Android性能专项测试之耗电量统计API
参考文章:Android应用的耗电量统计
深入浅出Android App耗电量统计
Battery stats - CPU total vs CPU foreground
深入浅出 Android App 耗电量统计
浅析Wakelock机制与Android电源管理
耗电量API
Android
系统中很早就有耗电量的API
,只不过一直都是隐藏的,Android
系统的设置-电池功能
就是调用的这个API
,该API
的核心部分是调用了com.android.internal.os.BatteryStatsHelper
类,利用PowerProfile
类,读取power_profile.xml
文件,我们一起来看看具体如何计算耗电量,首先从最新版本6.0开始看
6.0的API
源码
BatteryStatsHelper
其中计算耗电量的方法为490行的processAppUsage
,下来一步一步来解释该方法。
App耗电量的计算探究
private void processAppUsage(SparseArray<UserHandle> asUsers) {
方法的参数是一个SparseArray
数组,存储的对象是UserHandle
,官方文档给出的解释是,代表一个用户,可以理解为这个类里面存储了用户的相关信息.
final boolean forAllUsers = (asUsers.get(UserHandle.USER_ALL) != null);
然后判断该次计算是否针对所有用户,通过UserHandle
的USER_ALL
值来判断,该值为-1
,源码的地址在https://github.com/DoctorQ/platform_frameworks_base/blob/android-6.0.0_r1/core/java/android/os/UserHandle.java.
mStatsPeriod = mTypeBatteryRealtime;
然后给公共变量int类型的mStatsPeriod
赋值,这个值mTypeBatteryRealtime
的计算过程又在320行的refreshStats
方法中:
mTypeBatteryRealtime = mStats.computeBatteryRealtime(rawRealtimeUs, mStatsType);
这里面用到了BatteryStats(mStats)
类中的computeBatteryRealtime
方法,该方法计算出此次统计电量的时间间隔。好,歪楼了,回到BatteryStatsHelper
中。
BatterySipper osSipper = null;
final SparseArray<? extends Uid> uidStats = mStats.getUidStats();
final int NU = uidStats.size();
首先创建一个BatterySipper
对象osSipper
,该对象里面可以存储一些后续我们要计算的值,然后通过BatteryStats
类对象mStats
来得到一个包含Uid
的对象的SparseArray
组数,然后计算了一下这个数组的大小,保存在变量NU中。
for (int iu = 0; iu < NU; iu++) {
final Uid u = uidStats.valueAt(iu);
final BatterySipper app = new BatterySipper(BatterySipper.DrainType.APP, u, 0);
然后for
循环计算每个Uid
代表的App
的耗电量,因为BatterySipper
可计算的类型有三种:应用, 系统服务, 硬件类型,所以这个地方传入的是DrainType.APP
,还有其他可选类型如下:
public enum DrainType {
IDLE,
CELL,
PHONE,
WIFI,
BLUETOOTH,
FLASHLIGHT,
SCREEN,
APP,
USER,
UNACCOUNTED,
OVERCOUNTED,
CAMERA
}
列举了目前可计算耗电量的模块。
mCpuPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mWakelockPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mMobileRadioPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mWifiPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mBluetoothPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mSensorPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mCameraPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
mFlashlightPowerCalculator.calculateApp(app, u, mRawRealtime, mRawUptime, mStatsType);
其中mStatsType
的值为BatteryStats.STATS_SINCE_CHARGED
,代表了我们的计算规则是从上次充满电后数据,还有一种规则是STATS_SINCE_UNPLUGGED
是拔掉USB线后的数据。而mRawRealtime
是当前时间,mRawUptime
是运行时间。6.0的对各个模块的消耗都交给了单独的类去计算,这些类都继承于PowerCalculator
抽象类:
蓝牙耗电:BluetoothPowerCalculator.java
摄像头耗电:CameraPowerCalculator.java
Cpu耗电:CpuPowerCalculator.java
手电筒耗电:FlashlightPowerCalculator.java
无线电耗电:MobileRadioPowerCalculator.java
传感器耗电:SensorPowerCalculator.java
Wakelock耗电:WakelockPowerCalculator.java
Wifi耗电:WifiPowerCalculator.java
这一部分我一会单独拿出来挨个解释,现在我们还是回到BatteryStatsHelper
继续往下走
final double totalPower = app.sumPower();
BatterySipper#sumPower
方法是统计总耗电量,方法详情如下,其中usagePowerMah
这个值有点特殊,其他的上面都讲过.
