WO2023068959A1 - Système modulaire de collecte et d'analyse d'informations dans un environnement industriel - Google Patents

Système modulaire de collecte et d'analyse d'informations dans un environnement industriel Download PDF

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Publication number
WO2023068959A1
WO2023068959A1 PCT/RU2021/000448 RU2021000448W WO2023068959A1 WO 2023068959 A1 WO2023068959 A1 WO 2023068959A1 RU 2021000448 W RU2021000448 W RU 2021000448W WO 2023068959 A1 WO2023068959 A1 WO 2023068959A1
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data
sensors
computing
computing module
module
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PCT/RU2021/000448
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English (en)
Russian (ru)
Inventor
Вадим Вячеславович ПУТРОЛАЙНЕН
Максим Александрович БЕЛЯЕВ
Валентин Валерьевич ПЕРМИНОВ
Павел Владимирович ЛУНЬКОВ
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Акционерное общество "ДжиЭс-Нанотех"
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Priority to PCT/RU2021/000448 priority Critical patent/WO2023068959A1/fr
Publication of WO2023068959A1 publication Critical patent/WO2023068959A1/fr

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    • 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
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/048—Monitoring; Safety
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Definitions

  • the invention relates to the field of monitoring production processes and industrial equipment based on the collection and analysis of information obtained from a variety of sensors placed in an industrial environment.
  • the industrial environment is understood as a set of industrial equipment serving the infrastructure, including buildings, premises, communication lines, power and water supply lines used in the production of materials, air or water environment in which industrial equipment is located, as well as working personnel.
  • the invention is intended to analyze the current state of production processes and industrial equipment, to predict failures of industrial equipment or its individual components, to form recommendations on the modes of operation of industrial equipment.
  • the information collection system should provide functionality for preliminary processing of signals from sensors. For example, when analyzing a periodic signal, it often becomes necessary to represent it in the frequency domain, which can be implemented using the Fourier transform. Averaging the signal over a certain time period can be used to reduce the effect of noise. In some cases, it is necessary to use statistical processing of signals from several sensors using the methods of regression, correlation, and dispersion analysis. In some cases, it is useful to send status information production equipment or individual parts, which is determined based on the classification of a combination of signals from several sensors.
  • machine learning methods can be used, such as logistic regression, support vector machine, decision trees, Bayesian network, nearest neighbor method, neural networks, etc. These methods can be used for parallel processing of data obtained from multiple sensors and most often operate data presented in matrix form.
  • performing matrix calculations on traditional microprocessor architectures requires a lot of time due to the small number of arithmetic logic units.
  • the prior art is a device for collecting and processing data from multiple sensors [L. Sheynblat, T. Wolf, and A. Hodisan, “Multi-sensor data collection and/or processing,” US Patent application No. US20200029814A1, 01/30/2020], consisting of many sensors that can be placed both on the device itself and connected to it using wired and wireless interfaces, and a processing processor.
  • the processing processor may include various computing devices, such as special integrated circuits, digital signal processors, programmable logic devices, programmable logic integrated circuits, processors, controllers, microcontrollers, microprocessors, etc.
  • the processing processor may include analog-to-digital converters for converting data from analog sensors, memory for storing read and processed data, and sensor power supply control circuits.
  • the processing processor is used to collect, process and store data received from connected sensors.
  • Sensors can be of various types (accelerometers, gyroscopes, geomagnetic sensors, pressure sensors, biometric and temperature sensors, etc.) and connect using various wireless technologies (Bluetooth, Zigbee, NFC, Wi-Fi, etc.).
  • the data acquisition and processing device may be connected to external processing and transmitting devices and may transmit raw and processed data from connected sensors.
  • the disadvantages of this device are: the lack of a modular structure of the device for collecting and processing data, which complicates the use of the device to work with a large distributed array of sensors, as well as the lack of hardware acceleration of matrix calculations in the processing processor, which slows down data mining, including using computer methods. learning.
  • a system for monitoring and reporting equipment operating conditions and diagnostic information is known in the art [CL Boyd, M. Rajab, A. Borodaev, V. Fedishov, “Method and system for monitoring and reporting equipment operating conditions and diagnostic information,” WO Patent application No. WO2013036897A1, 03/14/2013], which provides the ability to monitor the status and assess the reliability of one or more industrial installations by creating a network consisting of many sensors temporarily or permanently placed on various parts of these installations. Sensors provide time-ordered information about vibration, temperature, electrical signals, or other operating conditions. The sensors are connected to data collection devices, which transmit the information collected from the sensors over a local or global network using wired or wireless connections as a data stream.
