Optimization of industrial systems

Abstract

The invention relates to a computer-implemented method for controlling one or more systems each comprising a plurality of components, wherein parts and / or processes that are processed or carried out by one of the components are further processed or continued by one or more of the other components of the same system, the method comprising: determining state information regarding the systems, and storing the determined state information in a first data structure; determining energy types, energy sources, energy costs and / or costs for disposals of materials of the components for specified time periods and storing the determined data in a second data structure; determining state information in the environment of the systems, and storing the determined state information in a third data structure; creating a sequence plan for the systems, wherein operating times for the components are defined on the basis of the data structures, said operating times minimizing waiting times and also the amount of energy used; and converting the sequence plan into control commands for the components of the systems, and applying the control commands to the components.

Description

[0001] Optimization of industrial plants

[0002] TECHNICAL FIELD

[0003] The invention disclosed here lies in the field of optimization of industrial plants, in particular optimization with regard to interactions of plant components with each other and with external factors.

[0004] STATE OF THE ART

[0005] DE io 2020 213 114 Ai discloses a method for controlling an energy swarm system. The energy swarm system has one or more energy clusters, each energy cluster having one or more thermal, electrical, and / or material energy generators and / or energy consumers and / or energy storage devices. The method comprises the following steps, which are performed iteratively: a) Determining and updating future-oriented energy flow schedules for each individual energy cluster by cluster control units. The energy flow schedules map the individual energy flows in the energy cluster in a predetermined time frame; b) Determining and updating future-oriented energy flexibility offers of the individual energy clusters by the cluster control units.The flexibility offers form the future realizable energy potential of the energy cluster within the specified time frame, taking into account the associated energy flow schedule and boundary conditions for the energy cluster; c) transmitting the energy flow schedules and the flexibility offers of the individual energy clusters to a central swarm control unit of the energy swarm system after being determined or updated in accordance with one of steps a) or b); d) offering flexibility offers broken down according to the specified time frame by the swarm control unit to energy network operators and / or energy suppliers at least one time cycle of the time frame before the energy can be physically provided.

[0006] EP 2 633 600 Bi discloses a method for distributed, intelligent, short-cycle data acquisition and control for a large number of measuring points according to the requirements of an intelligent energy supply network, in which a plurality of servers distributed in the energy supply network are used, which together form a middleware; wherein the middleware, using a middleware interface, acquires measured values ​​(MW) from digital measuring devices and counter values ​​(ZW) in short cycles and controls the digital measuring devices as well as other sensors and actuators;wherein the measuring devices are grouped together, each of which is assigned to a server, wherein tariff information (TR) is assigned to the measured values ​​(MW) in the middleware, wherein the middleware interface is responsible for providing the meter values ​​(ZW) and forwarding higher-level control signals, and the short-cycle data acquisition means recording measured values ​​at time intervals of 20 ms to 5 s for, depending on the network size, from 1,000 to several 10,000 end devices and depending on the performance of the digital measuring devices; wherein the measured values ​​(MW) and the meter values ​​(ZW) are subjected to a validity check in the middleware by means of signature verification (SV); wherein an image of the state of the energy supply network is stored in memory variables (HD) and a raw value memory in the middleware;the servers are connected to each other via a communication network and communicate via internal network interfaces so that the measured values ​​and the counter values ​​are available at each server;

[0007] The invention disclosed here is based on the task of optimizing complex systems with regard to energy costs, environmental compatibility, and efficiency. SUMMARY

[0008] Embodiments of the invention comprise a computer-implemented method for controlling one or more systems, each comprising a plurality of components, wherein components and / or processing processes that are processed or carried out by one of the components are further processed or continued or continued by one or more of the other components of the same system, wherein the method comprises: determining status information of the systems, in particular the energy consumption, the consumption of material, the CO2 footprint, the sequence and the processing and / or processing time of the individual components of the systems, and storing the determined status information in a first data structure; determining energy types, sources, costs and / or costs for disposal of materials of the components for predetermined periods of time, and storing the determined data in a second data structure;Determining status information in the environment of the systems, and storing the determined status information in a third data structure; creating a schedule for the systems, wherein operating times for each of the components are set based on the first, second, and third data structures, which, on the one hand, minimize waiting times between interconnected components of a system and, on the other hand, energy consumption, costs, material disposal costs, and / or CO2 footprints during the operation of the components; and translating the schedule into control commands for the components of the systems, and applying the control commands to the components.

[0009] A plant is a physical, particularly industrial, plant that produces a product or generally provides a service through the interaction of its components. The components of a plant are linked to one another in order to pass on components processed or produced by one component, or also measured values, parameters, etc., to another component. The data is passed on, for example, via conveyor belts, mechanical grippers, automatic distribution systems, network connections, or others. The three data structures can be a single data structure. The data structures are, for example, databases or general data structures that are suitable for storing data in tabular form. The data structures can be stored distributed among the individual plants, particularly their components, or at a central location, for example on a server that is linked to the components and / or plants via a network connection.

[0010] The components preferably have microcontrollers and / or programmable logic controllers (PLCs) that control the components and are configured to interact with actuators and / or sensors of the components. The microcontrollers and / or PLCs can be programmed from outside the components. Data from the microcontrollers / PLCs can be sent to devices outside a plant.

[0011] Multiple systems or their components can be located in close proximity to one another or at locations far apart. The systems can be identical or different in design and can perform identical or different processes.

[0012] The status information of the systems reflects the status of components and thus the status of a system. Certain status information can be determined directly from a component, for example, through sensors whose values ​​are stored in a PLC of the component, or through continuously stored values ​​about the progress of processes, including the number of processed components, the current processing speed, the current consumption of material, and the generation or consumption of energy. The energy consumption of a system includes, for example, the measurement of consumed kilowatt hours per unit of time or the measurement of consumed joules per unit of time. Such values ​​can be continuously recorded and stored as a series of measurements in the respective component or the central server. The data is preferably stored together with the respective timestamps.Material consumption refers to measurements of input materials required for the process performed by a component. Such materials can include paints or varnishes, certain chemicals, water, air, or other gases. Such materials are supplied through appropriate devices, such as nozzles, valves, and pipelines. Material consumption can be recorded using continuous measurements tailored to the specific material, such as automatic weighing for solids, measuring pressure or volume for gases or liquids, or counting the amount of material added.

