Operation system transplantation method

Abstract

The invention relates to the technical field of computer software, and provides an operating system transplanting method which comprises the steps that initialization parameters of an operating system are configured on the basis of a self-adaptive parameter regulation and control model, and the initialization parameters comprise hardware dependency items and software setting items; dynamically adjusting a message transmission mode between system applications of the operating system by utilizing an inter-process communication management mechanism; adjusting the file access permission of the operating system; intelligently adjusting a power management mode of the operating system; and allocating computing resources to the system application according to a preset priority allocation algorithm. According to the technical scheme provided by one or more embodiments, it can be ensured that the transplanted operating system can achieve a stable and efficient operation effect on the target hardware.

Description

Technical Field

[0001] The present disclosure relates to the field of computer software technology, and particularly to a method for operating system transplantation. Background Art

[0002] Operating system transplantation refers to adapting an operating system to a specific hardware platform to ensure that the system can operate normally on a new device. For example, adapting the Android operating system to a specific hardware platform.

[0003] However, during the process of operating system transplantation, various technical challenges will be faced, especially in solving problems such as system performance optimization and resource management. For example, when the operating system starts up, uneven memory resource allocation will lead to a slow startup speed of the operating system; when the operating system performs multitasking, data transmission delay problems are likely to occur; the operating system needs to apply privacy and data isolation mechanisms; how to extend the battery life of a mobile device with a transplanted operating system; how to improve the slow application response speed under high load of the operating system. Summary of the Invention

[0004] In view of this, one or more embodiments of the present disclosure provide a method for operating system transplantation, which can ensure that the transplanted operating system can achieve stable and efficient operation effects on the target hardware.

[0005] On the one hand, the present disclosure provides a method for operating system transplantation, the method comprising: configuring initialization parameters of the operating system based on an adaptive parameter regulation model, the initialization parameters including hardware dependencies and software settings; dynamically adjusting the message transmission mode between various system applications of the operating system by using an inter-process communication management mechanism; adjusting the file access permissions of the operating system; intelligently adjusting the power management mode of the operating system; and allocating computing resources to the system applications according to a preset priority allocation algorithm.

[0006] On the other hand, the present disclosure also provides an operating system transplantation device, the device comprising: a parameter configuration unit for configuring initialization parameters of the operating system based on an adaptive parameter regulation model, the initialization parameters including hardware dependencies and software settings; a communication management unit for dynamically adjusting the message transmission mode between various system applications of the operating system by using an inter-process communication management mechanism; a file management unit for adjusting the file access permissions of the operating system; a battery management unit for intelligently adjusting the power management mode of the operating system; and a scheduling algorithm unit for allocating computing resources to the system applications according to a preset priority allocation algorithm.

[0007] On the other hand, the present disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, the above-mentioned operating system transplantation method is implemented.

[0008] On the other hand, the present disclosure also provides a computer-readable storage medium, which is used to store a computer program, and when the computer program is executed by a processor, the above-mentioned operating system transplantation method is implemented.

[0009] The technical solutions provided by one or more embodiments of the present disclosure optimize the configuration of initialization parameters based on an adaptive parameter regulation model, which can solve the problem of uneven memory allocation during the startup process of the operating system; dynamically adjust the inter-process communication mechanism, which can reduce the data transmission delay during multitasking of the operating system and enhance the real-time performance and efficiency of the operating system data stream; adjust the file access permission mechanism of the operating system, which can enhance data isolation and privacy protection of system applications; intelligently adjust the power management mode of the operating system, which can reduce the power consumption during standby of the operating system; allocate priorities to the computing resources of system applications, and in the case of high load of the operating system, it can solve the problem of slow response of system applications and improve the fluency of the operating system.

[0010] The technical solutions provided by one or more embodiments of the present disclosure propose a series of technical means to optimize the performance of the operating system for the key problems faced in the process of operating system transplantation. By carefully and comprehensively adjusting the working methods and parameter configurations of each core module, the computing performance, response speed and security characteristics of the transplanted operating system are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The features and advantages of the embodiments of the present disclosure will be more clearly understood by referring to the accompanying drawings. The drawings are schematic and should not be construed as limiting the present disclosure in any way. In the drawings:

[0012] Figure 1 A schematic diagram of the steps of the operating system transplantation method in one embodiment of the present disclosure is shown;

[0013] Figure 2 A schematic diagram of the functional modules of the operating system transplantation device in one embodiment of the present disclosure is shown;

[0014] Figure 3 A schematic diagram of the structure of the electronic device in one embodiment of the present disclosure is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0016] Please refer to Figure 1 , a method for operating system transplantation provided by an embodiment of the present disclosure may include the following multiple steps.

[0017] S1: Configure the initialization parameters of the operating system based on an adaptive parameter regulation model, where the initialization parameters include hardware dependencies and software setting items.

[0018] In this embodiment, optimizing and configuring the initialization parameters based on the adaptive parameter regulation model involves complex setting processes in multiple aspects and subsequent effect evaluation. This means that during the kernel startup stage of the operating system (such as the Android system), the most suitable set of initialization parameters for the operating system can be predicted and set automatically according to the hardware resource configuration and past operation records. These initialization parameters can cover hardware dependencies such as the CPU frequency range allocation table, RAM memory segmentation rules, as well as software setting items such as network connection preferences and user behavior patterns. For example, in practical applications, if a mobile phone has a high-performance processor but is equipped with a relatively small-capacity random access memory, in order to improve the opening speed of application programs while ensuring the stable and smooth operation of the overall system, the initialization script may tend to lock frequently used programs to the address range with a faster access bandwidth and compress and package background processes to avoid main memory overflow; if the device belongs to a large-screen tablet terminal, the initialization script may allocate more operating system resources to image rendering performance, so that the screen refresh rate reaches the best visual performance and supports more multi-screen operations without frame drops or crashes. This series of initialization parameter regulations can effectively solve the problem of application lags caused by uneven memory allocation after the operating system is transplanted to different models and can improve the user experience in different application scenarios.

