A method and system for optimizing the performance of an Android motherboard of a mobile phone

Abstract

The present application provides a method and system for optimizing the performance of an Android motherboard of a mobile phone. By monitoring the operating loads of each functional unit of the Android motherboard when running initial data; extracting a plurality of key loads and a plurality of auxiliary loads from all the operating loads, and further determining a plurality of emergency partitions of the Android motherboard for dealing with abnormal data, determining the emergency response degree of the Android motherboard according to all the emergency partitions and all the auxiliary loads; extracting a plurality of adjustable functional units from all the functional units of the Android motherboard according to the emergency response degree; determining a plurality of optimization partitions of the Android motherboard through all the adjustable functional units and initial scheduling information; driving each functional unit in the Android motherboard according to all the optimization partitions. The above solution can avoid the untimely processing of abnormal data by the Android motherboard, which affects the performance of the Android motherboard, thereby improving the performance of the Android motherboard.

Description

Technical Field

[0001] This application relates to the field of computer technology. More specifically, this application relates to a method and system for optimizing the performance of a mobile phone Android motherboard. Background Art

[0002] A computer is an electronic device that can accept, process, and store data, and can execute a series of instructions to complete specific tasks. It consists of hardware and software, and the hardware and software are controlled and communicated through a computer motherboard. Computers can be used for various applications such as document processing, data analysis, games, web browsing, etc.

[0003] Motherboard performance optimization refers to the process of improving the overall performance and efficiency of the motherboard by adjusting hardware configurations, software settings, and resource management. This includes reducing power consumption, increasing processing speed, improving data transfer rates, and optimizing resource allocation. Mobile phone Android motherboard performance optimization refers to improving the operating efficiency and performance of an Android motherboard and its related hardware components through a series of technical means and strategies. These methods can improve the device's response speed, extend battery life, and improve system stability. During the operation of an existing mobile phone Android motherboard, the performance of the motherboard is related to various associated software and hardware. If abnormal data (such as programming errors, logical errors, improper resource management) appears during motherboard operation, it usually requires timely manual processing. If the processing is not timely, long-term abnormal data will cause the hardware to age rapidly, affecting the service life of the device, and more likely causing the system to restart or freeze frequently. Therefore, how to avoid the Android motherboard from being unable to process abnormal data in a timely manner and affecting the performance of the Android motherboard, thereby improving the performance of the Android motherboard, has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a method and system for optimizing the performance of a mobile phone Android motherboard, which can avoid the Android motherboard from being unable to process abnormal data in a timely manner and affecting the performance of the Android motherboard, thereby improving the performance of the Android motherboard.

[0005] In a first aspect, this application provides a method for optimizing the performance of a mobile phone Android motherboard, including the following steps:

[0006] Monitor the operating loads of each functional unit when the Android motherboard is running initial data;

[0007] Extract multiple key loads and multiple auxiliary loads from all the operating loads, determine multiple emergency partitions of the Android motherboard for dealing with abnormal data based on the association characteristics between each functional unit and all the key loads, and determine the emergency response degree of the Android motherboard for dealing with abnormal data according to all the emergency partitions and all the auxiliary loads;

[0008] Extract multiple adjustable functional units of the Android main board when processing abnormal data from all functional units of the Android main board according to the emergency response degree;

[0009] Determine the initial scheduling information of the functional units when the Android main board performs data switching, and drive and optimize all the functional units of the Android main board during data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android main board;

[0010] Drive each functional unit in the Android main board according to all the optimized partitions.

[0011] In some embodiments, extracting multiple key loads and multiple auxiliary loads from all the running loads specifically includes:

[0012] Obtain the historical running load data of the Android main board;

[0013] Determine the running load thresholds of each functional unit according to the historical running load data;

[0014] Determine multiple key loads and multiple auxiliary loads according to all the running load thresholds and all the running loads.

[0015] In some embodiments, determining multiple emergency partitions of the Android main board when dealing with abnormal data based on the association characteristics between each functional unit and all the key loads specifically includes:

[0016] Obtain the association characteristics between each functional unit;

[0017] Determine multiple same-level load sets according to all the key loads;

[0018] Determine multiple emergency partitions of the Android main board when dealing with abnormal data according to all the same-level load sets and the association characteristics.

[0019] In some embodiments, determining the emergency response degree of the Android main board when dealing with abnormal data according to all the emergency partitions and all the auxiliary loads specifically includes:

[0020] Determine the historical access data of the functional units in each emergency partition;

[0021] Determine the abnormal information of the Android main board when dealing with abnormal data according to all the historical access data;

[0022] Determine the emergency response degree of the Android main board when dealing with abnormal data according to the abnormal information and all the auxiliary loads.

