EP3827318A1 - Système de communication et de commande/réglage pour une installation de remplissage - Google Patents

Système de communication et de commande/réglage pour une installation de remplissage

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Publication number
EP3827318A1
EP3827318A1 EP19727980.5A EP19727980A EP3827318A1 EP 3827318 A1 EP3827318 A1 EP 3827318A1 EP 19727980 A EP19727980 A EP 19727980A EP 3827318 A1 EP3827318 A1 EP 3827318A1
Authority
EP
European Patent Office
Prior art keywords
communication
machine
operator
control system
ksr3
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP19727980.5A
Other languages
German (de)
English (en)
Inventor
Timo Pronold
Wolfgang Hahn
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Krones AG
Original Assignee
Krones AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Krones AG filed Critical Krones AG
Publication of EP3827318A1 publication Critical patent/EP3827318A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/409—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by using manual data input [MDI] or by using control panel, e.g. controlling functions with the panel; characterised by control panel details or by setting parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65B—MACHINES, APPARATUS OR DEVICES FOR, OR METHODS OF, PACKAGING ARTICLES OR MATERIALS; UNPACKING
    • B65B57/00—Automatic control, checking, warning, or safety devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24168—Identify connected programmer to allow control, program entry
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to the field of bottling plants and in particular to the control / regulation of bottling plants with the aid of operating devices.
  • Filling systems for beverages or the like comprise a plurality of production units connected in series, such as filling machines, labeling machines and packaging machines.
  • these can at least partially be designed as rotary machines which are coupled to one another by means of rotating transfer devices.
  • the production units can also be designed as straight-line rotors and / or can be connected to one another via linear transport devices, distribution devices and product buffers.
  • Corresponding system concepts are described, for example, in DE 4 442 586 W4, DE 20 2004 012 848 U1 and EP 2 218 664 A2.
  • a bottling plant is generally operated via a touch screen, via which instructions for the control or regulation of individual machines of the bottling plant can be entered by an operator.
  • Authentication of the operator for example by means of a transponder on which the identity of the operator is encoded, may be required to ensure that only operations for which the operator is authorized are carried out in response to inputs by the operator. Confirmations of operator input as well as information about the operating status or operational malfunctions can be displayed on the touch screen.
  • a communication and control / regulation system for at least one bottling plant for at least one bottling plant (in particular for bottling drinks; it is also possible to provide a number of juxtaposed plants), the bottling plant having a machine with a software Communication robot, in particular a chatbot, which is designed to recognize a voice input and / or text input by an operator and / or to output or display information about the operating state of the machine; a control device that is connected to the software communication robot for data communication and is designed to control and / or regulate the machine on the basis of the voice input and / or text input recognized by the software communication robot.
  • Said machine can be, for example, a filling machine, labeling machine, packaging machine, direct printing machine or blowing machine.
  • a transport device / transport route can fall under the term machine here.
  • the term control system is understood here to mean a control and / or regulating system.
  • the control system can be part of a central control system of the entire bottling plant, which controls all the machines.
  • the software (software-implemented) communication robot can be at least partially placed on one of the machines of the filling plant and can be a chatbot or can include one.
  • the software communication robot can comprise or be connected to a display device and it can comprise a speech output or be connected to such. A dialog with an operator can be conducted via such a voice output.
  • the software communication robot can be implemented in a central computing unit of the filling system. In any case, it receives a voice input recorded by a microphone or a text input from an operator.
  • the chatbot is a computer-implemented dialogue system that can be used to communicate via text input or voice, so that it intelligently supports the operator of a machine in the filling system.
  • the software communication robot significantly simplifies the operation of the filling system in comparison to the known touch screen terminals of the prior art.
  • the software communication robot can comprise a speech recognition module which allows the recognition of text and / or (spoken) speech inputs in several languages or dialects, so that the operator is able to operate the filling plant in his preferred language.
  • the software communication robot can be designed to question inputs and to show solutions for desired operations or problems mentioned by the operator or independently recognized problems.
