WO2024018671A1

WO2024018671A1 - Cld management device, cld management system, and cld management method

Info

Publication number: WO2024018671A1
Application number: PCT/JP2023/006103
Authority: WO
Inventors: アレッシアマソラ; 尚起吉本
Original assignee: 株式会社日立製作所
Priority date: 2022-07-20
Filing date: 2023-02-21
Publication date: 2024-01-25
Also published as: JP2024013498A

Abstract

The present invention provides a CLD managing means that makes it easier to understand causality among various indicators in fields such as urban planning that require abundant specialized knowledge.　Provided is a CLD management device including: a CLD configuration information generation unit that generates first CLD configuration information including at least two KPIs and a relationship indicator that indicates a relationship among the KPIs; a question generation unit that generates question information indicating a question pertaining to the relationship indicator in the first CLD configuration information; a CLD configuration information correction unit that generates second CLD configuration information by adding or removing the KPI to or from the first CLD configuration information; a calculation unit that acquires answer information indicating an answer to the question pertaining to the relationship indicator in the first CLD configuration information and calculates importance and a temporal feature of each relationship indicator in the second CLD configuration information on the basis of the acquired answer information; and a CLD generation unit that generates and outputs CLD on the basis of the second CLD configuration information, the importance and the temporal feature.

Description

CLD management device, CLD management system and CLD management method

The present invention relates to a CLD management device, a CLD management system, and a CLD management method.

In recent years, there has been a focus on cities, such as smart cities and eco-cities, and the concept of improving cities while also being considerate of the global environment, as well as various initiatives to realize this concept. In order to improve cities, it is necessary to properly understand the current situation, and many urban indicators have been proposed for this purpose.

In formulating appropriate urban plans to improve cities, it is important to understand the relationship between these urban indicators.

Proposals have been made to support understanding of causal relationships in specific events.
For example, International Publication No. 2019/220653 (Patent Document 1) states, ``The query identifying unit 81 identifies a query that is a combination of a variable for which an intervention operation is performed on a causal relationship and a value of that variable. The intervention data generation unit 82 generates intervention data that includes the query and the value of the target variable obtained by the intervention operation based on the query.The causality update unit 83 uses the generated intervention data to Update the relationship. At this time, the query identifying unit 81 identifies a query that minimizes the expected loss by updating, among the queries identified based on the expected loss representing the estimation error of the target variable by the query. Are listed.

International Publication No. 2019/220653

However, the causal relationships that exist among urban indicators that occur in various aspects of modern cities, such as energy, water, transportation, waste treatment, information and communication, economy, crime prevention and disaster prevention equipment, and medical facilities, are extremely complex. , a lot of specialized knowledge is required to understand it properly.
However, in many cases, urban plans are formulated by stakeholders other than urban development experts, such as politicians and local governments.
Therefore, in order to facilitate the formulation of urban plans by stakeholders, there is a need for a means to easily understand the causal relationships between urban indicators related to various aspects of the city.

The above-mentioned Patent Document 1 describes a means for efficiently estimating a causal relationship with respect to a predetermined variable. In the means described in Patent Document 1, a causal relationship between variables is determined based on a query from a user and a statistical model.

However, although the method described in Patent Document 1 estimates the relationship between predetermined variables, it does not consider how to easily understand the estimated relationship. Therefore, with the means described in Patent Document 1, data and statistical models necessary for estimating the relationship between predetermined variables are selected, and in order to understand the output estimation results, it is necessary to Expert knowledge is required. For this reason, it may be difficult to apply it to situations where the causal relationships between variables are extremely complex, such as urban development.

Therefore, the present disclosure aims to provide a CLD management means that makes it easier to understand the causal relationships between various indicators in a field that requires a lot of specialized knowledge, such as city planning.

In order to solve the above problems, one of the typical CLD management devices of the present invention includes a processor and a memory, and the memory includes at least two KPIs and a relationship index indicating the relationship between the KPIs. a CLD configuration information generation unit that generates CLD configuration information of 1; a question generation unit that generates question information indicating a question regarding the relationship index in the first CLD configuration information; , a CLD configuration information modification unit that generates second CLD configuration information by adding or removing KPIs, and obtaining answer information indicating an answer to the question regarding the relationship index in the first CLD configuration information. , a calculation unit that calculates the importance and temporal characteristics of each KPI in the second CLD configuration information based on the acquired answer information; and the second CLD configuration information, the importance and the temporal characteristics. The present invention is characterized in that it includes a processing instruction for causing the processor to function as a CLD generation unit that generates and outputs a CLD based on the above.

According to the present disclosure, it is possible to provide a CLD management means that makes it easier to understand the causal relationships between various indicators in a field that requires a lot of specialized knowledge, such as city planning.
Problems, configurations, and effects other than those described above will be made clear by the description in the detailed description below.

FIG. 1 is a diagram illustrating a computer system for implementing embodiments of the present disclosure. FIG. 2 is a diagram illustrating an example of a configuration of a CLD management system according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating an example of the flow of the first CLD configuration information generation process according to the embodiment of the present disclosure. FIG. 4 is a diagram showing the flow of data exchange in the CLD management system when the first CLD configuration information generation process according to the embodiment of the present disclosure is performed. FIG. 5 is a diagram illustrating an example of the flow of the second CLD configuration information generation process according to the embodiment of the present disclosure. FIG. 6 is a diagram showing the flow of data exchange in the second CLD configuration information generation process according to the embodiment of the present disclosure. FIG. 7 is a diagram illustrating an example of a configuration of first CLD configuration information according to an embodiment of the present disclosure. FIG. 8 is a diagram illustrating an example of CLD candidates corresponding to the first CLD configuration information according to the embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example of a configuration of second CLD configuration information according to an embodiment of the present disclosure. FIG. 10 is a diagram illustrating an example of a CLD corresponding to second CLD configuration information according to an embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of the flow of CLD generation processing according to the embodiment of the present disclosure. FIG. 12 is a diagram showing the flow of data exchange in the CLD generation process according to the embodiment of the present disclosure.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to this embodiment. In addition, in the description of the drawings, the same parts are denoted by the same reference numerals.

Additionally, terms such as "first," "second," and "third" may be used in the present disclosure to describe various elements or components; It will be understood that there should be no limitation by the terms. These terms are only used to distinguish one element or component from another. Accordingly, a first element or component discussed below may also be referred to as a second element or component without departing from the teachings of the inventive concept.

(Summary of this disclosure)
As mentioned above, in order to facilitate the formulation of urban plans by stakeholders, there is a need for a means to facilitate understanding of the causal relationships between urban indicators related to various aspects of the city.

