Information processing device, information processing system, logic determination method, and logic determination program

Abstract

To automatically determine a control content corresponding to a target facility and improve the ease of managing a determination logic of the control content.SOLUTION: An information processing device (1) comprises: a target identifying unit (102) that identifies a target facility; and a logic determining unit (106) that, based on logic allocation information (112) in which common logic (41) and individual logic (42) are allocated to each of a plurality of facilities (61), determines the common logic (41) and the individual logic (42) to be used in the target facility.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing system or the like that performs control in accordance with the results of predictions made using a prediction model. [Background technology]

[0002] A technique for determining appropriate control content for various facilities by utilizing prediction results from a prediction model is known as a conventional technique. For example, the following Patent Document 1 describes a waste treatment plant facility where a predicted value of the amount of steam generated is calculated using a learning model that predicts the amount of steam generated after a predetermined time. It also describes controlling a primary combustion air damper by applying an operating value that makes the difference between the predicted value of the amount of steam generated and a target amount of steam generated zero. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-242524 A Summary of the Invention [Problem to be solved by the invention]

[0004] Although the above-mentioned conventional technology is based on the premise that there is one control target, in reality, there may be multiple control targets. For example, a waste treatment plant may be equipped with multiple incinerators. When there are multiple incinerators, the characteristics of each incinerator may differ, so it may not be appropriate to determine the control content for each incinerator using the same decision logic. However, if the control content is determined using an individual decision logic for each incinerator, it becomes necessary to manage each of the multiple decision logics individually, which reduces manageability.

[0005] In view of the above, when multiple facilities exist, a technology is desired that enables automatic determination of appropriate control content according to the target facility to be controlled while improving manageability of the decision logic of the control content, but such a technology has not existed in the past. One aspect of the present invention aims to realize an information processing device or the like that enables determination of control content according to the target facility while also enabling improvement of manageability of the decision logic of the control content. [Means for solving the problem]

[0006] In order to solve the above problems, an information processing device according to one embodiment of the present invention includes a target identification unit that identifies equipment to be controlled among a specified number of pieces of equipment as target equipment, and a logic determination unit that determines the common logic and individual logic to be used in the target equipment based on logic assignment information that assigns, for each of the multiple pieces of equipment, a common logic that is a determination logic for control content that can be commonly applied to the multiple pieces of equipment, and an individual logic for determining individual control content for each piece of equipment.

[0007] In order to solve the above problems, an information processing system according to one embodiment of the present invention includes an information processing device that determines, based on logic assignment information in which a common logic, which is a decision logic for control content commonly applicable to a plurality of pieces of equipment, and an individual logic for deciding individual control content for each piece of equipment are assigned to each piece of equipment, the common logic and the individual logic to be used in a target piece of equipment to be controlled among the plurality of pieces of equipment, and a control content decision device that determines the control content for the target equipment using the common logic and the individual logic decided by the information processing device.

[0008] In order to solve the above problems, a logic determination method according to one embodiment of the present invention is a logic determination method executed by one or more information processing devices, and includes a target identification step of identifying equipment to be controlled among a specified number of pieces of equipment as target equipment, and a logic determination step of determining, for each of the multiple pieces of equipment, a common logic, which is a determination logic for control content commonly applicable to the multiple pieces of equipment, and an individual logic for determining individual control content for each piece of equipment, based on logic assignment information that assigns the common logic and the individual logic to be used in the target equipment. Effect of the Invention

[0009] According to one aspect of the present invention, it is possible to determine control content according to a target facility, and also to improve the manageability of the decision logic for the control content. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of a configuration of a main part of an information processing device according to an embodiment of the present invention; [Diagram 2] 1 is a diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Diagram 3] 11A and 11B are diagrams illustrating examples of model allocation information and logic allocation information. [Figure 4] FIG. 2 is a diagram illustrating an example of time-series data that is the source of training data. [Diagram 5] FIG. 11 is a diagram illustrating an example of pseudo time-series data and teacher data. [Figure 6] 1 is a flowchart illustrating an example of a model determination method / logic determination method according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] [System Configuration] 2 is a diagram showing an example of the configuration of an information processing system 9 according to an embodiment of the present invention. The information processing system 9 is a system having a function of predicting a predetermined event related to the control of equipment 61 included in the facility 6, and determining the control content for the equipment 61 based on the prediction result.

[0012] As shown in the figure, the information processing system 9 includes an information processing device 1, a prediction device 2a, a prediction device 2b, a control content determination device 3, a storage device 4, a storage device 5, and a facility 6. When there is no need to distinguish between the prediction device 2a and the prediction device 2b, they will be simply referred to as "prediction device 2." The same applies to prediction models 51a, 51b, equipment 61a to 61c, individual logics 42a to 42c, and setting information 43a to 43c, which will be described later.

[0013] The information processing device 1 identifies equipment to be controlled among the equipment 61 as target equipment, and determines the prediction model 51 to be used for the above-mentioned prediction of the target equipment. The information processing device 1 also determines a decision logic for determining the control content of the target equipment. Note that the decision logic defines a method for determining the control content, and can also be called a control content decision logic. For example, a computer program for determining the control content may be used as the decision logic. The above-mentioned "control content" also includes at least one of what kind of control to perform and what kind of control amount to apply.

[0014] The prediction device 2 performs the above-mentioned prediction for the target facility using the prediction model 51 determined by the information processing device 1. Since the prediction model 51 is stored in the storage device 5, the prediction device 2 acquires the prediction model 51 determined by the information processing device 1 from the storage device 5, and performs prediction using the acquired prediction model 51.

