Mobile phone Android mainboard performance optimization method and system

Abstract

The invention provides a mobile phone Android mainboard performance optimization method and system. The method comprises the following steps: monitoring an operation load of each functional unit when an Android mainboard operates initial data; extracting a plurality of key loads and a plurality of auxiliary loads from all the operation loads, further determining a plurality of emergency partitions when the Android mainboard deals with the abnormal data, and determining the emergency responsivity of the Android mainboard according to all the emergency partitions and all the auxiliary loads; extracting a plurality of adjustable functional units from all functional units of the Android mainboard according to the emergency responsivity; determining a plurality of optimization partitions of the Android mainboard through all the adjustable function units and the initial scheduling information; and driving each functional unit in the Android mainboard according to all the optimized partitions. According to the scheme, the situation that the performance of the android mainboard is affected due to the fact that the android mainboard does not process the abnormal data in time can be avoided, and therefore the performance of the android mainboard is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to a method and system for optimizing the performance of an Android motherboard of a mobile phone. 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 the computer motherboard. Computers can be used for a variety of 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 configuration, software settings and resource management, which includes reducing power consumption, increasing processing speed, improving data transmission rate and optimizing resource allocation. Mobile Android motherboard performance optimization refers to improving the operating efficiency and performance of the Android motherboard and its related hardware components through a series of technical means and strategies. These methods can improve the response speed of the device, extend battery life, improve system stability, etc. During the operation of the existing mobile phone Android motherboard, the performance of the motherboard is related to the software and hardware associated with the motherboard. If abnormal data (such as programming errors, logical errors, improper resource management) occurs during the operation of the motherboard, it is usually necessary to manually handle it in a timely manner. If it is not handled in time, long-term abnormal data will cause accelerated aging of the hardware, affecting the service life of the equipment, and may even cause the system to restart frequently or freeze. Therefore, how to avoid the Android motherboard from affecting the performance of the Android motherboard due to its untimely processing of abnormal data, thereby improving the performance of the Android motherboard has become a problem faced by the industry. Summary of the invention

[0004] The present application provides a method and system for optimizing the performance of an Android motherboard of a mobile phone, which can prevent the performance of the Android motherboard from being affected by the Android motherboard's untimely processing of abnormal data, thereby improving the performance of the Android motherboard.

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

[0006] Monitoring the operating load of each functional unit of the Android mainboard when running initial data;

[0007] Extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions when the Android mainboard responds to abnormal data based on the correlation characteristics between various functional units and all key loads, and determine the emergency responsiveness of the Android mainboard when responding to abnormal data based on all emergency partitions and all auxiliary loads;

[0008] Extracting multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard according to the emergency response degree;

[0009] Determine the initial scheduling information of the functional units of the Android mainboard when data switching is performed, and optimize the driving of all functional units of the Android mainboard when data switching is performed through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard;

[0010] Each functional unit in the Android mainboard is driven according to all optimized partitions.

[0011] In some embodiments, extracting a plurality of key loads and a plurality of auxiliary loads from all operating loads specifically includes:

[0012] Obtaining historical operation load data of the Android mainboard;

[0013] Determining an operation load threshold of each functional unit according to the historical operation load data;

[0014] A plurality of critical loads and a plurality of auxiliary loads are determined according to all operating load thresholds and all operating loads.

[0015] In some embodiments, determining multiple emergency partitions when the Android mainboard responds to abnormal data based on the association characteristics between the various functional units and all critical loads specifically includes:

[0016] Obtaining correlation features between various functional units;

[0017] Determine multiple load sets of the same level based on all critical loads;

[0018] Determine multiple emergency partitions when the Android mainboard responds to abnormal data based on all load sets of the same level and the associated characteristics.

[0019] In some embodiments, determining the emergency responsiveness of the Android mainboard to deal with abnormal data based on all emergency partitions and all auxiliary loads specifically includes:

[0020] Determine historical access data for functional units in each emergency zone;

[0021] Determine the abnormal information when the Android mainboard responds to abnormal data according to all historical access data;

[0022] The emergency response degree of the Android mainboard in dealing with abnormal data is determined based on the abnormal information and all auxiliary loads.