A causal loop diagram (CLD) is known as one of the means for visualizing causal relationships. CLD is a tool for modeling the causal structure between elements of an event in the form of a directed graph network.
However, policies to improve cities vary greatly from city to city and are often determined by the subjective opinions of stakeholders, making it difficult to construct a standard CLD that is comprehensive and applicable to many cities. . Furthermore, even if it were possible to construct such a standard CLD, the complexity of the cause-and-effect relationships that exist between urban indicators in urban planning would make it difficult for stakeholders without specialized knowledge to understand, resulting in a heavy burden on users. .

In view of the above issues, this disclosure aims to facilitate understanding of the causal relationships between urban indicators related to various aspects of cities, as well as to facilitate the formulation of urban plans by stakeholders. An object of the present invention is to provide a CLD management means that generates a CLD that can be customized according to the user's needs.

In an embodiment, a CLD management means according to an embodiment of the present disclosure includes a first CLD configuration information generation process that generates first CLD configuration information that is a CLD template based on expert knowledge; A first CLD configuration information generation process that generates second CLD configuration information customized to a specific usage environment or application based on the first CLD configuration information, and a CLD that visualizes the second CLD configuration information. It may also include a CLD generation process in which the CLD is generated and the generated CLD is used to support a project or business.
Below, aspects of the CLD management means according to the embodiment of the present disclosure will be described with reference to the drawings.

Next, with reference to FIG. 1, a computer system 100 for implementing an embodiment of the present disclosure will be described. The mechanisms and apparatus of the various embodiments disclosed herein may be applied to any suitable computing system. The main components of computer system 100 include one or more processors 102 , memory 104 , terminal interface 112 , storage interface 113 , I/O (input/output) device interface 114 , and network interface 115 . These components may be interconnected via memory bus 106, I/O bus 108, bus interface unit 109, and I/O bus interface unit 110.

Computer system 100 may include one or more general purpose programmable central processing units (CPUs) 102A and 102B, collectively referred to as processors 102. In some embodiments, computer system 100 may include multiple processors, and in other embodiments, computer system 100 may be a single CPU system. Each processor 102 executes instructions stored in memory 104 and may include onboard cache.

In some embodiments, memory 104 may include random access semiconductor memory, storage devices, or storage media (either volatile or nonvolatile) for storing data and programs. Memory 104 may store all or a portion of programs, modules, and data structures that perform the functions described herein. For example, memory 104 may store CLD management application 150. In some embodiments, CLD management application 150 may include instructions or writing to perform functions described below on processor 102.

In some embodiments, CLD management application 150 operates on semiconductor devices, chips, logic gates, circuits, circuit cards, and/or other physical hardware devices instead of or in addition to processor-based systems. It may also be implemented in hardware via. In some embodiments, CLD management application 150 may include data other than instructions or descriptions. In some embodiments, cameras, sensors, or other data input devices (not shown) may be provided to communicate directly with bus interface unit 109, processor 102, or other hardware of computer system 100. .

Computer system 100 may include a bus interface unit 109 that provides communication between processor 102 , memory 104 , display system 124 , and I/O bus interface unit 110 . I/O bus interface unit 110 may be coupled to I/O bus 108 for transferring data to and from various I/O units. I/O bus interface unit 110 connects via I/O bus 108 to a plurality of I/

O interface units

112, 113, 114, also known as I/O processors (IOPs) or I/O adapters (IOAs). and 115.

Display system 124 may include a display controller, display memory, or both. A display controller may provide video, audio, or both data to display device 126. Computer system 100 may also include devices, such as one or more sensors, configured to collect data and provide the data to processor 102.

For example, the computer system 100 may include a biometric sensor that collects heart rate data, stress level data, etc., an environmental sensor that collects humidity data, temperature data, pressure data, etc., and a motion sensor that collects acceleration data, exercise data, etc. May include. Other types of sensors can also be used. Display system 124 may be connected to a display device 126, such as a standalone display screen, a television, a tablet, or a handheld device.

The I/O interface unit has the ability to communicate with various storage or I/O devices. For example, the terminal interface unit 112 may include a user output device such as a video display device, a speaker television, or a user input device such as a keyboard, mouse, keypad, touchpad, trackball, buttons, light pen, or other pointing device. It is possible to attach user I/O devices 116 such as: Using the user interface, a user operates user input devices to input input data and instructions to user I/O device 116 and computer system 100, and to receive output data from computer system 100. Good too. The user interface may be displayed on a display device, played through a speaker, or printed through a printer, for example, via the user I/O device 116.

Storage interface 113 may include one or more disk drives or direct access storage devices 117 (typically magnetic disk drive storage devices, but also an array of disk drives or other storage devices configured to appear as a single disk drive). ) can be installed. In some embodiments, storage device 117 may be implemented as any secondary storage device. The contents of memory 104 are stored in storage device 117 and may be read from storage device 117 as needed. I/O device interface 114 may provide an interface to other I/O devices such as printers, fax machines, etc. Network interface 115 may provide a communication path so that computer system 100 and other devices can communicate with each other. This communication path may be, for example, network 130.

In some embodiments, computer system 100 is a device that receives requests from other computer systems (clients) that do not have a direct user interface, such as a multi-user mainframe computer system, a single-user system, or a server computer. There may be. In other embodiments, computer system 100 may be a desktop computer, a portable computer, a laptop, a tablet computer, a pocket computer, a telephone, a smart phone, or any other suitable electronic device.

Next, with reference to FIG. 2, a CLD management system according to an embodiment of the present disclosure will be described.

FIG. 2 is a diagram illustrating an example of a configuration of a CLD management system 200 according to an embodiment of the present disclosure. The CLD management system 200 is a system for generating and providing a CLD that makes it easier to understand the causal relationships between various indicators in fields such as urban development that require a lot of specialized knowledge. As shown in FIG. 2, the CLD management system 200 includes a CLD management device 210, a communication network 250, and a user terminal 260. CLD management device 210 and user terminal 260 may be connected to each other via communication network 250.

The CLD management device 210 is a device for generating CLD, and as shown in FIG. 2, mainly includes a memory 220, a storage section 230, a processor 244, and an input/output section 246.
In some embodiments, CLD management device 210 may be implemented by computer system 100 shown in FIG. 1.

The memory 220 may be a memory for storing the CLD management application 150 for implementing the functions of the CLD management means according to the embodiment of the present disclosure. As shown in FIG. 2, this CLD management application 150 implements the functions of software modules such as a CLD configuration information generation unit 221, a question generation unit 222, a CLD configuration information modification unit 223, a calculation unit 224, and a CLD generation unit 225. It may also include processing instructions for.

