Patent Publication Number: US-2021166155-A1

Title: Composite model generation method and information processing apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of the prior Japanese Patent Application No. 2019-216323, filed on Nov. 29, 2019, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiment discussed herein is related to a composite model generation method and an information processing apparatus. 
     BACKGROUND 
     Due to the recent spread of the Internet of Things (IoT), attention has been focused on a predictive maintenance that predicts a failure and performs maintenance using measurement data from a sensor mounted on a facility or a machine. By performing a predictive maintenance, maintenance may be performed optimally and minimally, which may result in suppression of costs. Further, there is also known a technique of predicting the consumption of a battery mounted on an EV car, and presenting the prediction result to a driver. 
     In the related art, as a model used for the above-described prediction, a model created by a data driven method such as machine learning using training data or a model of a model based method created using physical laws or expert knowledge is known. Further, a hybrid model in which these models are fused is also known. 
     Related techniques are disclosed in, for example, Japanese National Publication of International Patent Application No. 2015-535900, Japanese Laid-Open Patent Publication No. 2019-040244, Japanese National Publication of International Patent Application No. 2010-536099, Japanese National Publication of International Patent Application No. 2014-525063, and Japanese National Publication of International Patent Application No. 2019-507444. 
     SUMMARY 
     According to an aspect of the embodiment, a non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process comprising: acquiring a first model that is created using a plurality of pieces of training data and outputs a first estimation result estimated with respect to first input data and a second model that is created based on a physical law or human knowledge and outputs a second estimation result estimated with respect to second input data; identifying, by comparing the first model and the second model with each other, a missing class that is not included in classes of the second input data among classes of the first input data; extending the second model with a correction term for input of input data of the missing class; and generating a composite model in which the extended second model and the first model are fused together. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating a configuration of an information processing system associated with an embodiment; 
         FIG. 2  is a diagram illustrating a hardware configuration of a server; 
         FIG. 3  is a functional block diagram of the server; 
         FIG. 4  is a table summarized for a model DM and a model M; 
         FIG. 5  is a flowchart illustrating a processing of the server  10 ; 
         FIG. 6A  is a diagram illustrating an example of the model M, and  FIG. 6B  is a diagram illustrating an example of the model DM; 
         FIG. 7  is a diagram illustrating a state where the model M is extended to generate an extended model MM; and 
         FIG. 8  is a diagram illustrating a hybrid model HM generated from the model DM and the extended model MM. 
     
    
    
