{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T05:07:35Z","timestamp":1778908055852,"version":"3.51.4"},"reference-count":19,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The widespread deployment of Industrial Internet of Things (IIoT) infrastructures in modern power systems has introduced large\u2010scale, heterogeneous generation\u2010side data streams with varying quality, semantics, and reliability. Under the emerging Industry 5.0 paradigm, generation scheduling must be not only automated but also human\u2010centric and resilient, placing strong demands on trustworthy, transparent, and actionable data processing in real\u2010time decision loops. However, inconsistencies across multi\u2010source data can propagate through dispatch constraints and lead to erroneous operational actions. To address this challenge, we propose an IIoT\u2010enabled framework for trustworthy parsing and cross\u2010source consistency verification of generation\u2010side scheduling data. The framework integrates semantic parsing, confidence estimation, provenance\u2010weighted robust fusion, and constraint\u2010graph\u2010based inconsistency modeling into a unified online pipeline. An adaptive trust update mechanism dynamically reweights data sources based on streaming residual evidence, enabling rapid isolation of drifting or faulty sources while preserving reliable ones. Experiments on four public datasets\u2014ENTSO\u2010E, OPSD, PJM\u20105\u2009min, and NREL\u2010WIND\u2014demonstrate consistent improvements in parsing accuracy, fusion quality, and inconsistency localization, with practical streaming latency suitable for real\u2010world deployment.<\/jats:p>","DOI":"10.1002\/itl2.70258","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T07:07:10Z","timestamp":1775459230000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>IIoT<\/scp>\n                    \u2010Enabled Trustworthy Parsing and Constraint\u2010Graph Consistency Verification for Multi\u2010Source Heterogeneous Generation Scheduling Data"],"prefix":"10.1002","volume":"9","author":[{"given":"Han","family":"Zheng","sequence":"first","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5503-3792","authenticated-orcid":false,"given":"Weinan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Guangdong 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