing times, etc.) and specific resource adjustment strategies (including dynamic increases and decreases in thread pool capacity and flexible adjustments to the number of core threads) and task allocation strategies (including the number and mode of task allocation). This relationship is established through data-driven analysis based on extensive operational examples and optimization experience, ensuring that any given combination of monitoring metrics can find the most appropriate resource allocation and task scheduling path.

[0076] Specifically, in terms of resource adjustment strategies, the intelligent graph defines precise adjustments to thread pool capacity and core thread count based on the specific numerical ranges of monitoring indicators. This approach directly responds to the current load status of the thread pool, preventing waste caused by excess resources while ensuring rapid capacity expansion under high loads and maintaining stable system performance. The task allocation strategy is even more sophisticated, considering the impact of subtle changes in monitoring indicators on task allocation efficiency. It matches the corresponding task allocation amount and the optimal allocation form to maximize throughput and minimize latency.

[0077] Through this intelligent matching process, the present invention can not only respond to the dynamic changes of the thread pool in real time, but also proactively plan the optimal layout of resources and tasks, significantly improving the adaptability, stability and overall efficiency of the system.

[0078] In one embodiment, Figure 3 As shown, the thread pool load adjustment intelligent map is established as follows, including:

[0079] Step 301: Establish a training data set based on the collected thread pool load adjustment historical data; the thread pool load adjustment historical data includes: thread pool capacity adjustment amount, thread pool core thread number adjustment amount, task allocation amount and task allocation form associated with different values ​​of thread pool monitoring indicators;

[0080] Step 302: Based on the training data set, generate nodes and edges of a thread pool load adjustment intelligent graph; the nodes are different monitoring indicators, resource adjustment strategies, and task allocation strategies; the edges are edges connecting different monitoring indicators and different resource adjustment strategies and task allocation strategies;

[0081] Step 303: establishing associations between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies based on the nodes and edges and relevant expert experience;

[0082] Step 304: Based on graph neural network technology, an intelligent graph for thread pool load adjustment is established according to the correlation between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies.

[0083] Here is an example of building an intelligent graph for thread pool load adjustment:

[0084] 1. Historical Data Collection and Training Dataset Construction: First, we systematically collected and organized historical data on thread pool load adjustments. This data covered specific cases of thread pool capacity adjustments, core thread count adjustments, task allocations, and task allocation methods under different monitoring metrics. Through meticulous analysis of this data, we constructed a comprehensive and representative training dataset, laying a solid foundation for subsequent model training. Monitoring metrics included key parameters such as thread activity, wait queue length, and task execution time.

[0085] 2. Node and Edge Definition of the Intelligent Graph: Based on this foundation, the core structure of the thread pool load adjustment intelligent graph was constructed using the training dataset. The graph consists of nodes and edges. Nodes represent different monitoring indicators (such as the aforementioned parameters), resource adjustment strategies (such as increasing or decreasing thread pool capacity or adjusting the number of core threads), and task allocation strategies (such as task balancing or priority allocation). Edges connect monitoring indicators with corresponding resource adjustment strategies and task allocation strategies, intuitively demonstrating the logical dependencies and impact relationships between them.

[0086] 3. Establishing Relationships: By combining the nodes and edges of the graph and integrating the experience and knowledge of domain experts, we further deepen and refine the relationships between nodes. This process not only relies on the statistical patterns of historical data but also incorporates experts' intuition and insights into strategic choices in specific situations, ensuring that the graph reflects the deep integration of best practices with actual business scenarios.

[0087] 4. Application of Graph Neural Network Technology: Finally, advanced graph neural network technology is used to perform deep learning processing on the constructed intelligent graph. Graph neural networks capture the characteristics of nodes in the graph and the complex relationship patterns between them. Using node feature vectors and edge weights, they learn and infer the implicit connections between different monitoring metrics and resource adjustment and task allocation strategies. Through iterative training, the model gradually learns how to predict and recommend the most appropriate adjustment plan based on the current monitoring metric values, thereby achieving the goal of accurately optimizing thread pool load.

[0088] Through the above steps, the present invention successfully constructs a highly intelligent and adaptive thread pool load adjustment intelligent map, which can not only reflect the system status in real time, but also proactively guide the adjustment of thread pool parameters, significantly improving the system's operating efficiency and stability.

[0089] In one embodiment, it further includes:

[0090] Based on the real-time collected operating status parameters of the target thread pool and the data of real-time parameter adjustment of the target thread pool, the association weights and node attributes in the thread pool load adjustment intelligent map are adjusted to obtain an optimized thread pool load adjustment intelligent map.

[0091] In this embodiment, the target thread pool's operating status parameters are continuously collected, including but not limited to key indicators such as thread activity, task wait time, execution efficiency, and resource utilization. Furthermore, the details of each parameter adjustment operation are recorded, including the timing of the adjustment, the adjustment range, and the system feedback data after the adjustment, forming a real-time adjustment data sequence.

