Patent Publication Number: US-2023143297-A1

Title: Production knowledge management system, production knowledge management method, and production knowledge management program

Description:
TECHNICAL FIELD 
     The present invention relates to a production knowledge management system, a production knowledge management method, and a production knowledge management program. 
     BACKGROUND ART 
     As a background art of the present technical field, there is JP 2006-31213 A (PTL 1). This publication describes that “In the problem case registration search, a problem case in which a problem content and a countermeasure taken against the problem content are described is edited/created and registered in a problem case registration search device with respect to a problem event that occurred in the past, and a problem case serving as a reference can be searched/extracted and used from the registered problem case when necessary. In such a problem case registration search, regarding a plurality of constituent events constituting a problem event, one of the constituent events is set as a main event and the others are set as sub-events, an event chain in which the main event and the sub-events are associated with each other in a causal chain is edited and set for each problem case, and regarding the event chain set for each problem case, an event chain network in which the event chains common to the main events are associated with each other by the common main event is edited and set.” (see Abstract). 
     As another background art, there is JP 2007-241774 A (PTL 2). This publication describes that “The product/process model DB stores an integrated model in which a product structure information model and a process configuration information model are integrated. On the basis of this integrated model, in addition to the modeling of problems of the design model of the product, problems in preparation and manufacturing are also modeled. Further, since the integrated model expresses the process by the state transition, it is possible to express a problem occurrence mechanism based on a causal chain relationship of problems in the production system. The quality knowledge DB stores an entity/state data model in which each of an entity (unit/part) and a state (process) is expressed by an attribute and a method based on the integrated model, and is capable of systematically describing knowledge and know-how in a production system.” (see Abstract). 
     CITATION LIST 
     Patent Literature 
     PTL 1: JP 2006-31213 A 
     PTL 2: JP 2007-241774 A 
     SUMMARY OF INVENTION 
     Technical Problem 
     PTL 1 and PTL 2 disclose a technique of constructing a database of a tree structure by associating certain knowledge with other knowledge by a causal or parent-child definition. 
     However, when the knowledge obtained at various manufacturing sites is compiled into a database, there is a problem that a burden of work of constructing the database by applying the knowledge to a tree structure is large. 
     Therefore, an object of the present invention is to provide a production knowledge management system, a production knowledge management method, and a production knowledge management program capable of searching for knowledge using a database with a simple construction. 
     Solution to Problem 
     In order to solve the above problem, an embodiment of the present invention includes: a database; and a search unit which searches the database, wherein the database includes: a classification master table which registers a classification name obtained by classifying processing performed in each process of a production line and a classification ID which is a unique key thereof in association with each other; a process table which registers a process name of the process and a process ID which is a unique key thereof in association with each other; a process order table which registers the process ID and a next process ID which is a unique key of a process next to a process indicated by the process in association with each other; a process classification table which registers the process ID and the classification ID in association with each other; a knowledge table which registers a problem content occurring in each process, a factor thereof, and a knowledge ID which is a unique key thereof in association with each other; and a knowledge classification table which registers the knowledge ID and the classification ID in association with each other, the search unit performs a first search for specifying a first related knowledge record group by receiving a problem keyword and a problem occurrence process, by using the database, narrowing records in the knowledge table by determination of similarity of a character string between the problem keyword and the problem content stored in the knowledge table, and arranging an order of the narrowed records such that a record more related to the classification name in the problem occurrence process or a process upstream of the problem occurrence process in the production line is prioritized. 
     Advantageous Effects of Invention 
     According to the present invention, it is possible to provide a production knowledge management system, a production knowledge management method, and a production knowledge management program capable of searching for knowledge using a database with a simple construction. 
     Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram illustrating a system configuration of a production knowledge management system according to a first embodiment of the present invention. 
         FIG.  2    is a functional block diagram of the production knowledge management system according to the first embodiment. 
         FIG.  3    is a table configuration diagram of the production knowledge management system according to the first embodiment. 
         FIG.  4    is a conceptual diagram of a classification master table of the production knowledge management system according to the first embodiment. 
         FIG.  5    is a conceptual diagram of a process table of the production knowledge management system according to the first embodiment. 
         FIG.  6    is a conceptual diagram of a process order table of the production knowledge management system according to the first embodiment. 
         FIG.  7    is a conceptual diagram of a process classification table of the production knowledge management system according to the first embodiment. 
         FIG.  8    is a conceptual diagram illustrating a process flow of a sample for explaining processing of the production knowledge management system according to the first embodiment. 
         FIG.  9    is a conceptual diagram of a knowledge table of the production knowledge management system according to the first embodiment. 
         FIG.  10    is a conceptual diagram of a knowledge classification table of the production knowledge management system according to the first embodiment. 
         FIG.  11    is a plan view of a knowledge registration screen used in the production knowledge management system according to the first embodiment. 
         FIG.  12    is a plan view of a knowledge list screen used in the production knowledge management system according to the first embodiment. 
         FIG.  13    is a conceptual diagram of a table and a view stored in a search information storage unit used in the production knowledge management system according to the first embodiment. 
         FIG.  14    is a plan view of a knowledge search screen used in the production knowledge management system according to the first embodiment. 
         FIG.  15    is a flowchart of search processing executed by the production knowledge management system according to the first embodiment. 
         FIG.  16    is a conceptual diagram of an inter-process node number table of the production knowledge management system according to the first embodiment. 
         FIG.  17    is a conceptual diagram of a most upstream node number view generated by the production knowledge management system according to the first embodiment. 
         FIG.  18    is a conceptual diagram of a node number classification view generated by the production knowledge management system according to the first embodiment. 
         FIG.  19    is a conceptual diagram of a nearest classification view generated by the production knowledge management system according to the first embodiment. 
         FIG.  20    is a conceptual diagram of a first-stage candidate view generated by the production knowledge management system according to the first embodiment. 
         FIG.  21    is a conceptual diagram of a first-stage arrangement order determination view generated by the production knowledge management system according to the first embodiment. 
         FIG.  22    is a conceptual diagram illustrating a method of determining &lt;first-stage node number&gt; and &lt;first-stage process ID&gt; in the production knowledge management system according to the first embodiment. 
         FIG.  23    is a conceptual diagram of a second-stage candidate view generated by the production knowledge management system according to the first embodiment. 
         FIG.  24    is a conceptual diagram of a second-stage arrangement order determination view generated by the production knowledge management system according to the first embodiment. 
         FIG.  25    is a conceptual diagram illustrating a method of determining &lt;second-stage node number&gt; and &lt;second-stage process ID&gt; in the production knowledge management system according to the first embodiment. 
         FIG.  26    is a conceptual diagram of a third-stage candidate view generated by the production knowledge management system according to the first embodiment. 
         FIG.  27    is a conceptual diagram of a third-stage arrangement order determination view generated by the production knowledge management system according to the first embodiment. 
         FIG.  28    is a conceptual diagram illustrating a method of determining &lt;third-stage node number&gt; and &lt;third-stage process ID&gt; in the production knowledge management system according to the first embodiment. 
         FIG.  29    is a functional block diagram of a production knowledge management system according to a second embodiment of the present invention. 
         FIG.  30    is a plan view of a knowledge search screen used in a production knowledge management system according to a third embodiment. 
         FIG.  31    is a flowchart of search processing executed by the production knowledge management system according to the third embodiment. 
         FIG.  32    is a block diagram illustrating network connection between a production knowledge management system and a production state monitoring system according to the fourth embodiment. 
         FIG.  33    is a conceptual diagram of an error knowledge table used in the production knowledge management system according to the fourth embodiment. 
         FIG.  34    is a conceptual diagram of an error occurrence process table used in the production knowledge management system according to fourth embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described with reference to the drawings. 