[0019] S2: Use the inter-process communication management mechanism to dynamically adjust the message transmission mode between the various system applications of the operating system.

[0020] In this embodiment, dynamically adjusting the message transmission mode between system applications can reduce the data transmission delay during multitasking of the operating system. For example, it can be achieved through an intelligent IPC (Inter-Process Communication) management model. This model can not only sense the information transfer demand frequencies between various applications, but also switch to an appropriate type of message transmission mode according to the real-time load status of the operating system, such as direct shared cache, Socket communication, and Binder driver. In one embodiment, when multiple graphical applications simultaneously request to obtain data from the server side, the operating system may temporarily hand over the network download task to a lightweight thread pool and concatenate the result values expected to be received by each APP in the form of a pipeline. At the same time, if there is an emergency notification event (such as an incoming call vibration prompt) that needs to be immediately synchronized to the foreground interface, the method of local signal broadcast plus priority queuing is preferentially used for quick delivery. This way ensures that all requests receive appropriate and immediate attention. At the same time, it also ensures that even in an environment with unstable network conditions, the system applications of the operating system can maintain good service accessibility and stability. In addition, by making minor improvements to the existing framework interfaces, asynchronous update of cache data can be achieved to prevent blocking the execution of other non-related processes.

[0021] S3: Adjust the file access permissions of the operating system.

[0022] In this embodiment, adjusting file access permissions can enhance the isolation and privacy protection of application data, which involves fine-grained control of the operation permission levels for local storage resources. Usually, each independent software entity only has the right to read and write files within its own namespace and cannot access the data of other software entities. However, considering the functional linkage requirements in some special cases, an exception mechanism must be introduced to safely and orderly authorize cross-border call operations. Specifically, a security policy engine similar to SELinux mandatory access control can be added. This engine can match and determine whether a specific action is allowed according to a predefined rule list. For example, a certain photo-taking social application applies to obtain album content for posting a post. Then, the application information must first go through a strict legal verification process, including identity verification to confirm that the source is true and error-free, the purpose is clear and appropriate, and it needs to be encrypted and recorded for future reference throughout the process. In this way, malicious programs can be effectively prevented from abusing the system's underlying application programming interfaces (APIs), thereby protecting users' personal information. It can also help regular development teams build a better ecosystem, promoting innovation while maintaining the security and credibility of the entire community.

[0023] S4: Intelligently adjust the power management mode of the operating system.

[0024] In this embodiment, intelligent scheduling of the power management mode can reduce the power consumption when the operating system is in standby. For example, a dynamic power management framework can be created to monitor and analyze the remaining percentage of battery power and the charging and discharging curve trajectory, and based on this, an energy-saving plan can be formulated. For instance, during nighttime sleep, except for a few long-resident services such as alarm reminders, the vast majority of the remaining background processes of the operating system can be frozen and no longer consume electrical energy. Only when a trigger event is received will these background processes be awakened as needed, resume normal operation, and then re-enter the low-energy sleep state loop after meeting the established conditions. By doing so, it can not only extend the usage duration of a single charging cycle but also improve potential hazards such as overheating caused by excessive heat dissipation, thereby extending the service life of the device with the transplanted operating system.

[0025] S5: Allocate computing resources to the system application according to the preset priority allocation algorithm.

[0026] In this embodiment, for the problem of slow response of system applications under high load of the operating system, it can be improved specifically to ensure that without affecting the normal operation of other services, more core computing power is allocated for the key tasks to have the priority right of use. The traditional way of using computing resources in an operating system mostly adopts the principle of first in, first out. However, this is prone to the situation where long transactions block short requests, especially more obvious during the peak period of network traffic. Therefore, a priority allocation algorithm can be introduced. Combining historical statistical data and the characteristics of elements in the current task processing queue, the optimal sorting sequence is calculated, so that those more important application tasks can be identified in time and assigned a higher processing level until all work is completed and then it's the turn of the ordinary application tasks waiting behind. This method significantly improves the overall working efficiency of the operating system and can reduce the anxiety of users.

[0027] The operating system transplantation method of this embodiment optimizes and improves the working performance of the operating system in essence through in-depth reforms in five main directions, enabling the transplanted operating system to run better and faster on various hardware platforms, greatly facilitating the user's life and entertainment needs, and providing more possibilities for enterprise developers.

[0028] The operating system transplantation method provided by an embodiment of the present disclosure, step S1 may include the following multiple steps.

[0029] S11: Analyze the memory allocation data during the startup process of the operating system.

[0030] In this embodiment, through monitoring and logging, memory allocation data generated during the operating system startup process can be obtained, and comprehensive analysis can be performed on this data. The purpose of this step is to determine the exact amount of memory required by various applications or services during the initialization phase. For example, in one embodiment, the basic components and services loaded by the operating system at different stages during startup, such as core processes like SystemUI and Zygote, can be tracked, and their memory consumption can be recorded.

[0031] S12: Based on the memory allocation data, use a dynamic prediction algorithm to estimate the expected memory space of each application process of the operating system.

[0032] In this embodiment, a pre-trained dynamic prediction algorithm is used to estimate how much memory space each application process will actually use during subsequent operation. The dynamic prediction algorithm can make a relatively accurate inference based on previously collected similar data or the learning results of similar application scenarios. The working principle of the dynamic prediction algorithm includes considering factors such as the type and scale of the application program. In a practical application example, for the porting process of a certain version of the Android operating system, through historical data, the memory occupancy trend of a certain communication application of the Android operating system in a stable state after each startup can be analyzed, and then the approximate memory requirements when a new user first starts this application program can be estimated.

[0033] S13: According to the expected memory space, adjust the kernel parameters of the operating system to allocate processor resources and memory resources to each application process.

[0034] In this embodiment, by adjusting the kernel parameters of the operating system, it can be ensured that the central processing unit (CPU) resources and random access memory (RAM) resources can achieve a reasonable proportional allocation among different application processes, thereby improving the overall resource utilization rate of the operating system. The kernel needs to dynamically balance the allocation relationship between the two resources according to the changes in the application load at different time periods. In one embodiment, for the total memory denoted as T (which represents the total available RAM magnitude of the device), the known occupied memory is A, and the remaining part available for other task allocation is R = T - A. In the formula, T, A, and R are all positive integers in kilobytes (KB) or megabytes (MB). Ideally, the kernel will try to minimize problems such as application response latency caused by resource competition. In this process, the goal is to enable all processes to obtain sufficient CPU time slices, and at the same time, avoid a large amount of swapping due to overcrowded RAM.

