Canon lens digital correction dataCanon lens 'digital correction data included lens correction parameters such as distortion, color difference, halo, and so on. This data could help the photographer better deal with the relevant problems in the image to improve the quality of the image. Not every Canon lens had aberrations, so it was not necessary to turn on the aberrations correction every time. It was only necessary to correct the photos with obvious aberrations or in specific scenes. Different shooting scenes may require different correction parameters. The reasonable use of lens correction data can make the picture more in line with the photographer's intention. For example, when shooting landscape, portrait, and macro, aberrations correction can improve the quality of the photo. For landscape photography, turning on aberrations correction can improve the depth of field and color reproduction ability. In addition, the lens correction data could help reduce the color difference problem. For example, the application of Canon 1dx lens correction data could improve the overall image quality, allowing the photographer to obtain a more delicate and clear picture, and enhance the artistic sense and appreciation of the picture.
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Big data and artificial intelligence in the digital economy eraIn the digital economy era, big data and artificial intelligence were closely related and had many important meanings.
* * 1. Big data provides basic support for artificial intelligence **
1. * * Data is the fuel for research and development **
- Big data played a key role in the research and development of artificial intelligence, such as algorithm optimization, model training, data analysis and prediction. For example, artificial intelligence models required a large amount of data to learn and train. This data was like fuel that drove the model to continuously improve and improve performance.
2. * * The challenges and solutions to excavate the potential of data elements **
- Although the scale of data element resources in our country is huge, there are problems such as scattered data subjects, poor utilization, and low acquisition, which makes it difficult to meet the demands of high-quality data across industries and fields.
- In order to tap the potential of data elements, it was necessary to break the data barriers between government, enterprises, and countries. Between government and enterprises, encourage data integration and docking. On the basis of ensuring security compliance and data desolation, speed up the opening of government data and establish a resource coordination and dispatching mechanism. Between enterprises, strengthen system supervision and guidance to solve the "information island" and "traffic wall" phenomenon of mobile Internet applications, and realize the "de-unification" of the Internet ecosystem. Between regions, implement the digital silk road policy, formulate clear and complete data exit management standards, and cultivate a global data element market.
* * 2. Artificial Intelligence Boosts the Development of the Digital economy **
1. * * Digitization of enabling industries **
- Artificial intelligence and big data have achieved positive results in promoting new forms of business, creating new forms of employment, and digitizing the enabling industry. For example, artificial intelligence technology could promote the transformation of traditional industries from three aspects: application interaction, information processing, and decision analysis, accelerate the implementation of "artificial intelligence +" in emerging service industries and traditional manufacturing industries, realize the deep integration of artificial intelligence, advanced technology, and intelligent equipment manufacturing, and cultivate new industries and new tracks such as intelligent robots and autonomous driving.
2. * * Change the economic development model **
- In the digital economy era, artificial intelligence changed the economic development model through algorithms. For example, the recommendation algorithm made the cost of knowledge transmission almost zero, reduced the incompleteness of information, reduced the error of information, and accurately captured the interests of users, thereby reducing transaction costs and information search costs. It had brought about earth-shaking changes to various industries.
3. * * Promotion of data value circulation and mining **
- Artificial intelligence could help to tap into the potential of high-quality data sets and promote the safe and efficient circulation of data value. In vertical fields such as industrial manufacturing, transportation, and trade circulation, data has released a lot of value. For example, in the urban traffic scene of the transportation field, the "digital wisdom green wave" product created by big data and artificial intelligence technology integrated a variety of data to improve the wisdom level and operational efficiency of urban traffic management.
* * III. Problems and countermeasures for the coordinated development of the two **
1. * * The bottleneck of computing power resource supply **
- There was still a bottleneck in the supply of data elements and computing power. Although the domestic digital infrastructure construction achievements are remarkable, in specific scenarios, there are problems such as the storage computing investment is not synchronized, the upstream and downstream industries are not closely coordinated, and the technical performance is not up to standard.
2. * * Countermeasure **
- In the new stage of digital infrastructure construction, the construction of servers, storage equipment, security equipment, and network equipment should be balanced, so as to realize the integrated development of storage capacity, computing power, and network transmission capacity, and the cooperative research and development of software and hardware should make up for the shortcomings of performance. The facility construction side should cooperate with the chip manufacturing side, the talent training side, and the software application side to promote the deep cooperation between the upstream and downstream of the industrial chain. The traditional computing system centered on computing power should be transformed into a "data-centric" model to reduce the cost of "data relocation" and maximize the value of the computing power network.
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