Automatic method for monitoring a facility comprising a plurality of interrupting members, and associated processing terminal and system

Abstract

One aspect of the invention relates to an automatic method (100) for monitoring a facility comprising a plurality of interrupting members and a plurality of labels each associated with one of the interrupting members, each interrupting member being capable of assuming at least two separate operating states and being referenced by means of a functional reference mark, the functional reference mark being displayed on the associated label, wherein monitoring is carried out from a video stream that potentially captures one or more of the interrupting members of the facility and defines an item of monitoring information that associates the functional reference mark and the operating state of each captured interrupting member.

Description

DESCRIPTION TITLE: AUTOMATIC METHOD FOR MONITORING AN INSTALLATION COMPRISING A PLURALITY OF SECTIONING DEVICES, PROCESSING TERMINAL AND ASSOCIATED SYSTEM. TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of the systematic monitoring of an industrial installation comprising a plurality of materials, organs or equipment capable of occupying at least two distinct functional states.

[0002] The present invention relates to a method for remotely monitoring an operating state of a sectioning device, such as a valve or a switch, of an industrial installation. More particularly, the present invention relates to a monitoring method based on automatic image recognition. The present invention further relates to a processing terminal and a system for implementing the monitoring method.

[0003] The invention finds particular application for the monitoring and management of a nuclear installation or any industrial installation implementing a network, or line, of water, gas or electricity, comprising sectioning devices. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0004] Current industrial installations reach a very high degree of complexity due to the number and diversity of functional positions and / or the equipment constituting them.

[0005] In order to ensure the safety of these installations, as well as their proper functioning, regular monitoring is generally carried out by an operator, called a patrolman, carrying out a round following a specific route.

[0006] During this round, the operator ensures the identification of the equipment to be checked by reading and writing, on paper or in an electronic file, a coded identification mark attached to this equipment and referenced in a database relating to the installation. Then, the operator records, checks and possibly modifies the functional state of the equipment.

[0007] The equipment is, for example, a sectioning device capable of taking two visually distinct operating states, such as a valve that can be operated between an open state and a closed state.

[0008] The results of the readings associated with the identified organs are transmitted, after validation by the operator, to the data centralization station such as the installation's process control room.

[0009] This type of monitoring, when carried out manually, is costly in terms of time and personnel. The number of checks that can be carried out is therefore limited and insufficient to enable a reactive response regarding the configuration of the process or installation.

[0010] To remedy this, it is known to equip each component of the installation with a sensor capable of recording the operating status of the component and transmitting this status to a control center. However, this solution is particularly expensive and time-consuming to implement.

[0011] In order to avoid modifying the installation, document CN11 1028225A describes the use of a camera oriented so as to acquire the image of a valve of the installation, and of an image analysis module in which automatic image processing algorithms are applied to the acquired image to determine the state of the valve. These algorithms use several neural networks previously trained on learning bases. They are successively implemented by the image analysis module, to determine, in the acquired image, a) a first region of interest corresponding to the valve, b) in this region of interest, a second region of interest corresponding to the handle of the valve, c) in this second region of interest, the angle made by the handle with respect to a closed orientation and the closed (or open) state of this valve, and d) the open or closed state of the valve.

[0012] This solution requires a large number of steps to obtain the operating status of a single valve. It also has the disadvantage of not including a preliminary step of automatic identification of the component to be controlled. By "automatic identification" we mean the determination of the reference - or registration - assigned to this component and which allows its location within the installation.

[0013] However, this step, when carried out manually, is subject to a number of errors. For example, the operator may misidentify the equipment whose operating status they wish to check, mainly due to data entry errors. These errors can have harmful consequences: malfunction of the installation leading to a partial or total shutdown, endangerment of operators, etc.

[0014] To improve the reliability of the identification task and ultimately the reliability of monitoring, solutions include installing paper or digital identification media on each component to be monitored. The operator in charge of the patrol is then equipped with a reading system specifically dedicated to reading these identification media.

[0015] Document CN216771532U describes a one-dimensional barcode or two-dimensional barcode or QR code (using the acronym "Quick Response code") installed on each part of the installation.

[0016] However, such identification labels can deteriorate over time. To remedy this, patent EP0650169B1 describes an electronic identification medium in the form of an electronic circuit comprising a programmable memory in which the reference of the component in the installation is written.

[0017] This latter solution, although robust, remains costly to implement.

[0018] There remains a need for a monitoring system and associated process that is more reliable, simpler and less expensive to implement and deploy than existing solutions.

[0019] This need exists for monitoring sectioning devices having at least two visually distinct operating states, such as valves, switches, etc. SUMMARY OF THE INVENTION

[0020] The invention offers a solution to the problems mentioned above, by using a reliable identification medium that is already an integral part of the installation. and by enabling the identification and recognition of the functional state of the organ to be controlled jointly, using a single artificial intelligence trained on a training basis specific to the organs to be controlled and to the identification labels of the installation.