[0035] S14: Calculate the remaining physical memory of the operating system, and increase the virtual memory page swapping frequency of the operating system when the remaining physical memory is lower than the baseline value.

[0036] In this embodiment, when the remaining unused physical memory is less than the minimum limit level L (which is usually determined according to the baseline required for running the most important application in the system), the virtual memory page swapping (pageswap) frequency can be increased. L is also a critical value parameter defined in KB or MB. If it is found that R is less than L, it means that the operating system faces an increased risk of memory pressure. Appropriately increasing the swapping frequency can, to a certain extent, alleviate the potential performance bottleneck problem caused by the shortage of physical memory. For example, when R is close to zero and some background processes are inactive for a long time, more and faster transfer of temporarily unused data blocks in such processes to the external memory swap area can increase the chance for more important tasks to be processed in a timely manner. The purpose of this is to avoid serious failures such as the overall operating efficiency of the entire operating system being low or even deadlocked due to excessive squeezing of physical RAM.

[0037] For the operating system transplantation method provided by an embodiment of the present disclosure, step S11 may include the following multiple steps.

[0038] S111: Classify according to time periods, and collect and summarize the application loading times of each of the system applications.

[0039] In this embodiment, the collection and summary operations of the application loading times in different time periods can be performed to understand the startup required time of each application program under different circumstances. By recording the time points of each loading and statistically analyzing them at a certain period, a detailed application startup performance benchmark can be obtained. For example, during the morning rush hour from 8:00 to 9:00 and at noon from 12:00 to 1:00 on weekdays, collecting the loading time information of social software and office software can evaluate the differences in each period.

[0040] S112: Detect the memory fragmentation rate during the loading process of each of the system applications, and calculate the average memory fragmentation rate of each of the system applications.

[0041] In this embodiment, the degree of memory fragmentation encountered by each application during the loading process is detected, and these values are added up and averaged to obtain the average memory fragmentation rate F. This value can reflect whether the overall memory allocation is efficient. This detection is an inspection process that runs immediately after the system starts or the application starts to initialize, so as to ensure that there is a basis for subsequent optimization. In one embodiment, assume that there are multiple different types of applications on a certain Android system. When starting up, first record the proportion of fragmented and ineffectively utilized memory space generated during the loading of each software. Finally, through multiple samplings and calculations, the specific value of F can be determined.

[0042] S113: Adjust the initial buffer area of the operating system according to the current device hardware parameters.

[0043] In this embodiment, by flexibly adjusting the initial reserved buffer size according to the current hardware configuration (such as factors like CPU performance and the amount of built-in RAM), the probability of memory fragmentation can be minimized. Specifically, if the target device for transplanting the operating system is equipped with high-spec core components, the original setting range of the reserved buffer can be expanded; under the opposite conditions, the scale of the reserved buffer can be moderately compressed, while ensuring the stable operation of the operating system, leaving enough space for other important functions.

[0044] S114: Calculate the current memory allocation efficiency according to the average memory fragmentation rate, and evaluate the current memory allocation scheme according to the comparison result between the current memory allocation efficiency and the historical average allocation efficiency.

[0045] In this embodiment, the memory allocation efficiency E = (allocation success rate S * average fragmentation rate F * weight W) / T (time consumption) can be calculated. When E > H (the previously recorded performance reference index), it means that the current memory allocation strategy has a good effect and needs to be retained. Here, S can take a floating-point number between 0 and 1, representing the proportion of correct request processing; F represents the average memory fragmentation rate obtained during a complete operation cycle mentioned above; and W is a subjective weight coefficient introduced to balance the possible unequal relationship between the two. W is defaulted to a positive integer or semi-positive number, and W is close to 0 to 1; T refers to the total time limit unit, usually set in seconds or milliseconds. The reason for constructing the formula like this is to quantitatively evaluate the actual completion quality after a complete series of instructions from reception to release, and thereby judge whether there is a need for improvement, so as to achieve a better user experience and resource utilization. For example, historical data shows that the historical average allocation efficiency H of a certain type of operation is within a certain range. If the new efficiency measured after the adjustment of the new mechanism is higher than it, it is considered that progress has been made; otherwise, it is necessary to consider re-evaluating and correcting.

[0046] The operating system porting method provided by an embodiment of the present disclosure, step S112 may include the following multiple steps.

[0047] S1121: Trace the memory allocation paths of each of the system applications, and record the memory fragments generated by each of the system applications.

[0048] In this embodiment, by using a dedicated library, the memory allocation path can be traced, and the specific path of memory allocation during the startup and operation of each application, as well as the corresponding generated memory fragments, can be recorded. This step ensures that memory changes can be comprehensively captured, providing accurate data for subsequent analysis and laying a solid foundation for calculating the memory fragmentation rate later.

[0049] In one embodiment, through a memory monitoring tool integrated into the Android system, whenever a certain application program makes a new memory application or release operation, the occurrence location of this request (for example, which function or method) can be recorded in detail.

[0050] S1122: Calculate the ratio between the actual occupancy and the nominal occupancy of each allocated memory block, and this ratio is used to determine the memory fragmentation rate.

[0051] In this embodiment, in an ideal state, a whole segment of memory allocated should be fully used. However, in reality, due to various unpredictable factors, it is easy to cause some areas to become gaps that cannot be effectively used, or small unused intervals, that is, the so-called holes are formed. Therefore, this ratio reflects the loss in actual efficiency. Using this as one of the evaluation criteria, the fragmentation degree caused by all active components in the entire operating system can be measured.