[0023] In some embodiments, the emergency response degree extracts multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard, specifically including:

[0024] Determining a fault adjustment strategy for the Android mainboard in response to abnormal data according to the emergency responsiveness and the auxiliary load;

[0025] Determining the fault correlation of each functional unit in the Android mainboard according to the fault adjustment strategy;

[0026] Determining multiple confidence correlations when the Android mainboard processes abnormal data based on all fault correlations;

[0027] Multiple adjustable functional units when the Android mainboard processes abnormal data are determined through all confidence associations and all functional units.

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

[0029] Monitoring the operation data of the Android mainboard when switching data;

[0030] The initial scheduling information of the functional units of the Android mainboard when performing data switching is determined through the operating data.

[0031] In some embodiments, all functional units of the Android mainboard are driven and optimized during data switching by using all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard, specifically including:

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

[0033] Determine the operation drive ratio of each functional unit when the Android mainboard data is switched by adjusting the scheduling information;

[0034] A plurality of optimized partitions of the Android mainboard are determined according to the proportion of all running drivers.

[0035] In a second aspect, the present application provides a mobile phone Android motherboard performance optimization system, comprising:

[0036] A monitoring module, used to monitor the operating load of each functional unit of the Android mainboard when running initial data;

[0037] A processing module, used to extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions when the Android mainboard responds to abnormal data based on the correlation characteristics between the various functional units and all the key loads, and determine the emergency responsiveness of the Android mainboard when responding to abnormal data based on all the emergency partitions and all the auxiliary loads;

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

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

[0040] The execution module is used 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, comprising a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned mobile phone Android motherboard performance optimization method.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned mobile phone Android motherboard performance optimization method.

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

[0044] In the mobile phone Android motherboard performance optimization method and system provided by the present application, the running load of each functional unit of the Android motherboard when running initial data is first monitored; multiple key loads and multiple auxiliary loads are extracted from all the running loads, and multiple emergency partitions of the Android motherboard when responding to abnormal data are determined based on the correlation characteristics between the various functional units and all the key loads, and the emergency responsiveness of the Android motherboard when responding to abnormal data is determined according to all the emergency partitions and all the auxiliary loads; multiple adjustable functional units of the Android motherboard when processing abnormal data are extracted from all the functional units of the Android motherboard according to the emergency responsiveness; the initial scheduling information of the functional units of the Android motherboard when switching data is determined, and all the functional units of the Android motherboard when switching data are driven and optimized through all the adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android motherboard; and each functional unit in the Android motherboard is driven according to all the optimized partitions.

[0045] It can be seen that in the process of optimizing the performance of the Android motherboard of a mobile phone, the present application analyzes the operating load of each functional unit of the Android motherboard when running the initial data, and determines the emergency area composed of the functional units when the Android motherboard responds to abnormal data in combination with the correlation characteristics between the various functional units, thereby obtaining multiple emergency partitions; the emergency partitions enable the Android motherboard to respond to sudden abnormal data; secondly, the parameter values ​​of the response degree of the Android motherboard when responding to the abnormal data in an emergency are determined through all the emergency partitions, thereby obtaining the emergency response degree, which enables the Android motherboard to temporarily process the abnormal data to avoid the harm to the Android motherboard caused by the untimely processing of the abnormal data; thereby determining the emergency response degree for adjusting the Android motherboard when responding to abnormal data Functional units are obtained to obtain multiple adjustable functional units, and the adjustable functional units reduce the power consumption of the Android mainboard when processing abnormal data; further, all functional units of the Android mainboard are driven and optimized through all adjustable functional units and the initial scheduling information when the data is switched, and multiple optimized partitions of the Android mainboard are obtained. The optimized partitions reflect the partitions after the functional units of the Android mainboard are optimized when switching data, and are used to drive the functional units in the Android mainboard, improve the processing efficiency when abnormal data appears on the Android mainboard, avoid the situation that the Android mainboard does not process abnormal data in time, and reduce the power consumption of the Android mainboard; finally, drive each functional unit in the Android mainboard according to all optimized partitions. The above scheme can avoid the performance of the Android mainboard affected by the Android mainboard's untimely processing of abnormal data, thereby improving the performance of the Android mainboard has become a problem faced by the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is an exemplary flow chart of a method for optimizing the performance of an Android motherboard of a mobile phone according to some embodiments of the present application;