[0023] In some embodiments, the multiple adjustable functional units when the Android main board processes abnormal data extracted from all functional units of the Android main board by the emergency response degree specifically include:

[0024] Determine the fault adjustment strategy of the Android main board when dealing with abnormal data according to the emergency response degree and the auxiliary load;

[0025] Determine the fault correlation degree of each functional unit in the Android main board according to the fault adjustment strategy;

[0026] Determine multiple confidence correlation degrees when the Android main board processes abnormal data based on all the fault correlation degrees;

[0027] Determine multiple adjustable functional units when the Android main board processes abnormal data through all the confidence correlation degrees and all the functional units.

[0028] In some embodiments, determining the initial scheduling information of the functional units when the Android main board performs data switching specifically includes:

[0029] Monitor the running data of the Android main board when switching data;

[0030] Determine the initial scheduling information of the functional units when the Android main board performs data switching through the running data.

[0031] In some embodiments, driving optimization of all functional units when the Android main board performs data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android main board specifically includes:

[0032] Adjust the initial scheduling information according to all the adjustable functional units to obtain adjusted scheduling information;

[0033] Determine the running drive ratio of each functional unit when the Android main board performs data switching through the adjusted scheduling information;

[0034] Determine multiple optimized partitions of the Android main board based on all the running drive ratios.

[0035] In a second aspect, the present application provides a mobile phone Android main board performance optimization system, including:

[0036] A monitoring module for monitoring the running load of each functional unit of the Android main board when running initial data;

[0037] A processing module, configured to extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions of the Android mainboard for coping with abnormal data based on the association characteristics between each functional unit and all key loads, and determine the emergency response degree of the Android mainboard for coping with abnormal data according to all the emergency partitions and all the auxiliary loads;

[0038] The processing module is further configured to extract multiple adjustable functional units of the Android mainboard for processing abnormal data from all functional units of the Android mainboard according to the emergency response degree;

[0039] The processing module is further configured to determine the initial scheduling information of the functional units when the Android mainboard performs data switching, and drive and optimize all the functional units of the Android mainboard during data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard;

[0040] An execution module, configured to drive each functional unit in the Android mainboard according to all the optimized partitions.

[0041] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for optimizing the performance of the mobile phone Android mainboard.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for optimizing the performance of the mobile phone Android mainboard is implemented.

[0043] The technical solution provided by the disclosed embodiments of the present application has the following beneficial effects:

[0044] In the method and system for optimizing the performance of a mobile phone Android mainboard provided by this application, first, the running loads of each functional unit of the Android mainboard when running initial data are monitored; multiple key loads and multiple auxiliary loads are extracted from all the running loads, and multiple emergency partitions of the Android mainboard for dealing with abnormal data are determined based on the association characteristics between each functional unit and all the key loads. The emergency response degree of the Android mainboard for dealing with abnormal data is determined according to all the emergency partitions and all the auxiliary loads; multiple adjustable functional units of the Android mainboard for processing abnormal data are extracted from all the functional units of the Android mainboard by the emergency response degree; the initial scheduling information of the functional units when the Android mainboard performs data switching is determined, and all the functional units of the Android mainboard during data switching are driven and optimized through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard; each functional unit in the Android mainboard is driven according to all the optimized partitions.

[0045] It can be seen that during the performance optimization of the mobile phone Android mainboard in this application, by analyzing the running loads of each functional unit of the Android mainboard when running initial data, and combining the association characteristics between each functional unit, the emergency area composed of functional units of the Android mainboard for dealing with abnormal data is determined, and then multiple emergency partitions are obtained; the emergency partitions can enable the Android mainboard to deal with sudden abnormal data; secondly, the parameter value of the response degree when the Android mainboard responds emergently to abnormal data is determined through all the emergency partitions, and then the emergency response degree is obtained. The emergency response degree can enable the Android mainboard to temporarily process abnormal data and avoid the harm caused by untimely processing of abnormal data to the Android mainboard; thus, the functional units for adjustment when the Android mainboard deals with abnormal data are determined to obtain multiple adjustable functional units, and the adjustable functional units reduce the power consumption when the Android mainboard processes abnormal data; furthermore, all the functional units of the Android mainboard during data switching are driven and optimized through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard. The optimized partitions are the partitions reflecting the optimization of the functional units of the Android mainboard during data switching, and are used to drive the functional units in the Android mainboard, improve the processing efficiency when abnormal data appears in the Android mainboard, avoid the situation of untimely processing of abnormal data by the Android mainboard, and reduce the power consumption of the Android mainboard; finally, each functional unit in the Android mainboard is driven according to all the optimized partitions. The above solution can avoid the situation that the Android mainboard affects its performance due to untimely processing of abnormal data, so improving the performance of the Android mainboard has become a problem faced by the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is an exemplary flowchart of a method for optimizing the performance of a mobile phone Android mainboard according to some embodiments of this application;

