4.5. Route Selection

本文深入探讨了Linux系统中路由选择的原理与实现,包括常规路由选择流程及基于策略的路由特性。介绍了如何利用最长前缀匹配来确定数据包的最佳路径,并讨论了多路由表和路由策略数据库(RPDB)的应用。

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4.5. Route Selection

 

Crucial to the proper ability of hosts to exchange IP packets is the correct selection of a route to the destination. The rules for the selection of route path are traditionally made on a hop-by-hop basis [18] based solely upon the destination address of the packet. Linux behaves as a conventional routing device in this way, but can also provide a more flexible capability. Routes can be chosen and prioritized based on other packet characteristics.

The route selection algorithm under linux has been generalized to enable the powerful latter scenario without complicating the overwhelmingly common case of the former scenario.

4.5.1. The Common Case

The above sections on routing to a local network and the default gateway expose the importance of destination address for route selection. In this simplified model, the kernel need only know the destination address of the packet, which it compares against the routing tables to determine the route by which to send the packet.

The kernel searches for a matching entry for the destination first in the routing cache and then the main routing table. In the case that the machine has recently transmitted a packet to the destination address, the routing cache will contain an entry for the destination. The kernel will select the same route, and transmit the packet accordingly.

If the linux machine has not recently transmitted a packet to this destination address, it will look up the destination in its routing table using a technique known longest prefix match [19]. In practical terms, the concept of longest prefix match means that the most specific route to the destination will be chosen.

The use of the longest prefix match allows routes for large networks to be overridden by more specific host or network routes, as required in Example 1.10, “Removing a static network route and adding a static host route”, for example. Conversely, it is this same property of longest prefix match which allows routes to individual destinations to be aggregated into larger network addresses. Instead of entering individual routes for each host, large numbers of contiguous network addresses can be aggregated. This is the realized promise of CIDR networking. See Section I.1.3, “General IP Networking Resources” for further details.

In the common case, route selection is based completely on the destination address. Conventional (as opposed to policy-based) IP networking relies on only the destination address to select a route for a packet.

Because the majority of linux systems have no need of policy based routing features, they use the conventional routing technique of longest prefix match. While this meets the needs of a large subset of linux networking needs, there are unrealized policy routing features in a machine operating in this fashion.

4.5.2. The Whole Story

With the prevalence of low cost bandwidth, easily configured VPN tunnels, and increasing reliance on networks, the technique of selecting a route based solely on the destination IP address range no longer suffices for all situations. The discussion of the common case of route selection under linux neglects one of the most powerful features in the linux IP stack. Since kernel 2.2, linux has supported policy based routing through the use of multiple routing tables and the routing policy database (RPDB). Together, they allow a network administrator to configure a machine select different routing tables and routes based on a number of criteria.

Selectors available for use in policy-based routing are attributes of a packet passing through the linux routing code. The source address of a packet, the ToS flags, an fwmark (a mark carried through the kernel in the data structure representing the packet), and the interface name on which the packet was received are attributes which can be used as selectors. By selecting a routing table based on packet attributes, an administrator can have granular control over the network path of any packet.

With this knowledge of the RPDB and multiple routing tables, let's revisit in detail the method by which the kernel selects the proper route for a packet. Understanding the series of steps the kernel takes for route selection should demystify advanced routing. In fact, advanced routing could more accurately be called policy-based networking.

When determining the route by which to send a packet, the kernel always consults the routing cache first. The routing cache is a hash table used for quick access to recently used routes. If the kernel finds an entry in the routing cache, the corresponding entry will be used. If there is no entry in the routing cache, the kernel begins the process of route selection. For details on the method of matching a route in the routing cache, see Section 4.7, “Routing Cache”.

The kernel begins iterating by priority through the routing policy database. For each matching entry in the RPDB, the kernel will try to find a matching route to the destination IP address in the specified routing table using the aforementioned longest prefix match selection algorithm. When a matching destination is found, the kernel will select the matching route, and forward the packet. If no matching entry is found in the specified routing table, the kernel will pass to the next rule in the RPDB, until it finds a match or falls through the end of the RPDB and all consulted routing tables.

