邊緣計算
邊緣計算（Edge AI Computing）
Edge AI Computing
邊緣計算也稱為邊緣處理，是一種將伺服器放置在設備附近的網路技術。這有助於減少系統處理負載和解決數據傳輸延遲。這些過程在感測器或設備生成數據的位置執行，也稱為邊緣。
Edge calculator , also known as Edge Processing, is the location of near the setup . The location of 邊緣計算不同於雲計算，雲計算依賴於在雲上或中心位置要處理全部數據。通過邊緣計算，數據被處理和存儲在當地收集的。
Edge calculations differ from , depending on the amount of data to be processed on the cloud or at the centre. By margin calculations, the data are processed and stored locally collected. 邊緣計算明顯優於雲計算因為它可以實時毫秒數據處理。邊緣計算解決了與有限帶寬和延遲問題相關的問題，在一些應用中計算必須非常迅速地進行。
Edge calculations are clearly better than cloud calculations because they solve the problems associated with the limited data processing >. 邊緣計算的發展意味著邊緣人工智慧變得越來越重要。各行各業都是如此，尤其是在處理延遲和數據隱私方面。
The development of the margin calculation means that becomes increasingly important. This is true of industries, especially in dealing with delays and digital privacy. 企業使用邊緣計算來改善遠程設備的響應時間，並從設備數據中獲得更豐富、更及時的洞察。邊緣計算使實時計算在通常不可行的地方成為可能，並減少了支持邊緣設備的網路和數據中心的瓶頸。
Enterprises use edge calculations to improve the response time for remote devices and to obtain a richer and more timely insight from the data. Editation calculations make it possible where it is not usually feasible and reduce . 如果沒有邊緣計算，邊緣設備生成的大量數據將淹沒當今的大多數企業網路，阻礙受影響網路上的所有運營。成本可能會飆升。不滿意的顧客可能會去別處做生意。貴重的機器可能會被損壞，或者只是生產效率降低。但最重要的是，在依賴智能感測器來保護工人安全的行業，工人的安全可能會受到損害。
If there is no margin calculation, much of the data generated by the edge device will be flooded today . Cost may rise. Unsatisfied customers may go elsewhere to do business. Valuable machines may be damaged, or only . 當設備數據無法通過雲處理的情況越來越多。工廠機器人和汽車經常是這種情況，它們需要高速處理，因為當增加的數據流產生延遲時會出現問題。
There are more and more cases when the equipment data cannot be processed through the cloud. This is often the case for factory robots and cars, which need to be handled at high speed, because problems arise when the is delayed. 例如，想象一個自動駕駛汽車在檢測道路上的物體或操作剎車或方向盤時遭受雲延遲。數據處理的任何減速都會導致車輛的響應變慢。如果減速導致車輛不能及時響應，這可能會導致事故。有生命危險。
For example, imagine that a auto-drive vehicle is experiencing cloud delays in detecting objects on a road or in operating brakes or steering wheels. Any deceleration in data processing slows the response of the vehicle. If it causes the vehicle to fail to respond in time, this could lead to an accident. Life is in danger. 對於這些物聯網設備，實時響應是必要的。這意味著設備能夠在現場分析和評估圖像和數據，而不依賴於雲人工智慧。
For these devices, this means that they can analyse and evaluate images and data on the ground without relying on cloud. 通過將通常委托給雲的信息處理委托給邊緣設備，我們可以實現無傳輸延遲的實時處理。此外，通過將雲數據傳輸限製為僅傳輸重要信息，可以減少數據量並最大限度地減少通信中斷。
By entrusting of information entrusted to clouds, we can realize the current no-transmission delay in real-time processing. Furthermore, by transmitting the cloud important information for transfer purposes only, we can reduce the amount of information and minimize the loss of communications. 為了使智能應用和物聯網感測器的實時功能成為可能，邊緣計算解決了三個相互關聯的挑戰:
In order to make it possible to use smart applications and connect web sensors in real time, edge calculations solve three interconnected challenges: 5G無線等網路技術的進步使得在全球商業規模上解決這些挑戰成為可能。5G網路可以近乎實時地處理設備和數據中心之間往來的大量數據。(甚至有一個無線網路使用加密貨幣鼓勵用戶將覆蓋範圍擴大到難以到達的地區。)
wireless made it possible to address these challenges on the global business scale 倉庫中的安全攝像頭使用人工智慧來識別可疑活動，並只將特定數據發送到主數據中心進行即時處理。因此，攝像機只發送相關的視頻片段，而不是每天24小時不停地傳輸所有的鏡頭，給網路帶來負擔。這釋放了公司的網路帶寬和計算處理資源用於其他用途。
For example: security cameras in remote centre. Thus, video cameras only send video clips of relevance, instead of transmitting all cameras 24 hours a day 24 hours a day to impose a burden on the Internet. 邊緣計算讓更多使用案例成為可能:
