Method for integrating and coordinating a measurement and / or control system

Abstract

The present invention relates to a method for integrating and coordinating measurement and / or control systems by a system based on a functional data structure, where the integrated measurement and / or control systems each generate or process data values ​​of the data structure and can generate and modify data structure elements, the method comprising the steps of: a. generating a functional data structure having variables that map the data values ​​of the measurement and / or control systems; b. describing the content of the variables by several definition attributes, where at least one attribute may contain variable references to other variables to map a network of variables; c. generating a primary clone of the variable in the event that at least one of the definition variable attribute characteristics of the variable is changed by one of the integrated measurement and / or control systems; and d. generating machine clones of variables that are on the dependency path of the network of variables to which the primary clone variable belongs.

Description

Technical Field

[0001] The present invention relates to a method for integrating and coordinating measurement and / or control systems by a system based on a functional data structure, wherein the measurement and / or control systems to be integrated can each generate or process data values of the data structure and can create and modify data structure elements.

Background Art

[0002] A control system basically processes actual values and target or planned values, and in more complex use cases, this is regularly performed in an aggregation-interpretation manner across multiple processing levels (“layers”). Starting from raw data (“atomic information”) on as small a scale as possible, the data is aggregated across multiple processing layers from various perspectives (and in some cases various distribution systems) for various purposes. In the context of a data warehouse system, such a process is also referred to as, for example, an “ETL process” (Extract, Transform, Load).

[0003] The aggregation step can facilitate the understanding and use of the input information. However, if its preprocessing (“data lineage analysis”) cannot be reconstructed, aggregation can also cause incorrect interpretation and loss of information, and may even disrupt the production process.

[0004] Generally, system integration can be distinguished by the following items. - Integration interface type (synchronous / asynchronous) - Integration direction (horizontal / vertical) - Integration content (logical / structural or physical / value-based) - Integration scope (complete / incomplete)

[0005] Horizontal control integration means that control information is combined on a comparable scale. Vertical control integration requires the corresponding availability of a comprehensive data series across vertical layers (ideally down to the basic data), including related processing steps. Therefore, vertical control integration is driven by basic data and is essentially a complex problem.

[0006] Examples of commercially available solutions that are logically focused on a typical business intelligence data warehouse environment are known, for example, from German Patent Application Publication No. 10 2016 055 19. Even in the case of data processing where most is standardized in the ETL context, strong layer-oriented logical vertical integration is a major challenge.

[0007] In contrast, when trying to integrate a data processing system that is not overall very standardized, both structurally and in terms of value, without loss (i.e., when it is necessary to be able to understand not only the (logical) information structure and processing rules but also the processed (physical) content), the difficulty becomes very high. Empirically given requirements are at best of the degree that can be satisfied by simple hierarchical processing for each layer only in exceptional cases, and in industrial practice, a more flexible network structure is regularly required.

[0008] Commercially available integration solutions (e.g., SAP in enterprise resource planning including production planning and control environments) mainly address this issue by specifying standard processes, which can only be customized by users to a limited extent. In fact, due to the technical and economic limitations of this system-specific standardization, the process-related demand gaps are usually still satisfied by individual solutions ("individual data processing system", abbreviated as IDV). Therefore, since these IDV solutions are hardly integrated, they usually become the most difficult use cases for further comprehensive integration. SUMMARY OF THE INVENTION

Problems to be Solved by the Invention

[0009] One object of the present invention is to completely, synchronously, logically and physically integrate a distributed measurement and / or control system. By using this method, any complex integration problem can be addressed without having to tolerate a significant loss of information.

Means for Solving the Problems

[0010] This problem is solved by the method according to claim 1. Advantageous embodiments of the method are the subject matter of the dependent claims.

[0011] For lossless vertical integration with complete controllability of the processing procedure and the ability to connect to heterogeneous interfaces, an appropriate functional data structure design is required. Therefore, according to the present invention, the functional data structure is defined for processing the data of the measurement and / or control system. , for mapping the variable several with respect to The data values, also referred to as measured values, can be, for example, actual values and target values, planned values, predicted values, estimated values, etc.