Here is a snippet of python-esque pseudocode to illustrate the kernel's route selection process again. Each of the lookups below occurs in kernel hash tables which are accessible to the user through the use of various iproute2 tools.

Example 4.4. Routing Selection Algorithm in Pseudo-code

if packet.routeCacheLookupKey in routeCache :
    route = routeCache[ packet.routeCacheLookupKey ]
else
    for rule in rpdb :
        if packet.rpdbLookupKey in rule :
            routeTable = rule[ lookupTable ]
            if packet.routeLookupKey in routeTable :
                route = route_table[ packet.routeLookup_key ]
          

This pseudocode provides some explanation of the decisions required to find a route. The final piece of information required to understand the decision making process is the lookup process for each of the three hash table lookups. In Table 4.1, “Keys used for hash table lookups during route selection”, each key is listed in order of importance. Optional keys are listed in italics and represent keys that will be matched if they are present.

Table 4.1. Keys used for hash table lookups during route selection

route cacheRPDBroute table
destinationsourcedestination
sourcedestinationToS
ToSToSscope
fwmarkfwmarkoif
iifiif 

The route cache (also the forwarding information base) can be displayed using ip route show cache. The routing policy database (RPDB) can be manipulated with the ip rule utility. Individual route tables can be manipulated and displayed with the ip route command line tool.

Example 4.5. Listing the Routing Policy Database (RPDB)

[root@isolde]# ip rule show
0:      from all lookup local
32766:  from all lookup main
32767:  from all lookup 253
        

Observation of the output of ip rule show in Example 4.5, “Listing the Routing Policy Database (RPDB)” on a box whose RPDB has not been changed should reveal a high priority rule, rule 0. This rule, created at RPDB initialization, instructs the kernel to try to find a match for the destination in the local routing table. If there is no match for the packet in the local routing table, then, per rule 32766, the kernel will perform a route lookup in the main routing table. Normally, the main routing table will contain a default route if not a more specific route. Failing a route lookup in the main routing table the final rule (32767) instructs the kernel to perform a route lookup in table 253.

A common mistake when working with multiple routing tables involves forgetting about the statelessness of IP routing. This manifests when the user configuring the policy routing machine accounts for outbound packets (via fwmark, or ip rule selectors), but forgets to account for the return packets.

4.5.3. Summary

For more ideas on how to use policy routing, how to work with multiple routing tables, and how to troubleshoot, see Section 10.3, “Using the Routing Policy Database and Multiple Routing Tables”.

Yeah. That's it. So there.



[18] This document could stand to allude to MPLS implementations under linux, for those who want to look at traffic engineering and packet tagging on backbones. This is certainly not in the scope of this chapter, and should be in a separate chapter, which covers developing technologies.

[19] Refer to RFC 3222 for further details.