  • This data stream is stored in one of the databases and passed to the data collection service processor for analysis according to the diagnostic configuration.
  • the data acquisition processor divides the received information into a plurality of data packets, each containing only a portion of the original information.
  • the data packets are then stored in multiple databases to facilitate subsequent data retrieval and analysis.
  • the data packets are stored in a cloud storage system.
  • the data collection processor is responsible for distributing data packets to specific tables within databases, depending on their source.
  • the information from the databases is used in the process control services of the industrial equipment control system. Information from the databases is also available to software configured to extract data packets and analyze time-ordered information to determine the functional characteristics of the monitored object based on the diagnostic configuration.
  • the disadvantages of this system are: the lack of data pre-processing in the data collection devices, which significantly increases the load on the data transmission channels between the data collection devices and the data collection processor, as well as the lack of hardware acceleration of matrix calculations in the data collection processor, which slows down data mining, in including using machine learning methods.
  • This device is designed to collect and analyze data from industrial equipment.
  • this device consists of a mother processing system and one or more data acquisition systems.
  • the data acquisition system may have built-in sensors that are located directly on the board. Also, the data acquisition system may have many input interfaces and ports through which it can receive data from external sources. External data sources can be external sensors with a digital or analog interface, as well as other data sources, interaction with which is carried out through a standardized data transfer technology (Wi-Fi, Bluetooth, NFC, Ethernet).
  • the data source for the data acquisition system may be a training feedback signal from the analytical system as part of the parent processing system. The data coming from the sensors can be combined using the built-in multiplexer. The collected data may be buffered or cached in the built-in buffer/cache and may be read by external systems, such as the parent processing system, through the available output interfaces and ports.
  • the data collection system also includes an intelligent input selection system, a self-organizing data warehouse and an analytical system.
  • the intelligent input selection system of the data acquisition system is responsible for selecting the list of input signals that will be used to collect and analyze information using machine learning methods.
  • the selection of input signals can be made using the mother processing system, which also includes an intelligent input selection system.
  • the intelligent input selection system can take advantage of artificial intelligence and machine learning and information about captured system states obtained from external sources or from the parent processing system, which may relate to the operating state of the equipment, the state of the environment, the state of the workflow, the state of the malfunction, etc. Optimization and tuning of the choice of input signals may be performed using a training feedback signal from the mother processing system, which may contain training data and quality assessment metrics calculated by the analytical system of the mother computing system.
  • a data stream consisting of a certain combination of inputs allows positive results to be achieved under a given set of conditions (such as improved pattern recognition, improved prediction, improved diagnostics, increased profitability, increased ROI, increased efficiency, or the like)
  • metrics related to such results from the analytic system of the parent processing system can be provided via a training feedback signal to an intelligent input selection system to help configure the future data collection process. This allows you to later disable other input sources.
  • An intelligent input selection system can be based on genetic programming techniques using a training feedback signal and can be used to select the most efficient input sets from all possible options to optimize and adapt the data acquisition system to unique environmental conditions.
  • the intelligent input selection system of the data acquisition system can be used to combine (fuse) data from various sensors and other data sources into one or several combined data streams, for example, using the built-in multiplexer, to create various signals that represent combinations, permutations, mixtures, etc. of the original analog and / or digital data.
  • the self-organizing memory of the data acquisition system is used for intelligent data aggregation and storage.
  • Data coming from sensors can consume large amounts of storage capacity, especially since multiple sensors can be connected to a data acquisition system.
  • Simply storing all data indefinitely is usually not the best option for a data collection system, and transferring all data may result in exceeding the allowed bandwidth limits (for example, exceeding the volumes of transmitted data of a cellular plan), etc. .
  • data storage strategies are needed that deal with capturing only a part of the collected data, or storing data for a limited period of time, or storing data with the loss of part of the information (data are represented in intermediate or abstract forms).
  • a self-organizing storage can use a learning feedback signal to obtain evaluation metrics (measures of the entire system, analytical indicators, local performance indicators) from an analytical system or an intelligent input selection system of the parent processing system.