[0012] According to the present invention, the potential for reduced complexity controllability with respect to the potentially increasing complexity in the case of dynamic integration is the result of accepting an increase in the complexity inherent in the process or system regarding the functional data structure, contrary to the initial intuition. The essential problem of the potentially extreme coordination complexity in the control of distributed systems is solved by the steps of a simple lossless method.

[0013] The measurement and / or control system integrated via the described method can generate not only the data content but also its structure during operation while potentially completely controlling all changes and processing events for all involved interfaces (thus enabling parameter optimization in specific process executions, for example, via a structure that can be optimized in real time).

[0014] This objective is achieved, inter alia, by omitting the update process for the definition part of the functional data structure. Thus, the present invention realizes a reduction in procedural complexity by increasing the controllable structural complexity.

[0015] This method also enables organization-independent use by virtue of the decision on its specific functional data structure design. This method is "optionally" connectable basically for a base data-driven approach.

[0016] The basic elements of the data structure are variables having a set of attributes. These attributes are also referred to hereinafter as "variable attributes". With respect to the content, a variable is identified by a set of defined attributes. At least one of the defined variable attributes includes a variable reference, within the scope of which it indicates to which variable in the variable population within the system each variable is subordinate (for example, including self-reference for supporting the distinction of the comparison (c.p.) period). The variable attribute value characteristic is distinguished from the variable characteristic representing the data value of the measurement and / or control system or the variable value assigned to the variable.

[0017] In the data structure described in more detail hereinafter, the variables and data values of the measurement and / or control system can be modeled as a network of variables (for example, in the form of a directed acyclic graph in a computational context), the nodes of which are formed by individual variables and the edges of which result from variable references. A set of adjacent edges is hereinafter referred to as a "path" or "network path".

[0018] In this basic form, any measurement and / or control system can be procedurally integrated independently of local conventions.

[0019] In the simplest case, an attribute with a variable reference only includes an associative assignment of other variables to the variables under consideration. However, usually, an attribute with a variable reference includes more detailed information regarding the determination or calculation rules of the data values of the variables under consideration, such as functions dependent on one or more variables.

[0020] The measurement and / or control system has access to the functional data structure as an interface system and can also create new variables. The system interface is defined as a set of variables. In order to enable lossless and interference-free integration and cooperation between interface systems, usually, a collaborative cloning process of network paths dependent on one or more interface elements subject to definition changes is triggered.

[0021] A definition change is at least one change in the defining variable attribute characteristics of one or more variables.

[0022] This process (synonymous with the set of variables affected by the definition change) is hereinafter referred to as an "edition". When the edition is completed, usually, clones of the changed variables and dependent variables (i.e., dependent paths within the variable network) are created.

[0023] In this specification, clones of variables definedly changed by the measurement and / or control interface system are referred to as primary clones. In addition to these primary clones, variables (hereinafter referred to as "predecessors") that include the original of the primary clone within the variable reference are also cloned in a collaborative manner. The clone predecessor variables are replaced by the clones within the variable reference.

[0024] The clones thus generated are directly or indirectly dependent on the primary clone and are hereinafter referred to as secondary clones or machine clones. When a secondary clone is generated, a further variable cloning operation is triggered. The further variable refers to the predecessor of the machine clone, and similar operations are performed until no additional dependencies are found or a network node that is explicitly interpreted as final is reached.

[0025] The last clone (and its predecessor) on these network paths is referred to as the "final variable".

[0026] Variables that do not have variable reference content are referred to herein as "atomic variables". In the context of a directed variable network, the extreme nodes of a path can also be interpreted as "final input variables" or "atomic output variables".

[0027] In the cloning process, all attributes of variables that have not been changed by the interface system are copied, and attributes affected by the changes are inherited in an appropriately modified way. It is also beneficial to add undefined context information.

[0028] Therefore, variables are identified by defined attributes (the aforementioned cloning process is led by changes in values by the interface system). Variables can also be described by undefined attributes.

