Method for controlling and / or monitoring one or more industrial plant sites

Abstract

The invention relates to a method for assisting in controlling / monitoring a plant site. Manufacturing data associated with the product is available on a separate data system. A graph database model associated with an industrial plant site is received. If manufacturing data associated with a graph element associated with the manufacturing data in the graph database model is available on a different data system, the graph element is further associated with an analytic function. The analytic function indicates access information to different data systems. A request associated with the control / monitoring task is received and the corresponding associated manufacturing data is accessed with access information provided by the analytic function to apply the request to the graph database model. A result of applying the request to the graph database model is provided. A control signal is provided based on a result of the request associated with the control / monitoring task of the plant site.

Description

Technical Field

[0001] This invention relates to a method, apparatus, and computer program product for assisting in the control and / or monitoring of one or more industrial plant sites. Further, this invention relates to a method, apparatus, and computer program product for generating a graph database model associated with one or more plant sites, and the corresponding generated graph database model. Background Technology

[0002] Today, industrial products are often produced in highly complex processes involving multiple different industrial plant sites. Furthermore, modern industrial plant sites are often highly complex, with multiple production assets that must be controlled and monitored to produce the corresponding products. Therefore, any assistance in controlling and / or monitoring one or more industrial plant sites is invaluable, improving not only the safety of industrial production but also its efficiency. Summary of the Invention

[0003] One object of the present invention is to provide a method, apparatus, and computer program product that allows for automated, objective, and technically resource-efficient assistance in the control and / or monitoring of industrial plant sites, particularly large-scale chemical industrial plant sites. Controlling and / or monitoring industrial plant sites often requires highly trained and experienced personnel familiar with the specific production process and the industrial plant site. However, optimizing the large-scale production of industrial products, such as reducing energy consumption and waste generation, often requires not only monitoring and controlling, particularly optimizing, a single production process at one industrial plant site, but also monitoring and controlling, particularly optimizing, production processes at multiple industrial plant sites involved in the production. All these industrial plant sites typically provide multiple data points, particularly manufacturing data relevant to the production process of the product. Manufacturing data can be provided by multiple sensors monitoring production within the industrial plant site. However, manufacturing data can also be associated with other information related to the product, such as transportation information, quality testing information, product research data, product management data, etc. For monitoring and / or controlling, and particularly for optimizing product production, it will be necessary not only to access all manufacturing data from all industrial plant sites and associated operating units, but also to query, analyze, and otherwise evaluate the relevant manufacturing data. In particular, it is necessary to find the relationships between different parts of the manufacturing data, which can allow answers to relevant questions for the monitoring and / or control of the industrial production process.

[0004] However, in monitoring and / or controlling industrial plant sites, especially large chemical production sites, such queries are often inefficient, time-consuming, and fail to yield the desired results. One reason for this is that production data is often widely dispersed across different storage locations, data management systems, and applications, making it nearly impossible to answer even simple questions like "What parameters were used in the production of a particular product in previous production processes?" without time-consuming manual work, including manually accessing multiple databases, questioning relevant plant personnel, and analyzing the data.

[0005] Therefore, monitoring and / or controlling industrial plant sites, especially for optimizing production processes in complex, large-scale industrial plants, remains a challenging task. Consequently, it would be advantageous to provide an automated, objective, and technologically resource-efficient means of monitoring and / or controlling, particularly for optimizing, production processes in industrial plant sites.

[0006] By utilizing a graph database model associated with one or more industrial plant sites, where the relationships between manufacturing data represented by edges in the graph database model are associated with analytic functions, and if the manufacturing data associated with the graph edges is available on different data systems, where the analytic functions indicate access information regarding access to relevant manufacturing data on different data systems, knowledge naturally integrated into the graph database model regarding the processes at the industrial plant site, the hardware of the industrial plant site, the organizational structure of the industrial plant site, etc., can be easily accessed without any knowledge about when, where, or how a particular manufacturing dataset is stored. Therefore, monitoring and / or control tasks at industrial plant sites, particularly for optimizing production at the industrial plant site, can be based on querying the graph database plant model, which allows for rapid and resource-efficient access to and evaluation of manufacturing data.

[0007] In a first aspect of the invention, a computer-implemented method for assisting in the control and / or monitoring of one or more industrial plant sites is provided, wherein manufacturing data associated with one or more products produced by the one or more industrial plant sites is available on multiple independent data systems, wherein the method comprises: a) receiving a graph database model associated with the one or more industrial plant sites, wherein the graph database model is formed by a graph database including graph elements, the graph elements including graph nodes and graph edges, wherein the graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database includes graph elements associated with manufacturing data, wherein if the manufacturing data associated with the graph elements associated with the manufacturing data in the graph database model is available on different data systems, the graph element is further associated with a parsing function, wherein the parsing function indicates access information regarding access to the associated manufacturing data on these different data systems; b) receiving a request associated with a control and / or monitoring task of the one or more industrial plant sites, wherein the request is associated with a graph element of the graph database model; c) The request is applied to the graph database model by querying and / or following the graph element according to the received request, wherein querying and / or following the graph element includes accessing the corresponding associated manufacturing data when following and / or querying the graph element associated with the parsing function using the access information provided by the parsing function; d) applying the request to the graph database model as a result; and e) providing control signals based on the result of the request associated with the control and / or monitoring tasks of the one or more industrial plant sites.

[0008] This method refers to a computer-implemented method, and therefore can be executed by a general-purpose or special-purpose computer adapted to perform the method, for example, by executing a corresponding computer program. The method is configured to assist in the monitoring and / or control of an industrial plant site, preferably a chemical industrial plant site for producing one or more chemical products. Specifically, "assist" can refer to helping a user given the task of monitoring and / or controlling the industrial plant, for example, by providing relevant data, providing control recommendations, directly controlling at least a portion of the industrial plant site, notifying the user by marking a process or part of the industrial plant site according to predetermined criteria, or automatically monitoring at least a portion of the industrial plant site with respect to certain objectives. Preferably, the control and / or monitoring is performed with respect to predetermined optimization objectives. For example, the optimization objectives can refer to optimizing the energy consumption of the industrial plant site, reducing waste generation at the industrial plant site, reducing resource consumption at the industrial plant site, reducing CO2 emissions from the industrial plant site, etc.

[0009] Manufacturing data is associated with one or more products produced by one or more industrial plant sites. Generally, manufacturing data may include any data or information related to the corresponding product itself, its production process, its transportation, its handling, its operation, its management, etc. For example, manufacturing data may include production and processing parameters used during the production of the product, data related to corresponding pre-products necessary for the production of the product, tracking data from the transportation of the product, accounting and supplier details, etc. In a preferred embodiment, manufacturing data includes sensor data, particularly time-series data, from sensors monitoring the production of the product or its corresponding pre-products. Such sensors may be, for example, mass flow sensors, temperature sensors, pressure sensors, CO2 sensors, oxygen sensors, particle sensors, etc., providing the corresponding sensor data. As described above, manufacturing data can refer to multiple different aspects of a product and therefore originate from multiple different sources; therefore, such manufacturing data is generally not available in a central data system but on multiple independent data systems.