[0021] A first aspect of the invention relates to an automatic method for monitoring an installation comprising a plurality of sectioning members and a plurality of labels intrinsic to the installation, each label being associated with one of said sectioning members, each sectioning member being capable of occupying at least two distinct operating states and being referenced by a functional reference, said functional reference being displayed on the associated label, said method comprising the following steps: Reception of a video stream potentially capturing one or more sectioning devices of the installation to be monitored, said video stream having an acquisition frequency restoring real time, and being represented by a time series of images, the method comprising the following steps, carried out upon reception of each image of the time series of images: Use, on the image concerned, of an artificial intelligence trained offline on a training database to obtain a first set of location and operating state classification characteristics relating to each sectioning member present in the image concerned, and a second set of location characteristics relating to each label present in the image concerned, If the first and second sets each have at least one location, Merging the features of the first and second sets to obtain a set of location pairs, each location pair associating one of the locations of the first set with the closest location of the second set in the image, and corresponding to the sectioning organ of the location of the first set and its associated label, For each pair obtained, identification of the associated label from the location of the second set of said pair, and association of the identification obtained with the classification of the operating state corresponding to the sectioning member of said pair, said association defining monitoring information for the sectioning member corresponding to said pair, said monitoring information comprising the operating state and the reference of said sectioning member.

[0022] Thus, the monitoring of the installation is carried out by obtaining monitoring information for each sectioning device captured by the video stream, this monitoring information associating the operating state and the functional reference of said sectioning device.

[0023] Thanks to trained artificial intelligence, we obtain jointly - or simultaneously - the location and operating status of all the sectioning devices present in an image of the video stream, as well as the locations of all the labels present in this image.

[0024] The information thus obtained is then merged and combined to obtain for each sectioning organ present and identifiable in the time series of images a pair {operating state - functional reference}, also called monitoring information.

[0025] It should be noted that these merging and combining steps are performed only for each sectioning organ present and identifiable in a certain manner in the processed image.

[0026] This makes it possible to obtain, in a reduced number of steps, complete monitoring information (including the operating status and registration of the sectioning device).

[0027] The process is thus simpler than existing solutions. One advantage is that it is easily implementable in an on-board processing terminal, which is a key point for its deployment in a facility.

[0028] The process does not require a reading system dedicated to identifying the sectioning device, which implies simplicity and reduced cost of implementation.

[0029] It should be added that the use of a label intrinsic to the installation and containing the functional reference of the sectioning device, compared to a label added specifically to carry out the process, reduces the risk of making an error in identifying the sectioning devices. In fact, the label intrinsic to the installation is placed in the installation at the time of assembly of the sectioning device, and the functional reference that it displays provides an individual (unique) and certain identification of the sectioning device.

[0030] The use of the intrinsic label also avoids the tasks related to referencing the organs to be monitored: creation of a database referencing each organ to be monitored, installation of labels, validation of the label-reference correspondence, etc. An advantage is that the time, and cost, of deploying the monitoring process are reduced.

[0031] In addition to the characteristics which have just been mentioned in the preceding paragraph, the monitoring method according to the first aspect of the invention may have one or more additional characteristics among the following, considered individually or according to all technically possible combinations.

[0032] The method includes the following steps performed after the time series of images has been processed: Consolidation of monitoring information obtained for each sectioning organ captured in said time series of images, Transmission of consolidated monitoring information to a central terminal in the installation and / or to a human-machine interface.

[0033] Using a time series of images to confirm the monitoring information allows for variations in scale, movement, and lighting to be taken into account, which could potentially lead to erroneous determination of operating status or identification. The method thus improves the estimation of monitoring information and is more robust.

[0034] The monitoring information consolidation step can then include the following sub-steps: Sorting the monitoring information obtained for all the images in the time series of images, to obtain a set of monitoring information associated with each sectioning member present in the time series of images, determining characteristics of said set of monitoring information, selecting a final monitoring information as monitoring information of the sectioning member on the basis of said characteristics.

[0035] Thus, the surveillance information obtained over a time series of successive images is analyzed and the final surveillance information is determined using this analysis. The final surveillance information is thus determined more reliably. This also allows surveillance information to be obtained even if, for lighting or environmental reasons (occlusion, texture, etc.), no classification could be given on a given image in the image set.

[0036] The monitoring information consolidation step may also include the following sub-steps: Merging the label locations obtained for all images in the image time series, to obtain a set of label locations associated with each pair of locations obtained in the image time series, Determination of characteristics of said set of label locations, Selecting a final label location as the label location of said pair based on said determined characteristics.

[0037] Thus, we analyze the label locations relating to the same label, obtained on a time series of successive images, and we determine, on the basis of this analysis, the final label location which will be used to read (identify) the functional marker. This thus makes it possible to take into account variations in scale of a image to another, and to choose the label location that will be most easily identifiable. The robustness and reliability of the process are thus improved.

[0038] Said characteristics may then include the number of occurrences of the same classified operating state, and the final operating state is the operating state corresponding to the highest number of occurrences.

[0039] The number of images in the time series of images can then in practice be between 2 and 30, preferably equal to 10.

[0040] The series of images corresponds to images acquired at a frequency of between 1 image per second and 30 images per second, preferably between 10 images per second and 20 images per second, preferably equal to 10 images per second.

[0041] Artificial intelligence is a neural network trained in a supervised manner on a training base to provide a location and operating state of each sectioning organ present in an image, and to provide a location of each label present in said image.

[0042] The training base comprises a plurality of images of drive sectioning members, each drive sectioning member being represented in each of the operating states to be provided, and a plurality of images of training labels, each image of the plurality of images of drive sectioning members being associated with information relating to the location and operating state of each sectioning member present in the image, and each image of the plurality of images of training labels being associated with the location of each training label present in said image.

[0043] Preferably, the training base is acquired on at least one installation.

[0044] Alternatively, part of the training base is acquired on at least one installation, and another part of the training base is acquired outside the context of the installation.

[0045] The term "outside the context of the installation" means that the images of the training base are not acquired in the installation. It is sufficient to acquire images of the sectioning devices alone, without the context of the equipment located behind and around these organs in the installation. An advantage is that it is simple to build this training base, and simple to increment it.