我运行完app.py然后在algorithmselection中上传报出以下错误:127.0.0.1 - - [21/Jun/2025 19:31:25] "POST /upload HTTP/1.1" 500 - Traceback (most recent call last): File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 1536, in __call__ return self.wsgi_app(environ, start_response) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 1514, in wsgi_app response = self.handle_exception(e) File "D:\python3.13.2\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function return cors_after_request(app.make_response(f(*args, **kwargs))) ~^^^^^^^^^^^^^^^^^ File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 1511, in wsgi_app response = self.full_dispatch_request() File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 919, in full_dispatch_request rv = self.handle_user_exception(e) File "D:\python3.13.2\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function return cors_after_request(app.make_response(f(*args, **kwargs))) ~^^^^^^^^^^^^^^^^^ File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 917, in full_dispatch_request rv = self.dispatch_request() File "D:\python3.13.2\Lib\site-packages\flask\app.py", line 902, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^ File "d:\Users\Administrator\Desktop\project\gas_sensor_app\app.py", line 130, in upload_files if not preprocess_data(): ~~~~~~~~~~~~~~~^^ File "d:\Users\Administrator\Desktop\project\gas_sensor_app\app.py", line 87, in preprocess_data sensor_columns = [col for col in dataset.columns if col.startswith('sensor')] ^^^^^^^^^^^^^^ AttributeError: 'int' object has no attribute 'startswith'
06-22
<think>根据错误信息,问题发生在预处理数据的步骤中。具体是在寻找以'sensor'开头的列名时,出现了`AttributeError:'int'objecthasnoattribute'startswith'`。这通常意味着在`dataset.columns`中有一个整数(int)类型的列名,而整数没有`startswith`方法。可能的原因:1.数据集中的列名包含整数(例如,列名是数字而不是字符串)。2.在读取文件时,列名被解释为整数。解决方案:1.在预处理之前,将所有的列名转换为字符串。2.在读取文件后,检查列名的类型。修改`preprocess_data`函数,在特征工程之前将列名转换为字符串:在`preprocess_data`函数中添加:```python#确保所有列名都是字符串dataset.columns=dataset.columns.astype(str)```修改后的`preprocess_data`函数如下:```pythondefpreprocess_data():"""预处理数据集"""globaldatasetifdatasetisNone:logger.error("Nodatasetavailableforpreprocessing")returnFalsetry:#1.处理缺失值dataset.dropna(inplace=True)logger.info(f"AfterdroppingNA:{dataset.shape}")#2.删除不必要的列if'source_file'indataset.columns:dataset.drop('source_file',axis=1,inplace=True)#3.将列名转换为字符串(解决列名为整数的问题)dataset.columns=dataset.columns.astype(str)#4.特征工程(示例)#添加特征:传感器响应值的平均值sensor_columns=[colforcolindataset.columnsifcol.startswith('sensor')]ifsensor_columns:dataset['sensor_mean']=dataset[sensor_columns].mean(axis=1)logger.info("Addedsensor_meanfeature")#5.特征缩放scaler=StandardScaler()numeric_cols=dataset.select_dtypes(include=['float64','int64']).columnsdataset[numeric_cols]=scaler.fit_transform(dataset[numeric_cols])logger.info("Scalednumericfeatures")returnTrueexceptExceptionase:logger.error(f"Preprocessingfailed:{str(e)}")returnFalse```另外,在加载文件后,也可以确保列名是字符串,例如在`load_and_merge_files`函数中,读取每个文件后转换列名为字符串:在`load_and_merge_files`函数中,读取每个文件后添加:```python#确保列名为字符串df.columns=df.columns.astype(str)```两种方法都可以,选择一处修改即可。建议在读取每个文件后就转换列名,这样在后续处理中就不会出现类型问题。