The CLD configuration information generation unit 221 is a functional unit for generating first CLD configuration information. In the present disclosure, "CLD configuration information" is information that defines relationships between elements in a certain field, project, business, etc., and is used when generating a CLD. In addition, in this disclosure, the embodiments of the present disclosure will be described with reference to a plurality of different pieces of CLD configuration information. This will be explained using terms such as "3".

The first CLD configuration information generated by the CLD configuration information generation unit 221 may include a plurality of key performance indicators (hereinafter referred to as "KPIs") and relationship indicators indicating relationships between these KPIs. . As will be described later, this first CLD configuration information may be created as a highly versatile CLD template so that it can be applied to various uses and specification environments. In some embodiments, the first CLD configuration information may be list or tabular information that defines a plurality of KPIs and relationships between the KPIs. As described later, the CLD configuration information generation unit 221 may generate the first CLD configuration information based on input from a user such as an expert.

In general, KPIs are quantitative indicators that indicate goals that organizations and projects should achieve in society and business. As an example, the KPI in the CLD configuration information may be a KPI regarding urban development. These KPIs related to urban development may be any information related to urban infrastructure, organizations, and institutions, such as city population, city emissions, park area, public transportation, and are not particularly limited. As described later, KPIs in the CLD configuration information may be expressed as nodes of the CLD.

Furthermore, the relationship index in the CLD configuration information is information indicating the relationship between a plurality of KPIs. In one embodiment, this relationship index may include a directional element that indicates a causal relationship between a plurality of KPIs, and a polar element that indicates the polarity (positive or negative) of this causal relationship.
More specifically, the directional element is information that defines a KPI that causes a KPI that indicates a predetermined result. As discussed below, this directional element may be illustrated by the arrows connecting the nodes of the CLD.
Furthermore, the polarity element is information indicating whether the relationship between the KPIs is a proportional relationship or an inversely proportional relationship. As described below, this polarity element may be indicated by a "+" (proportional relationship) or "-" (inversely proportional relationship) near the arrow connecting to the node of the CLD.

The question generation unit 222 is a functional unit that generates question information indicating a question regarding the relationship index in the first CLD configuration information generated by the CLD configuration information generation unit 221. As described below, this question may be generated based on input from a user, such as an expert, and may be used to calculate the importance and temporal characteristics of the KPI's relationship indicators.

The CLD configuration information modification unit 223 is a functional unit that generates second CLD configuration information by adding or removing KPIs and related indicators to the first CLD configuration information. Therefore, this second CLD configuration information is the first CLD configuration information with KPIs added or removed. In some embodiments, the CLD configuration information modification unit 223 may create the second CLD configuration information based on input from the user. As will be described later, this second CLD configuration information is information that defines a CLD configuration that is customized to suit a specific purpose and usage environment, based on the first CLD configuration information, which was a highly versatile template. There may be.

The calculation unit 224 acquires answer information indicating an answer to the question regarding the relational index in the first CLD configuration information, and based on the acquired answer information, the calculation unit 224 generates second CLD configuration information created by the CLD configuration information correction unit 223. This is a functional unit that calculates the importance and temporal characteristics of the relationship index of each KPI.
Here, the importance of the KPI relationship index is a value indicating the importance of the relationship between predetermined KPIs. As will be described later, this degree of importance may be calculated based on the user's subjective opinion.
Further, the temporal feature here is information indicating the time required for the related index of the KPI (that is, the time from a predetermined cause to the realization of a result).

The CLD generation unit 225 generates and outputs a CLD based on the second CLD configuration information generated by the CLD configuration information modification unit 223, the importance of the KPI relationship index, and the temporal characteristics of the KPI relationship index. It is a functional part for More specifically, the CLD generation unit 225 visualizes the plurality of KPIs and their relationships defined in the second CLD configuration information based on the importance and temporal characteristics of the calculated KPI relationship index, and creates a directed graph. Generate the CLD shown in the network.

The storage unit 230 is a storage area that accommodates a database (hereinafter referred to as "DB") for storing various information according to the embodiment of the present disclosure, and as shown in FIG. 2, the first CLD configuration information DB 231, It may also include a question DB 232 and a second CLD configuration information DB 233.
The first CLD configuration information DB 231 is a database for storing first CLD configuration information generated by the CLD configuration information generation unit 221.
The question DB 232 is a database for storing questions generated by the question generation unit 222.
The second CLD configuration information DB 233 is a database for storing the second CLD configuration information generated by the CLD configuration information correction unit 223 and information on importance and temporal characteristics calculated based on questions. .

The processor 244 is a processing unit for executing processing instructions that define the functions of each functional unit of the CLD management application 150 stored in the memory 220.

The input/output unit 246 is a functional unit that receives information input to the CLD management device 210 and outputs information such as CLD generated by the CLD management device 210. In an embodiment, the input/output unit 246 may include, for example, a keyboard, a mouse, a display that displays a GUI (Graphical User Interface), and the like.

Communication network 250 may include, for example, a local area network (LAN), a wide area network (WAN), a satellite network, a cable network, a WiFi network, or any combination thereof.

The user terminal 260 is a terminal device that can be used by a user of the CLD management device 210. By using the user terminal 260, the user can input information for generating the first CLD configuration information and the second CLD configuration information, and check the CLD output by the CLD management device 210. Can be done. As an example, the user terminal 260 may include, for example, a smartphone, a smart watch, a tablet, a personal computer, etc., and is not particularly limited.
Note that in FIG. 2, for convenience of explanation, a configuration including one user terminal 260 is described as an example, but the number of user terminals 260 is not limited. As will be described later, in reality, the CLD management device 210 inputs and outputs information to a plurality of users (for example, urban development experts, stakeholders formulating urban plans, etc.), so the number of user terminals 260 is limited. There may be more than the number of users.

According to the CLD management system 200 described above, it is possible to provide a CLD that makes it easier to understand the causal relationships between various indicators in fields that require a lot of specialized knowledge, such as city planning.

Next, with reference to FIG. 3, the first CLD configuration information generation process according to the embodiment of the present disclosure will be described.

FIG. 3 is a diagram illustrating an example of the flow of the first CLD configuration information generation process 300 according to the embodiment of the present disclosure. The first CLD configuration information generation process 300 shown in FIG. This is performed by the question generation unit 222.