Edge calculations make it possible to use more cases: 邊緣計算通過在收集數據的本地站點或其附近快速處理大量數據，幫助企業優化日常運營。這比將所有收集的數據發送到幾個時區之外的中央雲或主數據中心更有效，後者會導致過度的網路延遲和性能問題。
Edge calculations help businesses to optimize their day-to-day operations by quickly processing large amounts of data at or near local sites where data are collected. This is more effective than sending all collected data to central clouds or major data centres outside of time zones, and later causes excessive network delays and performance problems. 繞過集中式雲和數據中心位置，公司可以更快、更可靠地實時或接近實時地處理數據。想象一下，當試圖將來自數千個感測器、攝像機或其他智能設備的信息同時發送到中央辦公室時，可能會出現數據延遲、網路瓶頸和數據質量下降。相反，邊緣計算使位於或靠近網路邊緣的設備能夠立即向關鍵人員和設備發出機械故障、安全威脅和其他關鍵事件的警報，以便採取快速行動。
邊緣計算使企業能夠更快地交付員工儘可能高效地完成工作職責所需的數據。在利用自動化和預測性維護的智能工作場所中，邊緣計算使員工所需的設備平穩運行，沒有中斷或容易預防的錯誤。
Edge calculations enable businesses to deliver quickly as efficiently as possible the data /a. In smart workspaces using automation and prognostic maintenance, the margin calculates the equipment required by the worker to smooth down the operation without disruption or pre-emption of errors. 在設備故障或工作條件變化可能導致傷害或更糟的工作環境中，物聯網感測器和邊緣計算可以幫助保護人們的安全。例如，在海上石油鑽井平臺、石油管道和其他遠程工業使用案例中，預測性維護和在設備現場或附近分析的實時數據有助於提高工人的安全性，並將環境影響降至最低。
working conditions working environment working environment > > [編輯]
邊緣計算使得利用在互聯網連接時斷時續或網路帶寬有限的遠程地點收集的數據變得更加容易，例如，在白令海的一艘漁船上或在義大利鄉村的一個葡萄園裡。像水或土壤質量這樣的操作數據可以由感測器持續監控，併在需要時採取行動。一旦互聯網連接可用，相關數據就可以傳輸到中央數據中心進行處理和分析。
Edge calculations make it easier to collect data using long-range locations with limited interconnection periods or Internet bandwidth, for example, on a fishing vessel in the Bering Sea or in a vineyard in an Italian village. are monitored continuously by sensors, and action can be taken when needed.
對於企業來說，向網路中添加數以千計的聯網感測器和設備的安全風險是一個真正的問題。邊緣計算允許企業在本地處理數據並離線存儲，有助於降低這種風險。這減少了通過網路傳輸的數據，有助於企業減少安全威脅。
For businesses, adding thousands of combination sensors and devices to the Internet is a real problem. Edge calculations allow companies to process data locally and offline storage, which helps to reduce the risk. This reduces the number of data transmitted through the Internet, and helps businesses reduce security threats.
在收集、處理、存儲和以其他方式使用客戶數據時，組織必須遵守數據收集或存儲所在國家或地區的數據隱私法規，例如歐盟的一般數據保護法規(GDPR)。跨越國界將數據移動到雲或主數據中心會使遵守數據主權法規變得困難，但藉助邊緣計算，企業可以通過在數據收集地附近處理和存儲數據來確保遵守本地數據主權準則。
In collecting, processing, storing and otherwise using client data, the organization is required to comply with the privacy of the data of
藉助邊緣計算，企業可以通過在本地而非雲中處理數據來優化其IT支出。除了最小化公司的雲處理和存儲成本，邊緣計算通過在收集數據的位置或附近清除不必要的數據來降低傳輸成本。
By helping to calculate the edges, companies can better their IT expenditures by processing data locally rather than in the clouds. Except for , the edge calculation reduces transfer costs by removing unnecessary data in or near the location of the collected data. 在邊緣計算中，大部分處理能力在物理上位於收集數據的地方或附近。邊緣計算硬體通常由以下物理組件組成:
In edge calculations, most of the processing capacity is physically located in or near the place where the data are collected. 邊緣硬體需要耐用可靠。通常，這種設備必須能夠承受極端天氣、環境和機械條件。特別是，它通常必須是:
Edge hardness requires durability. Normally, this device must be able to withstand extreme weather, environmental and mechanical conditions. In particular, it must be: 邊緣和霧計算是中間計算技術，有助於將遠程位置的物聯網設備收集的數據移動到公司的雲。讓我們探討一下邊緣計算與霧計算和雲計算有何不同，以及三者如何協同工作:
Edge and fog are intermediate computing techniques that help to move data collected by the remote into the company's clouds. Let's look at the difference between margin and fog and cloud calculations and how they work together: 雲計算使公司能夠在通過互聯網托管的遠程服務器上存儲、處理和使用他們的數據。商業雲計算提供商，如微軟Azure提供數字計算平臺和服務集合，公司可以使用它們來減少或消除物理IT基礎設施和相關成本。雲計算還使組織能夠為其員工提供安全的遠程工作能力，更輕鬆地擴展其數據和應用，並利用物聯網。
