A multi-threaded statistical method and system based on distributed storage

Abstract

The present application discloses a multi-threaded statistical method and system based on distributed storage, including obtaining a list of configurable items and thread data in a cloud storage system, determining multiple main configuration items in the list of configurable items based on multiple performance indicators of each configuration item in the list of configurable items that has been configured multiple times in a historical state, and screening key configuration items based on each main configuration item and an information threshold. Based on the multiple performance indicators of each configuration item in the historical state system, the necessary degree of optimizing the configuration item can be determined based on different performance indicators, so that the main configuration items can be determined based on the importance, and the key configuration items that have a greater impact on system performance than other configuration items can be screened out with a set information threshold, so that the key configuration items can be optimized in a targeted manner. After optimizing the key configuration items, the system can speed up the automatic tuning speed, ensuring the efficiency of system tuning.

Description

Technical Field

[0001] The present application relates to the field of computer storage technology, and in particular to a multi-threaded statistical method and system based on distributed storage. Background Art

[0002] With the widespread use of new storage media such as NVME, the performance of storage media in distributed storage systems is no longer a performance bottleneck in distributed storage systems. Instead, because storage programs cannot adapt well to multi-core architectures, the CPU has become the bottleneck of the entire distributed storage system. In multi-core programs, there are not only lock competition, but also cache failures, cacheline alignment and other issues. So how to better and more efficiently utilize multi-core CPUs has become a key issue.

[0003] In the prior art, due to multi-thread competition, even a simple statistical counting function will bring unexpectedly huge CPU consumption to the stored program. Generally, atomic variables are used for counting in multi-threaded programs, but as the number of threads increases, the execution time of atomic variables increases linearly. Summary of the invention

[0004] This application aims to solve the performance and CPU consumption problems of multi-threaded programs in counting statistics, and proposes a multi-threaded statistics method based on distributed storage. The specific technical solution is as follows:

[0005] In a first aspect of an embodiment of the present application, a multi-threaded statistical method based on distributed storage is provided, characterized in that the method comprises:

[0006] Get the list of configurable items and thread data in the cloud storage system;

[0007] Determine a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list configured multiple times in a historical state;

[0008] According to each of the main configuration items and the information threshold, key configuration items are screened, and timing data is obtained according to the thread data.

[0009] Optionally, the step of determining a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list configured multiple times in a historical state comprises:

[0010] Acquire multiple performance indicators of each configuration item in the configurable item list that is configured multiple times in a historical state;

[0011] Determine, according to the multiple performance indicators of each configuration item and the weight parameter corresponding to each performance indicator, an evaluation value of each configuration item, wherein the evaluation value represents the importance of the configuration item in the configurable list;

[0012] The plurality of main configuration items are determined according to the evaluation value of each of the configuration items and a preset threshold.

[0013] Optionally, the step of screening key configuration items according to each of the main configuration items and the information threshold includes:

[0014] The key configuration item among the multiple main configuration items is determined according to the information amount of each of the main configuration items and the size relationship of the information threshold.

[0015] Optionally, after the step of screening key configuration items according to each of the main configuration items and the information threshold, the method further includes:

[0016] Optimize the key configuration items.

[0017] Optionally, before the step of determining a plurality of main configuration items in the configurable item list according to a plurality of performance indicators of each configuration item in the configurable item list configured multiple times in a historical state, the method further comprises:

[0018] According to a preset collection method, the configuration items of the cloud storage system are collected to obtain the configurable item list.

[0019] Optionally, the step of collecting configuration items of the cloud storage system according to a preset collection method to obtain the configurable item list includes:

[0020] For multiple configuration items of the cloud storage system, if the amount of information contained in the configuration items exceeds the information amount threshold, the configuration items are segmented, and threads are created for each segment of the configuration items, and the threads concurrently collect the segments of the configuration items.

[0021] Optionally, the method further comprises:

[0022] Calculate and collect the total collection time of multiple configuration items of the cloud storage system, compare the total collection time with a preset time threshold, and when the total collection time exceeds the time threshold, optimize the number of threads for concurrently collecting configuration items, and record the number of optimized threads in the optimization strategy library, wherein the number of optimized threads is obtained by adding a preset value to the number of threads set in this round.