[0015] 2 shows two prediction devices 2, prediction devices 2a and 2b. The prediction device 2a performs prediction using a prediction model 51a that is commonly used for predictions in a plurality of facilities 61. On the other hand, the prediction device 2b performs prediction using a prediction model 51b that is used for predictions in a specific facility 61. The number of prediction devices 2 included in the information processing system 9 is arbitrary.

[0016] The control content determination device 3 determines the control content for the target equipment based on the prediction result by the prediction device 2. The decision logic determined by the information processing device 1 is used to determine the control content. Since the decision logic for the control content is stored in the storage device 4, the control content determination device 3 acquires the decision logic determined by the information processing device 1 from the storage device 4 and determines the control content using the acquired decision logic. Note that the information processing device 1 may be configured to acquire the decision logic from the storage device 4 and transfer the acquired decision logic to the control content determination device 3.

[0017] The storage device 4 stores a common logic 41, an individual logic 42, and setting information 43. Both the common logic 41 and the individual logic 42 are logic for determining the control content (which can also be rephrased as control content determination logic). Although details will be described later, the common logic 41 is applicable commonly to a plurality of pieces of equipment 61, and the individual logic 42 is for determining the individual control content in each piece of equipment 61. The setting information 43 indicates settings (for example, the values ​​of constants included in a program or formula that determines the control content) to be applied when performing processing for determining the control content using the common logic 41 and the individual logic 42 in each piece of equipment 61.

[0018] One decision logic is completed by combining the common logic 41 and the individual logic 42 and applying the settings indicated in the setting information 43. Then, the prediction results of the prediction model 51 are input into the completed decision logic, and control contents are output. Such common logic 41 can be generated by generating one decision logic for each piece of equipment 61 (a decision logic adapted to each piece of equipment 61) and then extracting a part of the created decision logic that is common to each piece of equipment 61. Then, the remaining part that is not extracted can be used as the individual logic 42 for each piece of equipment 61.

[0019] Although details will be described later, the allocation of the common logic 41, the individual logic 42, and the setting information 43 to each piece of equipment 61 is regulated by logic allocation information. Therefore, the information processing device 1 determines the common logic 41, the individual logic 42, and the setting information 43 to be used in the target equipment based on this logic allocation information.

[0020] The storage device 5 stores the prediction model 51. Although details will be described later, a prediction model 51 is assigned to each facility 61, and the assignment of the prediction model 51 to each facility 61 is specified by model assignment information. Therefore, the information processing device 1 determines the prediction model 51 to be used for prediction of the target facility based on this model assignment information.

[0021] FIG. 2 shows two prediction models 51, ie, prediction models 51a and 51b. The prediction model 51a is a prediction model 51 commonly used for predictions about a plurality of pieces of equipment 61. On the other hand, the prediction model 51b is a prediction model 51 used for predictions about a specific piece of equipment 61. For example, the prediction model 51a may be used for predictions about the pieces of equipment 61a and 61b, and the prediction model 51b may be used for predictions about the piece of equipment 61c. Note that there is no particular limit to how many types of prediction models 51 are stored in the storage device 5.

[0022] The facility 6 is a facility that includes a plurality of pieces of equipment 61 that are targets of control in the information processing system 9. Although three pieces of equipment 61, 61a to 61c, are shown in FIG. 2, the number of pieces of equipment included in one facility 6 is arbitrary. Furthermore, the information processing system 9 can also determine the control content of each piece of equipment 61 included in each facility 6, targeting a plurality of facilities 6. Note that the equipment 61 may be a single device or apparatus, or may include a plurality of devices or apparatuses. Furthermore, each piece of equipment 61 included in the facility 6 may operate individually, or may cooperate with each other.

[0023] (Example of processing) For example, in the information processing system 9, when controlling the equipment 61a, prediction data acquired by the equipment 61a and an equipment ID (IDentification) which is identification information of the equipment 61a are input to the information processing device 1. The prediction data is data used for prediction using the prediction model 51. For example, time-series measurement values ​​measured by a sensor or the like installed in the equipment 61a may be used as the prediction data. In addition, the process of transmitting the prediction data and the equipment ID to the information processing device 1 may be performed by, for example, an information processing device (not shown) installed in the facility 6. In addition, the user of the information processing system 9 may input these data to the information processing device 1.

[0024] Next, the information processing device 1 determines the prediction model 51 to be used for prediction of the facility 61a, and determines the prediction device 2 to perform the prediction, based on the above-mentioned model allocation information. For example, assume that the prediction model 51 assigned to the facility 61a is the prediction model 51a. In this case, the information processing device 1 determines to use the prediction model 51a for prediction of the facility 61a, and determines to cause the prediction device 2a capable of prediction using the prediction model 51a to perform the prediction.

[0025] The information processing device 1 also performs a predetermined pre-processing on the prediction data of the facility 61a to generate input data to be input to the prediction model 51a, and transmits the generated input data to the prediction device 2a. The prediction device 2a acquires the prediction model 51a from the storage device 5, inputs the input data received from the information processing device 1 to the acquired prediction model 51a, obtains a prediction result, and notifies the information processing device 1 of the prediction result.

[0026] Furthermore, the information processing device 1 determines the common logic 41, the individual logic 42, and the setting information 43 to be used in determining the control content in the equipment 61a based on the above-mentioned logic allocation information. For example, if the common logic 41, the individual logic 42a, and the setting information 43a are allocated to the equipment 61a, the information processing device 1 determines that these determination logics and setting information are to be used in determining the control content in the equipment 61a.