     DESCRIPTION OF EMBODIMENT 
     In order to create such a hybrid model, the dimensions of models to be fused (the classes of input data that is input to models) need to match, which makes it difficult to create a hybrid model from models with different dimensions. 
     Hereinafter, an embodiment of an information processing system will be described in detail with reference to  FIGS. 1 to 8 . The information processing system  100  illustrated in  FIG. 1  is a system that estimates battery consumption in a case of traveling on a candidate route when a user searches for a route in a car navigation system of an electric vehicle (EV) car  70 , and provides the user with estimation results as well as the candidate route. 
     As illustrated in  FIG. 1 , the information processing system  100  includes a server  10  and the EV car  70 . The server  10  and the EV car  70  are connected to a network  80  such as the Internet, and thus, data may be exchanged between the server  10  and the EV car  70 . 
     The server  10  is an information processing apparatus that generates a model used for the estimation of battery consumption in each EV car  70  and provides the model to the EV car  70 . The model generated by the server  10  is a hybrid model (composite model) in which two models are fused together. One of the two models (e.g., first model) is a model created by a data driven method such as machine learning using training data. Further, the other model (e.g., second model) is a model of a model-based method created using physical laws or expert knowledge. In addition, each model is assumed to be created for each same vehicle type equipped with the same EV battery (hereinafter simply referred to as “same vehicle type”). When creating a model used in a certain vehicle type, the server  10  is assumed to use various data obtained from the same vehicle type. 
       FIG. 2  illustrates a hardware configuration of the server  10 . As illustrated in  FIG. 2 , the server  10  includes a Central Processing Unit (CPU)  90 , a Read Only Memory (ROM)  92 , a Random Access Memory (RAM)  94 , a storage (here, Hard Disk Drive (HDD))  96 , a network interface  97 , and a portable storage medium drive  99 . These components of the server  10  are connected respectively to a bus  98 . In the server  10 , the CPU  90  executes programs (including a composite model generation program) stored in the ROM  92  or the HDD  96  or programs (including a composite model generation program) read by the portable storage medium drive  99  from a portable storage medium  91 , thereby implementing functions of respective units illustrated in  FIG. 3 . In addition, the functions of the respective units in  FIG. 3  may be implemented by, for example, an integrated circuit such as an Application Specific Integrated Circuit (ASIC) or a Field Programmable Gate Array (FPGA). 
       FIG. 3  illustrates a functional block diagram of the server  10 . The CPU  90  of the server  10  functions as a model acquisition unit  20  as an acquisition unit, an identifying unit  22 , an extension unit  24 , and a generation unit  26  by executing programs. 
     The model acquisition unit  20  acquires a model (first model) created by a data-driven method such as machine learning using training data and a model (second model) of a model-based method created using physical laws or expert knowledge. In the following, the first model will be referred to as “model DM” and the second model will be referred to as “model M.” 
     For example, as illustrated in  FIG. 4 , the model DM includes main terms (three-dimensional): current, voltage, and resistance, and correction terms (three-dimensional): acceleration, gradient, and motor rotation speed. More specifically, the model DM is capable of estimating the output current of an EV battery by inputting the voltage, the resistance, the acceleration of the EV car  70 , the gradient of the road on which the EV car  70  travels, and the motor rotation speed. 
     Here, since the model DM is created from training data by machine learning, the accuracy of prediction is high when there is a lot of training data, but the accuracy of prediction is low when there is little data. Further, the model DM is not capable of explaining about the prediction result as to why the prediction result was obtained. The model acquisition unit  20  executes machine learning using, as training data, data obtained from the EV car  70  of the same vehicle type to create the model DM. The model DM is, for example, a kernel regression function generated by k-nearest neighbor crossed kernel regression. 
     Meanwhile, for example, as illustrated in  FIG. 4 , the model M is an equivalent circuit model of an EV battery and includes main terms (three-dimensional): current, voltage, and resistance. More specifically, the model M is capable of estimating the output current of an EV battery by inputting input data such as voltage and resistance. 
     Here, the model M is created using physical laws or knowledge, and therefore, is capable of explaining the reason for the estimation result. Further, when the physical laws are not so complicated, some degree of prediction accuracy may be anticipated. 
     Referring back to  FIG. 3 , the identifying unit  22  compares the model M with the model DM and identifies a term not included in the model M (the classes of input data that is input to the model M) among terms included in the model DM (the classes of input data that is input to the model DM). In the example of  FIG. 4 , the correction terms (acceleration, gradient, and motor rotation speed) of the model DM are identified. 