  • Self-organizing storage can automatically change storage parameters such as data storage location (local storage, storage on neighboring data collection system, remote storage), data storage volume, data storage time, data storage type (values from individual sensors, or combined and multiplexed values from several sensors), type of storage (RAM, flash memory, hard drive), method of organizing data storage (“raw” data, hierarchical structures, etc.). Changes in parameters can be made over time based on feedback signals, thereby allowing the data acquisition system to adapt to changing environmental conditions.
  • the analytical system which is part of the data collection system, is used to implement data mining. It can use a wide range of analytical methods, including statistical and econometric methods (e.g., linear regression analysis, similarity matrices, heat map methods, etc.), inductive methods (Bayesian inference, rule-based methods, inductive reasoning, and etc.), iterative methods (forward and feedback, recursion, etc.), signal processing methods (Fourier transforms, etc.), pattern recognition methods (using digital filters, such as Kalman filter, etc.), search methods, probabilistic methods (random walk methods, random forest algorithm, etc.), modeling methods (linear optimization, etc.) and a number of other methods, including machine learning methods.
  • statistical and econometric methods e.g., linear regression analysis, similarity matrices, heat map methods, etc.
  • inductive methods Bayesian inference, rule-based methods, inductive reasoning, and etc.
  • iterative methods forward and feedback, recursion, etc.
  • decision trees For example, decision trees, association rules, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machine, Bayesian network, reinforcement learning, rule based learning, sparse learning methods, similarity and metrics based learning, systems of training classifiers, logistic regression, “random forest”, K-means method, gradient enhancement, K-nearest neighbors method.
  • the analytical system of the data collection system can be used to calculate metrics, such as indicators efficiency, energy usage, storage usage, redundancy, entropy and other parameters.
  • the parent computing system consists of an analytical system, an intelligent input selection system, a self-organizing network system, a state monitoring system, a computing architecture, and a policy automation engine.
  • the analytical system of the parent computing system is used to implement data mining and performs and can use the same methods as the analytical system of the data acquisition system, but at the same time is used for more resource-intensive calculations.
  • a learning feedback signal it can pass the value of the evaluation metrics to the data collection system to assist its self-organizing storage, intelligent input selection system, and analytical system.
  • the intelligent input selection system of the parent computing system serves to assist in selecting the list of input signals of data acquisition systems and the configuration of the self-organizing storage, including based on the analysis of data from the totality of all data acquisition systems connected to the parent computing system. For example, if one of the data acquisition systems is already collecting X-axis vibration data, the X-vibration sensor for the other data acquisition system can be disabled in favor of receiving data only on the Y-axis. Thus, due to coordinated data collection, the activity of multiple systems data collection connected to many different sensors can provide a sufficient data set for the parent computing system without undue loss of power, network infrastructure bandwidth, storage space.
  • the self-organizing network system of the parent computer system is used to select the values of the network exchange parameters using information from the training feedback signal and the analytical system.
  • These parameters may include: the method of data transmission in the network (local, cellular, satellite, Wi-Fi, Bluetooth, NFC, Zigbee, etc.), the functional characteristics of the network (for example, the choice of a network that provides the required functions and properties : connection security, cognitive properties, authentication methods, access control, etc.), network data encoding method (random linear network encoding, fixed encoding, etc..), network cost characteristics (for example, network settings based on conditions pricing for data delivery and/or traffic transmission costs, etc.), quality characteristics of data transmission (e.g. in the current environment), choice of network protocol (e.g.
  • the self-organizing network system can find configurations that are well adapted to the environment monitored by the mother computer system.
  • the monitoring system of the state of the parent computing system provides information about the events that have occurred, the state of the environment, operating conditions, states of work processes, the presence of errors or other diagnostic conditions. Based on the input information, the condition monitoring system can calculate the current state, or predict the future state, relating to data collection systems, of the environment in which data collection systems are located, such as the state of equipment, component, workflow, process, event (e.g., whether an event occurred or not), object, person, and etc. Up-to-date information about such states allows the parent computing system to use it in the operation of the analytical system to determine contextual information, apply semantic and conditional logic, and perform a number of other functions.
  • the computing architecture is a set of computing components of the parent computing system.