/**
* Sum all the powers and store the value into `value`.
* @return the sum of all the power in this BatterySipper.
*/
public double sumPower() {
return totalPowerMah = usagePowerMah + wifiPowerMah + gpsPowerMah + cpuPowerMah +
sensorPowerMah + mobileRadioPowerMah + wakeLockPowerMah + cameraPowerMah +
flashlightPowerMah;
}
然后根据是否是DEBUG版本打印信息,这个没啥可说的,然后会把刚才计算的电量值添加到列表中:
// Add the app to the list if it is consuming power.
if (totalPower != 0 || u.getUid() == 0) {
//
// Add the app to the app list, WiFi, Bluetooth, etc, or into "Other Users" list.
//
final int uid = app.getUid();
final int userId = UserHandle.getUserId(uid);
if (uid == Process.WIFI_UID) {
mWifiSippers.add(app);
} else if (uid == Process.BLUETOOTH_UID) {
mBluetoothSippers.add(app);
} else if (!forAllUsers && asUsers.get(userId) == null
&& UserHandle.getAppId(uid) >= Process.FIRST_APPLICATION_UID) {
// We are told to just report this user's apps as one large entry.
List<BatterySipper> list = mUserSippers.get(userId);
if (list == null) {
list = new ArrayList<>();
mUserSippers.put(userId, list);
}
list.add(app);
} else {
mUsageList.add(app);
}
if (uid == 0) {
osSipper = app;
}
}
首先判断totalPower
的值和当前uid号
是否符合规则,规则为总耗电量不为0或者用户id为0.当uid
表明为WIFI或者蓝牙时,添加到下面对应的列表中,一般情况下正常的应用我们直接保存到下面的mUsageList
中就行就行,但是也有一些例外:
/**
* List of apps using power.
*/
private final List<BatterySipper> mUsageList = new ArrayList<>();
/**
* List of apps using wifi power.
*/
private final List<BatterySipper> mWifiSippers = new ArrayList<>();
/**
* List of apps using bluetooth power.
*/
private final List<BatterySipper> mBluetoothSippers = new ArrayList<>();
如果我们的系统是单用户系统,且当前的userId
号不在我们的统计范围内,且其进程id
号是大于Process.FIRST_APPLICATION_UID
(10000,系统分配给普通应用的其实id号),我们就要将其存放到mUserSippers
数组中,定义如下:
private final SparseArray<List<BatterySipper>> mUserSippers = new SparseArray<>();
最后判断uid
为0的话,代表是Android
操作系统的耗电量,赋值给osSipper
(494行定义)就可以了,这样一个app
的计算就完成了,遍历部分就不说了,保存这个osSipper
是为了最后一步计算:
if (osSipper != null) {
// The device has probably been awake for longer than the screen on
// time and application wake lock time would account for. Assign
// this remainder to the OS, if possible.
mWakelockPowerCalculator.calculateRemaining(osSipper, mStats, mRawRealtime,
mRawUptime, mStatsType);
osSipper.sumPower();
}
主流程我们已经介绍完了,下面来看各个子模块耗电量的计算
Cpu耗电量
Cpu的计算要用到PowerProfile类,该类主要是解析power_profile.xml:
<device name="Android">
<!-- Most values are the incremental current used by a feature,
in mA (measured at nominal voltage).
The default values are deliberately incorrect dummy values.
OEM's must measure and provide actual values before
shipping a device.
