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ECOFACT (Eco-Innovative Energy FACTory Management System)

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说明

ECOFACT is a €12 million, Horizon 2020, EU-funded project made up of 20 consortium members from several European countries aiming at developing an ECO-innovative energy factory management platform using improved dynamic LCA/LCCA toward holistic manufacturing sustainability. The platform will be deployed in four different demo sites: a biscuit factory (Gullon), a Stellantis Group automotive factory (Tofas), a Heineken Group brewery (Athenian Brewery) and a multinational household appliances factory (Arçelik). One team is leading the development of the digital twin platform (DTP), based on Autodesk Forge software and composed of production lines, 3D models, and different applications (material-flow simulations, energy simulations, production planning and scheduling, industrial energy disaggregation, and industrial energy flexibility). The DTP will display simulation results and real-time Internet of Things (IoT) data alongside the 3D models maximizing Autodesk Forge functionalities.

主要学习内容

  • Assess the potential of a solution based on the Autodesk Forge platform and its APIs.
  • Learn about how to optimize production—and operation and maintenance—processes and reduce energy consumptions and costs.
  • Learn about validating industrial digital twins in the Autodesk Forge environment, integrating real-time IoT data.
  • Learn about combining energy and resource management systems with a dynamic LCA and LCCA approach.

讲师

  • Andrea Perego
    Andrea Perego is a Management Engineer graduated with full marks at Politecnico di Milano who firmly believes in the power of ideas supported by sweat and tears. Currently in One Team Andrea leads the business unit dealing with the scouting and management of value-added projects, including Research & Innovation (R&I) projects funded by the European Union (EU), mainly in the field of BIM, Digital Twins and Augmented and Virtual Reality (AR/VR). Andrea and One Team's goal is to create value through innovation, fostering by the way the compliance with current sustainability paradigms. Recently Andrea and his team have been working at three Horizon 2020 (H2020) and three Horizon Europe (HEU) projects: www.bim4eeb-project.eu (7M € budget); www.infinitebuildingrenovation.eu (10.1M € budget); www.ecofact-project.eu (12.3M € budget); www.buildon-project.eu (6.8M € budget); www.crete-valley.eu (25.2M € budget); www.retime-project.eu (5.5M € budget). Furthermore, as a Board Member of the company, Andrea is also in charge of managing strategic alliances with existing partners and scouting new ones. As Marketing Director, Andrea helped his team to manage the rebranding of the company, including the development of One Team's new website (www.oneteam.it). In closing he recently he became CEO of One Team Iberia.
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Transcript

ANDREA PEREGO: Hello, everybody. Thank you for being here with us. We are here for presenting ECOFACT. ECOFACT stands for ECO-innovative FACTory management system. But let's not focus on the acronym and let's see what the project is about.

So let's have a look at the project in a nutshell. In a big nutshell, actually, because it's a huge project. It's a 12 million Euro project. It started in 2020. It lasts 48 months. And it's carried out by a consortium of 20 organizations.

This project has been founded by Horizon 2020 program. So by the European Union. And from our point of view, the core part of this project is the Digital Twin platform based on Forge.

This Digital Twin platform will be the Energy and Resource Management System of the project. And we will see later on what we mean with Energy and Resource Management System.

But first, let's focus on the learning objectives of this class. So first of all, the first learning objective is understanding how to build up a Digital Twin Platform on Forge, exploiting its relevant APIs. Then we will see how to deepen the knowledge on the whole ECOFACT platform. And especially on the part that is composed by the Energy and Resource Management System.

So by the Digital Twin Platform itself, combined with the dynamic LCA and LCC. So lifecycle analysis and lifecycle cost assessment. Assess the potential of the integrated platform and understand how it could support the industrial process optimization and more in general, learn how European Union is contributing to boost sustainability through digitalization thanks to Horizon 2020 program.

But let's start with why. So why have we done this? Why ECOFACT? So basically, we all know that more is inevitable nowadays. So we are producing more and more. We are growing. Population is growing, so we need more cars. We need more streets. We need more buildings. But from the other side, we need to produce more with less, because we have less resources. We can exploit less resources. And furthermore, we have to produce more with less consumption of energy, if possible, and especially with less CO2 emission, because we are all aware of the environmental issues we are all facing.

So there is a huge opportunity, basically. The opportunity of doing better. The opportunity of doing better and do it differently because we cannot solve our problems with the same thinking we used when we created them. And this is basically why the European Union has promoted the Horizon 2020 program.