[0013] The CO2 footprint of a component is recorded based on the component's direct or indirect carbon dioxide emissions. This information can be determined using the component's recorded energy consumption, as mentioned above. This information takes into account the specific amount of energy consumed over a specific period of time, as well as the type of energy. Possible energy types include coal, natural gas, oil, nuclear energy, wind power, solar energy, and combinations thereof. To determine the CO2 footprint, the type of energy used to operate the component over a specific period of time is first determined. This information can be set during the creation of an ongoing schedule and stored, for example, in the component or on a server. The CO2 footprint is then determined based on the CO2 values ​​also stored for each energy type and the amount of energy consumed.The carbon footprint is also stored as a series of measurements with timestamps. In one embodiment, the carbon footprint can be negative if the component generates energy instead of consuming it, and if no carbon dioxide is emitted in the process. In this case, the generated energy can be converted into carbon dioxide emissions that would have occurred when generating the energy using a specific energy source (coal, gas, etc.). An example of such a component would be a wind turbine or a solar panel.

[0014] The sequence for a component includes information about other components that are upstream or downstream of the component in question and thus supply components and / or materials to or receive them from the component. This sequence is usually static and is not changed once a system has been put into operation. Alternatively, the sequence can be adapted dynamically, for example if a component can receive a component from one of several components in order to process this component. In this case, the sequence can contain the component that supplies the component in question with a component or material, etc. at a specific time. Sequences can be modeled using graphs whose nodes represent the coupled components and whose edges represent the influencing variables of the components. The edges are directed. The graphs can also model multiple systems.Graphs are suitable for implementation in relevant data structures and as a basis for determining concrete interactions between components and / or systems.

[0015] The processing time and processing time of a component refer to the time from receipt of a component / material / measured value to the transfer of the produced product or new values ​​to the next component. This information is also stored in the form of time-stamped measurement series.

[0016] Determining energy types, sources, costs, and / or costs for disposal of components' materials is a measure to prepare for further control of the component. Therefore, it is not the energy currently consumed and costs generated by the components that are determined, but rather the energy and costs available as alternatives in order to develop further control of the components. These parameters are obtained from various sources, for example, energy providers and disposal service providers. The parameters can be provided by these providers and service providers via a standardized interface to enable automated reading. In one embodiment, a table is created or updated in the second data structure that compares time periods, energy types, sources, costs, and disposal costs.The table can then be used to determine the most cost-effective type of energy and the corresponding provider (energy source) or the most cost-effective waste disposal company for an upcoming period. Determining condition information in the vicinity of the facilities includes parameters such as humidity, temperature, noise level, wind speed, brightness, and others, depending on the type of facility. For example, the ambient temperature may be relevant for a facility used to paint components, as paints can usually only be applied within a certain temperature range. Another example is a wind turbine, which can only be operated within a certain wind speed range. Humidity may be relevant for facilities for processing or manufacturing paper.These parameters can be recorded by sensors on or near the plant, or can be determined by accessing continuously updated databases.

[0017] Creating a schedule for the systems serves as preparation for the creation of control commands for the systems and their components. The goal of the schedule is to coordinate and operate all systems and their components. The aforementioned parameters—waiting times, energy consumption and costs, as well as material disposal costs and carbon footprints—should be kept as low as possible.

[0018] With regard to waiting times, creating the schedule involves analyzing which components depend on the states of previous components. Waiting times arise when a component in question is ready for processing or machining another component, and when the required component or materials are not available. Waiting times also arise when status information in the component's or system's environment does not meet certain requirements of the component, or when certain energy parameters (costs, etc.) or disposal costs do not meet such requirements.The analysis also includes determining, based on the system's status information, the stage at which the preceding components directly linked to a component are currently running, such as whether they are currently processing components, how quickly the components are being processed, when the components are transferred to the respective component, and how far a process for generating an output value has progressed. Depending on whether a component or output value, etc., is expected within a specified time limit, a component can be kept in idle mode despite the waiting time or shut down.Furthermore, based on the analysis, the scheduler can also prepare control commands for the preceding components, for example, for accelerated processing to reduce the waiting time of the subsequent component, or for slowed processing to avoid exposing a component that is still in operation to a backlog of components or a large number of output values. Such relationships can be analyzed for many components and formulated as control commands for all components of a system.

[0019] For each component, rules are stored that affect the relevant parameters and define the range within which the parameters should lie. The rules can be stored directly on or at the component or on a server that can be accessed to determine the rules. A component can only operate if all rules are met. Applying a rule can either return a binary value—the rule is met or not—or a standardized value, for example, a value between zero and one, that indicates the extent to which the rule is met.

[0020] For example, a wind turbine may have a rule that, for a given wind speed (currently measured as status information in the turbine's surroundings), returns a value of zero if the wind is stronger than an upper threshold or weaker than a lower threshold; the schedule would then require that this turbine or component be shut down. Furthermore, the rule may return a value of one if the wind speed allows full power operation, i.e., if it is between the two thresholds and within a predetermined distance of both thresholds. In this case, the schedule would require the turbine to operate at full power. For values ​​that are between the thresholds but do not allow full power, the rule may return a value between zero and one, so that the schedule would require that the turbine / component be throttled based on the wind speed (e.g., proportionally).In this example, the system's energy yield is maximized while simultaneously minimizing wear and tear caused by unnecessary slow operation or damage from excessive wind. Creating the schedule can also minimize energy expenditure / consumption and energy costs. This can be done by considering current energy consumption and current energy costs based on the system's status information, as well as determined energy types, sources, and costs. This data is taken from the first and second data structures explained above. Essentially, the current values ​​of the first data structure are compared with the available values ​​of the second data structure, and a decision is made as to whether changing the energy type and / or energy source makes sense. The decision is also based on the current utilization of the systems.If these systems are heavily utilized and there is a risk of processing bottlenecks due to the system components, parameters such as energy costs or energy sources can be given less weight than if the system is currently fully or partially affected by waiting times anyway. On the other hand, a switch to a more environmentally friendly energy source can be considered even at higher costs if the system is currently characterized by waiting times and consumes less energy than at full load. These relationships can also be defined in the form of rules, which, however, are not assigned to individual components but characterize the system as a whole and can also encode user preferences.

[0021] Disposal costs for materials can be handled analogously to energy costs and energy types. Different components can consume different materials, as already explained. For each material—alternatively, for each component—rules can be defined to determine which costs are borne. For example, an upper threshold can be defined that prevents further supply or intake of a material if current or planned costs for the material exceed this threshold. Furthermore, a lower threshold can be defined; if the costs are below this value, a precautionary order for the material can be triggered or interactively suggested to a user. Both states can be coded by standardized values—such as one and zero.Costs that lie between the thresholds can take on a value between the standardized values ​​and can be interpreted during scheduling to make components that use the material run slower at higher costs and faster at lower costs. These rules can be combined or weighted with other rules, for example, with the rules defining behavior for energy costs or waiting times.

[0022] The thresholds mentioned in some embodiments can either be specified by a user or queried interactively by the user, or they can be determined automatically based on the explained measurement series. For example, the measurement series can be used to continuously assess which cost values ​​are considered "high" or "low," for example, by determining the mean or median and standard deviations or predetermined quantiles. In this case, more recent data can be weighted more heavily than older data.