     The extension unit  24  adds, as a new term that plays a correction role, the term identified by the identifying unit  22  to the model M to create an extended model (hereinafter referred to as “extended model MM”) having the dimension matched with the model DM. That is, the dimension of the extended model MM matches with the dimension of the model DM. 
     The generation unit  26  fuses the extended model MM created by the extension unit  24  and the model DM to generate a hybrid model HM. At this time, since the dimension of the extended model MM and the dimension of the model DM match each other, the hybrid model HM may be easily generated. The generation unit  26  transmits the generated hybrid model HM to the EV car  70 . 
     The EV car  70  is a vehicle that is equipped with an EV battery and runs using a motor as a power source. The EV car  70  includes not only a sensor capable of measuring the voltage and resistance of the EV battery but also a sensor capable of measuring the acceleration of the EV car  70 , the gradient of the road on which the vehicle is traveling, and the rotation speed of the motor. 
     The EV car  70  is equipped with a car navigation system (car navigation). The car navigation acquires the hybrid model HM generated in the server  10 . Further, when the user searches for a route, the car navigation inputs information on a candidate route or data obtained by various sensors of the EV car  70  to the hybrid model HM, thereby estimating the output current of the EV battery after traveling on the candidate route and at the same time, estimating battery consumption. Then, the car navigation provides the user with information on the estimated battery consumption along with information on the candidate route. The user selects a route to be adopted from candidate routes by referring to the output battery consumption. 
     (About Processing of Server  10 ) 
     Hereinafter, a processing flow of the server  10  will be described in detail while appropriately referring to the flowchart of  FIG. 5  and other drawings. In addition, the processing of  FIG. 5  is a processing executed at the timing depending on a manager&#39;s input, at a predetermined period, or at the timing when data collected in the EV car  70  reaches a predetermined amount or more. In the following description of the processing, a case where the model M is two-dimensional and the model DM is three-dimensional will be described for simplification of description and drawings. 
     When the processing of  FIG. 5  starts, first, in operation S 10 , the model acquisition unit  20  acquires the models DM and M. Here, the model M is a model of a model based method created using physical laws or expert knowledge, and is assumed to be stored in, for example, a storage device of the EV car  70 . The model acquisition unit  20  acquires the model M from the EV car  70  via the network  80 . Here, it is assumed that a two-dimensional model y=P θ1 (x 1 ) as illustrated in  FIG. 6A  is acquired as the model M. In addition, “θ1” is a parameter. This model M is a model capable of estimating “y” by inputting “x 1 ” as input data. Here, the model M is, for example, a model for estimating battery consumption created by a manufacturer of the EV battery. Therefore, when estimating the battery consumption of the EV battery, the model does not consider the correction terms such as the acceleration of the EV car  70 , the road gradient, and the motor rotation speed. 
     Meanwhile, the model DM is obtained by machine learning and obtained using a large number of pieces of training data as illustrated in  FIG. 6B . Here, the model acquisition unit  20  acquires a large number of pieces of training data from a large number of EV cars  70  to perform machine learning, and is assumed to obtain a three-dimensional model y=DM(x 1 , x 2 ) as the model DM. This model DM is capable of estimating “y” by inputting “x 1 ” and “x 2 ” as input data. Here, the model DM is, for example, a model that estimates the battery consumption using various data obtained from the EV car  70  equipped with the EV battery. For this reason, when estimating the battery consumption of the EV battery, the model considers the correction terms such as the acceleration of the EV car  70 , the road gradient, and the motor rotation speed. 
     Next, in operation S 12 , the identifying unit  22  compares the models DM and M. Specifically, the identifying unit  22  compares the classes of input data that may be input to the models DM and M. 
     Next, in operation S 14 , the identifying unit  22  identifies the dimension that exists in the model DM, but is missing in the model M, i.e., a term that is not included in the classes of input data that may be input to the model M among the classes of input data that may be input to the model DM. In the example of  FIGS. 6A and 6B , “x 2 ” is identified. 
     Next, in operation S 16 , the identifying unit  22  determines whether or not there is a missing dimension. When the determination in operation S 16  is affirmative, the processing proceeds to operation S 18 . 
     When the processing proceeds to operation S 18 , the extension unit  24  extends the model M to generate the extended model MM. In the example of  FIG. 6A , the extension unit  24  prepares the following equation (1) by using P θ1 (x 1 ) as a main term and adding a correction term Q θ2 (x 1 , x 2 ) thereto. 
         y=P   θ1 ( x   1 )+ P   θ2 ( x   1   ,x   2 )  (1)
 
