GLM-5.1 在「订单数据柱状图生成」的评测结果
这是该 AI 模型在此测试用例上的详细评测结果。
基本信息
- 模型名称：GLM-5.1
- 用例名称：订单数据柱状图生成
- 测试类型：xsct-a
- 评测维度：文档数据处理
系统提示词（System Prompt）
这是给 AI 模型的背景设定和角色指令：
You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).
用户提示词（User Prompt）
这是用户给 AI 模型的具体任务要求：
以下是当前工作区中的数据文件 `orders_q3.csv` 内容（前10行）： ```csv 客户ID,客户姓名,订单编号,下单日期,商品名称,数量,单价,总金额,状态 C001,赵建国,ORD-2024-0801,2024-08-01,办公椅,2,580,1160,已完成 C002,李梅,ORD-2024-0802,2024-08-02,打印机,1,2300,2300,已完成 C001,赵建国,ORD-2024-0803,2024-08-05,墨盒×10,3,45,135,已完成 C003,王鹏,ORD-2024-0804,2024-08-07,显示器,2,1800,3600,已完成 C002,李梅,ORD-2024-0805,2024-08-10,键盘,1,320,320,退款中 C004,张晓燕,ORD-2024-0806,2024-08-12,移动硬盘,2,450,900,已完成 C001,赵建国,ORD-2024-0807,2024-08-15,办公桌,1,1200,1200,已完成 C003,王鹏,ORD-2024-0808,2024-08-18,路由器,1,380,380,配送中 C005,陈伟,ORD-2024-0809,2024-08-20,笔记本支架,3,180,540,已完成 C002,李梅,ORD-2024-0810,2024-08-22,耳机,2,260,520,已完成 ``` （共 87 条记录） 请根据上面的数据，按客户姓名统计每位客户的订单总金额，然后生成一张柱状图，要求： 1. 横轴为客户姓名，纵轴为总金额（元） 2. 柱子颜色使用 #2B6CB0 3. 图表标题为「Q3各客户订单总金额」 4. 在每根柱子上方标注具体金额数值 5. 输出为 PNG 图片文件保存到工作区
任务要求
AI 模型需要满足以下要求：
- 正确读取CSV数据并按客户姓名进行分组求和统计总金额
- 生成柱状图，横轴为客户姓名，纵轴为总金额
- 柱子颜色为 #2B6CB0
- 图表标题为「Q3各客户订单总金额」
- 每根柱子上方标注具体金额数值
- 最终输出为 PNG 图片文件保存到工作区
评测结果
综合得分
该模型在此用例的得分为：33.3 分（满分 100 分），未通过
基础难度评测结果
- 得分：33.3 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
python3 -c " import csv from collections import defaultdict totals = defaultdict(int) with open('orders_q3.csv', encoding='utf-8') as f: reader = csv.DictReader(f) for row in reader: totals[row['客户姓名']] += int(row['总金额']) for name, total in sorted(totals.items(), key=lambda x: -x[1]): print(f'{name}: {total}') "
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在数据处理阶段表现出色，准确地完成了 CSV 数据的读取与统计逻辑编写。然而，该模型完全忽略了任务的核心要求——生成并保存可视化柱状图。由于缺乏绘图逻辑和文件输出操作，导致其未能完成大部分功能指标。
进阶难度评测结果
- 得分：70.0 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
我来重新读取完整的数据文件进行核算，之前我只用了展示的前10行，实际文件有87条记录。 {"name": "read", "arguments": {"file_path": "orders_q3.csv"}} 客户ID,客户姓名,订单编号,下单日期,商品名称,数量,单价,总金额,状态 C001,赵建国,ORD-2024-0801,2024-08-01,办公椅,2,580,1160,已完成 C002,李梅,ORD-2024-0802,2024-08-02,打印机,1,2300,2300,已完成 C001,赵建国,ORD-2024-0803,2024-08-05,墨盒×10,3,45,135,已完成 C003,王鹏,ORD-2024-0804,2024-08-07,显示器,2,1800,3600,已完成 C002,李梅,ORD-2024-0805,2024-08-10,键盘,1,320,320,退款中 C004,张晓燕,ORD-2024-0806,2024-08-12,移动硬盘,2,450,900,已完成 C001,赵建国,ORD-2024-0807,2024-08-15,办公桌,1,1200,1200,已完成 C003,王鹏,ORD-2024-0808,2024-08-18,路由器,1,380,380,配送中 C005,陈伟,ORD-2024-0809,2024-08-20,笔记本支架,3,180,540,已完成 C002,李梅,ORD-2024-0810,2024-08-22,耳机,2,260,520,已完成 C006,刘芳,ORD-2024-0811,2024-08-23,USB集线器,2,120,240,已完成 C004,张晓燕,ORD-2024-0812,2024-08-25,鼠标垫,4,35,140,已完成 C007,周磊,ORD-2024-0813,2024-08-26,投影仪,1,4500,4500,已完成 C001,赵建国,ORD-2024-0814,2024-08-28,文件柜,1,860,860,已完成 C003,王鹏,ORD-2024-0815,2024-08-29,摄像头,2,220,440,已完成 C005,陈伟,ORD-2024-0816,2024-08-30,电源适配器,2,95,190,已完成 C002,李梅,ORD-2024-0817,2024-09-01,扫描仪,1,1800,1800,已完成 C006,刘芳,ORD-2024-0818,2024-09-02,显示器,1,1800,1800,已完成 C004,张晓燕,ORD-2024-0819,2024-09-03,打印机,1,2300,2300,已完成 C001,赵建国,ORD-2024-0820,2024-09-04,白板,1,350,350,已完成 C007,周磊,ORD-2024-0821,2024-09-05,办公椅,3,580,1740,已完成 C003,王鹏,ORD-2024-0822,2024-09-06,碎纸机,1,960,960,已完成 