[0010] Independent data systems are generally data systems that operate independently of each other. For example, each data system has independent data management, data structure, user and / or data interface, communication protocol, storage location, capabilities, and architecture. Specifically, different industrial plant sites are expected to include different, and in most cases, independent, data systems. Transportation providers for transporting products, or providers of pre-products of products, also include data systems independent of the product's manufacturer. Furthermore, in today's industrial plant sites, for security reasons, different layers are often provided for managing, controlling, and monitoring production, each layer independent of the others and interacting only through corresponding interfaces. However, although the respective data systems are independent, they can be communicatively coupled and provide corresponding interfaces for data transfer between independent data systems. Therefore, the data systems are independent but not closed off from each other. Generally, each independent data system includes at least one data storage device on which a corresponding portion of the manufacturing data is stored and accessible by a corresponding data interface.

[0011] In one step, the method includes receiving a graph database model associated with one or more industrial plant sites. This graph database plant model is formed by a graph database. Generally, a graph database is a database that uses a graph structure with nodes, edges, and attributes to represent and store data, where data items are stored as being associated with a set of nodes and edges in the graph database. In this context, edges in the graph database represent relationships between nodes connected by corresponding edges. Nodes represent items of interest, for which the graph database tracks their corresponding relationships. Furthermore, attributes refer to information associated with the corresponding node item. Therefore, a graph database not only allows the storage of data items but also the relationships between data items and the attributes of data items, allowing data to be linked together in a context-sensitive manner, making the corresponding complex data structures visible and queried. At least a portion of the graph database includes graph elements associated with manufacturing data. Generally, manufacturing data can be indirectly associated with graph elements, for example, via related graph elements associated with the manufacturing data. Preferably, a parse function is associated with an edge representing a relationship between two nodes, at least one of which is associated with the manufacturing data.

[0012] If manufacturing data associated with at least one graph edge is available on different data systems, then at least one graph element in the graph database model associated with the manufacturing data is associated with a parse function. Preferably, the graph element associated with the manufacturing data is an edge connecting the manufacturing data of two nodes, where the edge is then associated with the parse function. In other examples, nodes associated with the manufacturing data may also be associated with parse functions. The parse function indicates access information regarding access to the associated manufacturing data on different data systems. In particular, the parse function can be a constant, mapping information, or a custom access function. Constant parse functions can be used to directly add missing data to the graph database. Mapping information, for example in the form of a mapping table, can be configured to connect the graph database to a relational database. For example, mapping information can define a key relationship between the graph database and an external database to be accessed, which must be utilized during data queries along the graph database. Custom access functions are configured to allow access to the database in a proprietary format. Such databases may provide very specific access interfaces, drivers, or data formats. Custom access functions can then be adapted to the corresponding database by encoding this access information.

[0013] Therefore, parse functions allow access to, retrieval of, and concatenation of relevant manufacturing data provided by different data systems during a query. For example, if production records for multi-product, multi-batch production must be prepared for monitoring purposes, where production is carried out in an industrial plant site with multiple reactors simultaneously producing various different products, such a graph database model, including parse functions connecting manufacturing data provided on different data systems, can be utilized. Specifically, in the exemplary scenario described above, the information required for such production records is typically stored on different, distributed, and homogeneous systems and data sources that are independent of each other. Graph database models allow for the identification of, for example, recipes for a specific product, where to find the operational production steps for that product, the measurement results of each sensor, and which sensor belongs to which reactor. Furthermore, parse functions also allow, for example, direct retrieval of sensor measurement data from each individual, independent data system. Thus, the values ​​of the measurement results from those sensors attached to the reactors used to produce the corresponding products at the corresponding times are retrieved and can be concatenated to the corresponding records. Preferably, the access information associated with the parse function includes at least one of authorization information, storage location information, and access protocol information. Additionally, the parse function may be associated with contextual information related to the data context. For example, contextual information may refer to at least one of the model depth, data model, and primary data key of the data to be accessed.

[0014] Generally, a graph database model associated with an industrial plant site can be created using a known graph database and relevant knowledge about the industrial plant site. This knowledge can originate, for example, from operators of the industrial plant site who input their knowledge into the graph database model, and can also originate from construction information, maintenance information, installation information, process information, blueprints, organizational information, operational information, geographical information, etc. For utilization, the graph database model is preferably generated and stored on a corresponding storage unit, making it usable for multiple applications. Receiving the graph database model can then access the storage device and / or application on which the graph database model is stored, and, for example, query the graph database model for relevant information about the graph database plant model. In particular, the graph database model may be provided with a programming interface configured to provide users with functionality for querying the graph database. In this case, receiving the graph database can refer to accessing this programming interface, for example, using an application programming interface. Furthermore, the graph database may also be provided with a user interface for allowing users to input corresponding queries into the graph database. In this case, receiving the graph database may include accessing this user interface. Users can also use input units to indicate, for example, which graph database model should be used, so that the graph database model indicated by the user is retrieved.

[0015] In a further step, a request is received that is associated with a control and / or monitoring task for one or more industrial plant sites. Generally, the association with this control and / or monitoring task can refer to any type of data retrieval that facilitates the performance of that task. For example, a request associated with a monitoring task could refer to generating a report on one or more aspects of the production of a particular product during a specific time period, allowing for the monitoring of one or more targets of the corresponding production. A request associated with a control task could refer to determining the quality of one or more parameters currently utilized in production or requesting the quality of different pre-products to adapt the product's production process to the corresponding quality. The request is associated with graph elements of a graph database model, i.e., data and information provided in the graph database model in the form of one or more graph elements. Generally, the request can be received via an input unit, for example, implemented as a user interface into which a user can input the corresponding request. However, the request can also be received from a storage unit, for example, if a predetermined rule instructs for an automatic request. The request can then be provided in any known manner that allows the request to be applied to the graph database model or to translate the request into a way that makes it applicable to the graph database model. For example, the request can utilize a dedicated application programming interface or a known graph query language, such as GraphQL or Cypher. In one example, the request could also be received in the form of a natural language question. In this case, applying the received request to the next step of the graph database model could refer to translating the natural language question into a graph database request using semantic rules and the model, for example, translating it into a graph query language. However, the request can also be provided directly in the form of a graph database request, for example, in the form of objects and relationships between objects that can be searched in the graph database.

[0016] The method further includes applying the received request to a graph database model. Application refers to querying and / or following graph elements according to the request. For example, a graph node can be treated as a data filter during a query, ensuring that only the data represented by that node is accessed, while relations can be followed according to the query to access related data. Querying and / or following graph elements includes accessing relevant manufacturing data while following and / or querying relations associated with a parse function, utilizing access information provided by the parse function. For example, the parse function allows access to a specific database via a data interface and receives the corresponding manufacturing data associated with and / or represented by the queried node in the graph database. The manufacturing data thus accessed can then be retrieved and joined with further manufacturing data as part of the query results. However, the accessed manufacturing data can also be used to further follow another relation provided in the graph database to other manufacturing data. For example, a query could be "retrieve the temperature of a specific production process step during the production of a specific product". Applying the request to the graph database model can then refer to the relation of following the product's identifier to the corresponding industrial plant site that has produced the corresponding product. The relation of the industrial plant site to its operating assets can then be followed to identify the operating assets that performed the corresponding production process step at the corresponding production time. This allows the temperature sensor that measures the temperature of the corresponding asset to be located by following further relationships. The storage location of the temperature data measured by the sensor at a specific time can then be accessed. The access information required during the follow-up query is provided as a corresponding parsing function for accessing the data.

