需求描述
开发一个分布式待办事项应用,要求:
实现方案
// 1. 定义分布式数据模型
import distributedData from '@ohos.data.distributedData';
class TodoItem {
id: string;
content: string;
isCompleted: boolean = false;
timestamp: number = new Date().getTime();
}
// 2. 创建分布式数据表
const kvManager = distributedData.createKVManager({
bundleName: 'com.example.todo',
options: {
securityLevel: distributedData.SecurityLevel.S1,
isEncrypted: true
}
});
const kvStore = await kvManager.getKVStore<distributedData.SingleKVStore>('todo_store', {
createIfMissing: true,
encrypt: true,
backup: false,
autoSync: true
});
// 3. 数据监听与同步
kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_ALL, (data) => {
data.insertions.forEach(item => {
// 处理新增/更新
const todo = JSON.parse(item.value as string) as TodoItem;
updateLocalUI(todo);
});
data.deletions.forEach(key => {
// 处理删除
removeFromLocalUI(key);
});
});
// 4. 数据操作封装
async function addTodo(content: string) {
const todo = new TodoItem();
todo.id = generateUUID();
todo.content = content;
await kvStore.put(todo.id, JSON.stringify(todo));
}
async function toggleTodo(id: string) {
const entry = await kvStore.get(id);
if (entry) {
const todo: TodoItem = JSON.parse(entry.value as string);
todo.isCompleted = !todo.isCompleted;
await kvStore.put(id, JSON.stringify(todo));
}
}
技术要点:
@ohos.data.distributedData
实现跨设备数据同步需求描述
实现一个包含10,000+条目的通讯录列表,要求:
优化实现
// 1. 使用LazyForEach + 缓存池
@Component
struct ContactList {
@State contacts: Contact[] = [];
build() {
List() {
LazyForEach(this.contacts, (item: Contact) => {
ListItem() {
ContactItem({ contact: item }) // 复用组件
.cachedCount(10) // 缓存10个ListItem
}
}, (item) => item.id)
}
.listDirection(Axis.Vertical)
.edgeEffect(EdgeEffect.None) // 禁用边缘效果
}
}
// 2. 字母索引快速跳转
class IndexBar {
private scroller: Scroller = new Scroller();
jumpToSection(key: string) {
const index = this.findFirstIndex(key);
this.scroller.scrollToIndex(index);
}
private findFirstIndex(key: string): number {
// 二分查找优化
let low = 0, high = contacts.length - 1;
while (low <= high) {
const mid = Math.floor((low + high) / 2);
if (contacts[mid].pinyin[0] < key) {
low = mid + 1;
} else {
high = mid - 1;
}
}
return low;
}
}
// 3. 内存优化策略
class ContactItem {
@ObjectLink contact: Contact;
aboutToReuse(params: Record<string, Object>) {
// 复用前重置状态
this.contact = params.contact as Contact;
}
}
性能优化点:
LazyForEach
+ cachedCount
减少内存占用需求描述
开发一个天气服务卡片,要求:
实现代码
// 1. 定义卡片配置
"forms": [
{
"name": "weather_card",
"description": "实时天气卡片",
"src": "./ets/widget/WeatherCard.ets",
"uiSyntax": "arkts",
"window": {
"designWidth": 720,
"autoDesignWidth": true
},
"colorMode": "auto",
"isDefault": true,
"updateEnabled": true,
"scheduledUpdateTime": "10:00",
"formConfigAbility": "WeatherWidgetConfig"
}
]
// 2. 卡片UI组件
@Entry
@Component
struct WeatherCard {
@LocalStorageProp('weatherData') weather: WeatherData = new WeatherData();
build() {
Column() {
Text(this.weather.temperature)
.fontSize(24)
Text(this.weather.city)
.fontSize(16)
Image(this.weather.icon)
.width(48)
.height(48)
}
.onClick(() => {
postCardAction({
action: {
bundleName: 'com.example.weather',
abilityName: 'MainAbility',
params: { }
}
});
})
}
}
// 3. 卡片数据更新
import formProvider from '@ohos.app.form.formProvider';
function updateWeatherCard(formId: string) {
const newData = {
"temperature": "26℃",
"city": "北京",
"icon": "cloudy.png"
};
formProvider.updateForm(formId, newData)
.catch(err => console.error('Update form failed: ' + JSON.stringify(err)));
}
关键技术:
onCreate
/onDestroy
)LocalStorage
实现数据绑定需求描述
优化图像处理模块性能:
混合开发实现
// 1. 原生层C++代码 (native_filter.cpp)
#include <hilog/log.h>
#include <multimedia/image/image_pixel_map.h>
extern "C" {
void ApplyGaussianBlur(OH_ImagePixelMap* pixelMap, int radius) {
// 获取像素数据
uint32_t width = OH_ImagePixelMap_GetWidth(pixelMap);
uint32_t height = OH_ImagePixelMap_GetHeight(pixelMap);
uint8_t* pixels = OH_ImagePixelMap_GetPixels(pixelMap);
// SIMD优化算法
#pragma omp parallel for collapse(2)
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
// 高斯模糊计算...
}
}
}
}
// 2. TS层调用
import native from 'libnative.so';
function processImage(pixelMap: image.PixelMap) {
const nativePixelMap = pixelMap.getNativePixelMap();
native.ApplyGaussianBlur(nativePixelMap, 5);
}
优化策略: