JP2020161167A - Server device, learned model providing program, learned model providing method, and learned model providing system - Google Patents

Abstract

To provide a server device, a learned model providing program, a learned model providing method, and a learned model providing system that can select and supply optimal learned models for various devices with different environments, conditions, and the like.SOLUTION: A server device 10 capable of communicating via a communication network with at least one or more devices each equipped with a learner that uses a learned model to perform processing includes: a storage unit 15 that is made to a plurality of shared models that have learned in advance according to environments and conditions of various devices; a device data acquisition unit 11 that acquires device data including information on the environment and condition from the devices; a target shared model selection unit 12 that selects a shared model optimal for the device based on the acquired device data; and a transmission unit that transmits the selected shared model to the device.SELECTED DRAWING: Figure 1

Description

The present invention relates to a technique for introducing and optimizing a trained model at low cost for an industrial device that performs processing such as judgment and classification using a trained model by deep learning or the like. ..

Conventionally, in devices such as machine tools and abnormality detection devices for finished products, work targets have been identified and abnormality detection processing has been performed using a learned model generated by deep learning or the like. In these devices, the work accuracy and abnormality detection accuracy are improved by performing learning specialized in the work environment and work conditions of each device.

Examples of the device using such a trained model include Patent Document 1 and Patent Document 2. The advanced image automatic classification device described in Patent Document 1 is a device that classifies images by a learner from various feature quantities, and the metal surface quality evaluation device described in Patent Document 2 photographs the surface of metal. It is a device for evaluating the surface quality of metal with a learning device based on an image.

Japanese Unexamined Patent Publication No. 2007-21340 Japanese Unexamined Patent Publication No. 2011-191252

When making judgments, classifications, etc. using a learning device learned by machine learning, etc., including Patent Document 1 and Patent Document 2, the configuration of the learning device specialized for the working environment, working conditions, etc. of the device is used. It is necessary to set and then learn. It takes a considerable cost to make such a setting, learn from scratch, and perform highly accurate judgment / classification. And even if the trained model is obtained at such a cost, the same trained model cannot be used on devices with different working environments, working conditions, etc., so it is necessary to train from scratch again. There was a problem that there was.

In order to solve this, a method of preparing a general-purpose learning model that can correspond to various work environments, work conditions, etc. can be considered. However, the general-purpose learning model has the advantage that it can be applied to various situations because it can correspond to various work environments and work conditions, but because it is general-purpose, it is specialized in any environment and condition. There was a problem that the accuracy was lower than that of the model. In addition, there is a problem that the complexity of the model increases, the amount of information required to realize it increases, the calculation cost increases, and the memory cost increases. Furthermore, when each device has individual characteristics, there is also a problem that versatility must be ensured to absorb the individual differences.

The present invention has been made in view of the above problems, and is a server device capable of selecting and supplying an optimum trained model for various devices having different environments, conditions, etc., a trained model providing program, and trained. The purpose is to provide a model providing method and a trained model providing system.

The server device according to the present invention is a server device capable of communicating with at least one device provided with a learning device that performs processing using a trained model via a communication network, and can be used in various device environments and conditions. Optimal for the device based on the storage unit that stores a plurality of shared models that have been learned in advance, the device data acquisition unit that acquires device data including environment and condition information from the device, and the acquired device data. It is characterized by including a target shared model selection unit for selecting a shared model and a transmission unit for transmitting the selected shared model to the device.

Further, the server device according to the present invention stores and manages an additional learning processing unit that performs additional learning on the shared model using sample data for performing additional learning of the shared model, and an additional learning model. It is characterized in that it includes an additional learning model management unit, and the transmission unit transmits the additional learning model to the device when additional learning is performed on the shared model.

Further, in the server device according to the present invention, in the target shared model selection unit, the content of the device data acquired by the device data acquisition unit has been additionally learned based on another device stored by the additional learning model management unit. If the model is applicable, the additional trained model should be selected in preference to the shared model, and the transmitter should transmit the selected additional trained model to the device. It is characterized by having done it.

Further, the server device according to the present invention includes an additional learning model management unit that receives an additional learning model transmitted from a device having a function of performing additional learning processing on the shared model and stores it in the storage unit. It is characterized by that.

Further, in the server device according to the present invention, the target shared model selection unit calculates a score for evaluating the suitability of each shared model for the device based on the device data obtained from the device, and shares the score according to the score. The feature is that the model is selected.

Further, in the server device according to the present invention, the target shared model selection unit selects the optimum shared model by using machine learning based on the device data. The shared model is selected by the trained model learned in advance. It is characterized by having done it.

The trained model providing program according to the present invention executes a trained model selection process on a server device capable of communicating with at least one device provided with a learning device that performs processing using the trained model via a communication network. It is a trained model providing program for realizing each function for making the data, and is a storage function for storing a plurality of shared models that have been learned in advance according to the environment and conditions of various devices in the server device by a storage means. A device data acquisition function that acquires device data including environment and condition information from the device, a target sharing model selection function that selects the optimum sharing model for the device based on the acquired device data, and selected sharing. It is characterized by realizing a transmission function of transmitting a model to the device.

The trained model providing method according to the present invention provides a trained model for executing a process of selecting and providing an optimum trained model for a device provided with a learning device that performs processing using the trained model. It is a method, which is a storage process in which a plurality of shared models learned in advance according to various device environments and conditions are stored by a storage means, and device data for acquiring device data including environment and condition information from the device. It is characterized by including an acquisition process, a target shared model selection process for selecting the optimum shared model for the device based on the acquired device data, and a transmission process for transmitting the selected shared model to the device. ..

The trained model providing system according to the present invention includes at least one device provided with a learning device that performs processing using the trained model, and at least one server device capable of communicating with the device via a communication network. The server device and / or device is provided with a storage unit that stores at least one shared model that has been trained in advance according to the environment and conditions of various devices. , The device data acquisition unit that acquires device data including environment and condition information from the device that requires the trained model, and the target sharing that searches and selects the most suitable sharing model for the device based on the acquired device data. The server device is provided with a model selection unit, and the server device and / or the device is provided with a transmission unit for transmitting the selected shared model to the device that requires a trained model.

Further, in the trained model providing system according to the present invention, the target shared model selection unit evaluates the goodness of fit of each shared model to the device based on the device data obtained from the device that requires the trained model. Is calculated respectively, and the shared model is selected according to the score.

Further, in the trained model providing system according to the present invention, the device has a function of performing additional learning processing on the shared model, and the server device uses the additional learning model transmitted from the device. It is equipped with an additional trained model management unit that receives and stores it in the storage unit, and the target shared model selection unit of the server device selects the additional trained model in addition to the shared model. It is characterized by that.

Further, in the trained model providing system according to the present invention, the device has a function of performing additional learning processing on the shared model, and also has a storage unit for storing the additional trained model and an additional trained model. It is provided with an additional learned model information transmission unit that transmits information necessary for selection to the server device, and the target shared model selection unit of the server device is stored in a storage unit of the device in addition to the shared model. The feature is that the additional trained model is also included in the options to make the selection.

According to the present invention, a device is selected by selecting the optimum shared model in a server device in which a plurality of shared models that have been learned in advance according to the environment and conditions of various devices are classified according to the environment and conditions and stored. By sending to, the complexity of expressing with the learning model while realizing highly accurate discrimination / classification according to the situation compared to the case of using the conventional general-purpose learning model as described above There is an advantage that the cost of calculation and memory can be reduced because it is reduced. In addition, there is an advantage that the introduction cost can be significantly reduced as compared with the case where the trained model is generated independently by the device. Further, by providing the additional learning processing function, it is possible to obtain an additional learning model specialized according to the environment and conditions of the device, so that it is possible to additionally perform highly accurate inference processing on the device. In this additional learning process, by performing additional learning based on an appropriate shared model according to the environment and conditions of the device, many effects of an action called transfer learning can be obtained. Transfer learning is expected to efficiently perform learning in an environment where additional learning is desired by making good use of the weights of shared models created in other environments between environments where the device environment and conditions are not exactly the same. Is.
In addition, by storing and managing the additionally trained model in the server device, it is possible to immediately provide the additionally trained model when there is a request from another device with the same environment and conditions. Become. This makes it possible to reduce the calculation cost and the memory cost for additional learning as compared with the case of using a general-purpose learning model. Further, by configuring a trained model providing system including at least one or more devices and at least one server device, the shared model stored in the storage units of the plurality of server devices and / or the devices is optimized. Since it is possible to select an appropriate sharing model and provide it to the device, it is possible to select the optimum sharing model from a larger amount of data options.

It is a block diagram which showed the structure of the server apparatus 10 which concerns on this invention. It is a flowchart which showed the flow of the learning process of additional learning. It is a flowchart which showed the flow until the inference processing is performed in a device 20.

[First Embodiment]
Hereinafter, an example of the server device according to the first embodiment will be described with reference to the drawings. FIG. 1 is a block diagram showing a configuration of a server device 10 according to the present invention. The server device 10 is communicably connected to a plurality of devices 201, 202, ..., 20n via the communication network 30. The server device 10 and the devices 201 to 20n may be devices designed as dedicated machines, but can be realized by a general computer. In this case, the server device 10 and the devices 201 to 20n include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a memory that a general computer would normally have. , Storage such as a hard disk drive, etc. shall be provided as appropriate (not shown). Further, it goes without saying that various processes are executed by the program in order to make these general computers function as the server device 10 of this example.

The server device 10 includes at least a device data acquisition unit 11, a target sharing model selection unit 12, an additional learning processing unit 13, an additional learning model management unit 14, and a storage unit 15.

The device data acquisition unit 11 has a function of acquiring device data including information on the device environment and conditions generated in any of the devices 201 to 20n. Here, the device data is data necessary for defining attributes such as device environment, conditions, and data units, sample data with label information necessary for additional learning, and sensors in an actual device. It includes various data that can be acquired by the device, such as data and network log data. At a minimum, it must be device data that contains the data needed to select a shared model. Specifically, factory robot position data, actuator torque amount, acceleration sensor data, image data including or not including depth acquired by in-vehicle cameras and laser radar, displacement sensor data, various process data of process automation, Sensor data such as various data in infrastructure, agriculture, bio-healthcare, network log data, photo data of products including normal and abnormal products, voice data, machine type, work type, sensor type, Various data, such as geographic information, can be used as device data.

In addition, the device environment and conditions are required of the device, for example, when the device is a work machine for picking and the types of work shapes to be picked are divided into several types. The environment and conditions to be used differ individually. In addition, the functions required of the learning device, such as whether the product is a device for judging an abnormal product and a normal product or a device for classifying the product into a plurality of items, also differ depending on the device. Therefore, it is necessary to acquire information such as individual environments and conditions that differ for each device as device data. The information such as the environment and conditions may be input on the device side according to the format, or the information such as the environment and conditions may be defined by discriminating from various data in the server device 10. .. In that case, it may be a method of specifying the definition of information such as environment and conditions by machine learning using the acquired data.

The target shared model selection unit 12 has a function of selecting the optimum shared model for the device based on the device data acquired by the device data acquisition unit 11. Here, the shared model refers to a model that has been trained in advance according to the environment and conditions of various devices, and a plurality of shared models are stored in advance in the storage unit 15 described later. The degree of learning to be performed in advance may be set to any level, but at least the learning is performed to the extent that it is more efficient than learning from scratch on the device and contributes to cost reduction. Is preferable. The selection in the target shared model selection unit 12 is performed based on the acquired device data, but it is possible to appropriately determine which of the acquired device data is used for selecting the shared model. Further, the method of selecting the shared model may be automatically selected from the degree of matching of each item of the device data, or a plurality of shared models having a high degree of matching are presented to the user so that the user can select the shared model. You may. The degree of matching of items is determined by, for example, determining whether or not each item of device data matches for each item, and determining by the number of matching items. If a shared model that matches the definition of the device environment, conditions, etc. is not found, a new model having a neural network structure suitable for the definition may be generated. The method of selecting the target shared model may be a method of selecting the shared model based on a preset rule, or a shared model in which a learning model for selecting the optimum shared model is trained. It may be a method of selecting a shared model by a trained model for selection (a trained model different from the shared model and the additional trained model, which has learned the selection action of the shared model).

In addition, as a method of selecting the optimum shared model in the target shared model selection unit 12, a score evaluated for each shared model is calculated based on the environment and conditions obtained from the device, and selection is made according to the score. You may do it. The score, which is an evaluation of the suitability of the shared model, includes device data on the underlying environment and conditions such as machine type, work type, sensor type, and geographical information, as well as factory robot position data and Actuator torque amount, acceleration sensor data, image data including or not including depth acquired by in-vehicle camera or laser radar, displacement sensor data, various process data of process automation, various data in infrastructure, agriculture, bio-healthcare, etc. The evaluation is performed by adding more detailed device data such as sensor data, network log data, photo data of products including normal and abnormal products, and voice data. How to evaluate and score each of these items is set in advance, and the total score is calculated by summing the points for each item for each shared model. The actual shared model may be selected by automatically selecting the shared model having the highest score, or by presenting a plurality of shared models with the highest score to the user and letting the user select the shared model. In addition, a method may be used in which a learning model for calculating a score which is an evaluation of goodness of fit and selecting an optimum shared model is trained and a shared model is selected by a trained model. In this case, since the learning model also learns how to score each device data, it is possible to select the optimum shared model.