[0017] The results of applying the request to the graph database model can then be provided, for example, to an output screen or for further processing. Further control signals can be provided based on the results of a request associated with a control and / or monitoring task at one or more industrial plant sites. Generally, control signals can be any signal that allows control of one or more operating units at an industrial plant site that are subject to auxiliary control and / or monitoring. For example, a control signal could involve simply visualizing the results of a request to the graph database on a control and / or monitoring user interface, allowing the user to determine further actions in their task based on that visualization. However, control signals can also be more complex, for example, directly allowing control of one or more operating units used for product production. For example, if the query involves determining the production parameters used in product production during a past production period, the control signal could directly involve implementing the found production parameters into the operating unit performing production. Regarding monitoring tasks, control signals can also involve controlling the monitoring interface. For example, one or more operating assets whose abnormal operating parameters have been identified by a routine query can be flagged. Furthermore, if the results of a corresponding monitoring query meet predetermined maintenance or safety criteria, the control signal could involve directly initiating maintenance actions, or the control signal could cause the corresponding operating unit to be turned on or off.

[0018] In a preferred embodiment, the industrial plant site includes one or more assets utilized in one or more industrial processes that produce the one or more products, wherein at least a portion of the graph database model represents the one or more industrial processes and the assets that perform the one or more industrial processes at the one or more industrial plant site by mapping these assets and material transport processes between the assets to graph elements, and wherein at least a portion of the manufacturing data is associated with the production process of the product, preferably with sensor measurements taken during the production process of the product.

[0019] For example, a running node can simply indicate the existence of a specific running asset performing a particular process at an industrial plant site. For instance, a running asset could indicate the presence of a mixer at the industrial plant site and its location within the site. However, if the running asset comprises different parts that can also operate independently of each other, more than one node can indicate a single running asset at the industrial plant site. For example, a running asset could include not only a mixer but also a heater and a grinder, where all these hardware units can operate independently of each other but are used to perform the same overall process at the industrial plant site. However, if the same process is performed at the industrial plant site, the corresponding hardware units can also be indicated as the same node in the graph database. For example, a mixer, heater, and grinder could also be indicated as a single running asset performing a specific mixing process at only one node. Furthermore, the graph database can include nodes called transport nodes, which are associated with mass transport between running assets. At transport nodes, data items related to the material transport, i.e., mass transport, between the corresponding running assets at the industrial plant site are stored. For example, if a fluid, after mixing, is transported from a mixer to a heater used to heat the fluid, a transport node indicating mass transport between the two operating assets is provided in the graph database. For example, at the mass transport node, information about the mass transport, such as mass flow rate, the type of substance transported, and flow direction, can be stored. Further, the graph database may include nodes referred to as sensor nodes, which are associated with sensors at the industrial plant site. For example, a sensor node may store information about sensors measuring the temperature of heaters at the industrial plant site. This information may refer to the type of sensor, the sensor's location, the type of measurement performed, the timing of the measurement performed, the sensor's identification, etc. Specifically, the time-series data of the sensors can be stored in a manner associated with the sensor nodes. Further, edges in the graph database can be used to indicate relationships between different nodes in the graph database model. In one example, substance is transported from one operating asset to another. A first edge can be provided between a first operating node indicating the first operating asset and a transport node indicating mass transport. A second edge can be provided between a transport node indicating mass transport and a second operating node indicating the second operating asset. Furthermore, if a sensor measures one or more quantities related to an operating asset, a corresponding edge can be provided between the corresponding operating asset and the corresponding sensor. Similarly, if a sensor measures one or more quantities during mass transport, for example, during the flow of fluid from one operating asset to another, an edge can be provided between the corresponding sensor and the corresponding transport graph element representing that transport. Such transport graph elements can represent material transport information, such as the material being transported and its characteristics, such as viscosity, temperature, and pressure.The corresponding parsing functions can then be configured to provide links or pointers to relevant tables in an enterprise resource planning or manufacturing execution system for material information, and / or links or pointers to a product information management system for measured values. The corresponding key relationships associated with the parsing functions can then be tags or measurement point numbers. Furthermore, the contextual information provided by relevant graph elements can be used in conjunction with the parsing functions to determine further information for accessing the relevant material data, such as facility, date, batch number, etc. Therefore, a graph database can include and construct available knowledge about an industrial plant site and the processes performed within that site. In particular, the advantage of a graph database is that it not only stores data but also provides relationships between the data and indicates the structure of the industrial plant site and all relationships between the industrial plant site hardware.

[0020] In one embodiment, the access information associated with the parsing function is provided as a function on an application programming interface (API) for the corresponding data system. Specifically, the parsing function is associated with functions on the API for the corresponding data system that can be used to access the corresponding manufacturing data associated with the graph element. For example, different independent data systems may include different APIs that allow functions to be defined on the respective data system, such as data search, location, and reception functions. These functions provided by the API can then be associated with the parsing function so that they can be used during the parsing of queries along the graph element associated with the parsing function.

[0021] In one embodiment, the one or more industrial plant sites are organized in the form of different operating units, each operating unit including and operating at least one data system on which manufacturing data is available from the plurality of independent data systems. The operating unit is associated with the operation of one or more industrial plant sites and / or portions of one or more industrial plant sites. The graph database model is organized in a hierarchical structure, where each layer represents an operating unit, and an edge between nodes spanning two layers is associated with a parse function. Operating units allow complex industrial plant sites to operate in an organized manner and can be based on technical structures, control structures, geographical structures, organizational structures, service structures, etc., or combinations of such possible organizational structures. A layer is defined in the graph database model as all portions comprising nodes belonging to the same operating unit. However, operating units can also overlap or be organized hierarchically. For example, an operational asset like a reactor in an industrial plant site can be associated with more than one operating unit. Alternatively, an operating unit itself can be associated with operating units at a higher level. The relationships between operational units can then be represented by a graph database model using a hierarchical structure, in which each layer represents an operational unit, and the edges between two layers, i.e., the edges between two nodes in two layers, represent the relationships between operational units. Preferably, the layers representing operational units in the graph database model are hierarchically organized in the graph database model based on the geographic context of the operational units. Organizing the graph database model based on the geographic context of the operational units, such as based on the location of the operational units or based on whether the operational units are inter-regional, regional, or local, allows for a clear and functional structure that allows operational units to be easily embedded in the graph database model. Additionally or alternatively, the graph database model can be organized by context layers of operational units. For example, an operational unit associated with a chemical process may be part of one layer, physical assets may be part of another layer, organizational operational units, such as control and management units, may be part of another layer, logistics operational units may be part of another layer, and so on. This allows the graph database model to also represent the organizational structures and hierarchies that operate in the production of products, particularly chemical products.