[0092] Leveraging this real-time data, we developed an algorithm that dynamically adjusts the weights of relationships within the intelligent graph based on the current thread pool's operational status and the effects of historical parameter adjustments. Specifically, the algorithm analyzes deviations between new data and the existing graph model, identifying node links that need strengthening or weakening—the logical connection between monitoring indicators and adjustment strategies. Based on this information, the algorithm updates edge weights to more accurately reflect the impact strength in real-world situations.

[0093] While adjusting weights, the algorithm also intelligently updates node attributes in the graph based on the latest operational status. These revisions take into account new normal values ​​for monitoring indicators, newly discovered efficient resource adjustment strategies, and the effectiveness of task allocation models, ensuring that every node in the graph reflects the latest and optimal operational guidance.

[0094] Through the iterative calculations and optimization processes described above, the original thread pool load adjustment intelligent map has evolved into an optimized version. This optimized map not only inherits the structural framework of the original map but also incorporates the essence of real-time operation data, making the decision paths in the map more precise, enabling rapid response to system changes and providing more personalized and efficient adjustment suggestions for thread pool management.

[0095] During specific implementation, after performing step 103: matching the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map, and obtaining the target resource adjustment strategy and target task allocation strategy of the target thread pool that match the target thread pool monitoring indicator, perform step 104: based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, perform real-time parameter adjustment on the target thread pool with the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0096] In the embodiment, an efficient and automated thread pool management mechanism is implemented for dynamically changing system environments and workloads, with particular emphasis on refined parameter control based on real-time monitoring data. Specifically, the mechanism covers the following core aspects:

[0097] 1. Real-time Monitoring and Data Collection: The system continuously monitors the target thread pool's operating status, collecting key metrics including but not limited to thread pool capacity utilization, core thread activity, task queue length, and task execution time. These metrics reflect the thread pool's current workload and resource allocation efficiency, forming the basis for subsequent adjustments.

[0098] 2. Intelligent Policy Matching and Analysis: Based on the target thread pool monitoring metric values ​​obtained from the monitoring phase, the system applies a pre-defined database of target resource adjustment strategies and target task allocation strategies. This database contains a rich set of policy rules, each of which specifies the optimal thread pool capacity adjustment, core thread count adjustment, task allocation, and task allocation method (e.g., balanced allocation, priority allocation, etc.) for different monitoring metric value ranges. Using an intelligent matching algorithm, the system quickly identifies the policy combination that best matches the current metric values.

[0099] 3. Dynamic Parameter Adjustment: Once a policy match is completed, the system immediately executes the corresponding parameter adjustments. This includes, but is not limited to, dynamically increasing or decreasing the total thread pool capacity to adapt to load fluctuations; adjusting the number of core threads to ensure efficient and stable basic processing capabilities; and flexibly adjusting the task allocation strategy and allocation amount based on the task queue status and task nature to optimize overall throughput and response time. All adjustments are performed in real time without affecting the normal operation of the system, enabling refined and intelligent management of the target thread pool.

[0100] 4. Feedback and Continuous Optimization: Adjusted parameter configurations and their impact on system performance are recorded and fed back to the monitoring and policy matching module, forming a closed-loop control system. Through continuous performance monitoring and effectiveness evaluation, the system continuously learns and optimizes policy rules, ensuring that adjustments are more aligned with actual operational needs, improving overall adaptability and efficiency.

[0101] In summary, the present invention has built a comprehensive and efficient thread pool management solution by integrating multiple key technical links such as real-time monitoring, intelligent strategy matching, dynamic parameter adjustment and feedback optimization, which effectively responds to the complex and changeable system load challenges and ensures high availability of services and efficient use of resources.

[0102] In one embodiment, the target thread pool monitoring indicator is matched with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy of the target thread pool that match the target thread pool monitoring indicator, including:

[0103] Based on the graph matching algorithm, according to the target thread pool monitoring indicators, the resource adjustment strategy and task allocation strategy with the highest degree of matching with the target thread pool monitoring indicators are found in the thread pool load adjustment intelligent graph as the target resource adjustment strategy and target task allocation strategy.

[0104] In order to achieve refined management and optimization of the target thread pool, the present invention proposes an innovative policy matching technology. This technology relies on a preset thread pool load adjustment intelligent map and an advanced graph matching algorithm to automatically identify and select the most suitable resource adjustment strategy and task allocation strategy. First, the target thread pool is comprehensively monitored to collect key performance indicators (KPIs), such as the number of active threads, task queue length, CPU and memory utilization, etc. These indicators are quantified using standardized methods to facilitate mathematical matching and comparison with the policy nodes in the intelligent map.

[0105] A sophisticated graph matching algorithm maps the quantified target thread pool monitoring metrics to the thread pool load adjustment intelligent graph. This algorithm deeply analyzes the complex correlations and dependencies between metrics. By calculating the similarity between the monitoring metric vector and the feature vectors of each policy node in the graph, it identifies the resource adjustment and task allocation strategies that best match the current thread pool state. This matching process not only considers the optimal solution for a single metric but also focuses on the synergistic effects of the overall strategy combination to ensure global optimization of strategy selection.

