大模型训练场景住宅小区用户洞察数据

大模型训练用住宅小区用户洞察数据核心价值，十亿级设备使用行为（比如用户手机的app连接上住宅小区wifi后的用户行为数据）经去标识化、聚合化处理，可转化为住宅小区宏观群体画像。这类高质量统计数据能让大语言模型高效学习真实用户分布、社会常识与市场趋势，优化推理能力、校准输出并减少幻觉，还可作为 “事实基准” 与 “认知图谱”，复用于大模型预训练、监督微调及效果评估。
其多维度群体统计数据，是大模型训练优化的关键特征与基准。在预训练与知识增强层面，年龄字段，为模型提供社会人口统计学与消费行为学知识，融入预训练语料后，能帮模型更精准理解现实用户群体，回答 “设计面向住户的产品” 等问题时更符合商业逻辑。优化对话与推荐能力时，人员年龄占比、TGI 指标等将群体偏好量化，微调垂直领域模型时，可据此构建指令样本，如指令 “分析某一小区典型用户画像”，期望输出 “以一线城市 25 - 40 岁男性为主，TGI=135”，助力模型形成 “量化分析” 思维，提升垂直领域对话专业性。
此外，该数据可校准模型输出并评估 “幻觉”，还能集成到 RAG 系统，确保模型回答用户画像问题时依据真实数据。1、数据收集：
通过个推软件开发工具包对海量、离散的设备使用行为数据进行收集，进而加工产出以群体画像为核心的数据资产。
2、数据处理：
首先，进行隐私保护处理。 为确保数据无法关联到特定自然人。通过数据管道与处理引擎，对数据进行清洗、脱敏和聚合；
所有涉及用户标识的数据均会经过密码学哈希函数进行单向、不可逆的混淆计算。此过程实现了数据的匿名化与去标识化，从源头切断了信息回溯至特定个人的可能性。
其次，执行群体统计聚合。 在匿名化数据的基础上，系统按照预先设定的分析维度，对设备使用行为进行群体层面的统计汇总。此过程不关注个体行为，而是将个体行为汇聚成宏观的统计量，从而生成如“年龄分布”等反映整体用户构成的数据集合。
3、算法加工：
引入机器学习模型进行标签预测：
1. 对于无法直接获取的人口属性，本方案采用预设的机器学习模型进行推断。该模型首先以用户授权的、经脱敏后的跨平台住宅小区内居民设备使用行为数据作为输入特征，通过算法计算，输出具有“18-25岁”等特定标签的潜在用户群体在全量用户中的预估分布比例，从而完成宏观层面的群体定义与基准量化。
2. 在此基础上，我们将同一分析框架应用于具体场景，聚焦于单个特定目标住宅小区，通过相同的算法计算得出上述已定义的各类目标群体在该住宅小区用户中的分布比例，即群体渗透率。所有预测结果均以概率分布形式呈现，服务于群体洞察，而非对个体进行精准刻画。
3. 同时通过计算TGI分步，来量化群体特征相对于总体的偏好强度，并将TGI指标作为洞察报告的关键维度之一。
4. 以报告形式产出标准化数据资产。 首先，基于具体业务场景向大模型下达精准指令，生成包含固定框架、动态模块与数据占位符的标准报告模板，适配多场景使用需求；随后，通过定制化算法程序，将经清洗、脱敏、聚合与预测流程处理后的多源异构数据，转化为格式统一、类型匹配、精度合规的高质量标准化格式数据，无缝适配报告模板填充需求，将数据填入到报告模板中。

Core Value of Residential Community User Insight Data for Large Language Model (LLM) Training: Billions of device usage behavior records—such as user behavior data generated after users' mobile apps connect to residential community WiFi—can be processed via de-identification and aggregation to create macroscopic group portraits of residential communities. This high-quality statistical data enables LLMs to efficiently learn real user distributions, social common sense and market trends, optimize reasoning capabilities, calibrate outputs and reduce hallucinations. It can also serve as a "fact benchmark" and "cognitive graph" for reuse in LLM pre-training, supervised fine-tuning and effect evaluation.

Its multi-dimensional group statistical data serves as key features and benchmarks for LLM training and optimization. At the level of pre-training and knowledge enhancement, the age field provides the model with socio-demographic and consumer behavior knowledge. After being integrated into pre-training corpora, it helps the model more accurately understand real user groups, and make responses that align with business logic when answering questions such as "designing products for residents". When optimizing dialogue and recommendation capabilities, indicators such as the age proportion of the population and the Target Group Index (TGI) quantify group preferences. When fine-tuning vertical-domain models, instruction samples can be constructed based on these data. For example, the instruction "Analyze the typical user portrait of a certain community" expects the output "mainly males aged 25-40 in first-tier cities, TGI=135", helping the model develop "quantitative analysis" thinking and improve the professionalism of vertical-domain dialogue.

In addition, this data can calibrate model outputs and evaluate hallucinations. It can also be integrated into Retrieval-Augmented Generation (RAG) systems to ensure that the model answers user portrait questions based on real data.

1. Data Collection: Massive and discrete device usage behavior data is collected through the GeTui Software Development Kit (SDK), and then processed to create data assets centered on group portraits.

2. Data Processing: First, privacy protection processing is carried out to ensure that the data cannot be linked to specific natural persons. Data is cleaned, desensitized and aggregated through data pipelines and processing engines; all data involving user identifiers will be subjected to one-way, irreversible obfuscation calculation via cryptographic hash functions. This process achieves data anonymization and de-identification, cutting off the possibility of tracing information back to specific individuals at the source. Second, group statistical aggregation is performed. Based on the anonymized data, the system conducts statistical summary of device usage behavior at the group level according to pre-set analysis dimensions. This process does not focus on individual behaviors, but aggregates individual behaviors into macroscopic statistics, thus generating data sets reflecting overall user composition such as "age distribution".

3. Algorithm Processing: Machine learning models are introduced for label prediction:
1. For demographic attributes that cannot be directly obtained, this solution uses pre-trained machine learning models for inference. The model first takes the user-authorized, desensitized cross-platform device usage behavior data of residents in the residential community as input features, and through algorithmic calculation, outputs the estimated distribution proportion of potential user groups with specific labels such as "18-25 years old" in the total number of users, thereby completing macroscopic group definition and benchmark quantification.
2. On this basis, we apply the same analysis framework to specific scenarios, focusing on a single specific target residential community, and use the same algorithm to calculate the distribution proportion of the above-defined target groups among the users of this residential community, namely group penetration rate. All prediction results are presented in the form of probability distributions, serving group insights rather than accurately portraying individuals.
3. Meanwhile, the TGI index is calculated to quantify the preference intensity of group characteristics relative to the overall population, and the TGI indicator is taken as one of the key dimensions of the insight report.

4. Produce standardized data assets in the form of reports. First, issue precise instructions to the LLM based on specific business scenarios to generate standard report templates containing fixed frameworks, dynamic modules and data placeholders, adapting to multi-scenario usage needs; subsequently, through customized algorithm programs, convert multi-source heterogeneous data processed through cleaning, desensitization, aggregation and prediction processes into high-quality standardized format data with unified format, matched types and compliant accuracy, which seamlessly adapts to the filling requirements of the report template, and fills the data into the report template.

搜集汇总

背景与挑战

背景概述

该数据集专注于住宅小区用户洞察，旨在支持大模型训练场景。它可能包含用户行为、偏好或人口统计特征等相关数据，用于提升模型在住宅领域的分析和预测能力。