[0046] The training base can advantageously be artificially increased from images of sectioning organs and / or images of training labels acquired on at least one installation and / or outside the context of an installation.

[0047] The functional marker is encoded with alphanumeric characters, and the label identification step is performed using an automatic alphanumeric character recognition algorithm.

[0048] When the functional marker is encoded with alphanumeric characters, the identification step comprises, after using the automatic character recognition algorithm, a step of verifying the result of the recognition algorithm against predetermined grammar rules to confirm the functional marker.

[0049] This allows you to confirm that the code read is indeed a functional reference and not a code relating to other equipment / functions.

[0050] Thus, the functional marker is read easily regardless of the orientation of the label, without the need for a training base specific to the installation.

[0051] The method comprises, after the step of associating the functional reference and the operating state, a step of transmitting said association to a central terminal and / or to a display-writing means.

[0052] Thus, the results are transmitted to the central terminal of the installation for updating the functional plan and / or the general file of the installation, and / or to a patrolman who can validate, or monitor, the result given by the process.

[0053] The shut-off device is a quarter-turn valve that can be operated between an open and a closed state.

[0054] The database can then contain less than 20,000 labels.

[0055] A second aspect of the invention relates to a processing terminal for monitoring an installation, configured to execute the method according to the first aspect of the invention.

[0056] The processing terminal can be portable.

[0057] A third aspect of the invention relates to a system for monitoring an installation comprising a plurality of sectioning members arranged in determined locations of the installation, each sectioning member being capable of occupying at least two distinct operating states and being referenced by a functional reference, and a plurality of labels, each label coding the functional reference of one of the sectioning members and being arranged in a determined location of the installation adjacent to a determined location for said sectioning member, comprising: a processing terminal according to the second aspect of the invention, a module configured to acquire a stream of images of the installation, a lighting system.

[0058] The system may comprise a display-write module communicating with the processing terminal and made available to an operator, the video module, the processing terminal and said display-write module being adapted to be worn by said operator.

[0059] The system may comprise a central terminal comprising means for transmitting and receiving data wirelessly to the processing terminal.

[0060] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0061] The figures are presented for information purposes only and in no way limit the invention. Figure 1 is a block diagram illustrating the sequence of steps of a method for monitoring an installation according to the invention, Figure 2 is a schematic representation of captured sectioning organs subjected to the monitoring process. Figure 3 shows a schematic representation of an example of a sectioning member to be monitored, said sectioning member being in an open operating state, Figure 4 shows an example of a label associated with the sectioning device of Figure 3, Figure 5 shows a schematic representation of an image obtained at the end of the second step of the method of Figure 1, Figure 6 shows a schematic representation of an image obtained from the image of Figure 5 at the end of the third step of the method of Figure 1, Figure 7 is a block diagram illustrating the sequence of sub-steps of the fourth step of the method of Figure 1, Figures 8A and 8B are block diagrams illustrating the sequence of sub-steps of the sixth step of the method of Figure 1, Figure 9 shows a schematic representation of a processing terminal configured to perform the method of Figure 1, and of a system for monitoring a facility DETAILED DESCRIPTION

[0062] The invention relates in particular to a method for monitoring an installation comprising a plurality of sectioning members and labels associated with said sectioning members, each sectioning member being capable of taking at least two distinct operating states and being referenced by a functional marker.

[0063] By "distinct operating states" we mean distinguishable optically, or, in other words, visually.

[0064] By "functional reference" is meant a unique coded reference allowing the location on the installation of the sectioning device to which it is assigned and its functional description.

[0065] Monitoring of the installation is carried out when monitoring information is determined for each sectioning device, this monitoring information being defined as the association of the functional reference and the operating state of this sectioning device.

[0066] Figure 1 is a block diagram illustrating the sequence of steps of an automatic method 100 for monitoring an installation according to the invention.

[0067] Monitoring is carried out from an FV video stream potentially capturing one or more sectioning devices of the installation.

[0068] For this, the method 100 begins with a step S101 of receiving the video stream FV.

[0069] This FV video stream has an acquisition frequency that restores real time, and is represented by a time series of images h, lj, In.

[0070] The acquisition frequency is preferably between 1 image per second and 30 images per second, preferably between 10 images per second and 30 images per second, and preferably equal to 15 images per second.

[0071] This FV video stream can be acquired directly by a video module (not shown in Figure 1) comprising a camera, an acquisition card and means of transmission to a processing terminal.

[0072] This video module can be manipulated (oriented) by a patrolman carrying out surveillance along a specific route in the installation, this route making it possible to capture in several video streams all of the sectioning devices of the installation. In this case, the video module can be installed on an arm held by the patrolman, or it can be installed on an accessory worn by the patrolman, such as glasses.

[0073] This video module can alternatively be installed on a mobile robot traveling through the installation and programmed to capture the installation's sectioning devices in the video stream.

[0074] Alternatively, the reception S101 of the FV video stream first comprises a reception of a primary video stream (not shown in FIG. 1) acquired by the video module then a conversion of this primary video stream to obtain the FV video stream used by the method 100.

[0075] The primary video stream then has an acquisition frequency higher than the acquisition frequency of the FV video stream used by the method 100. The frequency of the primary video stream is thus, for example, between 30 images per second and 60 images per second.

[0076] The time series of images h, lj, In representing the video stream FV preferably comprises, when the frequency of the primary video stream is 30 images per second, between 1 and 30 images, preferably 10 images.