     First Embodiment 
     [Overall Configuration] 
       FIG.  1    is a block diagram illustrating a system configuration of a production knowledge management system  101  according to a first embodiment of the present invention. The production knowledge management system  1  is a database system. The production knowledge management system  101  includes a central processing unit (CPU)  11  that performs various calculations and intensively controls each unit of the production knowledge management system  101 . A random access memory (RAM)  12  which is a work area of the CPU  11 , a ROM  13  in which a basic input output system (BIOS) and the like are stored, and a magnetic storage device (HDD)  14  (which may be a solid state drive (SSD) and the like) which is a nonvolatile storage device for storing various data are connected to the CPU  11 . Further, a communication interface (I/F)  105  for communicating with a network  120  ( FIG.  2   ) such as the Internet, and a storage medium reading device  17  such as an optical disk device for reading data of a storage medium  16  which is various media such as a Blu-ray (registered trademark) disc, a digital versatile disc (DVD), and a compact disc (CD) are connected to the CPU  11 . Furthermore, an input device  18  such as a keyboard, a mouse, and the like, and a display device  19  such as a liquid crystal display, an organic EL display, and the like are connected to the CPU  11 . A production knowledge management program  20  is set up in the magnetic storage device  14 . The production knowledge management program  20  may be downloaded from the Internet or the like and set up in the magnetic storage device  14 , or may be read from the storage medium  16  by the storage medium reading device  17  and set up in the magnetic storage device  14 . 
     In  FIG.  1   , for convenience, the production knowledge management system  101  is illustrated as a single server device, but may be implemented as a plurality of server devices on a network that operate in cooperation with each other. In this case, the production knowledge management program  20  illustrated in  FIG.  1    as a set of data is also an aggregate of program groups set up in a distributed manner in a plurality of server devices. In this case, the storage medium  16  is also an aggregate of storage medium groups including storage media corresponding to the respective server devices. 
     The production knowledge management system  101  operates based on the production knowledge management program  20 , constructs a database  107  ( FIG.  2   ) to be described later in the magnetic storage device  14  or another non-volatile storage device, and can search the database  107 . The production knowledge management system  101  is a database that is used at a certain production site and registers various problem cases in order to solve a problem occurring in a production line at the production site. In the present specification, the term “process” refers to a process in a production line. Furthermore, the term “upstream” refers to upstream viewed form a certain process in a production line. 
       FIG.  2    is a functional block diagram of the production knowledge management system  101 . The production knowledge management system  101  constructs a database  107 , and a classification master information storage unit  108 , a process information storage unit  109 , and a knowledge information storage unit  110  are provided in the database  107 . A data input/output processor  103  inputs and outputs data relative to the database  107 . A search unit  104  performs search processing of the database  107 . A search information storage unit  111  stores search information obtained by searching the database  107 . 
     In order to access the production knowledge management system  101 , a terminal device  121  connected to the production knowledge management system  101  through the network  120  is used. The terminal device  121  includes a communication I/F  122  that communicates with the production knowledge management system  101  via the network  120  and a data input/output unit  123  through which a user of the terminal device  121  inputs and outputs data. An access controller  102  controls access from the terminal device  121  via the communication I/F  105  and the network  120 . 
     Meanwhile, PTL 1 and PTL 2 described above disclose a technique of constructing a database of a tree structure by associating certain knowledge with other knowledge by a causal or parent-child definition. However, such a technology has the following problems. 
     (a) In the techniques of PTL 1 and PTL 2, when searching for other knowledge from a certain knowledge A, only knowledge connected to the knowledge A in advance in a tree structure can be extracted. However, knowledge obtained at production sites of various products often has no certainty of causality in the middle, and a burden of work of applying knowledge to the tree structure is large. Therefore, it is difficult to construct the database with the tree structure of knowledge. 
     (b) In the techniques of PTL 1 and PTL 2, it is difficult for a plurality of persons to accumulate knowledge without discussion because the recognition of the granularity of the tree structure, such as whether to divide the knowledge into several pieces of connected knowledge or combine the knowledge into one piece of knowledge, differs depending on persons. Therefore, also in this respect, it is difficult to construct the database with the tree structure of knowledge. 
     Therefore, in the following, a system and a processing process (production knowledge management method) that enable search of knowledge using a database with easy construction in the production knowledge management system  101  (production knowledge management program  20 ) will be described in detail. 
     [Database] 
       FIG.  3    is a block diagram illustrating types and relationships of tables stored in the database  107 . The classification master information storage unit  108  stores a classification master table T 3 . The process information storage unit  109  stores a process table T 4 , a process order table T 5 , a process classification table T 6 , and an inter-process node number table T 15 . The knowledge information storage unit  110  stores a knowledge table T 8  and a knowledge classification table T 9 . 
     The details of each table will be described below. Note that, in the present specification, information in units of rows stored in a table or a view is referred to as a “record”. Here, a table means a record group held in a state in which a value is fixed, and a view means a record group in a state in which processing is performed by temporarily referring to a part or the whole of the table. In addition, a column name defined by each table or view is described by surrounding it by &lt;&gt;. In addition, a value serving as the content of a record used as a sample of the embodiment is described by enclosing it with “”. 
     [Classification Master Information Storage Unit  108 ] 
       FIG.  4    is a conceptual diagram of the classification master table T 3 . The classification master table T 3  is a table in which &lt;classification name&gt; as a record and &lt;classification ID&gt; which is a unique key of the record are associated with each other. 
     &lt;Classification name&gt; is a classification name obtained by classifying processing performed in various processes in a production line. In principle, this classification name does not include a unique field terminology used only in a specific production line, and it is desirable that the classification name is configured by a general-purpose term (generic name) that is commonly used at least in a production line of the same type of products. 
     In the classification master table T 3 , a server administrator registers records in advance through the data input/output processor  103  before the operation of the production knowledge management system  101  is started, and thereafter, the server administrator adds and changes the records through the data input/output processor  103  as necessary. 
     [Process Information Storage Unit  109 ] 
       FIG.  5    is a conceptual diagram of the process table T 4 . In the process table T 4 , &lt;process name&gt; as a record and &lt;process ID&gt; which is a unique key of the record are registered in association with each other. &lt;Process name&gt; is a notation of a name of each process in the production line, and unlike the classification name in  FIG.  4   , a unique field terminology used only in a specific production line may also be included. 
       FIG.  6    illustrates a process order table T 5  in which a process order in a production line is registered. 
     Although the definition of the process order may be another method such as numbering, in the first embodiment, the process order is defined by association between &lt;process ID&gt; and &lt;next process ID&gt;. &lt;Next process ID&gt; is a &lt;process ID&gt; of a process next to the process indicated by &lt;process ID&gt;. 
     As described above, the method of associating &lt;process ID&gt; with &lt;next process ID&gt; can also define a process flow in which a process branches or processes are joined in the middle of a production line. Both &lt;process ID&gt; and &lt;next process ID&gt; are values selected from &lt;process ID&gt; in the process table T 4  ( FIG.  5   ). 
       FIG.  7    illustrates a process classification table T 6 . The process classification table T 6  stores &lt;process ID&gt; of the process table T 4  ( FIG.  5   ) and &lt;classification ID&gt; of the classification master table T 3  ( FIG.  4   ) in association with each other. This association may be one-to-many, many-to-one. For example, in the sample illustrated in  FIG.  7   , &lt;process ID&gt; of “P10611” is associated with two &lt;classification IDs&gt; of “KS2” and “KK5”. Further, three &lt;process IDs&gt; of “P10511”, “P10811”, and “P20411” are associated with a &lt;classification ID&gt; of “KY1”. 
