Engine for contextualising industrial data

Abstract

The invention relates to a method for processing items of data from industrial facility sensors used to monitor at least one item of facility equipment, the method comprising the following steps: - extracting items of sensor data relating to a phase of an operation carried out within the industrial facility; - generating items of traceability data relating to a position of the phase in a stream of operations within the industrial facility; - aggregating the items of sensor data and the items of traceability data to generate items of operation data specific to the operation in the stream.

Description

Description Title of the invention: Industrial data contextualization engine technical field [2] The present invention relates to a method for processing sensor data from an industrial installation in order to optimize it by controlling its equipment. [3] The invention makes it possible to create engines for contextualizing data from industrial production processes for monitoring, controlling and optimizing these processes and associated equipment. [4] Technological background [5] The management of industrial production processes requires the processing of a large amount of data from various sensors. These sensors provide information on numerous parameters that indicate the status of many pieces of equipment operating in various phases and / or operating modes. The same piece of equipment can operate with different parameters depending on the operating phase within a given operation of the overall production process. [6] Knowledge of the exact process is key in monitoring, controlling and optimizing the industrial installation. [7] In the process industry, particularly batch processes, this is the main obstacle today to being able to exploit sensor data beyond simple monitoring. [8] Indeed, in this type of process, the finished product is obtained following a series of tasks rather than continuously. Batch process control in industry is used in many fields: food processing, chemicals, pharmaceuticals, cosmetics, biotechnology, brewing, plant protection, detergents, enzymes, paints, metallurgy, materials, and others. In all these fields, it is necessary to precisely and constantly control the mixtures and dosages of the various reagents involved in the process. the production process of the final product and the conditions under which this production process is carried out. [9] For manufacturers, mastering batch process control is a guarantee of quality and productivity. However, these processes must comply with strict rules regarding traceability, as well as quality controls and process control.

[0010] Industries using batch process control typically need to manufacture different products from identical equipment setups: for example, a quantity of white paint followed by the same quantity of black paint in the same tank, which may or may not require time for changeover and / or cleaning. The equipment is therefore used for various operations, each of which may involve different phases.

[0011] Batch production processes thus allow for greater flexibility in production and can even reduce development time for new products. However, this requires rigorous process monitoring and complete traceability of all batches produced.

[0012] To address this need for monitoring, various approaches have been proposed.

[0013] Most are based on tracking time series data from sensors using a data model to describe production operations. These solutions allow visualization of time curves from sensors, with a start and end point corresponding to the relevant operation.

[0014] However, these solutions have many drawbacks. They do not allow for a comprehensive approach to the processes. Data is made available sensor by sensor and only over time. This makes any overall understanding of the process and the problems that can arise when an anomaly is detected difficult and slow. It is therefore very complex, or even impossible under certain conditions, to monitor, control, and optimize the processes.

[0015] Therefore, there is a need to improve the processing of sensor data from industrial installations to ensure its optimization through the control of its equipment. In particular, there is a need for an industrial tool to simplify and facilitate the monitoring, control and optimization of processes and equipment associated with such installations.

[0016] The present invention falls within this framework.

[0017] Summary of the invention

[0018] According to a first aspect, the invention relates to a method for processing sensor data from an industrial installation for the control of at least one piece of equipment in said installation, the method comprising the following steps: - extraction of status data relating to a phase of an operation carried out within said industrial installation, - generation of traceability data relating to a position of said phase in a flow of operations within said industrial installation, - aggregation of said state data and said traceability data to generate operational data specific to said operation in said flow.

[0019] A process according to the first aspect makes it possible to contextualize, in a standardized way, the sensor data of a production process according to the production monitoring (traceability and genealogy) in order to simplify and facilitate the monitoring, control and optimization of this process and the associated equipment.

[0020] This process reduces the time, effort, and costs required to improve the performance of industrial facilities, such as those found in factories. Furthermore, it accelerates the analysis of real-time and delayed data to identify the root causes of process inefficiencies.