[0077] Figure 2 is a schematic representation of sectioning members Oi- 1, Oi, Oi+i captured in the FV video stream and subjected to the monitoring method 100.

[0078] By "captured in the FV video stream" we mean that these sectioning organs are each present in at least one image lj of the FV video stream. They can be present individually (a single sectioning organ in an image) or together (several sectioning organs in an image).

[0079] The OM, Oi, Oi+i sectioning devices were installed in specific locations of the INST installation, along the LIGN line circuits of this INST installation.

[0080] The sectioning devices O, Oi, Oi+i can be valves, switches, or other.

[0081] Whatever the nature of the sectioning organs, the shape, size, and / or color of each sectioning organ O, Oi, Oi+i may vary from one sectioning organ to another.

[0082] Thus, if the sectioning members are valves, these can be of different types to serve different functions: isolation valves, stop valves, etc. In other words, the method according to the invention is compatible with different types of valves, provided that these types have at least two visually distinct operating states.

[0083] As mentioned previously, each sectioning member Oi-1, Oi, Oi+i has an operating state FM, Fi, Fi+i selected from at least two distinct operating states. In this same figure 2, the functional states FM, Fi, Fi+i of the sectioning members Oi-1, Oi, Oi+i are two in number, and are represented schematically by the graduation 0 (members Oi i and Oi) and 1 (member Oi+i).

[0084] Figure 3 shows a schematic representation of an example of a valve. The valve has a handle P that can be operated between a first position parallel to the X axis of the valve (shown in Figure 3) and a second position oriented to 90° relative to the first position (not shown in Figure 3). These two positions correspond to the two distinct functional states represented respectively by the graduations 1 (open state) and 0 (closed state) in Figure 3.

[0085] As mentioned previously, each sectioning organ is further associated with, or referenced by, a functional reference denoted RFM, RFi, RFi+i (respectively for the sectioning organs OM, Oi, Oi+i) which is unique (or individual) and allows the location and functional description of said sectioning organ O, Oi, Oi+i.

[0086] This functional reference RF, RFi, RFi+i is generally recorded in a central file stored in a central terminal (or central monitoring terminal, not shown in figure 2) of the INST installation with other information relating to the sectioning device (for example, the supplier, the date of the last inspection, etc.).

[0087] The functional reference is intrinsic to the installation, that is to say it is an integral part of the installation. It is implemented from the design of the installation to describe each sectioning device. Thus, to implement the method 100, there is no need to carry out a prior task of assigning an identifier to the sectioning devices. An advantage is that the deployment of the monitoring method 100 is faster and less expensive compared to solutions where it is a question of adding labels or any device carrying the identification function dedicated to automatic monitoring.

[0088] In Figure 2, the functional reference RFM, RFi, RFi+i is preferably coded with alphanumeric characters.

[0089] Figure 2 schematically represents the labels EM, Ei, Ei+i associated with the sectioning devices OM, Oi, Oi+i.

[0090] The Ei-i, Ei, Ei+i labels are said to be “intrinsic to the installation” because they are placed in the INST installation at the time of assembly of the sectioning devices with which they are associated and bear the functional reference assigned to these sectioning devices.

[0091] Each label Ei associated with a sectioning device Oi contains functional reference RFi assigned to this sectioning device Oi and is affixed visibly and as close as possible to this sectioning device Oi. The term "in a visible manner" "visible" means in such a way that a patrolman can read the label Ei. The term "as close as possible" means a location such that the patrolman can unambiguously connect the label Ei to the sectioning device Oi with which it is associated.

[0092] Figure 4 shows an example of a label Ei. This is, for example, in the form of a piece of black plastic with the functional reference RFi written in white characters. Such a label is stuck, or screwed, as close as possible to the sectioning device Oi with which it is associated.

[0093] Naturally, several types of labels can be used. For example, labels with a white background and black characters and labels such as those shown in Figure 4 can be used together.

[0094] The block of steps B1 consisting of steps S102, S103, S104, S105 and S106 is carried out as an extension of step S101 of receiving the video stream FV, on each image lj, j= 1 to n, of the time series of images h, lj, In of this video stream FV.

[0095] The step block B1 aims to provide, for each image lj of the time series of images, a set Ens4j of monitoring information ISOi, each monitoring information ISOi of the set Ens4i being relative to each sectioning organ Oi detected in the image lj. The monitoring information ISOM, ISOi, SOi+i are illustrated in figure 2.

[0096] It should be noted that the block of steps B1 is carried out in a duration less than the time separating two successive images lj, lj+i of this time series of images. Thus, this block of steps B1 is carried out in real time. In other words, the monitoring information relating to each image is considered to be delivered in real time.

[0097] Within the block of steps B1, and with reference to figure 2, step S102 consists of using, on an image lj of the image time series, an artificial intelligence previously trained offline on a training base, to simultaneously obtain characteristics relating to the sectioning members O, Oi, Oi+i and to the labels present in the image.

[0098] Thus, a single trained artificial intelligence can simultaneously detect multiple sectioning organs in the image as well as the elements (labels) allowing them to be identified. Detection is therefore rapid.

[0099] Figure 5 shows a schematic representation of an image lj obtained at the end of step S102 and in which a sectioning member Oi and two labels Ei, Ei+i have been captured.

[0100] The characteristics relating to the sectioning organs present in the image lj form a first set Enslj of characteristics, and the characteristics relating to the labels Ei, Ei+i present in the image lj form a second set Ens2j of characteristics.