Example real-world values are given in comments, but they
are totally dependent on the platform and can vary
significantly, so should be measured on the shipping platform
with a power meter. -->
<item name="none">0</item>
<item name="screen.on">0.1</item> <!-- ~200mA -->
<item name="screen.full">0.1</item> <!-- ~300mA -->
<item name="bluetooth.active">0.1</item> <!-- Bluetooth data transfer, ~10mA -->
<item name="bluetooth.on">0.1</item> <!-- Bluetooth on & connectable, but not connected, ~0.1mA -->
<item name="wifi.on">0.1</item> <!-- ~3mA -->
<item name="wifi.active">0.1</item> <!-- WIFI data transfer, ~200mA -->
<item name="wifi.scan">0.1</item> <!-- WIFI network scanning, ~100mA -->
<item name="dsp.audio">0.1</item> <!-- ~10mA -->
<item name="dsp.video">0.1</item> <!-- ~50mA -->
<item name="camera.flashlight">0.1</item> <!-- Avg. power for camera flash, ~160mA -->
<item name="camera.avg">0.1</item> <!-- Avg. power use of camera in standard usecases, ~550mA -->
<item name="radio.active">0.1</item> <!-- ~200mA -->
<item name="radio.scanning">0.1</item> <!-- cellular radio scanning for signal, ~10mA -->
<item name="gps.on">0.1</item> <!-- ~50mA -->
<!-- Current consumed by the radio at different signal strengths, when paging -->
<array name="radio.on"> <!-- Strength 0 to BINS-1 -->
<value>0.2</value> <!-- ~2mA -->
<value>0.1</value> <!-- ~1mA -->
</array>
<!-- Different CPU speeds as reported in
/sys/devices/system/cpu/cpu0/cpufreq/stats/time_in_state -->
<array name="cpu.speeds">
<value>400000</value> <!-- 400 MHz CPU speed -->
</array>
<!-- Current when CPU is idle -->
<item name="cpu.idle">0.1</item>
<!-- Current at each CPU speed, as per 'cpu.speeds' -->
<array name="cpu.active">
<value>0.1</value> <!-- ~100mA -->
</array>
<!-- This is the battery capacity in mAh (measured at nominal voltage) -->
<item name="battery.capacity">1000</item>
<array name="wifi.batchedscan"> <!-- mA -->
<value>.0002</value> <!-- 1-8/hr -->
<value>.002</value> <!-- 9-64/hr -->
<value>.02</value> <!-- 65-512/hr -->
<value>.2</value> <!-- 513-4,096/hr -->
<value>2</value> <!-- 4097-/hr -->
</array>
</device>
这个里面存储了Cpu(cpu.speeds)的主频等级,以及每个主频每秒消耗的毫安(cpu.active),好,现在回到CpuPowerCalculator
中,先来看构造方法
public CpuPowerCalculator(PowerProfile profile) {
final int speedSteps = profile.getNumSpeedSteps();
mPowerCpuNormal = new double[speedSteps];
mSpeedStepTimes = new long[speedSteps];
for (int p = 0; p < speedSteps; p++) {
mPowerCpuNormal[p] = profile.getAveragePower(PowerProfile.POWER_CPU_ACTIVE, p);
}
}
第一步获得Cpu
有几个主频等级,因为不同等级消耗的电量不一样,所以要区别对待,根据主频的个数,然后初始化mPowerCpuNormal
和mSpeedStepTimes
,前者用来保存不同等级的耗电速度,后者用来保存在不同等级上耗时,然后给mPowerCpuNormal
的每个元素附上值。构造方法就完成了其所有的工作,现在来计算方法calculateApp
,
final int speedSteps = mSpeedStepTimes.length;
long totalTimeAtSpeeds = 0;
for (int step = 0; step < speedSteps; step++) {
mSpeedStepTimes[step] = u.getTimeAtCpuSpeed(step, statsType);
totalTimeAtSpeeds += mSpeedStepTimes[step];
}
totalTimeAtSpeeds = Math.max(totalTimeAtSpeeds, 1);
首先得到Cpu
主频等级个数,然后BatteryStats.Uid
得到不同主频上执行时间,计算Cpu
总耗时保存在totalTimeAtSpeeds
中,
app.cpuTimeMs = (u.getUserCpuTimeUs(statsType) + u.getSystemCpuTimeUs(statsType)) / 1000;
Cpu
的执行时间分很多部分,但是我们关注User
和Kernal
部分,也就是上面的UserCpuTime
和SystemCpuTime
。
double cpuPowerMaMs = 0;
for (int step = 0; step < speedSteps; step++) {
final double ratio = (double) mSpeedStepTimes[step] / totalTimeAtSpeeds;
final double cpuSpeedStepPower = ratio * app.cpuTimeMs * mPowerCpuNormal[step];
if (DEBUG && ratio != 0) {
Log.d(TAG, "UID " + u.getUid() + ": CPU step #"
+ step + " ratio=" + BatteryStatsHelper.makemAh(ratio) + " power="
+ BatteryStatsHelper.makemAh(cpuSpeedStepPower / (60 * 60 * 1000)));
}
cpuPowerMaMs += cpuSpeedStepPower;
}
上面的代码就是将不同主频的消耗累加到一起,但是其中值得注意的是,他并不是用各个主频的消耗时间*主频单位时间内消耗的电量,而是用一个radio变量来计算得到各个主频段执行时间占总时间的百分比,然后用cpuTimeMs
来换算成各个主频的Cpu实际消耗时间,这比5.0的API多了这么一步,我估计是发现了计算的不严谨性,这也是Android
迟迟不放出统计电量方式的原因,其实google自己对这块也没有把握,所以才会造成不同API
计算方式的差异。好,计算完我们的总消耗后,是不是就算完事了?如果你只需要得到一个App的耗电总量,上面的讲解已经足够了,但是6.0的API计算了每个App的不同进程的耗电量,这个我们就只当看看就行,暂时没什么实际意义。
// Keep track of the package with highest drain.