[0023] Optionally, the configuration items include one or more of the following information: system information, disk, shared directory, startup items, redundant services, password policy, user, audit policy, installed patches, installed software, anti-virus software, process, active port, user rights allocation, security options, group policy, firewall, service information, motherboard, network card, memory, graphics card, CPU, screen saver, DDOS attack protection, operating system log, and remote desktop.

[0024] In a second aspect of an embodiment of the present application, a multi-threaded statistical system based on distributed storage is provided, including:

[0025] The acquisition module is used to obtain the list of configurable items and thread data in the cloud storage system;

[0026] A determination module, configured to determine a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list configured multiple times in a historical state;

[0027] The screening module is used to screen key configuration items according to each of the main configuration items and the information threshold, and obtain timing data according to the thread data.

[0028] In a third aspect of an embodiment of the present application, an electronic device is provided. The device includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the method described in the first aspect above.

[0029] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and a processor executes the computer program to implement the method described in the first aspect above.

[0030] This application has the following beneficial effects:

[0031] The embodiment of the present application determines multiple main configuration items of the configurable item list according to multiple performance indicators of each configuration item of the configurable item list that are configured multiple times in the historical state, and screens key configuration items according to each main configuration item and information threshold. And solves the performance problem of multi-threaded traditional counters in high concurrency scenarios, improves the hit rate of the CPU, reduces the time for cache startup, and reduces the impact of the counter itself on the normal business performance of the program. And no matter how many threads are increased, there will be no thread competition, and the operation time of each counter accumulation will not increase. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0033] Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present application.

[0034] Figure 2 The figure is a flowchart of a multi-threaded statistical method based on distributed storage according to an exemplary embodiment.

[0035] Figure 3 The figure is a block diagram of a multi-threaded statistical system based on distributed storage according to an exemplary embodiment. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0037] In related technologies, cloud computing is the development of distributed computing, parallel computing and grid computing. It is to automatically split huge computing programs into countless smaller subprograms through the network, and then send the processing results back to the user after calculation and analysis by a large system composed of multiple servers. As for the multi-threaded counters in cloud storage, they are generally composed of some atomic variables, and multiple threads share one counter.

[0038] Based on the above objectives, the specific concept proposed in the embodiment of the present application is to determine multiple main configuration items of the configurable item list according to multiple performance indicators of each configuration item in the configurable item list that are configured multiple times in the historical state, and to screen key configuration items according to each main configuration item and information threshold. Based on this concept, in order to find some key configuration items that have a greater impact on system performance than other configuration items, the key configuration items can be optimized to improve the speed of automatic tuning of the cloud storage system. And add an independent statistical thread as the control thread, and the working thread has exclusive counters.

[0039] Reference Figure 1 , Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present application.

[0040] like Figure 1As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0041] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0042] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and an electronic program.

[0043] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the multi-threaded statistical system based on distributed storage stored in the memory 1005 through the processor 1001, and executes the multi-threaded statistical method based on distributed storage provided in the embodiment of the present application.

[0044] Reference Figure 2 Based on the aforementioned hardware operating environment, an embodiment of the present application provides a multi-threaded statistical method based on distributed storage, comprising the following steps:

[0045] S201: Obtain a list of configurable items and thread data in a cloud storage system.

[0046] In a specific implementation, the configurable item list includes multiple configurable items, and the configurable items refer to the items to be configured in all items included in the configuration management scope. For example, the configurable item list of the cloud storage system includes the configuration items on any module in the CPU, memory, disk, bandwidth, and computer room line, that is, the configurable item list can be more than one.

[0047] S202: Determine a plurality of main configuration items in the configurable item list according to a plurality of performance indicators of each configuration item in the configurable item list that is configured multiple times in a historical state.

[0048] In a specific implementation, the strength of a computer's functions or the quality of its performance is not determined by a certain myocardial infarction indicator, but by a combination of factors such as its system structure, instruction system, hardware composition, and software configuration. Performance indicators include word length, computing speed, internal memory capacity, external memory capacity, I / O speed, video memory, hard disk speed, and main frequency. The main configuration items refer to the configuration items to be configured in the configurable item list. Generally, they may be configuration items that cause significant interference to the operating speed of the cloud storage system during historical operation. Based on the performance indicators of the configurable item list under the historical state, the configurable items with poor performance under the historical operating state can be determined.