[0101] The characteristics of the first set Enslj include location characteristics SOi of each sectioning member present in the image h, and classification characteristics FOi of each sectioning member Oi present in the image lj.

[0102] By localization characteristic SOi, we mean for example a bounding box surrounding the region of the image corresponding to the sectioning organ O.

[0103] By classification characteristic FOi, we mean the classification of the operating state Fi of this sectioning device Oi. When the sectioning device Oi is a valve, the classification FOi of the operating state Fi is either an open state 1, or a closed state 0, or an indeterminate state NA distinct from the open state 1 and the closed state 0. In Figure 5, the classified operating state FOi is the open state 1.

[0104] It should be noted that in this first set Enslj of characteristics, each classification characteristic FOi corresponds to a location characteristic SOi, (and vice versa), the two characteristics then being linked to the same sectioning organ Oi.

[0105] The features of the second set Ens2j include localization features SEi, SEi+i of the labels present in the image lj. These localization features can be bounding boxes surrounding each region of the image corresponding to a label Ei, Ei+i.

[0106] The artificial intelligence is preferably an artificial neural network trained in a supervised manner on the training base to provide from an image lj a location SOi of each sectioning organ Oi present in the image lj and a location SEi of each label Ei present in the image lj, and for said location SOi of the sectioning member Oi, a classification FOi of the operating state of the corresponding sectioning member Oi.

[0107] In step S102, a convolutional neural network or CNN (acronym for “Convolutional Neural Network” in English) can be used for the neural network.

[0108] The training base preferably comprises a plurality of images of drive sectioning members, each drive sectioning member being represented in each of the operating states to be provided, and a plurality of images of drive labels. The drive sectioning members preferably comprise several types of drive members (several types of drive valves, or several types of drive members).

[0109] The training sectioning organs advantageously have morphological (shape, color) and arrangement (orientation, lighting) differences between them. The images of the training base are advantageously acquired with a variety of lighting conditions, distance to the training sectioning organ, etc. Thus, the performance of the trained neural network is more robust with respect to the type of sectioning organ and / or the shooting conditions. [001 10] Advantageously, the images of the training base are acquired in the context of at least one installation. [001 1 1 ] “In the context of an installation” means that the context of the installation is present on the images of the training base, in the environment of the training sectioning devices and the training labels. The images of the training base are then acquired on the installation. [001 12] Preferably, the images of the training base are acquired on three separate installations. [001 13] Tests carried out within the framework of the invention have shown that the variety of learning is then sufficient to generalize artificial intelligence well. [001 14] Advantageously, part of the images of the training base is acquired in the context of one or more installations and another part of the images of the training base is acquired outside the context of an installation. [001 15] By "outside the context of an installation" is meant that the images of the training members or training labels do not include the other elements of the installation (lineage circuits or other element). Thus, the training base can be enriched to provide missing diversity, in a non-invasive manner, that is to say without needing to travel through the installation or interfere with the operation and management of the installation. Furthermore, the training base makes it possible to obtain better generalization of the performance of the neural network compared to a training base acquired in the sole context of the installation. The monitoring method is thus easily adaptable to changes in installation. [001 16] The training base can furthermore be advantageously artificially increased from images of sectioning organs and / or images of training labels acquired on at least one installation and / or outside the context of an installation. This makes it possible to train the neural network simply and quickly on a greater variety of situations. [001 17] Thus, the adaptation of the method 100 to a new installation is quick and inexpensive to carry out. [001 18] Each image of the plurality of drive sectioning member images is associated with information relating to the location and operating state of each sectioning member present in the image. [001 19] Each image of the plurality of training label images is associated with information relating to the location of each training label present in said image.

[0120] Information relating to the location and operating status of the drive sectioning devices and the location of the drive labels are called instantiations or labelings. They are obtained, for example, manually, by annotating the images in the drive base.

[0121] Advantageously, the training base has fewer than 20,000 labels. This number is low enough that the training base is quick to build.

[0122] The training base may alternatively comprise a single plurality of images including the training sectioning members and the training labels.

[0123] The training base may alternatively include a first plurality of images of training sectioning members, a plurality of images of training labels, and a plurality of images including training sectioning members and training labels.

[0124] With reference to Figure 2, the monitoring method 100 continues with a step S103 which consists of merging, that is to say pooling, the characteristics of the first and second sets Ens2j and Ens3j to detect whether at least one potentially identifiable sectioning member Oi is actually present in the image lj.

[0125] To do this, we can determine the number Nsoj of location characteristics relating to the sectioning organs, and the number Nsej of location characteristics relating to the labels.

[0126] If the number Nsoj of location characteristics relating to the sectioning members and the number Nsej of location characteristics relating to the labels are both null, the response is considered to be negative Dneg, i.e. no sectioning member is present in the image lj, or if it is present, information (the associated label) likely to identify it is potentially missing. No other step is then carried out on the image lj. The method then continues with step S102 which is repeated on the image lj+i following the image lj, and so on on the following images of the time series of images, up to the last image In of this series.

[0127] Otherwise, that is to say, if this number Nsoj of location characteristics relating to the sectioning organs and the number Nsej of location characteristics relating to the labels are both unaffected, then the response is considered to be positive Dpos, that is to say that one or more organs potentially identifiable sectioning features are detected in the image. The method then continues with step S104.

[0128] Step S104 consists of merging the location characteristics SOi, SEi of the first and second sets Enslj, Ens2j to obtain a set Ens3j of location pairs.

[0129] Figure 6 shows a schematic representation of the image lj obtained at the end of step S104, in which a set Ens3j of location pairs has been obtained.