[0023] Translating the schedule into control commands requires that the decisions for controlling the components explained above are first recorded centrally (server or cloud) in a cross-device syntax, for example, in a common scripting language. It is assumed that not all components of all systems under consideration can be addressed in the same programming language. Therefore, an automatic translation of the control commands for each component is performed. One of the advantages of this approach is that the most recently created control commands for a component continue to exist centrally and can be compared with the new control commands for the component when translating the schedule.If a control command for a component is identical to a control command last applied to this component, the translation and application of this control command for this component can be omitted.

[0024] The described flow charts can be created for and applied to multiple systems. The invention therefore also covers situations in which the processes are influenced less by multiple components of a system, but rather by multiple systems. Interaction between systems that fundamentally operate independently of one another can arise, for example, when similar systems generate a common product. One example is an offshore wind farm in which the individual wind turbines operate independently of one another and are subject to similar factors. Wind farms extend over large areas. Each of the systems continuously measures weather parameters, in particular wind speeds. Within the meaning of the present invention, the measurements of the systems can be exchanged by determining status information of the systems and storing this information in a first data structure.The subsequent steps in the process also allow the turbines to react to the measured values ​​from other turbines. This way, a sudden strong wind at one of the turbines can, for example, lead to an appropriate response at the other turbines in good time.

[0025] In one embodiment, two of the systems are of the same type, i.e., they comprise essentially identical components and perform essentially the same function. In this embodiment, determining state information of the systems involves comparing the current state information of the systems. On the one hand, input or configuration variables and, on the other hand, output or performance variables are compared. If the input variables of one system are within a predetermined threshold of the input variables of the other system, a further check is carried out to determine whether the output variables of one system are within a second predetermined threshold of the other system. If this is not the case, the less efficient of the two systems is obviously incorrectly configured or defective, and the creation of the schedule is repeated for this system, or an error is output.Both can be omitted if the status information in the environments of both systems differs; however, a corresponding notice can still be issued in this case.

[0026] In one embodiment, determining the state information for a system can comprise reading in stored state information of past states of the system. From this stored state information, a normal state of the system can be determined, for example a particularly frequently occurring state or an assumed state determined from averaged individual values ​​of the stored states. The normal state is compared with the current state of the system. If the current state of the system deviates from the normal state (use of one or more threshold values), although current state information in the environment of the system is essentially identical to stored state information in the environment of the system, the creation of the schedule for the relevant system can be repeated and / or a corresponding message can be issued.

[0027] In one embodiment, determining the status information of the systems comprises capturing status information of the components via sensors. The determination of the energy types, sources, costs, and / or the costs for disposal of materials of the components is performed for a current point in time or period. The schedule is adjusted based on the status information of the systems, energy types, sources, costs, and / or the costs for disposal of materials, as well as the status information in the environment of the systems. The control commands are adjusted and applied.

[0028] Sensors include, on the one hand, sensors in or on the system, such as thermometers, hygrometers, and voltmeters. On the other hand, data stored in the components can also be referred to as sensor data in a broader sense. For example, a component can record the number of units produced per unit of time, voltage generation and / or consumption, etc., and store this locally as a series of measurements. Such information can be read out during the acquisition of status information and used in the same way as sensor data.

[0029] The recorded period can be a snapshot at a specific point in time, thus capturing sensor data at a current point in time. Alternatively, the period can cover a period immediately past the last change to any state or control command. States and control commands are continuously stored with timestamps. If a period is recorded that captures changes in states, averages can be calculated based on the changed values.

[0030] States, control commands, and other data generated during the execution of the inventive method can be stored continuously and time-stamped. This allows a specific past state of one or more systems to be restored at a later time. For example, stored control commands can be easily reapplied if the state information and all energy types, sources, etc., match or only slightly deviate from a stored set of state information, energy types, etc.

[0031] In one embodiment, adjusting the schedule includes accumulating material in individual components, or accelerating or slowing down the operation of the components, or taking up holding positions.

[0032] Accumulating material can involve making material available to a component, for example, filling a tank for liquids (water, paint, etc.). This can be initiated when the status information of the equipment or status information in the vicinity of the equipment indicates that a given material quantity is below a threshold.

[0033] The acceleration or deceleration of a component can depend on whether it currently has sufficient material and / or supplies from other components in the system. For example, if a component has a backlog of components from other components that are to be further processed by the component in question, the component can be accelerated. Conversely, the component can be decelerated if the status information indicates that other components are delivering their components late or not at all; this can prevent downtime and thus a waiting time for the component. The statements regarding components apply analogously to processes that do not process components but produce a product or provide another service.This allows processes in a component to be accelerated if they rely on output values ​​or data from other components and these already form a backlog at the component, for example, in the form of a data structure that is already largely occupied with such data. In certain cases, a component or system can enter a hold position in which the component or system is not in operation and a waiting period begins.

[0034] In one embodiment, the data structures are located in a cloud.

[0035] Cloud services are suitable for the invention in that data in a cloud is technically simple to store and retrieve. Furthermore, data can usually be retrieved both automatically, for example via an API, and manually, for example via a web service. Furthermore, cloud services are easily accessible from remote locations; alternative storage options, such as databases, are generally more suitable for local storage.

[0036] In one embodiment, the status information of the assets and / or the assets' environment includes indoor and outdoor temperature, solar radiation, current and forecast weather parameters, and / or humidity.

[0037] In general, status information includes all parameters and values ​​that can influence or are influenced by the systems, including system performance data. The information ultimately includes input and output data of the systems and their components. Input data can easily also include weather data or component quantities. The output data of one component or system can represent input data for other components, and vice versa. Creating a schedule involves analyzing a data model that records the influence of each component, system, and status information on other components, systems, etc.