[0047] Figure 2 is a partial view of the emergency partition shown in some embodiments of the present application;

[0048] Figure 3 is an exemplary flowchart for determining the emergency response degree shown in some embodiments of the present application;

[0049] Figure 4 is a schematic structural diagram of a mobile phone Android motherboard performance optimization system shown in some embodiments of the present application;

[0050] Figure 5 is a schematic structural diagram of a computer device for implementing the method for optimizing the performance of a mobile phone Android motherboard shown in some embodiments of the present application. Detailed implementation manners

[0051] To better understand the technical solutions in the present application, the technical solutions in the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0052] Refer to Figure 1 , which is an exemplary flowchart of the method for optimizing the performance of a mobile phone Android motherboard shown in some embodiments of the present application. The method 100 for optimizing the performance of a mobile phone Android motherboard mainly includes the following steps:

[0053] In step 101, monitor the operating loads of each functional unit when the Android motherboard is running initial data.

[0054] It should be noted that the operating load described in the present application represents the amount of data that the functional unit needs to process when the Android motherboard is running initial data. The operating load can be used to measure the resource utilization of the working unit in the Android motherboard. The initial data can be sensor data, system logs, memory allocation, etc. As a preferred embodiment, after starting the operation of the Android motherboard, the operating loads of each functional unit when the Android motherboard is running initial data are collected by a load acquisition device. Among them, the load acquisition device is, for example: an electronic load meter, a load spectrum acquisition device, etc. In other embodiments, other methods can also be used for collection, which will not be elaborated here.

[0055] In step 102, extract a plurality of key loads and a plurality of auxiliary loads from all the operating loads, and determine a plurality of emergency partitions of the Android motherboard for dealing with abnormal data based on the association characteristics between each functional unit and all the key loads.

[0056] In some embodiments, extracting a plurality of key loads and a plurality of auxiliary loads from all the operating loads can be implemented by the following steps:

[0057] Obtain the historical operating load data of the Android motherboard;

[0058] Determine the operating load thresholds of each functional unit according to the historical operating load data;

[0059] Determine multiple critical loads and multiple auxiliary loads according to all the operating load thresholds and all the operating loads.

[0060] When specifically implemented, obtain the historical operating load data of the Android main board from the database of the Android main board, where the historical operating load data is a set of the historical operating loads of each functional unit in the past month; determining the operating load thresholds of each functional unit according to the historical operating load data can be implemented in the following manner: select a functional unit as the selected functional unit, extract all the historical operating loads of the selected functional unit when processing data from the historical operation load data, and use the threshold calculation method in the prior art (such as the empirical method, machine learning method) combined with the extracted all historical operating loads as the operating load threshold of the selected functional unit, and continue to determine the operating load thresholds of the remaining functional units, where the operating load threshold represents the load judgment value for whether the functional unit is processing data and is used to judge the load of the functional unit during operation; in other embodiments, other methods can also be used for determination, which are not limited here.

[0061] When specifically implemented, determining multiple critical loads and multiple auxiliary loads according to all the operating load thresholds and all the operating loads can be implemented in the following manner, that is: select a functional unit as the selected functional unit, compare the operating load corresponding to the selected functional unit with the operating load threshold corresponding to the selected functional unit. If the operating load corresponding to the selected functional unit is greater than or equal to the operating load threshold corresponding to the selected functional unit, then use the operating load corresponding to the selected functional unit as the critical load. If the operating load corresponding to the selected functional unit is less than the operating load threshold corresponding to the selected functional unit, then use the operating load corresponding to the selected functional unit as the auxiliary load, and continue to judge the operating loads corresponding to the remaining functional units to obtain multiple critical loads and multiple auxiliary loads; in other embodiments, other methods can also be used for determination, which are not limited here.

[0062] It should be noted that the critical load in this application reflects the operating load of the functional unit that mainly processes the operating data when the Android main board is running and is used to analyze the trend of the operating data of the Android main board; the auxiliary load reflects the operating load of the functional unit that indirectly processes the operating data when the Android main board is running and is used to analyze the relevance of the operating data of the Android main board.

