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Build a Digital Value Chain with Revit+BiM360+SpaceIQ+Forge & its results

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When considering the "building life cycle" from the viewpoint of the process importance obtained through ISO19650 certification, to construct a Building database, using BIM360 as CDE and collaborating Information of BIM stored in Revit along with SpaceIQ is required. We will share our insights collected from real project. Also some of the insights on the evolution of data platforms by leveraging SpaceIQ solutions in the design phase, while it is commonly used in the construction to operation/maintenance phases. What is the digital value chain brought by the data collaboration? The effects and future projections will be presented

主要学习内容

  • How to utilize data throughout the building lifecycle
  • Explain the challenges and benefits to be gained when applying the Digital Twin to a real project
  • Synergy between Autodesk products and SpaceIQ
  • What is the Digital Value Chain?

讲师

  • 小川 拓真
    I have started to study BIM in university. To apply it into practice, I joined Daiwa House in 2019. In 2021, I have led the project in the company to have ISO19650 certification for the first time in Japan. Currently, I am in charge of establishing common data environment utilizing ACC and developing Web application used APS.
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Transcript

TAKUMA OGAWA: Good morning. We are going to present Build a Digital Value Chain with Revit, BIM 360, SpaceIQ, Forge, and its results. Let us introduce ourselves. My name is Takuma Ogawa. I've started to study BIM in University. To apply it into practice, I joined the Daiwa House in 2019. In 2020, Daiwa House obtained ISO 9650 certification for the first time in Japan. I was very excited to be a part of the project. In my past-time, I like cooking, especially with fresh whole fish. I cook by myself, for example, sushi and sashimi.

TOMOHIRO MIKAMI: Hi. My name is Tomohiro Mikami. I have also been studying BIM since University. After joining Daiwa House in 2019, I worked on a framework to streamline the information collaboration by utilizing BIM in the classification. My hobby is driving and traveling with my wife. I'm so lucky to be able to participate in Autodesk University for the first time this year. She's just [INAUDIBLE].

TAKUMA OGAWA: Daiwa House was established in 1955. At that time, we developed a prefabricated warehouse or railway facility called pipe house. It was the first prefab construction in Japan. It was created by quite a unique idea from the founder, utilizing the standardized component shown on your right.

Daiwa House has expanded its business into houses, [INAUDIBLE] houses, commercial facilities, hotels, warehouses, offices, and medical facility. We have grown into a $40 billion company. We are not just a housing company, but the largest construction company in Japan.

Daiwa House has been providing new products to customers around with the time and realize the value chain. Today, we live in a digital society. By adding all kinds of information generated by digital society to conventional products, we provide services to enrich people's activities, not only the customer, but also the employee, stakeholders, and the many people living there, and achieve digital value chain.

As the digitalization with BIM model's progress, it has created a new barrier that couldn't be brought about by conventional 2D work. For instance, interference checks by models or simulation analysis based on environmental information. However, these are only a small part of benefits of BIM.

By reforming our business operations through BIM and improving the productivity and the quality of the entire construction process, we can provide a better product to our customers, which in turn becoming the foundation to deliver new value and services. Improving the productivity and quality of the entire building lifecycle, it is necessary to share and utilize all kinds of digital information throughout the construction process.

To achieve this, it is necessary to build a data-centric data protocol. Furthermore, by associating information such as GIS data, product or specification data, construction data, quality data, and building operation data with a BIM model in each process, as value-added information, it enables a digital value chain in the construction process.

How will the construction industry change? By establishing the data brought to home and to achieve the data value chain. In this session, we will first introduce a case study that became a catalyst for this initiative. It is our operation and management BIM cooperating with the BIM models and databases. In conjunction with the case study, we will also share what we are currently working on with SpaceIQ. As many of you know, Autodesk and SpaceIQ formed a partnership last year. Our project is proceeding with the support of the partnership.

TOMOHIRO MIKAMI: So now, we would like to talk about a project we worked with our group company, focusing on the use of BIM in facility management. This building is called Kotokurie, our large-scale training facility located in our prefecture in Japan. It was completed in June last year.