The Horizon 2020 program, as a matter of fact, is a program financing innovation. So financing innovation with the aim of cutting greenhouse gas emissions. So financing innovation in different sectors, but with the aim of cutting greenhouse gas emissions.

So as I said before, ECOFACT, that is the project we are talking about today is part of this program. But as one thing, we are working also on other projects financed by the European Union with the same aim. So the final aim of those project is to cut the CO2 emission. In this case, you can see other projects as I said. So BIM4EEB, Infinite auto project where our role is to develop always let's call it the BIM platform, so the platform hosting the BIM models, and the BIM models themselves.

And the aim of this project in this case is to foster innovation and make existing buildings more efficient from an energy point of view, exploiting thanks to digitalization, thanks to BIM information modeling.

But today we are focusing, as I said before, on ECOFACT. ECOFACT is a focus on a different sector. So it's focus on manufacturing. It's focused on industry. As a matter of fact, ECOFACT, the goal of ECOFACT is the optimization of processes of industrial processes of factories. Especially for big factories, as we will see later.

And as I said before, in this project, it's necessary a change of paradigm. In this case, in the planning of the industrial processes, thanks to the ECOFACT project, the environmental part will be taking care at the beginning, at the very beginning of the planning, and not at the end of pipe.

So this is the big changes. So taking into consideration as a parameter the environmental issue since the very beginning of the planning of processes.

So let's say that at the end, the final goal is work through sustainability. And as one team-- as One Team is our company, we have always been interested in sustainability issues. As a matter of fact, our logo itself resembled the healthy sets and our motto is transform competencies and technology into value to make a better world, a more sustainable world. That's why we have been working in this industry, in these European-funded projects in [INAUDIBLE].

But let's stop one second before deep diving into who we are and presenting also ourselves. So me, Andrea, and my colleague, Alder. Let's have a look at the overall agenda.

So first we will start with an introduction. So introducing our company, myself, and Mister Alder Moriggi. Then we will talk about the project at a higher level. So we will give you a project overview. Then we will deep dive into the solution.

So we will see what is a digital platform, how is it made. And then we will focus on the next step. So what's next? Which are the future challenges and the call to actions?

OK. As I said before, now let's introduce better the company and ourselves.

So as you can see from the video, we are a consulting company working in different fields. So we are working in building formation modeling. We are working in manufacturing. So we talk about industry 4.0. We are working with the GIS system, so we're talking about [INAUDIBLE] system as a solution. And we have been doing this for over 25 years. And we are also one of the top 10 Autodesk Platinum partners at the EMEA level, and that's why we want to also thank Autodesk for the support and thank you for Autodesk for hosting us today and giving us the opportunity to share with you the results of this interesting, important project.

So these are our key numbers. So we are a staff of more than 100 people. We have 60k users. More than 60k users. Total revenue of about 37 millions, and 11 locations spread all over South Europe.

Last but not least, here we are. I am Andrea Perego. I'm a management engineer. I am the project manager of the ECOFACT project. And I'm here with Alder Moriggi, the leading engineer of the Forge development part. But we are here only as a representation, only as speakers, because we are representing actually a wider team. So we want to thank everybody for the nice work they have done until now.

But now let's focus on the project itself. So let's have a project overview. And starting from the targets, understanding how is the consortium made, et cetera, before deep diving into the solution made on Forge.

So let's start with the overall Gantt. Why I want to start with the Gantt? Not to, let's say, to analyze it in detail or understand each task, because as you see, it would be pretty difficult. But just to say that we are halfway. So the project still has two years. So we are presenting some results of the project. Some of the results will be presented at the end of the project. So stay tuned because maybe you will find us presenting the fact results of the project in the next years.

So let's start from the objectives, the targets of the project itself. There are very specific scientific, technological objectives defined since the very beginning of the project. And I want to focus on the second scientific, technological objective, because it's the solution impacted by the Energy and Resource Management System. So basically, implementing our solution. So the Energy and Resource Management System, the final aim is to cut on the factory energy bill by an average of 25%. So it's an important target.

I would also want to focus on the third scientific, technological objective because it's always linked with the sustainability, because in this case it's thanks to lifecycle analysis and lifecycle cost assessment, reduce environmental footprint of manufacturing processes by an average of four or eight percent.