     The above process information is information determined at a stage before design of a production line is completed and production is started at a manufacturing site. Therefore, the server administrator registers the records in the process table T 4  ( FIG.  5   ), the process order table T 5  ( FIG.  6   ), and the process classification table T 6  ( FIG.  7   ) by the data input/output processor  103  before the operation of the production knowledge management system  101  is started. Thereafter, when the contents of the processes constituting the production line are changed, such as in a change in product specifications and an improvement in a manufacturing method, and the like, the server administrator adds and changes the records by the data input/output processor  103 . 
       FIG.  8    is a conceptual diagram showing information regarding a process stored in each table of  FIGS.  5  to  7    for a sample (an example of a production line) shown in the first embodiment. In  FIG.  8   , each box shows an individual process  701  in the production line (In  FIG.  8   , a reference sign is added to only one box.). &lt;Process ID&gt;, &lt;process name&gt;, &lt;classification name&gt;, and &lt;classification ID&gt; were described in each box. An arrow connecting boxes indicates the flow of each process  701  in the production line. As illustrated in  FIG.  8   , because of the process table T 4  ( FIG.  5   ) and the process order table T 5  ( FIG.  6   ), the flow of each process in the production line is clear. &lt;Classification name&gt; associated with each &lt;process name&gt; is also clear from the process classification table T 6  (FIG.  7 ) and the classification master table T 3  ( FIG.  4   ). 
     The inter-process node number table T 15  ( FIG.  3   ) will be described later. 
     [Knowledge Information Storage Unit  110 ] 
       FIG.  9    is a conceptual diagram of the knowledge table T 8 . The knowledge table T 8  is configured by associating &lt;problem content&gt;, &lt;factor&gt;, and &lt;knowledge ID&gt; which is a unique key of these records. In addition, a column describing the content of knowledge in detail, such as &lt;countermeasure&gt;, and the like may be additionally associated with them. 
       FIG.  10    is a conceptual diagram of the knowledge classification table T 9 . The knowledge classification table T 9  is information that is searched at the production site and is used as a reference for countermeasures against problems. As a column to be added to the knowledge classification table T 9  in addition to those illustrated in  FIG.  10   , the exhibit information of information, the storage destination of photographs, materials, and the like, the responsible person, the registrant of the knowledge record, the registration date and time, and the like are exemplified. 
     &lt;Problem content&gt; in the knowledge table T 8  ( FIG.  9   ) indicates contents of various problems that may occur in the production line (occurred in the past or expected to occur in the future). &lt;Factor&gt; indicates contents (within a known range) of a factor causing the associated &lt;problem content&gt;. &lt;Countermeasure&gt; indicates contents of a countermeasure against the associated &lt;problem content&gt;. &lt;Problem content&gt;, &lt;factor&gt;, &lt;countermeasure&gt;, and the like may include a unique field terminology used only for a specific production line. 
     The knowledge classification table T 9  stores information related to association between &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) and &lt;classification ID&gt; of the classification master table T 3  ( FIG.  4   ). The association may be one-to-many, many-to-one. For example, in the sample illustrated in  FIG.  10   , &lt;knowledge ID&gt; of “AM01” is associated with two &lt;classification IDs&gt; of “KS1” and “KY1”. In addition, two &lt;knowledge IDs&gt; of “AX01” and “AX02” are associated with &lt;classification ID&gt; of “KT1”. 
     &lt;Process ID&gt; and the &lt;knowledge ID&gt; are associated by the knowledge classification table T 9  ( FIG.  10   ) and the process classification table T 6  ( FIG.  7   ). Therefore, &lt;process name&gt; is associated with &lt;problem content&gt;, &lt;factor&gt;, and &lt;countermeasure&gt;. In addition, &lt;classification name&gt; is further associated with them ( FIGS.  4  to  7 ,  9 , and  10   ). 
     [Knowledge Registration Screen  1001 ] 
       FIG.  11    is a plan view of the knowledge registration screen  1001  displayed on a display of the terminal device  121  via the data input/output unit  123 . By accessing the production knowledge management system  101  with the terminal device  121 , a user can display the knowledge registration screen  1001  on the own terminal device  121 . 
     As illustrated in  FIG.  11   , the knowledge registration screen  1001  includes a save button  1002 , a problem content input field  1003 , a classification display field  1004 , a classification addition button  1005 , a classification deletion button  1006 , a classification selection field  1007 , and a factor input field  1008 . 
     The user can add, edit, and delete records to and from the knowledge table T 8  ( FIG.  9   ) and the knowledge classification table T 9  ( FIG.  10   ) at any time using the knowledge registration screen  1001 . 
     On the knowledge registration screen  1001 , the contents of a record associated with each other in the knowledge table T 8  ( FIG.  9   ) are displayed. 
     In the problem content input field  1003 , &lt;problem content&gt; of the knowledge table T 8  is displayed in a state where the contents can be edited on the knowledge registration screen  1001 . 
     In the classification display field  1004 , &lt;classification names&gt; of all records are displayed in a selectable state from the classification master table T 3  ( FIG.  4   ) on the basis of &lt;classification ID&gt; of the knowledge classification table T 9 . 
     For example,  FIG.  11    illustrates a state in which a record of &lt;knowledge ID&gt;=“DP03” illustrated in the knowledge table T 8  ( FIG.  9   ) is displayed. 
     &lt;Classification name&gt; of the classification master table T 3  ( FIG.  4   ) is displayed in a selectable state in the classification selection field  1007 . 
     When a click of the classification addition button  1005  is detected, if there is a &lt;classification name&gt; selected in the classification selection field  1007 , the &lt;classification name&gt; is added to the classification display field  1004  and displayed. 
     For example, if the user selects “press-fitting” in the classification selection field  1007  and then clicks the classification addition button  1005  in the state of  FIG.  11   , “welding, performance inspection  1 , press-fitting” is displayed in the classification display field  1004 . 
     When a click of the classification deletion button  1006  is detected, if there is a &lt;classification name&gt; selected in the classification display field  1004 , the &lt;classification name&gt; is deleted from the classification display field  1004 . 
     For example, if the user selects “welding” in the classification display field  1004  and then clicks the classification deletion button  1006  in the state of  FIG.  11   , only “performance inspection 1” is displayed in the classification display field  1004 . 
     The factor input field  1008  displays &lt;factor&gt; of the knowledge table T 8  ( FIG.  9   ) in a state where the contents can be edited on the screen. 
     When the save button  1002  is clicked, the data input/output processor  103  updates &lt;problem content&gt; and &lt;factor&gt; in the knowledge table T 8  ( FIG.  9   ) to the contents displayed in the problem content input field  1003  and the factor input field  1008 . 
     In addition, the data input/output processor  103  adds or deletes a record to or from the knowledge classification table T 9  ( FIG.  10   ) so as to match the content displayed in the classification display field  1004 . 
     In a case where a column is added to the knowledge table T 8  ( FIG.  9   ), an input field for registering and editing the contents of the added column can be provided on the knowledge registration screen  1001 . 
       FIG.  12    is a plan view of the knowledge list screen  1101  displayed on the display of the terminal device  121  via the data input/output unit  123 . 
     The knowledge list screen  1101  includes an edition button  1102 , an addition button  1103 , a deletion button  1104 , and a knowledge list display field  1105 . 
     In the knowledge list display field  1105 , &lt;problem content&gt; and &lt;factor&gt; of all records of the knowledge table T 8  ( FIG.  9   ) can be displayed in a selectable state. 
     When the edition button  1102  is clicked, if there is a record selected in the knowledge list display field  1105 , the data input/output unit  123  ( FIG.  2   ) opens the knowledge registration screen  1001  ( FIG.  11   ) in a state where the record is displayed. 
     When the addition button  1103  is clicked, the data input/output unit  123  opens the knowledge registration screen  1001  in which the problem content input field  1003 , the classification display field  1004 , and the factor input field  1008  are blank. 