  • the software communication robot can be designed to present diagnostic information.
  • a software communication robot which is positioned on a specific machine of the filling system or is logically assigned to the machine, can also present data relating to the operating sequence of another machine of the filling system.
  • the data presentation can take place via a display device provided on or at the machine, via which information can generally be presented using, inter alia, virtual reality or augmented reality applications.
  • the information can be output via a voice output.
  • the voice output can take place via a headset worn by the operator.
  • the presentation of information / data can also be done via a mobile control device and / or smartphone.
  • the data communication with the control / regulating device of the bottling plant enables the software communication robot to initiate suitable operations in response to a dialogue with the operator.
  • the software communication robot can comprise a speaker recognition module, which is used in particular for speaker identification. It can also serve the speaker verification, ie, to verify the identity of a speaker specified by an operator.
  • the software communication robot can recognize an operator via the speaker recognition module and can thus enter into a dialogue with the operator adjust recognized operator to the same. For example, the dialog with an operator who is recognized as an experienced operator will differ from a dialog with another operator who will be recognized as a less experienced operator in terms of complexity and detail.
  • the dialogue can thus be suitably adapted to the training or experience of the operator.
  • the dialog can also be adapted to the competence of the recognized operator, so that the operator can be prevented from trying to initiate an operation for which he is not authorized.
  • the software communication robot can also have a data connection with a, in particular mobile, collaborative robot of the filling system, for example in radio communication or via an intranet, in order to give instructions in response to a dialog with the operator.
  • the software communication robot can be designed to communicate via a communication network and in response to a dialog with the operator with one or more other operators who are distant from the operator in dialog with the software communication robot.
  • the software communication robot can be equipped with an artificial intelligence (KL) module or be connected to one with which it can learn.
  • KL artificial intelligence
  • the software communication robot can be equipped with an artificial intelligence (KL) module or be connected to one with which it can learn.
  • KL artificial intelligence
  • an operator profile of an operator can be dynamically managed, saved and used for learning.
  • the operator profile can contain data about the qualifications and competencies of the operator, which determine to what extent the operator may influence the operation of which machines and components of the machines.
  • the operator profile can also be used in the voice recognition of a voice input by an operator.
  • the KL module can also be used for learning / training the above-mentioned speech recognition and speaker recognition.
  • the KL module can be designed for machine learning and can be or comprise an artificial neural network. Learning can be based, at least in part, on fuzzy logic.
  • the artificial neural network can be a Neuo-Fuzzy network.
  • the combination of fuzzy controllers with neural networks enables this automatic adaptation or generation of the fuzzy rules according to which the learning and the dialogue with an operator can take place.
  • each machine of the filling system for example a filling machine, a sealing machine, a labeling machine, a blowing machine and a packaging machine, can be equipped with a software communication robot or chatbot and the individual software communication robots or chatbots can be networked with one another his.
  • Figure 1 shows schematically communication and control systems in connection with machines of a filling plant according to an example of the present inven tion.
  • FIG. 2 shows a block diagram which illustrates a communication configuration in which a software communication robot can be implemented, such as can be used to operate a machine in a filling plant.
  • FIG. 3 shows an exemplary filling system that can be operated using a communication and control system according to the invention.
  • the present invention relates to the operation of machines in a filling plant.
  • the machines are operated via a text or voice input by an operator in a software communication robot.
  • a chatbot should representatively represent such a software communication robot, although any implementation of a software communication robot is included in this description.
  • the chatbot can be programmed at least partially in C ++ or Python.
  • FIG. 1 schematically shows a line of a filling plant 100, which has a number of machines M1, M2 and M3.
  • the machines each have components K1 1, K12 and K13 or K21, K22 and K23 or K31, K32 and K33, for example for processing bottles or preforms, via which they can process containers.
  • a first communication and control system KSR1 is connected to the first machine M1 for data exchange
  • a second communication and control system KSR2 is connected to the second machine M2 for data exchange
  • a third communication and control system Control system KSR3 is connected to the third machine M3 for data exchange.