As the owner of the facility, we explored how BIM can be effectively utilized in the operation and management phase. This output was reported to the Ministry of Land, Infrastructure, Transport, and Tourism as a part of a model project for facilitating building production and maintenance management processes using BIM initiatives. To begin with, please watch the [INAUDIBLE] video for Kotokurie.

[VIDEO PLAYBACK]

[MUSIC PLAYING]

- [SPEAKING JAPANESE]

[END PLAYBACK]

TOMOHIRO MIKAMI: As mentioned lately in the video, the collaboration between Revit and Archibus is a key in our project. First, I'd like to share a brief introduction to Archibus. Archibus is an FM system for facility owners, originated in the US. It provides a database environment for slowing data and web applications for implementing various functions.

The most important reason why we chose Archibus is data can be updated bidirectionally between database and BIM parameters in real-time. From the next slide, I'd like to return to Kotokurie case study and talk about operation and management BIM collaborating with Revit and the Archibus. Among the policy initiatives, we have tried to utilize operation and management-based BIM at the Kotokurie. We picked up two case studies to share.

The first case study is about the central supervisory control system. This is fundamental core to achieve digital twin. This figure shows a conceptual diagram of the central underlying system. At the initial wall layer, one, the Archibus display layer. And two, have the data storage layer. And three, have the data acquisition layer and the physical wall layer, actual building layer with sensors installed. Base is [INAUDIBLE] in layer four.

In order to capture the ever-changing conditions of actual activities in the building, air conditioning [INAUDIBLE] sensors, [INAUDIBLE] equipment sensors, electrical equipment sensors, and image sensors are installed. Data captured by the sensor passes through the acquisition layer [? via ?] a gateway approximately every 10 minutes and is stored in the storage layer. For instance, the [INAUDIBLE] sensors have been stored at the Kotokurie for various verifications.

Next, here is an example of automatic alarm function. Each sensor installed in Kotokurie has its own [INAUDIBLE] value. When the value debits largely from the [INAUDIBLE] value, the central monitoring system is allotted as highly important failure. The [INAUDIBLE] is also generated in the BIM viewer, the location where equipment can be performed. [INAUDIBLE] can also be checked.

Facility personnel will check the information and send instruction to the field worker if [INAUDIBLE] is clean. So field worker is notified in the BIM viewer. And the required cost is automatically issued. After executing the task, the worker will report the result of task execution on the tablet.

The [INAUDIBLE] as well as history of treatment on each facility can be recorded in the database. So it's a system. In addition, the information on BIM viewer is also shared to the [INAUDIBLE] layer, who is working remotely. So [INAUDIBLE], consultation, and instruction can be done smoothly. Some of the reported defect may be a problem with a particular piece of equipment. Or it may be a potential problem in the equipment itself.

Based on the [INAUDIBLE], we can analyze the issue and take necessary action in advance. This is a great advantage not only for building owners, who manage many properties, but also for a company like us, who construct many buildings per year and provide maintenance services. By linking and managing the operational data in physical world with additional work through the central monitoring system, they could achieve responding and analyzing political issues much more quickly and extensively than before.

The next case study is about the visualization of the space usage utilizing digital twin. Kotokurie is unique facility, including a wide variety of spaces to support people's creative activities. To maximize support for users facing limited space, verification of space optimization became a major theme of Kotokurie project.

We thought, it is important element to analyze what, kind of, space people prefer and what, kind of, activities they are engaged in there. One of the indicators to watch is the visualization of usage of each room. As an example, the dashboard displays the current number of users, carbon dioxide [INAUDIBLE], and room temperature for each space.

And here is a plot of the number of people unloading each space on the floor plan. Green indicates 10 or more people. And click on the link to see the number of uses for the day in chronological order. The usage history in the past year can also be displayed. It hasn't been easy to prepare the data to support decision or budget planning for mid to long-term periods or space allocation in office buildings.

However, if fiscal data can be checked from the manager's perspective, decision for better capital investments in the required space can be made more clearly. So we will develop an environment to design better spaces for users. By creating the digital twin with BIM model and Archibus, we could successfully respond to the building owner's requirements in a facility management phase much better than before.