     Here, when the model M is a differential equation system model, the correction term Q θ2 (x 1 , x 2 ) may be sufficiently approximated by a polynomial expression of nm order (“n” and “m” represent natural numbers). Therefore, assuming that the parameter θ2 is a i,j , the correction term Q θ2 (x 1 , x 2 ) may be represented by the following equation (2). 
     
       
         
           
             
               
                 
                   
                     
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     In addition, when the model M is a kinematic model, the correction term Q θ2 (x 1 , x 2 ) may be sufficiently approximated by a cubic polynomial expression. Thus, the correction term Q θ2 (x 1 , x 2 ) may be easily obtained. 
     The extension unit  24  adjusts “θ1” and “θ2” using training data in the equations represented by (1) and (2) to generate the extended model MM.  FIG. 7  schematically illustrates the extended model MM generated from the model M and the training data. The dimension of the extended model MM matches with the dimension of the model DM. In addition, in the following, the extended model MM will be described as being y=MM(x 1 , x 2 ). 
     Next, in operation S 20 , the generation unit  26  fuses the models DM and MM to generate the hybrid model HM. As an example, the generation unit  26  generates a model represented by the following expression (3) as the hybrid model HM. 
         y =α( x   1   ,x   2 )Ε DM ( x   1   ,x   2 )+(1−α( x   1   ,x   2 ))Ε MM ( x   1   ,x   2 )   (3)
 