[0029] Examples of defined attributes are shown below. - Context attributes (such as attributes used to identify an assigned process, resource, or organizational structure unit, etc.) - Measurement characteristics such as quantity, time, cost, or qualitative aspects - Perspectives of measurement or measurement of data values (e.g., current / goal / plan / forecast) - Period categories (e.g., year, month, week, day, hour, timestamp) - Periodic characteristics -(Variable reference as described above)

[0030] Examples of undefined attributes are shown below. The undefined attributes are related to this method although they do not trigger a system structure change by the cloning process when their values are changed. -Definition context and categorization of measurement -Comment -Attribute of access control -Attribute indicating clone trigger event type (primary / machine) -Attribute for retaining preprocessor reference (within clone context) -Attribute for marking CRUD (Create, Read, Update, Delete) events (e.g., event type, user, timestamp, clone sequence identifier function, etc.)

[0031] Preferably, all or at least some of the variables generated in the cloning process are provided with a unique clone number, which is stored, for example, in an appropriate attribute of the variable. By this means, the history of the clones can be documented. It is particularly preferred that all variables generated during a single cloning process are provided with the same clone number. This ensures that it can be clearly determined later which clones were created by a common edition event.

[0032] Also, it is particularly beneficial that the variable clones are stored together with corresponding information including a unique reference to the original variable, and that it is always possible to assign a clone to a cloned preprocessor variable. This reference is stored, for example, by the aforementioned attribute for recording the preprocessor reference.

[0033] Also, for variable clones storing , whether it is a primary clone (i.e., the primary clone is generated by access to a measurement and / or control system), or a machine clone it is considered possible to define a logical sub-structure This is beneficial. The latter is changed in a defined manner and is automatically generated during the cloning process due to a direct or indirect dependency on a variable that triggered the cloning process.

[0034] In this regard, a wide range of additional information regarding the variables can be stored to further optimize the traceability of the cloning process and thus the traceability of the entire data structure. The additional information stored in the appropriate attributes of the variables includes a more detailed description of a specific cloning process, and one or more events that triggered the cloning process, and / or the triggering interface system or user. Appropriate timestamps are also beneficial to ensure the chronological classification of the cloning process and related clones.

[0035] For the interface access to the data structure of individual measurement and / or control systems, different access variants can be distinguished. The first variant is characterized by value change or value assignment access, which promotes the change or assignment of the data value of at least one variable or its attribute value.

[0036] Changing the data value of a variable may affect the data values / measurement values of a number of dependent variables due to the network-like data structure.

[0037] Also, the change of an attribute value (especially a defined attribute value) can promote a structural change as a result of the cloning process triggered thereby, which is the second access variant of the interface system.

[0038] In addition to the structural design by generating variables as essential structural elements, the present invention also enables a structural change access to the interface system of the data structure, thereby enabling not only the change of the data value of individual variables but also the design of the structural connection of variables in particular.

[0039] In the case of variable access that purely changes data values, only the data values of variables interpreted as functionally related are determined. In this case, it is reasonable in terms of performance for the calculation / assignment of values to the data values of variables to start after all the determination / assignment of values of the referenced variables is completed.

[0040] It is possible to limit the interface access to a measurement and / or control system to a limited range of a data structure. Here , for example for example, by specifying a set of variables visible from an interface system [Figure 1] which is here referred to as an edition as above, or as a "segment" in the context of variable value determination hereinafter.

[0041] Also, from a physical perspective, it is possible to interpret a non-final variable as a final variable of the interface system or a final variable by the interface system, in which case the cloning and value determination process ends at that variable even if other variables referencing it actually exist within the network.

[0042] In addition or alternatively, from a physical perspective, non-atomic variables can also be logically interpreted as atomic variables, that is, their variable references are ignored from a logical perspective.

[0043] This enables the definition of a logical sub-structure of a functional data structure, and in other cases, it is complete from a physical perspective. Value assignment or structure change access that can cover a set of variables extended by functional connections can be restricted to such a logical sub-structure by such a method. On the other hand, it is also conceivable that such access brings about changes to the physical data structure.

[0044] Within the scope of this method, data structures and data values can be historized, and protocols or logging data can be generated for the technical method itself. According to any embodiment of this method, it is possible to visualize at least a part, preferably all, of the data structures and data values such as variable network structures, variable values, and value changes, whereby patterns, scenarios, and driver or sensitivity analysis can also be visually and intuitively performed, and it is possible to facilitate both system management and guidance for the interface system.

