Patent Description:
Patent document <NUM> describes "a diagnosis apparatus <NUM> configured to generate an analysis model for diagnosing an abnormal operation of a facility <NUM> based on detection signals of a plurality of sensors <NUM> and learn the analysis model to improve an accuracy of the diagnosis".

Patent document <NUM> discloses a gateway device comprising a control system interface component that is operable to provide a bidirectional communication between at least one external control system and said gateway device, a field device interface component, a direct data channel that is operable to exchange data between the control system interface component and the field device interface component, a communication interface component that is operable to provide a bidirectional communication between at least one external communication device and said gateway device.

Patent document <NUM> discloses a drive control system for controlling and regulating electric machines and drives, comprising power control electronics for providing a power control for drives, motors, actuators, and/or sensors of machine hardware. According to the invention, the drive control system comprises at least one control/interface unit for M2M protocol-based communication with the power control, wherein the power control has an M2M-protocol interface for bidirectional communication with the interface unit.

Patent document <NUM> discloses an apparatus includes at least one processor and memory storing instructions that, when executed, cause the at least one processor to determine a process control context based on a request for information associated with a field device of a process control system, the process control context based on a configuration of the process control system, identify a topic included in the request, the topic corresponding to the field device based on the process control context, map the topic to an action to be executed by the field device, generate a command to direct the field device to execute the action based on the mapping, and transmit the command to the field device to execute the action.

The dependent claims are directed to preferred embodiments of the invention. According to a first aspect, there is provided a system as defined in claim <NUM>.

The cloud communication unit may receive a third command using the protocol of the first form from the first cloud platform and convert the third command into a fourth command using the protocol of the common form, and the interface unit may transmit the fourth command to the AI processing unit.

The first command may include a sensor list acquisition request command for requesting to acquire a list of sensors provided in the facility managed by the first cloud platform.

The first command may include a sensor data acquisition request command for requesting to acquire measurement data of a sensor provided in the facility managed by the first cloud platform.

The first command may include a model information change request command for requesting to change information of the model managed by the first cloud platform.

The first command may include a configuration information change request command for requesting to change configuration information managed by the first cloud platform.

The interface apparatus does further include the AI processing unit.

According to a second aspect, there is provided an interface method as defined in claim <NUM>.

According to a third aspect, there is provided an interface program as defined in claim <NUM>.

Hereinafter, the present invention will be described by way of embodiments of the invention, but the following embodiments are not intended to limit the invention according to the claims. In addition, not all combinations of features described in the embodiments necessarily have to be essential to solving means of the invention.

<FIG> illustrates one example of a block diagram of an interface apparatus <NUM> according to the present embodiment together with a facility <NUM> and a cloud platform <NUM>. The interface apparatus <NUM> according to the present embodiment provides a function unit configured to execute artificial intelligence (AI) processing related to a state determination (for example, an abnormality diagnosis) of the facility <NUM>, and an interface for the cloud platform <NUM> configured to manage the facility <NUM>. At this time, the interface apparatus <NUM> is configured to receive a command using a protocol of a common form that is not dependent on the cloud platform <NUM> from the function unit configured to execute the AI processing. Then, the interface apparatus <NUM> is configured to convert the command using the protocol of the common form into a command using a protocol of a specific form dependent on any cloud platform, and transmit the converted command to any cloud platform.

The facility <NUM> is a structure set as a target of the state determination by the function unit configured to execute the AI processing. The facility <NUM> may be, for example, a plant. Examples of such a plant may include not only chemical or other industrial plants but also a plant that manages and controls a well source such as a gas field or an oil field and its surrounding area, a plant that manages and controls power generation such as hydroelectric, thermal, or nuclear power generation, a plant that manages and controls environmental power generation such as solar or wind power generation, a plant that manages and controls water and sewerage, a dam, or the like, or other plants. However, the plant is not limited to this. The facility <NUM> may be any structure different from the plant. One or a plurality of sensors <NUM> are provided in the facility <NUM>.

The sensor <NUM> is configured to measure a target physical quantity. For example, the sensor <NUM> may be a retrofitting industrial Internet of Things (IIoT) sensor provided in the plant. As one example, the sensor <NUM> may be capable of acquiring measurement data obtained by measuring a physical quantity such as vibration, a temperature, a humidity, illuminance, odor, a gas concentration, a pressure, an atmospheric pressure, stress, a magnetic field, sound, or an image in the plant. Note that in the above description, the case where the sensor <NUM> is the retrofitting IIoT sensor provided in the plant has been illustrated as one example, but is not limited to this. For example, the sensor <NUM> may be a process control (measurement) sensor or the like previously provided in an operational technology (OT) region, or may also be an industrial sensor or the like connected to or integrally configured with one or a plurality of field devices provided in the plant. The measurement data measured by the sensor <NUM> is collected in the cloud platform <NUM> via a wireless module (not illustrated) or the like provided in the facility <NUM>, for example, and managed in the cloud platform <NUM>.

The cloud platform <NUM> is a cloud system provided in the facility <NUM>. The cloud platform <NUM> has a necessary function such that the function unit configured to execute the AI processing constructs a state determination system of the facility <NUM>. For example, the cloud platform <NUM> may have a function of saving the measurement data of the sensor <NUM> collected via the wireless module in a database. In addition, the cloud platform <NUM> may have a function of activating and executing an application in a cloud.

A plurality of cloud systems respectively having different specifications may exist for providers who provide cloud services, but any type may be used as a cloud provided in the facility <NUM>. In the present drawing, a case is illustrated as one example where the cloud platform <NUM> has a first cloud platform 50A provided by a provider A and a second cloud platform 50B provided by a provider B (which are collectively referred to as the cloud platforms <NUM>). The first cloud platform 50A may provide, for example, a representational state transfer (REST) application programing interface (API) as the interface. In addition, the second cloud platform 50B may provide, for example, GraphQL as the interface. In this manner, the plurality of cloud platforms <NUM> may provide a plurality of interfaces respectively having different specifications.