     Here, α(x 1 , x 2 ) means the confidence of the model DM, and the range in which α(x 1 , x 2 ) is acquired is 0≤α(x 1 , x 2 )≤1. The confidence α(x 1 , x 2 ) has a large value in a range in which there is a lot of training data (a range in which training data exists densely) and has a small value in a range in which there is little training data (a range in which training data exists sparsely). In addition, when the model DM is a kernel regression function generated by k-nearest neighbor crossed kernel regression, the confidence may be obtained based on the magnitude of a confidence interval width. The confidence interval width is the value of standard deviation that indicates how much an estimated value output by the kernel regression function deviates. As the confidence interval width increases, the accuracy of the calculated estimated value decreases. Therefore, as the confidence interval width increases, α(x 1 , x 2 ) becomes a smaller value. 
       FIG. 8  schematically illustrates the hybrid model HM. As illustrated in  FIG. 8 , since the confidence of the model DM is relatively high in a region where there is a lot of training data, the degree of influence of the model DM increases, so that the hybrid model HM approaches the model DM. Meanwhile, since the confidence of the model DM is relatively low in a region where there is little training data, the degree of influence of the extended model MM increases, so that the hybrid model HM approaches the extended model MM. In addition, the hybrid model HM has descriptiveness characteristics since it includes at least the extended model MM. 
     Next, in operation S 22 , the generation unit  26  transmits the generated hybrid model HM to the car navigation of the EV car  70 . As described above, the car navigation estimates the consumption of the EV battery after traveling on the candidate route using the hybrid model HM and provides the user with the estimation result as well as the candidate route by displaying them on a screen. 
     In addition, when the determination in operation S 16  is negative, there is no need to extend the model M, and therefore, the processing proceeds to operation S 20  without passing through operation S 18 . After that, the generation unit  26  executes the processing of operations S 20  and S 22  as described above. Here, the determination in operation S 16  is negative even when the dimension of the model DM is lower than that of the model M, but, in this case, the dimension of the model DM may be matched with the dimension of the model M by increasing the types of training data. 
     As described above in detail, according to the present embodiment, the identifying unit  22  compares the model DM and the model M acquired by the model acquisition unit  20  to identify a missing class that is not included in the classes of input data that is input to the model M among the classes of input data that is input to the model DM. Then, the extension unit  24  extends the model M by the correction term for inputting the missing class of input data to generate the extended model MM, and the generation unit  26  fuses the extended model MM and the model DM to generate the hybrid model HM. Thus, in the present embodiment, the hybrid models HM may be generated by fusing the models DM and M having different dimensions. Accordingly, with the use of the hybrid model HM, the demerits of one of the models DM and M may be supplemented by the other model. For example, although the model DM has a descriptiveness problem, this descriptiveness problem may be solved by using the hybrid model HM. 
     Further, in the hybrid model HM of the present embodiment, when the training data used to create the model DM exists more densely in a first range than in a second range, the model DM has a higher degree of influence in the first range than in the second range and the extended model MM has a higher degree of influence in the second range than in the first range. As such, by increasing the influence of the model DM having relatively high estimation accuracy in the range in which the training data exists densely and increasing the degree of influence of the model MM in which the estimation accuracy is less affected by the density of training data in the range in which the training data exists sparsely, it is possible to improve the estimation accuracy by the hybrid model HM. 
     Further, in the hybrid model HM of the present embodiment, when the model DM is a kernel regression function generated by k-nearest neighbor crossed kernel regression, the degree of influence α(x 1 ,x 2 ) of the model DM and the extended model MM is determined based on the magnitude of the confidence interval width. Thus, when the model DM is a kernel regression function, it is possible to improve the estimation accuracy of the hybrid model HM. 
     Further, in the present embodiment, the correction term upon extension of the model M is a polynomial expression of nm order, so that the extended model MM may be appropriately generated from the model M when the model M is a differential equation system model. Further, when the model M is a kinematic model, the correction term upon extension of the model M may be approximated by a cubic polynomial expression. Thus, the extended model MM may be appropriately generated from the model M by a simplified and easy calculation. 
     In addition, in the embodiment, descriptions have been made on a case where the consumption of the EV battery after traveling on the candidate route is estimated by estimating the output current of the EV battery using the hybrid model HM, but the present disclosure is not limited thereto. For example, the hybrid model HM may be used to estimate the failure time of the EV battery or the failure probability of the EV battery. This makes it possible to perform a predictive maintenance of the EV battery. Therefore, maintenance may be appropriately performed. 
     Further, the hybrid model HM may also be used for various types of estimation or predictive maintenance of other devices (e.g., a motor) of the EV car  70 . Further, the hybrid model HM may be used for various types of estimation or predictive maintenance in other vehicles (e.g., motorcycles and ships) rather than being limited to the EV car  70 . Furthermore, the hybrid model HM may also be used for various types of estimation or predictive maintenance of various devices in power plants and factories. 
     In addition, the above embodiment has described a case where the server  10  generates the hybrid model HM, but the present disclosure is not limited thereto, and an in-vehicle device such as a car navigation may generate the hybrid model HM. When the in-vehicle device generates the hybrid model HM, the server  10  or the network  80  may be omitted from the information processing system  100 . 
     In addition, the processing function may be implemented by a computer. In that case, a program describing the processing content of a function that a processing apparatus needs to have is provided. By executing the program on a computer, the above processing function is implemented on the computer. The program describing the processing content may be recorded in a computer readable storage medium (excluding carrier waves). 
     When the program is distributed, the program is sold, for example, in the form of a portable storage medium such as a Digital Versatile Disc (DVD) or a Compact Disc Read Only Memory (CD-ROM) in which the program is recorded. Further, the program may be stored in a storage device of a server computer and may be transmitted from the server computer to another computer via a network. 
     The computer that executes the program stores, for example, the program recorded in the portable storage medium or the program transmitted from the server computer in a storage device thereof. Then, the computer reads out the program from the storage device thereof to execute a processing according to the program. In addition, the computer may also directly read out the program from the portable storage medium to execute a processing according to the program. Further, the computer may also sequentially execute a processing according to the received program whenever the program is transferred from the server computer. 
     The embodiment described above is an exemplary embodiment of the present disclosure. However, the present disclosure is not limited thereto, and various modifications may be made without departing from the scope of the present disclosure. 
     According to an aspect of the embodiment, it is possible to generate a composite model from first and second models in which the classes of input data are partially common. 
     All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to an illustrating of the superiority and inferiority of the invention. Although the embodiment of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.