PROCESS AND SYSTEM FOR PROCESSING AND USE OF WEIGHING DATA

Description

[0001] The present invention deals with a process and a system for processing and using weighing data.

[0002] Nowadays, weighing is performed by measuring the load to be weighted via sensors called load cells. Load cells are electronic transducers that pass the force applied by the mass to be measured into electric signals and one or more load cells interconnected create a weighing system. The load cells generate an electric signal in the range of millivolts, and this is used by an Electronic Display Unit (EDU) to be digitized and in most of the cases is represented in a display on the EDU. The EDU can also pass the measured value into a protocol to be used by a superior element like a Programmable Logic Controller (PLC) or a Human Machine Interface (HMI) to develop, for example, a manufacturing process like mixing materials with a manufacturing recipe. In the last years, load cells have already been digitized, so the output signal from the load cell is read as digital signal by the EDU or this signal can be used directly by a superior element. The measured data is used in this case for the process itself (packaging materials, dosing materials for manufacturing a specific recipe, etc.). This process is used in the food industry, chemical industry, packaging of goods, etc. It is important to understand that weighing is fundamental, since, without this weighing process, it is not possible to continue in the manufacturing process. For that reason, it is essential to keep the whole weighing system along with its electronics working at all times. The cost for maintaining, servicing and keeping up to date the weighing system is usually performed by external specialists. The cost of a halt of the production due to a failure in the weighing process represents a serious loss for the producer.

[0003] Document DE 10 2021 134294 A1 discloses an edge device and an electronic display unit. Document US 2008 / 150911 A1 discloses a hand-held electronic device, comprising a case having one or more major surfaces; a touch screen disposed on at least one of the major surfaces; a processor operably coupled to the touch screen.

[0004] Object of the present invention is solving the aforementioned prior art problems by providing a process and a cloud-based system for processing and using weighing data and which monitor continuously the technical situation of each sensor by reading the data produced by the EDU. Additionally, it is possible to read data from alternative sensors as temperature, relative humidity, vibration, pressure, concentration of specific gases, etc. that may have direct or indirect impact in the core process of weighing.

[0005] The above process and system are designed to revolutionize the weighing processes and the maintenance in various industries. By providing users with access to comprehensive data from their weighing systems, including load cell zero values, historical weighing and time of performed load on load cells, the present invention empowers businesses to enhance their maintenance strategies and optimize their weighing processes. This data-driven approach not only ensures efficient scale maintenance, but also leads to improved overall effectiveness and productivity for the end user's business. The present invention is based on technologies such as Industrial Internet of Things, Machine Learning, Artificial Intelligence and big data analytics.

[0006] The above and other objects and advantages of the invention, as will appear from the following description, are achieved with a process and a system like those claimed in their respective independent claims. Preferred embodiments and non-trivial variants of the present invention form the subject matter of the dependent claims.

[0007] It is understood that all attached claims form an integral part of the present description.

[0008] It will be immediately obvious that numerous variations and modifications can be made to what is described (for example relating to shape, dimensions, arrangements and parts with equivalent functionality) without departing from the scope of the invention, as appears from the attached claims.

[0009] The present invention will be better described by some preferred embodiments thereof, provided as a nonlimiting example, with reference to the attached drawings, in which Figure 1 shows a block diagram of a preferred embodiment of the system of the present invention.

[0010] Regarding the definitions used in the present description: Electronic Display Unit (EDU): Device which powers the sensor and digitizes its value and represents it on a display as a numeric value.

[0011] Load cell: A load cell is a transducer that converts a mechanical force or weight into an electrical signal.

[0012] Programmable Logic Controller (PLC) : is an industrial computer that has been ruggedized and adapted for the control of manufacturing processes, such as assembly lines, machines, robotic devices, or any activity that requires high reliability, ease of programming, and process fault diagnosis.