[0027] Then, the information processing device 1 notifies the control content determination device 3 of the logic ID indicating the common logic 41, the logic ID indicating the individual logic 42a, the setting information ID indicating the setting information 43a, and the prediction result of the prediction device 2a.

[0028] Next, the control content determination device 3 acquires the common logic 41, the individual logic 42a, and the setting information 43a indicated in the notified logic ID and setting information ID from the storage device 4, and determines the control content according to the prediction result of the prediction device 2a using these. Then, the control content determination device 3 notifies the equipment 61a (more precisely, a control device not shown that controls the equipment 61a) of the determined control content. This causes the equipment 61a to be controlled. Note that one control device may control one equipment 61, or one control device may control multiple equipment 61.

[0029] (About information processing systems) As described above, the information processing system 9 includes an information processing device 1 that determines a prediction model 51 to be used for predicting a predetermined event related to the control of a predetermined plurality of pieces of equipment 61, based on model allocation information that assigns to each of the plurality of pieces of equipment 61 a prediction model 51 to be used for predicting the above-mentioned event in a piece of target equipment to be controlled, a prediction device 2 that predicts the above-mentioned event in the piece of target equipment using the prediction model 51 determined by the information processing device 1, and a control content determination device 3 that determines control content for the piece of target equipment based on a prediction result by the prediction device 2. Thus, the information processing system 9 makes it possible to perform state prediction using an appropriate prediction model 51 according to the piece of equipment, and to determine appropriate control content based on the prediction result.

[0030] Moreover, the information processing system 9 includes a storage device 5 that stores a prediction model 51 assigned to each of the multiple facilities 61, and the prediction device 2 obtains the prediction model 51 determined by the information processing device 1 from the storage device 5 and performs prediction. With this configuration, the prediction models 51 used in each facility 61 can be managed collectively. Furthermore, by storing a prediction model 51a that is commonly used by the multiple facilities 61 (for example, facilities 61a and 61b) in the storage device 5, an efficient operation in which the prediction model 51a is reused among the facilities 61 can be realized.

[0031] Furthermore, when there are multiple target facilities and a common prediction model 51 is assigned to the multiple target facilities, the prediction device 2 may execute prediction processing using the prediction model 51 in parallel. This makes it possible to shorten the time until a prediction result for each target facility is output. In the information processing system 9, a prediction model 51a that is commonly used for predictions in multiple facilities 61 is used. For this reason, when a prediction using the prediction model 51a is being performed or will be performed in a certain target facility, it may become necessary to perform a prediction using the prediction model 51a in other target facilities as well. In such a case, the prediction device 2 executes prediction processing using the prediction model 51a in parallel (in other words, executes them simultaneously in parallel), thereby making it possible to quickly obtain a prediction result for each target facility.

[0032] As described above, the information processing system 9 includes an information processing device 1 that determines the common logic 41 and the individual logic 42 to be used in a target equipment to be controlled among a plurality of equipments 61 based on logic allocation information that assigns the common logic 41, which is a decision logic of control contents commonly applicable to the plurality of equipments 61, and the individual logic 42 for deciding individual control contents for each equipment 61, to each of the plurality of equipments 61, and a control content decision device 3 that determines the control contents for the target equipment using the common logic 41 and the individual logic 42 decided by the information processing device 1. Thus, according to the information processing system 9, it is possible to decide the control contents according to the target equipment by a combination of the common logic 41 and the individual logic 42. In addition, since the common logic 41 and the individual logic 42 can be managed according to their characteristics, it is also possible to improve the manageability of the decision logic.

[0033] As described above, the information processing system 9 includes a storage device 4 that stores the common logic 41 applicable to the multiple pieces of equipment 61 in common, and the individual logic 42 applied to each of the multiple pieces of equipment 61. The control content determination device 3 acquires the common logic 41 and the individual logic 42 determined by the information processing device 1 from the storage device 4. This configuration realizes efficient operation in which the common logic 41 stored in the storage device 4 is reused among the multiple pieces of equipment 61.

[0034] (Examples) The information processing system 9 is applicable to the control of various facilities and equipment. For example, the facility 6 may be a plant equipped with multiple devices. In this case, each device equipped in the plant is equipment 61. Here, a "plant" is a facility used industrially, equipped with multiple devices, and performing predetermined processing such as the production of products or the processing of objects using the devices. For example, a waste treatment facility is also included in the category of a plant.

[0035] In the following, an example will be described in which the facility 6 is a waste treatment facility that incinerates waste (e.g., combustible garbage) and generates power using the exhaust heat, and the equipment 61a-61c are incinerators. Then, in the equipment 61a-61c, prediction data related to waste incineration and power generation is collected, prediction is performed by the prediction model 51 using the prediction data, and the equipment 61a-61c is controlled based on the prediction results. This realizes stable automatic control of the waste treatment facility.

[0036] Moreover, the subject of prediction by the prediction model 51 may be anything related to the control of the facility 61, and is not particularly limited. For example, the prediction model 51 may be a model that predicts the quality of the combustion state in an incinerator. In order to generate power efficiently in the facility 61, it is necessary to stabilize the amount of steam for turning the turbine for power generation, but the combustion state in the incinerator changes with fluctuations in the quality and amount of waste to be incinerated, and the amount of steam also changes accordingly. By using the prediction model 51 that predicts the quality of the combustion state, it is possible to predict the quality of the combustion state in the facility 61, which is an incinerator, and appropriately control the facility 61 according to the prediction result, thereby stably incinerating waste and generating power.