Alongside these specific objectives, there are other objectives. There are other scientific, technological objectives more high level. So basically, these objectives are saying that we need to deliver something at the end of the project. So we need to deliver a platform as I said before. A platform that should be at technological readiness level seven.

What does it mean? Technological readiness level is a scale from one to nine. And the nine means that the solution is 100% marketable, so it's ready to be marketable. We have to deliver a solution at the technology readiness level seven. It means that the solution should be tested in relevant environment, but still not 100% ready to be scaled to be marketable.

Alongside, there are other non-technological objectives. In general, we could say that we need to exploit the result of the project and disseminate the results of the project. We are doing it also now. We are disseminating the results of the projects.

And now let's focus on who we are. So who is carrying out the work. So the consortium is made up of 20 partners from seven different countries. There are five research institutions, eight large industries, five small-medium enterprises, and two associations across the manufacturing environment.

But let's focus on the real protagonists of this project, so the demo sites. So first of all, we want to underline the fact that we wanted to test the solution on different industrial environments. So from one side, we have a discrete manufacturing demo site. In this case, we have Arcelik, which is a washing machine factory located in Romania. There is Tofas, which is an automotive factory located in Turkey, and is part of the [INAUDIBLE] Group.

While from the continuous manufacturing sector, continuous manufacturing industrial environment, we have Athenian Brewery, which is a brewery. So a factory of beer located in Greece. And it's a brewery from the Heineken group. There is Gullon, which is one of the largest biscuit factories in Europe, and is located in Spain.

Which is the architecture of the solution we are thinking of? Basically, we will start from the factories. So we will start from the data of the factory. So from one side, we will take data-- exploiting existing data, existing systems already in place in the factories. And from the other side, we will put in place, we will implement new sensors. We will implement an internet of things infrastructure in order to collect other datas.

Then this data will be delivered. We will convey to a data broker. And the data broker will harmonize, will clean the data and distribute the data to the application and services layer. In the application services layer, we have our Energy and Resource Management System that is composed by different models. We will see later which are these models.

And from the other side, we have also always in the upper services layer the dynamic LCA and the CCA systems and the supply collaboration system. So this solution integrated will make up the holistic digital sample system, so the ECOFACT platform itself.

So it could sound a little bit complicated. So we wanted just to sum it up focusing on the four milestones. So from one side, we have the local data. The local data, as I said before, local data taken directly from the field, from the factories, leveraging existing systems, existing PLC, existing SCADA, existing sensors from one side, and from the other side, putting in place specific IoT architecture in order to measure and convey other specific data.

Then we have the data broker. The data broker is used, as I said before, to convey data to the different application and services. In this case, the data broker is based on a software platform called Kafka.

Then we have, from our point of view, the most important, let's say, from the user point of view, also part that is the holistic DSS, because it's where the different algorithms, the different application and services are hosted. So from one side, we have the Forge-based Digital Twin Platform that will operate as an Energy and Resource Management System, I said before. And always, I said before, the supply chain collaboration and LCA and LCCA services. In this case, based on, always an existing software solution called SimaPro.

And then we have the last part, that is still very important. That is the user interface which will be used by the final user to interact with the platform and to have access to different data.

So now let's deep dive on the Digital Twin Platform itself. But first, let's clarify, at least from our point of view, let's clarify what the Digital Twin is, because it's a common trend, talking about the Digital Twin. We wanted just to focus on the definition from Dr. Michael Grieves. Dr. Michael Grieves said that a digital twin is a sensor-enabled digital model of a physical object that simulates the object in a live setting.

And that means, basically, that the digital twin is the digital representation of a physical asset, of a physical object, that could receive real time data and then simulate the behavior of the asset in the real setting in a digital environment. So as we can see, also from the schema below, the element scale, we talk about digital twin only when the digital asset, the digital model, we have we can call it, is enabled to receive data directly from the field. So we are referring to level 3 in this case.

Then there are [INAUDIBLE] factor levels that are, let's say, linked to the fact that the digital twin, from the other way around, could control the physical asset itself in a semi-automatic or automatic way. So we're talking about level 4 and level 5.

OK. But now, let's go back to our project. Let's focus on our digital twin and understand how is it made. And to do this, I leave the floor to my colleague, Alder Moriggi. Please, Alder.