[0013] Human Machine Interface (HMI): It refers to the design and development of interfaces that enable people to interact with machines, such as computers, software, and other digital devices.

[0014] Cloud Computing: is the delivery of computing services over the Internet, allowing users to access and utilize resources such as storage, servers, and software on-demand without the need for a physical infrastructure or a direct management. It offers scalability, cost-efficiency, accessibility, and agility.

[0015] Machine Learning (ML): Machine Learning is a subfield of artificial intelligence that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.

[0016] Artificial Intelligence (AI): Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI systems use algorithms and machine learning techniques to analyse data and make predictions or decisions based on that data.

[0017] Industrial Internet of Things (IIoT): refers to the integration of IoT technologies in industrial settings, such as manufacturing, logistics, and energy production, to improve efficiency, productivity, and decision-making. IIoT enables real-time monitoring and control of industrial processes, and predictive maintenance, among others.

[0018] Product as a Service (PaaS): is a business model where a company provides a product to customers on a subscription basis, rather than selling a one-time product. This allows customers to access the product without having to invest in the underlying infrastructure or technology.

[0019] With reference to Fig. 1, the system (1) of the present invention is employed for processing and using weighing data, and substantially comprises: an edge device (3) designed to read monitored weighing data from an Electronic Display Unit, EDU (9) a plurality of alternative sensors (5), including load cells, operatively connected to the edge device (3) and designed to send weighing signals to the Electronic Display Unit (9) a plurality of actuators (7), operatively connected to the edge device (3) and designed to perform actions following weighing results a weighing system (11), operatively connected to the Electronic Display Unit (9) a plurality of operating components (13), namely PLC, displays, HMI, operatively connected to the Electronic display unit (9) and designed to receive operating instructions following weighing results a back-end software cloud layer (15), communicatively connected to the edge device (3) in order to receive therefrom the monitored weighing data, and designed to operate as cloud computing environment, namely an environment of virtual servers and additional devices which can be deployed on a specific company hardware server or can be acquired as a service a front-end software cloud layer (17), communicatively connected to the back-end software cloud layer (15) and also being part of the cloud computing environment, the front-end software cloud layer (17) being designed to perform external accesses from the environment and to provide access from outside to the monitored weighing data processed by the environment.

[0020] In particular, the back-end software cloud layer (15) comprises: a back-end virtual server (19) designed to process the monitored weighing data received from the edge device (3), the back-end virtual server (19) comprising a plurality of applications, namely Machine Learning applications (23) and Artificial Intelligence applications (25), designed to process the monitoring weighing data through Machine Learning algorithms to perform tasks on the monitoring weighing data a database (21) operatively connected to the virtual server (19) and designed to store the monitored weighing data a IoT core (27) operatively connected to the back-end virtual server (19) and communicatively connected to the edge device (3).

[0021] Still in particular, the front-end software cloud layer (17) comprises: a front-end virtual server (29) comprising a Web application (31) and a SMS Gate (33) and operatively connected to an external network, for example Internet computer means operatively connected to the Web application and the SMS Gate.

[0022] Preferably, the computer means comprise personal computers, laptop computers, tablets, smartphones or similar components.

[0023] At the direct measurement level, the system (1) of the invention is compound of the edge device (3), a hardware device that is capable of reading data from the EDU (9), alternative sensors (5) (load cells included) and be connected to actuators (7) that might perform actions if required. This edge device (3) communicates the information obtained (this procedure is called monitoring) to a cloud computing environment. The cloud computing environment is an environment of virtual servers (19, 29) and additional services (23, 25, 31) that can be deployed on a specific company hardware server or can be acquired as a service (e.g. Amazon Web Services (AWS), Google Cloud Services (GCS), Microsoft (AZURE), etc.). As this service is a cloud one with access from any part of the world, it is very flexible. The data is sent from the edge device (3) to this Cloud computing environment via a wireless service (NB-IOT, 5G, etc.). The monitored data is stored in the database (21). The data is stored with one main objective, to be able to obtain historical view of the measurements and applying machine learning (ML) algorithms to perform various tasks on this data.

