Patent Publication Number: US-2021182734-A1

Title: Data analysis system and data analysis method

Description:
FIELD 
     The present disclosure relates to a data analysis system and a data analysis method. 
     BACKGROUND 
     Japanese Unexamined Patent Publication No. 2001-116705 discloses, as a conventional method for analyzing a sample, performing quantitative analysis by X-ray diffraction during which using a standardized sample made of the same material as a target of an X-ray source so as to calibrate an intensity of the diffraction X-rays. 
     SUMMARY 
     However, in the above-mentioned conventional method of analysis of a sample, at the stage of analysis of measurement data of the material (sample) being measured by the X-ray diffraction apparatus, there are parts requiring judgment of a human analyzer, so the results of analysis are liable to vary depending on the intuition, experience, etc. of the analyzer. Further, the results of analysis of the analyzer are not evaluated, so precision of analysis is liable to be unable to be secured. 
     The present disclosure was made focusing on such a problem and has as its object to keep the results of analysis of the measurement data from varying while improving the precision of analysis. 
     To solve above problem, the data analysis system according to one aspect of the present disclosure is provided with a processing device, a storage device connected to the processing device, and a communication part connected to the processing device and able to communicate with external terminals. The processing device is provided with a measurement data acquisition part configured to acquire measurement data of analysis of a material received through the communication part, a data analysis part configured to use a trained machine learning model to process the measurement data and output the results of analysis of the measurement data, a storage processing part configured to store a data set including the measurement data and results of processing obtained by processing the measurement data as an analysis result data set in an analysis result database of the storage device, a learning-use data set acquisition part configured to acquire a learning-use data set, which includes results of evaluation of the results of processing of the measurement data performed at the outside based on the analysis result data set, received through the communication part and, and a learning part retraining the machine learning model based on the learning-use data set. 
     Further, the data analysis method according to another aspect of the present disclosure is a data analysis method using a data analysis system provided with a processing device, a storage device connected to the processing device, and a communication part connected to the processing device and able to communicate with an external terminal, comprising a measurement data acquisition step of acquiring measurement data received through the communication part and analyzing a material, a data analysis step using a trained machine learning model to process the measurement data and outputting the results of analysis of the measurement data, a storage processing step storing in an analysis result database of a storage device a data set including the measurement data and results of processing obtained by processing the measurement data as an analysis result data set, a learning-use data set acquisition step acquiring a learning-use data set including results of evaluation of the results of processing of the measurement data performed at the outside based on the analysis result data set received through the communication part, and a learning step retraining the machine learning model based on the learning-use data set. 
     According to these aspects of the present disclosure, a trained machine learning model is used for processing the measurement data, so the results of analysis of the measurement data can be kept from ending up varying according to the intuition, experience, etc. of the analyzer analyzing the measurement data. Further, the machine learning model is retrained based on a learning-use data set including the results of evaluation of the results of processing of the measurement data, so the measurement data is analyzed and the analysis result data set is stored. As a result, the number of points of the learning-use data set increases. Along with this, the machine learning model can be improved in performance and the measurement data can be improved in precision of analysis. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic view of the configuration of a material information acquisition system provided with a data analysis system according to one embodiment of the present disclosure. 
         FIG. 2  is a view showing one example of an operation sequence of a material information system. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Below, referring to the drawings, embodiments of the present disclosure will be explained in detail. Note that, in the following explanation, similar constituent elements are assigned the same reference notations. 
       FIG. 1  is a schematic view of the configuration of a material information acquisition system  100  provided with a data analysis system  1  according to one embodiment of the present disclosure. 
     The material information acquisition system  100  is provided with a data analysis system  1  and a user terminal  2  and evaluator terminal  3  connected through the data analysis system  1  and network and able to communicate with the data analysis system  1 . 
     The material information acquisition system  100  is configured to analyze measurement data (input data) which is input to the data analysis system  1  by one or more users utilizing the material information acquisition system  100  and operating the user terminal  2  and which is measured by measurement devices for material analysis use etc. by the data analysis system  1  using a trained machine learning model and configured to be able to output the results of analysis of the measurement data such as the constituents of the material or chemical state, chemical structure, physical properties, and other related information (below, referred to as the “material information”) as output data to the user terminal  2  of the user inputting the measurement data. 