The interface apparatus <NUM> according to the present embodiment is configured to provide the interface between the cloud platforms <NUM> respectively having different specifications for the providers who provide the cloud services in this manner and the function unit configured to execute the AI processing.

The interface apparatus <NUM> may be a computer such as a personal computer (PC), a tablet computer, a smartphone, a work station, a server computer, or a general purpose computer, or a computer system in which a plurality of computers are connect to each other. Such a computer system is also the computer in a broad sense. In addition, the interface apparatus <NUM> may be implemented in one or a plurality of virtual computer environments which can be executed in the computer. Instead of this, the interface apparatus <NUM> may be a dedicated computer designed for the interface, or may also be dedicated hardware realized by a dedicated circuit. In addition, the interface apparatus <NUM> may be realized by cloud computing. For example, the interface apparatus <NUM> may be a system added in on the cloud platform <NUM> by execution of an interface program by the computer.

The interface apparatus <NUM> includes an interface unit <NUM> and a cloud communication unit <NUM>. In addition, the interface apparatus <NUM> may further include an AI processing unit <NUM>. These blocks are functional blocks functionally isolated from each other and do not necessarily need to match with actual device configurations. That is, one block illustrated in the present drawing does not necessarily need to be configured by one device. In addition, separate blocks illustrated in the present drawing do not necessarily need to be configured by separate devices.

In addition, in the present drawing, the case is illustrated as one example where the AI processing unit <NUM> is configured in an integrated manner with the interface unit <NUM> and the cloud communication unit <NUM> as a part of the functions of the interface apparatus <NUM>, but is not limited to this. A part or whole of the AI processing unit <NUM> may be configured to be separated from the interface apparatus <NUM>.

The interface unit <NUM> is configured to receive a first command using the protocol of the common form that is not dependent on the cloud platform <NUM> configured to manage the facility <NUM> from the AI processing unit <NUM> configured to execute at least either processing of generating the model for determining the state of the facility <NUM> by machine learning or processing of determining the state of the facility <NUM> using the model. Details of such a command will be described below. The interface unit <NUM> is configured to supply the first command received from the AI processing unit <NUM> to the cloud communication unit <NUM>.

The cloud communication unit <NUM> is configured to communicate with the cloud platform <NUM>. The cloud communication unit <NUM> has a plurality of specific communication units respectively corresponding to the plurality of cloud platforms <NUM>. In the present invention the cloud communication unit <NUM> has a first cloud communication unit 120A corresponding to the first cloud platform 50A and a second cloud communication unit 120B corresponding to the second cloud platform 50B (which are collectively referred to as the cloud communication units <NUM>).

The first cloud communication unit 120A is configured to convert the first command supplied from the interface unit <NUM> to the first cloud platform 50A into a command using a protocol of a form corresponding to REST API provided by the first cloud platform 50A, and transmits the converted command to the first cloud platform 50A. That is, the cloud communication unit <NUM> converts the first command supplied from the interface unit <NUM> into a second command using a protocol of a first form dependent on the first cloud platform 50A, and transmits the second command to the first cloud platform 50A. In the same manner, the second cloud communication unit 120B is configured to convert the first command supplied from the interface unit <NUM> to the second cloud platform 50B into a command using a protocol of a form corresponding to GraphQL provided by the second cloud platform 50B, and transmit the converted command to the second cloud platform 50B.

Note that in the above description, a case where the interface unit <NUM> and the cloud communication unit <NUM> relay the command from the AI processing unit <NUM> to the cloud platform <NUM> has been illustrated as one example, but the interface unit <NUM> and the cloud communication unit <NUM> may further relay the command in an opposite direction. That is, the interface unit <NUM> and the cloud communication unit <NUM> may relay the command from the cloud platform <NUM> to the AI processing unit <NUM>. For example, the first cloud communication unit 120A may receive a third command using a protocol of the form corresponding to REST API from the first cloud platform 50A. Then, the first cloud communication unit 120A may convert the third command into a fourth command using the protocol of the common form, and supply the fourth command to the interface unit <NUM>. That is, the cloud communication unit <NUM> may receive the third command using the protocol of the first form from the first cloud platform 50A, and convert the third command into a fourth command using the protocol of the common form. In the same manner, the second cloud communication unit 120B may receive the third command using the protocol of the form corresponding to GraphQL from the second cloud platform 50B. Then, the second cloud communication unit 120B may convert the third command into the fourth command using the protocol of the common form, and supply the fourth command to the interface unit <NUM>. Then, the interface unit <NUM> may transmit the fourth command supplied from the first cloud communication unit 120A or the second cloud communication unit 120B to the AI processing unit <NUM>.

In addition, in the above description, the case has been illustrated as one example where the cloud communication unit <NUM> is interposed between the interface unit <NUM> and the cloud platform <NUM>, and the cloud communication unit <NUM> converts the command, but is not limited to this. When the command conversion for each of the cloud platforms <NUM> is not necessary, the interface apparatus <NUM> may omit the command conversion by the cloud communication unit <NUM>. That is, the interface unit <NUM> and the cloud platform <NUM> may directly communicate with each other without the intermediation of the cloud communication unit <NUM>.