[0022] In a further aspect of the invention, a computer-implemented method is provided for generating a graph database model associated with one or more industrial plant sites, wherein the method includes: a) receiving manufacturing data information indicating storage conditions of the manufacturing data; b) receiving industrial plant site information indicating the physical and / or data organization structure of the one or more industrial plant sites; c) receiving data system information indicating access information regarding one or more data systems associated with the one or more industrial plant sites and the manufacturing data; d) A graph database model is generated based on the manufacturing data, the industrial plant site information, and the data system information. This graph database model is formed by a graph database comprising graph elements, including graph nodes and graph edges, where graph edges indicate relationships between graph nodes. At least a portion of the graph database includes graph elements associated with the manufacturing data. If the manufacturing data associated with a graph element in the graph database model is available on different data systems, the graph element is further associated with a parse function, which indicates access information regarding access to the associated manufacturing data on these different data systems. e) The generated graph database model is provided. Generally, the generated graph database model refers to the graph database model described above and can be used in the methods described above for assisting in the control and / or monitoring of industrial plant sites.

[0023] In a further aspect, a graph database model associated with one or more industrial plant sites is proposed, wherein the graph database model is formed by a graph database including graph elements, which include graph nodes and graph edges, wherein the graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database includes graph elements associated with manufacturing data, wherein if the manufacturing data associated with the graph element in the graph database model is available on different data systems, then the graph element is further associated with a parse function, wherein the parse function indicates access information regarding access to the associated manufacturing data on these different data systems. Generally, the generated graph database model refers to the graph database model described above and can be utilized in the methods described above for assisting in the control and / or monitoring of industrial plant sites.

[0024] In a further aspect, an apparatus for assisting in the control and / or monitoring of one or more industrial plant sites is proposed, wherein manufacturing data associated with one or more products produced by the one or more industrial plant sites is available on multiple independent data systems, wherein the apparatus includes one or more processors configured to: a) receive a graph database model associated with the one or more industrial plant sites, wherein the graph database model is formed by a graph database including graph elements, the graph elements including graph nodes and graph edges, wherein the graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database includes graph elements associated with manufacturing data, wherein if the manufacturing data associated with the graph elements associated with the manufacturing data in the graph database model is available on different data systems, the graph element is further associated with a parse function, wherein the parse function indicates access information regarding access to the associated manufacturing data on these different data systems; b) receive a request associated with a control and / or monitoring task of the one or more industrial plant sites, wherein the request is associated with a graph element of the graph database model; c) The request is applied to the graph database model by querying and / or following the graph element according to the received request, wherein querying and / or following the graph element includes accessing the corresponding associated manufacturing data when following and / or querying the graph element associated with the parsing function using the access information provided by the parsing function; d) applying the request to the graph database model as a result; and e) providing control signals based on the result of the request associated with the control and / or monitoring tasks of the one or more industrial plant sites.

[0025] In a further aspect, an apparatus for generating a graph database model associated with one or more industrial plant sites is proposed, wherein the apparatus includes one or more processors configured to: a) receive manufacturing data information indicating the storage conditions of the manufacturing data; b) receive industrial plant site information indicating the physical and data organization structure of the one or more industrial plant sites; c) receive data system information indicating access information regarding one or more data systems associated with the one or more industrial plant sites and the manufacturing data; d) A graph database model is generated based on the manufacturing data information, the industrial plant site information, and the data system information. This graph database model is formed by a graph database comprising graph elements, including graph nodes and graph edges, where graph edges indicate relationships between graph nodes. At least a portion of the graph database includes graph elements associated with the manufacturing data. If the manufacturing data associated with a graph element in the graph database model is available on different data systems, the graph element is further associated with a parse function, which indicates access information regarding access to the associated manufacturing data on these different data systems. e) The generated graph database model is provided.

[0026] In a further aspect, a computer program product for controlling and / or monitoring one or more industrial plant sites is proposed, wherein the computer program product includes program code means for causing the apparatus as described above to perform the methods as described above.

[0027] In a further aspect, control signals generated by the methods and / or devices described above are proposed.

[0028] In a further aspect, the use of the apparatus described above for controlling and / or monitoring one or more industrial plant sites is proposed.

[0029] It should be understood that the methods, apparatus and computer program products described above have similar and / or identical preferred embodiments, particularly as defined in the dependent claims.

[0030] It should be understood that the preferred embodiments of the present invention may also be any combination of the dependent claims or the above embodiments and the corresponding independent claims.

[0031] These and other aspects of the invention will become apparent and will be illustrated with reference to the embodiments described below. Attached Figure Description

[0032] In the following figures:

[0033] Figure 1 An embodiment of a system for assisting in the control and / or monitoring of one or more industrial plant sites is illustrated schematically and exemplary.

[0034] Figure 2 A flowchart illustrating, and exemplarily demonstrating, is provided for a method of assisting in the control and / or monitoring of one or more industrial plant sites, and...

[0035] Figure 3 An embodiment of an exemplary graph database model organization is illustrated schematically and exemplaryly. Detailed Implementation

[0036] Figure 1 A system 100 that allows for auxiliary control and / or monitoring of one or more industrial plant sites is illustrated schematically and exemplary. In this example, three industrial plant sites 131, 133, and 135 belonging to an industrial complex 130 are illustrated exemplary. These industrial plant sites are configured to produce one or more industrial products, preferably chemical products. During the production of the industrial products, manufacturing data is generated. For example, planning and formulation data may be generated during the planning phase. During production, process data, for example, from sensor measurements monitoring the production of the product, is generated. Furthermore, further auxiliary data, such as logistics data, pre-product and product quality data, status data, accounting and contract data, etc., are generated. All of this generated data can be considered as being associated with the product being produced. In most cases, the data will be generated from different data sources and at different data locations, and will not be available on a central platform. Exemplarily, Figure 1 Independent data systems 132, 134, and 136 are shown for each industrial plant site within an industrial plant area. Data systems 132, 134, and 136 are independent. These independent data systems are managed, organized, and constructed independently of each other and therefore in a decentralized manner. Therefore, for each independent data system, there is no central data organization unit that allows direct and centralized access to the manufacturing data available on the independent data systems. For example, manufacturing data generated during production steps performed by industrial plant site 131 is stored only on and available only on data system 132, manufacturing data generated during production steps performed by industrial plant site 133 is available only on independent data system 134, and so on. Furthermore, manufacturing data generated during the transportation of at least a portion of a product from one industrial plant site to another, such as transportation data, may even be stored... Figure 1 Further independent data systems for transportation providers not shown in the figure.

[0037] For many tasks related to controlling and / or monitoring one or more industrial plant sites, access to a combination of different manufacturing data components can be important. For example, determining process parameters previously used for producing a product can be important. To assist in this task, as well as other tasks related to controlling and / or monitoring one or more industrial plant sites, system 100 includes a corresponding device 110. Device 110 can be implemented in any form as one or more computers. For example, device 110 can be implemented as a combination of general or special-purpose computer hardware and / or software. However, the device can also be implemented in the form of distributed computing. For example, one or more processors can be configured to perform the methods for assisting in controlling and / or monitoring one or more industrial plant sites, as described below, in a distributed manner (e.g., in a cloud environment). Device 110 includes an input unit 111, one or more processors 112, and an output unit 113. Input unit 111 can be a general interface for accessing and / or receiving data. In particular, input unit 111 is configured to receive a graph database model associated with one or more industrial plant sites of industrial complex 130. For example, input unit 111 may receive the graph database model from graph database generation apparatus 120, which is configured to generate a graph database model as described below.