  • the composition of the computing architecture may include: microcontrollers, embedded microcontrollers, microprocessors, digital signal processors, specialized integrated circuits, programmable logic integrated circuits, coprocessors (mathematical, graphic, communication, etc.) and other types of computing devices.
  • the policy automation engine is used to deploy and manage Internet of Things (IoT) devices using policies. These policies can be access policies, network usage policies, storage usage policies, bandwidth usage policies, device connection policies, security policies, role and rule-based policies, and other policies that may be required to manage IoT devices. For example, since IoT devices can use many different networks to communicate with other devices, policies may be required that specify which devices a given device can connect to, what data can be transmitted and what data can be received.
  • the policy automation engine may use cognitive functions to create, configure, and manage policies, and obtain information about possible policies from a policy database or library, which may include one or more public policy sources.
  • the policy automation engine may apply policies according to one or more models, such as characteristics of a given device, equipment, or environment.
  • the policy automation engine can include cognitive functions (such as changing policy rules, policy configuration, etc.) based on information from a state monitoring system or learning feedback from an analytics system By changing and selecting policies, the policy automation engine can learn over time automatically create, deploy, configure and manage policies for a large number of devices.
  • cognitive functions such as changing policy rules, policy configuration, etc.
  • the disadvantage of the prototype is the lack of hardware acceleration of matrix calculations in the parent processing system, which are used in various methods of data preprocessing and analysis, including machine learning methods.
  • a modular system for collecting and analyzing information in an industrial environment which can provide accelerated processing and intelligent analysis of data coming from sensors, including using machine learning methods, due to hardware acceleration of matrix calculations.
  • a modular system for collecting and analyzing information in an industrial environment including a backplane on which a control computing module, a data storage module and a data transmission module are located, to which a plurality of sensor computing modules are connected via network connections, each of which connects to a variety of sensors with analog or digital outputs, it is proposed to use a tensor computing module, which is also located on the backplane and is connected to the control computing module to accelerate matrix calculations.
  • FIG. 1 shows a functional diagram of a modular system for collecting and analyzing information in an industrial environment.
  • a modular system for collecting and analyzing information in an industrial environment (1) consists of a backplane (2) on which a control computing module (3), a tensor computing module (4), data storage modules (5), a data transmission module (6) are installed , which in turn is connected to the set sensor computing modules (7), each of which is connected to a plurality of sensors with analog or digital outputs (8) placed in an industrial environment.
  • the backplane (2) has a built-in inertial sensor (9), which simultaneously performs the functions of a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer.
  • the communication module (6) supports wired Ethernet network connections, wired RS-485 serial connections, and wireless Bluetooth connections.
  • a Wi-Fi access point 10 is connected to the data transmission module (6) via a wired network connection of the Ethernet standard.
  • Sensors with digital connection interfaces (11) which use standards and data transmission technologies supported by the data transmission module (6) (Ethernet, RS-485, Bluetooth, Wi-Fi), can be directly connected to the data transmission module (6), bypassing the sensor computing module (7).
  • Sensor computing modules (7) can be connected to the data transmission module (6) either directly or using a network switch (12).
  • the sensor computing module (7) also includes a built-in inertial sensor (13) that measures linear acceleration and angular velocity.
  • Modular system for collecting and analyzing information in an industrial environment (1) can be connected to an external computing device (14) using an Ethernet network connection through a data communication module (6) or through a dedicated network interface (15) on the backplane to transfer collected and processed data from an industrial environment.
  • a dedicated network interface (15) is connected to the control computing module (3) using SGMII interface.
  • FIG. 1 A functional diagram of a modular system for collecting and analyzing information in an industrial environment is shown in Fig. 1.
  • the presented modular system for collecting and analyzing information in an industrial environment (1) assumes the presence of 4 levels of organization: the level of obtaining primary data from sensors, the level of preliminary processing of primary data, the level of transmission of processed data and the level of data analysis.
  • the first level is represented by sensors of various physical quantities (linear acceleration, angular velocity, temperature, voltage, current, etc.), sound and video recording devices. They allow you to obtain primary data that will be subjected to further processing and analysis.
  • Information is transmitted from sensors in analog, discrete or digital form using analog or digital (SPI, I2C, RS-485, Ethernet, Bluetooth, Wi-Fi, etc.) interfaces.