[0106] Based on the matching results output by the algorithm, the most closely matched set of resource adjustment and task allocation strategies is selected as the target strategy. This set of strategies directly addresses specific issues in the current thread pool, such as optimizing resource allocation by dynamically adjusting the number of threads or employing intelligent task scheduling mechanisms to reduce waiting times, thereby effectively improving the overall operating efficiency and responsiveness of the system.

[0107] In one embodiment, it further includes:

[0108] A visual monitoring interface is generated based on operating status parameters, target thread pool monitoring indicators, and the target resource adjustment strategy and target task allocation strategy of the target thread pool that match the target thread pool monitoring indicators; the visual monitoring interface is used to adopt data visualization technology to establish a customized monitoring view and data display dashboard, combined with the user's drag and drop selection of different information, to dynamically display the real-time changes of information in the monitoring interface.

[0109] In this embodiment, to enhance intuitive understanding and efficient management of the target thread pool's operating status, the present invention further proposes an innovative visual monitoring interface generation technology. This technology integrates operating status parameters, target thread pool monitoring indicators, and the resulting target resource adjustment strategy and target task allocation strategy. Through advanced data visualization methods, it provides users with a highly customized, interactive monitoring platform. Specific implementation details are described below:

[0110] First, the system integrates the target thread pool's real-time health parameters and monitoring indicators, including but not limited to key data such as thread activity, task completion rate, and system load, as well as detailed information on the target resource adjustment strategy and task allocation strategy determined by the aforementioned intelligent matching technology. This data is then cleaned and normalized to ensure it is suitable for subsequent data visualization needs.

[0111] Based on user needs and management habits, the system automatically generates customized monitoring views using dynamic layout algorithms and configurable templates. These views include various chart types, such as line charts, bar charts, and heat maps, to display multi-dimensional information such as thread pool operation trends, load distribution, and resource allocation efficiency. Users can freely select and organize these chart elements according to their actual needs to create personalized data display panels.

[0112] The developed visual monitoring interface incorporates highly interactive features, allowing users to instantly adjust the layout and content of the monitoring view with simple drag-and-drop operations, enabling rapid location and in-depth analysis of key information. The dashboard supports real-time updates to ensure the timeliness of monitoring data. It also provides features such as historical data comparison and abnormality alerts, helping users quickly identify and respond to potential issues in the system.

[0113] The monitoring interface utilizes advanced front-end technologies, such as Web Sockets and polling mechanisms, to ensure that all information displayed reflects real-time status changes of the target thread pool. When users drag and drop to select different monitoring items or adjust display dimensions, the system responds instantly, dynamically refreshing the view content. This allows users to intuitively perceive real-time dynamic changes in monitoring data, enabling more accurate and timely decision-making.

[0114] In summary, the visual monitoring interface generation technology provided by the present invention greatly improves the efficiency and convenience of target thread pool management by converting complex operating data and policy decisions into intuitive and easy-to-understand graphical displays, providing a powerful support tool for system operation and maintenance personnel, and ensuring the continuous optimization and stable operation of the system.

[0115] In the above embodiment, if Figure 4 As shown, it also includes:

[0116] Step 401: Establishing associations between preset alarm thresholds of different monitoring indicators and / or change trend thresholds between monitoring indicators and different alarm levels;

[0117] Step 402: When the target thread pool monitoring indicator meets the alarm threshold and / or the change trend threshold between monitoring indicators, an alarm message carrying the target thread pool monitoring indicator is issued in an alarm manner associated with the corresponding alarm level.

[0118] In this embodiment, to further enhance the proactiveness and efficiency of target thread pool monitoring, the present invention proposes a sophisticated alarm threshold configuration and dynamic response mechanism. This mechanism aims to scientifically establish preset alarm thresholds for different monitoring indicators and thresholds for changing trends between monitoring indicators, and closely associate these thresholds with different alarm levels. This allows for immediate identification of abnormal states in the target thread pool and tiered alarm notifications. The specific implementation plan is outlined below:

[0119] First, based on the characteristics of the target thread pool and analysis of historical operational data, a series of preset monitoring indicator alarm thresholds are set, including but not limited to upper and lower limits for key performance indicators such as thread pool capacity utilization, task wait time, and execution error rate. Furthermore, thresholds are defined for trends in monitoring indicators, such as abnormal growth or decline rates, to capture unusual changes in system status. These thresholds are designed to cover a wide range of scenarios that could impact system stability.

[0120] Based on the urgency and impact of monitored indicators, this invention defines multiple alert levels, such as minor, warning, severe, and emergency, each corresponding to a different response strategy and notification method. Through algorithmic logic, the system associates specific alert thresholds and trend thresholds with corresponding alert levels, ensuring that when a monitored indicator reaches or exceeds a set threshold, or when the trend between indicators exceeds a preset range, the corresponding level of alert response is automatically triggered.