[0063] In some embodiments, determining multiple emergency partitions of the Android main board for dealing with abnormal data based on the association characteristics between each functional unit and all the critical loads can be implemented by the following steps:

[0064] Obtain the association features between each functional unit;

[0065] Determine multiple sets of same-level loads according to all the critical loads;

[0066] Determine multiple emergency partitions of the Android main board when dealing with abnormal data according to all the sets of same-level loads and the association features.

[0067] When specifically implemented, obtain the association features between each functional unit from the database of the Android main board. The association features represent the association levels between different functional units in terms of information flow, control flow, resource usage, etc. Determining multiple sets of same-level loads according to all the critical loads can be implemented in the following way: obtain the historical operating load data of the Android main board, and use the standard deviation of all the historical operating loads in the historical operating load data as the load fluctuation coefficient. Among them, the load fluctuation coefficient is a parameter representing the fluctuation degree of the operating loads of each functional unit when the Android main board is running, and is used to judge the operating loads of each functional unit. Sort all the critical loads in ascending order, and use the obtained sequence as the critical load sequence. Divide the critical load sequence from front to back according to the size of the load fluctuation coefficient to obtain multiple critical load sequence segments, and use each critical load sequence segment as a set of same-level loads. Among them, the set of same-level loads is the set of all same-level loads, and all the same-level loads in the set of same-level loads represent the set of loads with similar processed data for the corresponding functional units when the Android main board is running. For example, the load fluctuation coefficient is 2, and the critical load sequence is (1, 2, 3, 4, 5, 6, 7, 8, 9). The critical load sequence (1, 2, 3, 4, 5, 6, 7, 8, 9) is divided according to the load fluctuation coefficient of 2, and the first critical load 1 is extracted, and the critical loads in the range from 1 to 1 + 2 = 3 are extracted as the critical load sequence segment (1, 2, 3). The critical load sequence is divided in turn to obtain multiple critical load sequence segments (1, 2, 3), (4, 5, 6), (7, 8, 9); in other embodiments, other methods can also be used for determination, which are not limited here.

[0068] When specifically implemented, multiple emergency partitions of the Android mainboard for dealing with abnormal data can be determined according to all the same-level load sets and the associated features, which can be implemented in the following way, that is: extract multiple same-level loads in all the same-level load sets that are lower than the average load of all running loads, select one same-level load set from all the extracted same-level loads as the selected same-level load set, use the functional units corresponding to each same-level load in the selected same-level load set as each node, and connect all the nodes according to the corresponding association relationships in the associated features, that is: there are sequence and association relationships in the connection. Connect all the nodes according to the priority order of the association levels, and use each same-level load in the selected same-level load set as the value of the corresponding node. The connected area is used as the emergency partition of the Android mainboard for dealing with abnormal data, and continue to determine multiple emergency partitions of the Android mainboard for dealing with abnormal data; for example, refer to Figure 2 As described, this figure is a partial view of the emergency partition shown in this embodiment. In this figure, 1, 2, 3, and 4 represent the priorities of driving functional units in the arrow direction, 1 is the highest priority, and the numbers in the functional units represent the running loads of the individual functional units; in other embodiments, other methods can also be used for determination, which are not limited here.

[0069] It should be noted that the emergency partition in this application is an emergency area reflecting the composition of functional units of the Android mainboard for dealing with abnormal data, and is used as the response area of the Android mainboard when running abnormal data, which is convenient for timely corresponding adjustments of the Android mainboard when running abnormal data; the abnormal data is data reflecting abnormalities in the Android mainboard, and the abnormal data includes system cache abnormalities, hardware restart abnormalities, temperature abnormalities, etc.

[0070] In step 103, according to all the emergency partitions and all the auxiliary loads, determine the emergency response degree of the Android mainboard for dealing with abnormal data, and then extract multiple adjustable functional units of the Android mainboard for processing abnormal data from all the functional units of the Android mainboard according to the emergency response degree.

[0071] In some embodiments, refer to Figure 3 As shown, this figure is a schematic flow chart for determining the emergency response degree in some embodiments of this application. In this embodiment, the emergency response degree of the Android mainboard for dealing with abnormal data can be determined according to all the emergency partitions and all the auxiliary loads by the following steps:

[0072] First, in step 1031, determine the historical access data of the functional units in each emergency partition;

[0073] Secondly, in step 1032, determine the abnormal information of the Android mainboard for dealing with abnormal data according to all the historical access data;

[0074] Finally, in step 1033, determine the emergency response degree of the Android main board when dealing with abnormal data according to the abnormal information and all auxiliary loads.