[0024] The invention also deals with a process for processing and using weighing data using the above-described system (1); the process comprises the steps of: reading, with the edge device (3), monitored weighing data from the Electronic Display Unit (9) sending, through the plurality of alternative sensors (5), including load cells, weighing signals from the weighing system (11) to the Electronic Display Unit (9) performing actions, through the plurality of actuators (7), following the weighing results sending to the plurality of operating components (13), namely PLC, displays, HMI, operating instructions following the weighing results sending from the edge device (3) to the back-end software cloud layer (15) the monitored weighing data through the front-end software cloud layer (17), performing external accesses from the environment and providing access from outside to the monitored weighing data processed by the environment.

[0025] In particular, on the back-end software cloud layer (15), the process comprises the steps of: through the back-end virtual server (19), processing the monitored weighing data received from the edge device (3) with the IoT core (27), using a plurality of applications, namely ML applications (23) and AI applications (25), designed to process the monitoring weighing data through Machine Learning algorithms to perform tasks on the monitoring weighing data storing the monitored weighing data on the database (21).

[0026] With the use of these tools, the system and process of the invention allow acquiring different resulting valuable data for the users: Trends on behaviours or each load cell and rest of the sensors to perform an analysis for predictive maintenance. (e.g. measuring the output of each load cell on zero during longer periods, can determine shift of zero and based on the speed of this shift, determine the predicted moment where the load cell is going to reach its limit of use) Mutual information (MI) analysis of each sensor in order to determine hidden parameters that may improve information on the production (e.g. MI between weighed poultry in a farm to obtain best results based on amount of food and water supplied, CO 2 concentrations during the day, temperature and RH in the place, etc.) Classification algorithms for establishing external factors influencing the accuracy of measurements (e.g. measuring the behaviour of the load cells under a silo can determine, after having monitored enough samples, if wind forces pushing on the silo are the source of the noise, and correct the measuring of supplied material during this moments with gusts of wind) Added value having a continuous checking of the system and supplying automated reports with the data acquired (e.g. managers of the company may have reports on the continuous measuring and reporting on anomalies out of range by the ML classification algorithms) Automated delivery of alarms by SMS, e-mail and phone calls with voice generated advice via Artificial Intelligence (AI) when reaching milestones predicted by the ML (e.g. measuring forces of struts in construction of basements, may run off range due temperature expansion of metal elements (no alarm) or by underground water pressure that can be a longer but more dangerous process, and need to be predicted and an alarm issued) Data driven decisions are improved by generating series of hidden correlations that can be observed only by long term monitoring.

[0027] The Cloud computing structure is divided between the backend, where has the stored data and the processing of data with the AI and ML tools, and the frontend, where there is an access to Internet and the web server, which gives access of the processed data to the client. The client gets the monitored data and the results of the processed data via a dashboard. Each client has a specific dashboard depending on measured data, type of processed data, etc. This process is a Product as a Service (PaaS) supplied with periodical payments of maintenance of the infrastructure, data processing and other programming services the client might require. This flexibility of software deployment that can be updated instantly as the client uses it via a web browser, is one main advantage compared with standard software packages.

Claims

1. System (1) for processing and using weighing data, the system (1) comprising: - an edge device (3) designed to read monitored weighing data from an Electronic Display Unit (9) - a plurality of alternative sensors (5), including load cells, operatively connected to the edge device (3) and designed to send weighing signals to the Electronic Display Unit (9) - a plurality of actuators (7), operatively connected to the edge device (3) and designed to perform actions following weighing results - a weighing system (11), operatively connected to the Electronic Display Unit (9) - a plurality of operating components (13), namely PLC, displays, HMI, operatively connected to the Electronic Display Unit (9) and designed to receive operating instructions following weighing results - a back-end software cloud layer (15), communicatively connected to the edge device (3) in order to receive therefrom the monitored weighing data, and designed to operate as cloud computing environment, namely an environment of virtual servers and additional devices which can be deployed on a specific company hardware server or can be acquired as a service - a front-end software cloud layer (17), communicatively connected to the back-end software cloud layer (15) and also being part of the cloud computing environment, the front-end software cloud layer (17) being designed to perform external accesses from the environment and to provide access from outside to the monitored weighing data processed by the environment.