[0037] When using a prediction model 51 for predicting whether the combustion state in an incinerator is good or bad, the prediction data may be various data related to the combustion state. For example, time-series sensing data related to automatic combustion control (ACC) may be used as the prediction data. By using such data as explanatory variables and a value indicating whether the combustion state is good or bad after a predetermined time has passed after the data is obtained as a target variable, and by machine learning the relationship between these, a prediction model 51 for predicting whether the combustion state is good or bad after a predetermined time has passed can be generated.

[0038] Similar prediction data can also be applied when using a prediction model 51 that predicts a numerical value indicating the state of the equipment 61 after a predetermined time. Examples of numerical values ​​indicating the state of the equipment 61 include the amount of steam, the pressure inside the furnace, and the temperature inside the furnace. Such a prediction model 51 can be generated by machine learning the relationship between various data used for prediction as explanatory variables and a numerical value to be predicted as a target variable.

[0039] Furthermore, the prediction model 51 may predict the content of control to be executed on the equipment 61. For example, various data acquired when an operator executes appropriate intervention control on the equipment 61 (or a predetermined time before) are used as explanatory variables, and a value indicating the content of the intervention control is used as a target variable, and the relationship between these is machine-learned to generate such a prediction model 51. Then, by using such a prediction model 51, it is possible to predict which of the intervention controls executed by the operator in the past should be executed.

[0040] The machine learning algorithm is not particularly limited. For example, the prediction model 51 may be a neural network prediction model, or a support vector machine, linear regression, random forest, or other prediction model.

[0041] [Device configuration] The configuration of the information processing device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of a main configuration of the information processing device 1. As shown in the figure, the information processing device 1 includes a control unit 10 that controls each unit of the information processing device 1, and a storage unit 11 that stores various data used by the information processing device 1. The information processing device 1 also includes a communication unit 12 that allows the information processing device 1 to communicate with other devices, an input unit 13 that accepts input of various data to the information processing device 1, and an output unit 14 that allows the information processing device 1 to output various data.

[0042] The control unit 10 includes a data acquisition unit 101, a target identification unit 102, a model determination unit 103, an input data generation unit 104, a prediction device determination unit 105, a logic determination unit 106, and a management unit 107. The storage unit 11 stores model allocation information 111 and logic allocation information 112.

[0043] The data acquiring unit 101 acquires various data used by the information processing device 1. For example, the data acquiring unit 101 acquires time series data acquired by the facility 61 as prediction data to be used for prediction using the prediction model 51. The time series data may be acquired from a sensor or the like arranged in the facility 61 via the communication unit 12 or the input unit 13, or may be input by a user of the information processing device 1 via the input unit 13. The data acquiring unit 101 may also acquire process data obtained by performing a predetermined preprocessing on the time series data.

[0044] The target identification unit 102 identifies the equipment 61 to be controlled from among a predetermined number of pieces of equipment 61 as the target equipment. The method of identifying the target equipment is arbitrary and may be determined in advance. For example, when data required for prediction using the prediction model 51 is acquired by the data acquisition unit 101, the target identification unit 102 may identify the equipment 61 from which the data was acquired as the target equipment.

[0045] The model determination unit 103 determines the prediction model 51 to be used for predicting an event in the target facility identified by the target identification unit 102, based on the model allocation information 111. The model allocation information 111 is information indicating the allocation of the prediction model 51 to be used for predicting a predetermined event related to the control of each of the multiple facilities 61.

[0046] The input data generation unit 104 generates input data to be input to the prediction model 51. More specifically, the input data generation unit 104 performs standardization or normalization as preprocessing on the time series data acquired by the data acquisition unit 101, thereby generating input data that can be input to the prediction model 51. Note that when the data acquisition unit 101 acquires preprocessed process data, the input data generation unit 104 is omitted.

[0047] The prediction device determination unit 105 determines a prediction device 2 that will perform prediction for a target facility from among a plurality of prediction devices 2 that are capable of performing prediction using the prediction model 51. By providing the information processing device 1 with the prediction device determination unit 105, it becomes possible to avoid concentration of processing load on one prediction device 2. The method of determining the prediction device 2 will be described later.

[0048] The logic determination unit 106 determines the common logic 41 and the individual logic 42 to be used for determining the control content in the target equipment identified by the target identification unit 102, based on the logic allocation information 112. The logic allocation information 112 is information indicating the allocation of the common logic 41 and the individual logic 42 for each of the multiple equipment 61. Furthermore, the logic determination unit 106 may determine setting information 43 to be used in the target equipment, in addition to the common logic 41 and the individual logic 42 to be used in the target equipment.

[0049] The management unit 107 manages the authority for each data stored in the storage device 4 (for example, authority to add, delete, and change data, authority to access data, etc.). Specifically, the management unit 107 individually sets a user to whom authority for the common logic 41 is to be given, a user to whom authority for the individual logic 42 is to be given, and a user to whom authority for the setting information 43 is to be given. Then, the management unit 107 identifies the user of the information processing device 1, and allows processing of each data stored in the storage device 4 within the scope of the authority given to that user.

[0050] As described above, the information processing device 1 includes a target identification unit 102 that identifies a piece of equipment 61 to be controlled as a target piece of equipment among a predetermined number of pieces of equipment 61, and a model determination unit 103 that determines a prediction model 51 to be used for predicting a predetermined event in the target equipment based on model allocation information 111 that assigns, to each of the plurality of pieces of equipment 61, a prediction model 51 to be used for predicting the above-mentioned event related to the control of the equipment 61. Thus, it is possible to determine an appropriate prediction model 51 according to the target equipment.