[0045] By using this method, a semi-automatic inventory in the application field with comprehensive data series analysis capabilities (from the perspectives of both structure and processing rules and processing results) can be obtained.

[0046] Also, by using this method to visualize processing operations (presumably automatically determined) using functional data structures, the reverse engineering process can be automated (in combination with an appropriate parser for source code in areas that would otherwise be insufficient or undocumented).

[0047] In addition to the method according to the present invention, the present invention also relates to a system comprising a plurality of, usually distributed, measurement and / or control systems and a central or distributed unit (integrated control system) for managing functional data structures.

[0048] The integrated control system has processing logic that, when called, executes the steps of the method according to the present invention. Therefore, this system is characterized by the same advantages and characteristics as described above based on the method according to the present invention. Therefore, duplicate explanations are omitted.

[0049] In the method described in this specification, machine-supported and simplified system initialization can be achieved, for example, by using a modified copy of the prototype network or the cloning logic inherent in the system (initial creation of the prototype network that will also be cloned after modification), or by system-specific migration procedures in which the interface system data structure is mapped to the method.

[0050] The structures and values available within the system can be exported to the standard format of the interface system in order to support local asynchronous editing processes (in some cases, such as in the case of a spreadsheet system, it may also be executable).

[0051] The present invention also includes a computer program, and by executing the computer program by a computer, the method according to the present invention is executed.

[0052] Further advantages and features of the present invention will be described in more detail below with reference to the exemplary embodiments illustrated in the drawings.

Brief Description of the Drawings

[0053] [Figures 2a - 2e] FIG. 1 is a simplified graphical representation of a directed acyclic graph as a special embodiment example of a data structure. [Figure 3] FIGS. 2a-2e are views of the graph according to FIG. 1 for explaining the procedure of value change or value assignment by the interface system. [Figures 3a - 3b] FIG. 3 is another simple exemplary representation of a variable network as an embodiment of a functional data structure. [Figures 4a - 4b] FIGS. 3a, 3b are variable networks according to FIG. 2 for explaining the cloning process according to the present invention, involving changes to two defined variables. [Figure 5] Figures 4a and 4b are variable networks according to FIG. 2 for explaining the cloning process according to the present invention, involving changes to two defined variables. Change of variable value or value assignment FIG. 5 is a schematic diagram of an integration and cooperation scenario between a plurality of interface systems.

DETAILED DESCRIPTION OF THE INVENTION

[0054] The core concept and possible application examples of this method are shown in detail again below. The novel method enables complete synchronous and logical and physical integration of measurement and / or control systems, thereby making it technically controllable even for very complex integration and cooperation problems, and also enabling the solution of not only the optimization problem of the value content but also the structural design. The potentially extremely complex cooperation in controlling a distributed system is here significantly simplified by simple procedural steps without loss of information. The measurement and / or control systems integrated via the described system optimized in terms of performance can also structurally change their content during operation, for example, enabling the optimization of parameters in specific process executions via a structure that can be flexibly designed in real time, and potentially completely controlling all changes and processing events for all related interface systems.

[0055] In this regard, the present system provides an essential basis for implementation modes of autonomous control solutions for a wide range of technical application fields. The design decisions described below regarding the functional data structure, as well as the method steps and overall system aspects, represent the essential basis of the present invention.

[0056] Since lossless vertical integration is driven by basic data, appropriate functional data structure design is required for complete traceability of processing procedures having connectivity to heterogeneous interfaces.

[0057] Here, the basic atomic element is a variable, which is identified from the perspective of content by a set of defined attributes. Examples of such a set of defined attributes include the following. - One or more context characteristics (e.g., assigned organizational structure unit, process, etc.) - Measured quantity - Perspective of measurement (e.g., actual / goal / plan / forecast / ... ) - Period category (e.g., year, month, week, day, hour, timestamp) - Periodic characteristic - Variable reference (function of other variables, e.g., mathematical functions (e.g., deterministic, probabilistic) or simple mapping, etc.)

[0058] Similarly, a variable can also have the following undefined attributes. - For categorization of context and metric - For comments - For access control - For identification of clone trigger event type (primary / machine) - For including reference to the variable's pre-processor (in the case of clone event) - For identification of CRUD events (create, read, update, delete) (e.g., event type, user, timestamp, clone sequence identifier function, etc.)

