Service management system and service management method

Abstract

The present invention relates to a system for creating or modifying business data to be applied to a control system or an information system for executing prescribed business by a terminal operation. The system comprises: a storage unit that stores at least service structure information, program structure information, and data structure information, which are about the business data; and a calculation unit that performs calculation based on the stored data. The calculation unit includes: a change range management unit that manages a change range for the business data; an influence range management unit that manages an influence range at the time when the business data is changed; and a comprehensiveness management unit that verifies the comprehensiveness of the business data.

Description

Service management system and service management method

[0001] The present invention relates to a service management system and a service management method.

[0002] Conventionally, when developing various service systems or programs, methods of managing functions, processes, and personnel, such as project management, or management methods using version control systems, have been used.

[0003] For example, Patent Document 1 describes an information processing device that displays on a screen the contents of conversations and comments of multiple workers when creating a single document based on the work of multiple workers.

[0004] JP 2023-65508 A

[0005] By applying the technology described in Patent Document 1, when multiple people are working on a single task, it is possible to manage the progress and approval of the content being produced by displaying the content of conversations and comments, etc. However, when the scale of the system being developed becomes large, there is a problem that simply displaying comments such as the approval status is insufficient.

[0006] For example, when the business data to be designed is large and includes event information, structure information, and status information, such as in a control system program, if the development or modification of this data is divided into separate tasks, it is impossible for one person to check the comprehensiveness or reliability of the content that each person has worked on. Furthermore, it is difficult for other people to automatically acquire the know-how gained by each person during their work. Therefore, conventional technologies have not been able to be said to be efficient when multiple people develop service systems or programs.

[0007] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a service management system and a service management method that can automate and improve the reliability of management and production support of business data such as service systems and programs.

[0008] In order to solve the above problem, for example, the configuration described in the claims is adopted. The present application includes multiple means for solving the above problem, and one example thereof is a service management system that creates or modifies business data applied to a control system or information system that executes a predetermined business operation through operation from one or more terminals, the system comprising: a storage unit that stores at least service structure information, program structure information, and data structure information for the business data; and a calculation unit that performs calculations based on the data stored in the storage unit, the calculation unit having a change range management unit that manages the change range for the business data, an impact range management unit that manages the impact range when the business data is changed, and a comprehensiveness management unit that verifies the comprehensiveness of the business data.

[0009] The present invention can contribute to the automation of development content management and production support, as well as to the improvement of reliability, when creating or modifying business data in a division of labor system, for example. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments.

[0010] 1 is a block diagram showing an example of the configuration of a service management system according to a first embodiment of the present invention. FIG. 2 is a flowchart showing the flow of service management processing according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of input information and an intermediate management table for automatically generating know-how information according to the first embodiment of the present invention. FIG. 4 is a diagram showing an example of a table for managing change contents according to the first embodiment of the present invention. FIG. 5 is a diagram showing an example of a table for managing information viewed by a user according to the first embodiment of the present invention. FIG. 6 is a diagram showing an example of a statistically processed know-how information management table according to the first embodiment of the present invention. FIG. 7 is a diagram showing an example of a table for managing technical terms, parts of speech, and a thesaurus structure according to the first embodiment of the present invention. FIG. 8 is a diagram showing an example of a coverage management table for the impact range of service and program changes and the user's skill range according to the first embodiment of the present invention. FIG. 9 is a diagram showing the concept of physical structure know-how being dispersed into file data, and the configuration of the management entity according to the first embodiment of the present invention. FIG. 10 is a block diagram showing an example of a configuration having a synchronization in-memory DB device and a backend server group externally according to the second embodiment of the present invention. FIG. 11 is a diagram showing an example of a configuration for synchronously executing logically and physically separated processing functions and analysis functions according to the second embodiment of the present invention. 1 is a flowchart showing an example of analysis (example 1) according to a second embodiment of the present invention. FIG. 2 is a flowchart showing an example of analysis (example 2) according to a second embodiment of the present invention. FIG. 3 is a diagram showing an example of utilizing classification AI and generative AI according to a second embodiment of the present invention. FIG. 4 is a diagram showing an example of an auxiliary input dictionary according to a second embodiment of the present invention. FIG. 5 is a diagram showing an example of a table for managing know-how approval according to a second embodiment of the present invention. FIG. 6 is a flowchart showing an example of impact scope analysis according to a second embodiment of the present invention. FIG. 7 is a flowchart showing examples of change scope analysis and suitable person analysis according to a second embodiment of the present invention. FIG. 8 is a diagram showing an example of displaying know-how management data according to a second embodiment of the present invention. FIG. 9 is a diagram showing an example of displaying know-how rankings, details, and comprehensiveness according to a second embodiment of the present invention. FIG. 10 is a diagram showing an example of displaying suitable person rankings according to a second embodiment of the present invention.10 is a diagram showing an example of displaying the correspondence between the extent of influence and the actual file location according to the second embodiment of the present invention. FIG. 11 is a diagram showing an example of displaying a know-how selection screen from a statistical log according to the second embodiment of the present invention.

[0011] <First Embodiment> A service management system and a service management method according to a first embodiment of the present invention will be described below with reference to FIGS.

[0012] [System Configuration] A service management system 100 according to this embodiment creates or modifies business data (programs) that are applied to a control system or information system that executes a predetermined business. Figure 1 shows the configuration of the service management system 100. The service management system 100 is made up of a service management device 200 and a plurality of user terminals 800a, 800b connected to the service management device 200 via a network.

[0013] 1 shows an example in which two user terminals 800a and 800b are used in order to explain handover from user 900a to user 900b and transfer of know-how, as will be described later. The use of two user terminals 800a and 800b is just an example, and the user terminals may be any number of terminals greater than or equal to one.

[0014] For example, if the design, production, testing, etc. of business data are performed by separate divisions of labor, a user terminal is installed in each department performing each division of labor. Even within a department performing design, production, testing, etc., multiple user terminals may be installed, allowing multiple users to perform tasks. Note that business data created or modified by user terminals 800a and 800b is stored in a storage unit (such as storage 400, described below) of the service management device 200, and is not generally saved in the user terminals 800a and 800b. Also, although not shown in FIG. 1 , a terminal operated by an administrator or the like may be connected to the service management device 200.