[0051] In the model allocation information 111, a common prediction model 51 may be assigned to a plurality of pieces of equipment 61. This allows the prediction model 51 used in the plurality of pieces of equipment 61 to be standardized. This makes it possible to simplify the system configuration related to prediction and reduce costs for generating and updating the prediction model 51, as compared to a case in which different prediction models 51 are used in each piece of equipment 61.

[0052] As described above, the information processing device 1 includes a target identification unit 102 that identifies a piece of equipment 61 to be controlled as a target equipment among a specified number of pieces of equipment 61, and a logic determination unit 106 that determines the common logic 41 and individual logic 42 to be used in determining the control content in the target equipment based on logic assignment information 112 that assigns, to each of the plurality of pieces of equipment 61, a common logic 41 that is a determination logic for control content commonly applicable to the plurality of pieces of equipment 61, and an individual logic 42 for determining individual control content in each piece of equipment 61.

[0053] According to the above configuration, the control contents for the target equipment can be determined using the common logic 41 and the individual logic 42 corresponding to the target equipment. Furthermore, according to the above configuration, the common logic 41 and the individual logic 42 can be managed according to their characteristics, so that the manageability of the decision logic can be improved. For example, according to the above configuration, by simply modifying one common logic 41, the modified content can be reflected in the determination of the control contents for multiple equipment 61. Furthermore, for example, it is possible to give the authority to modify, etc. only to some people for the common logic 41 that affects multiple equipment 61, and to give the authority to modify, etc. to more people for the individual logic 42 that affects only a specific equipment 61.

[0054] As described above, in the logic assignment information 112, the setting information 43 indicating the setting to be applied when performing the process of determining the control content using the common logic 41 and the individual logic 42 in each piece of equipment 61 may be assigned to each piece of equipment 61. Then, based on the logic assignment information 112, the logic determination unit 106 may determine the setting information 43 to be used in the target equipment in addition to the common logic 41 and individual logic 42 to be used in the target equipment.

[0055] According to this configuration, the setting information 43 indicating the settings to be applied when performing the process of determining the control contents using the common logic 41 and the individual logic 42 is data separate from the common logic 41 and the individual logic 42. Therefore, it is possible to improve the manageability compared to the case where the determination logic and the setting information are inseparably integrated. For example, it is possible to allow even a person who is not familiar with the determination logic of the control contents to modify the setting information 43.

[0056] As described above, the information processing device 1 includes the management unit 107 that individually sets the persons to whom authority regarding the common logic 41 is to be given, the persons to whom authority regarding the individual logic 42 is to be given, and the persons to whom authority regarding the setting information 43 is to be given. This configuration can achieve the improvement in manageability as described above.

[0057] For example, the management unit 107 can grant authority to modify both the common logic 41 and the individual logic 42 only to specific individuals, while granting authority to modify the individual logic 42 to other individuals. This makes it possible to easily modify the individual logic 42 in accordance with the actual conditions of each facility 61, while reducing the possibility of a problem occurring that affects multiple facilities 61 due to modification of the common logic 41.

[0058] [Examples of model allocation information and logic allocation information] The model allocation information 111 and the logic allocation information 112 may be, for example, information as shown in Fig. 3. Fig. 3 is a diagram showing an example of the model allocation information 111 and the logic allocation information 112.

[0059] 3 is information in which an equipment ID, which is identification information of the equipment 61, a model ID, which is identification information of the prediction model 51, and a prediction device ID, which is identification information of the prediction device 2, are associated with each other. By using such model allocation information 111, the model determination unit 103 can determine the prediction model 51 to be used for prediction of the target equipment identified by the target identification unit 102.

[0060] For example, assume that the equipment ID of the target equipment identified by the target identification unit 102 is A0002. In this case, the model determination unit 103 determines the prediction model 51 associated with A0002 and having a model ID of B0001 as the prediction model 51 to be used for prediction of A0002. In addition, in this case, the prediction device determination unit 105 determines that the prediction device 2 associated with A0002 and having a prediction device ID of C0001 should perform prediction using the prediction model 51 with a model ID of B0001.

[0061] 3 is information in which an equipment ID, which is identification information of the equipment 61, a logic ID, which is identification information of the common logic 41 or the individual logic 42, and a setting information ID, which is identification information of the setting information 43, are associated with each other. By using such logic assignment information 112, the logic determination unit 106 can determine the common logic 41 and the individual logic 42, and the setting information 43, which are used to determine the control content for the target equipment identified by the target identification unit 102.

[0062] For example, assume that the equipment ID of the target equipment identified by the target identification unit 102 is A0003. In this case, the logic determination unit 106 determines that the common logic 41 with a logic ID of D1001 and the individual logic 42 with a logic ID of D2001, which are associated with A0003, are to be used to determine the control content for A0003. In addition, in this case, the logic determination unit 106 determines that the setting information 43 with a setting information ID of E0003 is to be applied when determining the control content using the common logic 41 with a logic ID of D1001 and the individual logic 42 with a logic ID of D2001.

[0063] [Method of determining prediction device] As described above, if the allocation of the prediction device 2 is specified in the model allocation information 111, the prediction device determination unit 105 can determine the prediction device 2 to perform prediction by referring to the model allocation information 111. In this case, the model allocation information 111 also serves as prediction device allocation information indicating the allocation of the prediction device 2. The prediction device allocation information may be data separate from the model allocation information 111.

[0064] Furthermore, the prediction device determination unit 105 may determine the prediction device 2 to perform prediction so that the periods in which prediction is performed by each prediction device 2 do not overlap or the overlapping periods are minimized. For example, a plurality of prediction devices 2 capable of performing prediction using a prediction model 51a commonly used for a plurality of pieces of equipment 61 may be provided. In this case, the prediction device determination unit 105 may determine, among the plurality of prediction devices 2, one that is not performing prediction at that time as the prediction device 2 to perform prediction.