ALDER MORIGGI: Thank you, Andrea. And now I'm going to talk about the Digital Twin Platform that is composed by three different components. So the digital twin itself, then the 3D part that will be visualized with the Forge Viewer. And then in data, we have a modulator using in Inventor. And all the relevant documents are related.

Then in the web platform, we have also different tools. Here, we specifically mention OptimiST, because it's one of the tools that we are going to show in this presentation. But there will be other tools linked. There is also the Data Exchange layer, composed by some APIs. And so basically, the platform exchanges data with the other systems. Basically, the data broker that will be used within the holistic decision support systems. OK.

To model old factories lines-- Tofas, [INAUDIBLE], and Athenian Brewery, we used Autodesk Inventor. Machineries and the products are combined into an assembly to create the whole lines. And the after-modeling activity models were uploaded within the Digital Twin Platform in Forge. OK.

And we also used the integration with the Leica technology with the BLK3D a Leica device that realize 3D photos [INAUDIBLE] more photo cameras and the laser that calculated the distance from the machinery.

Measurements are readable directly to on the 3D photos, and the [INAUDIBLE] is easier. And in this case represented in this slide was Athenian Brewery demo site. And we went to Athenian Brewery demo site. And we took measurements. And then we model in line using these measurements. OK.

And this is the result, what we have uploaded in Forge. But we will see it more in detail in the next slides, in next video.

And now I'm going to show you the web application that we built for ECOFACT project. It's a front end to manage factories and all data related to factories. I'm talking about the groups, users, permissions, all those factory lines, documents, et cetera.

And the factories lines we modeled are at Tofas, [INAUDIBLE], Gullon and the Athenian Brewery and the two demo sites. And as I said before, we used Autodesk Inventor. For Tofas, until now we have finished modeling five production lines. And then we have uploaded on [INAUDIBLE] on ECOFACT platform [INAUDIBLE] and [INAUDIBLE]

For [INAUDIBLE], we made the one production line. One production line is completed. And another one is almost ready. For Athenian Brewery, we made two machineries. And for Gullon, we are still working on true assembly and one assembly with the three production lines is almost ready. And for the second one, we started working on.

And in a specific section on the web application, we disposed an interface to upload the [INAUDIBLE] model on Forge Bucket, in order to display them on a Forge view to charge model user as to package all assemblies and parts in Sipa and then upload it on a form that use modeling derivative APIs to upload the source file to OSS and translate the source files. OK.

In the video, after modeling activity for one production line of Tofas factories, in Inventor, we placed the sensors next to machinery like [INAUDIBLE] to monitoring natural gas, tools, to monitoring water consumption, pumps and motors to monitoring electrical energy. And there are also sensors to monitoring hot and chilled water and steam consumption and the compressed air.

We are mapping and codifying the sensor in order to get data coming from database and web services developed in other work packages. We have also prepared connectors to get data from sensors. And finally we prepared, as you can see in the video, a custom Forge viewer to view models. And then we created a custom extension to get all sensors placed in the model, and all components that sensors refers to, as you can see in the right part of the screen.

And we use the GetBulk properties to do better a specific function from Forge APIs. And then we prepared a list of sensors on the right and the user can zoom to the sensors, the related components, and user can also consult daily data graph and also real time data. This is the first integration. But we have to realize ordering integrations like that in the other two here and the other custom extensions. OK.

And here I reported the Forge APIs that we used right now to develop the Autodesk part of the project. We used the authentication APIs to allow the application to use other Forge APIs in a two-legged way. Next, we have to upload the models we produced with the Inventor to the Forget bucket. To manage the directories in the Forge bucket, we use the Model Derivative APIs. And then to build a custom viewers I showed in the previous slides, in the previous video, we used viewer APIs.

We used the GetBulk Properties to extract data required, especially sensors data, and sensors, and the machinery [INAUDIBLE]. We also used some extensions like PDF extension. And finally, we used data visualization APIs to visualize a sensor and data coming from sensor. Andrea, I leave the floor.

ANDREA PEREGO: OK. Thank you, Alder, for the presentation of the Digital Twin Platform and how it's made.

So now let's focus on a specific application integrated within the platform, the OptimiST application. As you can see, it's not the only application integrated within the platform, but it's the more mature one. So we will focus on this specific application.

So how it works, what is the application about? Basically, it's an application that optimizes the minimization of changeover time. So let's say that thanks to the minimization of changeover time, we are able to save time, and not only time, as we will see from the next slide, and redefine the production scheduling accordingly.