[0021] The embodiments of the invention provide an industrial process engineering tool. These embodiments allow for the concrete monitoring, control, and optimization of the behavior of industrial processes. This makes it possible to guide their improvement with sufficient precision, and within reasonable time and cost constraints.

[0022] The embodiments of the invention thus provide the industrial tool currently lacking in the development of industrial facilities. A treatment A purely intellectual analysis of sensor data is not possible. In practice, it is impossible to perform the calculations necessary to obtain sufficiently precise and meaningful results to enable the necessary industrial decisions to be made within a reasonable timeframe.

[0023] For example, the generation of operation data involves the application of a time window relating to said phase of said operation.

[0024] For example, the generation of operational data includes the location of data relating to said phase of said operation.

[0025] According to some embodiments, the operating data is transmitted to a control module of said at least one piece of equipment.

[0026] The process according to the first aspect may also include the following steps: - generation of genealogy data relating to a sequencing of said operation within said flow of operations within said industrial installation, - aggregation of said operational and genealogical data.

[0027] For example, the generation of genealogy data involves the creation of a link relating to their data between one or more downstream operations using products from one or more upstream operations in a process implemented within said installation.

[0028] For example, genealogy data allows the determination of data for one or more downstream operations from data relating to one or more upstream operations by applying a function representing said link.

[0029] According to achievements, the operation carried out within said industrial installation is an upstream operation whose results are used by a plurality of downstream operations, and in which said operational data specific to said upstream operation generated are used for the generation of operational data specific to said downstream operations.

[0030] According to the findings, the operation carried out within the said industrial installation is a downstream operation using results from a plurality upstream operations, and in which the generation of said operational data specific to said downstream operation involves the application of a function for merging operational data specific to said upstream operations.

[0031] According to some implementations, the aggregation of said operational and genealogical data is stored in a data structure comprising - a first set of data describing the operations of said flow of operations, - a second set of data describing equipment of said installation for the implementation of said operations of said flow of operations and associated with said sensors of said installation, - a third dataset comprising said aggregation of said data.

[0032] For example, the first dataset includes, for each operation of said flow, a subset of data describing phases of operation of said operation.

[0033] For example, the third dataset includes, for each operation in said flow, a genealogy type that indicates a sequencing link between said operations in said flow of operations.

[0034] According to implementations, the aggregation of said operational and genealogical data is transmitted to a control module of said at least one piece of equipment.

[0035] According to implementations, the state data includes at least some of the data from sensors of said installation and values ​​of state variables of equipment of said installation.

[0036] According to a second aspect, the invention relates to a device for implementing a process according to the first aspect.

[0037] Brief description of the figures

[0038] Other features and advantages of the invention will become apparent from the following detailed description, by way of non-limiting example, and the accompanying figures, among which:

[0039] [Fig. 1] illustrates a context for the implementation of embodiments,

[0040] [Fig. 2] illustrates a data structure according to embodiments for the description of a production process,

[0041] [Fig. 3] illustrates a data structure according to embodiments for the description of an operation in the production process,

[0042] [Fig. 4] illustrates a genealogy data structure according to different implementation modes,

[0043] [Fig. 5] illustrates a data fusion module according to different embodiments,

[0044] [Fig. 6] illustrates a first type of genealogy according to modes of realization,

[0045] [Fig. 7] illustrates a second type of genealogy according to modes of realization,

[0046] [Fig. 8] is a step diagram of a process according to embodiments,

[0047] [Fig. 9] schematically illustrates a device according to different embodiments,

[0048] [Fig. 10] schematically illustrates the implementation of a process according to embodiments in an agri-food production facility.

[0049] Detailed description of the invention

[0050] As will become apparent from reading the detailed description, the embodiments of the invention make it possible to contextualize the data from sensors associated with equipment in an industrial installation.

[0051] This contextualization can, for example, be done according to the state at a given moment of the installation in which these sensors are present.

[0052] Contextualization can be achieved by enabling data "traceability." For example, based on the equipment's operating cycle, the operation, the production phase, or other factors.