     When a click of the deletion button  1104  is detected, if there is a record selected in the knowledge list display field  1105 , first, a record of the same &lt;knowledge ID&gt; is deleted from the knowledge classification table T 9  ( FIG.  10   ) on the basis of &lt;knowledge ID&gt; of the record. Next, a record of the same &lt;knowledge ID&gt; is deleted from the knowledge table T 8  ( FIG.  9   ). 
     As described above, the user can accumulate information in the knowledge information storage unit  110  by operating the terminal device  121  at any time. 
     As described above, in a case where a column is added to the knowledge table T 8  ( FIG.  9   ), the added column is also displayed. 
     [Search Information] 
     The search information storage unit  111  ( FIG.  2   ) is a memory area that temporarily stores records extracted and processed from the database  107  ( FIG.  2   ) on the basis of search conditions in search processing executed by the search unit  104  ( FIG.  2   ) described later. 
       FIG.  13    illustrates a conceptual diagram of a table and a view stored in the search information storage unit  111 . The search information storage unit  111  temporarily stores an inter-process node number table T 15  ( FIG.  16   ), a most upstream node number view V 16  ( FIG.  17   ), a node number classification view V 17  ( FIG.  18   ), a nearest classification view V 18  ( FIG.  19   ), and a first-stage candidate view V 19  ( FIG.  20   ). In addition, the search information storage unit  111  temporarily stores a first-stage arrangement order determination view V 20  ( FIG.  21   ), a second-stage candidate view V 22  ( FIG.  23   ), an arrangement order determination view V 23  ( FIG.  24   ), a third-stage candidate view V 25  ( FIG.  26   ), and a third-stage arrangement order determination view V 26  ( FIG.  27   ). In  FIG.  13   , only a part thereof is illustrated. 
     In  FIG.  13   , an arrow indicates a reference relationship between a view and a table. For example, the nearest classification view V 18  is a view obtained by referring to and processing a record of the node number classification view V 17 . Details of each table and view will be described in the description of [Search processing] to be described later. 
     [Search Screen] 
       FIG.  14    is a plan view of the knowledge search screen  1301  displayed on the display of the terminal device  121  via the data input/output unit  123 . 
     The knowledge search screen  1301  includes a problem keyword input field  1302 , a problem occurrence process selection field  1303 , a knowledge search execution button  1304 , a search result display field  1305 , and a knowledge detail display button  1306 . 
     The problem keyword input field  1302  is displayed in a state where a user can input an arbitrary character string. The initial state is blank. In the problem keyword input field  1302 , contents of a problem occurred in the production line are input. 
     In the problem occurrence process selection field  1303 , &lt;process name&gt; of the process table T 4  ( FIG.  5   ) can be displayed as a selectable pull-down menu. Here, the user selects a process in which the problem input in the problem keyword input field  1302  has occurred in the production line. In the field of the problem occurrence process selection field  1303 , one &lt;process name&gt; selected by the user from the process table T 4  is displayed. 
     A click of the knowledge search execution button  1304  serves as a command to execute the search processing by the search unit  104 . 
     Although nothing is displayed in the initial state in the search result display field  1305 , after the search processing is executed, as illustrated in  FIG.  14   , the result of the search processing is displayed in the first-stage display field  1307 , the second-stage display field  1308 , and the third-stage display field  1309  in a state where each record can be selected. 
     When the knowledge detail display button  1306  is clicked, if there is a record selected in the search result display field  1305 , the data input/output unit  123  opens the knowledge registration screen  1001  ( FIG.  11   ) in a state where the record is displayed. 
     [Search Processing] 
       FIG.  15    is a flowchart illustrating search processing executed by the search unit  104 . First, in response to receiving a search execution command by the knowledge search execution button  1304  ( FIG.  14   ) being clicked as a trigger (Yes in step S 1 ), the search unit  104  acquires a search condition (step S 2 ). Specifically, the search condition receives the character string input in the problem keyword input field  1302  illustrated in  FIG.  14    as the problem keyword. The problem keyword is input by the user in simple words of the contents of a problem that has occurred in the production line. Further, &lt;process name&gt; selected in the problem occurrence process selection field  1303  is received as a problem occurrence process. In this case, specifically, &lt;process ID&gt; (origin process ID) extracted from the process table T 4  ( FIG.  5   ) based on the selected problem occurrence process is received. The problem occurrence process is &lt;process name&gt; indicating a process in the production line in which the problem has occurred input as the problem keyword. 
     Next, the search unit  104  performs node analysis (step S 3 ). Next, the search unit  104  extracts the first-stage knowledge record (step S 4 ). “Knowledge record” is a record registered in the knowledge table T 8  ( FIG.  9   ). Next, the search unit  104  determines the arrangement order of the first stage (step S 5 ). Next, the search unit  104  extracts a second-stage knowledge record (step S 6 ). Next, the search unit  104  determines the arrangement order of the second stage (step S 7 ). Next, the search unit  104  extracts the knowledge record of the third stage (step S 8 ). Next, the search unit  104  determines the arrangement order of the third-stage knowledge records (step S 9 ). Next, the search unit  104  determines the display contents of the first to third stages (step S 10 ). Next, the search unit  104  displays the display contents of the first to third stages determined as described above in the search result display field  1305  ( FIG.  14   ) (step S 11 ). As described above, the search processing ends, and a standby state starts. In the following, steps S 4  and S 5  are referred to as a first search, steps S 6  and S 7  are referred to as a first second search, and steps S 8  and S 9  are referred to as a second second search. 
     [Node Analysis] 
     Here, details of the node analysis (step S 3 ) will be described.  FIG.  16    illustrates an inter-process node number table T 15  ( FIG.  3   ). In the inter-process node number table T 15 , &lt;origin process ID&gt;, &lt;process ID&gt;, &lt;node type&gt;, and &lt;node number&gt; are registered in association with each other. 
     &lt;Origin process ID&gt; and &lt;process ID&gt; are any values registered as &lt;process ID&gt; in the process table T 4  ( FIG.  5   ). 
     The inter-process node number table T 15  ( FIG.  3   ) is information in which “closeness” considering a causal relationship between processes is defined by &lt;node type&gt; and &lt;node number&gt; on the basis of the process order table T 5  ( FIG.  6   ) for brute-force pairs of all &lt;process IDs&gt; (&lt;origin process ID&gt;) stored in the process table T 4  and all &lt;process IDs&gt; including itself. &lt;Node type&gt; and &lt;node number&gt; are concepts that define “closeness” in consideration of causality between processes. That is, &lt;node number&gt; is a numerical value representing the closeness between processes, and &lt;node type&gt; indicates a type of &lt;node number&gt;. 
     Here, the definition of “closeness” in the first embodiment will be described. First, a relationship of a process with respect to another process at a certain origin is divided into three types of (a) : a process that is the same as or upstream of the origin in the same production line, (b): a process that is downstream of the origin in the same production line, and (c): a process of another factory or the like that is not in the same production line. In the first embodiment, it is defined that the process is close to the process of the origin in the order of (a), (c), and (b). 
     The reason why (c) is set in the order from (b) is that a causal relationship between a problem and a factor is considered. It is based on the idea that knowledge information about similar processes present in other manufacturing lines is more likely to include a solution to a problem that occurred in the origin process than knowledge information about processes downstream of the origin process. However, this idea is an example of defining the arrangement order of the knowledge records, and the closeness may be defined by another idea. 
     In the inter-process node number table T 15  ( FIG.  3   ), &lt;node type&gt; and &lt;node number&gt; are defined and stored as follows for &lt;process ID&gt; with respect to &lt;origin process ID&gt;. 