[0047] Figure 2 is a partial diagram of an emergency zone according to some embodiments of the present application;

[0048] Figure 3 is an exemplary flow chart for determining emergency response level according to some embodiments of the present application;

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

[0050] Figure 5 It is a structural schematic diagram of a computer device for implementing a method for optimizing the performance of an Android motherboard of a mobile phone as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0051] In order to better understand the technical solution in the present application, the technical solution in the present application will be described in detail below in conjunction with the accompanying drawings in the specification and specific implementation methods.

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

[0053] In step 101, the operating load of each functional unit of the Android mainboard when running initial data is monitored.

[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 mainboard runs the initial data. The operating load can be used to measure the resource utilization of the working unit in the Android mainboard. The initial data can be sensor data, system logs, memory allocation, etc. As a preferred embodiment, after starting the operation of the Android mainboard, the operating load of each functional unit of the Android mainboard when running the initial data is collected through a load collection device, wherein the load collection device is, for example: an electronic load meter, a load spectrum collection device, etc. In other embodiments, other methods can also be used for collection, which will not be repeated here.

[0055] In step 102, a plurality of key loads and a plurality of auxiliary loads are extracted from all running loads, and a plurality of emergency partitions of the Android mainboard when dealing with abnormal data are determined based on the correlation characteristics between the functional units and all the key loads.

[0056] In some embodiments, extracting multiple key loads and multiple auxiliary loads from all operating loads can be achieved by using the following steps:

[0057] Obtaining historical operation load data of the Android mainboard;

[0058] Determining an operation load threshold of each functional unit according to the historical operation load data;

[0059] A plurality of critical loads and a plurality of auxiliary loads are determined according to all operating load thresholds and all operating loads.

[0060] In the specific implementation, the historical operation load data of the Android mainboard is obtained from the database of the Android mainboard, wherein the historical operation load data is a collection of the historical operation loads of each functional unit in the past month; the operation load threshold of each functional unit is determined according to the historical operation load data, which can be implemented in the following manner: a functional unit is selected as the selected functional unit, and all historical operation loads of the selected functional unit when processing data are extracted from the historical operation load data, and the operation load threshold of the selected functional unit is combined with the threshold calculation method in the prior art (such as the empirical method, the machine learning method) and all the extracted historical operation loads as the operation load threshold of the selected functional unit, and the operation load thresholds of the remaining functional units are continued to be determined, wherein the operation load threshold represents a load judgment value of whether the functional unit is processing data, and is used to judge the load of the functional unit when it is running; in other embodiments, other methods can also be used for determination, which are not limited here.

[0061] In specific implementation, determining multiple critical loads and multiple auxiliary loads based on all operating load thresholds and all operating loads can be achieved in the following manner, namely: selecting a functional unit as a selected functional unit, comparing 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 the operating load corresponding to the selected functional unit is used 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 the operating load corresponding to the selected functional unit is used as the auxiliary load, and the operating loads corresponding to the remaining functional units are continued to be judged 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 key load in this application is the operating load of the functional unit that reflects the main processing of the operating data at the Android mainboard during operation, and is used to analyze the trend of the operating data of the Android mainboard; the auxiliary load is the operating load of the functional unit that reflects the indirect processing of the operating data at the Android mainboard during operation, and is used to analyze the correlation of the operating data of the Android mainboard.

[0063] In some embodiments, determining multiple emergency partitions of the Android mainboard to deal with abnormal data based on the association characteristics between the functional units and all critical loads can be implemented by the following steps:

[0064] Obtaining correlation features between various functional units;

[0065] Determine multiple load sets of the same level based on all critical loads;

[0066] Determine multiple emergency partitions when the Android mainboard responds to abnormal data based on all load sets of the same level and the associated characteristics.