[0015] The user terminals 800a and 800b are equipped with input units 840a and 840b such as keyboards and display units 850a and 850b. The user terminals 800a and 800b store user identification information 810a and 810b and auxiliary input dictionaries 820a and 820b in a main memory unit (not shown). The user identification information 810a and 810b are information for identifying the users 900a and 900b who operate the respective terminals.

[0016] The auxiliary input dictionaries 820a and 820b are dictionary data that assist users 900a and 900b in input conversion when they input business data from input units 840a and 840b. The user terminals 800a and 800b also include change and reference acquisition units 830a and 830b. The processing performed by the change and reference acquisition units 830a and 830b will be described later (FIG. 3).

[0017] In addition to providing information to users 900a and 900b, user terminals 800a and 800b also transmit conditions necessary for information searches by users 900a and 900b and work order information to server 300 of service management device 200. For example, correction of input errors by users 900a and 900b and correction of variations in expression are performed by auxiliary input dictionaries 820a and 820b of terminals 800a and 800b, respectively. Here, user identification information 810a and 810b, which is information on user identification and job role, is stored in the main memory device within each terminal.

[0018] Furthermore, the work logs of users 900a and 900b via user terminals 800a and 800b are used as source data for management know-how. Therefore, change / reference acquisition units 830a and 830b acquire the work logs of user terminals 800a and 800b. The work logs acquired by change / reference acquisition units 830a and 830b are transmitted to service management device 200 via communication packets 700a and 700b.

[0019] Next, the configuration and processing of the service management device 200 will be described. The service management device 200 includes a server 300 and a storage 400 as a memory unit for storing data. In addition to providing information to user terminals 800a and 800b, the server 300 receives information from users 900a and 900b regarding conditions and work order information required for information searches, and corrects user input errors and inconsistencies in expression.

[0020] The server 300 also receives user identification information 810a, 810b such as a user ID and a job role as communication packets 700a, 700b, and manages the logs thereof. As a result, the server 300 converts the received user identification information 810a, 810b into skill management for individual users and skill and know-how information required for job roles.

[0021] The server 300 includes a CPU (Central Processing Unit) 301, a memory 302, a NIC (Network Interface Card) 303, a disk controller 304, and an internal storage device 305. These components are connected via a bus line 306 so that data can be transferred between them.

[0022] The CPU 301 executes programs implemented in the memory 302 or the like, thereby configuring a calculation unit that performs various processes on the memory 302. For example, as shown in FIG. 1 , a change range management unit 310, an impact range management unit 320, and a coverage management unit 330 are configured on the memory 302.

[0023] The change scope management unit 310 performs a change scope management process to determine and manage the scope of changes when business data is changed through user terminal operations. The impact scope management unit 320 performs an impact scope management process to check the interrelationships of data and manage the scope of changes when business data is changed. The coverage management unit 330 performs a coverage verification process to determine whether any necessary data is missing when business data is changed. An operating system (OS) 307 is also implemented in the memory 302. Computing units such as the change scope management unit 310, impact scope management unit 320, and coverage management unit 330 are configured by running the operation system 307 and utilizing the functions of the operation system 307.

[0024] Furthermore, role / user structure management information 340, process information 360, editing purpose / editing history information 370, and cyber-physical data models are stored in the memory 302. The role / user structure management information 340 includes user identification information 350. The process information 360 includes information on the scope of responsibility of a group or individual.

[0025] The storage 400 stores information on tabular data structures defined by table schemas, and information having directory structures and files. When the data stored in the storage 400 is database information, "storage / table schema ID / table column ID / table row identification key value" is created as identification information (structured ID: structured identification information).

[0026] Furthermore, when the data stored in the storage 400 is a file, "storage / logical partition ID / directory ID / file ID / page ID / object identification information" is created as identification information (structured ID). This allows information scattered throughout a complex device to be uniquely identified by an encoded and structured identification ID.

[0027] In the structure described above, a cyber-physical data model is stored in storage 400. That is, the cyber-physical data model stores service information of a control target in which physics and logic are intricately intertwined, such as physical structure information of a control system or the like, event information, state information, their prerequisites, function execution conditions, and post-processing. Specifically, storage 400 stores, as the cyber-physical data model, information such as service structure information 410, program structure information 420, data structure information 430, task / target / reason information 440, and awareness / target information 450.

[0028] Here, the service structure information 410, program structure information 420, and data structure information 430 form a tree structure as a taxonomic system model. This tree structure information exhibits characteristics as a classification system structure. The task / target / reason information 440 and the insight / target information 450 exhibit characteristics as folksonomic additional / derivative information, such as hashtags. This information governs the functions and command processing for the control system. Furthermore, since this information also includes instruction information for human work on site, this cyber-physical data model can be used to control control systems and information systems.

[0029] [Processing Flow Performed in the Service Management System] Figure 2 is a flowchart showing the processing flow performed by the user terminals 800a, 800b and the server 300. First, the user 900a inputs business data, such as creating or modifying it, into the user terminal 800a (step S11). When inputting data into the user terminal 800a, an auxiliary input dictionary 820a stored in the user terminal 800a is used to correct any input errors or to correct any variations in expression. In addition, the user terminal 800a generates user identification information 810a that identifies the user 900a, for example, through a login operation when starting an application program.

[0030] When the user 900a creates or modifies business data on the user terminal 800a, the user 900a may display similar business data (existing data) that has already been created on the screen of the display unit 850a of the user terminal 800a and perform a reference process. The existing data to be referenced is read from the storage 400, for example.

[0031] When the input is made in step S11, the change / reference acquisition unit 830a of the user terminal 800a acquires information about the changes made to the business data being worked on and the references made during the work (step S12).Then, the server 300 acquires the work log and user identification information about the business data created or modified on the user terminal 800a (step S13).

[0032] Then, the server 300 stores a cyber-physical data model for the created or modified business data in the storage 400 (step S14). As already described, the cyber-physical data model includes information such as service structure information 410, program structure information 420, data structure information 430, task / target / reason information 440, and awareness / target information 450.