[0052] Taking a social application program as an example, every time this program loads multimedia resources such as pictures and videos, it must apply for enough space to store these files and their metadata, and at the same time, a part of the buffer is reserved to handle possible overflow situations. In this case, if it is found that only 60% of a certain allocated buffer segment is fully occupied, and the remaining 40% is left unused, obviously a relatively serious fragmentation situation has occurred.

[0053] S1123: Adjust the free list management policy of the operating system according to the generation record of the memory fragments and the memory fragmentation rate.

[0054] In this embodiment, the free list management policy of the operating system is adjusted, which represents the policy for dynamically adjusting the internal policy of the operating system for managing and maintaining the remaining unused fragmented address space. Specifically, some linked list structures in the operating system can be changed so that the linking method can more reasonably and effectively organize the existing idle memory segments, combine them to form larger contiguous available areas, improve the success probability when future similar events occur, and reduce the overall complexity. The optimized free list management policy tends to integrate small scattered segments and preferentially supply tasks with moderate demands.

[0055] For example, when the detector senses that there are currently many small-scale memory intermittent bands (such as less than 2k), and there are frequent short-cycle jobs that need to be quickly turned around in the short term, a bidirectional chain can be considered to accelerate the retrieval speed; on the contrary, a stacking mode is recommended, which is beneficial to the long-term planning requirements of the continuous growth of large-scale projects.

[0056] S1124: Compare the average length of the memory fragments with the expected fragment size to determine whether to perform a compacting task, where the compacting task is used to merge or clean up the memory fragments.

[0057] In this embodiment, such an evaluation rule can be introduced: First, calculate that the length D of a single memory segment conforms to the expression D = Z / N + C*Z / N. Where Z represents the total memory that has been allocated, usually in bytes; N is the corresponding cumulative quantity, that is, how many times such events have occurred; the constant coefficient C is used to adjust the weighting influence, and the default setting is between 1.3 - 2.5, aiming to balance accuracy and robustness. When the result D obtained is less than the predetermined target value P (such as 4KB), it indicates that the existing problems are prominent and urgent, and compact reorganization measures must be taken to merge and clean up the scattered residues, so as to improve the comprehensive performance.

[0058] In this embodiment, the operating system in a specific scenario can make targeted responses and ensure a long-term stable user experience and good hardware support performance.

[0059] The operating system transplantation method provided by an embodiment of the present disclosure, step S1121 may include the following multiple steps.

[0060] S11211: Statistically analyze the changes in the free areas of each of the allocated memory blocks.

[0061] In this embodiment, the usage differences of memory blocks before and after each allocation operation can be recorded and analyzed. By monitoring memory allocation and release operations, it is possible to determine whether new allocations cause fragmentation, thereby better predicting future resource requirements. In this process, accurate data information can be obtained by comparing with a pre-constructed free area management table, which helps to dynamically adjust the memory management strategy.

[0062] S11212: For consecutive access requests to the system application, prioritize memory allocation.

[0063] In this embodiment, if an application or task requests a large amount of memory in a sequential manner, it will be given a higher priority to ensure that continuous space is reserved for these tasks, while reducing the increase in small, unused memory fragments caused by irregular allocations. In one embodiment, when running a multimedia editing software on an Android system, the writing of video frames with high continuity is given high priority, thereby avoiding unnecessary fragmentation and improving the stability and performance of the system.

[0064] S11213: Use a machine learning module to evaluate the impact of different memory allocation strategies on the future load of the operating system.

[0065] In this embodiment, by using intelligent algorithms to identify historical patterns, the system response under different allocation methods can be predicted, such as the average seek time or I / O latency metrics during a long working cycle. In one embodiment, after sufficient training, the machine learning tool can recommend the best practice configuration options when the user starts a multitasking mode (such as running social applications and heavy-load game programs simultaneously).

[0066] S11214: Determine whether the memory allocation strategy of the operating system adopts the large page management mechanism according to the comparison result of the memory usage difference and the reasonable growth rate of the allocated memory block.

[0067] In this embodiment, according to the formula V = M_after-M_before, if V≥G, consider switching to a more flexible large page management mechanism to reduce the risk of irregular segmentation. Here, V represents the total amount of change between two memory states; M_after is the total available space after the memory operation is completed; M_before is the number of values ​​that already exist before the operation starts at the same time point; G defines a growth rate limit, which can be set to 5% to 10% based on experience by default. The basis for choosing a reasonable growth rate is that: too small will affect flexibility and cause excessive adjustment and waste resources; on the contrary, it may delay the response and fail to quickly adapt to the ever-changing application scenarios. Specifically, assuming that an Android tablet equipped with a customized ROM is performing multiple background updates and service tasks, due to the sudden increase in data transmission volume, the memory pressure surges. It is particularly important to convert to a large page solution in time according to this rule, which will enable the operating system to optimize the memory segment distribution structure in a large range to meet the strong coherence storage area required by the real-time communication protocol, without falling into the dilemma of frequent trivial segmentation.

[0068] In this embodiment, all of the above logic can be implemented in a dedicated library to ensure that all operations can be executed efficiently and safely without affecting the user experience, and continuously provide high-quality service experience to end users of the operating system.

[0069] An operating system transplantation method provided by an embodiment of the present disclosure, step S11213 may include the following multiple steps.

[0070] S112131: Periodically update the training data set of the machine learning module.

[0071] In this embodiment, the training data set is refreshed periodically to cover the latest important metrics related to system performance. For example, in the interaction between the application layer and the underlying resource management of the Android system, CPU utilization, memory occupancy, and response time are considered key performance indicators. Specifically, these operating state parameters are captured every day or every hour and used to retrain existing or improved prediction mechanisms. This approach ensures that the prediction algorithm of the machine learning module can always obtain the latest and most practical performance information, thereby improving the judgment of future workloads.

[0072] S112132: Incorporate a new behavior pattern recognition component into the existing classifier of the machine learning module.

[0073] In this embodiment, new behavioral features are incorporated into the existing classification system, which can enhance the recognition accuracy of the machine learning module. That is, the machine learning module can identify unprecedented operation patterns or user activities, and accordingly adjust its internal logic to match the expected output results in the new situation. For example, when an unrecorded game application scenario appears, if it is found that the game frequently performs graphic redrawing and consumes a large amount of processor resources, such a scenario should be added to the existing knowledge base so that subsequent similar situations can be correctly classified.