[0130] In the set Ens3j, each POi pair of locations associates one of the sectioning organ locations SOi with the nearest label location SEi. In Figure 3, the set Ens3j has only one POi pair.

[0131] To do this, a distance calculation between the sectioning member's bounding box and each label bounding box can be performed, with the smallest distance then being used to determine the nearest label bounding box.

[0132] Naturally, other methods can be used in combination or not with this distance calculation and distance comparison. For example, in addition to the distance comparison, it is possible to perform a comparison of the size of the bounding boxes in the image, the largest size then being chosen to determine the closest bounding box. It should be noted that, in this case, the labels are all the same size and are positioned either strictly vertically or strictly horizontally throughout the installation.

[0133] The method 100 continues with a step S105, illustrated in FIG. 1, consisting of determining, for each location pair POi obtained in the set Ens3j, the monitoring information ISOi relating to the sectioning member Oi corresponding to said pair POi.

[0134] For this, step S105 comprises sub-steps S1051, S1052 and S1053, illustrated in figure 7.

[0135] The first sub-step S1051 consists of identifying the functional reference RFi of the sectioning member corresponding to said pair POi from the label location SEi of said pair. When the functional reference is coded with alphanumeric characters, the identification advantageously consists of the use of a character recognition algorithm or OCR (acronym for "Optical Character Recognition" in English). The OCR algorithm preferentially uses a trained recurrent neural network or RNN (acronym for "Recurrent Neural Network" in English). Such trained algorithms are available free of charge, and do not require the construction of a training base specific to the INST installation. The identification of the sectioning device is thus simple, fast and inexpensive to implement.

[0136] The result of the OCR character recognition algorithm can be checked with predetermined rules, called grammar rules, to confirm that it is a functional identifier. This avoids using an incorrect code as a functional identifier because it relates to other functions / equipment. This improves the reliability of automatic identification.

[0137] Sub-step S1051 is extended by a step S1052 consisting of selecting the classification FOi of the operating state corresponding to the location SOi of the sectioning member of said pair.

[0138] Sub-step S1052 is then extended by a sub-step S1053 consisting of associating the RFi identification of the label location, i.e. the functional reference corresponding to the sectioning member of said pair, with the selected FOi operating state.

[0139] Thus, at the end of step S105, a set Ens4j of monitoring information ISOi is determined, each monitoring information ISOi corresponding to the association of the functional reference and the operating state of each sectioning member detected in the image h.

[0140] After step S105, the method may comprise a step S106 of transmitting the set Ens4j of ISOi monitoring information determined in the image lj to a processing terminal and / or to a human-machine interface.

[0141] The human-machine interface can be implemented on a display device made available to the patrolman.

[0142] Once transmitted to the processing terminal, the monitoring information can be used to update a functional plan of the installation (which is for example displayed on a display screen) and / or update the general installation file. This file is, for example, part of installation management software. During this update, an operating anomaly and a malfunction alert may be generated by the central terminal.

[0143] When the monitoring information is transmitted to a patrol officer, the latter can confirm it, thus carrying out a double validation. The monitoring method 100 thus provides help, assistance, to the patrol officer.

[0144] The method 100 then continues with step S102 which is repeated on the image lj+i following the image lj, this up to the last image In of the time series of images.

[0145] Once all the images of the time series h, lj, In have been processed, we obtain a set Ens4pv of monitoring information relating to the time series of images, which comprises the plurality of sets Ens4j of monitoring information obtained for each image lj where a sectioning organ has been detected.

[0146] The method 100 can then be extended by a step S107 of consolidation (or confirmation) of the monitoring information obtained in the set Ens4pv of monitoring information relating to the time series of images.

[0147] Referring to Figure 8A and Figure 8B, this step S107 may comprise substep S1071 and / or substep S1072. The order of substeps S1071 and S1072 is irrelevant.

[0148] Substep S1071 aims to consolidate / confirm the operating status information in each ISOi monitoring information of the Ens4pv set.

[0149] For this, the sub-step S1071 comprises a sub-step S1071 A, illustrated in FIG. 8A, consisting of sorting the monitoring information obtained in the set Ens4pv of monitoring information relating to the time series of images, to obtain sets Ens5i of monitoring information, each set Ens5i being associated with one of the sectioning members Oi present in the time series of images.

[0150] Still with reference to FIG. 8A, sub-step S1071A is extended by a sub-step S1071 B consisting of determining characteristics for each set Ens5i of monitoring information.

[0151] These characteristics may include the number of occurrences of the same monitoring information.

[0152] Alternatively, these characteristics may include the presence of the same monitoring information, i.e. the same {operating state-functional reference} pair on several consecutive images (i.e. on several consecutive positions in the Ens5i set), for example on five consecutive images.

[0153] Sub-step S1072B is extended by a sub-step S1072C, also illustrated in FIG. 8A, consisting of selecting, for each set Ens5i, a final monitoring information as monitoring information of the sectioning member on the basis of the characteristics determined in step S1072B.

[0154] When the characteristics include the number of occurrences of the same monitoring information, the final monitoring information selected is the monitoring information with the greatest number of occurrences.

[0155] When the characteristics include the presence of surveillance information on five consecutive images in the Ens5i set, the final surveillance information selected is the surveillance information that is present on these five consecutive images.

[0156] Sub-step S1072 aims to consolidate / confirm the functional reference information in each ISOi monitoring information of the Ens4pv set.

