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2022年3月29日，施耐德電氣在生態(tài)圈逐漸強(qiáng)大之時(shí)，重磅開啟“綠色智能制造創(chuàng)贏計(jì)劃”第三季。在2季和第二季實(shí)踐的基礎(chǔ)上，施耐德電氣在第三季中給我們帶來了更多、更2的全新亮點(diǎn)。工業(yè)和信息化部國際經(jīng)濟(jì)技術(shù)合作中心副主任李毅鍇在啟動(dòng)儀式上特別表示：“盡管受復(fù)雜多變的國內(nèi)外環(huán)境和疫情影響，中小企業(yè)作為支撐經(jīng)濟(jì)發(fā)展2活躍的主體和創(chuàng)新活動(dòng)的主力軍，為經(jīng)濟(jì)社會發(fā)展做出了重要貢獻(xiàn)，但也面臨著諸多挑戰(zhàn)。國家高度重視中小企業(yè)發(fā)展。今年《政府工作報(bào)告》從加大企業(yè)創(chuàng)新激勵(lì)力度、堅(jiān)持紓困和培優(yōu)兩手抓，著力培育‘專精特新’中小企業(yè)等方面，出臺了若干惠企政策。工業(yè)和信息化部作為國家促進(jìn)中小企業(yè)發(fā)展工作的主管部門，也在不斷加大中小企業(yè)紓困幫扶力度，強(qiáng)化2企業(yè)培育，引導(dǎo)廣大中小企業(yè)走‘專精特新’發(fā)展道路，促進(jìn)產(chǎn)業(yè)數(shù)字化綠色化轉(zhuǎn)型。”



創(chuàng)贏計(jì)劃的推出，將幫助具有智能制造技術(shù)創(chuàng)新潛力的中小企業(yè)，突破技術(shù)與商業(yè)壁壘，讓中小企業(yè)找到成長壯大的新突破口，快速成長為2的綠色智能制造工程和系統(tǒng)集成解決方案提供商，真正為擁有某一領(lǐng)域技術(shù)專長的中小企業(yè)和工業(yè)應(yīng)用場景之間搭建“橋梁”，合力打通工業(yè)企業(yè)數(shù)字化轉(zhuǎn)型的“2后一公里”。



借此契機(jī)，工控網(wǎng)很高興與施耐德電氣工業(yè)自動(dòng)化業(yè)務(wù)中國區(qū)數(shù)字化生態(tài)圈進(jìn)行了深入交流，她為我們分享了她對于“綠色智能制造創(chuàng)贏計(jì)劃”的深刻理解，前兩季創(chuàng)贏計(jì)劃的復(fù)盤感受以及對第三季創(chuàng)贏計(jì)劃的期望。截至目前，“綠色智能制造創(chuàng)贏計(jì)劃”已經(jīng)為二十余家初創(chuàng)型企業(yè)提供技術(shù)和資源，涌現(xiàn)出了大量創(chuàng)新的工業(yè)技術(shù)和應(yīng)用場景。在今年的啟動(dòng)儀式上，參加過2021年創(chuàng)贏計(jì)劃的悠樺林信息科技（上海）有限公司市場總監(jiān)胡奇豪更是表示：“我們在培訓(xùn)和POC實(shí)踐中收獲頗豐，更是超預(yù)期地開拓了一個(gè)新的營銷思路和業(yè)務(wù)賽道。”



單打獨(dú)斗不是好漢，凝聚眾心才是英雄。從前兩季創(chuàng)贏計(jì)劃的報(bào)名、落地、場景和合作數(shù)據(jù)來看，施耐德電氣已經(jīng)將整個(gè)生態(tài)圈一步步發(fā)展壯大，創(chuàng)贏計(jì)劃的關(guān)注度也越來越多。



：“經(jīng)過對前兩季創(chuàng)贏計(jì)劃的深度復(fù)盤，我們在未來還是會繼續(xù)把信息化、工業(yè)化深度融合，踐行地更加扎實(shí)，尋找對于工業(yè)企業(yè)用戶更有價(jià)值的場景，再融合到創(chuàng)新型中小企業(yè)或者科技型企業(yè)，大家一起打造一系列聯(lián)創(chuàng)方案。”