[0075] When specifically implemented, the historical access data of the functional units in each emergency partition can be determined in the following manner, that is: obtain the historical access frequency of each functional unit from the database of the Android main board, where the historical access frequency represents the average access frequency of the functional unit within one month and is used to measure the access situation of the functional unit. Select an emergency partition as the selected emergency partition, and use the set of historical access frequencies corresponding to each functional unit in the selected emergency partition as the historical access data of the functional units in the selected emergency partition, and continue to determine the historical access data of the functional units in the remaining emergency partitions; in other embodiments, other methods can also be used for determination, which is not limited here.

[0076] When specifically implemented, the abnormal information of the Android main board when dealing with abnormal data can be determined according to all the historical access data in the following manner, that is: select an emergency partition as the selected emergency partition, obtain the total number of times each functional unit in the selected emergency partition deals with abnormal data from the database of the Android main board, divide the maximum historical access frequency in the historical access data corresponding to the selected emergency partition by the minimum historical access frequency in the historical access data corresponding to the selected emergency partition, perform a logarithmic operation with the base of 2 on the obtained value, multiply the value obtained from the logarithmic operation by the standard deviation of the total number of times all functional units in the selected emergency partition deal with abnormal data, and use the value obtained from the multiplication as the abnormality degree of the selected emergency partition when dealing with abnormal data. Continue to determine the abnormality degree of the remaining emergency partitions when dealing with abnormal data. Among them, the abnormality degree represents the parameter value of the accessibility of the emergency partition to abnormal data when the Android main board deals with abnormal data, that is: the situation of the emergency partition dealing with abnormal data when the Android main board deals with abnormal data. Use the set of all abnormality degrees as the abnormal information of the Android main board when dealing with abnormal data, where the abnormal information represents the information of the situation of each emergency partition when the Android main board deals with abnormal data; the emergency response degree of the Android main board when dealing with abnormal data can be determined according to the abnormal information and all auxiliary loads in the following manner, that is: multiply the total number of all auxiliary loads by the entropy of all abnormality degrees in the abnormal information, divide the value obtained from the multiplication by the standard deviation of all auxiliary loads, and use the value obtained from the division as the emergency response degree of the Android main board when dealing with abnormal data; in other embodiments, other methods can also be used for determination, which is not limited here.

[0077] It should be noted that the emergency response degree in this application represents the parameter value of the response degree when the Android main board makes an emergency response to abnormal data, and is used for the Android main board to process abnormal data, and can temporarily process abnormal data to avoid the harm of abnormal data to the Android main board.

[0078] In some embodiments, the multiple adjustable functional units when the Android main board processes abnormal data extracted from all the functional units of the Android main board according to the emergency response degree can be implemented by the following steps:

[0079] Determine the fault adjustment strategy of the Android main board for dealing with abnormal data according to the emergency response degree and the auxiliary load;

[0080] Determine the fault correlation degree of each functional unit in the Android main board according to the fault adjustment strategy;

[0081] Determine multiple confidence correlations when the Android main board processes abnormal data according to all the fault correlation degrees;

[0082] Determine multiple adjustable functional units when the Android main board processes abnormal data through all the confidence correlations and all the functional units.

[0083] Specifically, when implemented, determining the fault adjustment strategy of the Android main board for dealing with abnormal data according to the emergency response degree and the auxiliary load can be implemented in the following manner, that is: initialize a fault adjustment strategy model, use the abnormal information as the constraint parameter of this fault adjustment strategy model, use all the auxiliary loads as the initialization parameters of this fault adjustment strategy model, and obtain the fault adjustment strategy of the Android main board for dealing with abnormal data through this fault adjustment strategy model. Among them, the fault adjustment strategy model is a strategy model for establishing a fault adjustment strategy using machine learning algorithms (such as regression algorithms, neural networks, etc.). The strategy model is, for example: fault adjustment strategy = abnormal information * A + all the auxiliary loads * B, where A and B are weight coefficients, and A and B can be determined according to a large number of fault adjustment strategies. In other embodiments, other methods can also be used for determination, which is not limited here.

[0084] It should be noted that the fault adjustment strategy in this application is a strategy for reflecting the adjustment when the Android main board has abnormal data. The fault adjustment strategy includes the adjustment conditions of each functional unit in the Android main board, that is, the power consumption required by each functional board for abnormal data, which is used to process the abnormal data of the Android main board, facilitating the Android main board to make corresponding adjustments in a timely manner, and thus improving the operating stability of the Android main board.