We also found it even more effective when multiple buildings are managed at the same time. On the other hand, we saw challenges as well. In this project, it took more effort and time than anticipated to prepare for AIM. Because geometrical data, known geometrical data and document data in PIM, were not collaborated each other, the information had to be conserved to prepare for AIM.

If we update PIM around with the process, and maintain the quality of PIM and links information to AIM in real-time, we can significantly reduce the work time. So we want to leverage the benefits of collaboration between Revit and the Archibus to overcome the challenges and build that framework as a service.

TAKUMA OGAWA: Here, we would like to talk about our project with SpaceIQ. To share information across each process throughout the building lifecycle, it's a common data environment to encompass the entire process and the database environment to manage across each project as necessary. We are establishing the data [INAUDIBLE] home by using Revit as a BIM too, BIM 360 as a common data environment, Archibus as a database ranking to Revit, and for the API, as a technology connecting each solution.

There are three main areas we are currently working on with SpaceIQ, establishing the building database. The building database managed all projects handled by Daiwa House. It is responsible for correlating ERP information with BIM 360 and Revit data using technologies such as Forge API.

The element of technology of SpaceIQ project are ranked on the basis of the building database. The building database is based upon the Archibus database. Beyond this FM system, a prime Archibus from the start of the project, the BIM model and the database are ranked at an early stage in the lifecycle. This centralizes information source and enables seamless transition between each process. It also improves traceability by allowing buildings and assets to be managed across project boundaries.

Second, building a common design element to library, DEL. DEL is a rivalry of technical elements for designer. It works with the BIM model and enrich the information. DEL manages all types of data, master data to monitor the data parameters of each manufacturer's product and as that information, and other data. [INAUDIBLE] combination of multiple master data.

Project knowledge data contains product specification information defined in design, construction, and maintenance process. Physical data, operational data, including billing materials and equipment such as walls, glass, and grasses. With DEL, we can utilize downstream information in upstream. It enables designers to work data-centric approach.

Third, creating the proposal database. Proposal database manages two types of data. One is a project of data. And it's in the proposal process. The other is a project data, completed construction. Proposal database searches for similar project attribute information such as building use, site, configuration, cost, floor plan, et cetera, and provide information to designers. Combining it with generative design technology, creating a similar planning suggestion, might be possible.

To achieve them, we first brought the process down to the functional Revit. Then we've defined LOD and LOI for each process. We also define who, when, where, how, and what information is created before asked and updated. To connect Revit attribute information with the database information, we provide a corresponding database table with Revit family category or more subdivided classification Revit. We also mapped the parameters of each family to the feed in the database.

The challenges we are working on, having a major challenge for the entire construction industry for many years. The key to [INAUDIBLE] Revit model, Autodesk Construction Cloud, Archibus database, and the Forge API technology to connect them, especially after this Construction Cloud has enhanced attribute integration. And for the API, it started to update. [INAUDIBLE] with the right to share some of our near future to be revolutionized rights by establishing the data protocol.

TOMOHIRO MIKAMI: But first, we will look into the design phase. Till today, designers made a decision on specification based mainly on their own knowledge and experience. It is highly dependent on individual skill level and the quality and the cost for value. By building a digital platform, we can decide optimal specification based on static data such as cost, functionality, and performance rather than relying on individual skills. Bio engineering is achieved.

[INAUDIBLE] digital platform [INAUDIBLE] data from operation and management phase, where we link to the design phase. With this, designers will be able to select the product and specifications based on the real performance data rather than catalog values published by manufacturers. This will trigger to create new value or to provide an added value with the proposed activities for clients.

Next, let's look at the construction-to-handover phase. In conventional work, the supervisor checks the delivered product, whether it has adequate performance and costs. Then the result is recorded and saved, attaching to the delivery note. After construction is completed, the model and other related documents are updated based on changes during the construction.

These include [INAUDIBLE] human error and the lead time from the building handover to the delivery of [INAUDIBLE] model. As we saw in Kotokurie case study, the lead time would be further extended while [INAUDIBLE] the maintenance management system. Sorry. Instead of paper drawings or delivery notes, the supervisor will visit the job site with a tablet. That installed product information will be shown on the tablet. He or she can check, confirm, and update [INAUDIBLE].