[0038] The apparatus 120 for generating a graph database model may include an input unit 121, one or more processors 122, and an output unit 123. In this case, the input unit may also be implemented as any kind of data interface that allows access to and / or reception of data. Specifically, the input unit 121 is configured to receive manufacturing data information, industrial plant site information, and data system information. The manufacturing data information indicates the storage conditions of the available manufacturing data. For example, the manufacturing data information may indicate the storage location of the manufacturing data, data identifiers, etc. Figure 1In the example shown, manufacturing data information may, for example, indicate that sensor measurements from monitoring the production process performed by industrial plant site 131 are stored on data system 132. Industrial plant site information indicates the physical and / or data organization structure of the one or more industrial plant sites. For example, the physical organization structure may refer to the operating assets of the industrial plant site and the relationships between those assets. The data organization structure of one or more industrial plant sites may indicate the data structures associated with the industrial plant sites. These structures may be security structures, management and control structures, monitoring structures, operational structures, etc., associated with data processing at the industrial plant site. Data system information indicates access information regarding one or more data systems associated with the one or more industrial plant sites and manufacturing data. For example, access information may refer to the communication protocols used by the data systems of the industrial plant sites, the security protocols used by the industrial plant sites, the data formats used by the data systems, the application programming interface functions provided by the data systems, etc. All this information may be received, for example, by accessing relevant databases, querying the operators of the industrial plant sites, and accessing and analyzing information such as blueprints of the industrial plant sites, data system diagrams, and structural diagrams of the industrial organization. In particular, it is preferred that the information is analyzed, filtered, and cleaned by the user during user-machine interaction.

[0039] One or more processors 122 are then configured to generate a graph database model based on manufacturing data, industrial plant site information, and data system information. The graph database model can be generated based on known graph database generation methods. For example, appropriate templates can be used. Preferably, the graph database model is generated during user-machine interaction, in which the user utilizes the corresponding functions provided by device 120 to generate the graph database based on the received information.

[0040] A graph database model is formed by a graph database comprising graph elements. Graph elements include graph nodes and graph edges. Graph edges indicate relationships between graph nodes. At least a portion of the graph database includes graph elements associated with manufacturing data. For example, graph nodes may represent manufacturing data, and graph edges may represent relationships between manufacturing data. However, graph nodes may also refer to assets and assets that generate manufacturing data, such as production assets or sensors, so that graph nodes do not directly represent manufacturing data but are associated with it. If the manufacturing data associated with a graph element is available on different data systems, a parse function is further associated with the graph element. For example, if the relationships between manufacturing data represented by edges in the graph database model can be associated with a parse function to access the manufacturing data of the corresponding node. For example, manufacturing data stored on data system 132 may be associated with manufacturing data stored on data system 134 in the graph database model. For example, the relationship may be a pre-product / product relationship. Since the manufacturing data is stored in different data systems in this case, the relationship can be associated with a parse function. The parse function then indicates access information regarding access to the associated manufacturing data on the different data systems. For example, the parsing function in the above example could provide access information for both data system 132 and data system 134. However, if the graph database indicates a hierarchical or directional relationship in this case, where a user can only follow manufacturing data of a pre-product in graph database 132 from manufacturing data on graph database 134, the parsing function could also include only access information about graph database 132. Access information can refer to any information that allows users of the graph database model to access the data systems and optionally retrieve the corresponding manufacturing data. For example, access information could include authentication data, application programming interface functions, communication protocols, data storage locations, data formats, and data structure information. Depending on the specific data system and the required access information, the parsing function could be a constant, a mapping function, or a custom access function.

[0041] Figure 3 A schematic example of a graph database model is shown, and will be described in more detail when discussing specific, more detailed examples. The output unit 123 of device 122 can then also be implemented as a data interface that allows the provision of the correspondingly generated graph database model. For example, output interface 123 can output a graph database to a storage unit on which the corresponding graph database model is stored. However, output unit 123 can also directly provide the graph database model to input unit 111.

[0042] Further, input unit 111 is configured to receive requests associated with control and / or monitoring tasks for one or more industrial plant sites. For example, the request may be received via a user interface into which a user can input their request. Generally, the request is associated with a graph element of a graph database model, and thus refers to manufacturing data associated with the graph database model. The request can be provided in any human-understandable format, such as a simple text question in natural language. However, other possible request formats may also be utilized. For example, allowing the user to input a graph pattern to search for, providing a special language or graph-based functionality to express the request, etc. One or more processors 112 of device 110 are then configured to apply the received request to the graph database model. For example, the application of the received request may include analyzing the request according to its context in a first step. For example, if the request is provided as a natural language question, then in the first step, the request may be analyzed using a corresponding natural language processing method. Based on this analysis, the request can then be translated into a format and structure as defined for querying and / or following graph elements of the graph database model according to the request. Generally, corresponding query and / or follow methods are known for querying such graph database models. During queries and / or follow-ups on graph elements, when following and / or querying relationships associated with parse functions, the access information provided by the parse functions can be used to access the corresponding related manufacturing data. For example, a request might refer to manufacturing data related to a pre-product manufactured by industrial plant site 131, where the product is manufactured by industrial plant site 133. Queries on graph elements of the graph database model can then, at some point, result in a relationship between product-related manufacturing data stored in data system 134 and pre-product-related manufacturing data stored in data system 132. Since this manufacturing data is stored in different data systems, the relationship will include parse functions, whereby, during a query, the parse functions allow access to data system 132 to retrieve the pre-product's manufacturing data and, for example, present the manufacturing data to the user as part of the query results.

[0043] Output unit 113 can also be configured as a data interface, but may also be additionally or alternatively considered as a user interface, and can then provide the results of applying a request to a graph database model. Specifically, output unit 113 can be configured to provide control signals based on the results of a request associated with control and / or monitoring tasks for one or more industrial plant sites. For example, the control signal may refer to generating a report about the results or based on the results, which can be presented to the user on a corresponding user display. The user can then, for example, decide on further control and / or monitoring actions based on the report. However, the control signal may also directly refer to the control and / or monitoring of the industrial plant site. For example, the control signal may, based on the results of a request, cause at least a portion of the product process parameters to be implemented in the production process of the product. In another example, depending on certain criteria and predetermined rules applied to the results of a request, control signals leading to certain monitoring actions, such as safety measures, maintenance measures, testing measures, etc., may be provided.