[0065] [Forecast models assigned to multiple pieces of equipment] The prediction model 51 assigned to multiple facilities 61 may be a model generated by machine learning using training data generated by applying standardization or normalization processing to pseudo-time series data that concatenates time series data related to the event to be predicted.

[0066] By performing machine learning using the above-mentioned teacher data, the characteristics of the time-series data for each facility 61 are learned, thereby generating a prediction model 51 capable of outputting valid prediction results for each facility 61. Therefore, according to the above-mentioned configuration, it is possible to standardize the prediction model 51 used for the multiple facilities 61 while ensuring the validity of the prediction results.

[0067] The above teacher data will be described below with reference to Fig. 4 and Fig. 5. Fig. 4 is a diagram showing an example of time series data on which the teacher data is based, and Fig. 5 is a diagram showing an example of pseudo-time series data generated from the time series data shown in Fig. 4 and teacher data generated from the pseudo-time series data.

[0068] 4 shows an example in which time-series data 114a to 114c used to generate teacher data are generated from steam volume data 113a to 113c including measured values ​​of steam volume measured by a steam volume sensor that measures the volume of steam generated from a boiler provided in the equipment 61. The steam volume data 113a to 113c indicate the steam volumes measured in the equipment 61a to 61c in FIG. 2, respectively.

[0069] As shown in Fig. 4, the steam amount data 113a to 113c include measured values ​​(PV: Process Variable) for each time. The steam amount data 113a to 113c shown in Fig. 4 also include set values ​​corresponding to each measured value. The set value (SV: Set Variable) indicates a target value (steam amount in this example) at the time associated with the set value. In other words, in the equipment 61a to 61c from which the steam amount data 113a to 113c are obtained, control is performed so that the steam amount approaches the set value.

[0070] From such steam amount data 113a-113c, time series data 114a-114c can be generated, whose elements are the PV / SV value at each time, i.e., the ratio between the measured value and the set value. When the PV / SV value is close to 1, it can be said that the condition of the facility is normal. Note that the difference between PV and SV, i.e., the difference between the measured value and the set value, may be applied instead of PV / SV.

[0071] The time series data 114a to 114c also include an element called an abnormality flag. The value of the abnormality flag at each time indicates whether the state of the equipment 61a to 61c is normal or not. Specifically, in the example of Fig. 4, if the state is normal, the value of the abnormality flag is set to 0, and if the state is not normal, the value of the abnormality flag is set to 1. The criteria for determining whether the state is normal or not may be appropriately determined.

[0072] The value of the abnormality flag is correct data in machine learning, in other words, the objective variable of the generated prediction model 51. In other words, the prediction model 51 generated using the time-series data 114a to 114c is a model that predicts whether or not the state of the equipment 61 is normal. Of course, the objective variable of the prediction model 51 is arbitrary and is not limited to whether or not the state of the equipment 61 is normal.

[0073] The difference or ratio between the measured value and the set value is a more versatile index than the measured value measured by the equipment 61. Therefore, by using such a versatile index, the pseudo-time series data can be made natural. In other words, the above configuration can reduce the variation in data caused by different equipment 61, and by linking the data collected for each of the multiple equipment 61, the bias that is applied to the fluctuation of the numerical values ​​due to the time series can be reduced. Therefore, the above configuration can generate a prediction model 51 with high prediction accuracy.

[0074] FIG. 5 shows pseudo time series data 115 generated from the time series data 114a to 114c shown in FIG.

[0075] The pseudo-time series data 115 is generated by concatenating the time series data 114a to 114c in this order. However, simply concatenating the time series data 114a to 114c as is will result in duplications and inconsistencies in the "time" values.

[0076] Therefore, when generating the pseudo-time series data 115, the time values ​​in the time series data 114a to 114c are replaced with consecutive values ​​of 1 to 15. These values ​​indicate the order of each data element included in the time series data 114a to 114c, and can be called order information. By providing new order information in place of the original "time" values, it is possible to prevent inconsistencies in the "time" values ​​while maintaining the continuity of the data.

[0077] In general, when creating a prediction model using multiple variables with different data distributions, a highly accurate prediction model can be created by standardizing or normalizing the variables. Here, in order to perform the standardization or normalization process, statistics of the target data set are required, but the statistics are specific to the data set. Therefore, in the conventional technology, it is impossible to combine the time series data after the standardization or normalization process into a single data set, or even if it is possible to combine them, learning using such data significantly reduces the performance of the inference model.

[0078] In contrast, according to the above configuration, multiple time series data are concatenated in advance to create a single pseudo-time series data, so that standardization or normalization processing and concatenation processing of multiple time series data can be performed at the same time.

[0079] 5, the teacher data 116 is generated by performing a standardization process on the pseudo-time series data 115. Specifically, the teacher data 116 is generated by performing a standardization process on each of three data elements, namely, the measured value, the set value, and the PV / SV, included in the pseudo-time series data 115.

[0080] Specifically, the difference between the value of a data element included in the pseudo-time series data 115 and the average value is divided by the standard deviation for each data element to generate the teacher data 116. For example, in the pseudo-time series data 115, the average value of the measured values ​​is 9.387 and the standard deviation is 0.519. Therefore, the measured value of 9.8 at time=1 in the pseudo-time series data 115 is standardized to a value of (9.8-9.387) / 0.519=0.795.