     In a case where &lt;process ID&gt; for &lt;origin process ID&gt; is type (a), it is assumed that &lt;node type&gt;=0, and &lt;node number&gt;=0, 1, 2, 3, . . . from the origin toward the upstream. In a case of the type (b), it is assumed that &lt;node type&gt;=2, and &lt;node number&gt; is numbered so as to be larger toward the downstream side starting from &lt;node number&gt;+2 on the most upstream side. Here, the reason why &lt;node number&gt; starts from &lt;node number&gt;+2 on the most upstream side is to allocate &lt;node number&gt;+1 on the most upstream side to (c) in rearrangement of records to be described later. 
     The inter-process node number table T 15  ( FIG.  3   ) can be automatically generated based on the process order table T 5  ( FIG.  6   ) by this definition. 
     In addition, in step S 3 , the following views are generated.  FIG.  17    is a conceptual diagram of the most upstream node number view V 16 . The most upstream node number view V 16  is obtained by extracting a record having the maximum &lt;node number&gt; at &lt;node type&gt;=“0” for each &lt;origin process ID&gt; from the inter-process node number table T 15  and arranging &lt;origin process ID&gt; and &lt;node number&gt; in association with each other. 
       FIG.  18    is a conceptual diagram of the node number classification view V 17 . The node number classification view V 17  is a record group in which &lt;classification ID&gt; of the process classification table T 6  ( FIG.  7   ) is combined with each record of the inter-process node number table T 15  ( FIG.  16   ) using &lt;process ID&gt; as a key, and &lt;origin process ID&gt;, &lt;node number&gt;, &lt;process ID&gt;, and &lt;classification ID&gt; are arranged in association with each other. 
       FIG.  19    is a conceptual diagram of the nearest classification view V 18 . The nearest classification view V 18  is a record group obtained by extracting a record in which &lt;node number&gt; is the smallest in each &lt;classification ID&gt; with respect to &lt;origin process ID&gt; from the node number classification view V 17 . 
     Note that the values of the inter-process node number table T 15  ( FIG.  16   ), the most upstream node number view V 16  ( FIG.  17   ), the node number classification view V 17  ( FIG.  18   ), and the nearest classification view V 18  ( FIG.  19   ) can be determined regardless of the search condition received in step S 2 . Therefore, the processing in step S 3  may be executed independently by using, as a trigger, opening of the knowledge search screen  1301  by the user or updating of the process information by the server administrator, in addition to using the receiving of the search execution command as a trigger. 
     For the inter-process node number table T 15  ( FIG.  16   ), the concept of the definition of “closeness” considering the causal relationship between processes includes various approaches such as closeness of an implementation location and closeness of arrangement of a target component, and the like. &lt;Node type&gt; and the &lt;node number&gt; may be determined according to a definition other than the definitions described above, and the &lt;node number&gt; may be a real number with a decimal point instead of an integer. 
     In addition, for example, an arbitrary numerical value may be manually registered as a value of &lt;node number&gt; by a server administrator. In this case, the inter-process node number table T 15  may be stored in the process information storage unit  109  of the database  107  by the server administrator before the operation of the production knowledge management system is started. 
     Details of steps S 4  to S 9  will be described below. 
     [Knowledge Record Extraction: First Stage (First Search)] 
     In step S 4 , the knowledge record strongly related to the problem keyword serving as the search keyword is extracted from the knowledge table T 8  ( FIG.  9   ), and the first-stage candidate view V 19  illustrated in  FIG.  20    is generated. 
     The first-stage candidate view V 19  is configured by associating &lt;problem occurrence process ID&gt;, &lt;first-stage knowledge ID&gt;, and &lt;first-stage classification ID&gt;. &lt;Problem occurrence process ID&gt; is &lt;process ID&gt; indicating a process in which a problem serving as a problem keyword has occurred. &lt;First-stage knowledge ID&gt; is &lt;knowledge ID&gt; for specifying the knowledge record extracted as the first stage from the knowledge table T 8  ( FIG.  9   ). &lt;First-stage classification ID&gt; is &lt;classification ID&gt; associated with &lt;first-stage knowledge ID&gt; in the knowledge classification table T 9  ( FIG.  10   ). 
     Specifically, the processing here determines whether or not the problem keyword obtained as a search keyword is included in the character string of &lt;problem content&gt; in the knowledge table T 8 , and extracts the knowledge record included therein. 
     In the sample of the first embodiment, a knowledge record (&lt;knowledge ID&gt;=“AX02”, “BY01”, and “EZ04”) including the problem keyword “fracture” in &lt;problem content&gt; is extracted as &lt;first-stage knowledge ID&gt; of the first-stage candidate view V 19  ( FIG.  20   ). 
     In addition, &lt;classification ID&gt; obtained by combining the knowledge classification table T 9  ( FIG.  10   ) with &lt;knowledge ID&gt; as a combination key with respect to the obtained &lt;first-stage knowledge ID&gt; is set as &lt;first-stage classification ID&gt;. 
     In the sample of the present embodiment, for example, for &lt;knowledge ID&gt; of “BY01”, there are two records of &lt;classification ID&gt;=“KK6” and “KS2” in the knowledge classification table T 9  ( FIG.  10   ). Therefore, there are two records for &lt;first-stage knowledge ID&gt;=“BY01” of the first-stage candidate view V 19  ( FIG.  20   ) also has 2 records. 
     In step S 5 , the knowledge records extracted in the first-stage candidate view V 19  are rearranged in the order of “closeness” between processes on the basis of the nearest classification view V 18  ( FIG.  19   ), and the first-stage arrangement order determination view V 20  illustrated in  FIG.  21    is generated. 
     The first-stage arrangement order determination view V 20  ( FIG.  21   ) is obtained by adding the columns of &lt;first-stage node number&gt; and &lt;first-stage process ID&gt; to the first-stage candidate view V 19  ( FIG.  20   ) and excluding &lt;first-stage classification ID&gt;. 
     A method of determining &lt;first-stage node number&gt; and &lt;first-stage process ID&gt; will be described below using the conceptual diagram illustrated in  FIG.  22   . First, the first-stage candidate view V 19  ( FIG.  20   ) and the nearest classification view V 18  ( FIG.  19   ) are externally combined with &lt;problem occurrence process ID&gt; and &lt;origin process ID&gt; serving as a first combination key and &lt;first-stage classification ID&gt; and &lt;classification ID&gt; serving as a second combination key. 
     When &lt;node number&gt; obtained from the nearest classification view V 18  ( FIG.  19   ) exists in this external combination, &lt;node number&gt; can be obtained as &lt;first-stage node number&gt;, and &lt;process ID&gt; can be obtained as &lt;first-stage process ID&gt;. 
     On the other hand, when &lt;node number&gt; obtained from the nearest classification view V 18  ( FIG.  19   ) does not exist in this external combination, the first-stage candidate view V 19  ( FIG.  20   ) and the most upstream node number view V 16  ( FIG.  17   ) can be combined using &lt;problem occurrence process ID&gt; and &lt;origin process ID&gt; as combination keys, and &lt;most upstream node number&gt;+1 obtained from the most upstream node number view V 16  ( FIG.  17   ) can be obtained as &lt;first-stage node number&gt;. 
     In the sample of the first embodiment, for example, for a record of &lt;first-stage knowledge ID&gt;=“AX02” and &lt;classification ID&gt;=“KT1” of the first-stage candidate view V 19  ( FIG.  20   ), a process record of “&lt;origin process ID&gt;=‘P10711’ ‘and’ &lt;classification ID&gt;=‘KT1’” do not exist in the nearest classification view V 18  ( FIG.  18   ). Therefore, &lt;first-stage process ID&gt; remains blank. Further, from the most upstream node number view V 16  ( FIG.  17   ), since &lt;most upstream node number&gt;=“4” ( FIG.  18   ) of &lt;origin process ID&gt;=“P10711”, &lt;first-stage node number&gt;=4+1=“5” is obtained. 