據(jù)了解，2022年，創(chuàng)贏計(jì)劃第三季將會打造“加速營”和“成長營”雙營模式、“綠色智能制造技術(shù)融合創(chuàng)新專家委員會”以及豐富的“工業(yè)場景”等全新亮點(diǎn)。具體來說，為賦能更多的“專精特新”企業(yè)，第三季將延續(xù)招募、篩選報(bào)名企業(yè)的加速營模式，針對一線工業(yè)場景亟需的高可復(fù)制性的數(shù)字化解決方案，“由0到1”進(jìn)行創(chuàng)新，從而解決終端客戶和市場需求，為構(gòu)建工業(yè)底層的數(shù)字化能力添磚加瓦。

成長營

此外，還將開啟全新的“從1到N、快速復(fù)制推廣”的成長營模式，從前兩季PoC成果中篩選出獲得客戶及市場認(rèn)可、有前景且較為完整的聯(lián)創(chuàng)方案，進(jìn)行更有針對性的改進(jìn)和提升，使之快速落地形成規(guī)模化發(fā)展。


據(jù)蔡婷婷介紹，新增加的成長營模式，是希望將加速營打造的聯(lián)創(chuàng)方案進(jìn)行落地和規(guī)模化復(fù)制，并見到實(shí)際的落地效果。對于施耐德電氣來說，希望不僅能夠助力中小企業(yè)業(yè)務(wù)發(fā)展，也能夠?qū)崿F(xiàn)通過創(chuàng)新帶來新的業(yè)務(wù)增長點(diǎn)。8GB local storage +SD card support, support local data cache and offline applications;

4, three-in-one serial port, support RS485/RS232/RS422 three kinds of electrical interface;

5. Support edge computing, realize data optimization, real-time response, agile connection, model analysis and other services at the edge nodes of the Internet of Things, effectively share cloud computing resources and support simultaneous access of multiple devices;

6, support DC9~36V wide voltage input, adapt to a variety of complex industrial site;

7, support LED lamp customization, users can define LED lamp according to need (such as equipment status, edge calculation results, etc.);

8, no client, support on-demand remote upload, download, effectively save network traffic;

9, support gateway health self-diagnosis, fast detection of gateway failure;

Support a variety of standard VPN (PPTP/ L2TP/IPSec/OpenVPN);

11, support network active/standby mode, according to the network situation intelligent switch network access mode (support intelligent network diagnosis);

12, powerful cloud software center support, according to the actual application scenarios to install the corresponding firmware, applications, etc.

13, support a variety of remote control mode (no password/password/disabled), at the same time with physical remote control switch, one button switch remote control function;

14. Support multi-link well development data collection;

15. Support 4G traffic detail analysis and flow control;

16. Support remote gateway management; Support network self-recovery;

17, support base station and GPS mixed positioning mode and local WEB GPS position presentation;

18, support local WEB endpoint table configuration, support local configuration design and presentation;

Industrial edge computing gateway, data collection support 5000 points;