[0059] Hereinafter, for better content understanding (orientation), - The value / characteristic of a variable (i.e., the measured value for the measured quantity attribute, and "measurement" is generally interpreted as the assignment of a value to a variable. The measured quantity can be qualitative or quantitative as part of it) and - The value / characteristic of a variable attribute are distinguished as "variable value" and "attribute value" in order to distinguish them.

[0060] A many-to-many relationship between variables, i.e., a network of variables, is made possible so that even a very complex interface system can be mapped. This ensures the representability of any organizational model. In an organizational model, the arrangement of system components as organizational units and their processes are described. The network represents the most common form of a structural organizational model and can interpret more specific features of other organizational structures (e.g., hierarchical arrangement relationships). Thus, the basic data structure enables, for example, a flexible distribution between the centralization and decentralization of production processes and their control.

[0061] In this regard, variables correspond to the nodes of the variable network. Edges can be identified at least by the variable references of the nodes. Depending on the application, the coordination and control of related network changes are performed via variable design, whereby the encompassing "physical" network can be identified. In this specification, a subset of the physical network is referred to as a "logical" network.

[0062] The variables determine the structure of the mapped system. Variables as individual structural elements can be assigned any number of values (variable values), and the value assignment can be further specified, for example, by an "attachment".

[0063] In a special example of the integration of distributed systems focused on computing, these are treated as directed acyclic graphs. In this basic form, the distributed computing model can be integrated, shared, cloned, or generally modified in content independently of local specifications. The associated high technical complexity can be controlled by appropriately selecting the attribute characteristics of the variables, whereby all changes can be fully controlled.

[0064] A variable acts as an input variable when other variables refer to it during the calculation with respect to another variable.

[0065] In particular, depending on the position of the variables within the network - Atomic input variables (there are variables that depend on these, but no variables that affect them), and - Final output variables (there are input variables, but they do not affect other variables) are distinguished.

[0066] Figure 1 shows only an exemplary simple directed acyclic graph. Variables a, b, and c are atomic input variables, and variables e and g are final output variables. The assignment of values to variables within the network triggers the assignment of values to dependent variables along the dependency relationships defined by variable references. These value assignments are identified by unique sequence values. Comments can be added to the execution of the assignment, or identification or explanatory attributes can be specified.

[0067] A narrow - sense scenario is a set of value assignments for specific variables in the network, representing atomic input variables from a physical or logical perspective. Furthermore, a broad - sense scenario includes the entire set of all value assignments triggered by the narrow - sense scenario in the corresponding network paths that depend on the narrow - sense scenario.

[0068] The system interface is defined as a set of variables for the interface system to communicate with the integrated control system. There are the following two basic perspectives on the system for the interface system. - Structural design (modifying the network by editing nodes, especially by changing the defined attribute values of nodes. In addition to the process itself, the set of nodes affected is also referred to as an "edition" in this specification). Within an edition, it is possible to distinguish which variables should be logically interpreted as the final output (thus, after the final determination of the edition, no further transfer to additional variables occurs in the context of the cloning process triggered thereby). - Value change or value assignment: The set of variables for which a variable value is assigned or determined is referred to herein as a "segment". Within a segment, it is possible to distinguish which variables operate logically as atomic inputs or which variables should be logically interpreted as final outputs. If no atomic input is specified, the physical-atomic input to the elements of the segment is determined. If no final output is specified, the value of the path of the network that is dependent on the atomic input is determined as the assignment target.

[0069] A physical network is defined by a set of variables that are mutually dependent by the definition of variable references. A segment (as a subset of the physical network) can also be interpreted as a logical network.

[0070] Thus, if not all network variables are fully selected, the edition and the segment result in a logical view of the physical network given by the variables.

[0071] The viewpoints of the structural network and the value network are mutually dependent in that interpreting a non-atomic input variable as an atomic input can implicitly cause a structural change event (by implicitly modifying the variable reference characteristics as a result of variable value override, at least along with the edition of further defining attributes of the affected variables).

[0072] The identification of a broader scenario is - indirectly initiated via the identification of the target variable for which the atomic input was found, or - initiated via the identification of the atomic input (optionally, with an additional explicit identification of the target variable for which the value determination is made).