[0033] After storing this information, the server 300 converts the information into individual user skill management, skills required for job roles, and know-how information required for job roles based on the work log and user identification information (step S15). The converted know-how information is saved in the memory 302 as process information and information on the scope of responsibility of a group or individual.

[0034] Thereafter, when, for example, a user 900b other than the user 900a who input the business data in step S11 inputs other business data from a user terminal 800b, the know-how information obtained in step S15 is transmitted to the user terminal 800b (step S16). Note that the know-how information provided here is know-how information required for the job role of the user 900b. Details of the know-how information here will be described later, but one example includes information about an auxiliary input dictionary and information about the data to be referenced.

[0035] [Example of Processing by Change Scope Management Unit] Figure 3 shows a specific example of task, target, and reason information 440 automatically created by change scope management unit 310. When a user terminal performs an operation on information with a tabular data structure defined by a table schema or information having a directory structure and files stored in storage 400, change scope management unit 310 creates task, target, and reason information 440. In Figure 3, the change scope import information is shown as directory 400F-1 for data from Nth work (N is an arbitrary number) and as directory 401F-1 for data from N+1th work.

[0036] The directory contains information that can be identified as Nth operation data by data ID 400F-1-a, specification ID 400F-1-b, and program ID 400F-1-c. Similarly, the directory contains information that can be identified as N+1th operation data by data ID 401F-1-a, specification ID 401F-1-b, and program ID 401F-1-c.

[0037] The change range management unit 310 takes in this new and old information, that is, the information before and after the work, and creates work, target, and reason information 440. As a result of creating work, target, and reason information 440, difference information 440A-1 including a structured ID and update time information 440A-2, as shown in information 440A in FIG. 3, are automatically generated.

[0038] For example, in the example of Fig. 3, the data is output in the following flexible data structure. That is, as a result of creating the task / target / reason information 440, a set of information is output: [{"Structured ID": "Common Root ID / Specification ID / Item 1", "Technical Part of Speech": "Abbreviation / Document Type / Target", "Change Type": "Added", "Reason": "Not entered",}] and the update time. In Fig. 3, the reason column is left blank, but the user 900a who entered the information can also separately add the reason as know-how to the reason column using the user terminal 800a.

[0039] In this way, every time an update is performed, the update difference position and content of the task / target / reason information 440 and the update time information are automatically generated in sequence.

[0040] [Example of Processing by the Change / Reference Acquisition Unit] Figure 4 shows an example of the change / reference acquisition processing by the change / reference acquisition units 830a, 830b. The change range management unit 310 of the server 300 described in Figure 3 cannot acquire know-how, which is the content viewed by the workers 900a, 900b on the user terminals 800a, 800b, in managing differences in stored data. Therefore, the change / reference acquisition units 830a, 830b in the user terminals 800a, 800b acquire logs such as internet browsing history, directory structure, file browsing history, and which active window items were used for operations using input units such as the mouse and keyboard.

[0041] Fig. 4 shows task, target, and reason information 440B generated by change and reference acquisition units 830a and 830b. Fig. 4 shows the generated task, target, and reason information 440B, including viewing, operation position and content 440B-1, time 440B-2, and duration (seconds) 440B-3 of the active window being viewed.

[0042] In this case, the structured ID, which is structured identification information, is obtained from the file server ID and the directory ID including the directory hierarchical structure, for example, as follows: For example, in the case of the file data ID, "file server ID / directory ID / file data ID" is created as the structured ID.

[0043] That is, the structured ID is created based on the inclusion relationships indicated in various data, including hierarchical directories, database schema tables, column and row identifiers in the database schema, and inclusion relationships in the chapter and section structure of a manual document. The inclusion relationships indicated in various data are also called path structures. The inclusion relationships in the chapter and section structure of a document are identified from the hierarchy established in the document, such as the document title, chapters, sections, headings, and words. The structured ID is structured to represent the inclusion relationships indicated in the various data themselves and the path structures used to manage them in a uniform format, and is, in other words, an indication of the structured inclusion relationships.

[0044] [Example of Change Scope Management Unit Acquiring User Know-How] Figure 5 shows an example of processing in which logs acquired by the change / reference acquisition units 830a and 830b of the user terminals 800a and 800b described in Figure 4 are used as user know-how. The change scope management unit 310 takes in information 440A (Figure 3) generated from the update history and information 440B (Figure 4) generated by browsing and operation, and integrates the relationships between the browsed information in chronological order to create update information.

[0045] As a result of the integration, the change range management unit 310 automatically generates operation position and content information 440C, as shown in Figure 5. The operation position and content information 440C has an operation position and content column 440C-1, an action content column 440C-2, a time 440C-3, and a period (seconds) 440C-4. The operation position and content column 440C-1 indicates the task name, target location, etc., and the action content column 440C-2 indicates the start of the task, saving, viewing, completion of the task, etc.

[0046] The operation position and content information 440C shown in FIG. 5, which lists the task name and its processing content, is stored as know-how in the storage 400. The task name is registered in advance as a list of task names, and the user can select a name when starting and finishing the task. On the other hand, the user 900 can also input the task name at any time as part of the user's task. This allows, for example, the task know-how of user 900a who is familiar with the task content to be automatically registered, and allows a newly appointed user 900b who is not familiar with the task content to reuse the task know-how including time-series information. In addition, the estimated time required for the task of user 900b who is not familiar with the task content can also be confirmed.

[0047] [Example of Providing Know-how by Converting to Relative Time] Figure 6 shows an example in which the change range management unit 310 provides know-how to the user by converting it into relative time. As explained in Figure 5, the operation position and content information 440C is information indicating a past time, and it is not appropriate to provide the user with know-how based on that past time as is. For this reason, the change range management unit 310 converts the time 440C-3 in the operation position and content information 440C into relative time information 441, as shown in the lower left of Figure 6.

[0048] As a result, the From-To relationship of the operation content is obtained from the relative time, and relationship link information 442 is generated to indicate the order in which the work should be done. The relationship link information 442 can have source information called From and To information as the destination of directional information (direction). Since the relationship link information 442 can have To information in an array, directional information such as work procedures is registered as the relationship link information 442.