2. System according to claim 1, wherein the back-end software cloud layer (15) comprises: - a back-end virtual server (19) designed to process the monitored weighing data received from the edge device (3), the back-end virtual server (19) comprising a plurality of applications, namely Machine Learning applications (23) and Artificial Intelligence applications (25), designed to process the monitoring weighing data through Machine Learning algorithms to perform tasks on the monitoring weighing data - a database (21) operatively connected to the virtual server (19) and designed to store the monitored weighing data - a IoT core (27) operatively connected to the back-end virtual server (19) and communicatively connected to the edge device (3).

3. System according to claim 1 or 2, wherein the front-end software cloud layer (17) comprises: - a front-end virtual server (29) comprising a Web application (31) and a SMS Gate (33) and operatively connected to an external network, for example Internet - computer means operatively connected to the Web application and the SMS Gate.

4. System according to claim 3, wherein the computer means comprise personal computers, laptop computers, tablets or smartphones.

5. System according to any one of the previous claims, wherein the monitored weighing data, after being processed, provide one or more of the following results: - Trends on behaviours or each load cell and rest of the sensors to perform an analysis for predictive maintenance - Mutual information (MI) analysis of each sensor in order to determine hidden parameters that may improve information on the production - Classification algorithms for establishing external factors influencing the accuracy of measurements - Added value having a continuous checking of the system and supplying automated reports with the data acquired - Automated delivery of alarms by SMS, e-mail and phone calls with voice generated advice via Artificial Intelligence when reaching milestones predicted by the Machine Learning - Data driven decisions are improved by generating series of hidden correlations that can be observed only by long term monitoring.

6. Process for processing and using weighing data using the system (1) according to any one of the previous claims, the process comprising the steps of: - reading, with the edge device (3), monitored weighing data from an Electronic Display Unit (9) - sending, through a plurality of alternative sensors (5), including load cells, weighing signals from the weighing system (11) to the Electronic Display Unit (9) - performing actions, through the plurality of actuators (7), following the weighing results - sending to the plurality of operating components (13), namely PLC, displays, HMI, operating instructions following the weighing results - sending from the edge device (3) to the back-end software cloud layer (15) the monitored weighing data - through the front-end software cloud layer (17), performing external accesses from the environment and providing access from outside to the monitored weighing data processed by the environment.

7. Process according to claim 6, comprising, on the back-end software cloud layer (15), the steps of: - through the back-end virtual server (19), processing the monitored weighing data received from the edge device (3) with the IoT core (27), using a plurality of applications, namely Machine Learning applications (23) and Artificial Intelligence applications (25), designed to process the monitoring weighing data through Machine Learning algorithms to perform tasks on the monitoring weighing data - storing the monitored weighing data on the database (21).

8. Process according to claim 6 or 7, wherein the monitored weighing data, after being processed, provide one or more of the following results: - Trends on behaviours or each load cell and rest of the sensors to perform an analysis for predictive maintenance - Mutual information (MI) analysis of each sensor in order to determine hidden parameters that may improve information on the production - Classification algorithms for establishing external factors influencing the accuracy of measurements - Added value having a continuous checking of the system and supplying automated reports with the data acquired - Automated delivery of alarms by SMS, e-mail and phone calls with voice generated advice via Artificial Intelligence when reaching milestones predicted by the Machine Learning - Data driven decisions are improved by generating series of hidden correlations that can be observed only by long term monitoring.