[0067] In the specific implementation, the correlation characteristics between each functional unit are obtained from the database of the Android mainboard, and the correlation characteristics represent the correlation level between different functional units in terms of information flow, control flow or resource usage; determining multiple load sets of the same level based on all key loads can be implemented in the following way, namely: obtaining the historical operation load data of the Android mainboard, and taking the standard deviation of all historical operation loads in the historical operation load data as the load fluctuation coefficient, wherein the load fluctuation coefficient represents a parameter of the fluctuation degree of the operation load of each functional unit when the Android mainboard is running, and is used to judge the operation load of each functional unit, and all key loads are arranged in ascending order, and the sequence obtained by the arrangement is used as the key load sequence, and the key load sequence is divided from front to back according to the size of the load fluctuation coefficient to obtain multiple key load sequence segments, and each key load is divided into The load sequence segments are all used as the same level load set, wherein the same level load set is a collection of all the same level loads, and all the same level loads in the same level load set represent the load set of the corresponding functional unit of the Android mainboard when the processing data is similar. For example, the load fluctuation coefficient is 2, the key load sequence is (1,2,3,4,5,6,7,8,9), and the key load sequence is (1,2,3,4,5,6,7,8,9). According to the load fluctuation coefficient of 2, the first key load 1 is extracted, and the key loads in the range of 1 to 1+2=3 are extracted as the key load sequence segment (1,2,3). The key load sequence is divided in turn to obtain multiple key load sequence segments (1,2,3), (4,5,6), (7,8,9); in other embodiments, other methods can also be used for determination, which is not limited here.

[0068] In specific implementation, the following method can be used to determine the multiple emergency partitions of the Android mainboard when responding to abnormal data based on all the load sets of the same level and the associated characteristics, namely: extract multiple loads of the same level that are lower than the average load of all running loads from all the load sets of the same level, select a load set of the same level from all the extracted loads of the same level as the selected load set of the same level, use the functional units corresponding to each load of the same level in the selected load set of the same level as each node, connect all the nodes according to the corresponding association relationship in the associated characteristics, namely: there is a sequence of connection, an association relationship, etc., connect all the nodes according to the priority order of the association level, use each load of the same level in the selected load set of the same level as the value of the corresponding node, use the connected area as the emergency partition of the Android mainboard when responding to abnormal data, and continue to determine the multiple emergency partitions of the Android mainboard when responding to abnormal data; for example, refer to Figure 2 As described above, this figure is a partial diagram of the emergency partition shown in this embodiment, in which 1, 2, 3, and 4 represent the priority of driving the functional units in the direction of the arrow, 1 is the maximum priority, and the numbers in the functional units represent the operating load of the functional units; in other embodiments, other methods can also be used for determination, which is not limited here.

[0069] It should be noted that the emergency partition in the present application is an emergency area composed of functional units that reflects when the Android mainboard responds to abnormal data. It is used as the response area when the Android mainboard is running abnormal data, so as to facilitate timely corresponding adjustments to the Android mainboard when running abnormal data; the abnormal data is data that reflects the existence of abnormalities in the Android mainboard, and the abnormal data includes system cache abnormalities, hardware restart abnormalities, temperature abnormalities, etc.

[0070] In step 103, the emergency responsiveness of the Android mainboard in dealing with abnormal data is determined based on all emergency partitions and all auxiliary loads, and then the emergency responsiveness is used to extract multiple adjustable functional units of the Android mainboard when processing abnormal data from all functional units of the Android mainboard.

[0071] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the emergency response degree in some embodiments of the present application. In this embodiment, the emergency response degree of the Android mainboard in response to abnormal data is determined according to all emergency partitions and all auxiliary loads, which can be implemented by the following steps:

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

[0073] Secondly, in step 1032, abnormal information when the Android mainboard responds to abnormal data is determined based on all historical access data;

[0074] Finally, in step 1033, the emergency response degree of the Android mainboard in dealing with abnormal data is determined based on the abnormal information and all auxiliary loads.

[0075] In specific implementation, determining the historical access data of the functional units in each emergency partition can be achieved in the following manner, namely: obtaining the historical access frequency of each functional unit from the database of the Android mainboard, wherein the historical access frequency represents the average access frequency of the functional unit within one month, which is used to measure the access situation of the functional unit, selecting an emergency partition as the selected emergency partition, and taking the set of corresponding historical access frequencies of each functional unit in the selected emergency partition as the historical access data of the functional units in the selected emergency partition, and continuing 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 are not limited here.