[0121] During real-time monitoring, if any monitoring metric in the target thread pool meets the preset alarm conditions, the system will immediately initiate an alert based on the associated alarm level. This may include, but is not limited to, sending emails, SMS notifications, in-system pop-up alerts, and even directly invoking emergency communication interfaces for operations and maintenance personnel. The alert information not only includes the specific monitoring metric value that triggered the alert, but also provides trend analysis and preliminary troubleshooting suggestions, allowing the recipient to quickly assess the severity of the problem and take action.

[0122] The monitoring system of the present invention also has a built-in feedback learning mechanism, which can continuously optimize the settings of alarm thresholds and alarm strategies at various levels based on historical alarm records and actual processing effects, ensuring that the accuracy and effectiveness of the alarm mechanism improve over time, reducing the probability of false alarms and missed alarms, and improving the pertinence and efficiency of operation and maintenance responses.

[0123] In summary, the present invention realizes intelligent monitoring and graded alarms of target thread pool monitoring indicators by constructing a sophisticated alarm threshold system and a flexible dynamic response mechanism, effectively ensuring the stable operation and rapid problem response capabilities of the system.

[0124] A specific embodiment is given below to illustrate the specific application of the method of the present invention. In this embodiment, the specific implementation process of the thread pool configuration method is described in detail:

[0125] 1. Data collection stage:

[0126] Initialize data collection: Design and deploy a data collection mechanism that can periodically or in real time capture operational parameters from the target thread pool, including but not limited to the number of core threads, thread pool activity, thread pool queue capacity, and thread pool volume.

[0127] Record historical data of monitoring indicators: Continuously record these parameters and store them together with the corresponding thread pool monitoring indicators (such as thread pool utilization, thread pool capacity limit, task execution time, task queue time, number of abnormal tasks and processing results) to form a thread monitoring indicator historical database to prepare data for subsequent model training.

[0128] 2. Model training phase:

[0129] Build a neural network model: Design a thread pool monitoring indicator generation neural network model architecture, which can input the operating status parameters of the target thread pool and output the corresponding monitoring indicators.

[0130] Training the neural network model: Use the historical dataset of thread monitoring metrics to train the preset neural network model. Use the backpropagation algorithm to adjust the model parameters to minimize prediction error. This process may require multiple iterations until the model reaches the predetermined performance standard.

[0131] Verification and optimization: After training is completed, an independent validation set is used to test the generalization ability of the model, and the model is fine-tuned based on the test results to ensure the accuracy and robustness of the model.

[0132] 3. Intelligent graph construction stage:

[0133] Build an intelligent graph for thread pool load adjustment: Based on the output of the trained monitoring indicator model and historical experience, we construct an intelligent graph that associates various possible monitoring indicator values ​​with corresponding resource adjustment strategies (such as thread pool capacity adjustment and core thread number adjustment) and task allocation strategies (task allocation amount and task allocation form). The graph should flexibly reflect the optimal adjustment strategy for different business scenarios.

[0134] 4. Real-time adjustment and optimization stage:

[0135] Real-time calculation of monitoring indicators: Utilize the trained neural network model to calculate the monitoring indicators of the target thread pool in real time.

[0136] Intelligent graph matching: Input the real-time calculated monitoring indicator values ​​into the intelligent graph to find the resource adjustment strategy and task allocation strategy that best matches them.

[0137] Parameter adjustment implementation: Based on the matched strategy, the parameters of the target thread pool are adjusted in real time, including but not limited to dynamically adjusting the capacity of the thread pool, the number of core threads, and optimizing the distribution method and amount of tasks to adapt to the current system load and business needs.

[0138] Feedback and adaptive learning: After implementing adjustments, continue to monitor the running status and business performance of the thread pool, collect feedback data, and periodically update the intelligent graph and neural network model to achieve continuous optimization and adaptive learning.

[0139] Another specific embodiment is given below to illustrate the implementation of the above method of the present invention:

[0140] This embodiment addresses the shortcomings and deficiencies of existing thread pool monitoring solutions by providing an improved thread pool monitoring component. The technical problem to be solved by this invention is to overcome the problems of existing thread pool monitoring solutions, such as the lack of customized monitoring indicators, the lack of intelligent tuning mechanisms, and the limited visualization and alerting capabilities, by providing a thread pool monitoring solution suitable for risk control intelligent decision engines.

[0141] This embodiment aims to provide the following technical effects:

[0142] 1. Customized monitoring indicators: By designing and implementing more customized monitoring indicators, such as task execution time distribution and abnormal task statistics, we can meet the monitoring needs of the risk control intelligent decision-making engine under special business requirements.

[0143] 2. Intelligent tuning mechanism: Introducing an intelligent tuning mechanism to adaptively adjust the thread pool size and task scheduling strategy based on real-time load conditions, improve system stability and performance, and meet the dynamically changing business needs of the risk control intelligent decision-making engine.