[0073] To the atomic input variable Change of network structure Thereby, the continuous variable values of the dependent paths of the variables up to the final output variable are determined. For performance reasons, the determination of the variable values of the dependent variables needs to start when all the new values of the input variables of the dependent variables have been determined. Separately, by considering an appropriate objective function, the sequence of variable value determination can be further optimized.

[0074] Variable values that belong together in the context of scenario determination are identified by the assigned unique sequence values and are marked with respect to that context to simplify the technical reconstruction of scenario execution.

[0075] Exemplary and non-exhaustive examples are shown in FIGS. 2a to 2e. FIG. 2a reprises the variable network structure of FIG. 1. Here, the scenario will be determined with respect to the atomic input variables a, b, c with respect to the explicitly given final output variable g. The physical network also includes the final output variable e, but it is not considered here for the logical partial view.

[0076] In FIG. 2b, values are directly assigned to the variables a, b, and c. The broad scenario limited to the final output variable g also includes the dependent variables d and f.

[0077] The variable d can be determined first, as shown in FIG. 2c.

[0078] The variable f can be determined only after the value of the variable d has been determined, as shown in FIG. 2d.

[0079] After the new values of the input variables a, d, and f required to determine g become available (FIG. 2d), the final output value of g can be determined in the last step (see FIG. 2e).

[0080] Role It is triggered by one or more changes to the defined variable attribute characteristics of one or more variables. To facilitate the technical and functional control of the change event and the structure, a change to a defined variable generally results in the cloning of the set of variables affected, and the cascading cloning up to the final output variable of each dependent path of the set of variables affected, provided that all other conditions are the same. Thus, the generation of structural elements that would otherwise be redundant is intentionally accepted in order to achieve comprehensive controllability simplified in a sustainable way. Any required structural cleanup can be performed differently, either rule-based or asynchronously (e.g., by a deletion flag of consent created by all relevant interface systems to avoid uncontrolled information loss).

[0081] Variables that are logically interpreted as final outputs can be explicitly specified to save resources (it is not necessary to clone the entire dependent path in all cases). Variables created during the execution of a clone receive the same clone sequence number and a reference to its predecessor, i.e., the resource variable. The execution of the clone can be further specified.

[0082] Variables uniquely define the physical net that contains them ("variable-net-equivalence rule"). However, the path cloning process may potentially result in variables that are (originally) redundant when viewed alone. Therefore, to identify a variable, the defined attribute characteristics of the variable alone are insufficient unless the variable is an atomic input variable. In principle, the network context of the variable also needs to be taken into account.

[0083] Cloned variables can be marked as to whether they are cloned as dependent path elements in a purely technical sense or whether they are primary (path) clone trigger elements (i.e., the first clone of a defined and modified variable, and the primary clone trigger event can additionally be recorded there for performance reasons). Also, by overwriting the value of a variable determined in the past with an externally given value, (for example, when a non-atomic input variable is cloned into an atomic input), it is possible to implicitly handle structural changes, which may trigger a clone of a dependent path similar to the described logic.

[0084] Unless all interface systems always require complete transparency or are not completely transparent, Lights / Role The concept can be implemented at the level of data records (both structurally and from a value perspective). The cloning process is basically independent of the concept of permissions. Having the permission to create a specific primary clone Role can also trigger the creation of variables that do not have permissions themselves, and existing preprocessor permissions are also cloned. Exceptions to this rule can be set. Role itself can trigger the creation of variables that do not have permissions themselves, and existing preprocessor permissions are also cloned. Exceptions to this rule can be set.

[0085] Exemplary and non-exhaustive examples are shown in FIGS. 3, 3a, and 3b, which also visualize the variable network as a directed acyclic graph. Here, the network consists of variable elements a to f, where a is an atomic input and f is the final output variable.

[0086] In the first step, according to FIG. 3a, variables b and c are changed by the interface system with respect to one or more defined attribute values (here, simplified and visualized without deletion or addition of network elements or change of the dependent structure), thereby triggering the cloning process. Thus, there are two common trigger events in the cloning process here.