First, in step S310, the CLD configuration information generation unit 221 in the CLD management device 210 acquires first candidate information. This first candidate information is information indicating candidates for KPIs related to a predetermined project and candidates for relationship indicators of these KPIs. As an example, in some embodiments, this first candidate information may include KPIs related to urban development and information indicating causal relationships between these KPIs. Here, the CLD configuration information generation unit 221 may obtain the first candidate information from a user such as an expert on the project. As an example, in one embodiment, urban development experts gathered at a regularly held workshop propose KPIs and causal relationships between the KPIs as the first candidate information based on their own knowledge and discussions. , may be input to the CLD configuration information generation unit 221 in the CLD management device 210.

Next, in step S320, the CLD configuration information generation unit 221 performs a predetermined data mining method and a predetermined statistical analysis method on the first candidate information acquired in step S310, and corrects the first candidate information. By doing so, second candidate information is generated. This second candidate information is the first candidate information further supplemented with KPIs and related indicators. In one embodiment, the CLD configuration information generation unit 221 searches the Internet or a predetermined database for keywords related to the KPIs included in the first candidate information. Identify new and relevant KPIs. Thereafter, by performing a predetermined statistical analysis, a relationship index indicating the relationship between the identified KPIs can be derived. In this way, by performing data mining and statistical analysis, it is possible to supplement or complement the first candidate information acquired from users such as experts with new KPIs and related indicators.

Next, in step S330, the CLD configuration information generation unit 221 generates first CLD configuration information by modifying the directional element and polarity element in the second candidate information. As described above, the directional element here is information that indicates the causal relationship between KPIs, and the polar element is information that indicates the positive or negative nature of the causal relationship between KPIs.

More specifically, at the time of step S320, there is a possibility that an error exists in the relationship indicated for the KPI in the second candidate information. For example, although the first KPI in the second candidate information is indicated as the cause of the second KPI, the second KPI is actually the cause of the first KPI (i.e., the directional element ), or the first KPI and the second KPI are shown to be in a proportional relationship, but in reality, the first KPI and the second KPI are in an inversely proportional relationship. (In other words, there is an error in the polarity element).

Therefore, in step S330, the CLD configuration information generation unit 221 presents the second candidate information to a user such as an expert (for example, the user who proposed the first candidate information in step S310), and receives the second candidate information from the user. The second candidate information may be modified based on the received input for modifying the directional element and polarity element in the second candidate information. In this way, it is possible to generate first CLD configuration information in which errors in the second candidate information are corrected.

Next, in step S340, the CLD configuration information generation unit 221 stores the first CLD configuration information generated in step S330 in the first CLD configuration information DB 231. Here, the CLD configuration information generation unit 221 may store the first CLD configuration information in any format such as a list, table, matrix, etc. in the first CLD configuration information DB 231. In an embodiment, the CLD configuration information generation unit 221 may extract a relationship index in the first CLD information, and store only information on the extracted relationship index in the first CLD configuration information DB 231.

Next, in step S350, the question generation unit 222 in the CLD management device 210 generates a question for each relationship index in the first CLD configuration information generated in step S330, and stores the generated question in the question DB 232. As will be described later, the answers to these questions are used in the second CLD configuration information generation process 500 shown in FIG. 5 to calculate the importance and temporal characteristics of the relationship index.
Here, the question generation unit 222 presents each relational index included in the first CLD configuration information to a user such as an expert (for example, the user who proposed the first candidate information in step S310), and An input indicating a question for determining the importance of the relationship index and a question for determining the temporal characteristics of the relationship index may be received from the user.

As an example, consider two KPIs: "resident population" and "park area." In principle, in urban development, from the perspective of improving citizens' QoL (Quality of Life), it is desirable to increase the "park area" as the "settled population" increases. In a directed graph network such as CLD, the causal relationship between these two KPIs can be expressed by a relationship index such as "resident population +-->park area".
As a question for determining the importance of this relationship index, for example, the following Likert scale type question (Question A) can be considered.
Question A:
“With the current permanent population, do you think it would be better to increase the area of the park?
0) Strongly disagree 1) Disagree 2) Neutral 3) Agree 4) Strongly agree.”
In this case, the answer to question A will be a numerical value within the range of -2 to 2.

As another example, the following two questions B and C, which specify the current value and target value, are used as questions to determine the importance of the relationship index between two KPIs: "settled population" and "park area." Conceivable.
Question B:
“Current park area per 1,000 people0) 0-5 acres of park area per 1,000 people1) 5-10 acres of park area per 1,000 people2) 10-20 acres of park area per 1,000 people3) 20 acres of park area per 1,000 people ~40 acres of park area 4) 40-60 acres of park area per 1,000 people.”
In this case, the answer to question B will be a numerical value within the range of 0 to 4. The following question can be considered as question C following question B.
Question C:
“Target park area per 1,000 people0) 0-5 acres of park area per 1,000 people1) 5-10 acres of park area per 1,000 people2) 10-20 acres of park area per 1,000 people3) 20 acres of park area per 1,000 people ~40 acres of park area 4) 40-60 acres of park area per 1,000 people.”
In this case, the answer to question C will be a numerical value within the range of 0 to 4.

In addition, the following question D can be considered as a question for determining the temporal characteristics of the relationship index between the two KPIs of "settled population" and "park area."
Question D:
“Based on the current trends in the permanent population, how much time do you think it will take to achieve the target park area?”
In this case, the answer to question D may be selected from answer candidates prepared in advance, or may be a free-form answer.

As described above, the question generation unit 222 generates a question and possible answers to the question based on the user input for each relationship index in the first CLD configuration information generated in step S330, and It is stored in the question DB 232.

Next, data exchange in the first CLD configuration information generation process according to the embodiment of the present disclosure will be described with reference to FIG. 4.

FIG. 4 is a diagram showing the flow of data exchange in the CLD management system 200 when the first CLD configuration information generation process 300 according to the embodiment of the present disclosure is performed.

First, the CLD configuration information generation unit 221 acquires first candidate information via the communication network 250.

Next, the CLD configuration information generation unit 221 performs a predetermined data mining method or statistical analysis method via the communication network 250 to obtain additional data from the Internet or a predetermined database to supplement the first candidate information. do.

Next, the CLD configuration information generation unit 221 transmits the second candidate information to the user via the communication network 250.

Next, the CLD configuration information generation unit 221 obtains a modification input for modifying the second candidate information from the user via the communication network 250.

Next, the CLD configuration information generation unit 221 stores the first CLD configuration information generated by modifying the second candidate information in the first CLD configuration information DB 231.

Next, the question generation unit 222 transmits the relationship index in the first CLD configuration information to the user via the communication network 250.

Next, the question generation unit 222 acquires a question regarding the relational index in the first CLD configuration information from the user via the communication network 250, and stores it in the question DB 232.