  • Each of the communication and control systems KSR1, KSR2 and KSR3 can also store data from all machines M1, M2 and M3, for example via a connection of the machines M1, M2, M3 to one another or via a superordinate data system, into the data about all Machines M1, M2, M3 come in, receive.
  • the communication and control systems KSR1, KSR2 and KSR3 can also be connected to each other for data exchange.
  • the communication and control system KSR1 has a control system SR1 in connection with the machine M1 for controlling and / or regulating the same and a chatbot K1
  • the communication and control system KSR2 has a control / Control system SR2 in connection with machine M2 for controlling and / or regulating the same and a chatbot K2
  • the communication and control system KSR3 has a control system SR3 in connection with machine M3 for control and / or or regulation of the same and a chatbot K3.
  • the chatbot K1 is connected to the control system SR1
  • the chatbot K2 is connected to the control system SR2
  • the chatbot K3 is connected to the control system SR3.
  • An operator can operate one of the machines M1, M2 and M3 via one of the chatbots K1, K2 and K3.
  • Each of the chatbots can be implemented in the communication configuration 200 shown in FIG. 2.
  • Each of the control systems SR1, SR2 and SR3 can be part of a central control system. In particular, each of the control systems SR1, SR2 and SR3 does not have to be physically placed on one of the machines M1, M2, M3. Each of the control systems SR1, SR2 and SR3 is at least logically assigned to one of the machines.
  • the communication configuration 200 shown in FIG. 2 has an input interface 210. This input interface 210 receives data representing a voice input from an operator, which is picked up by a microphone, not shown. Alternatively, the input interface 210 can receive text data. It can be designed as a combined software and hardware interface. This data is sent from the input interface 210 to a (data) processing unit 220.
  • Processing unit 220 can be part of a central processing unit for the filling plant, which can have different processing units for different machines. Processing unit 220 may include a processor for processing data. Furthermore, the communication configuration 200 has an output unit 230 and an output interface 240. A voice output is generated via the output unit 230 and can be output via the output interface 240 and a loudspeaker (not shown) for dialogue with the operator. The output interface 240 can also be designed as a combined software and hardware interface.
  • the processing unit 220 can be designed to carry out speaker recognition on the basis of the data received from the input interface 210. Furthermore, the central processing unit 220 determines the semantic content of the speech input. The semantic content and the speaker recognition can be determined using one or more language models or semantic models, which are stored in a model memory 250. The probability of certain words occurring in a certain order can be taken into account when determining the semantic content.
  • the semantic model can be implemented in the form of a neural network or a Bayesian classifier.
  • the dialog with an operator which is carried out with the aid of the output unit 230 is carried out based on a dialog model which is stored in a dialog model memory 260.
  • the processing unit 220 can be designed to recognize voice inputs in a plurality of languages and / or dialects, in which case different models for the different languages or dialects have to be provided in the model memory 250.
  • a self-learning chatbot can be implemented in the communication configuration 200 shown in FIG.
  • the learning process takes place with the aid of a training unit 270, which collects and evaluates data, and receives speech data and recognized semantic data. see content stored in a memory 280.
  • an operator profile of an operator can be stored and updated in the memory 280. For example, it can be learned how the level of knowledge of an operator recognized with the aid of speaker recognition changes over time, and the dialog with this operator controlled by processing unit 220 can be adapted over time to the changing level of knowledge of the operator.
  • the dialog may be shortened in a later time due to the increased level of knowledge of the operator and thus faster operation of the machine enables who.
  • the operator profile can also be taken into account when determining the semantic content of the voice input.
  • the dynamic learning of a dialog guidance of a chatbot can take place with the aid of artificial intelligence implemented in the communication configuration 200.
  • Artificial intelligence can also be used for the speech recognition of a speech input or speech recognition / identification / verification.
  • Artificial intelligence can be implemented in the form of neural networks. Neural networks can be understood as tools which are suitable for emulating any nonlinear functions and thus also rules, for example of fuzzy logic, if these functions are available on the basis of examples. Regularities and thus weights of the neural networks can be learned / trained from a large number of examples, which can then be expressed with the aid of predefined but also adaptable rules, for example fuzzy sets and fuzzy rules.