[0081] Alternatively, the teacher data may be generated by performing a normalization process instead of the standardization process. In this case, the maximum and minimum values ​​of the data elements included in the pseudo-time-series data 115 are calculated. Then, the difference between the value of the data element and the calculated minimum value is divided by the data range, i.e., the difference between the maximum and minimum values, and this process is performed for each data element to generate the teacher data.

[0082] As described above, by generating teacher data 116 by performing standardization or normalization on pseudo time-series data 115, it is possible to generate a prediction model 51 that absorbs the characteristics of the time-series data for each facility 61. Note that the conditions of the standardization or normalization (statistics such as the average, standard deviation, maximum value, and minimum value) are also used when making predictions using the generated prediction model 51.

[0083] A prediction model 51 is generated by performing machine learning using the teacher data 116 generated as described above. The objective variable of the prediction model 51 may be the value of the abnormality flag. The explanatory variables of the prediction model 51 may be at least one of the measured value, the set value, and the PV / SV value. Values ​​other than these may also be included in the explanatory variables. For example, at least one of the measured value, the set value, and the PV / SV value of the temperature time series may also be included in the explanatory variables. At least one of the values ​​obtained by standardizing or normalizing these values ​​may also be included in the explanatory variables.

[0084] By using teacher data 116 generated based on pseudo-time series data 115 obtained by concatenating time series data 114a-114c collected from a plurality of facilities 61, a prediction model 51 with higher versatility is generated compared to a case where time series data from a single source is used as teacher data. For example, the prediction model 51a may be generated using teacher data generated from the time series data originating from the facilities 61a and 61b. This allows the prediction model 51a to perform highly accurate predictions for both the facilities 61a and 61b.

[0085] Furthermore, the prediction model 51a thus generated can also be used for prediction in facilities 61 other than the facilities 61a and 61b, as long as they are the same type of facility. In other words, the prediction model 51a thus generated can also be used for prediction in facilities 61 that do not provide the time series data on which the teacher data 116 is based. Note that the time series data to be linked is not limited to data collected in a plurality of different facilities 61. For example, the prediction model 51 can also be generated using teacher data 116 that links time series data collected for a plurality of devices in one facility 61.

[0086] When using the prediction model 51 generated using the above-mentioned teacher data 116, the data acquisition unit 101 may acquire time-series data related to an event to be predicted in the target facility (for example, time-series steam volume data, time-series temperature data, or time-series pressure data, etc.). Then, the input data generation unit 104 may generate input data that can be input to the prediction model 51 by performing standardization processing or normalization processing as preprocessing on the acquired time-series data.

[0087] Furthermore, the input data generating unit 104 may generate input data by performing common pre-processing for target equipment having a common assigned prediction model 51. This makes it possible to efficiently perform pre-processing when making predictions for target equipment having a common assigned prediction model 51.

[0088] For example, assume that a prediction model 51a commonly used for the facilities 61a and 61b shown in Fig. 2 is generated using the teacher data 116. In this case, the input data generating unit 104 can commonize preprocessing of the time series data serving as prediction data acquired from the facility 61a and preprocessing of the time series data serving as prediction data acquired from the facility 61b.

[0089] [Flow of processing executed by information processing device 1] The flow of the process executed by the information processing device 1 will be described with reference to Fig. 6. Fig. 6 is a flow chart showing an example of the process (model determination method / logic determination method) executed by the information processing device 1.

[0090] In S1, the data acquiring unit 101 acquires prediction data. The prediction data acquired in S1 may include, for example, time-series data measured in any of the equipment 61 included in the facility 6. In this case, the data acquiring unit 101 may also acquire identification information (for example, the equipment ID shown in FIG. 3) of the equipment 61 from which the time-series data was measured.

[0091] In S2 (target identification step), the target identification unit 102 identifies, as target equipment, the equipment 61 to be controlled among a predetermined number of pieces of equipment 61. For example, when identification information of the equipment 61a is acquired in S1, the target identification unit 102 identifies the equipment 61a as the target equipment.

[0092] In S3 (model determination step), the model determination unit 103 determines, based on the model allocation information 111, the prediction model 51 to be used for predicting a predetermined event in the target facility identified in S2.

[0093] In S4, the prediction device determination unit 105 determines the prediction device 2 that will perform prediction for the target facility from among a plurality of prediction devices 2 that are capable of performing prediction using the prediction model 51 determined in S3.

[0094] In S5, the input data generation unit 104 performs preprocessing on the prediction data acquired in S1 to generate input data that can be input to the prediction model 51 determined in S3. Note that in S1, preprocessed process data may be acquired as prediction data, in which case the process of S5 is omitted.

[0095] In S6, the input data generation unit 104 notifies the prediction device 2 determined in S4 of the prediction model 51 determined in S3, transmits the input data generated in S5, causes prediction to be performed using the prediction model 51, and receives the prediction result from the prediction device 2.

[0096] In S7 (logic determination step), the logic determination unit 106 determines the common logic 41 and the individual logic 42 to be used in the target equipment identified in S2 based on the logic allocation information 112. The logic determination unit 106 also determines the setting information 43 to be applied when using these determined logics. Note that the process of S7 can be performed at any timing after S2, and is not limited to the example shown in the figure.

[0097] In S8, the logic determination unit 106 notifies the control content determination device 3 of the content determined in S7 and the prediction result received in S6. This ends the processing in FIG.