[0038] In one embodiment, the minimization of waiting times and energy expenditure and costs is performed by a neural network whose input parameters are configured based on the first, second, and third data structures. A neural network is known to consist of several layers, each containing nodes. A first layer is referred to as the input layer, and a final layer as the output layer. Within the context of the present invention, the input layer can contain, for example, the state information and energy parameters, while the output layer can contain the control commands and / or the new state information and energy parameters. The nodes are connected between layers and within layers by edges. In the present invention, the nodes can represent components and / or systems that perform specific functions. The edges represent parameters and their values.Using a suitable topology of such a network, the interactions between components and / or systems are modeled. The topology can be predefined or automatically determined or adapted. For example, a neural network can be used initially, with all nodes interconnected. The real components and systems modeled by the network are operated, and their values ​​are monitored and transferred to the network. Relationships between changes can be automatically recorded, such as increased solar radiation and faster material consumption (water evaporation), stronger wind and increased power generation, etc.These observations can give corresponding edges in the neural network greater or lesser weighting; for example, strong wind increases power generation at a wind turbine but not at a photovoltaic system, and a produced number of painted components output by one component of a painting system results in a corresponding demand for packaging material at a subsequent component or system. In this way, a topology can be gradually optimized. The determination of whether a causal relationship exists between two changes can, for example, be based on whether there is a temporal relationship (threshold) between the changes and whether the relationship is reproducible and / or proportional.For example, a measurement of stronger wind and increased power generation within a predetermined period of time after the measurement, a measurement of weaker wind and lower power generation within a predetermined period of time, and / or similar observations within a predetermined period of time can result in an automatic modification of a connecting edge between an input node that models the wind and a node that models the wind turbine, for example a weighting of the edge that reflects the causality between both nodes.

[0039] Embodiments with neural networks also allow, based on an analysis of the neural network, to determine at what point components, systems, etc. influence each other unilaterally or even mutually. In particular, cyclical dependencies can also be identified. A cyclical dependency exists when a change in one node (parameter, component, or system) causes a change in other nodes, which then flows back directly or indirectly via further nodes to the first node. The neural network can also be subjected to a connected components analysis. This identifies those node sets of the neural network whose nodes are directly or indirectly connected to each other via edges. Different node sets therefore have no connections to each other.This information can be used when creating the schedule: If the schedule is recreated due to a change in the state information or energy types, etc., it is sufficient to create the schedule and corresponding control commands only for those node sets that contain one or more nodes affected by the change; the remaining node sets remain unaffected by the schedule creation. The control commands are then applied to the actual components / systems.

[0040] In one embodiment, the invention is implemented by software modules, wherein the determination of status information of the systems is carried out by a first interface and the determination of status information in the environment of the systems as well as the determination of energy types, sources, costs and / or costs for disposal of materials of the components is carried out by a second interface, wherein the first interface and the second interface are coupled to the neural network via a central module.

[0041] The first interface is a software module, also known as the "Manufacturing Process Layer Interface," which communicates with systems and, in particular, their components. The software module reads status information from the systems and components. The module also serves to transmit control commands to the systems and components once the process plan has been compiled.

[0042] The second interface is a software module, also known as the "External Data Layer Interface," which is used to communicate with units in the environment. These units include, in particular, databases that provide information on available materials, energy, weather data, etc.

[0043] The central module is also referred to as an “Integrated Data Layer Processing Module” and connects the first interface with the second interface, for example via an API that is provided to the central module and whose routines can be called by the first and second interfaces to provide information for the central module. Conversely, the first and second interfaces also provide their own API to control the functions of their respective interfaces. The central module communicates with the neural network by supplying it with new or changed information from the first and second interfaces and by obtaining output values ​​from the neural network. These output values ​​can already contain the control commands that are transmitted from the central module to the first interface and from there applied to the components and systems.

[0044] In one embodiment, the neural network is trained with stored schedules and corresponding state information of the plants, energy types, sources, costs and / or the costs for disposal of materials of the components as well as state information in the environment of the plants, wherein the creation of a schedule comprises storing the state information, energy types, sources, costs and / or costs for disposal of materials of the components together with the schedule.

[0045] An example of training the neural network was already given above. The given example referred to real-life operation of components and systems, where measured values ​​and influences of values ​​and components, etc., are entered into the neural network and causalities are determined. Alternatively or additionally, training can be accelerated by training the network using previously stored flow charts. For this purpose, the state information, etc., stored with the stored flow charts is read out and written into the neural network.This can be repeated sequentially with multiple schedules and state information to train the neural network regarding its topology; as already explained, edges are given greater weight if they reflect real influences between state variables, and are given less weight or removed entirely if no influence between the connected entities exists.

[0046] In one embodiment, the status information in the environment of a first facility includes status information of another facility.

[0047] Typically, condition information surrounding a facility includes things like weather data, material availability, and other information. Because the condition information can also include condition information from other facilities, these are also considered environmental information in a broader sense. Real-world facilities can influence each other by emitting heat or humidity during operation or by being influenced by them. Furthermore, multiple facilities can consume the same material, so that the influence of one facility on another is that only one of the facilities consumes the material at a time to avoid bottlenecks in the other facility.

[0048] In one embodiment, applying the control commands to the components comprises programming processors of the components with the control commands.

[0049] The control commands are preferably provided in machine language. They can be tailored to the specific architecture of the component in question and, for example, include fully compiled code that can be executed directly on the component. Alternatively, commands can be sent to the components, which are then interpreted and mapped to local commands.

[0050] In one embodiment, the method further comprises performing a plausibility check of the determined state information, in particular wherein individual state information items are compared with respective threshold values ​​and / or wherein two or more state information items are compared with one another.

[0051] Such tests can be performed during the determination of status information, immediately after receiving a status value, or can be carried out in their entirety when creating the schedule. During the test, a value is compared with a lower and an upper threshold to ensure that the value lies within a permissible or reasonable range. For example, a measured wind speed cannot reasonably be below zero, nor can it be above a few hundred kilometers per hour. The test can also include comparing different measured values. For example, it can be checked whether the measured ambient temperatures of two turbines located in close proximity differ from each other within a threshold.

[0052] In one embodiment, creating the schedule comprises creating one or more alternative schedules optimized for material buildup or acceleration of the equipment; and displaying the schedules to a user and prompting the user to select one of the schedules.

[0053] In certain situations, a single optimized schedule is not possible, as often only a subset of parameters – such as cheap energy, ecologically generated energy, etc. – can be optimized. This allows multiple schedules to be created that are optimized according to different parameters. These are shown to the user, for example, via a display. Such a display includes a representation of the components and systems under consideration and a visualization of the information used for the schedule, as well as a representation of the control commands. In particular, a simulation of the components and systems can also be displayed, visualizing their states when the control commands of a particular schedule are applied. The user can select one of the schedules.Alternatively, instead of displaying the schedules to the user, an automatic selection can be made, for example, taking into account pre-specified preferred optimization criteria; the schedule that satisfies the largest subset of the specified optimization criteria is applied.

[0054] In one embodiment, the method further comprises: identifying holding positions of components in the facilities, wherein a holding position comprises a below-average consumption of material and / or energy; and using the holding positions as preferred states of the components when creating schedules to mitigate temporary above-average energy costs, carbon footprints, energy types marked as undesirable, and / or above-average costs for disposal of materials for components.