As I said before, we save time. But not only, because as we can see from this slide, the time saving corresponds to a CO2 emission saving of about 16%. This is an important result from our point of view, and from the project point of view.

We have applied this technology, this algorithm and this application, for now in different demo site. But the results are now recorded especially for the Athenian Brewery demo site. And as I said before, this application is directly integrated in the ECOFACT platform. As you can see, from the two sessions, you can access the application. You have to input some data about the consumption, about the production, about the cost. Then the application ran. And as you can see, you can have access to the result of the application in an Excel format, if you want, representing the new scheduling, the optimized scheduling of the production. Or also, you can see the representation of the data displayed directly in the Forge platform.

So as I said before, this application has been tested in different demo sites. We have specific results from Athenian Brewery, but also we have been testing this application in Gullon demo site. In this case, the result is always the minimization of the changeover time. And so we will have a different and optimized scheduling of the production for each line.

In the [INAUDIBLE] demo site, we have a different approach. So this application will help the decision maker to minimize the energy bill and redefine the production scheduling accordingly. And so defining also an optimized electrical energy asset management plan. Same we can say for Tofas demo site. So we have the minimization of the energy bill and the optimization of thermal and electrical and energy assets management plan.

Now we gave you a brief overview of the results of the project until now. But let's focus on the next step. What's next?

So as I said before, the OptimiST is not the only application that will be integrated within the platform. There are other four application services that will be integrated in the Digital Twin Platform. And we will focus on each of them very briefly.

So we will integrate a material flow simulation application based on Siemens plant simulation, and a production optimization application based on the Gurobi solver application, Gurobi solver software. Then we will develop an integration with an energy simulation for dynamic operation management and cost optimization. Basically, we will develop and integrate a predictive maintenance model to optimize the operation and maintenance tasks.

Then we will integrate an industrial energy disaggregation component. In this case, we will integrate this component in order to understand the energy consumed for the production of each different product. So it is an energy disaggregation byproduct.

And last but not least, we will integrate the results of our energy resource system. So the results of our application and services developed within the Digital Twin Platform with the dynamic LCA, the [INAUDIBLE] and supply collaboration services developed within SimaPro. And these two components [INAUDIBLE] will be the technological building blocks of the overall holistic digital upper system.

But what's next? What's next from a call to action point of view? Let's say that the ECOFACT project is open to receive feedback, is open to welcome new partners. So for exploitation purposes, we are looking for five other demo sites where to test the solution. So if anyone is interested to test the solution on a specific factory is more than welcome.

We are looking for, also, partners who want to help us develop faster the solution to reach full marketability of the solution by 2028 to 2030. So we are talking about TRL9, technology readiness level 9. And last but not least, if you want to get a deeper knowledge about the project itself, please download our handout. If you want to be updated on the development of the project, follow us. Follow us on LinkedIn, on Twitter, on YouTube. You can subscribe to our mailing list. And if you have any specific questions, please drop us a line to me or to my colleague, Alder.

So I want to close this class with this quote. Because as I said before, as one team, we have been working in different research and development projects. And it's hard. It's not easy. Sometimes you feel lost because it's not easy to start from a white paper and develop something new, and develop following new paradigms.

But it's very challenging and very satisfactory when you manage to do it. And it's the only way for real evolution. And it's the only way to tackle and to face and to solve the challenges we are all facing. So our last sentences is, do not go where the path may lead, go instead where there is no path and leave a trail. Thank you, everybody, for the attention.

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我们通过 Launch Darkly 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。. Launch Darkly 隐私政策
New Relic
我们通过 New Relic 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。. New Relic 隐私政策
Salesforce Live Agent
我们通过 Salesforce Live Agent 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。. Salesforce Live Agent 隐私政策
Wistia
我们通过 Wistia 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。. Wistia 隐私政策
Tealium
我们通过 Tealium 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。. Tealium 隐私政策
Upsellit
我们通过 Upsellit 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。. Upsellit 隐私政策
CJ Affiliates
我们通过 CJ Affiliates 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。. CJ Affiliates 隐私政策
Commission Factory
我们通过 Commission Factory 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。. Commission Factory 隐私政策
Google Analytics (Strictly Necessary)
我们通过 Google Analytics (Strictly Necessary) 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。. Google Analytics (Strictly Necessary) 隐私政策
Typepad Stats
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。