[0087] When the defined variable attributes are changed, cloning of the affected variables c and b is performed. The resulting primary clones are denoted here as c1 and b1. The cloning process also generates machine clones from the dependent variables d, g, e to the final output variable f. These variables are denoted here as d1, g1, e1 and f1. Thus, the original network remains intact and is only extended by the newly cloned elements b1, c1, d1, e1, f1.

[0088] Depending on the number, type, and location within the network of the changes, the network structure grows exponentially. However, the associated increase in complexity can be technically controlled through the following design elements. - Technical / object-specific binding by technical identification characteristics inherent to clone execution - Discrimination ability between machines (here, d1, e1, g1, f1) and primary clones (here, c1, b1) - Specification of further information regarding clone execution (e.g., system, timestamp, context, changes to trigger events (which variables were changed with which defined attribute values and in what ranges, etc.)) - Network context of variables for comparison

[0089] The above-described functional data structures, in combination with the basic procedural steps of the systems implemented based on them, enable comprehensive and lossless integration and cooperation between interface systems that require high flexibility, particularly focused on measurement and / or control. As an additional advantage, the method promotes structural non-redundancy by avoiding unintegrated archives. The system integrated by this method can not only optimize the parameter values with respect to the basis of a given control, but also dynamically adapt its structure during execution, minimize the risk of collisions (e.g., due to update anomalies), and potentially fully control all element and value assignments to the associated interface systems, as will be easily understood.

[0090] In addition to comprehensive consistency checks and performance optimizations, especially when using an artificial intelligence-based interface system, up to autonomous optimization of the control system, in particular, higher-order optimization of dynamic measurement and / or control processes becomes possible.

[0091] System embodiments based on this method can themselves serve as a basis for implementing an artificially intelligent integration and control system.

[0092] This main advantage is shown below using the highly simplified and non-representative examples shown in FIGS. 4a and 4b.

[0093] A production process 1 shown in FIG. 4a is given, where a workload 2 is distributed to production resources 3 at a period t, and the production resources 3 are assumed to complete all or part of the work. The result of the process execution is the production completion status 4, and the uncompleted part of the workload 2 is included in the load for the next production period t+1.

[0094] The corresponding control system 10 for the production process 1 can typically be described according to the control loop shown in FIG. 4b.

[0095] Measurement variables for mapping the workload, resource input, production, and completion status can be given from different perspectives as actual values 13, predicted values 11, and target or planned values 12. In particular, production planning in the production process can be based on predictions or can be done arbitrarily. The quality of the prediction or plan can be measured (both qualitatively and quantitatively) by the deviation of the predicted value 11 / plan or target value 12 from the actual value 13, which can result in adjustment of the prediction or plan.

[0096] As shown in Fig. 5, in the context of the mutually dependent and distributed production process 1 and the correspondingly distributed production control system 10, the described method enables lossless integration, coordination, and overall parameter optimization, as well as parallel structure adaptation in a globally coordinated and mapped manner in real time for any design, i.e., "higher-order optimization".

[0097] After integration, the boundaries of the interface system can also be freely selected. That is, since the previously separated subsystems can be easily networked down to the atomic data level, the independence of the basic organizational structure (e.g., vertical integration as free distribution between centralization and decentralization) can be achieved.

[0098] By enabling real-time integration of the mapping of all interdependencies in the integrated system, optimization between interface systems by freely selecting target variables, which is considered a prerequisite for the realization of intelligent systems, becomes possible.

[0099] Advantages of the method Since the advantages of this method basically depend on the application, the following is not claimed to be comprehensive but is a general overview of beneficial aspects.

[0100] General advantages: - Performance optimization - Risk minimization - Cost / resource / process / system efficiency - Improvement of response and lead time - Increase in information volume - Improvement of data quality - Expansion of function / performance range (e.g., increase in the degree of freedom of dedicated process functions)