[0157] For this, the sub-step S1072 comprises a first sub-step S1072A, illustrated in FIG. 8B, consisting of merging the label locations obtained for the set Ens4pv of monitoring information relating to the time series of images, to obtain sets Ens6i of label locations, each set Ens6i being associated with a label present in the time series of images.

[0158] Substep S1072B is performed for each set Ens6i of label locations. It consists of determining characteristics of said set Ens6i of label locations.

[0159] One feature can be the area (in square pixels) covered by the bounding box surrounding the label.

[0160] Substep S1072C which follows substep S1072B is also carried out for each set Ens6i of label locations. It consists of selecting, on the basis of the determined characteristics, a final label location as the label location of the label associated with said set Ens6i.

[0161] For example, when the extracted feature is the area of ​​the bounding box, the final label location is the one with the largest area.

[0162] This improves the chances of correctly identifying the label and robustly takes into account variations in scale or lighting between images in the time series of images.

[0163] At the end of step S107, a consolidated set of monitoring information Ens4CFv is obtained, in which each piece of monitoring information relates to each sectioning member present in the time series of images, and has been determined with increased reliability compared to the set Ens4pv.

[0164] Step S107 can then be followed by a transmission step S108 to the central terminal and / or the human-machine interface of the consolidated monitoring information Ens4Cpv for each sectioning device present in the time series of images.

[0165] The human-machine interface can be implemented on a display-writing device, such as a tablet, made available to the patrolman.

[0166] The transmission step S108 can replace the step S106. In this case, only the consolidated monitoring information is transmitted to the central terminal.

[0167] The method 100 can then comprise a step S109 illustrated in FIG. 1, of validation by the patrolman, using the display-writing means, of the consolidated monitoring information Ens4CFv.

[0168] During this validation step S109, the patrol officer can confirm the monitoring information obtained automatically by steps S101 to S108 of the process, or he can invalidate it.

[0169] In the event that the patrolman does not confirm the monitoring information obtained automatically for one of the sectioning members Oi present in the image series, the validation step S109 may comprise an additional sub-step consisting of carrying out the following actions: Acquire a first image centered on the sectioning device for which the monitoring information has not been confirmed by the patrolman, Acquire a second image centered on the label associated with said sectioning organ, Use artificial intelligence on the first and second images to obtain respectively location and classification characteristics relating to said sectioning organ, and location characteristics of the associated label, Identify the functional reference of the associated label from the location of said label, Combine the classification of the operating state and the functional reference to obtain the monitoring information for said sectioning device.

[0170] Once the patrolman has manually confirmed the monitoring information of the sectioning devices present in the time series of images, said images can be annotated and saved in a storage space for later use as images in the artificial intelligence training base. Thus, it is possible to enrich the training base and retrain the artificial intelligence based on feedback. The monitoring method is thus easily adaptable to types of sectioning devices or labels that are rare and potentially difficult to classify.

[0171] Advantageously, the monitoring information sets Ens4j, Ens4pv, Ens4CFv are obtained using a single neural network trained on a training base specific to the sectioning devices and installation labels.

[0172] The process 100 is thus simple and inexpensive to implement.

[0173] Figure 9 shows a schematic representation of a processing terminal 11 according to a second aspect of the invention and of a monitoring system 1 according to a third aspect of the invention.

[0174] The processing terminal 11 is configured to execute the method 100.

[0175] This processing terminal 1 1 comprises a processor 1 1 1 , a memory (not shown in Figure 9), and a means 112 for transmitting-receiving data to a video module 12.

[0176] It should be noted that the computing power of the processing terminal 11 and the artificial intelligence used in step S102 determine the number of images in the time series of images. At a minimum, this computing power and the artificial intelligence are chosen to process the number of images mentioned above.

[0177] The processing terminal may further comprise a means 1 13, N of transmission-reception, for example wireless, towards a central terminal 13 of the installation.

[0178] It may comprise a means 114 of transmission-reception towards a display-writing device 14 made available to a patrolman R and in which a human-machine interface is implemented.

[0179] The processing terminal 11 is advantageously portable.

[0180] The processing terminal can advantageously be coupled to a means 115 energy storage, such as a portable battery.

[0181] Still referring to figure 9, system 1 is carried by a patrolman and includes: The processing terminal 11 according to the second aspect of the invention, the video module 12 configured to acquire the FV video stream, a lighting system 15.

[0182] The lighting system 15 comprises a light source, for example a light-emitting diode, coupled to a means for controlling the terminal of The processing 11. The control means is configured to activate the emission of light from the light source 15 during the acquisition of the FV video stream by the video module. The light source is preferably installed on the video module so as to enable the lighting of the scene to be filmed. The lighting system 15 thus makes it possible to monitor sectioning members even when they are in a dark environment. It also makes it possible to make the method more robust to variations in lighting conditions.

[0183] The video module 12 is preferably configured to capture a video stream with a field of view including at least one sectioning member and its associated label.

[0184] The video module 12 may include a viewfinder that allows the operator R to determine how to film the installation. The video module can then be configured to display a viewfinder window in the viewfinder. This viewfinder window allows the operator to center the acquisition of the video stream preferentially on a cutting member and / or on its associated label. The images of the temporal series of images then have a lower density of cutting members to be controlled and / or fewer context elements, which improves the performance (classification, calculation time) of the artificial intelligence.