[0074] S112133: Use a feedback loop to calibrate the model parameters of the machine learning module.

[0075] In this embodiment, by adopting a feedback path, various configuration parameters inside the model can be dynamically fine-tuned until the best prediction effect is achieved. This means that the machine learning module continuously monitors its own prediction results, compares the errors with the actual situation, and corrects those variable factors based on this difference. In one embodiment, after observing that the prediction service request frequency deviates too much from the actual value for a period of time, greater weights are assigned to some input attributes with greater influence (such as the number of concurrent accesses), so that the final estimated value of the machine learning module can better fit the actual change law.

[0076] S112134: Determine whether to change the learning coefficient of the machine learning module according to the comparison result between the error rate and the error threshold.

[0077] In this embodiment, using the result obtained by dividing the misclassification ratio by the total number and then averaging it as the evaluation criterion, it can be measured whether the learning coefficient of the machine learning module needs to be kept unchanged. For example, the error rate Rr = (total number of misclassifications / total number of samples) / number of iterations n. In n cycle periods, if Rr <= Q (Q is a predetermined threshold), the current training weight is kept unchanged to ensure stability. The error rate Rr can represent the average value of the proportion of the total number of misclassified instances in all inspection items after N iteration periods; Q is a predefined small probability value used as a safeguard line for stability. Specifically, if Rr remains below 0.05 in multiple consecutive test periods, it is considered that the existing settings are reasonable and there is no need to change the weight. This is because an error rate Rr lower than the threshold Q means that the current solution is already excellent enough to avoid the risk of instability caused by excessive pursuit of improvement. The main purpose of setting the threshold Q is to seek a balance between accuracy and stability, and maintain consistency and reliability during the process of continuously adjusting and improving the machine learning module.

[0078] The operating system transplantation method provided by an embodiment of the present disclosure, step S111 may include the following multiple steps.

[0079] S1111: Monitor the first call time of the system application and the resource utilization rate of the system application.

[0080] In this embodiment, at the moment when the user or the system first starts the application program, the specific call time and detailed data such as the usage percentages of the CPU, memory, and other hardware resources at that time can be recorded. Doing so not only helps analyze the load condition in the initial stage of the operating system startup, but also provides a reference for formulating subsequent optimization plans. For example, for a drawing software used on a tablet Android system, the above information can be obtained when the user first runs this program. Through this example, performance bottlenecks existing under certain specific conditions can be discovered, and raw materials can be accumulated for improving the algorithm.

[0081] S1112: Establish an association graph of the internal components of the system application according to the first call time and the resource utilization rate.

[0082] In this embodiment, various parameters obtained in S1111 can be classified by different components and an association graph can be drawn. This step aims to find out the internal components of the system application that consume the most startup time, and identify those codes or component interfaces with high frequency but low efficiency, so as to focus on optimization. For example, when a news client is loading, it needs to request network interfaces, render web page elements, etc. Statistically analyzing the connection between these interaction operations and the entire startup period can assist developers in quickly locking down the areas that need to be rectified and improving the quality of the final user experience.

[0083] S1113: Adjust the resource scheduling level of the system application according to the device model differences.

[0084] In this embodiment, since the internal structures of many mobile phones and tablets on the market have their own characteristics, the performance of the same application deployed on each device will surely be different. Therefore, when designing, the differences in the ways of processing instruction sequences of different brand terminals must be considered, and the resource allocation rules should be reasonably planned so that all devices can smoothly run the target App and minimize the probability of delay as much as possible. For example, for mid - to - low - end Android devices, complex image rendering work should be restricted and the frame rate requirement should be reduced, while for flagship products, higher - quality presentation is allowed and the corresponding speed is accelerated. In this way, the breadth and stability of cross - model support are unified.

[0085] S1114: Compare the startup times and startup durations of the system application, and evaluate the startup efficiency of the system application.

[0086] In this embodiment, if the relationship between the number of application launches C and the launch time T satisfies the formula: T ∝ log(C + B) (where B represents the background service constant), the optimal launch process is determined. Here, the number of launches represents the cumulative number of times the user opens the specified application (generally C > 0), and T is the number of seconds required from when the user clicks until the main interface is fully displayed. The value of B is determined by the underlying service overhead of the operating system and is within a relatively fixed range between [1, 10]. After the application goes through multiple activation cycles, its launch consumption shows a downward trend and approaches a certain stable state, and this change pattern exactly conforms to the characteristics of the above logarithmic growth formula. The purpose of setting this equation is to explain and verify the principle of improved launch performance from a mathematical perspective: as the user becomes more familiar with how to correctly use the software and internal caching and other mechanisms gradually mature and become effective, each restart will be a little faster, and then the optimal configuration parameters are deduced to make the program reach the best performance point.

[0087] The operating system transplantation method provided by an embodiment of the present disclosure, step S1112 may include the following multiple steps.

[0088] S11121: Establish the association graph according to the activity and priority of the system application.

[0089] In this embodiment, component activity reflects the number of executions and frequencies of each application or system process within a certain period of time, and the priority is set by the developer or the system itself to indicate importance. In this scenario, the dependency network diagram can visualize the dependency relationships and interaction intensities between various components, helping to identify the internal structural characteristics of the system. For example, for the relationships between multiple core management tasks and services in a new intelligent device operating system optimized for transplantation on the Android platform, such a diagram is drawn.

[0090] S11122: Analyze the association graph, screen the main components in the internal components, and determine the communication paths of the main components.

[0091] In this embodiment, the so-called main components are those key elements with a large number of incoming or outgoing edges, which play a decisive role in the stability and response efficiency of the entire software architecture. And the so-called communication path is the key channel that constitutes the information flow transmission line between these main components. Specifically, when it is detected that a certain data reading module is a core component called by most processes, it is considered an important component, and along this context, key links such as the storage subsystem and network access mechanism connected to it are discovered, so as to find possible weak points for subsequent processing.