[0044] Figure 2 A flowchart illustrating, and exemplarily demonstrating, is provided for a method of assisting in the control and / or monitoring of one or more industrial plant sites. This method may, for example, be derived from... Figure 1 The described system execution. In a first step, the method optionally includes generating a graph database model. Therefore, these steps can be considered as a method for generating a graph database model and can be performed prior to executing methods for assisting control and / or monitoring of an industrial plant site. The method can be performed independently of another method or integrated into another method. First, the method includes receiving manufacturing data information, data system information, and plant site information as described above. In a next step, a graph database model is then generated based on all of this information, for example, during human-machine interaction or purely automatically. The graph database model can then be provided, for example, to a storage unit from which it can then be utilized. However, the graph database model can also be provided directly to the steps for assisting control and / or monitoring of the plant site. The method then includes receiving the graph database model directly from the generation step or from a storage device where the graph database model is already stored in the first step. Further, the method includes receiving a request associated with the control and / or monitoring task. The request can then be applied to the graph database model, and the result of the request can be provided. Based on the corresponding results, control signals can then be generated and utilized, for example, to control and / or monitor the production process of the product.

[0045] In the following sections, some exemplary and preferred embodiments will be described in more detail. Generally, it is preferred that the graph database model, for example as a digital twin, represents a production consortium (Verbund) comprising one or more industrial plant sites. The graph database model is preferably generated using graph-based contextual modeling.

[0046] In a preferred embodiment, the graph database model is generated using a set of typed nodes and edges. Typed is defined as providing nodes and edges of predefined types. For example, one type of node can be defined to represent manufacturing data, and another type of node can be defined to represent production assets. Preferably, the graph database model represents a hierarchy of consortium operating assets in a geographic context. For example, operating assets can be represented as being region-related, with the region pointing to a site, the site to a cluster, the cluster to a factory, the factory to a process group, and the process group to an operating unit. This allows for a clear structure of operating assets in the graph database model. Further, preferably, the transportation processes between operating units and / or process groups are also represented by the graph database model.

[0047] To enable this graph database model to represent portions of manufacturing data available on different independent systems, these systems are referenced using separately typed specialized nodes and cross-linking edges. Specifically, parsing functions are introduced into the edges connecting nodes that refer to data on different systems. This allows the graph database model to represent manufacturing data, for example, within corresponding contexts of its organization, accounting, production planning, reporting, and geographic features.

[0048] The parse function is configured to allow graph traversal across different layers of the graph. In this case, a layer can be defined as all nodes in the graph database model that are associated with a corresponding independent system. Preferably, a node is generated to include standardized information about the corresponding system, including the data represented by that node. Applying a request to the graph database can be achieved by traversing and accessing the data represented by the nodes of the graph database. Traversal of the graph database is facilitated by edge-related functions, particularly parse functions associated with corresponding edges. Generally, edge-related functions refer to functions associated with different edge types. For example, a parse function is associated with an edge that relates manufacturing data from different data systems. Other edge-related functions can be associated with other edge types. For example, an edge that relates a node representing a running asset can be associated with a transport function representing the quality transport between running assets. Thus, edge-related functions represent a network of segmented data traversal functions that allow access to the corresponding data. Therefore, the graph database provides an application programming interface framework for accessing independent data systems. As a result, users can traverse different data systems and find new data through graph search functions without knowing the individual join relationships at the data level.

[0049] Industrial production is often organized across multiple organizational, operational, and production units, resulting in heterogeneous data structures that often contain numerous closed data islands, or independent data systems, which can be communicatively coupled via separate data bridges. As users, it is often impossible to know what data exists, where it is located, and how to access it. For industrial organizations, maintaining all data under a single public access and storage solution is also impractical, as custom databases exist for specific use cases or process steps, such as Enterprise Resource Planning (ERP) or Manufacturing Execution Systems (MES). Therefore, providing a unified framework to connect these data systems offers numerous advantages. The graph database model described above generally allows for an application programming interface implemented as a contextualized graph that facilitates search options for manufacturing data. In particular, the graph database model provides a framework for providing separate bridges between data sources, mapping them to an ontology model, and providing search options for traversing the network. Therefore, graph database models, especially those utilizing parse functions, offer several advantages over other possible solutions, such as those using relational mapping tables. Specifically, graph database models do not restrict the mapping of information between data systems to relational key mappings. Custom clients are provided with parsing functions that allow traversal of different data systems using different interfaces. Specifically, these parsing functions are not limited to SQL joins but can be configured to allow traversal of any system providing any kind of interface, such as shared file systems, web APIs, proprietary systems (e.g., PIMS), etc. Furthermore, during contextual searches, all key relationships (e.g., provided by the parsing functions) and filters (e.g., provided by the nodes) are automatically resolved by traversing the graph, thereby gathering information by referencing and accessing different data system interfaces with different data structures. This allows queries to be placed in the form of start and end node attributes without needing knowledge of the individual data system interconnections between the start and end nodes, such as knowledge containing the necessary link information. Link information, i.e., information expressed in traditional join statements, does not need to be known to the user expressing the graph search query. Further, nodes in the graph database model can be interactively explored while traversing the graph using an application programming interface framework provided by the graph database model. The graph database model provides information about the connected nodes and allows interactive exploration of the graph database model. Furthermore, graph database models can be easily extended and enriched with new data, data connections, or data types, including data-based computations such as KPIs and AI models / agents.

[0050] In a preferred embodiment, the graph database model also allows for the replacement of missing information. For example, if a specific portion of manufacturing data is unavailable, the graph database model can be used to determine the missing manufacturing data based on available manufacturing data associated with that missing manufacturing data. In a particular example, manufacturing data related to the quantity of a product produced is missing in a database of industrial plants that have produced that product. The graph database can then be used to search for relevant data that allows for the determination of that quantity. For example, accounting information related to the product may include information about the price paid for the product and the price per unit, thereby allowing for the determination of the quantity of products produced.

[0051] The following will discuss... Figure 3 An example explaining the preferred graph database model. Figure 3 Only the overall organization of the graph database is shown, omitting some or more detailed nodes. In this example, different operational units (also referring to different physical entities) are represented as graph nodes and associated with information. For example, in Figure 3 In the graph database, nodes are represented as signal providers, unit operations, etc. However, manufacturing data, operating units, or organizational units can also be represented by nodes in the graph database. The graph database model is organized in different tree layers, where each layer includes nodes representing operating units comprising independent data systems. These layers are... Figure 3 The boundary is indicated by a dashed line. For example, one layer can represent an industrial process. Figure 3 In this context, it is referred to as material transportation. Planning, accounting, and organization can also refer to the corresponding operational units that provide manufacturing data. The layers of a graph database are related through connecting edges that may include analytic functions. A particularly preferred layer in a graph database model (also...) Figure 3 (As shown in the image) is an asset hierarchy, which constructs the graph database, for example, by region, site, cluster, plant, process group, and unit. However, another geographic asset hierarchy can also be utilized, such as country, region, city, site, etc. Figure 3 Other levels that can be used to organize a graph database model, exemplified in the diagram, are organizational levels, such as operating departments and business units; cost accounting levels, such as cost centers and business processes; and material flows, such as transport edges between unit operations or plant nodes. The interconnections between these layers are defined by typed directed edges. For example, an edge type can be defined for each layer. Typed directed edges can then include parsing functions that provide access information for accessing the corresponding data system of a layer. Specifically, each type of directed edge can be configured with a specific parsing function that provides access information for the corresponding layer connected by that edge. Generally, the structure of a graph database model can differ for different companies or production sites. However, the principles of graph database models, particularly regarding parsing functions, as described above, can be applied to any company and industry.