  • the combination of fuzzy controllers with neural networks enables the fuzzy rules to be set up and parameterized in an intelligent way based on learning.
  • a rule more precisely a linguistic rule, comprises a number of premises in the form of a number belonging to a number of input variables belonging to a number of linguistic values, which are linked by a logical link, the so-called precondition of the rule, and an action in the form of a membership function of an output to a linguistic value (commonly referred to as an 'if-then' form).
  • each rule can be specified by an expert and / or learned by an automated process. The automated method can in particular be carried out with the artificial neural network mentioned.
  • a predetermined or learned rule can also be adapted through optimization steps.
  • An optimization step can adjust the solution of the above-mentioned parameters of a fuzzy set belonging to a linguistic value used in a rule or a prioritization or elimination of the rule.
  • a prioritization or elimination can take place in particular by setting or adapting weights of a rule when determining an overall membership function according to the invention on the basis of the resulting membership function of the action of the rule.
  • the logical combination of two or more linguistic values can be done by the usual logical operators, in particular by AND, OR and XOR. Binary, ternary or even operators with more than three operands can be used. In addition, the unary operation of negation can be applied to any linguistic value.
  • the degree of the precondition of the rule can be formed in particular by the minimum of the degrees of affiliation of the input variables to their corresponding linguistic values.
  • the degree of the precondition can be formed in particular by the maximum of the degrees of membership of the input variables to their corresponding linguistic values.
  • the logical AND linkage and / or the logical OR linkage can be carried out using limited sums.
  • the determination of a resultant membership function of an action of a rule takes place by shifting the degree of the precondition of the rule, that is, the logically linked premises, the 'if' part of the rule to the linguistic value of the action of the rule, the 'then'. Part of the rule.
  • the flipping also called inference, can take place by forming the minimum between the degree of the precondition and the membership function of the action, that is to say by graphically cutting off the membership function of the action at the level of the precondition.
  • the switch can be made by product formation between the degree of precondition and the membership function of the action.
  • a rule can include two or more premises, thus two or more linguistic values, as a precondition. Two or more linguistic values can be the same. Alternatively or in addition, two or more process variables that belong to the logical values of the precondition can be the same.
  • the determination of an overall membership function on the basis of the first resulting membership function of the action of the at least first predetermined rule can be done in particular by equating the overall membership function with the resulting first membership function of the action.
  • the resulting first membership function can be additionally modified by weighting, in particular by multiplication with a weighting function over the range of an output variable of the rule, and / or by cutting off at predetermined limits of the value range of the output variable.
  • an output variable (defuzzification) from the overall membership function can be done in particular by determining the abscissa value of the center of gravity of the area located under the overall membership function.
  • any value of the output variable for which the overall membership function has a maximum can be selected according to the max criterion method.
  • the mean value over the set of values of the output variable for which the overall membership function assumes its (global) maximum can be selected as the value of the output variable.
  • a virtual reality or augmented reality output can be presented to the operator via the output unit 230 and a corresponding design of the output interface 240, in particular via a display device (not shown in FIG. 2).
  • the virtual reality or augmented reality output can be used both to support the dialog with the operator and to display diagnostic and other operating data.
  • the virtual reality or augmented reality output can contain a particularly simulated, animated representation of information, for example about operating processes of machines in the filling plant.
  • An elaborate filling system 300 which can be operated with the aid of the communication and control systems KSR1, KSR2 and KSR3 shown in FIG. 1 is shown by way of example in FIG. 3.
  • the filling system 300 for filling containers 302, 303 with a liquid product, such as a beverage or the like comprises a filling machine 305 for filling and closing the containers 302, 303 and a distribution device 306 provided downstream of the filling machine 305 for distributing the containers 302, 303 on two separately controllable transport routes 307,
  • container buffer 309, 310 in each of which at least one container buffer 309, 310 with adjustable container guides 309a, 310a is provided.