[0092] S11123: Determine the bottleneck link according to the main components and the communication paths, and optimize the bottleneck link.

[0093] In this embodiment, if there are some functional modules that take too long due to inefficient algorithms, they can be accelerated by replacing them with more efficient versions or adjusting the relevant hardware resource allocation strategies. Taking an implementation case as an example, in the case of heavy graphics rendering tasks, by offloading some calculations to the auxiliary GPU to share the CPU pressure, the frame refresh interval can be stably controlled within a range of about 15 milliseconds per second, with a fluctuation of no more than ±1% (here, let Tavg be the average time consumption for generating each frame, and Tmin and Tmax be the shortest / longest time limits allowed respectively. For a normal video frame rate of 60 FPS, ideally, Tavg should approach (Tmin + Tmax) / 2 = 16.67 ms). This not only ensures the quality of the user experience but also improves the ability to process the number of requests within a unit time period.

[0094] S11124: When the influence range of the main component exceeds the influence threshold, strengthen the scheduling right of the adjacent components of the main component until the influence range of the main component falls below the influence threshold.

[0095] In this embodiment, when the influence range R of the main component K in the network exceeds the set threshold X, it is necessary to apply the enhanced scheduling right I to its adjacent nodes for auxiliary support until R falls within the normal range Y. Since the main component is an important object occupying a strategic position in the entire system operation logic chain (for example, the worker thread pool responsible for communication coordination), the radius of its influence represents the degree of the problem affected area caused by the failure propagation of this node to the rest. The enhanced scheduling weight I is a technical means parameter used to increase the working ability of other potentially affected units, ensuring that the main component can return to the normal operation state within a short time. In one embodiment, if a serious overheating risk signal appears in the central processing core, triggering the protection mechanism and causing a series of abnormal temperature control sensor device monitoring situations due to the frequency reduction operation, the peripheral load request frequency can be appropriately reduced at this time to reduce the burden, so that the central processing core can return to the normal working temperature as soon as possible and continue to play its role until R returns to the preset safety boundary.

[0096] The operating system transplantation method provided by an embodiment of the present disclosure, step S11123 may include the following multiple steps.

[0097] S111231: Review the relevant component code of the bottleneck link to determine potential problem points.

[0098] In this embodiment, by delving deep into the relevant component code, potential problem points can be examined. This process involves reviewing line by line the source code of the components in the operating system that are involved in data processing and transmission. The aim is to identify code segments with poor performance and understand the factors affecting performance, including but not limited to resource consumption, response time, and algorithm complexity. For example, in one embodiment, after a detailed evaluation of the Android framework layer graphics drawing code, the logic with high-frequency calls and large computational overhead in a specific function was identified as an area that needed improvement.

[0099] S111232: Set up a temporary buffer pool for the potential problem points to accelerate the data exchange efficiency of the potential problem points.

[0100] In this embodiment, by creating and managing a temporary area to store information that does not need to be processed immediately but will be frequently accessed soon, the reading pressure on the potential problem points can be alleviated, thereby reducing the likelihood of the potential problem points being blocked and accelerating the data flow rate. In one embodiment, when the Android porting system faces continuous input collected by sensors, a small space allocated in memory can be used as a pre-loader or transfer station to perform format unification and fast transfer in advance before the hardware driver passes the data to higher-level application services. In this way, the interaction latency of the Android porting system is greatly reduced, and the work fluency is improved.

[0101] S111233: Implement targeted patches or optimizations for the potential problem points.

[0102] In this embodiment, for the specific deficiencies pointed out in the preliminary diagnosis phase, corresponding strategies can be tailored, or ready-made improvement scripts can be cited to repair vulnerabilities and weaknesses, strengthening the weak links to ensure stable and reliable operation. For example, for the problem that the implementation of the network communication protocol stack in some versions is not efficient enough, a specially customized and optimized library file is used to replace the corresponding segment in the original version. After actual verification, it can significantly shorten the DNS resolution time and increase the connection stability, laying a good foundation for the entire operating system and facilitating the subsequent development to build rich features.

[0103] S111234: According to the component performance index formula, detect the performance changes of the potential problem points before and after optimization.

[0104] In this embodiment, after confirming the completion of the above work, according to the formula Ic = I_before * effect gain α, the performance change before and after optimization can be detected. Here, Ic refers to the value of a specific performance metric after improvement; I_before represents the original level under the pre-optimization conditions. The effect gain α reflects the positive promotion ratio generated by this rectification measure, and its value range usually starts from 1.1 or above, meaning that it is expected to have at least a 10% improvement space. And W is the pre-established minimum allowable percentage increase, ensuring that even in extreme cases, there is still enough confidence to believe that the effort is worth the resulting effect. If α ≥ W, it means that the implemented method has successfully achieved the expected goal. An example where the effect can be recognized may occur in the audio playback scenario: initially, due to the encoding and decoding process consuming too much processor time, synchronization misalignment faults frequently occurred. After introducing the self-adaptive volume adjustment algorithm and decompression method, the test results showed that the playback fluency score increased by 32%, far exceeding the initial expected threshold, indicating that this reform measure has actual effects and potential for promotion.

[0105] The operating system transplantation method provided by an embodiment of the present disclosure, step S111232 may include the following multiple steps.

[0106] S1112321: Plan the size of the temporary buffer pool.

[0107] In this embodiment, planning a buffer area with an appropriate size can prevent the temporary buffer pool from expanding excessively. The buffer size directly affects the data exchange efficiency of the middleware and the system stability. A reasonable plan should consider the balance between the maximum load and normal operations, meeting both the peak performance requirements and not causing resource idleness waste or system memory tension, thus slowing down the data processing flow. The buffer capacity C (C >= 0) needs to be dynamically adjusted to cope with different load patterns, ensuring that unnecessary memory resources are not occupied due to excessive capacity. In this method, the maximum buffer value is set as MaxSize, and the current capacity CurSize is automatically adjusted according to the load situation. When CurSize exceeds the preset warning ratio T1, the scaling mechanism is triggered until a stable state is restored.