[0052] The following sections provide illustrative examples of how to generate a graph database model or how to implement further data into it. To load new data into the graph database model—that is, to map the actual data source to the graph database model—data sources and connections are described. Further node attributes are also defined during the loading process. For example, a YAML file structure can be used to manage the loading process and edit the graph database model, such as modifying the model topology.

[0053] The following describes an example of how to query a graph database model. In this example, relations (i.e., edges between nodes representing data in the graph database) are resolved via a parsing function. The parsing function can provide constant values ​​(where access to a data system outside the current data system used for the graph database is not required) or connect to an external data system. The external data system may provide a corresponding application programming interface (API), which the parsing function can provide for access using these interfaces. For example, the parsing function may provide specific functionality, such as a client-based implementation of how to query an external API, or transactional database operations, such as SQL joins based on foreign key mappings. For example, to resolve the connection between the data systems of a cost center and an industrial plant, a track relation includes a parsing function that resolves the datasets between two sources: an Enterprise Data Lake (ADLS) and a Runtime Database (SQL), via a key mapping relation stored in the parsing function for track-type edges. This relation is resolved at query time, and the actual data is not stored in the graph database model. The parsing function may include user-defined functions configured to access specific legacy systems common in process industries, such as Process Information Management Systems (PIMS). Accessing these systems can involve very specific knowledge of their internal data structures, application programming interfaces, or even require dedicated drivers. Parsing functions can therefore include the appropriate information to overcome this problem and provide seamless data transfer for clients querying the graph database model. Based on the framework described above, corresponding graph database model search and traversal capabilities can be provided, utilizing query plan optimization, node folding, and collection of access requirements. Application examples are described in the following sections.

[0054] The corresponding graph database model enables users to traverse and search connections between distributed data sources, seamlessly access data, and return consistent datasets to graph clients. Contextual search allows paths to be found within the graph database model. For example, the question "Which operating department operates a given asset?" leads to a backward traversal of all owned assets within the asset hierarchy until an incoming edge of type "operates" is found, then traversing backward again until the operating department is found. This concept can be applied to finding interconnections between all nodes in a graph database model, traversing multiple layers and levels to find connections between two sets of information that are not directly connected at the data source level.

[0055] Another, more complex example of a possible query, involving many different data types and sources, is: "Find the temperature profile of material A produced in a reactor." To answer this question, for example, one must find a) when and in which plant the material was produced, b) in which reactor the material was produced in, and when the reactor was dispensed with the material, c) what temperature sensors were used in the reactor, and d) what measurements were taken by these sensors during this time frame. These questions can be answered using a parsed graph database model as described above.

[0056] Therefore, this invention refers to a method for organizing, modeling, and exposing data from multiple systems with heterogeneous data schemas and APIs within a central graph model. This is provided under a minimal, single, general syntax with highly adaptable and intuitive queries. The method progressively models different data sources and provides access to each of them through simple yet scalable queries. Explicit modeling of relationships between different data sources allows for automatic traversal of relevant data across multiple systems.

[0057] By studying the accompanying drawings, this disclosure, and the appended claims, those skilled in the art can understand and implement other variations of the disclosed embodiments when practicing the claimed invention.

[0058] The operations performed in the processes and methods disclosed herein may be implemented in different orders. Furthermore, the operations outlined are provided as examples only, and some of these operations may be optional, may be combined into fewer steps and operations, may be supplemented with more operations, or may be expanded into more operations without departing from the essence of the disclosed embodiments.

[0059] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a / an" does not exclude multiple / types.

[0060] A single unit or device can perform the functions of several items listed in the claims. The fact that certain measures are listed in different dependent claims does not indicate that combinations of these measures cannot be used advantageously.

[0061] Processes such as receiving graph databases, receiving queries, applying queries, and generating control data, which are performed by one or more units or devices, can be performed by any other number of units or devices. These processes can be implemented as program code devices and / or dedicated hardware for computer programs.

[0062] Computer program products can be stored / distributed on suitable media, such as optical or solid-state storage media provided with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0063] Any unit described herein can be a processing unit as part of a classical computing system. Processing units can include general-purpose processors and can also include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or any other special-purpose circuitry. Any memory can be physical system memory, which can be volatile, non-volatile, or some combination of both. The term "memory" can include any computer-readable storage medium, such as a non-volatile mass storage device. If the computing system is distributed, the processing and / or storage capabilities can also be distributed. A computing system can include multiple structures as "executable components." The term "executable component" is a structure that is well understood in the computing field to be software, hardware, or a combination thereof. For example, when implemented as software, those skilled in the art will understand that the structure of an executable component can include software objects, routines, methods, etc., that can be executed on the computing system. This can include executable components in the computing system heap or on a computer-readable storage medium. The structure of an executable component can exist on a computer-readable medium such that, when interpreted by one or more processors of the computing system (e.g., by processor threads), it causes the computing system to perform functions. This structure can be directly read by a processor, for example, if the executable is binary, or it can be constructed to be interpretable and / or compileable, for example, whether in a single stage or multiple stages, thereby generating such binary that can be directly interpreted by the processor. In other cases, the structure can be hard-coded or hard-wired logic gates, implemented specifically or almost specifically in hardware, such as within a field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or any other dedicated circuit. Thus, the term "executable" is a term for a structure well-known to those skilled in the art of computing, whether implemented in software, hardware, or a combination thereof. Any embodiments herein are described with reference to actions performed by one or more processing units of a computing system. If such actions are implemented in software, one or more processors direct the operation of the computing system in response to the execution of computer-executable instructions constituting the executable. The computing system may also include communication channels that allow the computing system to communicate with other computing systems via, for example, a network. A "network" is defined as one or more data links that enable the transfer of electronic data between computing systems and / or modules and / or other electronic devices. When information is transmitted or provided to a computing system via a network or another communication connection (e.g., hardwired, wireless, or a combination of hardwired and wireless), the computing system correctly treats that connection as a transmission medium. The transmission medium may include a network and / or a data link, which may be used to carry desired program code in the form of computer-executable instructions or data structures, and may be accessed by a general-purpose computing system or a special-purpose computing system or a combination thereof.While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for interaction with a user. The user interface, for example, acts as an input or output mechanism for the user via a display.

[0064] Those skilled in the art will understand that at least a portion of the present invention can be practiced in network computing environments with a variety of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, data centers, wearable devices (such as glasses), etc. The present invention can also be practiced in distributed system environments, where, for example, local and remote computing systems linked by a network via hardwired data links, wireless data links, or a combination of hardwired and wireless data links jointly perform tasks. In a distributed system environment, program modules can reside on both local and remote memory storage devices.