Cloud calculations enable companies to store, process and use their data on remote services hosted by the Internet. Business and accounting providers, such as 邊緣計算允許在組織網路的最遠端(即“邊緣”)捕獲、處理和分析數據這使組織和行業能夠實時處理緊急數據，有時甚至不需要與主數據中心通信，通常只需將最相關的數據發送到主數據中心進行更快的處理。這避免了像雲網路這樣的主要計算資源被不相關的數據充斥，從而降低了整個網路的延遲。它還降低了網路成本。
Edge Calculating allows capture, processing and analysis of data at the farthest end of the organizing network (i.e., the “front line”) which enables the organization and industry to deal with emergency data in a timely manner, sometimes without even communicating with the main data centre, usually only needs to send the most relevant data to the main data centre for faster processing. This avoids that major accounting sources such as cloud networks are overstretched by unrelated data sources, thus reducing the delay of the entire network. It also reduces the cost of the network. 考慮一個在海洋中央運行的石油鑽井平臺。跟蹤鑽孔深度、錶面壓力和流體流速等信息的感測器有助於保持鑽機上的機器平穩運行，並有助於保護工人和環境安全。為了在不降低網路速度的情況下做到這一點，感測器僅通過網路發送關於關鍵維護需求、設備故障和工人安全細節的數據，這使得能夠接近實時地識別問題並做出反應。
Consider an oil drilling platform operating in the middle of the ocean. The sensor tracking the depth of the drill, watch pressure, and flow speed, among other things, helps to keep the machines on the drill smooth and to protect workers and the environment. To do this without reducing the speed of the network, the sensor only sends through the network data on key maintenance needs, and worker safety details, which allows close identification and response. 霧計算在由於邊緣設備計算限制而無法處理邊緣數據的情況下，允許在雲和邊緣之間的計算層中臨時存儲和分析數據。
Mist Calculating allows the temporary storage and analysis of data between clouds and edges in cases where edge data cannot be handled due to edge design constraints. 從霧中，相關數據可以被髮送到雲伺服器，以便長期存儲和未來分析和使用。通過不將所有邊緣設備數據發送到中央數據中心進行處理，霧計算允許公司減少其雲伺服器上的一些負載，這有助於優化IT效率。
From the fog, the relevant data can be sent to for long-term storage and future analysis and use. By not sending all peripheral data to the Central Data Center for processing, mist allows companies to reduce some loads on their cloud servers, which helps to optimize IT efficiency. 例如，假設一家建築管理公司使用智能設備來自動控制其所有建築中的溫度控制、通風、照明、灑水裝置以及火災和安全警報。該公司不是讓這些感測器不斷向主數據中心傳輸數據，而是在每棟建築的控制室中安裝一臺伺服器來管理即時問題，並且只在網路流量和計算資源超出容量時才向主數據中心發送聚合數據。該霧計算層允許公司在不犧牲性能的情況下最大化其IT效率。
For example, assuming that a construction management company uses smart devices to control temperature control, ventilation, lighting, water spilling devices, and fire and security alerts in all its buildings, the company does not keep these sensors transmitting data to the main data centre, but installs a server in each building control room to manage immediate problems, and only and calculates the amount of information to be sent to the main data centre only when and when resources exceed capacity. The cloud allows the company to maximize its IT efficiency without sacrificing its performance. 值得註意的是，邊緣計算不依賴於霧計算。霧計算只是幫助公司在某些邊緣計算場景中獲得更高速度、性能和效率的附加選項。
It is interesting to note that margin calculations are not dependent on fog calculations. Fog calculations are just additional options that help companies to achieve higher speed, performance and efficiency in some edge computing scenarios. 物聯網設備和邊緣計算正在迅速改變全球各行業處理數據的方式。以下是的一些最顯著的用途商業中的邊緣計算：
自動駕駛汽車是應用邊緣計算最值得期待的領域。有很多情況下，自動駕駛汽車需要對情況進行即時評估，這就需要實時的數據處理。2019年12月，日本對《道路交通法》和《道路運輸車輛法》進行了修訂，使得3級自動駕駛汽車更容易上路。它規定了自動駕駛汽車應符合的安全標準，以及自動駕駛汽車可以運行的區域。因此，汽車製造商也在正在努力開發遵守這些標準的自動駕駛汽車。例如，豐田已經在測試TRI-P4的完全自動化（4級）自動駕駛系統了。
Self-driving cars is the area most expected to be handled by margin calculations