The additional learning processing unit 13 has a function of performing additional learning on the shared model selected by the target shared model selection unit 12. Although the shared model has been trained in advance, it is not trained under the environment and conditions specialized for the device, so it is added in order to perform high-precision judgment, classification, etc. It is preferable to perform fine adjustment by learning. Therefore, the device data acquisition unit 11 also acquires sample data to be used as input data in the additional learning, and additionally learns the shared model using the acquired sample data. Here, in the additional learning, the weight is not limited to re-learning for all layers of the neural network, but a part of the neural network is frozen and only the other layers are relearned, or additional layers are added. It also includes doing. As a result, the learning content under the environment and conditions specialized for the device is added, and the additionally trained model finely tuned as a more optimal model can be generated. In order to function as the additional learning processing unit 13, the server device 10 is required to have a configuration for functioning as a learning device.

The additional learning model management unit 14 has a function of storing the additional learning model generated in the additional learning processing unit 13 in the storage unit 15 described later and transmitting the additional learning model to the target device. Have. In addition, it has a function to manage the additionally trained model after setting definition information such as its environment and conditions so that it can be used by other devices having matching conditions. In this way, by defining the definition information such as the environment and conditions for the additionally trained model, when the target shared model selection unit 12 selects the shared model suitable for the device, the other device can be selected. The additional trained model generated based on the above can also be a candidate for the option.

The storage unit 15 has a function of storing a plurality of shared models that have been learned in advance according to the environment and conditions of various devices. Further, the storage unit 15 also stores the additionally trained model trained by applying the sample data for training the device-specific environment and conditions to the shared model. The storage unit 15 does not necessarily have to be in the server device 10, and may be a system provided on the device side. In that case, the server device 10 retains information on where the target shared model is stored, and transfers it from the storage location to the device as needed.

Next, the flow of processing from selecting the shared model in the server device 10 to performing additional learning will be described. FIG. 2 is a flowchart showing the flow of learning processing for additional learning. In FIG. 2, first, device data is collected in order to select a shared model suitable for the device (S11). Specifically, the device data acquisition unit 11 receives the device data transmitted from the device 20 and collects the device data. Based on the collected device data, the attributes of the device data are defined (S12). The device data attribute is information such as the device environment and conditions required to select the shared model, and defines this. Then, the shared model is searched based on the defined device data attributes (S13). The search target at this time may include an additionally trained model generated by performing additional learning on another device. As a result of the search, it is determined whether or not the corresponding shared model exists (S14). If the corresponding shared model exists, the shared model is selected and the process proceeds to the next step (S16). If the corresponding shared model does not exist, the neural network that matches the conditions of the device 20 is used. A new learning model consisting of the configuration is generated (S15), and the process proceeds to the next step (S16).

After selecting the shared model or generating a new learning model, additional learning is performed on the shared model or the new learning model by the learner (S16). The additional learning is performed by using the sample data for performing the additional learning collected from the device 20. After the completion of the additional learning, the generated additional learning model is stored in the storage unit 15 (S17). The server device 10 transmits the generated additional trained model to the device 20.

If the device 20 has a function to perform additional learning processing, or if the selected shared model matches the conditions of the device 20 without the need for additional learning, FIG. 2 shows. It is possible that step (S16) and step (S17) are omitted and the selected shared model is transmitted to the device 20 as it is.

Next, the flow from downloading the shared model to the device 20 and performing the inference process will be described. FIG. 3 is a flowchart showing a flow until the inference process is performed in the device 20. In FIG. 3, the device 20 that wants to perform inference processing first collects device data (S21). Based on the collected device data, the attributes of the device data are defined (S22). The attribute of the device data may be defined on the server device 10 side. Then, the device data is transmitted to the server device 10 in order to search for the optimum shared model using the device data (S23). In the server device 10 that has received the device data, the optimum sharing model is selected, and additional learning is performed as necessary. Then, in the device 20, the shared model or the additionally trained model selected by the server device 10 is downloaded to the learner and stored (S24). Finally, in the device 20, in a state where the shared model or the additionally trained model is stored in the learner, the device data is used to perform inference processing in the learner to obtain the inference result as output data (S25). ..