[0049] S203: Filter key configuration items according to each of the main configuration items and the information threshold, and obtain timing data according to the thread data.

[0050] In a specific implementation, the information threshold is a preset value, which can be used to limit the size of the configuration item according to the operation requirements of the cloud storage system. Generally, the key configuration items can be screened according to the size relationship between the information size of the main configuration items and the information threshold. For example, if the information size of any main configuration item is greater than the preset threshold, the main configuration item can be determined to be a key configuration item.

[0051] This embodiment determines multiple main configuration items in the configurable item list according to multiple performance indicators of each configuration item in the configurable item list that has been configured multiple times in the historical state, and screens key configuration items according to each main configuration item and the information threshold. Based on the multiple performance indicators of each configuration item in the historical state system, the necessary degree of optimizing the configuration item can be determined according to different performance indicators, so that the main configuration items can be determined according to the importance, and the key configuration items that have a greater impact on the system performance than other configuration items can be screened out with the set information threshold, so that the key configuration items can be optimized in a targeted manner. After optimizing the key configuration items, the system can speed up the automatic tuning speed, ensuring the efficiency of system tuning.

[0052] In some embodiments, the step of determining multiple main configuration items of the configurable item list according to multiple performance indicators of each configuration item of the configurable item list configured multiple times in the historical state includes:

[0053] Get multiple performance indicators of each configuration item in the configurable item list that is configured multiple times in the historical state;

[0054] According to multiple performance indicators of each configuration item and the weight parameter corresponding to each performance indicator, the evaluation value of each configuration item is determined, and the evaluation value represents the importance of the configuration item in the configurable list;

[0055] Based on the evaluation value of each configuration item and the preset threshold, multiple main configuration items are determined.

[0056] In a specific implementation, each performance indicator corresponds to a weight parameter, for example, the weight parameter of the operation speed is 0.5, the weight parameter of the I / O speed is 0.2, and the weight parameter of the video memory is 0.02. According to the evaluation value of each configuration item calculated according to different performance indicators, the configuration item that currently has a greater impact on the system performance can be evaluated. The preset threshold is a preset value. When the calculated evaluation value of the configuration item is greater than the preset threshold, the configuration item can be determined to be a major configuration item. Conversely, when the evaluation value of the configuration item is less than the preset threshold, the configuration item is not a major configuration item. In general, there can be more than one major configuration item. When the cloud storage system is in good operating condition, there may be no major configuration items and key configuration items, that is, the cloud storage system is in a state that does not require optimization.

[0057] In addition, if only the calculated evaluation value is relied upon to find key configuration items, the reliability is low. Therefore, it is necessary to continue to screen out key configuration items based on the amount of information of each main configuration item.

[0058] In some embodiments, the step of screening key configuration items according to each major configuration item and the information threshold includes:

[0059] According to the information amount of each major configuration item and the size relationship of the information threshold, the key configuration items among the multiple major configuration items are determined.

[0060] In a specific implementation, when the amount of information of any major configuration item is greater than the preset threshold, the major configuration item can be determined to be a critical configuration item. Conversely, when the amount of information of any major configuration item is less than the preset threshold, the major configuration item can be determined to be a sub-critical configuration item. It can be understood that major configuration items are configuration items that need to be optimized, and when the system optimizes the list of configurable items, it generally optimizes the critical configuration items first and then optimizes the sub-critical configuration items. In addition, a value less than the information threshold can be set as a threshold for screening sub-critical configuration items.

[0061] In some embodiments, after the step of screening the key configuration items according to each main configuration item and the information threshold, the method further includes: optimizing the key configuration items.

[0062] It is understandable that after finding out the key configuration items, they need to be optimized to improve the speed of system operation and further increase the efficiency of system automatic tuning.

[0063] In some embodiments, before the step of determining multiple main configuration items of the configurable item list according to multiple performance indicators of each configuration item in the configurable item list that are configured multiple times in the historical state, the method further includes:

[0064] According to the preset collection method, the configuration items of the cloud storage system are collected to obtain a list of configurable items.