下面是在`load_and_merge_files`中修改的示例:```pythondefload_and_merge_files(files):"""加载并合并多个数据文件"""globaldataset,gas_types,concentrationsdfs=[]forfileinfiles:try:#安全保存文件filename=secure_filename(file.filename)file_path=os.path.join(app.config['UPLOAD_FOLDER'],filename)file.save(file_path)logger.info(f"Savedfile:{file_path}")#根据文件类型加载数据iffilename.endswith('.csv'):df=pd.read_csv(file_path)eliffilename.endswith(('.xlsx','.xls')):df=pd.read_excel(file_path)else:logger.warning(f"Unsupportedfiletype:{filename}")continue#将列名转换为字符串df.columns=df.columns.astype(str)#添加文件来源标记(可选)df['source_file']=filenamedfs.append(df)logger.info(f"Loadedfile:{filename},shape:{df.shape}")exceptExceptionase:logger.error(f"Errorloadingfile{file.filename}:{str(e)}")continue#合并所有数据ifdfs:dataset=pd.concat(dfs,ignore_index=True)logger.info(f"Mergeddatasetshape:{dataset.shape}")#重置全局变量gas_types=[]concentrations=[]returnTruereturnFalse```选择其中一种方法修改即可。这里推荐在读取每个文件后立即转换列名(第二种方法),因为这样在合并前的每个数据框列名都是字符串,避免合并时可能出现的类型不一致问题。修改后重新运行应用,应该可以解决该错误。如果问题仍然存在,可以在日志中添加列名的输出,以便进一步调试:在`preprocess_data`函数中添加:```pythonlogger.info(f"Columns:{dataset.columns}")```或者在错误处理中输出列名:```pythonexceptExceptionase:logger.error(f"Preprocessingfailed:{str(e)}")logger.error(f"Columns:{dataset.columnsifdatasetisnotNoneelse'Nodataset'}")returnFalse```这样可以帮助确认列名是否都是字符串。完整修改后的`app.py`中`load_and_merge_files`函数部分(采用第二种方法)已经在上面的代码块中给出。请尝试修改并重新运行。</think>根据您提供的错误日志,问题出在预处理数据时尝试查找以 "sensor" 开头的列名时发生的。错误表明 `dataset.columns` 中包含整数类型的对象,而不是预期的字符串。让我提供一个完整的修复方案: ### 问题分析 错误的核心是: ```python sensor_columns = [col for col in dataset.columns if col.startswith('sensor')] AttributeError: 'int' object has no attribute 'startswith' ``` 这表示: 1. 数据集中的某些列名是整数类型(而不是字符串) 2. `startswith()` 方法只能用于字符串对象 3. 问题发生在预处理阶段的特征工程部分 ### 完整修复方案 ```python # 修改 preprocess_data 函数 def preprocess_data(): """预处理数据集""" global dataset if dataset is None: logger.error("No dataset available for preprocessing") return False try: # 1. 确保所有列名都是字符串 dataset.columns = dataset.columns.astype(str) # 关键修复 # 2. 处理缺失值 dataset.dropna(inplace=True) logger.info(f"After dropping NA: {dataset.shape}") # 3. 删除不必要的列 if 'source_file' in dataset.columns: dataset.drop('source_file', axis=1, inplace=True) # 4. 特征工程(修复后的版本) sensor_columns = [] for col in dataset.columns: # 确保列名是字符串并检查前缀 if isinstance(col, str) and col.lower().startswith('sensor'): sensor_columns.append(col) # 5. 添加传感器平均值特征 if sensor_columns: dataset['sensor_mean'] = dataset[sensor_columns].mean(axis=1) logger.info(f"Added sensor_mean feature using {len(sensor_columns)} sensor columns") else: logger.warning("No sensor columns found") # 6. 特征缩放 scaler = StandardScaler() numeric_cols = dataset.select_dtypes(include=['float64', 'int64']).columns dataset[numeric_cols] = scaler.fit_transform(dataset[numeric_cols]) logger.info("Scaled numeric features") return True except Exception as e: logger.error(f"Preprocessing failed: {str(e)}") # 添加详细日志以便调试 logger.debug(f"Dataset columns: {list(dataset.columns)}") logger.debug(f"Dataset dtypes: {dataset.dtypes}") logger.debug(f"Dataset head:\n{dataset.head()}") return False ``` ### 关键修复点解释 1. **强制列名为字符串**: ```python dataset.columns = dataset.columns.astype(str) ``` 这确保所有列名都是字符串类型,避免整数列名导致的问题 2. **更健壮的传感器列检测**: ```python sensor_columns = [] for col in dataset.columns: if isinstance(col, str) and col.lower().startswith('sensor'): sensor_columns.append(col) ``` - 显式检查列名是否为字符串 - 使用 `lower()` 处理大小写不一致 - 更安全地收集传感器列 3. **添加详细日志**: ```python logger.debug(f"Dataset columns: {list(dataset.columns)}") logger.debug(f"Dataset dtypes: {dataset.dtypes}") ``` 这些日志将帮助诊断数据加载问题 ### 完整的修复版 app.py ```python from flask import Flask, request, jsonify from flask_cors import CORS import os import pandas as pd import numpy as np from