  • the container buffers 309, 310 are followed by labeling machines 311, 312 and packaging machines 313, 314 for making container bundles 315. These are fed to a collecting and distributing device 316, so that the container bundles 315 are arranged on sorting tracks 317 provided downstream of the collecting and distributing device 316 can be distributed and fed to a picking device 318.
  • the transport routes 307, 308 each comprise first sections 307a, 308a on the input side, which are single-track and are designed for the pressure-free transport of the containers 302, 303. Furthermore, the transport routes 307, 308 comprise second sections 307b, 308b on the output side, which are each formed in multiple lanes for the pressure-free transport of the containers 302, 303.
  • the filling system 300 comprises a blow molding machine 319, 320.
  • separate blow molding machines 319, 320 are provided for making different containers 302, 303, for example containers of different geometric shapes.
  • At least one of the blow molding machines 319, 320 can be connected to the filling machine 3055 via an input-side transport path 321. Different incoming container streams can be fed for further processing via an inlet-side switch 305a.
  • Additional production units 323, 324 can be provided, for example, in the form of shrink tunnels.
  • a central control / regulation unit 322 is provided for controlling the filling system 300 according to the invention, which, in particular, includes the distribution device 306, the container buffers 309, 310, the labeling machines 311, 312 and production units upstream of the distribution device 306, such as the filling machine 305 and the blowing machines 319, 320 communicates.
  • the control / regulation systems KSR1, KSR2 and KSR3 or chatbots K1, K2, K3 shown in FIG. 1 can be part of the central control / regulation unit 322. Implementations with central and distributed data processing are possible.
  • the labeling machines 31 1, 312 are connected to chatbots K1, K2, the filling machine 305 to the chatbot K3 and the blow molding machines 319, 320 to the chat bots K4, K5.
  • the chatbots K1, K2, K3, K4 are logically assigned to the respective machines of the filling system 300.
  • all machines of the filling plant 300 can be equipped with chatbots, and the chatbots can be networked with one another so that they can exchange information about the operating states of the machines and the requirements of the operators.
  • the networking of chatbots with other machines, mobile collaborative robots, but also smartphones for operators, etc. can be restricted to a defined internal area (e.g. in the form of a company network) and at the same time an exchange on the Internet for independent learning of the chatbot , for example with regard to speech recognition or speaker identification).
  • the central control unit 322 is connected to the chatbots and can take over the coordination of the machines and transport technology at least partially, for example when organizing the system production and changing the type of product.
  • each machine can be assigned a communication and control / regulation system with a chatbot and control / regulation device.
  • An operator can operate the respective machines via the chatbots K1, K2, K3, K4, K5 with the aid of voice inputs and voice dialogs.
  • the chatbots can use display devices positioned at the machines to display information.
  • chatbots K1, K2, K3, K4, K5 can question inputs by the operator, submit solutions for presented or recognized problems or initiate specific actions.
  • each of the chatbots K1, K2, K3, K4, K5 can be designed to react to a dialog with an operator by another operator (depending on the qualification or for faster implementation / elimination, for example, of Setup processes or malfunctions) to call for support or to request a suitable free mobile collaborative robot and to commission it directly with a specific action (for example, the chatbot K2 in FIG. 3 is connected to a collaborative robot CR).
  • chatbots K1, K2, K3, K4, K5 can be designed to automatically terminate, prepare and start production via a control device, for example with the aid of the central control / regulation unit 322, and to accordingly adjust the material flow organize and tell the operator what tasks are to be done. It would also be possible to change an already initiated shutdown in such a way that the shutdown does not have to be carried out and then restarted.
  • the automatically extended production would also be conceivable because, for example, a new order for the product was received shortly and the necessary materials for the production are available or can be reordered in good time.
  • a reason for production that is longer or shorter in time could also be the receipt or lack of receipt of empties (for example from the beverage trade), which is delivered at short notice and can be fed into the filling system 300 or cannot be fed.