     Further, the material information acquisition system  100  is configured to retrain the machine learning model used for data analysis based on a later explained learning-use data set which an evaluator evaluating the results of analysis of the measurement data analyzed by the data analysis system  1  inputs to the data analysis system  1  by operating the evaluator terminal  3 . 
     Below, referring to  FIG. 1 , the hardware configuration of the data analysis system  1 , user terminal  2 , and evaluator terminal  3  forming the material information acquisition system  100  will be explained. 
     The user terminal  2  is a device for transfer of information through the network between the data analysis system  1  and the one or more users utilizing the material information acquisition system  100 . The user terminal  2 , for example, is a computer provided at the user side and provided with a keyboard, display, etc. Note that a user terminal  2  can be provided for each user when there are a plurality of users utilizing the material information acquisition system  100 . 
     The evaluator terminal  3  is a device for transfer of information through the network between the data analysis system  1  and the evaluator evaluating results of analysis of the measurement data analyzed by the data analysis system  1 . The evaluator is, for example, a human expert specializing in analysis of measurement data. The evaluator terminal  3  is, for example, a computer provided at the evaluator side and having a keyboard, display, etc. Note that, in this embodiment, the person at the supplier side providing the material information acquisition system  100  to the user was specified as the evaluator, but a person at the user side may also be specified as the evaluator. 
     The data analysis system  1  is provided with a communication part  10 , processing device  20 , and storage device  30 . 
     The communication part  10  is a communication interface circuit for connecting the data analysis system  1  through a network to the user terminal  2  and the evaluator terminal  3  and enabling communication between the data analysis system  1  and the terminals  2  and  3 . 
     The processing device  20  is a device running various types of programs stored in the storage device  30 , for example, a CPU (central processing unit). The processing device  20  performs processing according to the programs to thereby function as a measurement data acquisition part  21 , data analysis part  22  (pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25 ), analysis result transmission part  26 , analysis data storage processing part  27 , learning-use data set acquisition part  28 , and learning part  29  and operates as a functional part realizing a predetermined function (module). In the following explanation, when explaining the processing using a functional part as the subject, this will indicate that the processing device  20  is running a program for realizing the functional part. Details of the functional parts  21  to  29  will be explained later. 
     The storage device  30  is a device storing programs which the processing device  20  runs data used when running the programs, for example, a memory, HDD (hard disk drive), SSD (solid state drive), RAM (random access memory), ROM (read only memory), etc. The data stored in the databases of the storage device  30 , that is, a feature quantity database  31 , analysis result database  32 , and learning-use database  33 , will be explained in detail later. 
     Next, referring to  FIG. 1 , the operation of the material information acquisition system  100  will be explained. 
     The user can input measurement data of a material acquired at the user side through the user terminal  2  to the data analysis system  1  to thereby obtain results of analysis of the measurement data analyzed by the data analysis system  1 , that is, material information, through the user terminal  2  as output. As the measurement data, various types of data obtained by measurement for analysis of the material, for example, data obtained by firing X-rays or neutron beams or electron beams at the material (specifically, measurement data by X-ray diffraction (XRD) or X-ray absorption fine structure analysis (XAFS), X-ray photoemission spectrometry (XPS), X-ray absorption spectroscopy (XAS), X-ray absorption circular dichroism, small angle scattering (SAS), small-angle neutron scattering (SANS), neutral reflectance, inelastic scattering, electron diffraction, etc.), image data of the material observed by a microscope etc. (specifically, image data by an X-ray microscope, optical microscope, electron microscope, atomic force microscope, computer tomography, transmission electron beam imaging, scan electron beam imaging, etc.), etc. can be used. 
     In this embodiment, as the measurement data, the measurement data when analyzing a material (sample) by an X-ray diffraction apparatus, is input to the user. 
     If measurement data is input through the user terminal  2  by the user, the data analysis system  1  extracts the feature quantity of the measurement data and analyzes the material based on the extracted feature quantity. In this embodiment, as the feature quantity of the measurement data of the material analyzed by the X-ray diffraction apparatus, the diffraction peak position or intensity, crystal phase, phase fraction, peak width, or other diffraction pattern is extracted and the material is analyzed (phase identified etc.) based on the extracted diffraction pattern. 