[0053] Furthermore, this contextualization can be achieved by associating a "genealogy" with the data. This involves disseminating information derived from the sensors throughout the production process and the steps that comprise it.

[0054] This allows us to contextualize the information from the sensors by looking at how the process is unfolding or has unfolded. This makes it possible to generate accurate information, enabling us to monitor, control, and optimize production.

[0055] The data thus processed allows the combination of spatial context aspects (in which equipment) and temporal context (when such operation, such phase took place) of the installation.

[0056] The processes according to embodiments thus make it possible to aggregate the data from the sensors by combining the sensor data with the context of the realization of the industrial process which are necessary for the monitoring, control and optimization of the processes in question.

[0057] Figure 1 illustrates an implementation context for embodiments. A contextualization engine 100 processes data from various sensors on equipment in an industrial installation 101 implementing a production process. The engine 100 receives data via a real-time interface. 102 which connects it to an automated process control system 103. It also receives data entered directly by operators via a human-machine interface 104.

[0058] The data received from interface 104 includes, for example, data useful for process control. The automated process control system System 103, for example, is of the DCS (Distributed Control System) type. This type of system allows the control of industrial processes with an interface enabling supervision by operators and also digital communication with the industrial installation and its control infrastructure. System 103 can also be, for example, of the PLC (Programmable Logic Controller) type. This type of system is an industrial programmable logic controller designed to control industrial processes through sequential processing. It allows the computer generation and sending of commands to actuators in the industrial installation from sensor data.

[0059] The contextualization engine 100 also communicates with a control system 105 of the industrial installation 101. This system allows, based on the processing of the contextualization engine, to monitor, control, analyze and / or optimize the process implemented by the industrial installation.

[0060] The contextualization engine processes data from various modules. Module 106 is a traceability and genealogy generator. As described below, it enables the tracking of industrial plant status data from the perspective of the progress of the different stages of the process implemented by the plant. Status data can include data from sensors and / or status variable values ​​of plant equipment. These status variable values ​​can, for example, come from the automated control system 103. A status variable can indicate the operating phase of a piece of plant equipment. Module 106 generates traceability and genealogy information to feed into module 107 using data received via interface 102.

[0061] The contextualization engine also includes a production monitoring module 107 within the installation based on data received from interface 104 and module 106. The data from this module 107 is then processed by a data fusion module 109, responsible for aggregating it with the sensor data received and processed by a module 108 connected to interface 102.

[0062] The contextualization engine relies on a data structure illustrated by Figure 2.

[0063] This data structure aims at the reproduction and digital representation of the production process implemented by installation 101. It allows a standardized and exhaustive description of the process but also of the data attached to it.

[0064] The process implemented by industrial installation 101 is described by means of a dataset 200 describing operations (OP) respectively 201, 202, 203 of a flow of operations. These operations are implemented by means of various equipment (EQUIP #1 ... P..Q) described by a set of data 205, 206, ... , 207. Each of these equipment is associated with respective sensors (SNSR), 208, ... , 209, ... , 210 allowing to obtain state information of these equipment (for example temperature, pressure or other physical parameters).

[0065] Each operation is described according to the data structure 300 in Figure 3. An operation (OP) has a unique operation reference (REF) 301 that allows it to be uniquely identified. It is further described by means of a set of traceability data 302, 303, ..., 304 describing successive phases (PHS #1... R) of the operation. Each phase is described by means of data comprising data representing a respective starting point (STRT) 305, 306, 307 and data representing an ending point (END) 308, 309, 310. Each phase is also associated with a location (LOC) data 311, 312, 313 of the equipment that implements it. Finally, the described operation can also be described by means of other data stored in a structure 314 (DAT) provided for this purpose.

[0066] The process is also described using so-called "genealogy" data (GNLGY) 204, which links the data from the sensors of the different equipment in terms of the flow of operations during which they were acquired. This flow of operations is that described by data 201, 202, ..., 203. The genealogy data 204 are described in more detail with reference to Figure 4.