[0085] In specific implementation, the fault correlation degrees of each functional unit in the Android main board can be determined according to the fault adjustment strategy in the following way: select a functional unit as the selected functional unit, perform a natural exponential operation on the power consumption corresponding to the selected functional unit in the fault adjustment strategy, subtract the reciprocal of the natural exponential operation from 1, and use the obtained value as the fault correlation degree of the selected functional unit. Then continue to determine the fault correlation degrees of the remaining functional units. The fault correlation degree is a parameter value representing the degree of association of the functional unit when dealing with abnormal data, and is used to adjust the functional units in the Android main board. In other embodiments, other methods can also be used for determination, which are not limited here.

[0086] In specific implementation, the multiple confidence correlation degrees when the Android main board processes abnormal data can be determined according to all the fault correlation degrees in the following way: calculate the average value of all the fault correlation degrees, extract each fault correlation degree greater than the average value from all the fault correlation degrees, and use the extracted fault correlation degrees as the confidence correlation degrees when the Android main board processes abnormal data. The confidence correlation degree is a parameter value representing the degree of association of the main functional units when the Android main board processes abnormal data, and is used for the Android main board to process abnormal data. The multiple adjustable functional units when the Android main board processes abnormal data can be determined through all the confidence correlation degrees and all the functional units in the following way: select a confidence correlation degree as the selected confidence correlation degree, and use the functional unit corresponding to the selected confidence correlation degree among all the functional units as the adjustable functional unit when the Android main board processes abnormal data. Then continue to determine the multiple adjustable functional units when the Android main board processes abnormal data. In other embodiments, other methods can also be used for determination, which are not limited here.

[0087] It should be noted that the adjustable functional unit in this application is a functional unit used for adjustment when the Android main board deals with abnormal data, which is used for the Android main board to process abnormal data and reduce the power consumption when the Android main board processes abnormal data.

[0088] In step 104, determine the initial scheduling information of the functional units when the Android main board performs data switching, and optimize the driving of all the functional units when the Android main board performs data switching through all the adjustable functional units and the initial scheduling information, so as to obtain multiple optimized partitions of the Android main board.

[0089] In some embodiments, the initial scheduling information of the functional units when the Android main board performs data switching can be determined by the following steps:

[0090] Monitor the running data of the Android main board when switching data.

[0091] Determine the initial scheduling information of the functional units when the Android mainboard performs data switching based on the operation data.

[0092] When specifically implemented, monitoring the operation data of the Android mainboard when switching data can be achieved in the following manner, that is: monitor the operation data of the Android mainboard when switching data through a data monitoring system (such as Systrace, Android Studio CPU Profiler), where the operation data represents the operation data of the Android mainboard when switching data; determining the initial scheduling information of the functional units when the Android mainboard performs data switching based on the operation data can be achieved in the following manner, that is: judge the data type of the operation data through a type judgment operator in the prior art, and extract the initial scheduling information of the functional units when the Android mainboard performs data switching from the control system of the Android mainboard based on the data type of the operation data; in other embodiments, other methods can also be used for monitoring, which are not limited here.

[0093] It should be noted that the initial scheduling information in this application represents the information on the scheduling situation of each functional unit when the Android mainboard processes operation data. The initial scheduling information includes the adjustment situation of the functional units, that is: the priority and driving situation of each functional unit. The initial scheduling information is used to schedule the functional units of the Android mainboard when dealing with operation data.

[0094] In some embodiments, driving optimization of all the functional units when the Android mainboard performs data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard can be achieved by the following steps:

[0095] Adjust the initial scheduling information according to all the adjustable functional units to obtain adjusted scheduling information;

[0096] Determine the running drive ratio of each functional unit when the Android mainboard performs data switching through the adjusted scheduling information;

[0097] Determine multiple optimized partitions of the Android mainboard based on all the running drive ratios.

[0098] In specific implementation, the initial scheduling information is adjusted according to all adjustable functional units, and the adjusted scheduling information can be implemented in the following manner: extract the functional units that can replace each adjustable functional unit from the control center of the Android main board, replace the adjustable functional units in the initial scheduling information with the functional units that can replace each adjustable functional unit, and use the replaced initial scheduling information as the adjusted scheduling information. Among them, the adjusted scheduling unit represents the scheduling information of the functional units after adjustment when the Android main board performs data switching, and is used to drive the functional units of the Android main board; in other embodiments, other methods can also be used for determination, which are not limited here.

[0099] In specific implementation, the running drive ratio of each functional unit when the Android main board performs data switching can be determined according to the adjusted scheduling information in the following manner: arrange all functional units in the order of the priority in the adjusted scheduling information, and use the arranged sequence as the functional unit sequence. Select a functional unit in the functional unit sequence as the selected functional unit, divide the position of the selected functional unit in the functional unit sequence by the total number of all functional units, subtract the obtained value from 1, and use the obtained value as the running drive ratio of the selected functional unit when the Android main board switches data. Then continue to determine the running drive ratio of each remaining functional unit when the Android main board switches data. Among them, the running drive ratio represents the parameter value of the proportion degree when the functional module drives during data switching of the Android main board, that is, the order of driving, and is used to drive the functional units of the Android main board; in other embodiments, other methods can also be used for determination, which are not limited here.