According to the first CLD configuration information generation process 300 described above, the first CLD configuration information used to generate the CLD and the importance of the relationship index indicating the relationship between KPIs in the first CLD configuration information. and temporal features can be calculated.
In this way, by generating the first CLD configuration information in advance as a highly versatile template that can be applied to various uses and usage environments based on input from users such as experts, it is possible for non-experts to The user can easily customize this first CLD configuration information in accordance with the user's purpose and usage environment at a later stage.

Next, with reference to FIG. 5, the second CLD configuration information generation process according to the embodiment of the present disclosure will be described.

FIG. 5 is a diagram illustrating an example of the flow of second CLD configuration information generation processing 500 according to the embodiment of the present disclosure. The second CLD configuration information generation process 500 shown in FIG. 5 modifies (customizes) the first CLD configuration information generated by the first CLD configuration information generation process 300 shown in FIG. ), and is mainly performed by the question generation section 222, the CLD configuration information modification section 223, and the calculation section 224.

First, in step S510, the CLD configuration information modification unit 223 in the CLD management device 210 converts the first CLD configuration information generated by the first CLD configuration information generation process 300 shown in FIG. 3 into the first CLD configuration information DB 231. At the same time, a question generated regarding the relational index in the first CLD configuration information is obtained from the question DB 232.

Next, in step S520, the CLD configuration information modification unit 223 presents the first CLD configuration information acquired in step S510 to the user. In some embodiments, the CLD configuration information modification unit 223 may present the user with a list or table that constitutes the first CLD configuration information. On the other hand, in an embodiment, the CLD generation unit 225 in the CLD management device 210 may present CLD candidates (see FIG. 8) that visualize the first CLD configuration information to the user. Thereby, a user who confirms the first CLD configuration information can confirm the KPIs included in the first CLD configuration information and the relationship index between the KPIs as an easy-to-understand digraph network.
Note that even if the user here is different from the user who proposed the KPIs and related indicators for generating the first CLD configuration information during the first CLD configuration information generation process 300 shown in FIG. good. As an example, the user who proposed KPIs and related indicators for generating the first CLD configuration information during the first CLD configuration information generation process 300 shown in FIG. 3 may be an expert in urban development, etc. Often, the user who confirms the first CLD configuration information in step S520 may be a city stakeholder (i.e., a user other than an urban development expert), such as a city planner or a politician.

Next, in step S530, the CLD configuration information modification unit 223 determines whether to modify the first CLD configuration information presented to the user in step S520. Here, the CLD configuration information modification unit 223 acquires an input from the user indicating whether or not to modify the first CLD configuration information, and determines whether or not to modify the first CLD configuration information according to the acquired input. You may.
If it is determined that the first CLD configuration information is to be modified, the process proceeds to step S540. On the other hand, if it is determined that the first CLD configuration information is not to be modified, the process proceeds to step S550.

In step S540, the CLD configuration information modification unit 223 modifies the first CLD configuration information based on the user's input. Here, the CLD configuration information modification unit 223 modifies the first CLD information by adding or removing predetermined KPIs and related indicators to the first CLD configuration information based on the user's input. You can. In this way, by adding or deleting KPIs and related indicators in the first CLD configuration information, the first CLD configuration information, which was a highly versatile template, can be adjusted to the usage status of the user. It can be customized to suit your needs.
After the modification to the first CLD configuration information is completed, the process returns to step S510, and processes from step S510 onward are performed using the modified first CLD configuration information.

Next, in step S550, the CLD configuration information modification unit 223 determines the first CLD configuration information and generates the second CLD configuration information. When the first CLD configuration information is modified in step S540, this modified first CLD configuration information becomes the second CLD configuration information. Further, if the first CLD configuration information is not modified in step S540, the first CLD configuration information becomes the second CLD configuration information as it is.

Next, in step S560, the question generation unit 222 in the CLD management device 210 presents the user with the question acquired from the question DB 232 in step S510, and acquires an answer to the question from the user. As an example, the question generation unit 222 generates the above-mentioned questions B, C, and C as questions for determining the importance and temporal characteristics of the relationship index between the two KPIs of "settled population" and "park area." Question D may be presented to the user and an answer to the question may be obtained.

Next, in step S570, the calculation unit 224 in the CLD management device 210 calculates the importance and temporal characteristics of each relationship index based on the answers to the questions.
As an example, the calculation unit 224 may use the weighting associated with each answer option of the question as the degree of importance of the relational index. Further, as another example, when a plurality of questions (for example, questions B and C mentioned above) are presented in order to determine the importance of the relationship index, the calculation unit 224 calculates the The weighted difference may be used as the degree of importance of the relevant relationship index. For example, the answer to question B above is "0 to 5 acres of park area per 1000 people," which is associated with a weight of "0," and the answer to question C above is "4." In the case of "park area of 40 to 60 acres per 1,000 people", which is associated with the weighting of good.
Similarly, the calculation unit 224 may use an answer to a question that determines the temporal feature of the relational index as the temporal feature of the relational index. As an example, if the answer to question D mentioned above is "one year", the temporal characteristic of the related index may be "one year".
As will be described later, the importance and temporal characteristics of each relationship index calculated here are used together with the second CLD configuration information to generate the CLD.

Next, in step S580, the CLD configuration information modification unit 223 converts the second CLD configuration information generated in step S550 and the importance and temporal characteristics of each relationship index calculated in step S570 into second CLD configuration information. Store in DB233.

Next, data exchange in the second CLD configuration information generation process 500 according to the embodiment of the present disclosure will be described with reference to FIG. 6.

First, the CLD configuration information modification unit 223 acquires first CLD configuration information from the first CLD configuration information DB 231. Further, the CLD configuration information modification unit 223 acquires questions regarding the relationship index from the question DB 232.

Next, the CLD configuration information modification unit 223 presents the first CLD configuration information to the user via the communication network 250.
As described above, the user here is a user different from the user who proposed the KPIs and related indicators for generating the first CLD configuration information during the first CLD configuration information generation process 300 shown in FIG. There may be. As an example, the user who proposed KPIs and related indicators for generating the first CLD configuration information during the first CLD configuration information generation process 300 shown in FIG. 3 may be an urban development expert. Here, the user who confirms the first CLD configuration information may be a stakeholder of the city, such as a city planner or a politician.

Next, the CLD configuration information modification unit 223 obtains a modification input for modifying the first CLD configuration information from the user via the communication network 250.

Next, the CLD configuration information generation unit 221 stores the second CLD configuration information generated by modifying the first CLD configuration information in the second CLD configuration information DB 233.

Next, the question generation unit 222 transmits the question regarding the relational index obtained from the question DB 232 to the user via the communication network 250.