The AI processing unit <NUM> is configured to execute at least either processing of generating the model for determining the state of the facility <NUM> by machine learning or processing of determining the state of the facility <NUM> using the model. Herein, the state determination processing of the facility <NUM> may be facility abnormality diagnosis processing of diagnosing an abnormality of the facility <NUM> as one example. However, the configuration is not limited to this. The state determination processing of the facility <NUM> may be deployed to processing related to various industrial solutions such as pipe corrosion diagnosis processing of diagnosing corrosion of a pipe.

The AI processing unit <NUM> has a learning unit <NUM>, a learning model generation unit <NUM>, a learning model management unit <NUM>, a determination unit <NUM>, and a determination processing unit <NUM>. Note that these blocks are functional blocks respectively functionally isolated and do not necessarily need to match with actual device configurations. That is, one block illustrated in the present drawing does not necessarily need to be configured by one device. In addition, separate blocks illustrated in the present drawing do not necessarily need to be configured by separate devices.

The learning unit <NUM> is configured to generate the model for determining the state of the facility <NUM> by machine learning. For example, the learning unit <NUM> performs machine learning using data input by the learning model generation unit <NUM>, and generates a learning model (AI model) for determining the state of the facility <NUM>.

The learning model generation unit <NUM> is configured to operate in cooperation with the learning unit <NUM>, and execute new creation and update processing of the model for determining the state of the facility <NUM>. For example, the learning model generation unit <NUM> inputs data acquired from the cloud platform <NUM> via the interface unit <NUM> and the cloud communication unit <NUM> to the learning unit <NUM>, and executes the new creation and update processing of the learning model using the learning unit <NUM>.

The learning model management unit <NUM> is configured to manage information related to the model for determining the state of the facility <NUM>. For example, the learning model management unit <NUM> stores and manages the information of the learning model generated by the learning unit <NUM> and the learning model generation unit <NUM> together with configuration information indicating a basic setting related to the learning model (for example, information of the sensor <NUM> used for the generation of the learning model, and information of a sampling interval of the sensor <NUM>).

The determination unit <NUM> is configured to determine a state of the facility <NUM> using the model for determining the state of the facility <NUM>. For example, the determination unit <NUM> determines the state of the facility <NUM> by the learning model generated by the learning unit <NUM> and the learning model generation unit <NUM> using the data input by the determination processing unit <NUM>.

The determination processing unit <NUM> is configured to operate in cooperation with the determination unit <NUM> and execute determination processing of the facility <NUM> to write a determination result in a database of the cloud platform <NUM>. For example, the determination processing unit <NUM> inputs data acquired from the cloud via the interface unit <NUM> and the cloud communication unit <NUM> to the determination unit <NUM>, and executes the state determination processing of the facility <NUM> using the determination unit <NUM>. Then, the determination processing unit <NUM> writes a determination result in the database of the cloud platform <NUM> via the interface unit <NUM> and the cloud communication unit <NUM>.

<FIG> illustrates one example of a generation flow of a learning model using the interface apparatus <NUM> according to the present embodiment. Note that processing of initializing the AI processing unit <NUM> may be executed before this flow. For example, the interface unit <NUM> may transmit an initialization command to the learning model generation unit <NUM>. In response to this, the AI processing unit <NUM> may be initialized. Then, the learning model generation unit <NUM> may transmit a SUCCESS command indicating that the initialization is successful to the interface unit <NUM>.

In step <NUM>, the AI processing unit <NUM> requests to acquire a list of the sensors <NUM> provided in the facility <NUM> managed by any of the cloud platforms <NUM>. For example, the learning model generation unit <NUM> transmits a sensor list acquisition request command for requesting to acquire the list of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A to the interface unit <NUM> as the first command using the protocol of the common form that is not dependent on the cloud platform <NUM>. The interface unit <NUM> receives the sensor list acquisition request command using the protocol of the common form as the first command. Then, the interface unit <NUM> supplies the first command received from the learning model generation unit <NUM> to the first cloud communication unit 120A corresponding to the first cloud platform 50A. In this manner, the first command received by the interface unit <NUM> may include the sensor list acquisition request command for requesting to acquire the list of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A.

In step <NUM>, the cloud communication unit <NUM> converts the first command using the protocol of the common form which is supplied in step <NUM> into the second command using the protocol of the specific form dependent on any of the cloud platforms <NUM>. For example, the first cloud communication unit 120A converts the first command using the protocol of the common form that is not dependent on the cloud platform <NUM> which is the sensor list acquisition request command for requesting to acquire the list of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A into the second command using the protocol of the first form dependent on the first cloud platform 50A (form corresponding to REST API).

In step <NUM>, the cloud communication unit <NUM> transmits the second command converted in step <NUM> to any of the cloud platforms <NUM>. For example, the first cloud communication unit 120A transmits the second command converted in step <NUM> to the first cloud platform 50A.

In step <NUM>, any of the cloud platforms <NUM> responds to the second command received in step <NUM>. For example, in response to the second command received in step <NUM>, the first cloud platform 50A transmits a sensor list acquisition command indicating a list of the sensors <NUM> provided in the facility <NUM> managed by itself to the first cloud communication unit 120A as the third command using the protocol of the first form.

In step <NUM>, the cloud communication unit <NUM> converts the third command using the protocol of the specific form which is received in step <NUM> into the fourth command using the protocol of the common form. For example, the first cloud communication unit 120A converts the third command using the protocol of the first form dependent on the first cloud platform 50A which is the sensor list acquisition command indicating the list of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A into the fourth command using the protocol of the common form that is not dependent on the cloud platform <NUM>.

In step <NUM>, the cloud communication unit <NUM> transmits the fourth command converted in step <NUM> to the AI processing unit <NUM> via the interface unit <NUM>. For example, the first cloud communication unit 120A supplies the fourth command converted in step <NUM> to the interface unit <NUM>. Then, the interface unit <NUM> transmits the sensor list acquisition command using the protocol of the common form to the learning model generation unit <NUM>. In response to this, the learning model generation unit <NUM> supplies the fourth command received from the interface unit <NUM> to the learning model management unit <NUM>.