[0108] In one embodiment, historical usage data is analyzed for the application running environment of a specific Android device to determine the optimal MaxSize value and the corresponding T1 threshold. Specifically, it is found in a certain mobile device that when the CPU utilization rate of the application usually reaches 80% during peak hours, high concurrent access will occur. Therefore, in this scenario, T1 = 75% and MaxSize = 50MB are set. If the load is detected to be higher than this critical point at this time, the non-core task cache space will start to be decreased to release resources for more important activities, making the overall performance smoother and reducing the risk of performance degradation caused by excessive caching.

[0109] S1112322: Create two copies, one primary and one standby, of the temporary buffer pool.

[0110] In this embodiment, the two copies, one primary and one standby, can be updated synchronously to ensure transaction consistency. For the consistency and reliability of the primary and standby copies, every time there is a data change, it must be synchronized to both the primary and standby storage media to prevent data loss or out-of-sync problems caused by the failure of any node. By adopting a log-based recording scheme, it can effectively support fast recovery after a crash, that is, each change will be first written to the write-ahead log file on the disk, and then the main database will be updated. After all changes are successfully committed, the log will be copied to the standby side and the change application will be synchronized to achieve strong consistency under high performance.

[0111] For example, consider a chat server in a distributed Android application environment. Frequent message sending by users may cause a sharp increase in the pressure on the main server, resulting in errors or increased latency. By configuring a redundant backup database and securely replicating each chat record insertion to two instances simultaneously, the probability of such accidents can be significantly reduced. This approach not only improves service stability but also simplifies the failover process to reduce the negative impact of downtime on users.

[0112] S1112323: Set up a data recycling mechanism for the temporary buffer pool.

[0113] In this embodiment, information that has accumulated over a long time and is no longer needed will occupy a large amount of unnecessary space, which is not conducive to the efficient operation of the operating system and the optimization of response speed. Therefore, intelligent cleaning rules should be introduced to periodically check and remove expired or very low-frequency data objects. In this process, an effective lifespan ExpireDuration (ExpireDuration > 0) can be set, and elements that have not been accessed for more than the time limit will be eliminated. In addition, the real-time utilization rate UtilizationRate needs to be monitored, and combined with the access popularity H (0 < H ≤ 1), when the product of the two is lower than the set threshold L, a compression operation will be performed to delete unpopular items to ensure that the entire system always maintains a good operating state.

[0114] Specifically, when deploying inside a certain news and information App, it is noted that the reading volume of article resources gradually decreases over time. For such large text files, a reasonable preservation period can be evaluated based on the last access time and the average daily growth rate, and a background program can be used to scan once an hour to automatically clear the old version materials that have not been noticed by users for a long time, thus freeing up valuable storage space for the push of the latest important content, which helps to improve the quality of the user experience and relieve the server bandwidth burden.

[0115] S1112324: If the capacity ratio of the current workload of the temporary buffer pool to the storage pool capacity exceeds the warning value of the temporary buffer pool, trigger the expansion program of the temporary buffer pool until the capacity ratio returns to normal.

[0116] In this embodiment, if the ratio of the current workload U (U>0) to the storage pool capacity G (G>0) exceeds the warning value V, the expansion program is immediately triggered until the ratio drops to N or lower. This measure aims to prevent potential bottlenecks and prepare additional space in advance before the resources are about to be exhausted to ensure the continuous service ability. The V value is defined as the percentage form of U / G. Usually, empirical values are selected as the judgment baseline, such as the common range of 50%-70%. If this limit is exceeded, it is considered that there is an urgent expansion need, and then the elastic scaling mechanism is activated, and it is gradually expanded until the ratio returns to the normal range. During this period, the ideal target size OptimalSize will also be calculated with reference to the current available resources and the expected expansion scale, and new storage units will be added in segments according to the strategy to avoid abrupt changes from interfering with the business continuity.

[0117] For example, on a social platform, sudden bursts of topic discussions often occur, resulting in the server being instantly hit by a super-high peak request volume. To avoid this situation from affecting the user experience, when it is detected that the CPU load U reaches more than 60% of the total physical memory G and is close to the system hardware limit, the storage expansion instruction is responded to in a timely manner. Without affecting the original jobs, it can smoothly transition to a higher specification configuration until the workload distribution returns to a reasonable level again. In this way, not only the anti-pressure performance is enhanced, but also enough elastic space is left for the development of more advanced functions in the future.

[0118] The technical solutions provided by one or more embodiments of the present disclosure optimize the configuration of the initialization parameters based on the adaptive parameter regulation model, and can solve the problem of uneven memory allocation during the operating system startup process; dynamically adjust the inter-process communication mechanism, which can reduce the data transmission delay during the multitasking processing of the operating system and enhance the real-time performance and efficiency of the operating system data stream; adjust the file access permission mechanism of the operating system, which can enhance the data isolation and privacy protection of system applications; intelligently adjust the power management mode of the operating system, which can reduce the power consumption when the operating system is on standby; allocate priorities to the computing resources of system applications. In the case of high load of the operating system, it can solve the problem of slow response of system applications and improve the fluency of the operating system.

[0119] The technical solutions provided by one or more embodiments of the present disclosure address the key issues faced in the process of operating system transplantation and propose a series of technical means to optimize the performance of the operating system. By carefully and comprehensively adjusting the working modes and parameter configurations of each core module, the computing performance, response speed, and security features of the transplanted operating system are effectively improved. Please refer to Figure 2 In addition, the present disclosure also provides an operating system transplantation device, which includes:

[0120] A parameter configuration unit 100, configured to configure the initialization parameters of the operating system based on an adaptive parameter regulation model, where the initialization parameters include hardware dependencies and software setting items;

[0121] A communication management unit 200, configured to dynamically adjust the message transmission mode between various system applications of the operating system by using an inter-process communication management mechanism;

[0122] A file management unit 300, configured to adjust the file access permissions of the operating system;

[0123] A battery management unit 400, configured to intelligently adjust the power management mode of the operating system;

[0124] A scheduling algorithm unit 500, configured to allocate computing resources to the system applications according to a preset priority allocation algorithm.