Next, the question generation unit 222 obtains an answer to the question from the user via the communication network 250 and stores it in the second CLD configuration information DB 233.

Next, the calculation unit 224 calculates the importance and temporal characteristics of the relationship index based on the user's answer to the question, and stores it in the second CLD configuration information DB 233.

According to the second CLD configuration information generation process 500 described above, the first CLD configuration information, which is a highly versatile template, is changed to the second CLD configuration information customized according to the usage status of the user. can be generated.

Next, with reference to FIG. 7, an example of the first CLD configuration information according to the embodiment of the present disclosure will be described.

FIG. 7 is a diagram illustrating an example of the configuration of first CLD configuration information 700 according to the embodiment of the present disclosure. As described above, the first CLD configuration information 700 is information including a plurality of KPIs and relational indicators indicating the relationships between these KPIs, and is a highly versatile template that can be applied to various uses and usage environments. becomes.

As shown in FIG. 7, the first CLD configuration information 700 includes an identifier 710 for uniquely identifying a pair of predetermined KPIs, a first KPI 720, a second KPI 730, and a first KPI 720 and a second KPI 730. It may also include information such as the polarity element 740 of the relationship with the KPI 730 of No. 2.

As shown in FIG. 7, the first KPI 720 and the second KPI 730 are a pair that are associated with each other. Additionally, the first KPI 720 may indicate a cause that causes the associated second KPI 730. In other words, the first KPI 720 may indicate a cause and the second KPI 730 may indicate an effect caused by the cause of the first KPI 720.

Furthermore, the polarity element 740 is information indicating whether the relationship between a pair of KPIs consisting of a predetermined first KPI 720 and a second KPI 730 is positive or negative (that is, whether it is a proportional relationship or an inversely proportional relationship). In FIG. 7, "+" indicates that a predetermined pair of KPIs have a reference relationship, and "-" indicates that a predetermined pair of KPIs have a counter-reference relationship.

As an example, consider a KPI whose identifier 710 is "L3". As the "number of high-emission vehicles" increases, the "emissions" of the entire city increases, so the relationship between the "number of high-emission vehicles" and the "emissions" is a proportional relationship, and the polarity element 740 is "+". It has become.
On the other hand, consider the KPI whose identifier 710 is "L4". As the "emissions" of the city as a whole increases, the "environmental quality" decreases, so the relationship between "emissions" and "environmental quality" is a counter-example relationship, and the polarity element 740 becomes "-". ing.

Next, with reference to FIG. 8, CLD candidates corresponding to the first CLD configuration information according to the embodiment of the present disclosure will be described.

FIG. 8 is a diagram illustrating an example of a CLD candidate 800 corresponding to the first CLD configuration information according to the embodiment of the present disclosure. As described above, in step S520 of the second CLD configuration information generation process 500 shown in FIG. 5, the first CLD configuration information is presented to the user. At this time, the first CLD configuration information may be presented to the user in a table format as shown in FIG. 7, for example, or as a CLD candidate 800 of a directed graph network as shown in FIG. .

As shown in FIG. 8, in the CLD candidate 800, each KPI shown in FIG. 7 is expressed as a node, and the relationship index indicating the relationship between the KPIs is expressed as an edge between the nodes. As an example, in FIG. 8, the KPI "QoL" is expressed as a node 810, and the relationship index indicating the relationship between the two KPIs "environmental quality" and "QoL" is expressed as an edge 820.

As described above, the relationship index expressed as an edge includes a directional element indicating a causal relationship between KPIs and a polarity element indicating the polarity of the relationship. In FIG. 8, the directional element is expressed as the direction of an arrow between nodes, and the polar element is expressed as a "+" or "-" near the arrow.

Additionally, as shown in FIG. 8, the CLD candidate 800 may include an editing interface 850 for adding new KPIs or deleting existing KPIs. For example, when modifying the first CLD configuration information in step S540 of the second CLD configuration information generation process 500 described above, the user uses this editing interface 850 to create a new KPI (the first KPI that is the cause), etc. By defining the resulting second KPI) and the relationship (polarity element, etc.) between the KPIs, deleting any KPI in the CLD candidate 800, or modifying the relationship index of the existing KPI, The second CLD configuration information described above can be generated.

Next, with reference to FIG. 9, an example of the second CLD configuration information according to the embodiment of the present disclosure will be described.

FIG. 9 is a diagram illustrating an example of the configuration of second CLD configuration information 900 according to the embodiment of the present disclosure. As described above, the second CLD configuration information 900 can be used to add new KPIs, delete existing KPIs, and add relationship indicators between KPIs to the first CLD configuration information 700 shown in FIG. This is CLD configuration information generated by adjusting the .

As shown in FIG. 9, the second CLD configuration information 900 includes an identifier 910 for uniquely identifying a pair of predetermined KPIs, a first KPI 920, a second KPI 930, and a first KPI 920 and a second KPI 930. It may also include information such as a polarity element 940 indicating the relationship with KPI 930 of No. 2.
Note that the configuration of the second CLD configuration information 900 is substantially the same as the first CLD configuration information 700 described with reference to FIG. 7, so a repetitive description will be omitted here.

As shown in FIG. 9, in the second CLD configuration information 900, a new KPI pair 950 with the identifier "L12" is added to the first CLD configuration information 700 shown in FIG. This KPI pair 950 includes the first KPI 920 with "environmentally friendly vehicles" and the first KPI with "number of high emission vehicles" because the "number of high emission vehicles" decreases as the number of "environmentally friendly vehicles" increases. The relationship between KPI 930 of 2 is an inversely proportional relationship (the polarity element 940 is "-").

This KPI pair 950 may be added, for example, when modifying the first CLD configuration information in step S540 of the second CLD configuration information generation process 500 shown in FIG. By modifying the first CLD configuration information in this way, the first CLD configuration information can be customized to a specific usage environment.

Next, with reference to FIG. 10, a CLD corresponding to the second CLD configuration information according to the embodiment of the present disclosure will be described.

FIG. 10 is a diagram illustrating an example of a CLD 1000 corresponding to second CLD configuration information according to an embodiment of the present disclosure. This CLD 1000 visualizes second CLD configuration information customized to a specific usage environment, and shows KPIs included in the second CLD configuration information and relationship indicators indicating relationships between KPIs in a directed graph network format.
Note that the configuration of the CLD 1000 is substantially the same as the CLD candidate 800 described with reference to FIG. 8, so a repetitive description will be omitted here.

As shown in FIG. 10, a new KPI of "environmentally friendly vehicles" added to the second CLD configuration information is expressed as a new node 1010 in the CLD 1000. Furthermore, a relationship index indicating the relationship between this new node 1010 and other nodes is expressed as a new edge 1020.