[0144] 3. Improved visualization and alerting capabilities: Strengthen the visualization capabilities and alerting mechanisms of monitoring tools, provide intuitive and comprehensive monitoring information display and timely and accurate alert notifications, and meet the high reliability and stability requirements of the risk control intelligent decision-making engine.

[0145] The thread pool monitoring component of this embodiment overcomes the shortcomings of existing thread pool monitoring solutions and provides a more customized, intelligent, and reliable thread pool monitoring solution to meet the special needs of risk control intelligent decision engines. The complete technical solution provided by this invention is an improved thread pool monitoring component for use in risk control intelligent decision engines. This solution includes the following key technologies:

[0146] 1. Customized monitoring indicators:

[0147] 1. Design and implement customized monitoring indicators, such as task execution time distribution and abnormal task statistics, to meet the special business needs of the risk control intelligent decision-making engine.

[0148] 2. Add a monitoring module to the thread pool to collect and analyze the execution status of tasks such as intelligent decision-making and decision-making results entering the lake, which have low timeliness requirements, in real time, and generate corresponding monitoring indicators.

[0149] 2. Intelligent tuning mechanism:

[0150] 1. Introduce real-time tuning of the number of thread pool cores, dynamically adjust the thread pool size and task scheduling strategy based on real-time load conditions, and ensure rational resource usage:

[0151] (1) When the thread pool usage is full, the number of queues is not full, and the thread pool size has not reached the preset upper limit, adjust the thread pool size and the number of core threads to 1.5 times the original value. This allows the thread pool configuration to handle more tasks.

[0152] (2) When the thread pool usage is full, the number of queues is full, and the thread pool size has not reached the preset upper limit, adjust the thread pool size and the number of core threads to 1.5 times the original, and adjust the rejection strategy to discard. By discarding excessive tasks, more tasks can be processed while ensuring engine performance. Engine performance is crucial, and it is necessary to appropriately discard some tasks to ensure engine performance.

[0153] 2. Monitor system load, including the length of the task queue in the thread pool, the number of idle threads in the thread pool, etc., and trigger dynamic adjustments based on preset thresholds:

[0154] When the number of active threads in the thread pool reaches the preset value, the thread pool size and the number of core threads are adjusted according to the preset strategy. The thread pool configuration can be expanded to handle more tasks or reduced to save more resources.

[0155] 3. Improved visualization and alerting capabilities:

[0156] 1. Develop a monitoring tool interface to provide intuitive and comprehensive monitoring information display, including thread pool status, task execution status, monitoring indicators, etc., so that administrators and developers can understand the system status in real time.

[0157] 2. Set up an alarm mechanism to trigger alarm notifications in a timely and accurate manner by setting thresholds and rules, including SMS and email, so as to quickly respond to abnormal situations.

[0158] By implementing the above technical solution, we can improve the thread pool monitoring of the risk control intelligent decision engine and have the following advantages:

[0159] 1. Provide customized monitoring indicators to meet the special business needs of the risk control intelligent decision-making engine.

[0160] 2. Introduce an intelligent tuning mechanism to automatically adjust the thread pool size and task scheduling strategy to improve system stability and performance.

[0161] 3. The visual tool interface displays monitoring information, making it easier for administrators and developers to understand the system status in real time.

[0162] 4. Set up an alarm mechanism to promptly notify abnormal situations for quick response and processing.

[0163] In summary, the technical solution provided by the present invention can overcome the shortcomings of existing thread pool monitoring solutions and provide a more customized, intelligent and reliable thread pool monitoring solution for the risk control intelligent decision-making engine.

[0164] Of course, it is understandable that the above detailed process may have other variations, and all relevant variations should fall within the scope of protection of the present invention.

[0165] In the embodiment of the present invention, the operating status parameters of the target thread pool are obtained; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume; the operating status parameters of the target thread pool are input into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on the thread monitoring indicator historical data; the thread monitoring indicator historical data includes the operating status parameters of different thread pools and the historical data of the thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, abnormal The number of tasks and the results of abnormal task processing; matching the target thread pool monitoring index with the preset thread pool load adjustment intelligent map to obtain the target resource adjustment strategy and target task allocation strategy of the target thread pool that matches the target thread pool monitoring index; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different values ​​of the thread pool monitoring index, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring index, the associated task allocation amount and task allocation form; the target thread pool monitoring index obtained based on real-time monitoring The target indicator value is used to adjust the target thread pool parameters in real time based on the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy. By building a neural network model based on historical data training, more accurate and targeted monitoring indicators can be generated. These indicators are more in line with the special business needs of the risk control intelligent decision-making engine; using the constructed thread pool load adjustment intelligent map, efficient mapping between monitoring indicators and resource adjustment strategies and task allocation strategies can be achieved, and the optimal adjustment strategy can be automatically matched according to real-time monitoring data, such as dynamic adjustment of thread pool capacity and core thread number, as well as intelligent allocation. tasks to ensure that the system can maintain the best operating state when facing different loads; in addition, based on real-time monitoring data, resource adjustment and task allocation strategies are automatically executed without human intervention, which greatly improves the response speed and flexibility of thread pool configuration, and improves the applicability of risk control intelligent decision-making engines that have extremely high requirements for performance and stability; it effectively solves the performance bottlenecks and resource waste problems that traditional solutions may encounter when facing high loads, improves the overall stability and processing efficiency of the system, thereby better supporting the efficient operation of the risk control intelligent decision-making engine, ensuring business continuity and risk controllability, solving the specific needs of the risk control intelligent decision-making engine in monitoring and resource management, and significantly improving the intelligence level and operation and maintenance efficiency of the thread pool configuration.