[0055] Before a component or system can be put into a stop position, it must be determined how such a state can be defined for the component / system. For this purpose, previous saved states of the component or system can be determined. For example, it can be checked whether no power was consumed or generated in a previous state, and what values ​​the other parameters of the component or system in question had at that time. This state can be designated and saved as a stop position for the component or system. Alternatively, stop positions can be predefined for each component or system, for example through a parameter set with fixed default values. When creating a schedule, stop positions can be used specifically to temporarily stop a component or system when certain criteria are met.For example, a component can be placed in a holding position if it cannot accept any components, process values, materials, or similar, or if it cannot produce / process components or continue processes. Such obstacles can be due to a defect in the component or a lack of supplies from other components. Adopting a holding position can involve putting the component into a rest state, for example by moving mechanical moving parts of the component into a predefined position and, if necessary, taking safety measures. In the case of a wind turbine, for example, the rotor blades can be placed in a rest position and the rotor blades can assume a holding position. If the component consumes energy, it can be switched off in a holding position.

[0056] In one embodiment, creating a schedule further comprises displaying the determined energy types, sources, costs, and / or material disposal costs to a user, and prompting the user to select one or more of the displayed alternatives; and optimizing the schedule with respect to the selected alternatives.

[0057] The parameters mentioned (energy types, etc.) can be displayed to involve the user in the concrete design of the schedule. The user has the option of weighting some of these parameters higher than others by selecting them. Optimizing the schedule involves considering this selection and prioritizing the selected parameters. For example, a schedule for which the parameters "energy costs" and "disposal costs" have been selected, but not the parameters "energy type" and "energy source," can select the energy types / sources with the lowest costs, regardless of available energy types / sources.

[0058] The user selection can be a binary selection in which the user can select or deselect the displayed parameters, or it can be a respective scale in which the user enters a value between zero and one or another range of values ​​for each parameter, such as in the form of a graphically displayed slider. The displayed elements can be shown with a default selection or a currently applicable selection, so that the user can decide through their selection to what extent they will deviate from the current settings. In one embodiment, creating the schedule further comprises optimizing the schedule based on a preselected criterion, wherein the criterion includes an energy type, source, cost, or cost of material disposal.

[0059] Optimizing one or more preselected criteria can be seen as an alternative to prompting the user to make a selection when weighting the parameters / criteria. This underscores the potential for fully automated, complex analysis and control of all systems and components.

[0060] In one embodiment, the method further comprises determining the components required by the components; determining an existing inventory for each of the determined components; and determining the availability of further copies of the determined components; wherein creating the schedule comprises prioritizing those components that require components with lower availability over those components that require components with higher availability.

[0061] Determining the required components can involve using the component's sensors to record the current inventory of components in the component and reading out the maximum possible inventory. The sensors can be cameras, for example, that record the components currently being processed or machined and can be connected to logic that recognizes and counts these components in the captured image material. The maximum possible inventory can be a manufacturer specification for the component and read directly from the component's memory.

[0062] Determining the availability of additional components may involve consulting external databases that offer such components for delivery and also specify the availability in quantities. Alternatively or additionally, communication can be established with components in the same or another plant that supply the components of the component in question in order to determine the quantities available there. By comparing these available components with the required components (maximum possible inventory minus currently processed components of the component), the required number of components is determined. When creating the schedule, control commands can be created that trigger an automatic order from the aforementioned databases.Furthermore, the processing speed of the component can be adjusted based on the determined availability and required quantities; if the components are readily available from the databases and / or other components (delivery time less than or equal to the time until additional components can be added to the component), the component's function can be accelerated, provided other criteria - such as current energy costs, material supply, etc. - do not conflict with this. Conversely, the component can be throttled back if longer delivery times have been determined or other components will not be supplying components in the foreseeable future. These decisions are made by comparing the time that the component requires until additional components can be added or the time required for delivery via database ordering or supply from other components.

[0063] In one embodiment, minimizing waiting times, energy consumption, costs, material disposal costs, and / or carbon footprints includes comparing higher energy input and consequent faster operation with lower energy input and slower operation in terms of energy costs.

[0064] To make this comparison, the current state of a component or system is first determined to determine how higher or lower energy input and acceleration or deceleration of the component / system would affect the aforementioned parameters. A higher energy input is generally associated with higher energy costs, a change in the type or source of energy, and, because the component produces or consumes more, with higher material disposal costs or a higher carbon footprint, but at the same time also with an increase in production. These parameters are weighed against each other using preset weightings, for example, selection by the user or prior training of a neural network, in order to minimize the aforementioned parameters.In one embodiment, creating the schedule includes modeling states of the components of the systems in a state diagram, simulating the schedule using this state diagram, and optimizing the schedule with regard to a number of state changes per unit of time, wherein the number of state changes per unit of time is reduced below a predetermined threshold.

[0065] A state diagram, also known as a state graph, is a graphical representation of a system in the form of a graph. The graph contains nodes connected by directed or undirected edges. The nodes represent states of real objects, and the edges symbolize transitions between states. A state is a set of parameters and associated values ​​or value ranges. Each edge preferably contains information about one or more conditions for the transition from a first to a second state.

[0066] Applied to the claimed method, multiple states can be defined for each component or system, for example, states that define operation of the component / system in a specific mode. For example, a wind farm may comprise multiple wind turbines, each associated with multiple states. A first state may represent normal operation. This state includes parameters such as wind strength, power generation, lubricating oil demand, and current lubricating oil supply. The parameters define value ranges within which this state is assumed. Further states may indicate operation during storms or calm conditions, or a halt position in the event of a lack of lubricating oil, a lack of wind, or a defect.The values ​​of these parameters are actually continuously measured, and the state diagram can be shown to a user on a display, highlighting the current state of a plant and showing the current values ​​as well as the value ranges, for example by illustrating each value range as a scale and showing the concrete current measured value as well as a trend (color or arrow) on the scale.

[0067] This provides the user with a clear representation of a complex system and early warning of anticipated changes in the operation of the systems or components. The displayed values ​​include not only the status information (here: lubricating oil, wind speed, etc.), but also the status information of the surrounding environment, which is either also part of the displayed status or displayed as separate variables, possibly with arrows pointing to the states influenced by these variables.

[0068] In one embodiment, a user can directly intervene in the automatic flow of the state diagram and control the system by interacting with the displayed diagram. A simple example is the user shutting down a component or system, for example, when a danger is imminent that is not detected by measurements and automatic operational adjustments of the flow diagram. In this case, the user can activate a corresponding graphical user element of a component / system in the display, which issues control commands to the component / system to bring it into a stop position.

[0069] The statechart can also be used by the user to influence the creation of flowcharts. As already explained, flowcharts can be created automatically, taking into account automatic adjustments to neural networks or previous flowcharts. The user can influence this process by revising states in the statechart. For example, the user can manually adjust parameter value ranges, add or delete states, and define transitions. Such changes trigger a new flowchart creation.