[0076] In specific implementation, the abnormal information of the Android mainboard in responding to abnormal data can be determined based on all historical access data in the following manner, namely: select an emergency partition as the selected emergency partition, obtain the total number of times each functional unit in the selected emergency partition responds to abnormal data from the database of the Android mainboard, 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 a base of 2 on the value obtained by the division, multiply the value obtained by the logarithmic operation by the standard deviation of the total number of times all functional units in the selected emergency partition respond to abnormal data, use the multiplied value as the abnormality degree of the selected emergency partition in responding to abnormal data, and continue to determine the abnormality degrees of the remaining emergency partitions in responding to abnormal data, wherein the abnormality degree represents the safety. The parameter value of the accessibility of the emergency partition to abnormal data when the Zhuo mainboard responds to abnormal data, that is: the response situation of the emergency partition when the Android mainboard responds to abnormal data, and the collection of all abnormal degrees is used as the abnormal information of the Android mainboard when responding to abnormal data, wherein the abnormal information represents the information of the response situation of each emergency partition when the Android mainboard responds to abnormal data; the emergency response degree of the Android mainboard in responding to abnormal data is determined according to the abnormal information and all auxiliary loads, which can be achieved in the following way, namely: multiplying the total number of all auxiliary loads by the entropy of all abnormal degrees in the abnormal information, dividing the multiplied value by the standard deviation of all auxiliary loads, and using the divided value as the emergency response degree of the Android mainboard in responding to abnormal data; in other embodiments, other methods can also be used for determination, which are not limited here.

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

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

[0079] Determining a fault adjustment strategy for the Android mainboard in response to abnormal data according to the emergency responsiveness and the auxiliary load;

[0080] Determining the fault correlation of each functional unit in the Android mainboard according to the fault adjustment strategy;

[0081] Determining multiple confidence correlations when the Android mainboard processes abnormal data based on all fault correlations;

[0082] Multiple adjustable functional units when the Android mainboard processes abnormal data are determined through all confidence associations and all functional units.

[0083] In specific implementation, the fault adjustment strategy of the Android mainboard in response to abnormal data is determined according to the emergency responsiveness and the auxiliary load, which can be implemented in the following manner, namely: initialize a fault adjustment strategy model, use the abnormal information as the constraint parameter of the fault adjustment strategy model, use all the auxiliary loads as the initialization parameters of the fault adjustment strategy model, and obtain the fault adjustment strategy of the Android mainboard in response to abnormal data through the fault adjustment strategy model, wherein the fault adjustment strategy model is a strategy model for establishing a fault adjustment strategy using a machine learning algorithm (such as a regression algorithm, a neural network, etc.), and the strategy model is, for example: fault adjustment strategy = abnormal information * A + all auxiliary loads * B, wherein A and B are weight coefficients, A and B can be determined based on a large number of fault adjustment strategies, and can also be determined in other ways in other embodiments, which are not limited here.

[0084] It should be noted that the fault adjustment strategy in the present application is an adjustment strategy that reflects what happens when abnormal data appears on the Android mainboard. The fault adjustment strategy includes the adjustment status of each functional unit in the Android mainboard, that is, the power consumption required by each functional mainboard for abnormal data, which is used to process the abnormal data of the Android mainboard, so that the Android mainboard can make corresponding adjustments in a timely manner, thereby improving the operating stability of the Android mainboard.

[0085] In specific implementation, determining the fault correlation of each functional unit in the Android mainboard according to the fault adjustment strategy can be achieved in the following manner, namely: selecting a functional unit as the selected functional unit, performing a natural exponential operation on the power consumption corresponding to the selected functional unit in the fault adjustment strategy, subtracting the inverse of the natural exponential operation from 1, and using the subtracted value as the fault correlation of the selected functional unit, and continuing to determine the fault correlation of the remaining functional units, wherein the fault correlation represents a parameter value of the degree of correlation when the functional unit responds to abnormal data, and is used to adjust the functional units in the Android mainboard; in other embodiments, other methods may also be used for determination, which are not limited here.