[0098] The above-described process in Fig. 6 includes the model determination method according to this embodiment. That is, the model determination method according to this embodiment includes a target identification step (S2) of identifying a piece of equipment 61 to be controlled as a piece of equipment to be controlled among a plurality of pieces of equipment 61, and a model determination step (S3) of determining a prediction model 51 to be used for predicting an event in the piece of equipment to be controlled based on model allocation information 111 that assigns a prediction model 51 to be used for predicting a predetermined event related to the control of the piece of equipment 61 to each piece of equipment 61. Therefore, it becomes possible to determine an appropriate prediction model 51 according to the piece of equipment to be controlled.

[0099] The above-described process of Fig. 6 also includes the logic determination method according to this embodiment. That is, the logic determination method according to this embodiment includes a target identification step (S2) of identifying a facility 61 to be controlled among a predetermined number of facilities 61 as a target facility, and a logic determination step (S7) of determining the common logic 41 and the individual logic 42 to be used in the target facility based on logic assignment information 112 in which a common logic 41, which is a determination logic of control contents commonly applicable to the plurality of facilities 61, and an individual logic 42 for determining individual control contents in each facility 61 are assigned to each of the plurality of facilities 61. Therefore, it is possible to determine the control contents according to the target facility and to improve the manageability of the determination logic.

[0100] [Modifications] The execution entity of each process described in each of the above-mentioned embodiments is arbitrary and is not limited to the above-mentioned examples. In other words, as long as the execution entity of each process described in each of the above-mentioned embodiments can be executed, the device constituting the information processing system 9 can be appropriately changed.

[0101] For example, in the information processing system 9 of FIG. 2, the information processing device 1 and the control content determination device 3 are separate devices, but they may be integrated into one device. Similarly, the storage device 4 and the storage device 5 may be integrated into one storage device. In addition, for example, the information processing device 1 may have the function of the prediction device 2a. In this case, the information processing device 1 performs prediction using the prediction model 51a by itself, and makes the prediction device 2b perform prediction using the prediction model 51b. In addition, the information processing device 1 determines both the prediction model 51 and the common logic 41 and the individual logic 42, but an information processing device that determines the prediction model 51 and an information processing device that determines the common logic 41 and the individual logic 42 may be provided separately.

[0102] 6 (model determination method / logic determination method) does not necessarily have to be executed by one device, and these processes can be shared and executed by any number of information processing devices (computers). In other words, the above-mentioned model determination method and logic determination method can be executed by one information processing device 1, or by multiple information processing devices.

[0103] [Software implementation example] The functions of the information processing device 1 can be realized by a program for causing a computer to function as the information processing device 1, and a program (model determination program / logic determination program) for causing a computer to function as each control block of the information processing device 1 (particularly each part included in the control unit 10).

[0104] In this case, the information processing device 1 includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the above program. The control device and storage device execute the above program to realize each function described in the above embodiment.

[0105] The above program may be recorded in one or more computer-readable recording media, not temporarily. The recording media may or may not be included in the information processing device 1. In the latter case, the above program may be supplied to the information processing device 1 via any wired or wireless transmission medium.

[0106] In addition, some or all of the functions of each of the control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0107] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0108] 1. Information processing device 102 Target Identification Department 103 Model Determination Unit 104 Input data generation unit 105 Prediction device determination unit 106 Logic decision section 107 Management Department 2. Prediction Device 3. Control Content Determination Device 4, 5 Storage device 61 Equipment 9. Information Processing Systems

Claims

1. A target identification unit that identifies a piece of equipment to be controlled from among a plurality of pieces of equipment as a target piece of equipment; An information processing device comprising: a logic determination unit that determines the common logic and the individual logic to be used in the target equipment based on logic assignment information that assigns, for each of the multiple pieces of equipment, a common logic that is a determination logic for control content that can be commonly applied to the multiple pieces of equipment, and an individual logic for determining individual control content for each piece of equipment.

2. In the logic allocation information, setting information indicating a setting to be applied when performing a process of determining control content using the common logic and the individual logic in each of the plurality of pieces of equipment is assigned to each of the pieces of equipment, The information processing device according to claim 1 , wherein the logic determination unit determines, based on the logic assignment information, the common logic and the individual logic to be used in the target equipment, as well as the setting information to be used in the target equipment.

3. The information processing apparatus according to claim 2 , further comprising a management unit that individually sets a person to whom authority regarding the common logic is to be given, a person to whom authority regarding the individual logic is to be given, and a person to whom authority regarding the setting information is to be given.

4. an information processing device that determines, based on logic assignment information that assigns a common logic, which is a determination logic for control content commonly applicable to a plurality of pieces of equipment, and an individual logic for determining individual control content for each piece of equipment, the common logic and the individual logic to be used in a target piece of equipment to be controlled among the plurality of pieces of equipment; a control content determination device that determines control content for the target facility using the common logic and the individual logic determined by the information processing device.

5. A storage device that stores the common logic that can be commonly applied to a plurality of the facilities and the individual logic that is applied to each of the plurality of the facilities, The information processing system according to claim 4 , wherein the control content determination device acquires the common logic and the individual logic determined by the information processing device from the storage device.

6. 1. A method of logic determination executed by one or more information processing devices, comprising: A target identification step of identifying a piece of equipment to be controlled from among a plurality of pieces of equipment as a target piece of equipment; A logic determination method including: a logic determination step of determining, for each of a plurality of pieces of equipment, the common logic, which is a determination logic for control content commonly applicable to the plurality of pieces of equipment, and the individual logic for determining individual control content for each piece of equipment, based on logic assignment information in which the common logic and the individual logic are assigned to the plurality of pieces of equipment.

7. A logic determination program for causing a computer to function as the information processing device according to claim 1 , the logic determination program causing a computer to function as the target specification unit and the logic determination unit.

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