[0070] Transitions from state to state can be generated automatically. As mentioned, different states define respective parameters with value ranges, and a component or system is always in the state whose value ranges contain the current values ​​of the component's / system's state information and the current values ​​of the state information in the component's / system's environment. If such a value changes so that it leaves one of the value ranges, the state of the affected component / system changes to another state whose value ranges include the changed value. This state change can be accompanied by the issuance of a control command to the component or system.For example, a change in the measured wind force from value ranges that specify strong wind to value ranges that specify storms can lead to a change in corresponding states and a control command to stop or throttle the turbine.

[0071] In one embodiment, the method further comprises determining that one of the state information of the assets, the energy types, sources, costs, and / or the costs of disposal of materials, or the state information in the environment of the assets has changed; and in response to the determining, repeating the creation of the schedule.

[0072] The impact of changes to any values ​​that can influence the schedule has already been mentioned several times. All relevant values ​​can be continuously monitored by monitoring the sensors and memory of the components / systems, as well as external databases. Continuous monitoring can be performed using a predetermined sampling rate to ensure that changes are detected within a maximum time. To achieve real-time capability, in one embodiment, a separate processor or even a separate processor core of a processor can be assigned to each monitored parameter or piece of information, which performs no other tasks besides this monitoring function.In this embodiment, the processes used for monitoring are tailored to the respective source of the respective parameter and include, for example, for a sensor, a routine for querying this sensor, for a database, a routine for querying the database, and for local values ​​stored in components or systems, routines that access programmable logic controllers and / or other memories of these components / systems.

[0073] If a change in a value is detected, the schedule can be recreated or adjusted. In one embodiment, in this case, it is first determined which components / systems and ultimately which parts of the schedule are directly or indirectly affected by the change, and the optimizations disclosed here are re-executed only for these.

[0074] Since changes are detected by monitoring sensors and memory contents, a response to the changes can be made very quickly. In one embodiment, real-time capable processors are used in the devices involved, i.e., the components, systems, servers, and the device that implements the invention, which ensure a response to a change within a predefined maximum time.

[0075] In one embodiment, repeating the creation of the schedule comprises determining those components that are directly or indirectly affected by the change and changing the schedule only with respect to those commands that are directed to the determined components.

[0076] To determine the affected commands, the aforementioned neural network can be used after it has already been adapted to the real system, i.e., the components and systems, and thus has information about those components, systems, their parameters, as well as external parameters (status information from the environment of the components / systems). As already explained, a connected component analysis can be used to determine which changed input parameters – energy type, individual status information, etc. – in the input layer of the neural network are directly and indirectly connected to other parameters, components, etc. These "components" of the network typically extend from one or more of the input parameters to control commands in the output layer of the neural network.Thus, only the part of the neural network containing the changed parameters needs to be adjusted, and only those control commands that are also contained in the relevant part of the neural network are applied. The invention thus enables particularly efficient control of a complex real-world system with regard to its energy consumption and productivity. Embodiments of the invention also include computer-readable media with instructions stored thereon that, when executed by one or more processors, carry out the method according to one of the above embodiments.

[0077] The embodiments disclosed here further comprise systems configured to carry out the methods explained. Such systems comprise one or more plants, each comprising a plurality of components, wherein the components are configured to process or carry out components and / or processing processes. This also includes processes for energy generation. Such systems comprise a separate device, in particular a server, which is configured to determine status information of the plants, in particular the energy consumption, the consumption of material, the CO2 footprint, the sequence and the processing and / or processing time of the individual components of the plants. The server is further configured to store the determined status information in a first data structure, which is either a component of the server or connected to it.The server is further configured to determine energy types, sources, costs, and / or material disposal costs of the components for predetermined periods of time and to store this information in a second data structure. The second data structure can be identical to the first data structure; in one embodiment, it is a database or, more generally, a table containing parameters and their values, as well as associated components or other sources of the respective data, and associated timestamps. The server is further configured to determine status information in the environment of the systems and to store this information in a third data structure. The third data structure can also be identical to the first and second data structures and store data in the described format.The server is also configured to create a schedule for the systems, wherein operating times or operating modes are set for each of the components based on the first, second and third data structures, which, on the one hand, minimize waiting times between interconnected components of a system and, on the other hand, minimize energy consumption, costs, costs for material disposal and / or CO2 footprints during operation of the components. The server is configured to translate the schedule into control commands for the components of the systems and to apply the control commands to the components. The components of the system, essentially servers, components and systems, can be geographically separated from one another and communicate with one another via network connections or via additional intermediate databases or devices.

[0078] The embodiments disclosed herein further include components and systems embedded in or connected to one of the systems disclosed herein. All systems and components are machines or other physical entities used to manufacture or process components or to generate energy, and may utilize supplied materials. All of these systems and components preferably have sensors that record values ​​for certain parameters of the invention described herein and deliver them to a central server.

[0079] BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 shows a method according to the invention.

[0081] Figure 2 shows an exemplary system that is set up to carry out the method according to the invention.

[0082] Figure 3 shows an exemplary data model that illustrates the data used and generated when carrying out the method according to the invention.

[0083] Figure 4 shows an exemplary system that can be controlled using the method according to the invention.

[0084] Figure 5 shows a method according to the invention for controlling similar systems.

[0085] DETAILED DESCRIPTION Figure 1 shows a computer-implemented method 100 for controlling one or more plants. Each of the plants comprises one or more components on which processes run. These processes serve to produce, process, or manipulate components or to generate energy or another industrial process. The components of a plant can be coupled to one another. The coupling of the components can be in the form of a linear coupling, also referred to as a pipeline. In addition or alternatively, the components can also include direct connections to more distant components. If there is more than one plant, the plants can also be coupled to one another and, in particular, the components of one plant can be coupled to components of the other plant.

[0086] In a first step 110, the method 100 determines status information of the systems and stores it in a first data structure. The status information includes operating parameters of the systems and, in particular, their components. Examples of status information include throughput, operating temperature, operating mode (hold position or active operation), energy consumption or generation, operating speed, and others. The status information can be provided by sensors of the components, either directly to the system executing the method or indirectly via an intermediate memory on the respective component or respective system.

[0087] In one embodiment, step 110 can determine input and output parameters of the components and systems, unless these are already known from predefined information. Thus, the set of input parameters for each component / system can be determined by querying details about the power supply, the supply of media such as air, water, wind, or other resources. Output parameters can be determined analogously by querying details about the current status of processing / production. Furthermore, for each component and system, those components and / or systems that are directly coupled to this component / system can be determined. In this way, the method 100 can create a topology of the systems. In step 120, the method 100 determines energy parameters and / or parameters for disposal of materials of the components / systems.These parameters include, for example, the energy types and sources currently used and the associated costs, as well as the costs for disposal or procurement of materials. The parameters are determined by querying relevant databases or other services provided by the respective suppliers. In addition to the current values, future values ​​are also determined. For example, electricity costs for different energy types or sources can change continuously, possibly within a single day. The determined parameters and their values ​​are stored in a second data structure.