[0067] This figure illustrates data (GNLGY) 400 that links a dataset (UPSTR) 401 representing upstream operations to a dataset (DWNSTR) 403 representing downstream operations. Each dataset contains respective upstream data 404 and downstream data 405 and 406 describing operations of the process implemented by the industrial plant. Genealogy type (TYP) data 402 indicate the type of link between operations, for example, whether a downstream operation is linked to several upstream operations (or vice versa – type 1 -> N or N -> 1), or whether several upstream operations are linked to several downstream operations (type N -> N).

[0068] Some of the data described above, particularly traceability and genealogy data, is generated by the traceability and genealogy generator. This module takes as input data from automated control systems. Specifically, the generator takes as input data representing the state of the equipment. For example, for a given piece of equipment (or set of equipment), it can take as input the phase it is in at a given time.

[0069] The data structure described above allows the data fusion module to combine data at two levels. First, the module merges traceability data with sensor data to build characteristic data for an operation. Second, the module merges traceability data with data associated with the operations.

[0070] At the first level, as illustrated in Figure 5, the Data Fusion Module (FSN) 109 combines traceability data (e.g., phase start, phase end, phase location of a piece of equipment) with sensor data to generate operation-related data. This figure replicates the data structure of Figure 2.

[0071] Initially, the data fusion module 109 selects, in step 500, the sensors 208 attached to the equipment 205 corresponding to the location of the relevant phase 302. Secondly, for these sensors, it selects, in step 501, the time window bounded by the start and end of the relevant phase as given by modules 305 and 308. Thirdly, in step 502, it aggregates the sensor data(s) over this window using a transformation function (f).

[0072] The data merging module can perform calculations on the fly (each time the data associated with the operation is requested) to ensure that the data is always up-to-date. Alternatively, it can pre-calculate and store the results with rules for triggering updates, enabling the execution of bulk queries across a large number of operations for use cases that require it.

[0073] At the second level, data from one or more upstream operations is propagated to one or more downstream operations. The propagation processes depend on the type of genealogy involved.

[0074] Figure 6 illustrates a 1-to-N genealogy. The results of upstream operations, represented by upstream operation data 600, are used by downstream operations, represented by downstream operation data 601. In the illustrated case, an upstream operation 602 is followed by two downstream operations 603 and 604. The data 605 from operation 602 are propagated as is and reused as eigendata 606 and 607 for operations 603 and 604, respectively. For example, a product A with a characteristic X having a value Y is produced in operation 602. Downstream operations 603 and 604, implementing the product from this upstream operation, retrieve the value Y for this characteristic X.

[0075] Figure 7 illustrates an N-to-N or N-to-1 genealogy. The results of upstream operations, represented by upstream operation data 700, are used by downstream operations, represented by downstream operation data 701. In this case, processing is performed to generate the data 702 and 703 associated with the downstream operations 704 and 705, because several data sets 706 and 707 from upstream operations 708 and 709 are needed to construct each of them. Thus, for example, a product A with a characteristic X having a value Y is produced during upstream operations 708 and 709. A downstream operation 704 or 705 implements the product resulting from these different upstream operations. To obtain the value Y of characteristic X for this downstream operation, a function f is applied by a data fusion module 710 to the Y values ​​of characteristic X from the upstream batches. For example, it is a weighted sum.The values ​​can be weighted by the mass of product used from the various upstream operations. Of course, other functions or treatments can be considered depending on the process implemented.

[0076] It appears from the above that these implementation methods make it possible to provide manufacturers facing the challenge of managing data collected within their industrial facilities with the means to optimize the processes they implement. These methods of These implementations provide a standardized data structure for describing production operations and phases, thereby standardizing how genealogy and traceability data (i.e., production tracking) are structured. Generating traceability and genealogy data allows the data structure to be populated in the expected format, adapting to the form in which this data is available in automated control systems. Data fusion, as described, generates data linked to an operation by combining production tracking data with sensor data. Specifically, this data fusion combines sensor data with traceability data to systematically obtain aggregated information at the phase level of an operation.It also allows, through genealogy, the combination of data from one or more upstream operations to one or more downstream operations.