     Below, the content of specific processing performed at the data analysis system  1 , that is, the contents of the functional parts  21  to  29  realized by the processing device  20  performing processing in accordance with programs, will be explained. 
     The measurement data acquisition part  21  acquires measurement data input by the user and outputs it to the data analysis part  22  and analysis data storage processing part  27 . 
     The pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25  of the data analysis part  22  use respectively trained machine learning models to process the measurement data and output results of analysis comprised of the material information as the final output data. The machine learning models used in the data analysis part  22  are not particularly limited. A neural network, support vector machine, random forest, or various other types of machine learning models can be used. 
     The pre-processing part  23  receives as input the measurement data acquired by the measurement data acquisition part  21 . The pre-processing part  23  pre-processes the input measurement data to, for example, smooth it, remove background, or otherwise reduce noise of the measurement data, that is, pre-processes it to raise the signal-noise ratio, and outputs the measurement data which was pre-processed (below, referred to as the “pre-processed measurement data”) to the feature quantity extraction processing part  24  and analysis data storage processing part  27 . 
     The pre-processing part  23  is trained in advance in accordance with the input measurement data to enable suitable smoothing or removal of background. For example, if the pre-processing part  23  smoothes the measurement data by the kernal density estimation method, it has to set the value of the smoothing parameter (band width) for smoothing to for example a suitable value in accordance with the number of data points etc., so the pre-processing part  23  is trained in advance to set a suitable value of the smoothing parameter in accordance with the input measurement data to enable smoothing of the measurement data 
     The feature quantity extraction processing part  24  receives as input the pre-processed measurement data. The feature quantity extraction processing part  24  is trained in advance so as to enable extraction of a feature quantity of the measurement data in accordance with the input pre-processed measurement data and outputs the extracted feature quantity to the feature quantity analysis processing part  25  and analysis data storage processing part  27 . In this embodiment, as explained above, the diffraction peak position or intensity, crystal phase, phase fraction, peak width, or other diffraction pattern is extracted as a feature quantity of the measurement data. Note that the extracted feature quantity is not limited to these. It is also possible to extract for example the distribution of the particle size of the material as the feature quantity in accordance with the measurement data or, if the measurement data is an image, extract a geometric feature in the image as the feature quantity. 
     The feature quantity analysis processing part  25  receives as input the feature quantity of the measurement data. The feature quantity analysis processing part  25  analyzes the material based on the input feature quantity of the measurement data and outputs the results of analysis to the analysis result transmission part  26  and analysis data storage processing part  27 . In this embodiment, the feature quantity analysis processing part  25  compares the diffraction pattern input as the feature quantity of the measurement data with data relating to the feature quantities of known materials stored in the feature quantity database  31 , that, the diffraction patterns of known materials, is retrained so as to be able to select a diffraction pattern with a high similarity from the diffraction patterns of known materials, and outputs the material information specified from the selected diffraction pattern as the results of analysis to the analysis result transmission part  26  and analysis data storage processing part  27 . 
     In the past, for example the value of the smoothing parameter was set in accordance with the number of data points by the intuition, experience, etc. of the analyzer. Further, a feature quantity of the measurement data was extracted, the material was analyzed based on the extracted feature quantity, etc. in the same way by intuition, experience, etc. of the analyzer. As opposed to this, in this embodiment, the trained machine learning model was used to process the measurement data. For this reason, the smoothing parameter can be set, the feature quantity of the measurement data can be extracted, and the material can be analyzed based on the feature quantity while keeping them from ending up being dependent on the analyzer like in the past. 
     The analysis result transmission part  26  sends the input results of analysis, that is, material information, as the results of analysis of the measurement data which the user input to the user terminal  2 . 
     The analysis data storage processing part  27  links the data input to the analysis data storage processing part  27 , that is, the measurement data, the pre-processed measurement data obtained by pre-processing that measurement data, the feature quantity extracted from the measurement data, and the results of analysis of the measurement data (material information) and stores them as the analysis result data set in the analysis result database  32 . 