In step <NUM>, the AI processing unit <NUM> stores information related to the list of the sensors <NUM> provided in the facility <NUM> managed by any of the cloud platforms <NUM>. For example, in response to the fourth command received in step <NUM>, the learning model management unit <NUM> stores the information related to the list of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A. Thus, in step <NUM> to step <NUM>, the acquisition processing of the sensor list using the interface apparatus <NUM> is executed.

In step <NUM>, the AI processing unit <NUM> requests to acquire measurement data of the sensors <NUM> provided in the facility <NUM> managed by any of the cloud platforms <NUM>. For example, the learning model generation unit <NUM> identifies the sensors <NUM> needed to generate the learning model from the sensor list acquired in step <NUM>. Then, the learning model generation unit <NUM> transmits a sensor data acquisition request command for requesting to acquire the measurement data of the identified sensors <NUM> to the interface unit <NUM> as the first command using the protocol of the common form that is not dependent on the cloud platform <NUM>. The interface unit <NUM> receives the sensor data acquisition request command using the protocol of the common form as the first command. Then, the interface unit <NUM> supplies the first command received from the learning model generation unit <NUM> to the first cloud communication unit 120A corresponding to the first cloud platform 50A. In this manner, the first command to be accepted by the interface unit <NUM> may include the sensor data acquisition request command for requesting to acquire the measurement data of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A.

In step <NUM>, the cloud communication unit <NUM> converts the first command using the protocol of the common form which is supplied in step <NUM> into the second command using the protocol of the specific form dependent on any of the cloud platforms <NUM>. For example, the first cloud communication unit 120A converts the first command using the protocol of the common form that is not dependent on the cloud platform <NUM> which is the sensor data acquisition request command for requesting to acquire the measurement data of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A into the second command using the protocol of the first form dependent on the first cloud platform 50A.

In step <NUM>, any of the cloud platforms <NUM> responds to the second command received in step <NUM>. For example, in response to the second command received in step <NUM>, the first cloud platform 50A transmits the sensor data acquisition command indicating the measurement data of the sensors <NUM> provided in the facility <NUM> managed by itself to the first cloud communication unit 120A as the third command using the protocol of the first form.

In step <NUM>, the cloud communication unit <NUM> converts the third command using the protocol of the specific form which is received in step <NUM> into the fourth command using the protocol of the common form. For example, the first cloud communication unit 120A converts the third command using the protocol of the first form dependent on the first cloud platform 50A which is the sensor data acquisition command indicating the measurement data of the sensors <NUM> provided in the facility <NUM> managed by the first cloud platform 50A into the fourth command using the protocol of the common form that is not dependent on the cloud platform <NUM>.

In step <NUM>, the cloud communication unit <NUM> transmits the fourth command converted in step <NUM> to the AI processing unit <NUM> via the interface unit <NUM>. For example, the first cloud communication unit 120A supplies the fourth command converted in step <NUM> to the interface unit <NUM>. Then, the interface unit <NUM> transmits the sensor data acquisition command using the protocol of the common form to the learning model generation unit <NUM>. Thus, in step <NUM> to step <NUM>, the sensor data acquisition processing using the interface apparatus <NUM> is executed.

In step <NUM>, the AI processing unit <NUM> calculates a sampling interval. For example, before the learning model is generated, the learning model generation unit <NUM> calculates the sampling interval from the measurement data acquired in time series in step <NUM>. For example, with regard to the calculation of the sampling interval in the learning model generation unit <NUM>, in addition to a time at which the sensors <NUM> have acquired the data (which is added to the sensor data), a time at which the interface unit <NUM> has received the fourth command or the like may be used for the calculation.

In step <NUM>, the AI processing unit <NUM> executes pre-processing. For example, before the learning model is generated, the learning model generation unit <NUM> applies the pre-processing to the measurement data acquired in step <NUM>. Examples of the above-mentioned pre-processing include, for example, processing of interpolation (a previous value, an average value, a median value, a mode value, a straight line, or the like), waveform compression, moving average, standardization, normalization, or the like.

In step <NUM> and step <NUM>, the AI processing unit <NUM> generates the model for determining the state of the facility <NUM> by machine learning. For example, in step <NUM>, the learning model generation unit <NUM> inputs the measurement data which is acquired in step <NUM> and to which the pre-processing is applied in step <NUM> to the learning unit <NUM>. In response to this, the learning unit <NUM> performs machine learning, and generates the learning model (AI model) for determining the state of the facility <NUM>. Then, in step <NUM>, the learning model generation unit <NUM> acquires the generated learning model from the learning unit <NUM>. Herein, various algorithms may be used for the machine learning for generating the learning model. Since such learning processing itself has been proposed in related art, the description thereof will be omitted here.

In step <NUM>, the learning model generation unit <NUM> supplies the information of the learning model generated in step <NUM> and step <NUM> to the learning model management unit <NUM> together with the configuration information indicating the basic setting related to the learning model (for example, the information of the sensors <NUM> used for generating the learning model (the sensors <NUM> identified in step <NUM>) and the information of the sampling interval of the sensors <NUM>).

In step <NUM>, the AI processing unit <NUM> stores the information of the learning model together with the configuration information. For example, the learning model management unit <NUM> stores the information of the learning model which is supplied in step <NUM> in association with the information related to the sensors <NUM> used for generating the learning model and the information related to the sampling interval of the sensors <NUM>. Thus, in step <NUM> to step <NUM>, the generation processing of the learning model using the interface apparatus <NUM> is executed.