  • chatbots K1, K2, K3, K4, K5 are a dialogical clarification with the operator of which exact other product will be produced and in what quantity before switching production to another product or outputting information to the operator about which tasks for the production of a new product have to be done in which order (for example also cleaning and maintenance processes, general set-up processes such as material change, format part change, settings etc.).
  • operator support can be provided by the chatbots K1, K2, K3, K4, K5 in that automatic sub-processes such as cleaning / sterilization etc. are started in the correct order and / or in a predetermined time interval without the operator having to confirm again.
  • setup parts and tools / materials required for a setup process are sorted and provided by cobots, automated guided vehicles or forklift drivers,
  • animations or videos are offered and presented so that the location for the respective task to be performed is visually displayed on the machine, or
  • the respective presentation of information / data can also take place via a mobile control unit and / or smartphone.
  • expected states of the filling system 300 for example predictable / predictable stops or necessary interventions, for example accompanied by a voice output, can be displayed.
  • Preventive measures for example to avoid stops or malfunctions, CIP or cleaning processes and intermediate disinfection in aseptic systems etc. can also be recommended and / or initiated directly.
  • a dialogue with the operator can be carried out in advance, in which, for example, at the end of the shift in the evening / at the weekend after the start of the shift the next morning / Monday, the question is asked (for example: "When should the system / machine be ready for production?") ,
  • All of the above-mentioned operator support can be personalized based on a correspondingly created and updated operator profile, in particular depending on the level of knowledge and competence of the operator, since each chatbot can learn dynamically from the individual dialogues with the operator (see above).
  • the artificial intelligence implemented in the communication configuration 200 can learn in the course of regular use how long certain tasks, such as setting up and debugging, actually take. Based on this data, production planning is adjusted, for example, or suggestions are made to the shift manager. Data such as inventory and also the weather or the ambient conditions (temperature, humidity, time, etc.) can also be included in the optimization of the production data.

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  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
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  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Manipulator (AREA)
  • General Factory Administration (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

Système de communication et de commande/réglage pour une installation de remplissage La présente invention concerne un système de communication et de commande/réglage pour au moins une installation de remplissage, l'installation de remplissage comprenant une machine qui comprend un robot logiciel de communication, en particulier un agent conversationnel, lequel est conçu pour reconnaitre une entrée vocale et/ou une entrée de texte par un opérateur ; et un dispositif de commande/réglage, qui est connecté au robot logiciel de communication pour une communication de données et qui est conçu pour commander et/ou régler la machine de l'installation de remplissage sur la base de l'entrée vocale et/ou entrée de texte reconnue par le robot logiciel de communication.
EP19727980.5A 2018-07-26 2019-05-24 Système de communication et de commande/réglage pour une installation de remplissage Pending EP3827318A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102018212503.1A DE102018212503A1 (de) 2018-07-26 2018-07-26 Kommunikations- und Steuerungs-/Regelungssytem für eine Abfüllanlage