  • the modular system for collecting and analyzing information in an industrial environment (1) uses sensors with analog and digital outputs (8) connected to sensor computing modules (7), built-in inertial sensor (9) on the backplane (2), built-in inertial sensor ( 13) on the sensor computing module (7), and sensors with digital connection interfaces (I), which can be directly connected to the data transmission module (6).
  • the second level of collection and analysis of information in an industrial environment (1) is associated with the preprocessing of data received from sensors.
  • This function is implemented by sensor computing modules (7), which perform: matching of electrical signals (signal amplification, conversion of electrical and logical levels of signals from sensors, etc.), signal capture and representation of data coming from sensors in digital form, accumulation of data coming from data sensors in internal memory, preliminary processing of data coming from sensors, transfer of preprocessed data for further analysis to the control computing module (3) through the data transmission module (6), as well as power management of sensors connected to the sensor computing module (7).
  • Signal level matching is carried out using electronic matching circuits based on discrete radio components.
  • Capture of the signal, representation of the data coming from the sensors into digital form, accumulation and pre-processing of these data is carried out using a microcontroller, and an integrated analog-to-digital converter is used to convert the analog signal.
  • Data pre-processing refers to various mathematical operations on a data array, such as averaging values, calculating root mean square values, Fourier transform, etc.
  • the transmission of preprocessed data is carried out using an Ethernet transceiver connected to the microcontroller using the RMI interface.
  • the built-in inertial sensor (13) is connected to the microcontroller using the SPI interface.
  • the sensor computing module is powered by the technology of power transmission over Ethernet (Power over Ethernet).
  • the third level of the modular system for collecting and analyzing information in an industrial environment (1) is responsible for the transfer of data between the control computing module (3) and other functional elements of the modular system: sensor computing modules (7) using Ethernet , sensors with digital connection interfaces (11) using Ethernet, RS-485, Bluetooth, Wi-Fi and an external computing device (14) using Ethernet.
  • sensor computing modules (7) using Ethernet sensors with digital connection interfaces (11) using Ethernet, RS-485, Bluetooth, Wi-Fi and an external computing device (14) using Ethernet.
  • a microcontroller is used to perform the main function communication module (6), i.e. data reception, their aggregation and transmission.
  • an integrated microcircuit is used, which is connected to the microcontroller using the SPI interface.
  • transceiver chips connected via the UART interface are used.
  • the wired Ethernet connection is implemented using the Ethemet switch chip, which also implements the connection between the data communication module (6) and the control computer module (3) using the SGMII interface.
  • the module is powered by a voltage converter chip supplied from the
  • the last level of a modular system for collecting and analyzing information in an industrial environment (1) performs data analysis and storage and is represented by three modules that are located on the backplane (2): a control computing module (3), a tensor computing module (4) and data storage modules (5).
  • the control computing module (3) is the main controller of the modular system and is a package-on-package chip in which the microprocessor is integrated with operational (DDR) and flash (NAND and NOR) memory. Its functions include: control of data sampling from sensors connected to a modular system for collecting and analyzing information in an industrial environment (1), including through sensor computing modules (7); execution of requests for data processing using a tensor computing module (4); sending data analysis results to data storage modules (5); providing data analysis results to an external computing device (14).
  • Operating systems can be used as system software. systems of the Unix/Linux family.
  • the control computing module is connected to the tensor computing module (4) using the selected interface (PCI, USB, SPI or US ART), and to the data storage modules (5) using the SATA interface.
  • the built-in inertial sensor (9) on the backplane (2) is connected to the control computer module (3) using the SPI interface.
  • Data storage modules (5) perform the functions of writing, reading and storing data and consist of a disk controller chip and several NAND memory chips. The connection between the disk controller and the NAND memory chips is carried out using the ONFi or Toggle interface.
  • the tensor computing module (4) is used to accelerate the processing and intellectual analysis of data collected from sensors, including using machine learning methods, due to hardware acceleration of matrix calculations performed in a modular system for collecting and analyzing information in an industrial environment (1).
  • Matrix calculations are used in data mining, including using machine learning methods such as logistic regression, support vector machine, decision trees, Bayesian network, nearest neighbors, artificial neural networks, etc.
  • a tensor computing module (4) available: a general-purpose microprocessor, a hardware acceleration unit for tensor calculations, RAM and flash memory, an audio signal processing unit, and a hardware acceleration unit for Fourier transforms. Tasks for performing matrix calculations for the tensor computing module (4) are received from the control computing module (3).