double highestDrain = 0;
app.cpuFgTimeMs = 0;
final ArrayMap<String, ? extends BatteryStats.Uid.Proc> processStats = u.getProcessStats();
final int processStatsCount = processStats.size();
for (int i = 0; i < processStatsCount; i++) {
final BatteryStats.Uid.Proc ps = processStats.valueAt(i);
final String processName = processStats.keyAt(i);
app.cpuFgTimeMs += ps.getForegroundTime(statsType);
final long costValue = ps.getUserTime(statsType) + ps.getSystemTime(statsType)
+ ps.getForegroundTime(statsType);
// Each App can have multiple packages and with multiple running processes.
// Keep track of the package who's process has the highest drain.
if (app.packageWithHighestDrain == null ||
app.packageWithHighestDrain.startsWith("*")) {
highestDrain = costValue;
app.packageWithHighestDrain = processName;
} else if (highestDrain < costValue && !processName.startsWith("*")) {
highestDrain = costValue;
app.packageWithHighestDrain = processName;
}
}
// Ensure that the CPU times make sense.
if (app.cpuFgTimeMs > app.cpuTimeMs) {
if (DEBUG && app.cpuFgTimeMs > app.cpuTimeMs + 10000) {
Log.d(TAG, "WARNING! Cputime is more than 10 seconds behind Foreground time");
}
// Statistics may not have been gathered yet.
app.cpuTimeMs = app.cpuFgTimeMs;
}
上面统计同一App
下不同的进程的耗电量,得到消耗最大的进程名,保存到BatterySipper
对象中,然后得出App
的Cpu
的foreground
消耗时间,将foreground
时间与之前计算得到的cpuTimeMs
进行比较,如果foreground
时间比cpuTimeMs
还要大,那么就将cpuTimeMs
的时间改变为foreground
的值,但是这个值的变化对之前耗电总量的计算没有丝毫影响。
// Convert the CPU power to mAh
app.cpuPowerMah = cpuPowerMaMs / (60 * 60 * 1000);
最后的最后,将耗电量用mAh单位来表示,所以在毫秒的基础上除以60*60*1000
。
总结:Cpu
耗电量的计算是要区分不同主频的,频率不同,单位时间内消耗的电量是有区分的,这一点要明白。还有一点就是不同主频上的执行时间不是通过BatteryStats.Uid#getTimeAtCpuSpeed
方法得到的,二十是通过百分比和BatteryStats.Uid#getUserCpuTimeUs
和getSystemCpuTimeUs
计算得到cpuTimeMs
乘积得到的。最后一点就是,cpuTimeMs
时间是会在计算完毕后进行比较,比较的对象是CPU
的foreground
时间。
WakeLock耗电量的计算
从构造方法开始,
public WakelockPowerCalculator(PowerProfile profile) {
mPowerWakelock = profile.getAveragePower(PowerProfile.POWER_CPU_AWAKE);
}
首先得到power_profile.xml
中cpu.awake
表示的值,保存在mPowerWakelock
变量中。构造方法只做了这么点事,下面进入calculateApp
方法。
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
long wakeLockTimeUs = 0;
final ArrayMap<String, ? extends BatteryStats.Uid.Wakelock> wakelockStats =
u.getWakelockStats();
final int wakelockStatsCount = wakelockStats.size();
for (int i = 0; i < wakelockStatsCount; i++) {
final BatteryStats.Uid.Wakelock wakelock = wakelockStats.valueAt(i);
// Only care about partial wake locks since full wake locks
// are canceled when the user turns the screen off.