PCT/EP2019/063525 WO2020020515A1 (fr) 2018-07-26 2019-05-24 Système de communication et de commande/réglage pour une installation de remplissage

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EP3827318A1 true EP3827318A1 (fr) 2021-06-02

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EP19727980.5A Pending EP3827318A1 (fr) 2018-07-26 2019-05-24 Système de communication et de commande/réglage pour une installation de remplissage

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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11520304B2 (en) * 2019-04-04 2022-12-06 William B. Pappas Automation of self-lifting forklift
EP3983859A1 (fr) * 2019-06-17 2022-04-20 Grundfos Holding A/S Système et procédé implémentés par ordinateur pour commander et surveiller une pompe
DE112020007632A5 (de) * 2020-10-23 2023-07-20 Cafer Tosun Verfahren zur steuerung eines prozesses mit einem programmprodukt
DE102022102748A1 (de) 2022-02-07 2023-08-10 Arburg Gmbh + Co Kg Verfahren zur Steuerung von Prozessen an Kunststoff verarbeitenden Maschinen

Family Cites Families (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4442586B4 (de) 1994-11-30 2004-02-12 Krones Ag Vorrichtung zum Verteilen von Gefäßen
DE10026263B4 (de) * 1999-08-27 2004-04-08 Siemens Ag Steuerungsverfahren für eine industrielle technische Anlage, insbesondere eine Werkzeugmaschine oder einen Roboter
JP2004164024A (ja) 2002-11-08 2004-06-10 Toshiba Mach Co Ltd 管理支援装置、そのシステム、その方法、そのプログラム、および、そのプログラムを記録した記録媒体
CN1515993A (zh) * 2003-01-10 2004-07-28 刘书铭 可携式语音输入模块
DE202004012848U1 (de) 2004-08-17 2005-03-31 Krones Ag Vorrichtung zum Transportieren von Gegenständen
DE102007024106B4 (de) * 2007-05-22 2009-12-03 Khs Ag Füllsystem
US7676294B2 (en) * 2007-09-27 2010-03-09 Rockwell Automation Technologies, Inc. Visualization of workflow in an industrial automation environment
DE102009003475A1 (de) 2009-02-12 2010-08-19 Krones Ag Förderer und Verfahren zum Beschicken einer Weiterverarbeitungseinheit
DE102009040977B4 (de) 2009-09-11 2022-12-15 Krones Aktiengesellschaft Behältnisbehandlungsanlage und ein Behältnisbehandlungsverfahren zum Behandeln von mit einem Produkt befüllbaren Behältnissen
US9529343B2 (en) * 2010-06-30 2016-12-27 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Dialogue system and method for examining machining processes
DE102011017448A1 (de) * 2011-04-18 2012-10-18 Krones Aktiengesellschaft Verfahren zum Betreiben einer Behältnisbehandlungsanlage mit Störungsdiagnose
DE102013214052A1 (de) * 2013-07-17 2015-02-19 Krones Ag Behälterbehandlungsmaschine mit Display
JP6346480B2 (ja) 2014-03-25 2018-06-20 アンリツインフィビス株式会社 表示装置および物品検査システム
JP6337556B2 (ja) 2014-03-25 2018-06-06 岩崎電気株式会社 容器に対する殺菌処理システム
DE102015204922A1 (de) 2015-03-18 2016-09-22 Krones Ag Datenaustausch zwischen einer Maschine und einem externen Steuermodul in der Getränkemittelindustrie
US10528020B2 (en) * 2016-08-09 2020-01-07 Johnson Controls Technology Company Building control system with adaptive user interface
US20180129181A1 (en) * 2016-08-17 2018-05-10 BioHiTech America, LLC Chatbot Systems and Methods for Industrial Machinery
DE102016115694A1 (de) * 2016-08-24 2018-03-01 Krones Ag Behälterbehandlungsanlage und Verfahren für Behälterbehandlungsanlage mit Signalen für geplante Zustände
CN206121530U (zh) * 2016-08-27 2017-04-26 彩城（惠州）化工有限公司 一种基于语音控制功能的漆料搅拌装置
US9947319B1 (en) * 2016-09-27 2018-04-17 Google Llc Forming chatbot output based on user state
CN206108878U (zh) * 2016-10-18 2017-04-19 冷昕玥 一种可以语音控制的矿泉水生产用自动灌装机
US20190339671A1 (en) * 2017-01-05 2019-11-07 Itamar Izhak Yona Systems and methods for automatic three-dimensional object printing
DE102017109736A1 (de) * 2017-05-05 2018-11-08 Storopack Hans Reichenecker Gmbh Vorrichtung und Verfahren zum Polstern mindestens eines Gegenstands in einem Behälter

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JP2021532467A (ja) 2021-11-25
WO2020020515A1 (fr) 2020-01-30
DE102018212503A1 (de) 2020-01-30
BR112021001129A2 (pt) 2021-04-20
CN112771459A (zh) 2021-05-07
JP7476469B2 (ja) 2024-05-01
US11772836B2 (en) 2023-10-03
US20210292025A1 (en) 2021-09-23

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