The sensor data acquisition processing (step <NUM> to step <NUM>) and the learning model generation processing (step <NUM> to step <NUM>) described above may be repeated and executed by the same number of times as the number of the sensors <NUM> configured to generate the learning model. Note that in the above description, the case has been illustrated as one example where each one of the learning models is generated by the AI processing unit <NUM> using the measurement data acquired from one of the sensors <NUM>, but is not limited to this. The AI processing unit <NUM> may generate one or a plurality of the learning models using the measurement data acquired from the plurality of sensors <NUM>.

<FIG> illustrates one example of a determination flow by the learning model using the interface apparatus <NUM> according to the present embodiment. This flow may be executed, for example, after the generation flow of the learning model illustrated in <FIG>.

In step <NUM> and step <NUM>, the AI processing unit <NUM> prepares a model for determining the state of the facility <NUM>. For example, in step <NUM>, the determination processing unit <NUM> requests the learning model management unit <NUM> for the learning model used for the state determination. Then, in step <NUM>, the learning model management unit <NUM> supplies the learning model used for the state determination to the determination unit <NUM>. Note that the learning model supplied herein may be the learning model generated by the flow of <FIG>, for example, or a different learning model.

Since the processing in step <NUM> to step <NUM> and the processing in step <NUM> are respectively similar to the processing in step <NUM> to step <NUM> and the processing in step <NUM> in <FIG>, the description thereof will be omitted here.

In step <NUM> and step <NUM>, the AI processing unit <NUM> determines the state of the facility <NUM> using the model. For example, in step <NUM>, the determination processing unit <NUM> inputs, to the determination unit <NUM>, the measurement data which is acquired in step <NUM> and to which the same pre-processing as that at the time of the learning is applied in step <NUM>. In response to this, the determination unit <NUM> executes the determination processing using the learning model and determines the state of the facility <NUM>. Then, in step <NUM>, the determination processing unit <NUM> acquires a determination result obtained by determining the state of the facility <NUM> from the determination unit <NUM>. Since such determination processing itself has been proposed in related art, the description thereof will be omitted here.

In step <NUM>, the AI processing unit <NUM> requests to write the determination result obtained by determining the state of the facility <NUM> in the database of any of the cloud platforms <NUM>. For example, the determination processing unit <NUM> transmits a determination result write request command for requesting to write the determination result obtained by determining the state of the facility <NUM> managed by the first cloud platform 50A in the database of the first cloud platform 50A to the interface unit <NUM> as the first command using the protocol of the common form that is not dependent on the cloud platform <NUM>. The interface unit <NUM> receives the determination result write request command using the protocol of the common form as the first command. Then, the interface unit <NUM> supplies the first command received from the determination processing unit <NUM> to the first cloud communication unit 120A. In this manner, the first command to be accepted by the interface unit <NUM> may include the determination result write request command for requesting to write the determination result obtained by determining the state of the facility <NUM> by the AI processing unit <NUM> in the database of the first cloud platform 50A.

In step <NUM>, the cloud communication unit <NUM> converts the first command using the protocol of the common form which is supplied in step <NUM> into the second command using the platform of the specific form dependent on any of the cloud platforms <NUM>. For example, the first cloud communication unit 120A converts the first command using the protocol of the common form that is not dependent on the cloud platform <NUM> which is the determination result write request command for requesting to write the determination result obtained by determining the state of the facility <NUM> managed by the first cloud platform 50A in the database of the first cloud platform 50A into the second command using the protocol of the first form dependent on the first cloud platform 50A.

In step <NUM>, any of the cloud platforms <NUM> writes the determination result. For example, in response to the second command received in step <NUM>, the first cloud platform 50A writes the determination result obtained determining the state of the facility <NUM> by the AI processing unit <NUM> into its own database.

In step <NUM>, any of the cloud platforms <NUM> transmits a response to the second command to the cloud communication unit <NUM>. For example, in response to the successful write of the determination result in its own database, the first cloud platform 50A transmits the SUCCESS command indicating the successful write to the first cloud communication unit 120A as the third command using the protocol of the first form.

In step <NUM>, the cloud communication unit <NUM> converts the third command using the protocol of the specific form which is received in step <NUM> into the fourth command using the protocol of the common form. For example, the first cloud communication unit 120A converts the third command using the protocol of the first form dependent on the first cloud platform 50A which is the SUCCESS command indicating the successful write of the determination result in the database of the first cloud platform 50A into the fourth command using the protocol of the common form that is not dependent on the cloud platform <NUM>.

In step <NUM>, the cloud communication unit <NUM> transmits the fourth command converted in step <NUM> to the AI processing unit <NUM> via the interface unit <NUM>. For example, the first cloud communication unit 120A supplies the fourth command converted in step <NUM> to the interface unit <NUM>. Then, the interface unit <NUM> transmits the SUCCESS command using the protocol of the common form to the determination processing unit <NUM>. Thus, in step <NUM> to step <NUM>, the determination processing by the learning model using the interface apparatus <NUM> is executed.

Such determination processing by the learning model (step <NUM> to step <NUM>) may be repeated and executed by the same number of times as the number of the learning models used for determining the state of the facility <NUM>. Note that in the above description, the case has been illustrated as one example where the AI processing unit <NUM> determines the state of the facility <NUM> using the measurement data acquired from one of the sensors <NUM>, but is not limited to this. The AI processing unit <NUM> may determine the state of the facility <NUM> using the measurement data acquired from the plurality of sensors <NUM>.

<FIG> illustrates one example of a flow for updating the learning model from an external system <NUM> using the interface apparatus <NUM> according to the present embodiment. Herein, the external system <NUM> is, for example, a system having a function of generating a customized learning model which is a system provided outside the interface apparatus <NUM>.