BatteryStats.Timer timer = wakelock.getWakeTime(BatteryStats.WAKE_TYPE_PARTIAL);
if (timer != null) {
wakeLockTimeUs += timer.getTotalTimeLocked(rawRealtimeUs, statsType);
}
}
app.wakeLockTimeMs = wakeLockTimeUs / 1000; // convert to millis
mTotalAppWakelockTimeMs += app.wakeLockTimeMs;
// Add cost of holding a wake lock.
app.wakeLockPowerMah = (app.wakeLockTimeMs * mPowerWakelock) / (1000*60*60);
if (DEBUG && app.wakeLockPowerMah != 0) {
Log.d(TAG, "UID " + u.getUid() + ": wake " + app.wakeLockTimeMs
+ " power=" + BatteryStatsHelper.makemAh(app.wakeLockPowerMah));
}
}
首先获得Wakelock
的数量,然后逐个遍历得到每个Wakelock
对象,得到该对象后,得到BatteryStats.WAKE_TYPE_PARTIAL
的唤醒时间,然后累加,其实wakelock
有4种,为什么只取partial
的时间,具体代码google
也没解释的很清楚,只是用一句注释打发了我们。得到总时间后,就可以与构造方法中的单位时间waklock
消耗电量相乘得到Wakelock
消耗的总电量。
Wifi耗电量的计算
首先来看构造方法,来了解一下WIFI的耗电量计算用到了power_profile.xml
中的哪些属性:
public WifiPowerCalculator(PowerProfile profile) {
mIdleCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_IDLE);
mTxCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_TX);
mRxCurrentMa = profile.getAveragePower(PowerProfile.POWER_WIFI_CONTROLLER_RX);
}
我们去PowerProfile.java
找到上面三个常量代表的属性:
public static final String POWER_WIFI_CONTROLLER_IDLE = "wifi.controller.idle";
public static final String POWER_WIFI_CONTROLLER_RX = "wifi.controller.rx";
public static final String POWER_WIFI_CONTROLLER_TX = "wifi.controller.tx";
知道对应的xml的属性后我们直接看calculateApp
方法:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
final long idleTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_IDLE_TIME,
statsType);
final long txTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_TX_TIME, statsType);
final long rxTime = u.getWifiControllerActivity(BatteryStats.CONTROLLER_RX_TIME, statsType);
app.wifiRunningTimeMs = idleTime + rxTime + txTime;
app.wifiPowerMah =
((idleTime * mIdleCurrentMa) + (txTime * mTxCurrentMa) + (rxTime * mRxCurrentMa))
/ (1000*60*60);
mTotalAppPowerDrain += app.wifiPowerMah;
app.wifiRxPackets = u.getNetworkActivityPackets(BatteryStats.NETWORK_WIFI_RX_DATA,
statsType);
app.wifiTxPackets = u.getNetworkActivityPackets(BatteryStats.NETWORK_WIFI_TX_DATA,
statsType);
app.wifiRxBytes = u.getNetworkActivityBytes(BatteryStats.NETWORK_WIFI_RX_DATA,
statsType);
app.wifiTxBytes = u.getNetworkActivityBytes(BatteryStats.NETWORK_WIFI_TX_DATA,
statsType);
if (DEBUG && app.wifiPowerMah != 0) {
Log.d(TAG, "UID " + u.getUid() + ": idle=" + idleTime + "ms rx=" + rxTime + "ms tx=" +
txTime + "ms power=" + BatteryStatsHelper.makemAh(app.wifiPowerMah));
}
}
这里的计算方式也是差不多,先根据Uid得到时间,然后乘以构造方法里对应的wifi类型单位时间内消耗电量值,没什么难点,就不一一分析,需要注意的是,这里面还计算了wifi
传输的数据包的数量和字节数。
蓝牙耗电量的计算
蓝牙关注的power_profile.xml
中的属性如下:
public static final String POWER_BLUETOOTH_CONTROLLER_IDLE = "bluetooth.controller.idle";
public static final String POWER_BLUETOOTH_CONTROLLER_RX = "bluetooth.controller.rx";
public static final String POWER_BLUETOOTH_CONTROLLER_TX = "bluetooth.controller.tx";
但是还没有单独为App计算耗电量的,所以这个地方是空的。
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// No per-app distribution yet.