[0049] By having multiple pieces of this relationship link information 442, the change range management unit 310 can generate one-to-one, one-to-many, many-to-one, and many-to-many relationships. This generated information can be confirmed as a screen display on the user terminal. An example of the screen display will be described later. This directional information such as work procedures is stored in the storage 400, but when processed and utilized, it is expanded and processed as editing purpose / editing history information 370.

[0050] [Example of Role / User Structure Management Information] Figure 7 shows an example of role / user structure management information stored in storage 400. The data shown in Figure 7 is an example of data generated by impact scope management unit 320 to identify the impact scope of changes based on task / target / reason information 440 generated by change scope management unit 310. Here, the impact scope can be the impact scope of the work content for the work name, as shown in information 440C (Figure 5), or the impact scope based on the content log of subsequent work. The service structure information 410, program structure information 420, and data structure information 430 touched by these tasks are managed as impact scopes by the impact scope management unit 320.

[0051] The impact range management unit 320 manages, for example, term management information 400D, such as service identification 400D-1, term and its location 400D-2, term usage amount 400D-3, and impact score 400D-4. This allows the impact range management unit 320 to manage even the terms used by users. The service identification 400D-1 indicates the common root ID, service term, and abstract concept for the term. The term usage amount 400D-3 indicates the specialized part of speech ID and usage history.

[0052] The impact range management unit 320 also manages program identification 400E-1, terms 400E-2, usage amounts of those terms 400E-3, impact scores 400E-4, and the like as program management information 400E. The program identification 400E-1 manages terms such as functions, tasks, and variables. In this way, the impact range management unit 320 manages the connections and impact ranges of various data stored in the storage 400.

[0053] Furthermore, the impact range management unit 320 manages the person in charge identification 400F-1, the target 400F-2, the specialized part of speech ID and usage history 400F-3, the impact score 400F-4, etc. as person in charge management information 400F. This allows the impact range management unit 320 to manage the history of people and terminology, and people and work information. By acquiring role / user structure management information at the time of login, the impact range management unit 320 can add and save the user ID and role, which is the service identification, to the log.

[0054] In this way, by saving log information, the impact scope management unit 320 can manage the user identification of the person in charge, the role identification and experience content, and the correspondence between the task name and the operation part. Furthermore, in user identification, the impact scope management unit 320 can also include the identification of groups, such as team structure, and can manage teams that can take on roles as services. In other words, the change scope management unit 310 can digitize the skills of the person in charge by generating data such as information 400F. This information 400F, such as the person in charge, is stored in the storage 400, and when know-how is used, it is read and reused as process information, group or individual responsibility range information 360 ( FIG. 1 ).

[0055] [Example of Processing in the Coverage Management Unit] Figure 8 shows an example of processing in the coverage management unit 330. The coverage management unit 330 reads the impact scope information 400H calculated by the impact scope management unit 320 from the work area of ​​the editing purpose / editing history information 370. The impact scope information 400H includes a program identification 400H-1 indicating the common root ID / impact scope analysis result, a term 400H-2 indicating the impact scope or experience scope, a specialized part-of-speech ID and usage history 400H-3 indicating details of the impact scope, and a proficiency score 400H-4. The specialized part-of-speech ID and usage history 400H-3 indicating details of the impact scope represents an array of identifiers for the range of the development area in charge.

[0056] The coverage management unit 330 also extracts information 400G about users (persons in charge) who have been involved in the past scope of influence from the process information, group, or individual responsibility range information 360 area. The information 400G about users who have been involved in the scope of influence includes a common root ID and a program identification 400G-1 indicating the person in charge, terms 400G-2 indicating the scope of influence or experience, a specialized part-of-speech ID and usage history 400G-3 indicating details of past experience, and a proficiency score 400G-4. The specialized part-of-speech ID and usage history 400G-3 indicating details of past experience are an array of identifiers that manage the scope of the development area they were responsible for.

[0057] The coverage management unit 330 can manage the coverage of the person in charge for the impact range by comparing the information 400H and the information 400G. This allows the coverage management unit 330 to select the most suitable person based on the comparison result between the experience range and the impact range analysis result. Note that the coverage management unit 330 can also manage the coverage as the progress status of the process by comparing the operation logs for the impact range as a type of coverage.

[0058] [Example of User-Added Know-How] Figure 9 shows an example of task, target, and reason information 440, which is one example of know-how stored in storage 400. As already explained, know-how is automatically generated as the information described in Figures 3 and 8, but the user can also add know-how by specifying a part using a structured ID. For example, the comprehensiveness management unit 330 generates data with the content shown in information 400J in Figure 9 and stores the data as insight and target information 450 in the cyber-physical data model of the target. This allows the comprehensiveness management unit 330 to digitize personal know-how and link it to data and structures.

[0059] For example, the information 400J includes a program identification 400J-1, a term 400J-2, a specialized part of speech ID and usage history 400J-3, and a proficiency score 400J-4. The user 900a inputs the specialized part of speech ID and usage history 400J-3 and what he or she noticed. This allows the user 900a to digitize the personal know-how of each person in charge.

[0060] Next, as shown in the lower part of Figure 9, an example of associating know-how with services and system target positions that involve structures such as control systems will be described. For example, when considering business data for a manufacturing line, conventionally, concepts such as manufacturing lane structure have not been linked to data IDs, specification IDs, or program IDs. Here, in this embodiment, information 400K that governs the characteristics of the physical structure of these manufacturing lines is managed as directory ID information 400K-1, which serves as a structured ID. The directory ID information 400K-1 includes data ID information 400K-1-a, specification ID information 400K-1-b, and program ID information 400K-1-c.

[0061] As a result, the service management device 200 of this embodiment can digitize physical control objects that have complex inclusion relationships, hierarchical structures, and interrelationships, and further associate information 400J shown in Figure 9 with information 400K using From-To relationship link information, thereby managing digital information for associating know-how with physical structures.

[0062] <Second Embodiment> Next, a service management system and a service management method according to a second embodiment of the present invention will be described with reference to Figures 10 to 24. In Figures 10 to 24, parts corresponding to those in Figures 1 to 9 described in the first embodiment are given the same reference numerals, and duplicated explanations will be omitted.