[0088] In step 130, condition information in the environment of the equipment is determined. The type of condition information depends on the consumption of energy, materials, and other parameters by the equipment components. Examples include wind, indoor or outdoor temperature, air pressure, and others. For example, wind speed is a key parameter in the environment of a wind turbine, and relative humidity and indoor temperature are key parameters for a paint shop. The condition information is stored in a third data structure.

[0089] In step 140, method 100 creates a schedule for the systems and their components. Step 140 considers the values ​​stored in the three data structures and the topology of the components and systems. The goal of the schedule is efficient control of the components / systems from the perspectives of productivity, costs, and material availability. The specific optimization of all components and systems results from weighing the individual parameters against each other and can be determined, for example, using a neural network, using suitable weighting parameters.

[0090] Step 150 translates the schedule into control commands. The control commands include commands for accelerating or decelerating individual components, as well as for starting or stopping them. The control commands can be generated in advance in a format used by the respective component or system. Alternatively, the control commands can be provided in an abstract command set that is understood by all components and translated into a native format and applied in the respective component.

[0091] Figure 2 shows a system 200 for implementing the methods described here. The core of the system is a first interface 210, a second interface 220, a central module 230, and a neural network 250, which are preferably housed in a common device—indicated here by a common frame. Alternatively, the interfaces and modules can also be housed in separate and spatially separated devices.

[0092] The first interface 210 is connected to one or more systems 2isa-n. The connections serve, firstly, for communication to determine status information of the systems and, secondly, for transmitting control commands of a schedule to the systems. The first interface 210 is further coupled to the central module 230 in order to transmit the determined status information to the central module 230 and, secondly, to receive schedules or control commands. In one embodiment, the schedules are transmitted from the central module 230 to the first interface 210, where they are translated into control commands.

[0093] The second interface 220 is coupled to one or more databases 240. The databases 240 are preferably accessible via a cloud and provide information about energy types, sources, and / or costs for disposal of material from the components of the systems 2isa-n. The databases 240 are provided, for example, by suppliers of energy, materials, etc., and preferably contain both current availability and costs as well as planned or time-dependent availability and costs. This data is retrieved by the second interface 220 and forwarded to the central module 230.

[0094] The central module 230 collects the information obtained from the databases 240 and the systems 215a-n, as well as from other sources—this also includes status information in the environment of the systems—and prepares it for optimization. All information obtained is preferably present in data structures, for example, in the first and second interfaces 210 and 220, and is used by the central module 230 as parameters for optimization. In the illustrated embodiment, the central module 230 is coupled for this purpose to a neural network 250, which is configured with the parameters.

[0095] The neural network 250 maps parameter values ​​to parts of a schedule. The neural network models the relationships between systems and components, and in particular between their states. The neural network 250 uses an optimization process to determine a schedule that determines control commands for all components involved. This minimizes costs, energy expenditure, material consumption, and waiting times, while maximizing operating speed and efficiency. The neural network 250 returns the results to the central module 230, which transmits them to the systems 2isa-n via the first interface 210. The control commands are executed in the systems 2isa-n. The steps can be repeated if individual parameters change. Additionally or alternatively, the steps can be repeated at regular intervals, for example, once a day or once per hour.

[0096] Figure 3 shows an exemplary data model 300 for the method according to the invention. Information for the method originates from three types of sources. These sources include, first, the systems and components 310 to be controlled; second, the environment 320 of these systems / components; and third, the providers—and possibly also consumers—of certain services or products (materials).

[0097] The information about current states and / or offer data obtained from systems / components 310, environment 320, and providers 330 is stored in data structures 340. The data in data structures 340 can be organized according to its source and stored, for example, in three separate data structures 340. Alternatively, a single data structure, such as a database, can be considered that contains all the data. A current schedule 350 is determined from the data stored in data structures 340, for example, by optimizing certain parameters. This schedule is applied to systems / components 310 in the form of control commands.

[0098] Figure 4 shows two exemplary systems 410 and 420 that can be controlled by the methods disclosed herein. System 410 comprises two components 415 and 416, while the second system 420 comprises three components 425, 426, and 427. In both systems 410 and 420, components 415 and 416, or 425, 426, 427, are shown in a linear arrangement. For example, system 410 and 420 may be a painting system for metal parts; in this embodiment, component 425 of system 420 receives a component for painting and automatically removes rust and deposits. The component is then transferred to the second component 426, where it is galvanized. Finally, component 427 applies a paint layer. In this embodiment, the system 410 can be used for painting wooden parts and comprises a first component 415 for leaching and a second component 416 for painting.

[0099] Components 415, 416, and 425 to 427 can influence each other even though they are located in different systems. For example, the status information of system 410 can include the waste heat generated by components 415 and / or 416, and the status information in the environment of system 420 can include status information of one or more of components 425 to 427, whose operating speed depends, among other things, on the ambient temperature and thus on the waste heat of system 410. Such dependencies are indicated in Figure 4 by arrows between components of different systems. Such insights into the interactions between components of the same system and / or different systems can either be predefined or determined through automatic measurements during operation of the components and subsequently taken into account to optimize schedules for the systems.For example, based on the status information of component 415, it can be determined that this component is currently processing a component, and based on a current operating speed, it can be estimated when this component will be processed by component 416. Thus, it is known from when and, if applicable, for how long component 416 will generate waste heat, and it can also be determined when and for how long this waste heat will affect the ambient temperature and thus the system 420. This information can be taken into account in order to commission components of the system 420 during the corresponding period in order to benefit from the waste heat.

[0100] Figure 5 shows an exemplary method 500 for controlling multiple similar systems. Systems are similar if the input variables of the components of one system are identical to the components of another, for example, because the systems are similar in design and comprise identical components that are also coupled in an identical topology. The method 500 begins after step 150 of the method 100; the systems in question are therefore already controlled, and the status information and other parameters that influence the systems and their components have already been determined and configured.

[0101] In step 510, the method 500 determines for each of two systems those parameters that, according to the current schedule or its underlying topology, are used as input variables for the components of this system and that simultaneously originate from outside the system, i.e., either from another system or from status information from the system's environment. Step 510 can be performed with the aid of the diagram shown in Figure 4. The diagram shows, for example, that component 426 of system 420 is influenced by output parameters of components 415 and 416 outside of system 420. If two systems are of the same type, the set of determined input variables of all components of the first system matches the determined input variables of all components of the second system; otherwise, the systems are not of the same type; in this case, the method is not continued.