[0086] In specific implementation, determining multiple confidence correlations when the Android mainboard processes abnormal data based on all fault correlations can be implemented in the following manner, namely: calculating the average of all fault correlations, extracting each fault correlation that is greater than the average among all fault correlations, and using each extracted fault correlation as the confidence correlation when the Android mainboard processes abnormal data, wherein the confidence correlation represents a parameter value of the degree of correlation of the main functional units when the Android mainboard processes abnormal data, and is used for the Android mainboard to process abnormal data; determining multiple adjustable functional units when the Android mainboard processes abnormal data through all confidence correlations and all functional units can be implemented in the following manner, namely: selecting a confidence correlation as the selected confidence correlation, using the functional unit corresponding to the selected confidence correlation among all functional units as the adjustable functional unit when the Android mainboard processes abnormal data, and continuing to determine multiple adjustable functional units when the Android mainboard 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 the present application is a functional unit used for adjustment when the Android mainboard responds to abnormal data. It is used for the Android mainboard to process abnormal data and reduce the power consumption of the Android mainboard when processing abnormal data.

[0088] In step 104, the initial scheduling information of the functional units of the Android mainboard when performing data switching is determined, and all functional units of the Android mainboard when performing data switching are driven and optimized through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard.

[0089] In some embodiments, determining the initial scheduling information of the functional unit when the Android mainboard performs data switching can be implemented by the following steps:

[0090] Monitoring the operation data of the Android mainboard when switching data;

[0091] The initial scheduling information of the functional units of the Android mainboard when performing data switching is determined through the operating data.

[0092] In specific implementation, monitoring the running data of the Android mainboard when switching data can be achieved in the following manner, namely: monitoring the running data of the Android mainboard when switching data through a data monitoring system (such as Systrace, Android Studio CPU Profiler), wherein the running data represents the running data of the Android mainboard when switching data; determining the initial scheduling information of the functional units of the Android mainboard when switching data through the running data can be achieved in the following manner, namely: judging the data type of the running data through the type judgment operator in the prior art, and extracting the initial scheduling information of the functional units of the Android mainboard when switching data from the control system of the Android mainboard based on the data type of the running 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 the present application represents the information on the scheduling status of each functional unit when the Android mainboard processes running data. The initial scheduling information includes the adjustment status of the functional units, that is, the priority and driving status of each functional unit. The initial scheduling information is used to schedule the functional units when the Android mainboard responds to running data.

[0094] In some embodiments, all functional units of the Android mainboard are driven and optimized during data switching by all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard, which can be achieved by the following steps:

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

[0096] Determine the operation drive ratio of each functional unit when the Android mainboard data is switched by adjusting the scheduling information;

[0097] A plurality of optimized partitions of the Android mainboard are determined according to the proportion of all running drivers.

[0098] In specific implementation, the initial scheduling information is adjusted according to all the adjustable functional units, and the adjusted scheduling information can be implemented in the following manner, namely: functional units that can replace each adjustable functional unit are extracted from the control center of the Android mainboard, and the adjustable functional units in the initial scheduling information are replaced by functional units that can be replaced by each adjustable functional unit, and the replaced initial scheduling information is used as the adjusted scheduling information, wherein the adjusted scheduling unit represents the scheduling information of the functional unit after the Android mainboard adjusts when switching data, and is used to drive the functional unit of the Android mainboard; in other embodiments, other methods can also be used for determination, which are not limited here.

[0099] In specific implementation, determining the operating drive ratio of each functional unit when the Android mainboard data is switched through the adjustment scheduling information can be achieved in the following manner, namely: arranging all functional units in the order of priority of the functional units in the adjustment scheduling information, and using the arranged sequence as the functional unit sequence, selecting a functional unit in the functional unit sequence as the selected functional unit, dividing the position of the selected functional unit in the functional unit sequence by the total number of all functional units, subtracting the divided value from 1, and using the divided value as the operating drive ratio of the selected functional unit when the Android mainboard switches data, and continuing to determine the operating drive ratio of the remaining functional units when the Android mainboard switches data, wherein the operating drive ratio represents the parameter value of the ratio of the functional module when the Android mainboard switches data, that is, the order of driving, which is used to drive the functional units of the Android mainboard; in other embodiments, other methods can also be used for determination, which are not limited here.