[0063] [System Configuration] The service management device 200 of the service management system 100 according to this embodiment includes, in addition to the server 300, a synchronization in-memory DB device 500 and a back-end analysis server group 600. Other configurations of the service management system 100 are the same as those of the service management system 100 shown in FIG.

[0064] The backend analysis server group 600 is composed of multiple servers that are physically or logically separated. The backend analysis server group 600 simultaneously executes multiple backend analysis processes on the multiple servers. Here, the synchronization in-memory DB device 500 synchronously executes the analysis functions of the backend analysis server group 600 and executes processing to link with the server 300.

[0065] That is, the synchronization in-memory DB device 500 executes processing so that the server 300 can synchronously process the logically and physically separated analysis functions of the analysis units in the backend analysis server group 600. That is, the synchronization in-memory DB device 500 performs processing such as synchronizing processes, providing information of advanced data structures as arguments, and receiving them as return values.

[0066] Furthermore, in order to synchronously execute analysis functions, the backend analysis server group 600 may be provided with multiple storages 400. The storages 400 have a tabular data structure defined by a table schema, and store information having a directory structure and files.

[0067] 10 is an example in which the service management device 200 is configured to include a synchronization in-memory DB device 500 and a backend analysis server group 600. The synchronization in-memory DB device 500 and the backend analysis server group 600 may be provided outside the service management device 200. That is, as shown in FIG. 11, the server 300 of the service management device 200 may be configured to be able to transfer data with the external backend analysis server group 600 via the synchronization in-memory DB device 500.

[0068] In the example of Figure 11, the external backend analysis server group 600 includes a backend analysis unit 601 using classification AI (Artificial Intelligence), a backend analysis unit 602 using generative AI, and a backend analysis unit 603 using a quantum computer.

[0069] [Example of a Configuration of a Backend Analysis Server Group] Here, the analysis functions of each of the backend analysis units 601, 602, and 603 will be described. First, the backend analysis unit 603 using a quantum computer will be described. Unlike analysis using a general computer, a quantum computer does not have the concept of a search key and its value in a table. However, a quantum computer can very quickly determine whether the desired information is included in the management information. Therefore, the backend analysis unit 603 using a quantum computer can be said to be a preferable analysis method to execute first, as it does not waste search time.

[0070] Next, we will explain the analysis function of the generative AI back-end analysis unit 602. Generative AI technology is generally not suitable for high-precision analysis because even a slight change in the query wording can completely change the results obtained.

[0071] However, analysis by generative AI may provide a wider range of answers than classification AI. For example, when a query is made for an analysis of the meaning of A-F devices, generative AI is likely to analyze all of A, B, C, D, E, and F devices. On the other hand, when a query is made for an analysis of the meaning of A-F devices, classification AI may analyze only A and F devices and exclude B, C, and E devices from the analysis. For this reason, it is preferable to perform an analysis by the backend analysis unit 602 using generative AI before performing an analysis by classification AI to create a search query with a more accurate meaning.

[0072] Next, the back-end analysis unit 601 using classification AI (analysis AI) will be described. Since analysis AI does not produce fluctuations in analysis results, it is suitable for processing such as finding reference materials and programs. Therefore, analysis using analysis AI can more accurately find know-how, which is the analysis processing in this embodiment, and enables analysis that is suitable for accurately sharing the content among multiple people divided into different tasks.

[0073] As described above, when the system is equipped with analytical AI, generative AI, and quantum computer-based back-end analysis units 601 to 603, it is preferable that the synchronization process in the synchronization in-memory DB device 500 first performs analysis in the quantum computer-based back-end analysis unit 603. Next, it is preferable that the generative AI-based back-end analysis unit 602 performs analysis, and finally the analytical AI-based back-end analysis unit 601 performs analysis. Note that the system is equipped with analytical AI, generative AI, and a quantum computer-based analysis unit, and multiple analysis units may be provided in other combinations.

[0074] [Example of Processing in the Synchronization In-Memory DB Device] Figure 12 shows an example of synchronization processing in the synchronization in-memory DB device 500. In the first embodiment, the change range management unit 310, impact range management unit 320, and coverage management unit 330 of the server 300 performed logical processing, such as exact match and match determination using regular expressions. However, the change range management unit 310, impact range management unit 320, and coverage management unit 330 often require complex comparisons such as matching with words, search, similarity, and term distance (code distance). In other words, the change range management unit 310, impact range management unit 320, and coverage management unit 330 must each perform analysis using distance and similarity between terms. More specifically, these management units 310, 320, and 330 break down the identification information into character units, infer from rules that indicate the meaning of abbreviations, automatically generate search queries, compare terms in expressions with structured identification information, and calculate the difference between the compared information as a code distance to obtain the degree of relevance. For this reason, in this embodiment, a backend analysis server group 600 is provided to enable complex comparisons, but the server 300 needs to cooperate with each process and API (Application Programming Interface) of the backend analysis server group 600.

[0075] For this reason, the synchronization in-memory DB device 500 generates a synchronization namespace with a PubSub structure as shown in Fig. 12. That is, as shown in Fig. 12, the synchronization in-memory DB device 500 prepares an individual synchronization namespace PubSub structure for each of the analysis units 601, 602, and 603.

[0076] Furthermore, the synchronization in-memory DB device 500 enables argument activation processing and synchronous control of analysis processing by waiting for readout in the space of each PubSub structure and putting the analysis process on hold. For example, as described above, the synchronization in-memory DB device 500 can sequentially execute analysis using a quantum computer, analysis using generative AI, and analysis using analytical AI. Furthermore, by adjusting the queue length, the synchronization in-memory DB device 500 can handle everything from complete synchronization to asynchronous processing using a request ID.

[0077] 13 shows an example of the flow of processing when analysis is performed by one of the back-end analysis units 601 to 603, for example, the back-end analysis unit 603 using a quantum computer. First, the server 300 writes analysis data and an analysis result instruction command to the synchronization in-memory DB device 500 (step S300). Then, the synchronization in-memory DB device 500 waits for the analysis result from the back-end analysis unit 603 using a quantum computer, which is an external analysis device (step S301). The synchronization in-memory DB device 500 may asynchronously wait for the analysis result from this external analysis device.

