problem

1. 错误工单是什么概念?就是失败工单?? 错误工单处理? 没有数据?!!!

监控当前有几条失败的工单?

2. 工单简单是实时的? 间隔是多少? 是否需要监控之前的?


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1. 定时刷新
2. flex仪表盘
3. linechart
4. 柱表,柱表嵌套效果
5. flex嵌套到html


6. 扩大缩小?
Dynamic programming is an algorithm suitable for solving the Knapsack problem. It avoids repeated calculations by decomposing complex problems into overlapping sub - problems and storing the solutions to the sub - problems. The core of dynamic programming is state definition and state transition equations [^1]. The general steps of using dynamic programming to solve the Knapsack problem are as follows: 1. **Define the state**: Usually, two - dimensional states are defined. For example, let `dp[i][j]` represent the maximum value that can be obtained when considering the first `i` items and the capacity of the knapsack is `j`. 2. **State transition equation**: For the 0 - 1 Knapsack problem, if the weight of the `i` - th item is `w[i]` and the value is `v[i]`, then the state transition equation is: - When `j < w[i]`, `dp[i][j]=dp[i - 1][j]` (the current item cannot be put into the knapsack). - When `j >= w[i]`, `dp[i][j]=max(dp[i - 1][j], dp[i - 1][j - w[i]]+v[i])` (choose whether to put the current item into the knapsack). Here is a simple Python code example for the 0 - 1 Knapsack problem: ```python def knapsack(weights, values, capacity): n = len(weights) dp = [[0 for _ in range(capacity + 1)] for _ in range(n + 1)] for i in range(1, n + 1): for j in range(1, capacity + 1): if j < weights[i - 1]: dp[i][j] = dp[i - 1][j] else: dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - weights[i - 1]] + values[i - 1]) return dp[n][capacity] weights = [2, 3, 4, 5] values = [3, 4, 5, 6] capacity = 8 print(knapsack(weights, values, capacity)) ``` ### Application scenarios - **Resource allocation**: In project management, given a limited amount of resources (such as time, budget, manpower), and different tasks with different resource requirements and benefits, the Knapsack problem can be used to select the most profitable combination of tasks. - **Stock selection**: When an investor has a certain amount of funds and there are multiple stocks with different prices and expected returns, the Knapsack problem can be used to select the most profitable stock portfolio.
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