[0100] In specific implementation, the following method can be used to determine the multiple optimized partitions of the Android mainboard based on the proportion of all running drivers, namely: initialize an optimized partition model, use all running driver proportions as initialization parameters of the optimized partition model, and output multiple optimized partitions of the Android mainboard through the optimized partition model, wherein the optimized partition model is a partition model for establishing optimized partitions using a machine learning algorithm (such as a regression algorithm, a neural network, etc.), and the partition model includes thread optimization, scheduling measurement adjustment, scheduling framework optimization, memory management, etc., and the initialization parameters are input into the partition model, combined with the relevant optimization adjustments in the partition model to output multiple optimized partitions of the Android mainboard. In other embodiments, other methods can also be used to determine, which are not limited here.

[0101] It should be noted that the optimized partition in the present application is a partition that reflects the optimization of the functional units when the Android motherboard switches data. It is used to drive the functional units in the Android motherboard. The optimized partition includes the connection status of the functional units, the distribution status of the functional units, etc., which can improve the processing efficiency when abnormal data appears on the Android motherboard.

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

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

[0104] Obtaining driver information of the Android mainboard;

[0105] The driver information is adjusted through all optimized partitions to obtain the driver adjustment information of the Android mainboard;

[0106] Each functional unit in the Android mainboard is driven according to the drive adjustment information.

[0107] In specific implementation, obtaining the driving information of the Android mainboard can be achieved in the following manner, namely: accessing the Android source code of the Android mainboard through the access tools (Git and Repo tools) in the prior art to obtain the driving information of the Android mainboard, wherein the driving information represents the information for driving each functional unit in the Android mainboard, namely: controlling and communicating with each functional unit; adjusting the driving information through all optimized partitions, and obtaining the driving adjustment information of the Android mainboard can be achieved in the following manner, namely: adjusting the driving information through the debugging tools (dmesg and debugfs tools) in the prior art in combination with all optimized partitions, and using the adjusted driving information as the driving adjustment information of the Android mainboard, wherein the driving adjustment information represents the information after the driving information of each functional unit in the Android mainboard is adjusted, and driving each functional unit in the Android mainboard according to the driving adjustment information can be achieved in the following manner, namely: 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 is not limited here.

[0108] In addition, in another aspect of the present application, in some embodiments, the present application provides a mobile phone Android motherboard performance optimization system, referring to Figure 4 , which is a structural schematic diagram of a mobile phone Android motherboard performance optimization system according to some embodiments of the present application, the mobile phone Android motherboard performance optimization system 400 includes: a monitoring module 401, a processing module 402 and an execution module 403, which are respectively described as follows:

[0109] Monitoring module 401, in this application, monitoring module 401 is mainly used to monitor the operating load of each functional unit of the Android mainboard when running initial data;

[0110] Processing module 402, in the present application, is used to extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions when the Android mainboard responds to abnormal data based on the association characteristics between each functional unit and all key loads, and determine the emergency responsiveness of the Android mainboard when responding to abnormal data based on all emergency partitions and all auxiliary loads;

[0111] It should be noted that the processing module 402 in the present application is also used to extract multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard according to the emergency response degree;

[0112] In addition, it should be noted that the processing module 402 in the present application is also used to determine the initial scheduling information of the functional unit of the Android mainboard when performing data switching, and optimize the driving of all functional units of the Android mainboard during data switching through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard;

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

[0114] In addition, the present 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 motherboard performance optimization method.

[0115] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for optimizing the performance of an Android motherboard of a mobile phone according to some embodiments of the present application. The method for optimizing the performance of an Android motherboard of a mobile phone in the above embodiment can be performed by Figure 5 The computer device 500 shown in the figure is implemented, and the 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 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

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

[0118] The memory 503 may 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, or 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 compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a disk or other magnetic storage device, 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 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

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

[0120] The communication interface 504 uses any transceiver or other device for communicating 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, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0122] The above-mentioned computer device can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a portable 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 embodiment of the present application does not limit the type of computer device.