werkzeug.utils import secure_filename from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report import logging # 配置日志 logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) app = Flask(__name__) app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024 # 100MB 文件大小限制 # 详细的 CORS 配置 CORS(app, resources={ r"/*": { "origins": "*", "methods": ["GET", "POST", "PUT", "DELETE", "OPTIONS"], "allow_headers": ["Content-Type", "Authorization"] } }) # 配置上传文件夹 UPLOAD_FOLDER = 'uploads' if not os.path.exists(UPLOAD_FOLDER): os.makedirs(UPLOAD_FOLDER) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER # 允许的文件扩展名 ALLOWED_EXTENSIONS = {'csv', 'xlsx', 'xls'} # 全局数据集存储 dataset = None gas_types = [] concentrations = [] def allowed_file(filename): """检查文件扩展名是否合法""" return '.' in filename and \ filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS def load_and_merge_files(files): """加载并合并多个数据文件""" global dataset, gas_types, concentrations dfs = [] for file in files: try: # 安全保存文件 filename = secure_filename(file.filename) file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) file.save(file_path) logger.info(f"Saved file: {file_path}") # 根据文件类型加载数据 if filename.endswith('.csv'): df = pd.read_csv(file_path) elif filename.endswith(('.xlsx', '.xls')): df = pd.read_excel(file_path) else: logger.warning(f"Unsupported file type: {filename}") continue # 添加文件来源标记(可选) df['source_file'] = filename dfs.append(df) logger.info(f"Loaded file: {filename}, shape: {df.shape}") except Exception as e: logger.error(f"Error loading file {file.filename}: {str(e)}") continue # 合并所有数据 if dfs: dataset = pd.concat(dfs, ignore_index=True) logger.info(f"Merged dataset shape: {dataset.shape}") # 重置全局变量 gas_types = [] concentrations = [] return True return False def preprocess_data(): """预处理数据集""" global dataset if dataset is None: logger.error("No dataset available for preprocessing") return False try: # 1. 确保所有列名都是字符串 dataset.columns = dataset.columns.astype(str) logger.info(f"Column names converted to strings") # 2. 处理缺失值 dataset.dropna(inplace=True) logger.info(f"After dropping NA: {dataset.shape}") # 3. 删除不必要的列 if 'source_file' in dataset.columns: dataset.drop('source_file', axis=1, inplace=True) # 4. 特征工程(修复后的版本) sensor_columns = [] for col in dataset.columns: # 确保列名是字符串并检查前缀 if isinstance(col, str) and col.lower().startswith('sensor'): sensor_columns.append(col) # 5. 添加传感器平均值特征 if sensor_columns: dataset['sensor_mean'] = dataset[sensor_columns].mean(axis=1) logger.info(f"Added sensor_mean feature using {len(sensor_columns)} sensor columns") else: logger.warning("No sensor columns found") # 6. 特征缩放 scaler = StandardScaler() numeric_cols = dataset.select_dtypes(include=['float64', 'int64']).columns dataset[numeric_cols] = scaler.fit_transform(dataset[numeric_cols]) logger.info("Scaled numeric features") return True except Exception as e: logger.error(f"Preprocessing failed: {str(e)}") # 添加详细日志以便调试 if dataset is not None: logger.debug(f"Dataset columns: {list(dataset.columns)}") logger.debug(f"Dataset dtypes: {dataset.dtypes}") logger.debug(f"Dataset head:\n{dataset.head()}") return False @app.route('/upload', methods=['POST']) def upload_files(): """处理文件上传""" global gas_types, concentrations logger.info("Received upload request") # 检查是否有文件 if 'files' not in request.files: logger.error("No file part in request") return jsonify({'error': 'No file part'}), 400 files = request.files.getlist('files') if len(files) == 0 or files[0].filename == '': logger.error("No selected files") return jsonify({'error': 'No selected files'}), 400 # 过滤合法文件 valid_files = [f for f in files if allowed_file(f.filename)] if not valid_files: logger.error("No valid files found") return jsonify({'error': 'No valid files'}), 400 # 从表单获取传感器和气体信息 try: sensor_type1 = request.form.get('sensor_type1', 'MP2') gas_type1 = request.form.get('gas_type1', 'acetone') concentration1 = float(request.form.get('concentration1', 20)) sensor_type2 = request.form.get('sensor_type2', 'MP2') gas_type2 = request.form.get('gas_type2', 'acetone') concentration2 = float(request.form.get('concentration2', 20)) except Exception as e: logger.error(f"Error parsing form data: {str(e)}") return jsonify({'error': 'Invalid form data'}), 400 # 加载并合并文件 if not load_and_merge_files(valid_files): logger.error("Failed to load and merge files") return jsonify({'error': 'Failed to load files'}), 500 # 预处理数据 if not preprocess_data(): logger.error("Preprocessing failed") # 提供更多错误细节给前端 error_details = { 'error': 'Data preprocessing failed', 'columns': list(dataset.columns) if dataset is not None else 'No dataset', 'dtypes': str(dataset.dtypes) if dataset is not None else 'No dataset' } return jsonify(error_details), 500 # 更新气体和浓度信息 gas_types = [gas_type1, gas_type2] concentrations = [concentration1, concentration2] # 准备响应数据 response = { 'message': f'Successfully uploaded {len(valid_files)} files', 'sample_count': len(dataset), 'gas_types': gas_types, 'concentrations': concentrations, 'sensor_types': [sensor_type1, sensor_type2], 'columns': list(dataset.columns) # 返回列名供前端参考 } logger.info(f"Upload successful: {response}") return jsonify(response), 200 # 其他函数保持不变(analyze_data, reset_data等) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000, debug=True) ``` ### 修复说明 1. **列名强制转换**: ```python dataset.columns = dataset.columns.astype(str) ``` 确保所有列名都是字符串类型 2. **健壮的传感器列检测**: ```python sensor_columns = [] for col in dataset.columns: if isinstance(col, str) and col.lower().startswith('sensor'): sensor_columns.append(col) ``` 使用显式类型检查并忽略大小写差异 3. **增强的错误日志**: ```python logger.debug(f"Dataset columns: {list(dataset.columns)}") logger.debug(f"Dataset dtypes: {dataset.dtypes}") ``` 在错误发生时记录数据集详细信息 4. **前端错误反馈增强**: ```python error_details = { 'error': 'Data preprocessing failed', 'columns': list(dataset.columns) if dataset is not None else 'No dataset', 'dtypes': str(dataset.dtypes) if dataset is not None else 'No dataset' } return jsonify(error_details), 500 ``` 向前端返回更多错误细节,帮助调试 ### 数据格式要求说明 为了确保应用正常工作,上传的数据文件应满足以下要求: 1. **列名应为字符串**: - 即使列名是数字,也应表示为字符串(如 "1" 而不是 1) 2. **传感器列命名规范**: - 传感器列应以 "sensor" 开头(不区分大小写) - 例如:sensor1, SENSOR_2, Sensor_A 3. **目标变量要求**: - 必须包含一个名为 "label" 的列作为分类目标 4. **数据格式**: - 支持 CSV 和 Excel 文件 - 第一行应为列名 ### 测试建议 1. **测试数据准备**: ```python import pandas as pd # 创建测试数据 data = { 'sensor1': [1.2, 2.3, 3.4], 'sensor2': [4.5, 5.6, 6.7], 'label': [0, 1, 0], 1: [10, 20, 30] # 测试整数列名 } df = pd.DataFrame(data) df.to_csv('test_data.csv', index=False) ``` 2. **上传测试**: ```bash curl -X POST -F "files=@test_data.csv" -F "sensor_type1=MP2" -F "gas_type1=acetone" -F "concentration1=20" http://localhost:5000/upload ``` 3. **检查日志**: - 在控制台查看详细的预处理日志 - 确认列名已正确转换 这个修复版本应该能解决您的 "int object has no attribute 'startswith'" 错误,并提供更健壮的数据处理能力。
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