[0101] Specific advantages: - Comprehensive standardization of (local or distributed) data structures with few major restrictions regarding content or processing - Comprehensive integration of a decentralized ERP system - Bi-directional functional integration of the interface system (e.g., partial use possibility of the interface system format as a system front-end such as database connection to spreadsheet calculation, etc.) - Realization of extended comprehensive analysis, even fully machine-based analysis - Optimization of the content process in data analysis - Replacement of locally opaque control and processing procedures by a comprehensive and transparent audit certification procedure - Reduce complexity without losing relevant information, while maintaining any adaptability (including ad-hoc) and full control - Flexible and non-conflicting adjustment in all relevant aspects (e.g., model generation and model change, overwriting of calculated values...) - Promote non-redundancy with respect to variables (as an additional advantage, reduce the required storage space), and improve consistency through implicit data and process quality assurance - Realization of end-to-end process integration and quality assurance between processes - Implicit automatic inventory of mapped processes (e.g., data series analysis) - Simplified reconstruction / reverse engineering at any entry point - Extended visualization options (processing structure, value flow...) - Reduction of process costs (system and content maintenance, analysis, reconstruction...) - Improvement of response ability and shortening of lead time - Improvement of audit certification compliance - Enable true vertical control integration instead of a lossy interpretive control process - Simplify the preparation and execution of standard ERP migration through implicit "automatic" reverse engineering of the previously decentralized IDV system

Claims

1. A method for integrating and coordinating a measurement and / or control system by means of a system based on a functional data structure, wherein the measurement and / or control systems to be integrated are each capable of generating or processing data values of the functional data structure and of generating and modifying the functional data structure, the method comprising: a. generating a functional data structure having variables that map the data values of the measurement and / or control systems; b. describing the content of the variables by a set of defined attributes, at least one attribute being able to include a variable reference to other variables for mapping a variable network; c. creating a primary clone of the variable when at least one of the defining variable attribute characteristics of the variable is changed by one of the integrated measurement and / or control systems; d. creating a machine clone of a variable on the dependent variable network path of the variable for which the primary clone was created and including.

2. The method according to claim 1, characterized in that the reference of a variable to a reference variable is defined by a functional or associative mapping relationship.

3. The method according to claim 1 or 2, characterized in that each of the primary clone and the machine clone receives a unique cloning operation sequence number stored in one of its attributes and stores a reference to its associated original variable in the attributes of the variable.

4. The method according to any one of claims 1 to 3, characterized in that it stores in the respective attributes of the primary clone and the machine clone whether it is a primary clone or a machine clone.

5. The method according to any one of claims 1 to 4, characterized in that at least one of the primary clone and the machine clone is assigned further information regarding the creation of the primary clone and / or the machine clone via one or more further attributes.

6. The further information is information regarding one or more events that trigger the creation of the primary clone and / or the machine clone, and / or information regarding the user who triggers the creation of the primary clone and / or the machine clone, and / or information regarding the timestamp of the creation of the primary clone and / or the machine clone, characterized in that, the method according to claim 5.

7. One or more of the measurement and / or control systems have access to the functional data structure and the data values of the functional data structure via a system interface, and the access to change or assign data values is distinguished from the access to generate or change the structure, characterized in that, the method according to any one of claims 1 to 6.

8. The system interface of the measurement and / or control system is defined as a proper subset or a non-proper subset with respect to the population of variables of the functional data structure, characterized in that, the method according to claim 7.

9. The access to the subset of the functional data structure and its data values can be restricted by specifying variables, and the change does not adjust the variables dependent on them, or only some of the data values of the functional data structure are determined, characterized in that, the method according to claim 7 or 8.

10. In the case of changing or assigning data values and determining the data values of variables, a unique sequence value is assigned to the variable data value and stored in an attribute, characterized in that, the method according to any one of claims 7 to 9.

11. The variable and its data value include an explicit deletion request for collaborative deletion by an interface system, characterized in that, the method according to any one of claims 1 to 10.

12. Roles, rights, or permissions are effective for individual variables and the data values of the variables, characterized in that, the method according to any one of claims 1 to 11.

13. For the method itself, the functional data structure and the data values are historized, and protocol or logging data is generated, characterized in that, the method according to any one of claims 1 to 12.

14. A system comprising a measurement and / or control system as an interface system and a program memory storing control commands, wherein, when executed, the steps of the method according to any one of claims 1 to 13 are performed.

15. The system according to claim 14, characterized in that the individual steps of the method and / or the result of the method are visualized according to the method according to any one of claims 1 to 13.

16. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the method according to any one of claims 1 to 13.

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