Since processing in step <NUM> to step <NUM> is similar to the processing in step <NUM> to step <NUM> in <FIG> except that the learning model generation unit <NUM> is replaced with the external system <NUM>, the description thereof will be omitted here.

In step <NUM>, the external system <NUM> generates a model for determining the state of the facility <NUM> using the measurement data supplied in step <NUM>.

In step <NUM>, the external system <NUM> requests for a change (for example, addition, update, and deletion) of at least any of the information of the learning model or the configuration information which are stored in an internal memory (for example, the learning model management unit <NUM>) of the cloud platform <NUM>. For example, in step <NUM>, the external system <NUM> transmits at least any of a model information change request command for requesting to change the information of the model managed by the cloud platform <NUM> or a configuration information change request command for requesting to change the configuration information to the learning model management unit <NUM>.

In step <NUM>, the learning model management unit <NUM> changes the information managed by itself in response to the command received in step <NUM>. For example, in response to the model information change request command received in step <NUM>, the learning model management unit <NUM> changes (for example, adds, updates, and deletes) the information of the model managed by itself. In addition, in response to the configuration information change request command received in step <NUM>, the learning model management unit <NUM> changes (for example, adds, updates, and deletes) the configuration information managed by itself.

In step <NUM>, the learning model management unit <NUM> transmits a response to the command received in step <NUM> to the external system <NUM>. For example, in response to the successful change of the information managed by itself, the learning model management unit <NUM> transmits the SUCCESS command indicating the successful change of the information to the external system <NUM>.

In this manner, the interface apparatus <NUM> supports the external system <NUM> to acquire the measurement data for generating the learning model, and also, in response to the generation of the learning model by the external system <NUM>, changes the information of the model or the configuration information which are stored in the internal memory of the cloud platform <NUM>. With this configuration, when the flow of <FIG> is executed after the flow of <FIG> is executed, for example, the interface apparatus <NUM> can execute the state determination processing using the learning model generated by the external system <NUM>.

<FIG> illustrates another example of the flow for updating the learning model from the external system <NUM> using the interface apparatus <NUM> according to the present embodiment. Since the processing in step <NUM> to step <NUM> is similar to the processing in step <NUM> to step <NUM> in <FIG>, the description thereof will be omitted here. In the flow of <FIG>, the case has been illustrated where the external system <NUM> directly requests the learning model management unit <NUM> to change the information stored in the internal memory of the cloud platform <NUM>, but in this flow, the external system <NUM> requests the learning model management unit <NUM> via the cloud platform <NUM> to change the information stored in the internal memory of the cloud platform <NUM>.

In step <NUM>, the external system <NUM> requests to change at least any of the information of the learning model or the configuration information which are stored in the internal memory of any of the cloud platforms <NUM>. For example, in step <NUM>, the external system <NUM> transmits at least any of the model information change request command for requesting to change the information of the model managed by the first cloud platform 50A or the configuration information change request command for requesting to change the configuration information to the interface unit <NUM> as the first command using the protocol of the common form that is not dependent on the cloud platform <NUM>. Then, the interface unit <NUM> supplies the first command received from the external system <NUM> to the first cloud communication unit 120A corresponding to the first cloud platform 50A. In this manner, the first command to be accepted by the interface unit <NUM> may include the model information change request command for requesting to change the information of the model managed by the first cloud platform 50A. In addition, the first command to be accepted by the interface unit <NUM> may include the configuration information change request command for requesting to change the configuration information managed by the first cloud platform 50A.

In step <NUM>, the cloud communication unit <NUM> converts the first command using the protocol of the common form which is supplied in step <NUM> into the second command using the protocol of the specific form dependent on any of the cloud platforms <NUM>. For example, the first cloud communication unit 120A converts the first command using the protocol of the common form that is not dependent on the cloud platform <NUM> which is at least any of the model information change request command for requesting to change the information of the model managed by the first cloud platform 50A or the configuration information change request command for requesting to change the configuration information into the second command using the protocol of the first form dependent on the first cloud platform 50A.

In step <NUM>, any of the cloud platforms <NUM> transfers the second command received in step <NUM> to the learning model management unit <NUM>. For example, the first cloud platform 50A transfers the second command received in step <NUM> to the learning model management unit <NUM> added in to itself.

In step <NUM>, in response to the second command received in step <NUM>, the learning model management unit <NUM> changes the information to be managed by itself. Since step <NUM> is similar to the processing of step <NUM> in <FIG>, the description thereof will be omitted here.

In step <NUM>, the learning model management unit <NUM> transmits a response to the second command received in step <NUM> to any of the cloud platforms <NUM>. For example, in response to the successful change of the information to be managed by itself, the learning model management unit <NUM> transmits the SUCCESS command indicating the successful change of the information to the first cloud platform 50A.

In step <NUM>, any of the cloud platforms <NUM> transmits the response to the command received in step <NUM> to the cloud communication unit <NUM>. For example, in response to the reception of the SUCCESS command in step <NUM>, the first cloud platform 50A transmits the command to the first cloud communication unit 120A as the third command using the protocol of the first form.

In step <NUM>, the cloud communication unit <NUM> converts the third command received in step <NUM> into the fourth command using the protocol of the common form. For example, the first cloud communication unit 120A converts the third command using the protocol of the first form dependent on the first cloud platform 50A which is the SUCCESS command indicating the successful change of the information into the fourth command using the protocol of the common form that is not dependent on the cloud platform <NUM>.

In step <NUM>, the cloud communication unit <NUM> transmits the fourth command converted in step <NUM> to the external system <NUM> via the interface unit <NUM>. For example, the first cloud communication unit 120A supplies the fourth command converted in step <NUM> to the interface unit <NUM>. Then, the interface unit <NUM> transmits the SUCCESS command using the protocol of the common form to the external system <NUM>.