[0078] Furthermore, the synchronization in-memory DB device 500 receives the analysis result from the back-end analysis unit 603 using the quantum computer, and sends it to the server 300 (step S302). In this way, the analysis process is executed via the synchronization in-memory DB device 500.

[0079] Fig. 14 shows the processing flow when analysis is performed in sequence by multiple analysis units 601 to 603. In Fig. 14, steps S300 to S302 are the same as the example in Fig. 13. Here, the external analysis device in step S301 is the back-end analysis unit 603 using a quantum computer.

[0080] Then, in step S302, the synchronization in-memory DB device 500 receives the result from the quantum computer-based backend analysis unit 603, and then the synchronization in-memory DB device 500 checks whether a target for Grover's algorithm search exists (step S303). Here, if a target for Grover's algorithm search exists, a flag is set.

[0081] If the flag is set in step S303, the synchronization in-memory DB device 500 performs analysis by the back-end analysis unit 602 using the generative AI and analysis by the back-end analysis unit 601 using the analytical AI, and further analyzes the details of the analysis results (step S304). As a result, analysis is performed using multiple analysis units.

[0082] [Example of utilizing classification AI and generative AI] Figure 15 is a diagram illustrating the target detection and interlinking process steps performed during the analysis process. As shown in Figure 15, well-known information is collected as the directory structure, table information, and data definitions of existing data, taking advantage of the fact that there are many abbreviations but little change. Furthermore, information with little fluctuation that is automatically generated by the device is collected as log data for the database (DB) of existing data and existing data (files). An annotated dictionary is then generated based on this information.

[0083] Natural language data such as service specifications are collected, including variations in expressions, abbreviations, initialisms, and compound technical terms. This generates specification management information (Product B in FIG. 15). Furthermore, table contents of the analysis results are generated from the annotated dictionary and the specification management information (Product A in FIG. 15).

[0084] [Example of Dictionary Management] Figure 16 shows an example of dictionary management including annotations. The automatically collected statistics are, for example, data in the range of 0 to 90, and when approved by an expert or a person in charge, the approval score becomes 100. This allows a distinction to be made between information predicted by analytical AI, generative AI, or a quantum computer and data whose terms and relationships have been approved by a person in charge.

[0085] 16, the structured technical terminology dictionary table 3-11A has a column 3-11A-1 of the structured ID of a term that can be uniquely identified in the facility operation automation system, and a column 3-11A-2 of the notation data expressing that term. Furthermore, the structured technical terminology dictionary table 3-11A has a column 3-11A-3 that manages the structured technical part of speech information and usage history of that term, and an approval score column 3-11A-4 that identifies how approved that term is.

[0086] In row 3-11A-a, which manages Term 1, column 3-11A-2 shows that "Term 1" was used as the expression method. Column 3-11A-1 also shows that the structured ID is managed as "common root ID / Term 1." Column 3-11A-3 also shows that "Term 1" has a technical part of speech of "noun / program / filter device." Statistical information on the frequency of use of these terms is managed. Also, column 3-11A-4, which shows the approval score, has an authentication score of "100," indicating that the information has been manually corrected and is highly reliable structured technical terminology information.

[0087] In row 3-11A-b, which manages Term 2, it can be seen from column 3-11A-2 that "Term 2" was used as the expression method. However, this means that the structured ID is "Common Root ID / Term 1" from column 3-11A-1, which means that it is managed as a different notation that has the same meaning as "Term 1."

[0088] Furthermore, column 3-11A-3 shows that "Term 2" has the technical part of speech "Abbreviation / Service / Process 1." Column 3-11A-3 also shows statistical information on how often these terms are used. This usage shows that it is less common than "Term 1," which has the same meaning but is written differently.

[0089] The approval score column 3-11A-4 is set to "49." Therefore, although Term 2 in the second row, which manages Term 2, was automatically collected by the terminology analysis management function 3-8 or the data model / API analysis management process, it has not yet been manually corrected or corrected based on input assistance results, and it is determined that the reliability of this information is not yet high.

[0090] In row 3-11A-c, which manages Term 3, we can see from column 3-11A-2 that "Term 3" was used as the expression method. From column 3-11A-1, we can see that this means that the structured ID is "Common Root ID / Term 2." Furthermore, from column 3-11A-3, we can see that "Term 2" is a specialized part of speech for each production user in the division of labor system: "Noun / Data / Data 1." Furthermore, as can be seen from column 3-11A-3, statistical information on the amount of usage of these terms is managed.

[0091] The approval score column 3-11A-4 is set to "80," which indicates that although the terminology analysis and management function 3-8 and the data model / API analysis and management function have automatically collected the terminology, and corrections have been made based on input assistance results, no human checking or correction has been made.

[0092] 17 shows a management table 3-11B of approval score calculation rules in the structured technical dictionary. The contents of this data can also be confirmed from the display on the display units 850a and 850b of the user terminals 800a and 800b. The management table 3-11B of approval score calculation rules has information on an approval score calculation method 3-11B-2 for an action ID 3-11B-1 that identifies a detected action. The management table 3-11B also has a range 3-11B-3 of values ​​that can be given as the approval score.

[0093] Action 3-11B-a for a row of structured terminology 3-11B that has been manually corrected indicates that the approval score will be "100." Row 3-11B-b, where a dictionary is generated automatically and corrections have been made based on the results of input assistance, is assigned an approval score between "50-99." Row 3-11B-c, where a dictionary is generated automatically and no input assistance or manual corrections have occurred, is managed so that an approval score between "0-49" will be assigned.

[0094] This allows the reliability of terms managed in Table 3-11A of the structured technical terminology dictionary to be evaluated using approval scores. In this way, when a task that has been corrected or approved from a user terminal is recognized, the approval score of the term increases. When the approval score of a term increases, it is more likely to be adopted in role management information than automatically generated but unapproved terms. In other words, an increase in the approval score controls the priority of task selection. As a result, approved tasks can be selected and executed with priority.