[0065] Those skilled in the art will also understand that at least a portion of the present invention can be practiced in a cloud computing environment. A cloud computing environment can be distributed, but this is not required. When a cloud computing environment is distributed, it can be spread across multiple countries within an organization and / or have components across multiple organizations. In this specification and the appended claims below, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources, such as networks, servers, storage devices, applications, and services. The definition of “cloud computing” is not limited to any of the many other advantages that can be obtained from such a model when deployed. The computing system of the accompanying drawings includes various components or functional blocks that can implement the various embodiments disclosed herein as explained. These various components or functional blocks can be implemented on a local computing system or on a distributed computing system that includes elements residing in the cloud or aspects implementing cloud computing. These various components or functional blocks can be implemented as software, hardware, or a combination of software and hardware. The computing system shown in the figures may include more or fewer components than those shown in the figures, and some of these components may be combined as needed.

[0066] Any reference numerals in the claims should not be construed as limiting the scope.

Claims

1. A computer-implemented method for assisting in the control and / or monitoring of one or more industrial plant sites, wherein, Manufacturing data associated with one or more products produced from the one or more industrial plant sites is available on multiple independent data systems, wherein the method includes: - Receive a graph database model associated with the one or more industrial plant sites, wherein the graph database model is formed by a graph database including graph elements, the graph elements including graph nodes and graph edges, wherein the graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database includes graph elements associated with manufacturing data, wherein if the manufacturing data associated with the graph element in the graph database model is available on different data systems, then the graph element is further associated with a parsing function, wherein the parsing function indicates access information regarding access to the associated manufacturing data on the different data systems. - Receive requests associated with control and / or monitoring tasks for the one or more industrial plant sites, wherein the requests are associated with graph elements of the graph database model. - The request is applied to the graph database model by querying and / or following the graph element according to the received request, wherein the querying and / or following of the graph element includes accessing the corresponding associated manufacturing data when following and / or querying the graph element associated with the parsing function using the access information provided by the parsing function. - Provides the results of applying the request to the graph database model, and - Provide control signals based on the results of the requests associated with the control and / or monitoring tasks of the one or more industrial plant sites.

2. The method according to claim 1, wherein, An industrial plant site includes one or more assets utilized in one or more industrial processes that produce the one or more products, wherein at least a portion of the graph database model represents the one or more industrial processes and the assets that perform the one or more industrial processes at the one or more industrial plant site by mapping the assets and material transport processes between the assets to graph elements, and wherein at least a portion of the manufacturing data is associated with the production process of the product, preferably with sensor measurements taken during the production process of the product.

3. The method according to any one of the preceding claims, wherein, The access information associated with the parsing function is provided in the form of a function on the application programming interface for the corresponding data system.

4. The method according to any one of the preceding claims, wherein, The one or more industrial plant sites are organized in the form of different operating units, each operating unit including and operating at least one data system on which manufacturing data is available in the plurality of independent data systems, wherein the operating unit is associated with the operation of one or more industrial plant sites and / or portions of one or more industrial plant sites, wherein the graph database model is organized in a hierarchical structure, wherein each layer represents an operating unit, and wherein an edge between nodes spanning two layers is associated with a parse function.

5. The method according to claim 4, wherein, The layers representing the operational units in the graph database model are hierarchically organized within the graph database model based on the geographic context of the operational units.

6. The method according to any one of the preceding claims, wherein, The access information associated with the parsing function includes at least one of authorization information, storage location information, and access protocol information.

7. A computer-implemented method for generating a graph database model associated with one or more industrial plant sites, wherein, The method includes: - Receive manufacturing data information, which indicates the storage conditions for the manufacturing data. - Receive industrial plant site information, which indicates the physical and / or data organization structure of the one or more industrial plant sites. - Receive data system information indicating access information regarding one or more data systems associated with the one or more industrial plant sites and the manufacturing data. - A graph database model is generated based on the manufacturing data information, the industrial plant site information, and the data system information. The graph database model is formed by a graph database including graph elements, where each graph element includes graph nodes and graph edges. At least a portion of the graph database includes graph elements associated with the manufacturing data. If the manufacturing data associated with a graph element in the graph database model is available on different data systems, then that graph element is further associated with a parsing function, where the parsing function indicates access information regarding access to the associated manufacturing data on the different data systems. - Provides the generated graph database model.

8. A graph database model associated with one or more industrial plant sites, wherein, The graph database model is formed by a graph database including graph elements, which include graph nodes and graph edges, wherein graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database represents manufacturing data as graph nodes and the relationships between the manufacturing data as graph edges, wherein if the manufacturing data associated with an edge in the graph database model is available on different data systems, the relationship between the manufacturing data represented by the graph edge is associated with a parsing function, wherein the parsing function indicates access information regarding access to the associated manufacturing data on the different data systems.

9. An apparatus for assisting in the control and / or monitoring of one or more industrial plant sites, wherein, Manufacturing data associated with one or more products produced by the one or more industrial plant sites is available on multiple independent data systems, wherein the device includes one or more processors configured to: - Receive a graph database model associated with the one or more industrial plant sites, wherein the graph database model is formed by a graph database including graph elements, the graph elements including graph nodes and graph edges, wherein the graph edges indicate relationships between graph nodes, wherein at least a portion of the graph database includes graph elements associated with manufacturing data, wherein if the manufacturing data associated with the graph element in the graph database model is available on different data systems, then the graph element is further associated with a parsing function, wherein the parsing function indicates access information regarding access to the associated manufacturing data on the different data systems. - Receive requests associated with control and / or monitoring tasks for the one or more industrial plant sites, wherein the requests are associated with graph elements of the graph database model. - The request is applied to the graph database model by querying and / or following the graph element according to the received request, wherein the querying and / or following of the graph element includes accessing the corresponding associated manufacturing data when following and / or querying the graph element associated with the parsing function using the access information provided by the parsing function. - Provides the results of applying the request to the graph database model, and - Provide control signals based on the results of the requests associated with the control and / or monitoring tasks of the one or more industrial plant sites.

10. An apparatus for generating a graph database model associated with one or more industrial plant sites, wherein, The device includes one or more processors, the one or more processors being configured to: - Receive manufacturing data information, which indicates the storage conditions for the manufacturing data. - Receive industrial plant site information, which indicates the physical and data organization structure of the one or more industrial plant sites. - Receive data system information indicating access information regarding one or more data systems associated with the one or more industrial plant sites and the manufacturing data. - A graph database model is generated based on the manufacturing data information, the industrial plant site information, and the data system information. The graph database model is formed by a graph database including graph elements, where each graph element includes graph nodes and graph edges. Graph edges indicate relationships between graph nodes. At least a portion of the graph database represents production data as graph nodes and the relationships between the production data as graph edges. If the manufacturing data associated with an edge in the graph database model is available on different data systems, the relationship between the manufacturing data represented by that edge is associated with a parse function. The parse function indicates access information regarding access to the associated manufacturing data on the different data systems. - Provides the generated graph database model.

11. A computer program product for controlling and / or monitoring one or more industrial plant sites, wherein, The computer program product includes program code means that causes the apparatus according to claim 9 to perform the method according to any one of claims 1 to 6.

12. A control signal generated by the method according to any one of claims 1 to 6 and / or the apparatus according to claim 9.

13. Using the apparatus according to claim 9 to control and / or monitor one or more industrial plant sites.

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