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

[0124] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted 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 equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the performance of a mobile phone Android motherboard, characterized in that: The steps include: Monitoring the operating load of each functional unit of the Android mainboard when running initial data; Extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions when the Android mainboard responds to abnormal data based on the correlation characteristics between various functional units and all key loads, and determine the emergency responsiveness of the Android mainboard when responding to abnormal data based on all emergency partitions and all auxiliary loads; Extracting multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard according to the emergency response degree; Determine the initial scheduling information of the functional units of the Android mainboard when data switching is performed, and optimize the driving of all functional units of the Android mainboard when data switching is performed through all 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 optimized partitions.

2. The method according to claim 1, characterized in that Extract multiple key loads and multiple auxiliary loads from all operating loads, including: Obtaining historical operation load data of the Android mainboard; Determining an operation load threshold of each functional unit according to the historical operation load data; A plurality of critical loads and a plurality of auxiliary loads are determined according to all operating load thresholds and all operating loads.

3. The method according to claim 1, characterized in that Determining multiple emergency partitions when the Android mainboard responds to abnormal data based on the association characteristics between the various functional units and all key loads specifically includes: Obtaining correlation features between various functional units; Determine multiple load sets of the same level based on all critical loads; Determine multiple emergency partitions when the Android mainboard responds to abnormal data based on all load sets of the same level and the associated characteristics.

4. The method according to claim 1, characterized in that Determining the emergency response of the Android mainboard to abnormal data based on all emergency partitions and all auxiliary loads specifically includes: Determine historical access data for functional units in each emergency zone; Determine the abnormal information when the Android mainboard responds to abnormal data according to all historical access data; The emergency response degree of the Android mainboard in dealing with abnormal data is determined based on the abnormal information and all auxiliary loads.

5. The method according to claim 1, characterized in that The emergency response degree extracts multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard, specifically including: Determining a fault adjustment strategy for the Android mainboard in response to abnormal data according to the emergency responsiveness and the auxiliary load; Determining the fault correlation of each functional unit in the Android mainboard according to the fault adjustment strategy; Determining multiple confidence correlations when the Android mainboard processes abnormal data based on all fault correlations; Multiple adjustable functional units when the Android mainboard processes abnormal data are determined through all confidence associations and all functional units.

6. The method according to claim 1, characterized in that Determining the initial scheduling information of the functional unit of the Android mainboard when performing data switching specifically includes: Monitoring the operation data of the Android mainboard when switching data; The initial scheduling information of the functional units of the Android mainboard when performing data switching is determined through the operating data.

7. The method according to claim 1, characterized in that The drive optimization of all functional units during data switching of the Android mainboard is performed through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard, specifically including: Adjusting the initial scheduling information according to all adjustable functional units to obtain adjusted scheduling information; Determine the operation drive ratio of each functional unit when the Android mainboard data is switched by adjusting the scheduling information; A plurality of optimized partitions of the Android mainboard are determined according to the proportion of all running drivers.

8. A mobile phone Android motherboard performance optimization system, characterized in that: include: A monitoring module, used to monitor the operating load of each functional unit of the Android mainboard when running initial data; A processing module, used to extract multiple key loads and multiple auxiliary loads from all running loads, determine multiple emergency partitions when the Android mainboard responds to abnormal data based on the correlation characteristics between the various functional units and all the key loads, and determine the emergency responsiveness of the Android mainboard when responding to abnormal data based on all the emergency partitions and all the auxiliary loads; The processing module is further used to extract multiple adjustable functional units when the Android mainboard processes abnormal data from all functional units of the Android mainboard according to the emergency response degree; The processing module is further used to determine the initial scheduling information of the functional units of the Android mainboard when data switching is performed, and to optimize the driving of all functional units of the Android mainboard when data switching is performed through all adjustable functional units and the initial scheduling information to obtain multiple optimized partitions of the Android mainboard; The execution module is used to drive 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 codes, and the processor is configured to obtain the codes and execute the mobile phone Android motherboard performance optimization method as described in 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, the method for optimizing the performance of the Android motherboard of a mobile phone as described in any one of claims 1 to 7 is implemented.

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