     The evaluator operates the evaluator terminal  3  to access the analysis result database  32  and thereby acquire the analysis result data set, analyzes the relationship between the input data and the output data, and determines an evaluation score corresponding to the quality of the output data obtained from the input data. Furthermore, the evaluator operates the evaluator terminal  3  to input the learning-use data set linking the input data, the output data, and evaluation score to the data analysis system  1 . 
     In this embodiment, the evaluator referred to the measurement data and the pre-processed measurement data obtained by pre-treating that measurement data to, for example, evaluate whether a suitable value was set as the smoothing parameter or otherwise whether the measurement data was suitably pre-processed and assigns an evaluation score corresponding to the results of evaluation to the pre-processed measurement data. At that time, if the measurement data was suitably pre-processed, a high evaluation score is assigned. 
     Further, the evaluator refers to the measurement data (or pre-processed measurement data) and the feature quantity extracted from the measurement data to, for example, evaluate whether noise hasn&#39;t been extracted as the peak or otherwise whether a feature quantity has been suitably extracted from the measurement data and assigns an evaluation score corresponding to the results of evaluation to the feature quantity extracted from the measurement data. At that time, if the feature quantity is suitably extracted from the measurement data, a high evaluation score is assigned. 
     Furthermore, the evaluator refers to the feature quantity extracted from the measurement data and the results of analysis of the measurement data based on that feature quantity to, for example, evaluate whether a diffraction pattern with a high degree of similarity, whereby the diffraction pattern input as the feature quantity and the diffraction peak position or number of the same match or are similar, has been suitably selected from the feature quantity database  31  or otherwise whether the data was suitably analyzed based on the feature quantity extracted from the measurement data, and assigns an evaluation score corresponding to the results of evaluation to the results of analysis of the measurement data. At this time, if the data as suitably analyzed based on the feature quantity extracted from the measurement data, a high evaluation score is assigned. 
     Further, the evaluator links the measurement data, the pre-processed measurement data obtained by pre-processing that measurement data, the feature quantity extracted from the measurement data, the results of analysis of the measurement data, the evaluation score assigned to the pre-processed measurement data, the evaluation score assigned for the feature quantity, and the evaluation score assigned for the results of analysis and inputs them as the learning-use data set to the data analysis system  1 . 
     The learning-use data set input to the data analysis system  1  in this way is acquired by the learning-use data set acquisition part  28  and stored in the learning-use database  33  storage device  30 . That is, the learning-use data set acquisition part  28  acquires the learning-use data set input by the evaluator and stores the acquired learning-use data set to the learning-use database  33 . 
     The learning part  29  retrains the machine learning model of the data analysis part  22  based on the learning-use data set stored in the learning-use database  33  to optimize the machine learning model. For example, the learning part  29  retrains the machine learning model of the data analysis part  22  when the number of data points of the learning-use data set newly stored in the learning-use database  33  becomes equal to or greater than a predetermined number so as to update the values of the functions used for the machine learning model and optimize the machine learning model. 
     In this embodiment, the learning part  29  acquires, as the learning-use data set for training the pre-processing part  23 , the measurement data, the pre-processed measurement data obtained by pre-processing the measurement data, and the evaluation score assigned to the pre-processed measurement data from the learning-use database  33  and, based on these, optimizes the machine learning model used in the pre-processing part  23  so that the evaluation score of the pre-processed measurement data when the new measurement data is input is maximized. Due to this, for example, in this embodiment, the various types of parameters of the model for calculating the value of the smoothing parameter used at the time of kernal density estimation are optimized. 
     Further, the learning part  29  acquires, as the learning-use data set for training the feature quantity extraction processing part  24 , the measurement data (or pre-processed measurement data), the feature quantity extracted from the measurement data, and the evaluation score assigned for the feature quantity from the learning-use database  33  and, based on these, optimizes the machine learning model used in the feature quantity extraction processing part  24  so that the evaluation score of the feature quantity when new measurement data is input is maximized. 
     Further, the learning part  29  acquires, as the learning-use data set for training the feature quantity analysis processing part  25 , the feature quantity extracted from the measurement data, the results of analysis of the measurement data based on the feature quantity, and the evaluation score assigned for the results of analysis from the learning-use database  33  and, based on these, optimizes the machine learning model used in the feature quantity analysis processing part  25  so that the evaluation score of the results of analysis of the measurement data based on the feature quantity when a new feature quantity is input is maximized. 