[0166] The present invention also provides a thread pool configuration device, as described in the following embodiments. Since the principle of the device to solve the problem is similar to the thread pool configuration method, the implementation of the device can refer to the implementation of the thread pool configuration method, and the repeated parts will not be repeated.

[0167] The embodiment of the present invention also provides a thread pool configuration device for automatically executing resource adjustment and task allocation strategies based on real-time monitoring data, realizing the automation of thread configuration, improving the response speed and flexibility of thread pool configuration, and improving the intelligence level and operation and maintenance efficiency of thread pool configuration. Figure 5 As shown, the device includes:

[0168] The operating status parameter acquisition module 501 is used to obtain the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume;

[0169] The thread pool monitoring indicator determination module 502 is used to input the operating status parameters of the target thread pool into the thread pool monitoring indicator generation neural network model to obtain the target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on the thread monitoring indicator historical data; the thread monitoring indicator historical data includes the operating status parameters of different thread pools and the historical data of the thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results;

[0170] The resource adjustment strategy and task allocation strategy determination module 503 is used to match the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy of the target thread pool that match the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: the association relationship between different thread pool monitoring indicators and the corresponding resource adjustment strategy and task allocation strategy; the resource adjustment strategy includes different values ​​of the thread pool monitoring indicator, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring indicator, the associated task allocation amount and the task allocation form;

[0171] The real-time parameter adjustment module 504 is used to perform real-time parameter adjustment on the target thread pool based on the indicator value of the target thread pool monitoring indicator obtained through real-time monitoring, using the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0172] In one embodiment, obtaining the operating status parameters of the target thread pool includes:

[0173] Use event-driven architecture and real-time stream processing technology to establish a real-time data pipeline for the thread pool;

[0174] Based on the events of thread pool state changes collected and analyzed in the real-time data pipeline, the operating status parameters of the target thread pool are obtained.

[0175] In one embodiment, the thread pool monitoring metric generation neural network model is trained and validated as follows:

[0176] Clean, standardize, and normalize the collected historical data of thread monitoring indicators to obtain processed data;

[0177] Based on feature engineering technology, feature extraction is performed on the processed data to obtain a feature data set;

[0178] Using a deep learning framework, based on the feature data set, a preset variational autoencoder neural network model is trained to obtain a trained thread pool monitoring indicator generation neural network model;

[0179] The feature data set is used as a verification data set to verify the trained thread pool monitoring indicator generation neural network model; and a verified thread pool monitoring indicator generation neural network model is obtained.

[0180] In one embodiment, a thread pool load adjustment intelligent map is established as follows:

[0181] Establishing a training data set based on the collected thread pool load adjustment historical data; the thread pool load adjustment historical data includes: thread pool capacity adjustment amount, thread pool core thread number adjustment amount, task allocation amount and task allocation form associated with different values ​​of thread pool monitoring indicators;

[0182] Based on the training data set, nodes and edges of the thread pool load adjustment intelligent map are generated; the nodes are different monitoring indicators, resource adjustment strategies and task allocation strategies; the edges are edges connecting different monitoring indicators and different resource adjustment strategies and task allocation strategies;

[0183] Based on the nodes and edges, as well as relevant expert experience, establishing associations between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies;

[0184] Based on graph neural network technology, an intelligent graph for thread pool load adjustment is established according to the correlation between different thread pool monitoring indicators and the corresponding resource adjustment strategies and task allocation strategies.

[0185] In one embodiment, it further includes:

[0186] Based on the real-time collected operating status parameters of the target thread pool and the data of real-time parameter adjustment of the target thread pool, the association weights and node attributes in the thread pool load adjustment intelligent map are adjusted to obtain an optimized thread pool load adjustment intelligent map.

[0187] In one embodiment, the target thread pool monitoring indicator is matched with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy of the target thread pool that match the target thread pool monitoring indicator, including:

[0188] Based on the graph matching algorithm, according to the target thread pool monitoring indicators, the resource adjustment strategy and task allocation strategy with the highest degree of matching with the target thread pool monitoring indicators are found in the thread pool load adjustment intelligent graph as the target resource adjustment strategy and target task allocation strategy.