The output data is completely different depending on the inference processing to be performed. For example, judgment of correctness of planned action, judgment of abnormality degree of parts, judgment of system abnormality degree, inspection result of good or defective product, The name (classification process) of the object shown in the video, the characteristics such as the race and gender of the person shown in the video, the photograph, sound, text, etc. processed based on a specific rule are output as output data.

If the device 20 has a function of performing additional learning processing on the side, additional learning may be performed on the shared model after the step (S24) of FIG. When the additional learning is performed on the device 20 side, the additional learning model is uploaded to the server device 10, so that the additional learning model on the device 20 side can also be uploaded to the server device 10 on another device. It will be possible to use it.

Explaining a specific operation example of the present invention by taking the state of FIG. 1 as an example, for example, the shared model selected by the device 201 transmitting device data to the server device 10 is the “model A”, and the device. It is assumed that the additionally trained model obtained by performing additional training based on the sample data included in the device data of 201 is "model A'". Further, the shared model selected by the device 202 transmitting the device data to the server device 10 is the "model B", which is obtained by performing additional learning based on the sample data included in the device data of the device 202. It is assumed that the additional trained model is "model B'". In this way, the devices 201 and 202 can acquire the optimum and additionally trained trained model only by transmitting the device data including the information such as the environment and conditions of their own devices to the server device 10, respectively. , 202 has an advantage that the introduction cost can be significantly reduced as compared with the case where the trained model is independently generated.

Further, in FIG. 1, when the device 20n transmits device data to the server device 10 and requests a shared model, the environment, conditions, and the like defined from the device data of the device 20n are the same as those of the device 201. When the server device 10 determines that the same trained model can be applied, the additional trained model "model A'" is transmitted to the device 20n instead of performing additional training based on the "model A". For example, the device 20n can perform inference processing as it is. In this way, if there is an additional trained model generated based on other devices in the same environment and conditions, it can be used directly, so the introduction cost can be further reduced and the introduction can be performed. It is possible to shorten the time until. Further, since the optimum neural network size can be applied as compared with the case of using a general-purpose learning model, it is possible to reduce the calculation cost and the memory cost for additional learning.

Further, in a situation where the products handled in the same factory are changed, it is conventionally necessary to learn from scratch every time the products are changed, but according to the server device 10 of the present invention, the products handled are changed. You can search for and download the optimal sharing model again at the right time. That is, there is an advantage that it is easy to introduce the optimum shared model when the processing content in the same device 20 is changed. If the additional trained model is generated by another device with the same processing content, there is an advantage that the additional trained model that can perform highly accurate inference processing that requires time and effort for the additional learning process can be immediately introduced. .. As described above, it is also an advantage of the server device 10 of the present invention that a large number of devices 201 to 20n access the server device 10 and the data of the additionally learned model is accumulated.

In the first embodiment, the shared model and the additionally trained model have been described separately, but both are still trained models, only the degree of learning is different. .. That is, if the shared model and the additionally trained model can be appropriately selected according to the degree of learning when viewed from another device, they are necessarily stored separately as in the storage unit 15 of FIG. No need. It can be said that if the shared model and the additionally trained model are provided with information for searching for the optimum model at the time of searching, they can be treated as the same trained model. In this case, it can be said that the server device 10 of the present invention functions without the additional learned model management unit 14.

In the first embodiment, the server device 10 is provided with the additional learning processing unit 13, but the present invention is not limited to this, and each device 20 is provided with an additional learning processing function corresponding to the additional learning processing unit 13. It may be provided on the side. In this case, the additional trained model generated on the device 20 side may be transmitted to the server device 10, but the entire additional trained model is not transmitted to the server device 10 and is the additional trained model. The configuration may be such that only the information necessary for selection is transmitted to the server device 10. It can be configured to send directly to the server device 10 or the required device 20 only when the other device 20 needs the same additionally trained model. This makes it possible to reduce the data area required for the server device 10.

In the first embodiment, as shown in FIG. 1, a configuration in which one server device 10 and a plurality of devices 201 to 20n are connected via a communication network 30 has been described as an example. For example, by configuring the shared model (including the additionally trained model) in which a plurality of server devices 10 are stored so as to be able to communicate with each other via the communication network 30 in a state of mutual recognition. The shared model may be searched from another server device 10 and provided to the device. By configuring the trained model providing system including the plurality of server devices and the plurality of devices in this way, it is possible to provide the shared model stored in any of the plurality of server devices 10 to the device 20. The optimal sharing model can be selected from a larger amount of data options.