[0095] [Impact Scope Analysis Processing] Figure 18 shows an example of impact scope analysis processing performed by the server 300. First, the impact scope management unit 320 imports interrelationship information (step S1001). The impact scope management unit 320 sequentially analyzes the definitions for the imported interrelationship information and adds the analysis results to the score. Then, the impact scope management unit 320 determines whether all definitions have been processed (step S1002). If all definitions have been processed in step S1002 (Yes in step S1002), the impact scope analysis processing by the impact scope management unit 320 ends. If there are definitions remaining to be processed in step S1002 (No in step S1002), the impact scope management unit 320 determines whether the score of the analysis result is equal to or greater than a specified value (step S1003). In step S1003, if the score of the analysis result is not equal to or greater than the specified value (No in step S1003), the process returns to the analysis of the remaining definitions for the interrelation information acquired in step S1001.

[0096] Furthermore, in step S1003, if the score of the analysis result is equal to or greater than the specified value (Yes in step S1003), the influence extent management unit 320 outputs the score of the analysis result (step S1004) and ends the analysis process.

[0097] [Change Scope Analysis Processing] Figure 19 shows an example of the change scope analysis processing performed by the change scope management unit 310. First, the change scope management unit 310 imports interrelationship information and the person in charge's experience table (step S1101). The change scope management unit 310 sequentially analyzes the definitions for the imported interrelationship information and sets flags based on the analysis results. The change scope management unit 310 then determines whether all definitions have been processed (step S1102). If all definitions have been processed in step S1102 (Yes in step S1102), the change scope analysis processing by the change scope management unit 310 ends.

[0098] If there are any remaining definitions to be processed in step S1102 (No in step S1102), the change scope management unit 310 determines whether the matching ratio of the personnel based on the analysis results is equal to or greater than a specified value (step S1103). If the matching ratio is not equal to or greater than a specified value in step S1103 (No in step S1103), the process returns to analyzing the remaining definitions for the interrelation information acquired in step S1001.

[0099] Furthermore, in step S1103, if the matching ratio is equal to or greater than a specified value (Yes in step S1103), the impact extent management unit 320 outputs the selection result of the suitable person based on the matching ratio (step S1104), and ends the analysis process. Note that although a flowchart of the coverage analysis process performed by the coverage management unit 330 is not shown, it is possible to analyze the coverage by a process similar to the impact extent analysis process performed by the impact extent management unit 320 shown in FIG. 18, for example.

[0100] [Display Examples on User Terminals] Next, display examples on the display units 850a and 850b of the user terminals 800a and 800b are shown in Figs. 20 to 24. Fig. 20 shows an example of a screen that presents role management information to the user and allows correction. The top row of Fig. 20 shows an example of a scene representation 1-1a, the middle row shows a Sankey representation 1-1b, and the bottom row shows a Gantt chart representation 1-1c.

[0101] For example, in scene representation 1-1a, the analysis progress screen shows the mission target ID "Common Root ID / Mission 1 / Task 1" and indicates whether target IDs 1, 2, 3, 4, or 5 are being analyzed at relative times 1, 2, 3, 4, or 5. Target IDs 1, 2, and 3 indicate analysis by a quantum computer, analysis by analytical AI, and analysis by generative AI, respectively.

[0102] The Sankey representation 1-1b shows the scope of influence, displaying the mission target ID "common root ID / mission 1 / task 1" and indicating which ID's program, physical configuration, etc., the data of affected target ID 1 affects. The Gantt chart representation 1-1c shows the mission target ID "common root ID / mission 1 / task 1" and indicates the target swimlane as the target swimlane, displaying the Gantt chart in relative time.

[0103] Here, the user can specify and correct the part to be corrected by selecting and clicking the part to be corrected on the user terminal 800a. In this correction, the user can correct the expression, the management table, or the relationship link. For example, the display unit 850a can display a delete setting button (not shown) on the screen and delete the definition by operating the delete setting button. Alternatively, the display unit 850a can display a confirm setting button (not shown) on the screen and correct the definition by operating the confirm setting button. The server 300 can update the approval score and improve the accuracy of the management information by these explicit corrections and confirm settings by the user.

[0104] FIG. 21 shows a display screen for the ranking of points of interest 1-1d. As shown in the upper section, the ranking of points of interest 1-1d displays the common root ID / mission number 1-1d-1 of the related parts and their respective folksonomy point of interest scores 1-1d-2. When the user selects and clicks with the mouse pointer 1-1d-3, a detailed mission preview is displayed in the middle section of FIG. 21. The detailed mission preview column displays the mission target ID 1-1d-4 and a scene representation 1-1d-5 for each relative time.

[0105] 21, the coverage and degree of achievement of the rework range are shown as the assumed state 1-1d-8 at a specific relative time based on the folksonomy management data and the range selected by the mouse pointer 1-1d-7. The degree of achievement for each relative time is shown in the graph of the rework range achievement. The rework range 1-1d-6 is also displayed in the graph.

[0106] Fig. 22 shows a display screen of a ranking of suitable candidates 1-1e. In the example of Fig. 22, a list 1-1e-2 of the experience points of three suitable candidates is displayed, and the user selects a suitable candidate using a mouse pointer 1-1e-3 based on their experience points.

[0107] Figure 23 shows a display screen showing the correspondence between the scope of impact and the target entity file. In the example of Figure 23, details of directory 400F are displayed as the file structure explained in Figure 3 etc. In other words, information that can be identified by data ID 400F-1-a, specification ID 400F-1-b, and program ID 400F-1-c that exist in directory 401F is displayed.

[0108] Figure 24 shows a screen 1-1f for selecting know-how from statistical logs. The example in Figure 24 shows a display example of a screen for selecting information to be deleted from user information related to know-how for selecting the appropriate person, for example. That is, the mouse pointer 1-1f-1 can be used to select the information to be deleted by clicking the delete button in Figure 24.

[0109] As mentioned above, by managing a dictionary of information used in business operations and role information including the order in which it is processed in chronological order, unlike conventional AI which only displays the output for the input, it is possible to handle information in the dimension of relative time, such as fast-forwarding or rewinding, based on the concept of a time axis for display and control. This makes it possible to realize role management including time information for facility operation.