[0189] In one embodiment, it further includes:

[0190] A visual monitoring interface is generated based on operating status parameters, target thread pool monitoring indicators, and the target resource adjustment strategy and target task allocation strategy of the target thread pool that match the target thread pool monitoring indicators; the visual monitoring interface is used to adopt data visualization technology to establish a customized monitoring view and data display dashboard, combined with the user's drag and drop selection of different information, to dynamically display the real-time changes of information in the monitoring interface.

[0191] In one embodiment, it further includes:

[0192] Establish preset alarm thresholds for different monitoring indicators and / or change trend thresholds between monitoring indicators, and the association between them and different alarm levels;

[0193] When the target thread pool monitoring indicator meets the alarm threshold and / or the change trend threshold between monitoring indicators, an alarm message carrying the target thread pool monitoring indicator is issued in an alarm manner associated with the corresponding alarm level.

[0194] An embodiment of the present invention provides an embodiment of a computer device for implementing all or part of the content of the above-mentioned thread pool configuration method. The computer device specifically includes the following content:

[0195] A processor, a memory, a communications interface, and a bus; wherein the processor, the memory, and the communications interface communicate with each other via the bus; the communications interface is used to implement information transmission between related devices; the computer device can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the computer device can be implemented with reference to the embodiment of the embodiment for implementing the configuration method of the thread pool and the embodiment for implementing the configuration device of the thread pool, the contents of which are incorporated herein and repeated parts are not repeated.

[0196] Figure 6 1 is a schematic block diagram of the system structure of the computer device 1000 according to an embodiment of the present application. Figure 6 As shown, the computer device 1000 may include a central processor 1001 and a memory 1002; the memory 1002 is coupled to the central processor 1001. Figure 6 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0197] In one embodiment, the thread pool configuration function may be integrated into the central processing unit 1001. The central processing unit 1001 may be configured to perform the following control:

[0198] Obtaining the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity, and thread pool volume;

[0199] Inputting the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results;

[0200] The target thread pool monitoring indicator is matched with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator; the thread pool load adjustment intelligent map includes: an association relationship between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; the resource adjustment strategy includes different values ​​of the thread pool monitoring indicator, an associated thread pool capacity adjustment amount, and an associated thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of the thread pool monitoring indicator, an associated task allocation amount, and a task allocation form;

[0201] Based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, the target thread pool is adjusted in real time according to the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

[0202] In another embodiment, the thread pool configuration device can be configured separately from the central processing unit 1001. For example, the thread pool configuration device can be configured as a chip connected to the central processing unit 1001, and the thread pool configuration function is realized through the control of the central processing unit.

[0203] like Figure 6 As shown, the computer device 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 1000 does not necessarily have to include Figure 6 In addition, the computer device 1000 may also include all components shown in Figure 6 For components not shown, reference may be made to the prior art.

[0204] like Figure 6 As shown, the central processing unit 1001 is sometimes also referred to as a controller or an operation control unit, and may include a microprocessor or other processor device and / or logic device. The central processing unit 1001 receives inputs and controls the operations of various components of the computer device 1000 .

[0205] Memory 1002 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned device-related information and may also store programs that execute the relevant information. The CPU 1001 may execute the programs stored in memory 1002 to implement information storage or processing.

[0206] Input unit 1004 provides input to CPU 1001. Input unit 1004 may be, for example, a keypad or touch input device. Power supply 1007 is used to provide power to computer device 1000. Display 1006 is used to display objects such as images and text. This display may be, for example, an LCD display, but is not limited thereto.

[0207] The memory 1002 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 1002 may also be some other type of device. The memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 1022 for storing application programs and function programs or processes used by the central processing unit 1001 to execute the operations of the computer device 1000.

[0208] The memory 1002 may also include a data storage unit 1023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1024 of the memory 1002 may include various driver programs for the computer device for communication functions and / or for executing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0209] The communication module 1003 is a transmitter / receiver 1003 that sends and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the central processor 1001 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0210] Based on different communication technologies, multiple communication modules 1003 can be provided in the same computer device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010, thereby implementing common telecommunication functions. The audio processor 1005 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 1005 is coupled to the central processing unit 1001, enabling local recording via the microphone 1010 and playback of stored audio via the speaker 1009.

[0211] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned thread pool configuration method.

[0212] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned thread pool configuration method.

[0213] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0214] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0215] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0217] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A thread pool configuration method, characterized in that: include: Obtaining the operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity, and thread pool volume; Inputting the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator of the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks and abnormal task processing results; Matching the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator; The thread pool load adjustment intelligent map includes: the association between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; the resource adjustment strategies include different values ​​of thread pool monitoring indicators, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of thread pool monitoring indicators, the associated task allocation amount and task allocation form; Based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, the target thread pool is adjusted in real time according to the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

2. The method according to claim 1, wherein Get the operating status parameters of the target thread pool, including: Use event-driven architecture and real-time stream processing technology to establish a real-time data pipeline for the thread pool; Based on the events of thread pool state changes collected and analyzed in the real-time data pipeline, the operating status parameters of the target thread pool are obtained.

