Doctrine Cyber & Info

Data Also Requires "Panning and Washing": Maximizing the Release of Data Value

数据亦需“淘洗”，实现数据价值的最大化释放

PLA Daily (解放军报) 19 August 2026

Summary

A PLA Daily commentary lays out a three-part doctrinal framework for battlefield data filtering — distinguishing truth from falsehood, assessing timeliness across real-time/short-duration/long-duration tiers, and examining cross-domain correlation — under the concept of data 'panning and washing' (淘洗) in informatized and intelligentized warfare (信息化智能化战争). The article documents the PLA's ongoing effort to formalize data quality control as a command-and-decision problem distinct from raw data collection, framing corrupted or outdated data as an active threat to operational judgment rather than mere noise. The piece provides a baseline for how PLA doctrinal writing is currently framing the data-to-decision pipeline; what remains unknown is whether this framework is tied to any specific system, unit, or exercise, or is purely conceptual guidance.

Translation

In informatized and intelligentized warfare (信息化智能化战争), data is the core element supporting command and decision-making. However, battlefield data is massive and complex, with truth and falsehood intertwined, containing large quantities of "impurities." If data is not subjected to "panning and washing" (淘洗), it will not only be difficult to support decision-making, but may even interfere with judgment and delay action. At the same time, command and decision-making in modern warfare increasingly exhibits characteristics of refinement (精细化), with ever-higher demands for the layering and precision of data. This determines that data "panning and washing" cannot be conducted in a sweeping, generalized manner — it must be approached through categorized measures and precise application of effort, achieving the maximum release of data value in the process of separating the false from the true and the refined from the crude.

"Panning and washing" to distinguish truth from falsehood. The authenticity of data is the logical starting point for all operational decision-making; false data is more dangerous than no data at all. In modern warfare, the sources of false data are diverse — for example, deceptive data deliberately released by the enemy, noise data generated by the natural environment, and anomalous data produced by equipment malfunctions — together forming a complex picture in which truth and falsehood are intertwined. Under these circumstances, to better achieve the scientific validity and effectiveness of command decisions, one cannot avoid categorically distinguishing the true from the false in data; otherwise, there is a very real risk of distorting the basis for decisions and causing errors in operational judgment. At the practical level, a multi-dimensional indicator system for distinguishing truth from falsehood must be established to conduct initial screening and grading of data. For highly credible data, original information should be preserved to the maximum extent; for data under suspicion, contradictory information should be eliminated through multi-source comparison; and clearly false data should be thoroughly purged, with strict prevention of its infiltration into the decision-making chain.

"Panning and washing" to assess timeliness. Data value has a distinctly time-sensitive attribute; the same data carries entirely different operational significance at different points in time. As the operational tempo of informatized and intelligentized warfare continuously accelerates and the battlefield situation changes in an instant, the effective window period for data is sharply compressed. Outdated data not only loses its reference value but may also mislead commanders in their assessment of the current situation. Therefore, data "panning and washing" must fully account for the time dimension, ensuring that data plays the correct role within its effective timeframe. To this end, a tiered standard for data timeliness can be established, dividing data into three levels: real-time data, short-duration data, and long-duration data. For real-time data, invalid and false information should be rapidly eliminated, processes simplified, and latency compressed as much as possible to ensure it enters the command chain at the earliest possible moment. For short-duration data, deep filtering should be applied to improve data precision while guaranteeing timeliness, so that it better serves decision-making at the tactical level. For long-duration data, deep mining and pattern extraction should be conducted to provide support for situational assessment at the strategic level.

"Panning and washing" to examine correlation. Modern warfare is a contest of system against system (体系与体系的对抗), and cross-validation has always been an important method for separating truth from falsehood in information. This means that the value of a single piece of data is limited, whereas the intrinsic correlations between data are the key to filtering invalid information, revealing battlefield patterns, and anticipating enemy actions. Therefore, data "panning and washing" must examine the correlational relationships among data from a systems perspective, using categorized sorting to allow seemingly scattered and useless data to form an organic whole. On one hand, for data pertaining to the same element, horizontal comparative "panning and washing" (横向比对式"淘洗") should be applied to eliminate conflicts and contradictions between data from different sources on the same element. On the other hand, for data along the same chain of operations, vertical end-to-end "panning and washing" (纵向贯通式"淘洗") should be applied to remove data "impurities" generated by data barriers between nodes along the operational chain, ensuring that data always resonates in frequency with the operational system (与作战体系同频共振).

Original Chinese

在信息化智能化战争中，数据是支撑指挥决策的核心要素。然而，战场数据海量庞杂、真伪交织，包含了大量“杂质”。如果对数据不加以“淘洗”，不仅难以支撑决策，甚至可能干扰判断、迟滞行动。同时，现代战争的指挥决策越来越呈现出精细化特征，对数据的层次性、精准性要求越来越高。这就决定了数据“淘洗”不能笼而统之、大而化之，必须分类施策、精准发力，在去伪存真、去粗取精中实现数据价值的最大化释放。 “淘洗”辨真伪。数据的真实性是一切作战决策的逻辑起点，虚假数据比没有数据更危险。在现代战争中，虚假数据的来源多种多样，比如，敌方刻意释放的迷惑性数据、自然环境产生的噪声数据、装备故障生成的异常数据等，共同构成了数据真伪交织的复杂图景。在这种情况下，要更好实现指挥决策的科学性、有效性，就不能不对数据的真伪进行分类甄别，否则，就极有可能导致决策依据失真、作战判断失误。在实践层面，要建立多维度真伪甄别指标体系，对数据进行初筛分级。对高度可信数据，最大限度保留原始信息；对存疑数据，通过多源比对剔除矛盾信息等；对明确虚假数据彻底清除，严防其混入决策链路。 “淘洗”鉴时效。数据价值具有鲜明的时效属性，同一数据在不同时间节点的作战意义截然不同。信息化智能化战争的作战节奏不断加快、战场态势瞬息万变，数据的有效窗口期急剧压缩，过时的数据不仅会失去参考价值，还可能误导指挥员对当前态势的判断。因此，数据“淘洗”必须充分考虑时间维度，让数据在有效的时间内发挥正确的作用。为此，可以建立数据时效分级标准，将数据划分为实时数据、短时数据、长时数据三个层级。对实时数据，快速剔除其中的无效信息、虚假信息，尽可能地简化流程、压缩时延，确保其第一时间进入指挥链路；对短时数据，要深度过滤，在保证时效的前提下提升数据精度，使其更好服务于战术层面的决策；对长时数据，进行深度挖掘和规律提炼，为战略层面的态势研判提供支撑。 “淘洗”看关联。现代战争是体系与体系的对抗，同时，交叉验证始终是信息去伪存真的重要方法。这意味着，单一数据的价值是有限的，而数据之间的内在关联，则是过滤无效信息、揭示战场规律、预判敌方行动的关键。因此，数据“淘洗”要从体系视角审视数据之间的关联关系，通过分类梳理，让看似分散、无用的数据形成有机整体。一方面，对同要素数据，实施横向比对式“淘洗”，消除同一要素不同来源数据之间的冲突与矛盾。另一方面，对同链路数据，实施纵向贯通式“淘洗”，去掉作战链路各节点间因数据壁垒产生的数据“杂质”，确保数据始终与作战体系同频共振。