In this manner, the interface apparatus <NUM> also provides the interface for changing the information stored in the internal memory of the cloud platform <NUM> from the external system <NUM> via the cloud platform. With this configuration, even when any type of the cloud platforms <NUM> is used, it is facilitated to access the information saved in the internal memory of the cloud platform <NUM> from the external system <NUM>.

In recent years, selectable cloud platforms have been diversified, it has become less easy to incorporate the function unit configured to execute the AI processing in the diverse cloud platforms. At this time, a system in which the cloud platform is integrated with the function unit configured to execute the AI processing is preferably provided, but in reality, users having already introduced the cloud system exist, and there are cases where only the function unit configured to execute the AI processing is newly introduced in a retrofitting manner. In such a case, a system engineer responsible for cloud construction needs to understand interface materials of the already introduced platform and the newly introduced function unit configured to execute the AI processing for the incorporation, and construction operations are complex. In addition, an incorporation operational error in an engineering process tends to occur.

In contrast, the interface apparatus <NUM> according to the present embodiment provides the interface between the AI processing unit <NUM> configured to execute the AI processing and the cloud platform <NUM>. In particular, the interface apparatus <NUM> according to the present embodiment receives the first command using the protocol of the common form that is not dependent on the cloud platform <NUM> from the AI processing unit <NUM>. Then, the interface apparatus <NUM> converts the first command using the protocol of the common form into the second command using the protocol of the specific form dependent on any of the cloud platforms <NUM>, and transmits the converted second command to any of the cloud platforms <NUM>. With this configuration, in accordance with the interface apparatus <NUM> according to the present embodiment, the AI processing unit <NUM> does not need to generate the specific command for each of the cloud platforms <NUM>, and a load of the AI processing unit <NUM> can be alleviated. In addition, since the AI processing unit <NUM> is not dependent on various specifications of the cloud platforms <NUM>, the update of the AI processing unit <NUM> can be easily performed without taking influences on the cloud platforms <NUM> into account. In this manner, in accordance with the interface apparatus <NUM> according to the present embodiment, it is possible to easily incorporate the function unit configured to execute the AI processing in various cloud environments.

In addition, the interface apparatus <NUM> according to the present embodiment receives the third command using the protocol of the specific form dependent on the cloud platform <NUM> from the cloud platform <NUM>. Then, the interface apparatus <NUM> converts the third command using the protocol of the specific form into the fourth command using the protocol of the common form that is not dependent on the cloud platform <NUM>, and transmits the converted fourth command to the AI processing unit <NUM>. With this configuration, in accordance with the interface apparatus <NUM> according to the present embodiment, it is possible to provide the bidirectional interface from the AI processing unit <NUM> to the cloud platform <NUM> and from the cloud platform <NUM> to the AI processing unit <NUM>.

In addition, the interface apparatus <NUM> according to the present embodiment has the plurality of specific communication units respectively corresponding to the plurality of cloud platforms <NUM>. With this configuration, in accordance with the interface apparatus <NUM> according to the present embodiment, it is possible to provide the interface between the AI processing unit <NUM> and the plurality of cloud platforms <NUM> respectively having the different specifications. In addition, in accordance with the interface apparatus <NUM> according to the present embodiment, it is possible to provide the interface between the AI processing unit <NUM> and the new cloud platform <NUM> by simply adding the specific communication unit.

In addition, the interface apparatus <NUM> according to the present embodiment accepts at least any of the sensor list acquisition request command, the sensor data acquisition request command, the determination result write request command, the model information change request command, or the configuration information change request command as the first command. With this configuration, the interface apparatus <NUM> according to the present embodiment can relay the command related to the AI processing (such as the learning processing or the determination processing) for determining the state of the facility <NUM> between the AI processing unit <NUM> and the cloud platform <NUM>.

In addition, the interface apparatus <NUM> according to the present embodiment includes, for example, the AI processing unit having the learning unit and the determination unit. With this configuration, in accordance with the interface apparatus <NUM> according to the present embodiment, the interface function and the function for executing the AI processing can be configured as the integrated apparatus.

Various embodiments of the present invention may be described with reference to flowcharts and block diagrams whose blocks may represent (<NUM>) steps of processes in which operations are performed or (<NUM>) sections of devices responsible for performing operations. Certain steps and sections may be implemented by a dedicated circuit, programmable circuitry supplied with computer readable instructions stored on computer readable media, and/or processors supplied with computer readable instructions stored on computer readable media. A dedicated circuit may include digital and/or analog hardware circuits and may include integrated circuits (IC) and/or discrete circuits. Programmable circuitry may include reconfigurable hardware circuits including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements, etc., such as field-programmable gate arrays (FPGA), programmable logic arrays (PLA), etc..

Computer readable media may include any tangible device that can store instructions for execution by a suitable device, such that the computer readable medium having instructions stored therein comprises an article of manufacture including instructions which can be executed to create means for performing operations specified in the flowcharts or block diagrams. Examples of computer readable media may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, etc. More specific examples of computer readable media may include a floppy (registered trademark) disk, a diskette, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or Flash memory), an electrically erasable programmable read only memory (EEPROM), a static random access memory (SRAM), a compact disc read only memory (CD-ROM), a digital versatile disk (DVD), a BLU-RAY (registered trademark) disc, a memory stick, an integrated circuit card, etc..

Computer readable instructions may include assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages.

Computer readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing device, or to programmable circuitry, locally or via a local area network (LAN), wide area network (WAN) such as the Internet, etc., to execute the computer readable instructions to create means for performing operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc..