Updated data is linked to the database in real-time and reflected to the BIM model. Human error is eliminated. And the lead time from building handover to delivery of [INAUDIBLE] model is significantly shortened. It allows a seamless transition to the operation and the management phase.

When we talk about Kotokurie case study, we saw some benefits in operation management phase. Or is that all? We believe to have even greater impact from the data circulation. So that's the building lifecycle on the digital platform. Workers will use tablets to report the problem in the equipment. The collected data is linked to the database in real-time, where similar defects are reported in several locations. The data is updated as highly screened information.

The information from other buildings will also be consolidated. We can see at a glance where and how much asset and product is being used and investigate the necessary treatments when the program happens. Furthermore, the information is immediately linked to design and the construction phase through the digital platform.

Creating a digital platform will not only improve traceability in maintenance management, but also improve risk management for work in progress buildings during design or construction phase. The experience and knowledge of the buildings will be recorded in the database and be passed on to other buildings and the new buildings being created in the future. Our buildings will grow together with people. We will provide such services to our customers.

TAKUMA OGAWA: Ready to conclude this session. We are trying to build a digital platform and achieve a digital value chain by 2025. And then, we would like to move on to the next step to rewrite data transformation across industries. As a final [INAUDIBLE], please take a look at the video summarizing our vision for the future.

[VIDEO PLAYBACK]

[MUSIC PLAYING]

- The key to digital integration comes from the further possibilities created by pairing the building database with the database BIM model.

[MUSIC PLAYING]

For example, by essentially constructing digitized real and virtual data, it is possible to have real-time facility management after construction. And maintenance is more efficient and faster even after construction.

[MUSIC PLAYING]

Utilizing BIM digital information in the database will bring about a new future. This integrated database will then open the door to the future.

[MUSIC PLAYING]

Daiwa House industry will use a digital environment to create a new information infrastructure, promote collaboration with various industries, and start the creation of an unlimited amount of new businesses. This will lead to a digital transformation.

[MUSIC PLAYING]

One day, we will become a company that not only constructs buildings, but also utilizes them.

- [SPEAKING JAPANESE]

- In the near future the design department achieving automated design will become a reality, along with full automation and visualization on site.

- [SPEAKING JAPANESE]

- I think that the era of being able to do more than just save on labor, but also to monitor the safety of on-site productivity remotely, is coming.

[MUSIC PLAYING]

- [SPEAKING JAPANESE]

- Rather than people utilizing technology, BIM will link communities, companies, and departments. I truly think that the era of technology is coming.

[MUSIC PLAYING]

Daiwa House Industry will continue to make new challenges towards the future of the construction industry.

[MUSIC PLAYING]

[END PLAYBACK]

TAKUMA OGAWA: Thank you very much for your attention.

______
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我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
Geo Targetly
我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
SpeedCurve
我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
Qualified
Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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改善您的体验 – 使我们能够为您展示与您相关的内容

Google Optimize
我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
ClickTale
我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
OneSignal
我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
Optimizely
我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
Amplitude
我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
Snowplow
我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
UserVoice
我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
Clearbit
Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
YouTube
YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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定制您的广告 – 允许我们为您提供针对性的广告

Adobe Analytics
我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
Google Analytics (Web Analytics)
我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
AdWords
我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
Marketo
我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
Doubleclick
我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
HubSpot
我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
Twitter
我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
Facebook
我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
LinkedIn
我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
Yahoo! Japan
我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
Naver
我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
Quantcast
我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
Call Tracking
我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
Wunderkind
我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
ADC Media
我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
AgrantSEM
我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
Bidtellect
我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
Bing
我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
G2Crowd
我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
NMPI Display
我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
VK
我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
Adobe Target
我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
Google Analytics (Advertising)
我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
Trendkite
我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
Hotjar
我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
6 Sense
我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
Terminus
我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
StackAdapt
我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
The Trade Desk
我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

是否确定要简化联机体验?

我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

个性化您的体验,选择由您来做。

我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。