     [Knowledge Record Extraction: Second Stage (First Second Search)] 
     In step S 6 , the knowledge record strongly related to the knowledge record obtained in the first-stage arrangement order determination view V 20  ( FIG.  21   ) is extracted from the knowledge table T 8  ( FIG.  9   ), and the second-stage candidate view V 22  illustrated in  FIG.  23    is generated. 
     In the second-stage candidate view V 22 , &lt;first-stage process ID&gt;, &lt;first-stage knowledge ID&gt;, &lt;first-stage node number&gt;, &lt;second-stage knowledge ID&gt;, and &lt;second-stage classification ID&gt; are associated with each other. 
     Here, a value of &lt;factor&gt; included in the first related knowledge record group that is a data group obtained in the processing of [knowledge record extraction: first stage] is obtained as a search keyword. Specifically, first, for each record of the first-stage arrangement order determination view V 20  ( FIG.  21   ), a value of &lt;factor&gt; obtained by combining with &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) is obtained as a search keyword with &lt;first-stage knowledge ID&gt; as a key. 
     Next, it is determined whether or not the value of the search keyword is included in the character string of &lt;problem content&gt; in the knowledge table T 8  ( FIG.  9   ), and &lt;knowledge ID&gt; of the knowledge record including the value is extracted as &lt;second-stage knowledge ID&gt;. 
     In the sample of the first embodiment, for example, for &lt;knowledge ID&gt;=“BY01”, a knowledge record (&lt;knowledge ID&gt;=“CN01”) including &lt;factor&gt;=“deformation” in &lt;problem content&gt; is extracted as &lt;second-stage knowledge ID&gt; ( FIG.  23   ). 
     In addition, &lt;classification ID&gt; obtained by combining the knowledge classification table T 9  ( FIG.  10   ) with &lt;knowledge ID&gt; as a combination key with respect to the obtained &lt;second-stage knowledge ID&gt; is set as &lt;second-stage classification ID&gt;. 
     In the sample of the first embodiment, for example, for the &lt;knowledge ID&gt; of “CN01”, since there is a record of &lt;classification ID&gt;=“KA1” in the knowledge classification table T 9  ( FIG.  10   ), &lt;second-stage classification ID&gt;=“KA1” is obtained ( FIG.  23   ). 
     In step S 7 , the knowledge records extracted in the second-stage candidate view V 22  ( FIG.  23   ) are rearranged in order of “closeness” between processes on the basis of the nearest classification view V 18  ( FIG.  19   ), and the second-stage arrangement order determination view V 23  illustrated in  FIG.  24    is generated. 
     The second-stage arrangement order determination view V 23  is obtained by adding the columns of &lt;second-stage node number&gt; and &lt;second-stage process ID&gt; to the second-stage candidate view V 22  and excluding &lt;second-stage classification ID&gt;. 
     Hereinafter, a method of determining &lt;second-stage node number&gt; and &lt;second-stage process ID&gt; will be described using the conceptual diagram illustrated in  FIG.  25   . First, the second-stage candidate view V 22  ( FIG.  23   ) and the nearest classification view V 18  ( FIG.  19   ) are externally combined with &lt;first-stage process ID&gt; and &lt;origin process ID&gt; serving as a first combination key and &lt;second-stage classification ID&gt; and &lt;classification ID&gt; serving as a second combination key. 
     When &lt;node number&gt; obtained from the nearest classification view V 18  ( FIG.  19   ) exists in this external combination, &lt;node number&gt; is obtained as &lt;second-stage node number&gt;, and &lt;process ID&gt; is obtained as &lt;second-stage process ID&gt;. 
     On the other hand, when &lt;node number&gt; obtained from the nearest classification view V 18  ( FIG.  19   ) does not exist in this external combination, the second-stage candidate view V 22  ( FIG.  23   ) and the most upstream node number view V 16  ( FIG.  17   ) are combined using &lt;first-stage process ID&gt; and &lt;origin process ID&gt; as combination keys, and &lt;most upstream node number&gt;+1 obtained from the most upstream node number view V 16  ( FIG.  17   ) is obtained as &lt;second-stage node number&gt;. 
     In the sample of the first embodiment, for example, for a record of &lt;first-stage process ID&gt;=“P10611”, &lt;second-stage knowledge ID&gt;=“CN01”, and &lt;classification ID&gt;=“KA1” of the second-stage candidate view V 22  ( FIG.  23   ), a record of a process of “&lt;origin process ID&gt;=‘P10611’” and “&lt;classification ID&gt;=‘KA1’” does not exist in the nearest classification view V 18  ( FIG.  19   ), and thus &lt;second-stage process ID&gt; remains blank. Further, from the most upstream node number view V 16  ( FIG.  17   ), since &lt;most upstream node number&gt;=“1” for &lt;first-stage process ID&gt;=“P10611” ( FIG.  18   ), &lt;first-stage node number&gt;=1+1=“2”d. 
     [Knowledge Record Extraction: Third Stage (Second Second Search)] 
     In step S 8 , the knowledge record “strongly related” to the knowledge record obtained in the second-stage arrangement order determination view V 23  ( FIG.  24   ) is extracted from the knowledge table T 8  ( FIG.  9   ), and the third-stage candidate view V 25  illustrated in  FIG.  26    is generated. 
     In the third-stage candidate view V 25 , &lt;first-stage process ID&gt;, &lt;first-stage knowledge ID&gt;, &lt;first-stage node number&gt;, &lt;second-stage process ID&gt;, &lt;second-stage knowledge ID&gt;, &lt;second-stage node number&gt;, &lt;third-stage knowledge ID&gt;, and &lt;third-stage classification ID&gt; are associated with each other. 
     Here, a value of &lt;factor&gt; included in the second related knowledge record group that is a data group obtained in the processing of [knowledge record extraction: second stage] is obtained as a search keyword. Specifically, first, for each record of the second-stage arrangement order determination view V 23  ( FIG.  24   ), a value of &lt;factor&gt; obtained by combining with &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) is obtained as a search keyword with &lt;second-stage knowledge ID&gt; as a key. 
     Next, it is determined whether or not the value of the search keyword is included in the character string of &lt;problem content&gt; in the knowledge table T 8  ( FIG.  9   ), and &lt;knowledge ID&gt; of the knowledge record including the value is extracted as &lt;third-stage knowledge ID&gt;. 
     In the sample of the first embodiment, for &lt;second-stage knowledge ID&gt;=“DP03”, for example, a knowledge record (&lt;knowledge ID&gt;=“DP-gen”) including &lt;factor&gt;=“drop” in &lt;problem content&gt; is extracted as &lt;third-stage knowledge ID&gt; ( FIG.  25   ). 
     In addition, &lt;classification ID&gt; obtained by combining the knowledge classification table T 9  ( FIG.  10   ) with &lt;knowledge ID&gt; as a combination key with respect to the obtained &lt;third-stage knowledge ID&gt; is set as &lt;third-stage classification ID&gt;. 
     In the sample of the present embodiment, for example, for &lt;knowledge ID&gt;=“DP-gen”, since there is a record of &lt;classification ID&gt;=“KS1” in the knowledge classification table T 9  ( FIG.  10   ), &lt;third-stage classification ID&gt;=“KS1” is obtained ( FIG.  23   ). 
     In step S 9 , the knowledge records extracted in the third-stage candidate view V 25  ( FIG.  26   ) are rearranged in order of “closeness” between processes on the basis of the nearest classification view V 18  ( FIG.  19   ), and a third-stage arrangement order determination view V 26  illustrated in  FIG.  27    is generated. 