In the first embodiment, the target data for additional learning in the additional learning processing unit 13 was learned using the device data acquired only on that device, but it is not necessary to stay there, and the same environment and The data acquired by another device of the condition may be used, or the trained model generated by another device of the same environment and condition may be used for updating. Further, the additionally trained models generated in each of a plurality of devices under the same environment and conditions may be mixed to generate a mixed learning model. Various known methods can be applied to the mixing of learning models.

The present invention is a technique that can be applied to any field that requires inference processing using a trained model, and can be operated as a database of trained models.

10 Server device 11 Device data acquisition unit 12 Target shared model selection unit 13 Additional learning processing unit 14 Additional learning model management unit 15 Storage unit 20, 201-20n device 30 Communication network

Claims (17)

At least one storage device that stores multiple models with different characteristics learned in advance,
With at least one processor,
The at least one processor
To get information about the first device
To select a model to be transmitted to the first device from the plurality of models based on the information about the first device.
Sending the selected model to the first device and
Is configured to run
The first device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information regarding the first device includes at least information regarding the environment of the first device and any one of the conditions of the first device.
Model transmitter. The model transmitted to the first device is determined by the model transmitter, not by the first device.
The model transmitter according to claim 1. Each of the plurality of models is a model capable of inputting device data and outputting an inference result regarding the device data.
The model transmitter according to claim 1 or 2. The model transmitted to the first device is a model capable of outputting an inference result regarding data sensed by an external sensor included in the first device.
The model transmitter according to any one of claims 1 to 3. The first device is the work machine and
The information about the first device includes information about the work object of the work machine.
The model transmitter according to any one of claims 1 to 4. The information regarding the first device is information regarding the environment of the first device.
The at least one processor selects a model suitable for the environment of the first device from the plurality of models.
The model transmitter according to any one of claims 1 to 5. The information regarding the first device is information regarding the conditions of the first device.
The at least one processor selects a model that meets the conditions of the first device from the plurality of models.
The model transmitter according to any one of claims 1 to 6. With at least one storage device
A device with at least one processor,
The at least one processor
Sending information about the device to the model transmitter,
Receiving a model selected by the model transmitter based on information about the device from the model transmitter and
Acquiring device data and
Obtaining the inference result regarding the device data using the received model,
Is configured to run
The device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information about the device includes at least information about the environment of the device and any one of the conditions of the device.
device. The received model is determined to be transmitted to the device by the model transmitter.
The device according to claim 8. Equipped with an external sensor
The device data is data acquired by the external sensor.
The device according to claim 8 or 9. The work machine
The information about the device includes information about the work object of the work machine.
The device according to any one of claims 8 to 10. The information about the device is information about the environment.
The received model is a model suitable for the environment of the device.
The device according to any one of claims 8 to 11. The information about the device is information about the conditions of the device.
The received model is a model that meets the conditions of the device.
The device according to any one of claims 8 to 12. By at least one processor
To get information about the first device
To select a model to be transmitted to the first device from a plurality of models having different characteristics learned in advance based on the information about the first device.
Sending the selected model to the first device and
Is a model transmission method that includes
The first device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information regarding the first device includes at least information regarding the environment of the first device and any one of the conditions of the first device.
Model transmission method. By at least one processor on the device
Sending information about the device to the model transmitter
Receiving a model selected by the model transmitter based on information about the device from the model transmitter and
Acquiring device data and
Obtaining the inference result regarding the device data using the received model,
Is a way to prepare
The device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information about the device includes at least information about the environment of the device and any one of the conditions of the device.
Method. On at least one processor
To get information about the first device
To select a model to be transmitted to the first device from a plurality of models having different characteristics learned in advance based on the information about the first device.
Sending the selected model to the first device and
Is a program that executes
The first device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information regarding the first device includes at least information regarding the environment of the first device and any one of the conditions of the first device.
program. On at least one processor on the device
Sending information about the device to the model transmitter
Receiving a model selected by the model transmitter based on information about the device from the model transmitter and
Acquiring device data and
Obtaining the inference result regarding the device data using the received model,
Is a program that executes
The device is at least one of a robot, an automobile, a process automation device, an infrastructure device, an agricultural device, a healthcare device, a work machine, an abnormality detection device, and a classification device.
The information about the device includes at least information about the environment of the device and any one of the conditions of the device.
program.

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