<FIG> illustrates an example of a computer <NUM> through which a plurality of aspects of the present invention may be entirely or partially embodied. A program that is installed in the computer <NUM> can cause the computer <NUM> to function as or perform operations associated with apparatuses of the embodiments of the present invention or one or more sections thereof, or perform the operations or the one or more sections, and/or cause the computer <NUM> to perform processes of the embodiments of the present invention or steps of the processes. Such a program may be executed by the CPU <NUM> to cause the computer <NUM> to perform certain operations associated with some or all of the blocks of flowcharts and block diagrams described herein.

The computer <NUM> according to the present embodiment includes a CPU <NUM>, a RAM <NUM>, a graphics controller <NUM>, and a display device <NUM>, which are mutually connected by a host controller <NUM>. The computer <NUM> also includes input/output units such as a communication interface <NUM>, a hard disk drive <NUM>, a DVD-ROM drive <NUM> and an IC card drive, which are connected to the host controller <NUM> via an input/output controller <NUM>. The computer also includes legacy input/output units such as a ROM <NUM> and a keyboard <NUM>, which are connected to the input/output controller <NUM> through an input/output chip <NUM>.

The CPU <NUM> operates according to programs stored in the ROM <NUM> and the RAM <NUM>, thereby controlling each unit. The graphics controller <NUM> obtains image data generated by the CPU <NUM> on a frame buffer or the like provided in the RAM <NUM> or in itself, and causes the image data to be displayed on the display device <NUM>.

The communication interface <NUM> communicates with other electronic devices via a network. The hard disk drive <NUM> stores programs and data used by the CPU <NUM> within the computer <NUM>. The DVD-ROM drive <NUM> reads the programs or the data from the DVD-ROM <NUM>, and provides the hard disk drive <NUM> with the programs or the data via the RAM <NUM>. The IC card drive reads programs and data from an IC card, and/or writes programs and data into the IC card.

The ROM <NUM> stores therein a boot program or the like executed by the computer <NUM> at the time of activation, and/or a program depending on the hardware of the computer <NUM>. The input/output chip <NUM> may also connect various input/output units via a parallel port, a serial port, a keyboard port, a mouse port, or the like to the input/output controller <NUM>.

A program is provided by computer readable media such as the DVD-ROM <NUM> or the IC card. The program is read from the computer readable media, installed into the hard disk drive <NUM>, RAM <NUM>, or ROM <NUM>, which are also examples of computer readable media, and executed by the CPU <NUM>. The information processing described in these programs is read into the computer <NUM>, resulting in cooperation between a program and the above-mentioned various types of hardware resources. An apparatus or method may be constituted by realizing the operation or processing of information in accordance with the usage of the computer <NUM>.

For example, when communication is performed between the computer <NUM> and an external device, the CPU <NUM> may execute a communication program loaded onto the RAM <NUM> to instruct communication processing to the communication interface <NUM>, based on the processing described in the communication program. The communication interface <NUM>, under control of the CPU <NUM>, reads transmission data stored on a transmission buffering region provided in a recording medium such as the RAM <NUM>, the hard disk drive <NUM>, the DVD-ROM <NUM>, or the IC card, and transmits the read transmission data to a network or writes reception data received from a network to a reception buffering region or the like provided on the recording medium.

In addition, the CPU <NUM> may cause all or a necessary portion of a file or a database to be read into the RAM <NUM>, the file or the database having been stored in an external recording medium such as the hard disk drive <NUM>, the DVD-ROM drive <NUM> (DVD-ROM <NUM>), the IC card, etc., and perform various types of processing on the data on the RAM <NUM>. The CPU <NUM> may then write back the processed data to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium to undergo information processing. The CPU <NUM> may perform various types of processing on the data read from the RAM <NUM>, which includes various types of operations, processing of information, condition judging, conditional branch, unconditional branch, search/replace of information, etc., as described throughout this disclosure and designated by an instruction sequence of programs, and writes the result back to the RAM <NUM>. In addition, the CPU <NUM> may search for information in a file, a database, etc., in the recording medium. For example, when a plurality of entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored in the recording medium, the CPU <NUM> may search for an entry matching the condition whose attribute value of the first attribute is designated, from among the plurality of entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute satisfying the predetermined condition.

The above-explained program or software modules may be stored in the computer readable media on the computer <NUM> or near the computer <NUM>. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as the computer readable media, thereby providing the program to the computer <NUM> via the network.

Claim 1:
A system comprising:
a first cloud platform (50A);
a second cloud platform (50B);
characterized in that the system further comprises
an interface apparatus (<NUM>) comprising:
an Al processing unit (<NUM>) configured to execute at least either processing of generating a model for determining a state of a facility by machine learning or processing of determining the state of the facility using the model;
an interface unit (<NUM>) configured to receive, from the Al processing unit (<NUM>), a first command according to a protocol of a common form that is common for a plurality of cloud platforms respectively having different specifications and being configured to manage the facility; and
a cloud communication unit (<NUM>) configured to convert the first command into a second command according to a protocol of a form corresponding to an interface provided by a cloud platform and transmit the second command to the cloud platform; wherein
the cloud communication unit (<NUM>) includes
a first specific cloud communication unit (120A) corresponding to the first cloud platform (50A) and configured to convert the first command into the second command using a protocol of a first form corresponding to a first interface provided by the first cloud platform (50A), and transmit the second command using the protocol of the first form to the first cloud platform (50A), and
a second specific cloud communication unit (120B) corresponding to the second cloud platform (50B) and configured to convert the first command into the second command using a protocol of a second form corresponding to a second interface provided by the second cloud platform (50B), and transmit the second command using the protocol of the second form to the second cloud platform (50B).