     The third-stage arrangement order determination view V 26  is obtained by adding the columns of &lt;third-stage node number&gt; and &lt;third-stage process ID&gt; to the third-stage candidate view V 25  ( FIG.  26   ) and excluding &lt;third-stage classification ID&gt;. 
     Hereinafter, a method of determining &lt;third-stage node number&gt; and &lt;third-stage process ID&gt; will be described using the conceptual diagram illustrated in  FIG.  28   . First, the third-stage candidate view V 25  ( FIG.  26   ) and the nearest classification view V 18  ( FIG.  19   ) are externally combined with &lt;second-stage process ID&gt; and &lt;origin process ID&gt; serving as a first combination key and &lt;third-stage classification ID&gt; and &lt;classification ID&gt; serving as a second combination key. 
     When &lt;node number&gt; obtained from the nearest classification view V 18  exists in this external combination, &lt;node number&gt; is obtained as &lt;third-stage node number&gt;, and &lt;process ID&gt; is obtained as &lt;third-stage process ID&gt;. 
     On the other hand, when &lt;node number&gt; obtained from the nearest classification view V 18  ( FIG.  19   ) does not exist in this external combination, the third-stage candidate view V 25  ( FIG.  26   ) and the most upstream node number view V 16  ( FIG.  17   ) are combined using &lt;second-stage process ID&gt; and &lt;origin process ID&gt; as combination keys, and &lt;most upstream node number&gt;+1 obtained from the most upstream node number view V 16  ( FIG.  17   ) is obtained as &lt;third-stage node number&gt;. 
     In the sample of the present embodiment, for example, for a record of &lt;second-stage process ID&gt;=“P20411”, &lt;third-stage knowledge ID&gt;=“DP-gen”, and &lt;classification ID&gt;=“KY1” of the third-stage candidate view V 25  ( FIG.  26   ), since a process record of &lt;origin process ID&gt;=“P20411” and &lt;classification ID&gt;=“KY1” has &lt;node number&gt;=“0” and process ID =“P20411” in the nearest classification view V 18  ( FIG.  19   ), &lt;third-stage process ID&gt;=“P20411” and &lt;third-stage node number&gt;=“0” are obtained. 
     [Creation and Display of Search Result on First to Third Stages] 
     Details of step S 10  will be described. In step S 10 , the first-stage arrangement order determination view V 20  ( FIG.  20   ), the second-stage arrangement order determination view V 23  ( FIG.  24   ), and the third-stage arrangement order determination view V 26  ( FIG.  27   ) are displayed on the knowledge search screen  1301  ( FIG.  14   ) while maintaining the order of records. In this case, a case where the same &lt;knowledge ID&gt; appears again is excluded, and &lt;first-stage process ID&gt;, &lt;second-stage process ID&gt;, and &lt;third-stage process ID&gt; are displayed on the knowledge search screen  1301  ( FIG.  14   ) with &lt;first-stage knowledge ID&gt;, &lt;second-stage knowledge ID&gt;, and &lt;third-stage knowledge ID&gt; as keys. In addition, &lt;problem content&gt; and &lt;factor&gt; extracted from the knowledge table T 8  by combining with the &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) are displayed on the knowledge search screen  1301  ( FIG.  14   ). In this case, in the search result display field  1305  of the knowledge search screen  1301  ( FIG.  14   ), the information on the first stage is displayed in the first-stage display field  1307 , the information on the second stage is displayed in the second-stage display field  1308 , and the information on the third stage is displayed in the third-stage display field  1309 . 
       FIG.  14    illustrates an example of a screen display state when step S 10  ends. In this example, &lt;process name&gt; associated with &lt;first-stage process ID&gt;, &lt;process name&gt; associated with &lt;second-stage process ID&gt;, and &lt;process name&gt; associated with &lt;third-stage process ID&gt; are expressed as “related processes”. 
     In this manner, a list of knowledge records that serve as reference of factors of the problem that has occurred is displayed in an easily viewable state for the user. 
     According to the production knowledge management system  101  described above, the process table T 4  ( FIG.  5   ) and the knowledge table T 8  ( FIG.  9   ) may easily include in records proper nouns such as a field terminology used only in a specific production line. On the other hand, &lt;classification name&gt; registered in the classification master table T 3  ( FIG.  4   ) is configured by a general-purpose term (general name) that is commonly used in production lines of the same type of products. Therefore, if the classification master table T 3  ( FIG.  4   ) is prepared, the process in the production line and the knowledge in the knowledge table T 8  ( FIG.  9   ) can be associated later using the process classification table T 6  ( FIG.  7   ) and the knowledge classification table T 9  ( FIG.  10   ). That is, by using the classification master table T 3  ( FIG.  4   ), it is possible to perform the search processing by associating one knowledge with another knowledge for the first time at the time of executing the search. Therefore, the search processing can be performed even if a tree structure that links certain knowledge and other knowledge is not precisely generated in advance. Therefore, according to the production knowledge management system  101 , knowledge search can be performed using the database  107  that is easily constructed. 
     According to the production knowledge management system  101 , the search processing is performed not only once in the first search of the first stage but also in the first second search of the second stage and in the second second search of the third stage. However, the present invention is not limited thereto, and only one first search of the first stage may be performed. However, according to the production knowledge management system  101 , the necessary knowledge can be easily searched from a wide range by executing the second search. In this case, in the above example, the second search (the second second search) based on the result of the second search (the first second search) that has been most recently executed is performed once. The present invention is not limited to this, and the second search may be performed only once, and the second second search may not be executed. Alternatively, the number of times of the second search may be increased, and the third second search, the fourth second search, and the like may be executed. 
     In addition, according to the production knowledge management system  101 , a knowledge record having a high possibility as a factor of the problem that has occurred is displayed in a high order, and a related process can be presented. 
     Furthermore, according to the production knowledge management system  101 , a knowledge record registered regarding a problem of a production line different from the production line in which the problem has occurred can be presented as reference information with a relatively low priority. 
     Furthermore, according to the production knowledge management system  101 , since the registration of knowledge is simple, the latest knowledge obtained on site can be accumulated by a plurality of persons. 
     According to the first embodiment, since the search without omission of the production knowledge information can be performed, it is effective for the discussion of failure mode EA (FMEA) and fall tree analysis (FTA) in the design stage of the production line. 
     Further, according to the first embodiment, it is possible to register the information little by little from a stage where the information is not determined. 
     Second Embodiment 
     Next, the second embodiment of the present invention will be described. 
     In the following embodiments, differences from the first embodiment will be mainly described, and the same reference numerals as those of the first embodiment will be used for components and the like common to the first embodiment. 
       FIG.  29    is a functional block diagram of the production knowledge management system  101  according to the second embodiment. The production knowledge management system  101  is different from that of the first embodiment in that a text analysis processor  130  is added as a functional block. The function of the text analysis processor  130  is also realized by processing executed by the production knowledge management system  101  based on the production knowledge management program  20 . 
     The text analysis processor  130  executes processing of extracting &lt;knowledge ID&gt; including a content strongly related to the search keyword in &lt;problem content&gt; from the knowledge table T 8  ( FIG.  9   ) by a processing procedure in the following order of (1) to (6) which is a natural language processing method. 
     (1) A word is extracted by the morphological analysis of &lt;problem content&gt; of each record of the knowledge table T 8 . 
     (2) By the term frequency-inverse document frequency (TF-IDF), n vectors Xn (m×1) indicating the importance of all words (m items) extracted from &lt;problem content&gt; (n items) of all records of the knowledge table T 8  with respect to &lt;problem content&gt; of each record are generated. 
     (3) By latent semantic analysis (LSA), a k×n orthogonal matrix D of the degree of correlation around X (n items) and topics (k items) and an m×k orthogonal matrix T of the degree of correlation around words (m items) and topics (k items) are generated. 
     (4) For the search keyword, a search word group is extracted by morphological analysis. 
