优化代码，GPU加速 def temp_condtion(df, temp_upper, temp_low): return ((df['max_temp']<=temp_upper) & (df['min_temp']>=temp_low)) def soc_condtion(df, soc_upper, soc_low): return ((df['bat_module_soc_00']<=temp_upper) & (df['bat_module_soc_00']>=temp_low)) def current_condtion(df, min_curr, batt_state): if batt_state=='charge': return (df['bat_module_current_00'].abs()>=min_curr) & (df['bat_module_current_00']>=0) elif batt_state=="discharge": return (df['bat_module_current_00'].abs()>=min_curr) & (df['bat_module_current_00']<=0 # 板端运行逻辑 data = {'realtime':[], 'cell_volt':[], 'total_current':[]} index = [] # (total_current[j]<=0) for i in tqdm(df.index[temp_condtion(df, temp_upper, temp_low) & soc_condtion(df, soc_upper, soc_low) & current_condtion(df, min_curr, 'discharge')]: n = 0 k = i while (n <= data_point) & (i <= len(df)-100): idx_list = [] idx_list.append(i) for j in np.arange(i+1, len(df)): if ((sendtime.iloc[j]-sendtime.iloc[k]).total_seconds()>=time_interval): break elif (df['max_temp'].iloc[j]<=temp_upper) & (df['min_temp'].iloc[j]>=temp_low) & \ (df['bat_module_soc_00'].iloc[j]>=soc_low) & (df['bat_module_soc_00'].iloc[j]<=soc_upper) & \ ((sendtime[j]-sendtime[i]).total_seconds()>=sample_interval) & \ ((sendtime.iloc[j]-sendtime.iloc[k]).total_seconds()<=time_interval) & \ (np.abs(total_current[j]-total_current[i])>=curr_interval) & (np.abs(soc[j]-soc[i])<=soc_interval) & \ (np.abs(total_current[j])>=min_curr): n+=1 idx_list.append(j) i = j if ((sendtime.iloc[j]-sendtime.iloc[k]).total_seconds()>=time_interval): break if len(idx_list) >= data_point: print(idx_list) index.append(idx_list)

There are a few ways to optimize this code and potentially utilize GPU acceleration:

1. Use Numba: Numba is a just-in-time compiler for Python that can compile Python code to run on GPUs. This can significantly speed up code execution. You can decorate your functions with `@jit` to have them compiled by Numba.

2. Use Pandas' `query` method: Rather than using boolean indexing, you can use the `query` method of a Pandas DataFrame to filter rows based on conditions. For example, you can replace `temp_condition(df, temp_upper, temp_low)` with `df.query("max_temp <= @temp_upper and min_temp >= @temp_low")`.

3. Use vectorized operations: Instead of looping through each row of the DataFrame, you can use vectorized operations to apply your conditions across the entire DataFrame. For example, you can replace `df['bat_module_current_00'].abs()>=min_curr` with `np.abs(df['bat_module_current_00'])>=min_curr`.

4. Use Dask: Dask is a parallel computing library that can distribute computations across multiple CPUs or GPUs. You can use Dask to parallelize your code and potentially speed up execution. However, this may require significant changes to your code structure.