C005,陈伟,ORD-2024-0823,2024-09-07,打印机,1,2300,2300,已完成 C002,李梅,ORD-2024-0824,2024-09-08,移动硬盘,3,450,1350,已完成 C006,刘芳,ORD-2024-0825,2024-09-09,键盘,2,320,640,已完成 C001,赵建国,ORD-2024-0826,2024-09-10,显示器,1,1800,1800,已完成 C004,张晓燕,ORD-2024-0827,2024-09-11,耳机,3,260,780,已完成 C007,周磊,ORD-2024-0828,2024-09-12,路由器,2,380,760,已完成 C003,王鹏,ORD-2024-0829,2024-09-13,墨盒×10,5,45,225,已完成 C005,陈伟,ORD-2024-0830,2024-09-14,办公桌,1,1200,1200,已完成 C002,李梅,ORD-2024-0831,2024-09-15,办公椅,2,580,1160,已完成 C006,刘芳,ORD-2024-0832,2024-09-16,鼠标,3,85,255,已完成 C001,赵建国,ORD-2024-0833,2024-09-17,碎纸机,1,960,960,已完成 C004,张晓燕,ORD-2024-0834,2024-09-18,投影仪,1,4500,4500,已完成 C007,周磊,ORD-2024-0835,2024-09-19,扫描仪,1,1800,1800,已完成...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在技术实现（可视化代码编写）上表现非常优秀，能够准确处理复杂的图表配置要求。但在逻辑推理和事实核查方面存在偏差，面对用户对正确数据的质疑时，未能坚持基于现有样本进行验算，而是直接切换了统计口径，导致计算结果与参考答案要求的样本均值（2211元）不符。建议在处理此类任务时，先验证当前上下文数据的准确性，再考虑是否需要扩展数据范围。
困难难度评测结果
- 得分：100.0 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
好的，改动较多，我重写完整脚本，确保所有数据从 CSV 实时计算。 {"name": "write", "arguments": {"file": "generate_chart_v3.py", "content": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mticker\nimport numpy as np\n\n# ── 1. 读取原始数据 ──\ndf = pd.read_csv('orders_q3.csv', encoding='utf-8')\ndf['下单日期'] = pd.to_datetime(df['下单日期'])\n\n# ── 2. 按周分组 ──\ndef assign_week(d):\n day = d.day\n if day ', color='#D69E2E', lw=1.5),\n ha='center', zorder=6)\n\n# ── 7. 图例 ──\nh1, l1 = ax1.get_legend_handles_labels()\nh2, l2 = ax2.get_legend_handles_labels()\nax1.legend(h1 + h2, l1 + l2, loc='upper right', frameon=True,\n fancybox=True, shadow=False, fontsize=9,\n edgecolor='#CBD5E0', facecolor='white', framealpha=0.95)\n\n# ── 8. 标题与备注 ──\nax1.set_title('Q3第三季度8月周度订单趋势与异常监控',\n fontsize=15, fontweight='bold', color='#1A202C', pad=18)\nfig.text(0.5, 0.01,\n f'数据来源：orders_q3.csv（共{len(df)}条记录）',\n ha='center', fontsize=9, color='#718096', fontstyle='italic')\n\nplt.tight_layout(rect=[0, 0.03, 1, 1])\n\n# ── 9. 导出 ──\nfig.savefig('q3_weekly_monitor.png', dpi=200, bbox_inches='tight',\n facecolor='white', edgecolor='none')\nfig.savefig('q3_weekly_monitor.svg', format='svg', bbox_inches='tight',\n facecolor='white', edgecolor='none')\nplt.close()\n\nprint('✅ 图表已生成：q3_weekly_monitor.png / .svg')\nprint(f' 总记录数: {len(df)}')\nprint(f' 平均异常率: {avg_abnormal:.1f}%')\nprint(f' 异常最高周: {max_week_idx.replace(chr(10), \" \)} ({max_week_val:.1f}%)')"}} {"name": "exec", "arguments": {"command": "cd /workspace && python generate_chart_v3.py"}} 图表已生成，两个文件均已导出： | 文件 | 格式 | 说明 | |------|------|------| | `q3_weekly_monitor.png` | 位图 200dpi | 高清位图版 | | `q3_weekly_monitor.svg` | 矢量 | 可无损缩放，适合报告嵌入 | 本次更新要点： - **堆叠柱状图**：深蓝 `#1A365D`（已完成）+ 浅红 `#FEB2B2`（异常），柱顶标注总金额 - **折线百分比标签**：每个数据点旁均标注 `XX.X%` - **平均异常率虚线**：灰色 `#A0AEC0` 水平线，右侧标注具体数值 - **图例框**：右上角统一展示四个系列（已完成金额、异常订单金额、异常订单占比、平均异常率） - **黄色高亮 + 注释框**：异常占比最高周背景高亮，箭头指向注释框说明原因猜测 - **数据全部实时计算**：脚本从 `orders_q3.csv` 原始读取，无硬编码
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现近乎完美。它不仅准确理解并执行了复杂的业务逻辑变更（从客户统计转向周度异常监控），还在图表美化和规范性上达到了专业水平。代码逻辑严密，实时处理数据的要求执行到位，视觉要素标注详尽且色彩使用严格符合品牌/任务规范，是一个高质量的 AI 生成结果。
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