The CLD 1000 shown in FIG. 10 may be used, for example, in CLD generation processing 1100 described later. By using the CLD1000, which shows KPIs in a specific usage environment and the relationships between the KPIs in an easy-to-understand directed graph network format, it is easy to understand large-scale projects such as urban planning, and it also supports stakeholders' decisions. It becomes possible to do so.

Next, with reference to FIG. 11, CLD usage processing according to the embodiment of the present disclosure will be described.

FIG. 11 is a diagram illustrating an example of the flow of CLD generation processing 1100 according to the embodiment of the present disclosure. The CLD generation process 1100 shown in FIG. 11 is a process for generating a CLD that visualizes the second CLD configuration information generated by the second CLD configuration information generation process 500 shown in FIG. 5, and providing it to the user. , is mainly executed by the CLD generation unit 225 and calculation unit 224.

First, in step S1110, the CLD generation unit 225 generates the second CLD configuration information generated by the second CLD configuration information generation process 500 shown in FIG. degree and temporal characteristics are acquired from the second CLD configuration information DB 233.

Next, in step S1120, the CLD generation unit 225 generates a CLD that visualizes the second CLD configuration information acquired in step S1110, and presents it to the user. Here, when generating the CLD, the CLD generation unit 225 may determine the configuration and design of the CLD based on the importance and temporal characteristics calculated for the relationship index in the second CLD configuration information. As an example, a CLD such as CLD 1000 described with reference to FIG. 10 may be presented to the user here.
Note that here, any existing means may be used as a method for generating a CLD from the second CLD configuration information, and is not particularly limited.

As an example, in an embodiment, when the importance calculated for a certain relational index satisfies a predetermined importance standard (that is, the relational index is determined to be important), the CLD generation unit 225 generates a KPIs corresponding to related indicators may be highlighted on the CLD.

Further, in an embodiment, when the temporal feature calculated for a certain relational index satisfies a predetermined time criterion (that is, a long time is expected), the CLD generation unit 225 generates a response to the relational index or the relational index. The KPIs to be used may be highlighted on the CLD.
Here, "highlighting" may include, for example, displaying an edge or node corresponding to a specific KPI or related index in bold, in a conspicuous color, or in descriptive text. In this way, the user who checks the CLD can easily check particularly important related indicators, KPIs that require a long time to realize, and the like.

Next, in step S1130, the calculation unit 224 receives an input of a KPI value for CLD from the user. The KPI value here is a specific numerical value of each KPI in CLD. As an example, the calculation unit 224 may accept an input of a KPI value of "577,500 people" for the KPI of "urban population." In some embodiments, the calculation unit 224 may receive input of two or more KPI values, a current value and a target value, for each KPI.

Next, in step S1140, the calculation unit 224 identifies a constraint that becomes a bottleneck in CLD based on the KPI value received in step S1130, and then the CLD generation unit 225 highlights the identified bottleneck in CLD. do. Here, highlighting bottlenecks in CLD is similar to highlighting based on importance and temporal characteristics described above, by displaying edges and nodes corresponding to KPIs and related indicators that are expected to become bottlenecks in bold. This may include displaying the information in a conspicuous color, displaying descriptive text, etc.

Here, bottleneck means a constraint that is predicted to have a negative impact on efficiency, cost, production, speed, capacity, etc. in the project modeled by CLD. Here, in order to identify the bottleneck in CLD, the calculation unit 224 may use a predetermined statistical analysis method. Further, the bottleneck information identified by the calculation unit 224 here is reflected in the CLD and stored in the second CLD configuration information DB 233.

As an example, for the KPI "number of high-emission vehicles," if the current value is "186,522 vehicles" and the target value is "150,000 vehicles," the calculation unit 224 calculates the number of electric vehicles that can be substituted for the number of high-emission vehicles. A KPI with "battery production volume" may be identified as a bottleneck KPI (if the production volume of electric vehicle batteries is limited, the price of electric vehicles will increase and the reduction in the number of high-emission vehicles will be limited). ).

Next, in step S1150, the CLD with the bottleneck highlighted in step S1140 is output to the user.
Thereafter, the user may search the CLD, hide KPIs and related indicators that are of no interest, expand the area of interest, and make further modifications as necessary.

Next, data exchange in the CLD generation process according to the embodiment of the present disclosure will be described with reference to FIG. 12.

FIG. 12 is a diagram showing the flow of data exchange in CLD generation processing 1100 according to the embodiment of the present disclosure.

First, the CLD generation unit 225 generates the second CLD configuration information generated by the second CLD configuration information generation process 500 shown in FIG. The characteristics are acquired from the second CLD configuration information DB 233.

Next, the CLD generation unit 225 transmits the CLD that visualizes the second CLD configuration information to the user via the communication network 250.

Next, the calculation unit 224 obtains the KPI value for CLD via the communication network 250.

Next, the calculation unit 224 stores information on constraints such as bottlenecks identified based on the KPI values in the second CLD configuration information DB 233 and transfers it to the CLD generation unit 225.

Next, the CLD generation unit 225 reflects the bottleneck information received from the calculation unit 224 on the CLD, and transmits it to the user via the communication network 250.

According to the CLD generation process 1100 described above, it is possible to generate a CLD customized for a specific usage environment and provide it to the user. By using this customized CLD, users can easily confirm the cause-and-effect relationships between KPIs in CLD, so they can easily understand situations where the relationships between KPIs are complex, such as urban development, without having any specialized knowledge. be able to make appropriate judgments.

As mentioned above, the causal relationships that exist among urban indicators that occur in various aspects of modern cities such as energy, water, transportation, waste treatment, information and communication, economy, crime prevention and disaster prevention equipment, and medical facilities are very strong. It is complex and requires a lot of specialized knowledge to understand it properly.
However, in many cases, city plans are currently formulated by stakeholders other than urban development experts, such as politicians and local governments.
Therefore, in order to facilitate the formulation of urban plans by stakeholders, there is a need for a means to facilitate understanding of the causal relationships between urban indicators related to various aspects of the city.

A causal loop diagram (CLD) is known as one of the means for visualizing causal relationships.
However, policies to improve cities vary greatly from city to city and are often determined by the subjective opinions of stakeholders, making it difficult to construct a standard CLD that is comprehensive and applicable to many cities. . Furthermore, even if it were possible to construct such a standard CLD, the complexity of the cause-and-effect relationships that exist between urban indicators in urban planning would make it difficult for stakeholders without specialized knowledge to understand, resulting in a heavy burden on users. .

