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融合语义消歧与知识图谱约束的发电设备多源异构数据质量提升方法

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融合语义消歧与知识图谱约束的发电设备多源异构数据质量提升方法

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Data quality enhancement method for multi-source heterogeneous power generation equipment data integrating semantic disambiguation and knowledge graph constraints

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doi: 10.19666/j.rlfd.202606014

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【目的】针对发电设备多源异构数据治理中普遍存在的字段缺失、语义歧义及补全结果违反业务规则等难题，提出一种融合语义消歧与知识图谱约束的数据质量提升方法，以实现缺失字段的精准补全与故障文本的规范化。

【方法】以设备标识和时间为主键，关联设备台账、SCADA告警及运维日志构建多源语料库；基于DeepSeek-V3生成多类型候选补全结果，利用领域适配BERT模型评估语义一致性，并结合本地模块计算候选支持度；构建发电设备领域知识图谱，明确定义属性范围、设备层级、时序等强制规则与软约束；在此基础上设计双因子动态门控机制，以语义复杂度因子S和领域约束强度因子C动态融合语义评分、候选支持度及知识图谱软合规评分，通过硬约束过滤与综合重排序实现结果优选。

【结果】基于某大型发电集团区域公司2020—2024年76 360条火电、风电和光伏运营数据的实验表明，该方法宏平均补全准确率、语义一致性和业务合规率分别达到95.0%、93.9%和95.8%，均优于单一BERT、DeepSeek零样本及固定权重融合等对比方法。消融实验进一步显示：去除知识图谱约束后业务合规率降至84.3%；去除双因子门控后三项指标均出现下降，验证了各模块的有效性。

【结论】所提方法通过语义理解与业务规则的深度协同，显著提升了发电设备数据补全质量与文本规范化水平，为能源电力行业的数据治理提供了可靠的技术参考。

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[Objective] To confront the enduring challenges in governing multi-source heterogeneous data from power generation equipment-such as the frequent occurrence of missing fields, semantic ambiguity, and the persistent problem where completion results violate complex business rules-this paper introduces an innovative data quality enhancement method that seamlessly integrates semantic disambiguation with knowledge graph constraints, aiming to achieve highly accurate missing-field completion and standardized normalization of fault-related texts.

[Methods] Using equipment unique identifiers and timestamps as primary keys, a multi-source corpus is constructed by associating equipment ledgers, SCADA alarm records, and operation-maintenance logs. DeepSeek-V3 is then employed to generate multiple types of candidate completions based on existing fields and similar historical samples, while a domain-adapted BERT model-fine-tuned on power generation datasets with a partial freezing and layer-wise unfreezing strategy-evaluates the semantic consistency between candidates and contexts. The fine-tuning process uses a batch size of 16, the AdamW optimizer, and a maximum sequence length of 512. A local module further calculates candidate support degrees. Concurrently, a specialized knowledge graph for power generation equipment is built to explicitly define mandatory rules-including attribute ranges, equipment hierarchies, and temporal logic-alongside soft constraints. A dual-factor dynamic gating mechanism is designed to dynamically fuse semantic scores, candidate support degrees, and knowledge graph soft compliance scores using a semantic complexity factor S and a domain constraint strength factor C, where hard constraints strictly filter out invalid candidates and comprehensive reranking optimizes the final output.

[Results] Extensive evaluations are conducted on seventy-six thousand three hundred and sixty actual operational records from thermal power, wind power, and photovoltaic systems of a regional subsidiary of a major power generation group spanning 2020 to 2024. The dataset is divided into training, validation, and test sets in a ratio of 7:1:2. The proposed method achieves macro-average completion accuracy, semantic consistency, and business compliance rates of ninety-five point zero percent, ninety-three point nine percent, and ninety-five point eight percent respectively, significantly outperforming standalone BERT, DeepSeek zero-shot, and fixed-weight fusion methods. Ablation studies further indicate that removing the knowledge graph constraints causes the business compliance rate to plummet to eighty-four point three percent, and removing the dual-factor gating degrades all three metrics, fully confirming the effectiveness of each core module.

[Conclusion] By deeply synergizing semantic understanding with explicit business rules, the proposed method effectively overcomes semantic ambiguity and rule conflicts in data completion for power generation equipment, significantly improving data quality and text normalization levels, and providing a reliable technical reference for data governance in the energy and power sector.

引用本文

. 融合语义消歧与知识图谱约束的发电设备多源异构数据质量提升方法. 热力发电, : - . DOI: 10.19666/j.rlfd.202606014

. Data quality enhancement method for multi-source heterogeneous power generation equipment data integrating semantic disambiguation and knowledge graph constraints[J]. Thermal Power Generation, : - . DOI: 10.19666/j.rlfd.202606014

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doi: 10.19666/j.rlfd.202606014

首发时间：2026-11-05

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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