     (5) A strongly related topic is extracted by collating a search word group with the orthogonal matrix T, and a strongly related vector Xn is extracted by collating the extracted topic with the orthogonal matrix D. 
     (6) The &lt;knowledge ID&gt; of a record corresponding to the vector Xn is obtained from the knowledge table T 8 . 
     In addition, in the second embodiment, the contents of the search processing of the first embodiment described above are partially changed as in the following (a) to (c). 
     (a) In steps S 4  and  FIG.  20   , the processing by the text analysis processor  130  is executed using a problem keyword as a search keyword, and the obtained &lt;knowledge ID&gt; is set as &lt;first-stage knowledge ID&gt; of the first-stage candidate view V 19  ( FIG.  20   ). 
     (b) In steps S 6  and  FIG.  23   , each record of the first-stage arrangement order determination view V 20  ( FIG.  21   ) is combined with &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) by using &lt;first-stage knowledge ID&gt; as a key. Then, the processing by the text analysis processor  130  is executed using a value of &lt;factor&gt; as a search keyword, and the obtained &lt;knowledge ID&gt; is set as &lt;second-stage knowledge ID&gt; of the second-stage candidate view V 22  ( FIG.  23   ). 
     (c) In steps S 8  and  FIG.  26   , a value of &lt;factor&gt; obtained by combining each record of the second-stage arrangement order determination view V 23  ( FIG.  24   ) with &lt;knowledge ID&gt; of the knowledge table T 8  ( FIG.  9   ) by using &lt;second-stage knowledge ID&gt; as a key is set as a search keyword. Then, the processing by the text analysis processor  130  is executed, and the obtained &lt;knowledge ID&gt; is set as &lt;third-stage knowledge ID&gt; of the third-stage candidate view V 25  ( FIG.  26   ). 
     According to the second embodiment described above, since the search in which the notation fluctuation is absorbed by &lt;problem content&gt; and &lt;factor&gt; of the knowledge table T 8  ( FIG.  9   ) is performed, the user does not need to be sensitive for unifying terms when registering the knowledge record in the knowledge table T 8 . 
     Note that it is also possible to further improve the accuracy of search by adding processing such as collocation analysis, absorption of notation fluctuation using a dictionary, and stop word exclusion to the text analysis processor  130 . 
     Third Embodiment 
     Next, the third embodiment of the present invention will be described. 
     The third embodiment is different from the first embodiment in that, as illustrated in  FIG.  30   , a first-stage exclusion button  1310  is added to the knowledge search screen  1301  of the first embodiment, and accordingly, a new processing ( FIG.  31   ) is performed. 
     Basically, the processing of executing steps S 1  to 
     S 11  in  FIG.  15    by detection of a click of the knowledge search execution button  1304  is common to that of the first embodiment. As a result, a search result as illustrated in  FIG.  30    is output. 
       FIG.  31    is a flowchart illustrating search processing executed by the search unit  104  when the first-stage exclusion button  1310  is clicked. When a record obviously unrelated to a search target intended by the user is extracted in the first stage, the user selects the record in the first-stage display field  1307  of the screen in  FIG.  30    (indicated by hatching in the example in  FIG.  30   ). When the instruction to exclude from the result of the first stage is received by clicking the first-stage exclusion button  1310  when the search result as illustrated in  FIG.  30    is displayed (Yes in S 21 ), the search unit  104  overwrites the first-stage arrangement order determination view V 20  ( FIG.  21   ) with the record set excluding the selected record (S 22 ). Then, the search unit  104  executes the same processing as steps S 6  to S 11  in  FIG.  15    on the basis of the overwritten first-stage arrangement order determination view V 20  ( FIG.  21   ). That is, the processing of S 6  onward is performed again. 
     That is, if there is a record instructed to be excluded in the first related knowledge record group in the search result, the second related knowledge record group is specified again in the second search on the basis of the first related knowledge record group excluding the record. 
     According to the third embodiment, when a record obviously unrelated to the search target intended by the user is extracted in the first stage, it is possible to avoid that a record strongly related to the unrelated record is extracted in the second stage and the third stage, and it becomes difficult to search for target knowledge. 
     Fourth Embodiment 
     Next, the fourth embodiment of the present invention will be described. 
     As illustrated in  FIG.  32   , the fourth embodiment is different from the first embodiment in that the production knowledge management system  101  is connected to a production state monitoring system  311  which is a system for managing a production line via a network, and data can be transmitted and received to and from each other. Here, the production state monitoring system  311  is a system having a function of collecting an error code issued from each manufacturing facility  312  provided in the production line. The error code is a code for specifying a type of a problem which may occur in each manufacturing facility  312 . 
     In addition, in the fourth embodiment, an error knowledge table T 32  for registering &lt;knowledge ID&gt; and &lt;error code&gt; in association with each other as illustrated in  FIG.  33    is added to the knowledge information storage unit  110  ( FIG.  2   ) of the database  107 . 
     In addition, an error occurrence process table T 33  for registering &lt;process ID&gt; and &lt;error code&gt; in association with each other as illustrated in  FIG.  34    is added to the process information storage unit  109  ( FIG.  2   ). 
     In the search processing executed by the search unit  104 , the processing of first embodiment is changed in the following points. First, the processing in step S 2  ( FIG.  15   ) is changed so that &lt;error code&gt; is received from the production state monitoring system  311  and &lt;process ID&gt; extracted by collation between the received &lt;error code&gt; and the error occurrence process table T 33  ( FIG.  34   ) is obtained as &lt;problem occurrence process ID&gt;. As a result, unlike the processing of the first embodiment, it is possible to specify the problem occurrence process without the input work of the user. 
     Furthermore, the processing in step S 4  ( FIG.  15   ) is changed so that the &lt;knowledge ID&gt; extracted by collation between the &lt;error code&gt; received as described above and the error knowledge table T 32  ( FIG.  33   ) is set as the &lt;first-stage knowledge ID&gt; of the first-stage candidate view V 19  ( FIG.  20   ). As a result, the &lt;first-stage knowledge ID&gt; can be narrowed down by the &lt;error code&gt;. 
     Other steps S 3  and S 5  to S 11  are the same as those in the first embodiment. 
     According to the fourth embodiment, when an error code is issued, an input from the user as in the first embodiment is not required. In addition, since the related knowledge record is immediately displayed and the related knowledge record is displayed in the second and third stages, it is possible to improve the efficiency of factor analysis and countermeasures of the user. 
     Note that the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments have been described in detail in order to simply describe the present invention, and are not necessarily limited to those having all the described configurations. In addition, a part of the configuration of a certain embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of a certain embodiment. In addition, it is also possible to add, delete, and replace other configurations for a part of the configuration of each embodiment. 
     In addition, a part or all of the above-described configurations, functions, processors, processing means, and the like may be realized by hardware, for example, by designing with an integrated circuit. 
     In addition, the control lines and the information lines indicate those necessary for the description, and do not necessarily indicate all the control lines and the information lines on the product. In practice, it may be considered that almost all the configurations are connected to each other. 
     REFERENCE SIGNS LIST 
       16  storage medium 
       20  production knowledge management program 
       101  production knowledge management system 
       104  search unit 
       107  database 
     T 3  classification master table 
     T 4  process table 
     T 5  process order table 
     T 6  process classification table 
     T 8  knowledge table 
     T 9  knowledge classification table 
     T 32  error knowledge table 
     T 33  error occurrence process table 
     S 4 , S 5  first search 
     S 6 , S 7  first second search 
     S 8 , S 9  second second search 
     V 20  first-stage arrangement order determination view (first related knowledge record group) 
     V 23  second-stage arrangement order determination view (second related knowledge record group) 
     V 26  third-stage arrangement order determination view (second related knowledge record group)