3. The method according to claim 1, wherein Train and validate the thread pool monitoring metrics generation neural network model as follows: Clean, standardize, and normalize the collected historical data of thread monitoring indicators to obtain processed data; Based on feature engineering technology, feature extraction is performed on the processed data to obtain a feature data set; Using a deep learning framework, based on the feature data set, a preset variational autoencoder neural network model is trained to obtain a trained thread pool monitoring indicator generation neural network model; The feature data set is used as a verification data set to verify the trained thread pool monitoring indicator generation neural network model; and a verified thread pool monitoring indicator generation neural network model is obtained.

4. The method according to claim 1, wherein Create a thread pool load adjustment intelligence map as follows: Establishing a training data set based on the collected thread pool load adjustment historical data; the thread pool load adjustment historical data includes: thread pool capacity adjustment amount, thread pool core thread number adjustment amount, task allocation amount and task allocation form associated with different values ​​of thread pool monitoring indicators; Based on the training data set, nodes and edges of the thread pool load adjustment intelligent map are generated; the nodes are different monitoring indicators, resource adjustment strategies and task allocation strategies; the edges are edges connecting different monitoring indicators and different resource adjustment strategies and task allocation strategies; Based on the nodes and edges, as well as relevant expert experience, establishing associations between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; Based on graph neural network technology, an intelligent graph for thread pool load adjustment is established according to the correlation between different thread pool monitoring indicators and the corresponding resource adjustment strategies and task allocation strategies.

5. The method according to claim 1, wherein Also includes: Based on the real-time collected operating status parameters of the target thread pool and the data of real-time parameter adjustment of the target thread pool, the association weights and node attributes in the thread pool load adjustment intelligent map are adjusted to obtain an optimized thread pool load adjustment intelligent map.

6. The method according to claim 1, wherein Matching the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator, including: Based on the graph matching algorithm, according to the target thread pool monitoring indicators, the resource adjustment strategy and task allocation strategy with the highest degree of matching with the target thread pool monitoring indicators are found in the thread pool load adjustment intelligent graph as the target resource adjustment strategy and target task allocation strategy.

7. The method according to claim 1, wherein Also includes: A visual monitoring interface is generated based on operating status parameters, target thread pool monitoring indicators, and the target resource adjustment strategy and target task allocation strategy of the target thread pool that match the target thread pool monitoring indicators; the visual monitoring interface is used to adopt data visualization technology to establish a customized monitoring view and data display dashboard, combined with the user's drag and drop selection of different information, to dynamically display the real-time changes of information in the monitoring interface.

8. The method according to claim 1, wherein Also includes: Establish preset alarm thresholds for different monitoring indicators and / or change trend thresholds between monitoring indicators, and the association between them and different alarm levels; When the target thread pool monitoring indicator meets the alarm threshold and / or the change trend threshold between monitoring indicators, an alarm message carrying the target thread pool monitoring indicator is issued in an alarm manner associated with the corresponding alarm level.

9. A thread pool configuration device, characterized in that: include: An operating status parameter acquisition module is used to obtain operating status parameters of the target thread pool; the operating status parameters include the number of core threads, thread pool activity, thread pool queue capacity and thread pool volume; A thread pool monitoring indicator determination module is configured to input the operating status parameters of the target thread pool into a thread pool monitoring indicator generation neural network model to obtain a target thread pool monitoring indicator for the target thread pool; the thread pool monitoring indicator generation neural network model is obtained by training a preset neural network model based on thread monitoring indicator historical data; the thread monitoring indicator historical data includes operating status parameters of different thread pools and historical data of thread pool monitoring indicators corresponding to each thread pool; the monitoring indicators include thread pool utilization rate, thread pool capacity upper limit, task execution time and task queuing time, number of abnormal tasks, and abnormal task processing results; A resource adjustment strategy and task allocation strategy determination module is used to match the target thread pool monitoring indicator with a preset thread pool load adjustment intelligent map to obtain a target resource adjustment strategy and a target task allocation strategy for the target thread pool that match the target thread pool monitoring indicator; The thread pool load adjustment intelligent map includes: the association between different thread pool monitoring indicators and corresponding resource adjustment strategies and task allocation strategies; the resource adjustment strategies include different values ​​of thread pool monitoring indicators, the associated thread pool capacity adjustment amount and the thread pool core thread number adjustment amount; the task allocation strategy includes different values ​​of thread pool monitoring indicators, the associated task allocation amount and task allocation form; A real-time parameter adjustment module is used to adjust the parameters of the target thread pool in real time based on the indicator value of the target thread pool monitoring indicator obtained by real-time monitoring, using the thread pool capacity adjustment amount, the thread pool core thread number adjustment amount, the task allocation amount and the task allocation form corresponding to the indicator value in the target resource adjustment strategy and the target task allocation strategy.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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