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2025-03-26 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > IT Information >
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Shulou(Shulou.com)11/24 Report--
As a well-known domestic provider of data governance, data analysis products and technical services, Merrill Lynch data deeply ploughs the energy field, including the wind power industry.
Wind power enterprises are faced with four difficult problems in equipment monitoring and early warning: first, the operation status of wind power generation equipment mainly depends on manual on-site inspection and evaluation, and it is not accurate to rely on human experience; second, although the status of equipment can be monitored by video and other means, due to occasional equipment failures, it still needs people to make on-site inspection or patrol inspection. Third, the data model brought by the host manufacturer is obviously different from the on-site environment, the data standard is not unified, and it is difficult for users to obtain coordinated data; fourth, when equipment failure occurs, the disposal method is unclear.
Why is the monitoring and early warning of equipment operation risk very important? Yang Lei, a senior digital expert in Merrill Lynch data wind power industry, said that the unplanned shutdown of wind power equipment has brought huge losses to wind power stations. First of all, the complicated work such as equipment maintenance and replacement will cause direct economic losses, the core components such as the spindle are expensive, the offshore fan is huge, the sweeping area of the impeller is equivalent to several football fields, and the loss of a single blade may be as high as millions of yuan; secondly, downtime brings about a reduction in power generation. Moreover, the power generation of the wind farm station is subject to the grid power quota and unified dispatching, and the sudden change of power generation may be punished.
"solving pain points is the source of all digital solutions." Yang Lei analyzed that the "Tempo Wind Intelligent Wind Power big data platform" (hereinafter referred to as "Tempo Wind platform") independently developed by Merrill Lynch data brings the status of wind power equipment into health management, and carries out dynamic health assessment through historical data and daily inspection information to help power generation enterprises formulate reasonable maintenance strategies. Based on the AI model constructed by big data, the equipment operation information is analyzed around the clock, the possible failures are predicted, the major losses or safety accidents such as downtime are warned in time, and the safety situation of remote on-site operations is monitored.
Tempo Wind platform is like an early warning aircraft for power generation enterprises. The early detection and reminder of the faults of power generation equipment, especially large components, greatly help power generation enterprises to reduce losses and reduce the impact on the power grid. Therefore, the Tempo Wind platform which can solve practical problems is very popular among power generation enterprises. A power generation enterprise using Tempo Wind platform has gained more than 100 million yuan of economic value from it.
Not only the safety is greatly improved, but also the economic benefits of power generation enterprises are enhanced by the Tempo Wind platform. The platform analyzes the efficiency of wind power equipment and forms an evaluation report to help power generation enterprises continuously optimize wind power operation, wind turbine operation and maintenance, and improve the efficiency of operation and maintenance in an all-round way.
Yang Lei said that big data and AI applications to improve the level of data supply, at the same time must have sufficient computing power as the basis, high-efficiency hardware equipment is indispensable. Merrill Lynch data cooperated with Haiguang as early as 2019, and has been using Haiguang server, starting from 2022 to embed Haiguang's heterogeneous acceleration card into relevant models, migrate some algorithms, and accelerate the pace of on-site verification.
Part of the domestic equipment application of Merrill Lynch data is in accordance with the requirements of users in the energy industry, replacing the existing equipment, and the other part is the extensive use of domestic equipment such as Haiguang in the newly planned projects. Yang Lei said the process of moving from imported equipment to Haiguang equipment was smooth and smooth. Whether it is taking into account the greatly reduced cost of use, or a good level of computing power, users are very recognized by Haiguang's equipment.
Yang Lei said that Haiguang equipment has passed the strict security requirements of the energy industry, and the timely and effective technical support of Haiguang team has also helped Merrill Lynch data to meet the various needs in the application process.
As the winner of the second photosynthetic Organization AI solution Competition, Yang Lei was impressed by the unique "temperament" of the competition, which takes into account not only the technical level of the project, but also the suitability of technology and practical business.
Yang Lei said that there are two major benefits from participating in the competition. One is that enterprises from the software and hardware fields sat together and conducted full technical exchanges and business collisions. Merrill Lynch data also got to know many friends in the upstream and downstream of the AI industry. Second, through the competition, Merrill Lynch data and Haiguang achieved closer cooperation. Merrill Lynch is also very interested in the ongoing third photosynthetic tissue Solutions Competition.
Yang Lei said that the bubble period of AI is over, and now users no longer accept the appearance of grandeur, but pursue whether new technologies can solve practical problems. He said that "Tempo" is the name of Merrill Lynch data when it was developed many years ago, and it is the homonym of "treasure exploration". Whether in the past or in the future, Merrill Lynch data will keep its feet on the ground and continue to tap the great value of big data.
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