[0065] In a specific implementation manner, there are multiple ways of collecting, refer to the following implementation manner:

[0066] In one embodiment, the step of collecting configuration items of the cloud storage system according to a preset collection method to obtain a list of configurable items includes:

[0067] For multiple configuration items of the cloud storage system, if the amount of information contained in the configuration item exceeds the information amount threshold, the configuration item will be processed in segments, and a thread will be created for each segment of the configuration item. The threads will concurrently collect the segments of the configuration item, and a list of configurable items will be obtained based on the processed configuration items.

[0068] In a specific implementation, when each configuration item is collected, a working thread is automatically assigned, and multiple working threads collect all configuration items concurrently. The method of collecting configuration items concurrently using multiple threads can further improve the collection efficiency of configuration items.

[0069] For a single configuration item, there may be a problem that the amount of information contained in the configuration item is too large, resulting in low collection efficiency. As an optional implementation method, a configuration item information volume threshold can be set. For each configuration item, if the amount of information contained in the configuration item exceeds the configuration item information volume threshold, the configuration item is segmented and a thread is created for each segment of the configuration item. The thread concurrently collects each segment of the configuration item.

[0070] Specifically, for example, for a certain configuration item, there are 1000 records, then the 1000 records can be divided into 10 segments, and then 10 working threads are created for these 10 segments of configuration items. The 10 working threads collect concurrently, so as to improve the collection efficiency of a single configuration item.

[0071] As an optional implementation method, the number of threads for concurrently collecting configuration items in this round is set, and all configuration items of the operated and maintained devices are concurrently collected with the set number of threads according to the optimal configuration item extraction method.

[0072] In some embodiments, the method further comprises:

[0073] Calculate and collect the total collection time of multiple configuration items of the cloud storage system, and compare the total collection time with the preset time threshold. When the total collection time exceeds the time threshold, optimize the number of threads for concurrently collecting configuration items, and record the number of optimized threads in the optimization strategy library, where the number of optimized threads is obtained by adding a preset value to the number of threads set in this round.

[0074] As an optional implementation, the collection time threshold can be set to 10 seconds, and the accumulated preset value can be set to one. For example, if the number of threads set in this round is 10, but the total collection time obtained in the end is higher than the preset value of 10 seconds, the number of threads to be optimized is increased by one based on the number of threads set in this round, that is, in the next round, 11 threads are used to concurrently collect all configuration items of the operated and maintained devices. If the total collection time of the next round still exceeds the collection time threshold, 12 threads are used to concurrently collect in the next round. In this way, the collection efficiency of threads can be effectively improved without occupying too many system resources due to too many threads.

[0075] In some embodiments, the configuration items include one or more of the following information: system information, disk, shared directory, startup items, redundant services, password policy, user, audit policy, installed patches, installed software, anti-virus software, process, active port, user rights assignment, security options, group policy, firewall, service information, motherboard, network card, memory, graphics card, CPU, screen saver, DDOS attack protection, operating system log, and remote desktop.

[0076] Reference Figure 3 , Figure 3is a block diagram of a multi-threaded statistical system 300 based on distributed storage according to an exemplary embodiment. Figure 3 As shown, the system 300 includes:

[0077] An acquisition module 310 is used to acquire a list of configurable items and thread data in the cloud storage system;

[0078] A determination module 320, configured to determine a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list that is configured multiple times in a historical state;

[0079] The screening module 330 is used to screen key configuration items according to each main configuration item and the information threshold.

[0080] In some embodiments, the determination module 320 includes:

[0081] The acquisition submodule is used to obtain multiple performance indicators of each configuration item in the configurable item list that is configured multiple times in the historical state;

[0082] A first determination submodule is used to determine an evaluation value of each configuration item according to multiple performance indicators of each configuration item and a weight parameter corresponding to each performance indicator, where the evaluation value represents the importance of the configuration item in the configurable list;

[0083] The second determination submodule is used to screen key configuration items according to each main configuration item and the information threshold.

[0084] In some embodiments, the screening module 330 includes:

[0085] The third determination submodule is used to determine the key configuration items among the multiple main configuration items according to the information amount of each main configuration item and the size relationship of the information threshold.

[0086] In some embodiments, the system 300 further includes:

[0087] Optimization module, used to optimize key configuration items.