[0102] In step 520, method 500 checks whether not only the input variables of both systems, but also the current values ​​of these input variables, are comparable. The check includes a pairwise comparison of the input values ​​and a determination of whether any difference between these input values ​​is within a predetermined threshold; such a threshold can be set separately for each input variable. If two input values ​​differ, method 500 ends at step 140; method 500 thus routinely continues with re-determining the system's status information.

[0103] If, however, the two sets of input values ​​are comparable in step 520, the method 500 determines the current status information of both systems in step 530. This information is also compared pairwise to determine whether any deviations exist between the systems. If the deviations are minor (threshold value), both systems are apparently operating at a similar level of efficiency, and the method 500 ends at step 530.

[0104] If, however, there are significant deviations in at least one input value of both systems, method 500 performs a more detailed analysis. Through the previous determination in step 510 of components and their input variables, and through the specific knowledge from step 540 about differing input values, it is immediately known which of the components of the two systems diverge from each other. This knowledge can be used by method 500 to send a notification about the relevant components to an administrator or to repeat method 100 in its entirety to account for the lower efficiency of one system compared to the other. Method 500 then continues with step 140 as scheduled.

Claims

Claims 1. A computer-implemented method for controlling one or more installations, each comprising a plurality of components, wherein components and / or processing steps which are processed or carried out by one of the components are further processed or further processed or continued by one or more of the other components of the same installation, the method comprising: Determining status information of the systems, in particular energy consumption, material consumption, CO2 footprint, sequence and processing and / or machining time of the individual components of the systems, and storing the determined status information in a first data structure; Determining energy types, sources, costs and / or costs for disposal of materials of the components for specified periods of time, and storing the determined data in a second data structure; Determining status information in the environment of the systems and storing the determined status information in a third data structure; Creating a schedule for the systems, using the first, second and third data structures to determine operating times for each of the components, which, on the one hand, minimize waiting times between interconnected components of a system and, on the other hand, minimize energy consumption, costs, material disposal costs and / or CO2 footprints during the operation of the components; and Translating the schedule into control commands for the components of the system and applying the control commands to the components.

2. A computer-implemented method according to claim i, wherein determining state information of the systems comprises detecting state information of the components via sensors, and wherein the method further comprises: Determine the energy types, sources, costs and / or costs for disposal of materials of the components for a current period; Adapting the schedule based on the status information of the assets, energy types, sources, costs and / or costs for disposal of materials as well as the status information in the environment of the assets, and adapting and applying the control commands.

3. The computer-implemented method of claim 2, wherein adjusting the schedule includes accumulating material in individual components or accelerating or slowing down the operation of the components or assuming holding positions.

4. Computer-implemented method according to one of the preceding claims, wherein the data structures are located in a cloud.

5. A computer-implemented method according to any one of the preceding claims, wherein the status information of the equipment and / or the environment of the equipment includes indoor and outdoor temperature, solar radiation, current and forecast weather parameters and / or humidity.

6. A computer-implemented method according to any one of the preceding claims, wherein minimizing waiting times and energy expenditure and costs is performed by a neural network whose input parameters are configured based on the first, second and third data structures.

7. A computer-implemented method according to claim 6, wherein the determination of status information of the systems is carried out by a first interface and the determination of status information in the environment of the systems as well as the determination of energy types, sources, costs and / or costs for disposal of materials of the components is carried out by a second interface, wherein the first interface and the second interface are coupled to the neural network via a central module.

8. A computer-implemented method according to claim 6 or 7, further comprising: Training the neural network with stored schedules and corresponding status information of the plants, energy types, sources, costs and / or the costs for disposal of materials of the components as well as status information in the environment of the plants; wherein creating a schedule includes storing the status information, energy types, sources, costs, and / or material disposal costs of the components together with the schedule.

9. A computer-implemented method according to any one of the preceding claims, wherein the status information in the environment of a first plant comprises status information of another plant.

10. A computer-implemented method according to any one of the preceding claims, wherein applying the control commands to the components comprises: Programming processors of the components with the control commands.

11. A computer-implemented method according to any one of the preceding claims, further comprising: Carrying out a plausibility check of the determined status information, in particular wherein individual status information items are compared with respective threshold values and / or wherein two or more status information items are compared with each other.

12. A computer-implemented method according to any one of the preceding claims, wherein creating the schedule further comprises: Creating one or more alternative schedules that are optimized with regard to material accumulation or acceleration of the systems; and Displaying the schedules for a user and prompting the user to select one of the schedules.

13. A computer-implemented method according to any one of the preceding claims, further comprising: Identifying holding positions of components in the systems, where a holding position includes below-average consumption of material and / or energy; and Use the holding positions as preferred states of the components when creating schedules to temporarily avoid above-average energy costs, C02 Footprints, energy types marked as undesirable and / or above-average costs for disposal of materials for components.

14. A computer-implemented method according to any one of the preceding claims, wherein creating a schedule further comprises: Displaying the identified energy types, sources, costs and / or material disposal costs to a user and prompting the user to select one or more of the displayed alternatives; and Optimize the schedule with regard to the selected alternatives.

15. The computer-implemented method of any one of claims 1 to 13, wherein creating the schedule further comprises optimizing the schedule based on a preselected criterion, wherein the criterion includes an energy type, source, cost, or cost of material disposal.

16. A computer-implemented method according to any one of the preceding claims, further comprising: Determine the components required by the components; Determining an existing inventory for each of the identified components; and Determining the availability of additional copies of the identified components; whereby the creation of the schedule includes prioritizing those components that require components with lower availability over those components that require components with higher availability.

17. A computer-implemented method according to any one of the preceding claims, wherein minimizing waiting times, energy consumption, costs, material disposal costs and / or C02 footprints comprises: Comparing higher energy input and resulting faster operation with lower energy input and slower operation in terms of energy costs.

18. Computer-implemented method according to one of the preceding claims, wherein the creation of the schedule comprises modeling states of the components of the systems in a state diagram, simulating the schedule using this state diagram and optimizing the schedule with regard to a number of state changes per unit of time, wherein the number of state changes per unit of time is reduced below a predetermined threshold.

19. A computer-implemented method according to any one of the preceding claims, further comprising: Determining that any of the asset condition information, energy types, sources, costs, and / or material disposal costs, or the asset condition information surrounding the asset has changed; and in response to the determining, repeating the creation of the schedule.

20. The computer-implemented method of claim 19, wherein repeating the creation of the schedule comprises: Identify those components that are directly or indirectly affected by the change and modify the schedule only with regard to those commands that target the identified components.

21. A computer-readable medium having stored thereon instructions which, when executed by a processor, perform the method of any preceding claim.

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