[0100] In specific implementation, multiple optimized partitions of the Android main board can be determined according to all the running drive ratios in the following manner: initialize an optimized partition model, use all the running drive ratios as the initialization parameters of this optimized partition model, and output multiple optimized partitions of the Android main board through this optimized partition model. Among them, the optimized partition model is a partition model that uses machine learning algorithms (such as regression algorithms, neural networks, etc.) to establish optimized partitions. The partition model includes thread optimization, scheduling measurement adjustment, scheduling framework optimization, memory management, etc. By inputting the initialization parameters into this partition model and combining the relevant optimization adjustments in the partition model, multiple optimized partitions of the Android main board are output. In other embodiments, other methods can also be used for determination, which are not limited here.

[0101] It should be noted that the optimized partitions in this application reflect the partitions after the functional units of the Android main board are optimized during data switching, and are used to drive the functional units in the Android main board. The optimized partitions include the connection situation of the functional units, the distribution situation of the functional units, etc., which can improve the processing efficiency when abnormal data appears in the Android main board.

[0102] In step 105, each functional unit in the Android mainboard is driven according to all the optimized partitions.

[0103] In some embodiments, driving each functional unit in the Android mainboard according to all the optimized partitions can be implemented by the following steps:

[0104] Obtain the driving information of the Android mainboard;

[0105] Adjust the driving information through all the optimized partitions to obtain the driving adjustment information of the Android mainboard;

[0106] Drive each functional unit in the Android mainboard according to the driving adjustment information.

[0107] Specifically, obtaining the driving information of the Android mainboard can be implemented in the following way, that is: accessing the Android source code of the Android mainboard through the existing access tools (Git and Repo tools) in the prior art to obtain the driving information of the Android mainboard, where the driving information represents the information for driving each functional unit in the Android mainboard, that is: controlling and communicating with each functional unit; adjusting the driving information through all the optimized partitions to obtain the driving adjustment information of the Android mainboard can be implemented in the following way, that is: adjusting the driving information through the existing debugging tools (dmesg and debugfs tools) in combination with all the optimized partitions, and using the adjusted driving information as the driving adjustment information of the Android mainboard, where the driving adjustment information represents the information after adjusting the driving information of each functional unit in the Android mainboard, driving each functional unit in the Android mainboard according to the driving adjustment information can be implemented in the following way, that is: updating the driving information of the Android mainboard through the driving adjustment information, and then re-driving each functional unit in the Android mainboard; in other embodiments, other methods can also be used for determination, which are not limited here.

[0108] In addition, on the other hand of the present application, in some embodiments, the present application provides a mobile phone Android mainboard performance optimization system. Refer to Figure 4 , this figure is a schematic structural diagram of a mobile phone Android mainboard performance optimization system according to some embodiments of the present application. The mobile phone Android mainboard performance optimization system 400 includes: a monitoring module 401, a processing module 402, and an execution module 403, which are described as follows:

[0109] The monitoring module 401, in the present application, the monitoring module 401 is mainly used to monitor the running load of each functional unit when the Android mainboard is running the initial data.

[0110] The processing module 402. In this application, the processing module 402 is used to extract multiple critical loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions of the Android main board for dealing with abnormal data based on the association characteristics between each functional unit and all critical loads, and determine the emergency response degree of the Android main board for dealing with abnormal data according to all the emergency partitions and all the auxiliary loads;

[0111] It should be noted that in this application, the processing module 402 is further used to extract multiple adjustable functional units of the Android main board for processing abnormal data from all functional units of the Android main board according to the emergency response degree;

[0112] In addition, it should be noted that in this application, the processing module 402 is further used to determine the initial scheduling information of the functional units when the Android main board performs data switching, and perform drive optimization on all functional units of the Android main board during data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android main board;

[0113] The execution module 403. In this application, the execution module 403 is mainly used to drive each functional unit in the Android main board according to all the optimized partitions.

[0114] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned mobile phone Android main board performance optimization method.

[0115] In some embodiments, refer to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the mobile phone Android main board performance optimization method according to some embodiments of this application. The above-mentioned mobile phone Android main board performance optimization method can be implemented by Figure 5 The shown computer device, this computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0116] The processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0117] The communication bus 502 can be used to transmit information between the above components.