[0088] In some embodiments, the system 300 further includes:

[0089] The collection module is used to collect configuration items of the cloud storage system according to a preset collection method to obtain a list of configurable items.

[0090] In some embodiments, the collection module includes:

[0091] The processing submodule is used for processing multiple configuration items of the cloud storage system in segments if the amount of information contained in the configuration items exceeds the information amount threshold. A thread is created for each segment of the configuration items, and the threads concurrently collect the segments of the configuration items.

[0092] In some embodiments, the system 300 further includes:

[0093] The thread optimization module is used to calculate the total collection time of multiple configuration items of the cloud storage system, compare the total collection time with the preset time threshold, and when the total collection time exceeds the time threshold, optimize the number of threads for concurrently collecting configuration items, and record the number of optimized threads in the optimization strategy library, where the number of optimized threads is obtained by adding a preset value to the number of threads set in this round.

[0094] It should be noted that each module in the multi-threaded statistical system based on distributed storage in this embodiment corresponds one-to-one to each step in the multi-threaded statistical method based on distributed storage in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned multi-threaded statistical method based on distributed storage, which will not be repeated here.

[0095] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0096] at least one processor; and,

[0097] a memory communicatively connected to at least one processor; wherein,

[0098] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can perform an image semantic segmentation and annotation method based on deep learning.

[0099] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a circuit board image semantic segmentation and labeling method based on deep learning.

[0100] Based on the same inventive concept, an embodiment of the present application also provides a computer program product, which, when processed, implements an image semantic segmentation and labeling method based on deep learning.

[0101] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0102] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0103] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0104] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0105] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of more restrictions, the elements defined by the sentence "includes..." do not exclude the existence of other identical elements in the article or device including the elements.

[0106] The above is a detailed introduction to a multi-threaded statistical method based on distributed storage. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A multi-threaded statistical method based on distributed storage, characterized in that: The method comprises: Get the list of configurable items and thread data in the cloud storage system; Determine a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list configured multiple times in a historical state; Acquire multiple performance indicators of each configuration item in the configurable item list that is configured multiple times in a historical state; For multiple configuration items of the cloud storage system, if the amount of information contained in the configuration items exceeds the information amount threshold, the configuration items are processed in segments, and threads are created for each segment of the configuration items, and the threads concurrently collect the segments of the configuration items; Determine, according to the multiple performance indicators of each configuration item and the weight parameter corresponding to each performance indicator, an evaluation value of each configuration item, wherein the evaluation value represents the importance of the configuration item in the configurable list; Determining the plurality of main configuration items according to the evaluation value of each of the configuration items and a preset threshold; According to a preset collection method, the configuration items of the cloud storage system are collected to obtain the configurable item list; According to each of the main configuration items and the information threshold, critical configuration items are screened, and timing data is obtained according to the thread data; Determine the key configuration item among the multiple main configuration items according to the information amount of each of the main configuration items and the size relationship of the information threshold; Optimizing the key configuration items; Calculate and collect the total collection time of multiple configuration items of the cloud storage system, compare the total collection time with a preset time threshold, and when the total collection time exceeds the time threshold, optimize the number of threads for concurrently collecting configuration items, and record the number of optimized threads in the optimization strategy library, wherein the number of optimized threads is obtained by adding a preset value to the number of threads set in this round.

2. The method according to claim 1, characterized in that The configuration items include one or more of the following information: system information, disk, shared directory, startup items, redundant services, password policy, user, audit policy, installed patches, installed software, anti-virus software, process, active port, user rights allocation, security options, group policy, firewall, service information, motherboard, network card, memory, graphics card, CPU, screen saver, DDOS attack protection, operating system log, and remote desktop.

3. A multi-threaded statistical system based on distributed storage, characterized in that: A method for performing the process as claimed in claim 1, comprising: The acquisition module is used to obtain the list of configurable items and thread data in the cloud storage system; A determination module, configured to determine a plurality of main configuration items of the configurable item list according to a plurality of performance indicators of each configuration item of the configurable item list configured multiple times in a historical state; The screening module is used to screen key configuration items according to each of the main configuration items and the information threshold, and obtain timing data according to the thread data.

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