[0077] The steps of a process according to embodiments are summarized with reference to figure 8 which is a flowchart of steps.

[0078] During step 800, data from an automated process control system controlling an industrial installation is transmitted. Simultaneously, during step 801, other complementary data relating to the installation may also be transmitted by other information systems. This includes, for example, data transmitted by operators via a human-machine interface, as previously described.

[0079] The contextualization engine implementing the process receives this data during an 802 collection step. It is then routed to the various modules of the engine for processing.

[0080] In step 803, genealogy data from sensor data is extracted and then stored in step 805. This data is then used in step 808 to create genealogy data according to the structure described above to link upstream and downstream data.

[0081] In parallel, during a step 804, the data relating to the traceability of the data from the sensors are extracted and then stored during a step 806. This data is then used to generate, during a step 809, the traceability data in accordance with the structure described above and allowing to describe the successive phases of the operations from which the data originate (start and end point, location of the phases in terms of equipment used, etc.).

[0082] The sensor data itself is also extracted during step 807 and stored during step 810.

[0083] As described above, traceability data is aggregated with sensor data in step 811. The aggregation of this data is then used to generate the operational data specific to the operations implemented, in step 813. This generation is based on the operational data extracted in step 812 from the previously collected data.

[0084] Finally, the genealogy and operational data are aggregated in step 814 for transmission to the plant's control module. This provides the module with all the relevant information needed to optimally control the plant. Beyond the raw sensor data, the control module can determine the precise context in which it was collected.

[0085] A process, according to specific embodiments, can be implemented in various ways. It can be implemented using a computer connected to the industrial installation. Thus, a process, according to specific embodiments, can be implemented as part of so-called "on-premises" solutions. It can also be implemented on a remote computer connected to the installation. Thus, a process, according to specific embodiments, can be implemented as part of so-called "SaaS" solutions (acronym for "Software as a Service").

[0086] Figure 9 is a functional diagram of a 900 system for implementing one or more embodiments of the invention. The systems or servers described above may have the same structure.

[0087] The 900 system includes a communication bus to which the following are connected: - a processing unit 901, such as a microprocessor, called a CPU; - a 902 random access memory unit, called RAM, for storing executable code of a process according to an embodiment of the invention as well as registers adapted to record the variables and parameters necessary for the implementation of a process according to embodiments, the memory capacity of which can be extended by an optional RAM connected to an expansion port for example; - a memory unit 903, called ROM, for storing computer programs intended to implement the embodiments of the invention; - a network interface unit 904 connected to a communication network on which the digital data to be processed is transmitted or received. The network interface 904 can be a single network interface, or composed of a set of different network interfaces (for example, wired and wireless interfaces, or different types of wired or wireless interfaces). Data is written to the network interface for transmission or read from the network interface for reception under the control of the software application running in the CPU 901; - a graphical user interface unit 905 for receiving input from a user or displaying information to a user; - a 906GB hard drive marked HD - a 907 I / O module for receiving / sending data from / to external systems such as a video source or a screen.

[0088] The executable code can be stored either in read-only memory (ROM), on the hard drive (HDD), or on removable digital media such as a disk. In one variant, the executable code of programs can be received at means of a communication network, via network interface 904, in order to be stored in one of the communication system storage means 900, such as hard disk 906, before being executed.

[0089] The central processing unit 901 is adapted to control and direct the execution of instructions or parts of software code of the program(s) according to the embodiments of the invention, these instructions being stored in one of the aforementioned storage means. After power-up, the processing unit 901 is capable of executing the instructions from the main RAM 902 relating to a software application after these instructions have been loaded from the program ROM 903 or the hard disk drive (HDD) 906, for example. Such a software application, when executed by the central processing unit 901, causes the execution of the steps of a method according to various embodiments.

[0090] To illustrate the implementation of a process according to embodiments in an industrial installation, an example of an industrial installation is illustrated by figure 10.