[0125] Each unit described in the above embodiments can be specifically implemented by a computer chip or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0126] For the convenience of description, the above devices are described by dividing them into various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0127] Please refer to Figure 3 In addition, the present disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, the above-mentioned operating system transplantation method is implemented.

[0128] The present disclosure also provides a computer-readable storage medium, which is used to store a computer program, and when the computer program is executed by a processor, the above-mentioned operating system transplantation method is implemented.

[0129] Among them, the processor can be a Central Processing Unit (CPU). The processor can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., in the form of chips, or a combination of the above types of chips.

[0130] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present disclosure. By running the non-transitory software programs, instructions, and modules stored in the memory, the processor can execute various functional applications and data processing of the processor, that is, implement the methods in the above method embodiments.

[0131] The memory can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory can optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0132] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, optical disk, Read-Only Memory (ROM), Random Access Memory (RAM), Flash Memory, Hard Disk Drive (abbreviation: HDD), or Solid-State Drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0133] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0134] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0135] Although the embodiments of the present disclosure are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for transplanting an operating system, characterized in that: The method comprises: Based on the adaptive parameter control model, configuring the initialization parameters of the operating system, the initialization parameters including hardware dependency items and software setting items; Using an inter-process communication management mechanism, dynamically adjusting the message transmission mode between various system applications of the operating system; Adjusting file access permissions of the operating system; Intelligently adjusting the power management mode of the operating system; Allocate computing resources to the system application according to a preset priority allocation algorithm.

2. The method according to claim 1, characterized in that The configuring of the initialization parameters of the operating system based on the adaptive parameter control model includes: Analyzing memory allocation data during the startup of the operating system; Based on the memory allocation data, using a dynamic prediction algorithm, estimating the expected memory space of each application process of the operating system; According to the expected memory space, adjusting the kernel parameters of the operating system, and allocating processor resources and memory resources to each of the application processes; The remaining physical memory of the operating system is calculated, and when the remaining physical memory is lower than a baseline value, the virtual memory page swap frequency of the operating system is increased.

3. The method according to claim 2, characterized in that The analyzing the memory allocation data during the startup of the operating system includes: Classify according to time periods, collect and summarize the application loading time of each of the system applications; Detecting the memory fragmentation rate of each of the system applications during the loading process, and calculating the average memory fragmentation rate of each of the system applications; Adjusting the initial buffer area of ​​the operating system according to current device hardware parameters; According to the average memory fragmentation rate, the current memory allocation efficiency is calculated, and according to the comparison result of the current memory allocation efficiency and the historical average allocation efficiency, the current memory allocation scheme is evaluated.

4. The method according to claim 3, characterized in that The detecting the memory fragmentation rate of each of the system applications during the loading process and calculating the average memory fragmentation rate of each of the system applications includes: Tracking the memory allocation path of each of the system applications, and recording the memory fragments generated by each of the system applications; Calculating the ratio between the actual occupancy and the nominal occupancy of each allocated memory block, wherein the ratio is used to determine the memory fragmentation rate; According to the memory fragmentation generation record and the memory fragmentation rate, adjusting the free list management strategy of the operating system; The average length of the memory fragments is compared with the expected fragment size to determine whether to execute a compaction task, wherein the compaction task is used to merge or clean up the memory fragments.

5. The method according to claim 4, characterized in that The tracking of the memory allocation paths of the system applications and recording the memory fragments generated by the system applications include: Counting changes in the free areas of each of the allocated memory blocks; Prioritize memory allocation for continuous access requests of the system application; Using a machine learning module, evaluating the impact of different memory allocation strategies on future loads of the operating system; According to the comparison result of the memory usage difference of the allocated memory block and the reasonable growth rate, it is determined whether the memory allocation strategy of the operating system adopts the large page management mechanism.

6. The method according to claim 5, characterized in that The using of the machine learning module to evaluate the impact of different memory allocation strategies on the future load of the operating system includes: Periodically updating the training data set of the machine learning module; Adding a new behavioral pattern recognition component to an existing classifier of the machine learning module; Using a feedback loop, calibrating model parameters of the machine learning module; According to the comparison result between the error rate and the error threshold, determine whether to change the learning coefficient of the machine learning module.

7. The method according to claim 3, characterized in that The collecting and summarizing the application loading time of each of the system applications includes: Monitoring the first call time of the system application and the resource utilization rate of the system application; Establishing a correlation chart of internal components of the system application according to the first call time and the resource utilization rate; Adjusting the resource scheduling level of the system application according to differences in device models; The startup times and startup time of the system application are compared to evaluate the startup efficiency of the system application.

8. The method according to claim 7, characterized in that The establishing of a correlation chart of internal components of the system application according to the first call time and the resource utilization rate includes: Establishing the association graph according to the activity and priority of the system applications; Analyzing the association graph, screening main components among the internal components, and determining the connection paths of the main components; Determine the bottleneck link according to the main components and the connection path, and optimize the bottleneck link; When the influence range of the main component exceeds the influence threshold, the scheduling right of the adjacent components of the main component is strengthened until the influence range of the main component falls back below the influence threshold.

9. The method according to claim 8, characterized in that The optimizing of the bottleneck link comprises: Review the relevant component codes of the bottleneck link to identify potential problem points; Setting a temporary buffer pool for the potential problem point to speed up the data exchange efficiency of the potential problem point; Implement targeted patches or optimizations for the potential problem points; According to the component performance indicator formula, the performance changes before and after the optimization of the potential problem point are detected.

10. The method according to claim 9, characterized in that The step of setting a temporary buffer pool of the potential problem point to accelerate the data exchange efficiency of the potential problem point includes: Planning the size of the temporary buffer pool; Establishing two primary and backup copies of the temporary buffer pool; Setting a data recovery mechanism for the temporary buffer pool; If the capacity ratio of the current workload load of the temporary buffer pool to the storage pool capacity exceeds the warning value of the temporary buffer pool, the capacity expansion procedure of the temporary buffer pool is triggered until the capacity ratio returns to normal.

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