  • data is collected from all connected sensors.
  • Data from sensors with analog and digital outputs (8) and built-in inertial sensors (13) can be pre-processed (averaging values, calculating RMS values, Fourier transform, etc.) on sensor computing modules (7) before being sent to the data communication module (6).
  • Data from sensors with digital connection interfaces (I) enters the data transmission module (6) without preprocessing.
  • the data transmission module (6) receives, aggregates and transmits data from all connected sensors to the control computing module (3).
  • the control computing module (3) using data mining algorithms, including using machine learning methods, processes incoming data and generates an array of analytical information about the state of the production process, industrial equipment or its individual nodes.
  • This array of analytical information may include the following data: the presence of discrepancies between the technological parameters of the production process and the required values, the presence of malfunctions of industrial equipment or its individual components, the possibility of failures of industrial equipment or its individual components in the near future, diagnostic information about the state of the production process or industrial equipment, recommendations for technical inspection or scheduled repair of industrial equipment or its individual components, etc.
  • the control computing module (3) creates tasks for performing matrix calculations and transfers them to the tensor computing module (4). After the task is completed, the tensor computing module (4) transmits the results of the matrix calculations back to the control computing module (3).
  • Raw data received from the sensors, pre-processed data and intellectual analysis results can be stored by the control computing module (3) using data storage modules (5). These data can be provided for further analysis to an external computing device (14) using a network exchange.
  • the claimed invention is intended to collect data from a distributed array of sensors located in an industrial environment for monitoring production processes and industrial equipment based on data mining, including using machine learning methods.
  • the claimed invention can be used in various industries such as mechanical engineering, chemical industry, metallurgy, food industry, energy industry, timber industry, auto, aircraft and shipbuilding, etc. This is achieved due to the possibility of using sensors of various physical quantities that can be installed on the monitoring object in the form of a distributed array.

Abstract

L'invention se rapporte au domaine de la surveillance des processus de production et des équipements industriels sur la base de la collecte et de l'analyse d'informations reçues de plusieurs capteurs disposés dans un environnement industriel. Ce système modulaire de collecte et d'analyse d'informations dans un environnement industriel comprend une carte mère connectée à plusieurs modules informatiques de détection à chacun desquels vient se connecter une pluralité de capteurs ayant des sorties analogiques ou numériques. Sur la carte mère se trouvent un module informatique de commande, un module informatique tensoriel, des modules de stockage de données et un module de transmission de données. Les données obtenues des capteurs peuvent être préalablement traitées par les modules informatiques de détection, après quoi elles sont collectées et agrégées par le module de transmission de données en vue d'une analyse ultérieure par le module informatique de commande. Les données collectées et les résultats d'analyse peuvent être transmis en vue d'un traitement ultérieur à un dispositif informatique externe à l'aide d'une interface réseau. L'invention est caractérisée par l'utilisation d'un module informatique tensoriel pour l'accélération matérielle des calculs matriciels qui sont nécessaires lors du processus d'analyse intelligente des données obtenues depuis plusieurs capteurs, y compris en utilisant des procédés d'apprentissage machine.
PCT/RU2021/000448 2021-10-20 2021-10-20 Système modulaire de collecte et d'analyse d'informations dans un environnement industriel WO2023068959A1 (fr)

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Citations (4)

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US20190043201A1 (en) * 2017-12-28 2019-02-07 Christina R. Strong Analytic image format for visual computing
US20190222652A1 (en) * 2019-03-28 2019-07-18 Intel Corporation Sensor network configuration mechanisms
US20200279349A1 (en) * 2017-04-09 2020-09-03 Intel Corporation Machine learning sparse computation mechanism
US20210377279A1 (en) * 2018-09-28 2021-12-02 Intel Corporation Trust management mechanisms

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200279349A1 (en) * 2017-04-09 2020-09-03 Intel Corporation Machine learning sparse computation mechanism
US20190043201A1 (en) * 2017-12-28 2019-02-07 Christina R. Strong Analytic image format for visual computing
US20210377279A1 (en) * 2018-09-28 2021-12-02 Intel Corporation Trust management mechanisms
US20190222652A1 (en) * 2019-03-28 2019-07-18 Intel Corporation Sensor network configuration mechanisms

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