Therefore, according to the first CLD configuration information generation process 500 in the CLD management means according to the embodiment of the present disclosure described above, the first CLD configuration information is generated as a highly versatile template that can be applied to various uses and usage environments. Information is generated in advance. This first CLD configuration information may be created based on KPIs and their relationships proposed by experts in a predetermined field such as urban development, for example.

Thereafter, according to the second CLD configuration information generation process 300 in the CLD management means according to the embodiment of the present disclosure, a user such as a city planner modifies the first CLD configuration information created in advance. By doing so, it is possible to generate second CLD configuration information customized according to a specific usage situation. This allows the user to delete irrelevant KPIs or add KPIs specific to his/her usage status, thereby making it possible to generate CLD information specific to his/her usage status.

According to the CLD generation process 1100 in the CLD management means according to the embodiment of the present disclosure, users such as city planners can visualize the causal relationships between KPIs in their own usage situations in a directed graph network format that is easy to understand. CLD can be obtained. By using this CLD, urban stakeholders other than urban development experts, such as politicians and local governments, can easily It is possible to easily understand the causal relationships between necessary elements when formulating a city plan and bottlenecks that adversely affect efficiency, and it is also possible to make appropriate judgments when formulating a city plan.

In the above, the case where the CLD management means according to the embodiment of the present disclosure is applied to the formulation of a city plan has been described as an example, but the CLD management means according to the embodiment of the present disclosure is not limited to the formulation of a city plan, and for example, It can be applied to any field or application where visualization of cause-and-effect relationships between KPIs is desired, such as risk analysis, business analysis, and production management.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various changes can be made without departing from the gist of the present invention.

150: CLD management application, 200: CLD management system, 210: CLD management device, 220: memory, 221: CLD configuration information generation unit, 222: question generation unit, 223: CLD configuration information correction unit, 224: calculation unit, 225 : CLD generation unit, 230: Storage unit, 231: First CLD configuration information DB, 232: Question DB, 233: Second CLD configuration information DB, 244: Processor, 246: Input/output unit, 250: Communication network, 260: User terminal

Claims

A CLD management device,
Equipped with a processor and memory,
The memory is
a CLD configuration information generation unit that generates first CLD configuration information including at least two KPIs and a relationship index indicating a relationship between the KPIs;
a question generation unit that generates question information indicating a question regarding the relationship index in the first CLD configuration information;
a CLD configuration information modification unit that generates second CLD configuration information by adding or removing KPIs to the first CLD configuration information;
Obtain answer information indicating an answer to the question regarding the relational index in the first CLD configuration information, and determine the importance and temporality of each relational index in the second CLD configuration information based on the obtained answer information. a calculation unit that calculates features;
a CLD generation unit that generates and outputs a CLD based on the second CLD configuration information, the importance level, and the temporal characteristic;
A CLD management device comprising a processing instruction for causing the processor to function as a CLD management device.
The related index is
a directional element indicating a causal relationship between the KPIs;
a polarity element indicating the polarity of the causal relationship of the KPI;
The CLD management device according to claim 1, characterized in that:
The CLD configuration information generation unit includes:
obtaining first candidate information indicating a relationship candidate between at least two KPI candidates and the KPI candidate;
generating second candidate information to which new KPIs and related indicators are added by performing a predetermined data mining method and a predetermined statistical analysis method on the first candidate information;
generating the first CLD configuration information by modifying the directional element or the polar element of each relationship index in the second candidate information;
The CLD management device according to claim 2, characterized in that:
The calculation unit is
obtaining a KPI value corresponding to the KPI included in the CLD;
Based on the obtained KPI value, identify a bottleneck KPI that is a KPI that may become a bottleneck in the CLD,
The CLD generation unit includes:
highlighting the identified bottleneck KPI on the CLD;
The CLD management device according to claim 2, characterized in that:
The CLD generation unit includes:
In the CLD,
highlighting the first relationship indicator if the importance calculated for the first relationship indicator satisfies a predetermined importance criterion;
highlighting the second relational indicator if the temporal feature calculated for the second relational indicator satisfies a predetermined temporal criterion;
The CLD management device according to claim 1, characterized in that:
The above KPI is
These are KPIs related to urban development.
The CLD management device according to claim 1, characterized in that:
a user terminal;
In a CLD management system in which a CLD management device is connected via a communication network,
The CLD management device includes:
Equipped with a processor and memory,
The memory is
first candidate information indicating a relationship between at least two KPIs and the KPI is acquired from the user terminal, and a relationship index indicating the relationship between the KPI and the KPI based on the acquired first candidate information. a CLD configuration information generation unit that generates first CLD configuration information;
a question generation unit that acquires question information indicating a question regarding the relationship index in the first CLD configuration information from the user terminal;
a CLD configuration information modification unit that generates second CLD configuration information by adding or removing KPIs to the first CLD configuration information;
Obtain answer information indicating an answer to the question regarding the relational index in the first CLD configuration information, and determine the importance and temporality of each relational index in the second CLD configuration information based on the obtained answer information. a calculation unit that calculates features;
a CLD generation unit that generates a CLD based on the second CLD configuration information, the importance level, and the temporal characteristic, and outputs the CLD to the user terminal;
A CLD management system comprising processing instructions for causing the processor to function as a CLD management system.
A CLD management method,
memory for storing processing instructions;
In a computer system including a processor,
The processing instructions stored in the memory are:
acquiring first candidate information indicating at least two KPI candidates and relationship candidates of the KPI candidates;
By performing a predetermined data mining method and a predetermined statistical analysis method on the first candidate information, a new KPI, a directional element indicating a causal relationship between the KPI, and the KPI
generating second candidate information to which a relationship index including a polarity element indicating the polarity of the causal relationship is added;
generating first CLD configuration information by modifying the directional element or the polar element of each relationship index in the second candidate information;
generating question information indicating a question regarding the relationship index in the first CLD configuration information;
generating second CLD configuration information by adding or removing KPIs to the first CLD configuration information;
acquiring answer information indicating an answer to the question regarding the relationship index in the first CLD configuration information;
Calculating the importance and temporal characteristics of each relationship index in the second CLD configuration information based on the obtained answer information;
generating a CLD based on the second CLD configuration information, the importance level, and the temporal feature;
highlighting the first relationship indicator in the CLD if the importance calculated for the first relationship indicator in the CLD satisfies a predetermined importance criterion;
highlighting the second relational indicator in the CLD if the temporal feature calculated for the second relational indicator in the CLD satisfies a predetermined temporal criterion;
outputting the CLD;
A CLD management method characterized by causing the processor to execute.