[0091] The installation comprises a set (COMPNT) 1001 of tanks 1002 to 1009 of components (C #1, ..., #8). Each tank contains a component used in the manufacturing process of a product within the installation. For example, the final product is a cake, and the tanks contain the components of this cake, such as milk, sugar, flour, eggs, oil, yeast, almonds, and fruit.

[0092] The installation also includes various equipment blocks (BLK #1, ..., #5), 1010, 1012, 1015, 1019, 1022, utilizing various components. For example, block 1010 includes a single sourdough starter culture unit, 1011. Block 1012 includes two units, 1013 and 1014, for dough kneading and dough resting, respectively. Block 1015 includes three units, 1016, 1017, and 1018, for molding, baking, and cooling, respectively. Block 1019 includes two units, 1020 and 1021, for cake unmolding and inspection. Finally, block 1022 includes a cake packaging unit, 1023.

[0093] For example, components C #1 to C #3 are supplied to the sourdough culture equipment 1011 and components C #2 to C #8 are supplied to the kneading equipment 1013. Equipment 1011 produces sourdough as output which is also supplied to the kneading equipment which in turn produces cake batter. The dough is fed to equipment 1014 to rest. The rested dough is then fed to equipment 1016 to mold the cakes, which are then fed to equipment 1017 for baking. Once baked, the cakes are fed to equipment 1018 to cool. They are then unmolded in equipment 1020. A quality control check is performed in equipment 1021. Once the non-conforming cakes have been filtered, they are packaged in equipment 1023 for transport to their distribution point.

[0094] In what follows, the cooking stage in equipment 1017 will be given particular attention. For the implementation of the contextualization engine, refer to the description above, especially the flowchart in Figure 8.

[0095] The equipment is assumed to consist of two ovens, each equipped with a sensor measuring the temperature inside the oven and a sensor measuring the humidity inside the oven. Each oven is also equipped with an automated control system that can constantly transmit the oven's status (baking, standby) as well as the batch number of the dough being baked.

[0096] It is further assumed that data relating to dough quality (dry matter, viscosity) are collected for each batch number of dough used.

[0097] Implementing a process as described above in this installation allows the following operations to be carried out.

[0098] First, data from the various sensors, the status information of the two ovens, and the batch number of the dough used are collected. The sensor data is then stored. This corresponds to steps 802 and 807 described earlier.

[0099] Traceability data is then generated based on the oven status information. This corresponds to step 809 described above. In this example, each time the oven status changes from "standby" to "bake," a new operation with a single phase is created. The start date and time of this phase are the date and time the transition was observed. A unique operation number is then generated, for example, in the format YYYY-MM-DD-XXX (where YYYY is the year, MM the month, DD the day, and XXX is a sequential number reset to 0 at the beginning of each production day). The phase end date and time, determined later, correspond to the date and time the transition from "bake" to "standby" was observed. The location assigned to this phase is the reference of the oven whose status data was used to create the operation.This traceability creation process is carried out in parallel on the data from both furnaces. The collection, extraction, and storage of the data needed for this creation correspond to steps 802, 804, and 806.

[0100] In accordance with what was previously described for steps 803, 805, 808, genealogy information of type 1 ->N is generated by associating the batch number of the paste used (upstream) with the operation number that just implemented the paste (downstream).

[0101] For example, in accordance with step 811 already described, an initial traceability fusion operation is used to calculate temperature-derived indicators for each baking operation. For the operation in question, the temperature sensor corresponding to the oven designated in the location is selected. Then, according to step 812, the data from this sensor within the time window between the start and end of the phase as described in the operation are selected. In accordance with step 813, aggregation operations are then performed on this data. In this example, three different indicators can be calculated: the average, minimum, and maximum temperatures. The three results are stored for this operation for later use.

[0102] To supplement the data related to the cooking operation, a genealogy merging operation is performed in accordance with step 814. The data from quality (dry matter, viscosity) corresponding to the batch number of dough used in the cooking operation are recovered and stored at the cooking operation level.