[0110] Furthermore, by using structured IDs to manage the information required by AI and the labeling of results, it is possible to manage which tasks an analysis program contributes to. Furthermore, by breaking down structured IDs into delimiters or even characters, and then thinning and combining them, it becomes possible to recognize and correct inconsistent abbreviations and other words created by humans, as well as automate tasks. Furthermore, by providing an input assistance dictionary that brings human input closer to the information managed using structured IDs and allows unknown information containing disturbances to be corrected to known information as much as possible, it is possible to reduce the computer costs for analysis and automate tasks that previously required human intervention to recognize and match data.

[0111] <Modifications> Note that the embodiments described above have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the configurations described. Furthermore, some or all of the configurations and processes described in one embodiment may be combined with other embodiments.

[0112] For example, when generating an input assistance dictionary, the dictionary may be generated even if there is no exact match. For example, the number of concatenated identifiers of delimited character strings representing the inclusion relationships for generating the dictionary may be adjusted and compared, and the meaning may be interpreted even if the structured identification information for identifying the terms and meanings in the expressions does not exactly match, and the dictionary may be generated.

[0113] Furthermore, in each block diagram, only control lines and information lines that are considered necessary for explanation are shown, and not all control lines and information lines are necessarily shown in the actual product. In reality, it can be assumed that almost all components are interconnected. Furthermore, the process flow shown in each flowchart is only an example, and as long as the process results are the same, the order of some processes may be changed or multiple processes may be executed simultaneously.

[0114] 1 and other figures, the server 300 serving as the service management device 200 is configured as a computer equipped with a CPU and memory, and is equipped with a program that executes the processes described in each embodiment, so that the server 300 functions as a control device. Such a computer configuration is one example, and some or all of the functions performed by the server 300 may be realized by hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0115] Furthermore, when the server 300 is configured as a computer, the program to be implemented on the computer may be prepared in the memory 302 or storage 400 of the server 300, or may be stored on a recording medium such as an external memory, an IC card, an SD card, or an optical disk, and then transferred.

[0116] 100...service management system, 200...service management device, 300...server, 301...CPU, 302...memory, 303...NIC, 304...disk controller, 305...internal storage device, 306...bus line, 307...operation system, 310...change range management unit, 320...influence range management unit, 330...coverage management unit, 340...role / user structure management information, 350...user identification information, 360...process information, 370...editing purpose / editing history information, 400...storage, 500...synchronization in-memory DB device, 600...backend analysis server group, 601, 602, 603...backend analysis unit, 700a, 700b...communication packets, 800a, 800b...user terminal, 810a, 810b...user identification information, 820a, 820b...input auxiliary dictionary; 830a, 830b...change / reference acquisition unit; 840a, 840b...input unit; 850a, 850b...display unit; 900a, 900b...user

Claims

1. A service management system that creates or modifies business data applied to a control system or information system that executes specified operations through operation from one or more terminals, comprising: a storage unit that stores at least service structure information, program structure information, and data structure information for said business data; and a calculation unit that performs calculations based on the data stored in said storage unit, wherein said calculation unit has a change range management unit that manages the range of changes to said business data, an impact range management unit that manages the range of impact when said business data is changed, and a comprehensiveness management unit that verifies the comprehensiveness of said business data.

2. The service management system described in claim 1, wherein the memory unit stores existing data of the business data, and the calculation unit extracts information regarding the data meanings of terms contained in multiple types of existing data based on multiple types of existing data related to the business data stored in the memory unit and the path structure of the multiple types of existing data, and generates information regarding the degree of association between multiple terms based on the extracted information regarding the data meanings.

3. The service management system described in claim 2, wherein the change scope management unit, the impact scope management unit, and the coverage management unit calculate the change scope, the impact scope, and the coverage based on information regarding the path structure and the degree of association between the terms.

4. The service management system described in claim 2, wherein the calculation unit statistically processes the log of operations performed by the terminal to generate role management information including time-series information, and the impact scope management unit identifies tasks to be performed based on the time-series information and the role management information and calculates the impact scope of the tasks.

5. The service management system described in claim 1, wherein the business data, which is the multiple types of existing data, includes terms in expressions used in the business and structured identification information that identifies their meanings, the structured identification information indicating structured inclusion relationships based on at least one of inclusion relationships in hierarchical directories, database schema tables, database schema column and row identifiers, service structure information, program structure information, and chapter and section structures of data structure information, and the calculation unit uses the structured inclusion relationships to create a dictionary of the meanings of the terms.

6. The service management system according to claim 5, wherein the calculation unit breaks down the identification information into character units, automatically generates a search query by inferring from rules indicating the meaning of the abbreviation, compares the terms in the expression with the structured identification information, calculates the difference between the compared information as a code distance, and obtains the degree of relevance between the business data.

7. The service management system according to claim 5, wherein the calculation unit adjusts and compares the number of concatenations of identifiers in delimited character strings that represent inclusion relationships, interprets meanings even if the structured identification information that identifies the terms and meanings in expressions does not match exactly, and generates a dictionary.

8. The service management system according to claim 1, wherein the calculation unit is triggered by input from the terminal, determines the priority of the business information to be executed based on the matching rate of the structured identification information, and outputs the information corresponding to the input information.

9. The service management system according to claim 1, wherein the calculation unit preferentially selects and executes a task that has been corrected and / or approved by the terminal or the calculation unit.

10. The service management system according to claim 7, wherein when the calculation unit performs analysis processing on the terminal or the calculation unit, the calculation unit processes multiple analysis processing synchronously, and uses a common namespace during the multiple analysis processing so that each analysis processing can wait for synchronization.

11. A service management system as described in claim 1, wherein the calculation unit has a table that manages the scope of impact associated with service changes, etc. and the skill levels of personnel, and the calculation unit selects personnel who can deal with the scope of impact based on the information in the table.

12. The service management system according to claim 5, wherein the existing data has structured identification information that identifies terms used in the business and their meanings.

13. A service management method for creating or modifying business data applied to a control system or information system that executes specified business operations through operations on one or more terminals, the method comprising: a storage process for saving at least service structure information, program structure information, and data structure information for the business data; and an arithmetic process for performing calculations based on the data saved by the storage process, wherein the arithmetic process executes a change range management process for managing the range of changes to the business data, an impact range management process for managing the range of impact when the business data is changed, and a comprehensiveness verification process for verifying the comprehensiveness of the business data.

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