     In this way, in this embodiment, the performances of the machine learning models of the pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25  are evaluated by the evaluator and the results of evaluation are input to the data analysis system  1 . The thus input results of evaluation can be utilized to retrain the pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25 . Due to this, each time the measurement data is analyzed by the data analysis system  1  and the results of evaluation of the results of analysis is utilized to retrain the machine learning model, the machine learning models can be improved in performance. 
       FIG. 2  is a view showing one example of an operation sequence of the material information acquisition system  100 . 
     At step S 1 , if a user operates the user terminal  2  to input measurement data, the measurement data is sent through the network to the data analysis system  1 . 
     At step S 2 , the data analysis system  1  acquires the measurement data received through the communication part  10 . 
     At step S 3 , the data analysis system  1  analyzes the acquired measurement data and outputs the material information as the results of analysis. 
     At step S 4 , the data analysis system  1  sends the material information as the results of analysis through the communication part  10  to the user terminal  2  of the user inputting the measurement data. 
     At step S 5 , the data analysis system  1  stores the analysis result data set in the analysis result database  32 . 
     At step S 6 , the evaluator operates the evaluator terminal  3  to acquire the analysis result data set stored in the analysis result database  32  through the network. 
     At step S 7 , the evaluator evaluates the results of analysis of the measurement data by the data analysis system  1  based on the analysis result data set and prepares the learning-use data set. 
     At step S 8 , if the evaluator operates the evaluator terminal  3  to input the learning-use data set, the learning-use data set is sent through the network to the data analysis system  1 . 
     At step S 9 , the data analysis system  1  acquires the learning-use data set received through the communication part  10  and stores it in the learning-use database  33 . 
     At step S 10 , the data analysis system  1  retrains the machine learning model used for analysis of the measurement data based on the learning-use data set stored in the learning-use database  33 . 
     The data analysis system  1  according to the present embodiment explained above is provided with a processing device  20 , a storage device  30  connected to the processing device  20 , and a communication part  10  connected to the processing device  20  and able to communicate with the external terminals  2 ,  3 . The processing device  20  is provided with a measurement data acquisition part  21  acquiring measurement data received through the communication part  10  and obtained by analyzing a material, a data analysis part  22  using a trained machine learning model to process measurement data and output the results of analysis of the measurement data, an analysis data storage processing part  27  (storage processing part) storing a data set including measurement data and results of processing of processing of the measurement data as an analysis result data set in the storage device  30 , a learning-use data set acquisition part  28  acquiring a learning-use data set including results of evaluation of results of processing of the measurement data performed at the outside based on the analysis result data set received through the communication part  10 , and a learning part  29  retraining the machine learning model based on the learning-use data set. 
     In this way, since the data analysis system  1  according to the present embodiment uses a trained machine learning model to process the measurement data, the results of analysis of the measurement data can be kept from ending up varying depending on the intuition etc. and experience etc. of the analyzer analyzing the measurement data. Further, to retrain the machine learning model based on the learning-use data set including the results of evaluation of the results of processing of the measurement data performed at the outside based on the analysis result data set, the measurement data is analyzed and the analysis result data set is stored. As a result, the number of points of the learning-use data set is increased. Along with this, the machine learning model can be improved in performance and the precision of analysis of the measurement data can be improved. 
     Further, in this embodiment, the processing device  20  is further provided with an analysis result transmission part  26  sending the results of analysis of the measurement data to the outside user terminal  2  from which the measurement data was sent. For this reason, the user can acquire the results of analysis of the measurement data as the output data by just inputting the measurement data. 
     Further, the data analysis part  22  according to the present embodiment is provided with a feature quantity extraction processing part  24  trained in advance so as to extract and output a feature quantity of the measurement data based on the measurement data and a feature quantity analysis processing part  25  trained in advance so as to output results of analysis of the measurement data corresponding to the feature quantity based on the feature quantity of the measurement data. Further, the data analysis part  22  is further provided with a pre-processing part  23  trained in advance so as to process the measurement data acquired by the measurement data acquisition part  21  to raise the signal-noise ratio and output the pre-processed measurement data and is configured to input the pre-processed measurement data as the input data to the feature quantity extraction processing part  24 . 