[0103] At the end of this process, for each cooking operation, the following are available: a unique operation number in the form YYYY-MM-DD-XXX, a phase with a Start Date / Time and an End Date / Time and five data points: average temperature, minimum temperature, maximum temperature, viscosity of the dough used, dry matter of the dough used.

[0104] For each cooking operation the genealogy data gives the batch number of dough used (Upstream), the genealogy type: 1 ->N and the unique cooking operation number in the format YYYY-MM-DD-XXX in which the dough was used.

[0105] All this data allows for the control of the installation, by determining the setting to adjust the oven's setpoint temperature.

[0106] The present invention has been described and illustrated in this detailed description with reference to the accompanying figures. However, the present invention is not limited to the embodiments shown. Other variations, configurations, and combinations of features can be deduced and implemented by a person skilled in the art upon reading this description and the accompanying figures.

[0107] To meet specific needs, a person competent in the field of the invention may apply modifications or adaptations.

[0108] In the demands, the term “includes / 1 This does not preclude other elements or steps. The various features presented and / or claimed may be advantageously combined. Their presence in the description or in different dependent claims does not, in fact, preclude the possibility of combining them. Reference symbols shall not be construed as limiting the scope of the invention.

Claims

Demands

1. A method for processing state data of an industrial installation (101) for the control of at least one piece of equipment of said installation, wherein said state data comprises at least some of the following: data from sensors of said installation and values ​​of state variables of equipment of said installation, the method comprising the following steps: - extraction (802) of state data relating to a phase (302, 303, 304) of an operation (300) carried out within said industrial installation, - generation (809) of traceability data relating to a position of said phase in a flow of operations within said industrial installation, - aggregation (811) of said state data and said traceability data to generate (813) operation data specific to said operation in said flow.

2. A method according to claim 1, wherein said generation of operation data includes the application of a time window relating to said phase of said operation.

3. A method according to any one of the preceding claims, wherein said generation of operation data includes the localization of data relating to said phase of said operation.

4. A method according to any one of the preceding claims, wherein said operating data are transmitted to a control module of said at least one piece of equipment.

5. A method according to any one of the preceding claims, further comprising the following steps: - generation (808) of genealogy data relating to a sequencing of said operation in said flow of operations within said industrial installation, - aggregation (814) of said operational and genealogical data.

6. A method according to claim 5, wherein the generation of genealogy data involves creating a link relating to their data between one or more downstream operations using products of one or more upstream operations in a process implemented within said installation.

7. A method according to claim 6, wherein said genealogy data allows the determination of data for one or more downstream operations from data relating to one or more upstream operations by application of a function representing said link.

8. A method according to claim 7, wherein said operation carried out within said industrial installation is an upstream operation whose results are used by a plurality of downstream operations, and wherein said operation data specific to said upstream operation generated are used for the generation of operation data specific to said downstream operations.

9. A method according to claim 7, wherein said operation carried out within said industrial installation is a downstream operation using results from a plurality of upstream operations, and wherein the generation of said operation data specific to said downstream operation involves the application of a function for merging the operation data specific to said upstream operations.

10. A method according to any one of claims 5 to 9, wherein the aggregation of said operation and genealogy data is stored in a data structure comprising - a first set of data describing the operations of said flow of operations, - a second set of data describing equipment of said installation for the implementation of said operations of said flow of operations and associated with said sensors of said installation, - a third dataset comprising said aggregation of said data.

11. A method according to claim 10, wherein the first data set comprises, for each operation of said flow, a subset of data describing phases of operation of said operation.

12. A method according to claim 10, wherein the third data set comprises, for each operation of said flow, a genealogy type enabling the indication of a sequencing link between said operations of said flow of operations.

13. A method according to any one of claims 5 to 12, wherein the aggregation of said operation and genealogy data is transmitted to a control module of said at least one piece of equipment.

14. Device (900) comprising a processing unit (901) configured for carrying out steps according to a method according to any one of the preceding claims, i

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