20. Support data multi-channel forwarding and third-party platform access. Generally speaking, brain-like computing refers to making essential changes to existing computing systems and systems in hardware implementation, software algorithm and other aspects by referring to the basic rules of information processing in the brain, so as to achieve significant improvements in computing energy consumption, computing power and computing efficiency. Communication with the rapid development of computer technology in the past decades has brought the information revolution, but the development of the existing computing systems are still facing two serious bottleneck: one is the system energy consumption is too high, the second is for the human brain can easily do cognitive tasks (such as language and understanding of the complex scene, etc.) processing power is insufficient, it is difficult to support a high level of intelligence. The brain's obvious advantages in these two areas make borrowing from the brain a very promising direction. Class cerebral calculation are life science, especially the height of the cross and fusion brain science and information technology, the technical connotation includes for a deep understanding of the principle of brain information processing, on this basis to develop new type of processor, algorithm and system integration architecture, and applied it to a new generation of artificial intelligence, data processing, human-computer interaction and so on widely. Brain-like computing technology is expected to enable artificial information processing systems to produce intelligence comparable to the human brain with very low energy consumption. Many people believe that the substantial progress in this direction will be the prelude to the intelligent revolution and bring profound changes to social production and life. [2] Research on brain-like computing can be roughly divided into neuroscience research, especially research on the basic principles of brain information processing. The research of brain-like computing devices (hardware) and brain-like learning and processing algorithms (software) are three aspects. In neuroscience, there have been very rapid advances in the last couple of decades, especially in the last decade or so. Now we have accumulated a wealth of knowledge about how the brain works, which provides an important biological basis for the development of brain-like computing. The human brain is a complex network of nearly 100 billion neurons through trillions of contact points (synapses). The material basis for the realization of sensory, motor, cognitive and other brain functions is the orderly transmission and processing of information in this huge network. The structure and function of individual neurons is now well understood, thanks to generations of neuroscientists. But much remains to be done about how relatively simple neurons organize themselves through networks to form the most efficient information-processing system we know of. At the microscopic level, the brain network is represented by synaptic connections, at the mesoscopic level by connections between individual neurons, and at the macroscopic level by connections between brain regions and subregions. The information processing on different scales of brain network is a unified whole with important differences and close relationship. At present, the research hotspot of neuroscience mainly focuses on analyzing the structure of brain network at the above levels, observing the activities of brain network, and finally clarifying the function of brain network, namely the mechanism of information storage, transmission and processing. To achieve this goal, need to break through the key technology is for accurate and rapid determination of brain network structure, network activity of the brain mass detection and control, and efficient analysis for these huge amounts of data, moreover also needs in the experimental data under the constraints of the appropriate model and theory, form a complete understanding of brain information processing [3]. The original intention of brain-like computing device research is to greatly reduce power consumption without compromising performance, or to greatly increase speed at similar power consumption. Although modern computers have amazing computing power and speed, they are accompanied by high energy consumption. Mainframe computers tend to consume more than megawatts of power, compared with about 20 watts for an adult brain. The huge power consumption severely limits the further development of miniaturized systems (because of the difficulty in dissipating heat), and also makes complex embedded applications and remote applications, such as space exploration, lack sufficient computing power (because of the difficulty in carrying enough energy). An important reason for the high energy consumption of modern computers is the widespread use of computers. Neumann architecture. In Feng architecture, the information processing unit is separated from the storage unit, so in the process of calculation, it is bound to often transfer data between the processing unit and the storage unit, this seemingly simple process can contribute nearly 50% of the power consumption of the system. , by contrast, in the biological, brain information processing is implemented in the neural network, and the data itself is stored in the network of distributed in the various nodes (characterized by the concentration of ions in the neurons, for example) and the connection between nodes (such as characterized by the strength of the synapses), computing and storage is highly integrated in structure. Thus, using a small number of or even a single electronic device to mimic the function of a single neuron, and forming a large number of electronic "neurons" in a brain-like way to form large-scale parallel processing networks for computing, has become a very attractive direction. Current research hotspots include finding more suitable devices to simulate single neurons (such as memristor) and designing processors that are not based on Von system. The TrueNorth chip recently developed by IBM is a representative progress in this field. Due to the use of non-Von structure system and a series of other measures, the power consumption has been reduced by nearly 2 orders of magnitude (FIG. 1) [4]. Other important progress also includes the research and development of special processors, which are specially optimized for brain-like algorithms such as deep neural network to improve speed and reduce power consumption [5]. As algorithms in this field have been applied in image and speech recognition, such special processors are expected to be put into practical application at an early date.

Can greatly reduce the energy consumption and speed up the kind of processor to achieve a higher level of intelligence of the brain will no doubt have a lot of help, but the real implementation class level general artificial intelligence, in addition to need such hardware foundation, the key is to understand the biological brain information for calculation, the class and learning algorithm of the brain. A common concern about this line of research is that neuroscience does not yet know enough about how the brain works, so can we develop effective brain-like algorithms? We can take some cues from deep neural networks, which are now enjoying widespread success. From the neurons connection mode to the training rules many ways, such as the depth of the real brain neural network distance network there is quite a distance, but in essence it draw lessons from the multi-layer structure of brain networks (i.e., the source of the word "depth"), and in the brain, especially the multi-layer, step-by-step processing structure of visual way is already obtain the basic knowledge of neuroscience. This shows that we don't need to fully understand how the brain works to study brain-like algorithms. Instead, it is likely to be the relatively basic principles that inspire. Some of these principles may already be known to brain scientists, while others may yet be discovered, and the elaboration of each of these basic principles and their successful application to artificial information processing systems may lead to major or minor advances in brain-like computing research. Importantly, this process of discovery and transformation will not only promote the progress of ARTIFICIAL intelligence, but also deepen our understanding of why the brain can process information so efficiently [6], thus forming a virtuous circle in which brain science and ARTIFICIAL intelligence technology promote each other.