Claims

CLAIMS

1. Automatic method (100) for monitoring an installation (INST) comprising a plurality of sectioning members (Oi) and a plurality of labels (Ei) intrinsic to the installation, each label being associated with one of said sectioning members (Oi), each sectioning member (Oi) being capable of occupying at least two distinct operating states (0,1) and being referenced by a functional reference (RFi), said functional reference (RFi) being displayed on the associated label (Ei), said method (100) comprising the following steps: - Reception (S102) of a video stream (FV) potentially capturing one or more sectioning members (Oi) of the installation to be monitored, said video stream (FV) having an acquisition frequency restoring real time, and being represented by a time series of images (h, lj, In), the method (100) being characterized in that it comprises the following steps, carried out upon reception of each image (lj) of the time series of images (h, lj, In): - Use (S102), on the image (lj) concerned, of an artificial intelligence trained offline on a training database to obtain a first set (Enslj) of location (SEi) and classification (FOi) characteristics of the operating state relating to each sectioning member (Oi) present in the image concerned, and a second set (Ens2j) of location (SEi) characteristics relating to each label (Ei) present in the image (lj) concerned, - If the first and second sets (Ens1 j, Ens2j) each comprise at least one location (S103), o Merging (S104) the characteristics of the first and second sets (Enslj, Ens2j) to obtain a set (Ens3j) of pairs of locations (POi), each pair (POi) of locations associating one of the locations (SOi) of the first set with the location (SEi) of the second set closest in the image (lj), and corresponding to the sectioning member (Oi) of the location (SO) of the first set and its associated label (Ei), o For each pair (POi) obtained, identification (S105, S1051) of the associated label from the location (SEi) of the second set of said pair, and association (S105, S1052, S1053) of the obtained identification (RFi) with the classification (FO) of the operating state corresponding to the sectioning member of said pair, said association defining monitoring information (ISOi) of the sectioning member corresponding to said pair, said monitoring information (ISOi) comprising the operating state (FOi, Fi) and the reference (RFi) of said sectioning member (Oi).

2. Automatic method (100) for monitoring an installation according to claim 1, in which the method (100) comprises the following steps carried out after the time series of images (h-ln) has been processed: - Consolidation (S107) of the monitoring information obtained for each sectioning organ captured in said time series of images, - Transmission (S108) of consolidated monitoring information to a central terminal of the installation and / or to a human-machine interface.

3. An automatic method for monitoring an installation according to claim 2, wherein the step of consolidating (S107) the monitoring information comprises the following sub-steps: - sorting (S1071, S1071 A) of the monitoring information obtained for all the images of the time series of images (h-ln) to obtain a set of monitoring information (Ens5i) associated with each sectioning organ (Oi) present in the time series of images, - determination (S1071, S1071 B) of characteristics of said set of monitoring information (Ens5i), selection (S1071, S1071 C) of a final monitoring information as monitoring information (ISOi) of the sectioning member (Oi) on the basis of said characteristics.

4. Automatic monitoring method (100) according to one of claims 2 to 3, in which the step of consolidating (S107) the monitoring information comprises the following sub-steps: - Merging (S1072, S1072A) the label locations obtained for all the images in the image time series, to obtain a set of label locations associated with each pair of locations obtained in the image time series, - Determining (S1072, S1072B) characteristics of said set of label locations, - Selecting (S1072, S1072C) a final tag location as the tag location of said pair based on said determined characteristics.

5. Automatic monitoring method (100) according to one of claims 1 to 4, in which the artificial intelligence is a neural network trained in a supervised manner on a training base to provide a location (SOi) and an operating state (FOi) of each sectioning member (Oi) present in an image, and to provide a location (SEi) of each label present in said image, the training base comprising a plurality of images of training sectioning members, each training sectioning member being represented in each of the operating states to be provided, and a plurality of images of training labels, each image of the plurality of images of training sectioning members being associated with information relating to the location and the operating state of each sectioning member present in the image,and each image of the plurality of training label images being associated with the location of each training label present in said image., [Claim s] Automatic monitoring method (100) according to claim 5, in which the training base is acquired on at least one installation.

7. Automatic monitoring method (100) according to claim 5, in which a part of the training base is acquired on at least one installation, and another part of the training base is acquired outside the context of the installation. [Claim s] Automatic monitoring method (100) according to claim 1 to 7, wherein the functional marker is encoded with alphanumeric characters, and the step of identifying the label is carried out using an automatic alphanumeric character recognition algorithm. [Claim s] Automatic monitoring method (100) according to claim 1 to 8, in which the method comprises, after the step of associating the functional marker and the operating state, a step of transmitting said association to a central terminal and / or to a display-writing means.

10. Automatic monitoring method (100) according to claim 1 to 9, in which the sectioning member is a quarter-turn valve operable between an open state and a closed state. [Claim 1 1 ] Automatic monitoring method (100) according to claim 1 to 10, wherein the number of images in the time series of images is between 2 and 30, preferably equal to 10. [Claim ^] Processing terminal (1 1 ) for monitoring an installation (INST), configured to execute the method (100) according to one of claims 1 to 1 1 .

13. A processing terminal (11) according to claim 12, wherein the processing terminal (11) is portable.

14. System (1) for monitoring an installation comprising: a processing terminal (11) according to one of claims 12 to 13, - a video module (12) configured to acquire a stream of images from the installation, - a lighting system (15).

15. System (1) according to claim 14, wherein the system (1) comprises a display-writing module (14) communicating with the processing terminal (11) and made available to an operator (R), the video module (12), lighting system (15), the processing terminal (11) and said display-writing module (14) being adapted to be worn by said operator (R).

16. System (1) according to one of claims 14 to 15, wherein the system (1) comprises means for transmitting and receiving wireless data to a central processing terminal (13).

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