[0118] The memory 503 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0119] Among them, the memory 503 is used to store the program code for executing the solution of this application and is controlled by the processor 501 for execution. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The methods used in the above embodiments can be implemented by one or more software modules in the program code of the processor 501 and the memory 503.

[0120] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0121] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0122] The computer device described above may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0123] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for optimizing the performance of a mobile phone Android motherboard is implemented.

[0124] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0125] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the performance of an Android motherboard of a mobile phone, characterized in that, It includes the following steps: Monitor the running loads of each functional unit when the Android main board runs initial data; Extract multiple key loads and multiple auxiliary loads from all the running loads, determine multiple emergency partitions of the Android main board for dealing with abnormal data based on the association characteristics between each functional unit and all the key loads, and determine the emergency response degree of the Android main board for dealing with abnormal data according to all the emergency partitions and all the auxiliary loads; Extract multiple adjustable functional units of the Android main board for dealing with abnormal data from all the functional units of the Android main board according to the emergency response degree; Determine the initial scheduling information of the functional units when the Android main board performs data switching, and optimize the driving of all the functional units when the Android main board performs data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android main board; Drive each functional unit in the Android main board according to all the optimized partitions.

2. The method according to claim 1, characterized in that Specifically, extracting multiple key loads and multiple auxiliary loads from all the running loads includes: Obtain the historical running load data of the Android main board; Determine the running load thresholds of each functional unit according to the historical running load data; Determine multiple key loads and multiple auxiliary loads according to all the running load thresholds and all the running loads.

3. The method according to claim 1, wherein Specifically, determining multiple emergency partitions of the Android main board for dealing with abnormal data based on the association characteristics between each functional unit and all the key loads includes: Obtain the association characteristics between each functional unit; Determine multiple same-level load sets according to all the key loads; Determine multiple emergency partitions of the Android main board for dealing with abnormal data according to all the same-level load sets and the association characteristics.

4. The method according to claim 1, characterized in that Specifically, determining the emergency response degree of the Android main board for dealing with abnormal data according to all the emergency partitions and all the auxiliary loads includes: Determine the historical access data of the functional units in each emergency partition; Determine the abnormal information of the Android main board for dealing with abnormal data according to all the historical access data; Determine the emergency response degree of the Android main board for dealing with abnormal data according to the abnormal information and all the auxiliary loads.

5. The method according to claim 1, characterized in that, Specifically, extracting multiple adjustable functional units of the Android main board for dealing with abnormal data from all the functional units of the Android main board according to the emergency response degree includes: Determine the fault adjustment strategy of the Android main board for dealing with abnormal data according to the emergency response degree and the auxiliary loads; Determine the fault correlation degree of each functional unit in the Android main board according to the fault adjustment strategy; Determine multiple confidence correlation degrees of the Android main board for dealing with abnormal data according to all the fault correlation degrees; Determine multiple adjustable functional units of the Android main board for dealing with abnormal data through all the confidence correlation degrees and all the functional units.

6. The method according to claim 1, wherein Specifically, determining the initial scheduling information of the functional units when the Android main board performs data switching includes: Monitor the running data when the Android main board switches data; Determine the initial scheduling information of the functional units when the Android main board performs data switching through the running data.

7. The method according to claim 1, wherein Driving optimization of all functional units during the Android mainboard data switching through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard specifically includes: Adjust the initial scheduling information according to all adjustable functional units to obtain adjusted scheduling information; Determine the running drive ratio of each functional unit during the Android mainboard data switching through the adjusted scheduling information; Determine multiple optimized partitions of the Android mainboard based on all the running drive ratios.

8. A performance optimization system for an Android motherboard of a mobile phone, characterized in that, Including: A monitoring module for monitoring the running load of each functional unit of the Android mainboard when running the initial data; A processing module for extracting multiple key loads and multiple auxiliary loads from all the running loads, determining multiple emergency partitions of the Android mainboard for dealing with abnormal data based on the association characteristics between functional units and all the key loads, and determining the emergency response degree of the Android mainboard for dealing with abnormal data according to all the emergency partitions and all the auxiliary loads; The processing module is further configured to extract multiple adjustable functional units of the Android mainboard for processing abnormal data from all the functional units of the Android mainboard according to the emergency response degree; The processing module is further configured to determine the initial scheduling information of the functional units when the Android mainboard performs data switching, and perform driving optimization on all the functional units during the Android mainboard data switching through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard; An execution module for driving each functional unit in the Android mainboard according to all the optimized partitions.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the mobile phone Android mainboard performance optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the mobile phone Android mainboard performance optimization method according to any one of claims 1 to 7.

Images