}
摄像头耗电量的计算
摄像头的耗电量关注的是power_profile.xml
中camera.avg
属性代表的值,保存到mCameraPowerOnAvg
,
public static final String POWER_CAMERA = "camera.avg";
计算方式如下:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// Calculate camera power usage. Right now, this is a (very) rough estimate based on the
// average power usage for a typical camera application.
final BatteryStats.Timer timer = u.getCameraTurnedOnTimer();
if (timer != null) {
final long totalTime = timer.getTotalTimeLocked(rawRealtimeUs, statsType) / 1000;
app.cameraTimeMs = totalTime;
app.cameraPowerMah = (totalTime * mCameraPowerOnAvg) / (1000*60*60);
} else {
app.cameraTimeMs = 0;
app.cameraPowerMah = 0;
}
}
先计算摄像头打开的时间totalTime
,然后根据这个值乘以mCameraPowerOnAvg
得到摄像头的耗电量。
手电筒耗电量的计算
FlashlightPowerCalculator.java
public static final String POWER_FLASHLIGHT = "camera.flashlight";
跟摄像头类似,也是先得到时间,然后乘积,不想说了,没意思。
无线电耗电量的计算
MobileRadioPowerCalculator.java
关注的是power_profile.xml
中如下三个属性:
/**
* Power consumption when screen is on, not including the backlight power.
*/
public static final String POWER_SCREEN_ON = "screen.on";
/**
* Power consumption when cell radio is on but not on a call.
*/
public static final String POWER_RADIO_ON = "radio.on";
/**
* Power consumption when cell radio is hunting for a signal.
*/
public static final String POWER_RADIO_SCANNING = "radio.scanning";
当无限量连接上时,根据信号强度不同,耗电量的计算是有区别的,所以在构造方法,当无线电的状态为on时,是要特殊处理的,其他两个状态(active和scan)就正常取值就可以了。
/**
* Power consumption when screen is on, not including the backlight power.
*/
public static final String POWER_SCREEN_ON = "screen.on";
/**
* Power consumption when cell radio is on but not on a call.
*/
public static final String POWER_RADIO_ON = "radio.on";
/**
* Power consumption when cell radio is hunting for a signal.
*/
public static final String POWER_RADIO_SCANNING = "radio.scanning";
计算的方式分两种,以无线电处于active
状态的次数为区分,当active
大于0,我们用处于active
状态的时间来乘以它的单位耗时。另一种情况就要根据网络转化的数据包来计算耗电量了。
传感器耗电量的计算
只关注一个属性:
public static final String POWER_GPS_ON = "gps.on";
- 1
计算方式如下:
@Override
public void calculateApp(BatterySipper app, BatteryStats.Uid u, long rawRealtimeUs,
long rawUptimeUs, int statsType) {
// Process Sensor usage
final SparseArray<? extends BatteryStats.Uid.Sensor> sensorStats = u.getSensorStats();
final int NSE = sensorStats.size();
for (int ise = 0; ise < NSE; ise++) {
final BatteryStats.Uid.Sensor sensor = sensorStats.valueAt(ise);
final int sensorHandle = sensorStats.keyAt(ise);
final BatteryStats.Timer timer = sensor.getSensorTime();
final long sensorTime = timer.getTotalTimeLocked(rawRealtimeUs, statsType) / 1000;
switch (sensorHandle) {
case BatteryStats.Uid.Sensor.GPS:
app.gpsTimeMs = sensorTime;
app.gpsPowerMah = (app.gpsTimeMs * mGpsPowerOn) / (1000*60*60);
break;
default:
final int sensorsCount = mSensors.size();
for (int i = 0; i < sensorsCount; i++) {
final Sensor s = mSensors.get(i);
if (s.getHandle() == sensorHandle) {
app.sensorPowerMah += (sensorTime * s.getPower()) / (1000*60*60);
break;
}
}
break;
}
}
}
当传感器的类型为GPS时,我们计算每个传感器的时间然后乘以耗电量,和所有的耗电量计算都是一样,不同的是,当传感器不是GPS时,这个时候计算就根据SensorManager
得到所有传感器类型,这个里面保存有不同传感器的单位耗电量,这样就能计算不同传感器的耗电量。
总结
至此我已经把App耗电量的计算讲完了(还有硬件),前后花费3天时间,好痛苦(此处一万只草泥马),不过好在自己也算对这个耗电量的理解有了一定的认识。google官方对耗电量的统计给出的解释都是不能代表真实数据,只能作为参考值,因为受power_profile.xml的干扰太大,如果手机厂商没有严格设置这个文件,那可想而知出来的值可能是不合理的。
提示
腾讯的GT团队前几天推出了耗电量的计算APK,原理是一样的,大家可以试用下GT