     Due to this, it is possible to extract a feature quantity based on the measurement data with the heightened signal-noise ratio, that is, the pre-processed measurement data of the measurement data from which noise has been removed, so it is possible to further improve the precision of analysis of the measurement data. 
     Further, in this embodiment, the analysis data storage processing part  27  is configured to store in the storage device  30 , as an analysis result data set, the measurement data, pre-processed measurement data output from the pre-processing part  23 , feature quantity of the measurement data output from the feature quantity extraction processing part  24 , and results of analysis of the measurement data output from the feature quantity analysis processing part  25 . Further, the learning-use data set includes evaluation scores assigned to output data converted to numerical values corresponding to the quality of the output data output from the pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25  as results of evaluation of the results of processing of the measurement data. The learning part  29  is configured to retrain the pre-processing part  23  based on the measurement data, pre-processed measurement data output from the pre-processing part  23 , and evaluation score assigned to the pre-processed measurement data, retrain the feature quantity extraction processing part  24  based on the measurement data, feature quantity of the measurement data output from the feature quantity extraction processing part  24 , and evaluation score assigned for the feature quantity, and retrain the feature quantity analysis processing part  25  based on the measurement data, results of analysis of the measurement data output from the feature quantity analysis processing part  25 , and evaluation score imparted to the results of analysis. 
     Due to this, the pre-processing part  23 , feature quantity extraction processing part  24 , and feature quantity analysis processing part  25  can be retrained in accordance with the respective results of processing. For this reason, it is possible to improve the performances of the machine learning models of the processing parts  23  to  25  and improve the precision of analysis of the measurement data. 
     Further, the material information acquisition system  100  according to the present embodiment is provided with a data analysis system  1  and is provided with, as external terminals able to communicate with the data analysis system  1 , a user terminal  2  (first terminal) for the user using the data analysis system to input measurement data to the data analysis system  1  and receive the results of analysis of the measurement data as the output data and an evaluator terminal (second terminal) for acquiring an analysis result data set from the storage device  30  and inputting into the data analysis system  1  a learning-use data set including results of evaluation of the results of processing of the measurement data performed based on the acquired analysis result data set. 
     Due to this, by just inputting the measurement data into the user terminal  2 , the user can easily acquire the material information as the results of analysis of the measurement data. Further, in accordance with need, the evaluator can operate the evaluator terminal  3  to acquire the analysis result data set and just input the results of evaluation of the results of analysis as learning-use data set to easily improve the performance in analysis of the data analysis system  1 . 
     Above, embodiments of the present disclosure were explained, but the above embodiments only show some of the examples of application of the present disclosure and are not meant to limit the technical scope of the present disclosure to the specific configurations of the embodiments. 
     For example, in the above embodiments, the data analysis part  22  was provided with the pre-processing part  23 , but if the measurement data does not require pre-processing, the pre-processing part  23  may be omitted. Note that, in this case, the analysis data storage processing part  27  is configured to store in the storage device  30 , as an analysis result data set, the measurement data, the feature quantity of the measurement data output from the feature quantity extraction processing part  24 , and the results of analysis of the measurement data output from the feature quantity analysis processing part  25 . Further, the learning-use data set includes the evaluation score assigned to the output data converted to numerical values according to the quality of the output data output from the feature quantity extraction processing part  24  and feature quantity analysis processing part  25  as the results of evaluation of the results of processing of the measurement data. The learning part  29  is configured for retraining the feature quantity extraction processing part  24  based on the measurement data, the feature quantity of the measurement data output from the feature quantity extraction processing part  24 , and the evaluation score assigned for the feature quantity and for retraining the feature quantity analysis processing part  25  based on the measurement data, the results of analysis of the measurement data output from the feature quantity analysis processing part  25 , and the evaluation score assigned for the feature quantity. 
     Further, in the above embodiments, the evaluator was a human expert, but the evaluation itself may also be performed by for example using a machine learning model etc. for mechanical evaluation and inputting the results of evaluation to the data analysis system  1 .