Data processing system and data processing method for supporting realization of target future image

Abstract

To support determination of a measure for achieving a target future image.SOLUTION: The data processing system performs second data processing using first result data of the first data processing as an input. In the first data processing, an evaluation value of a future image as a target is calculated for each time based on an index value set for each time. The first result data includes an evaluation value at each time and an index value set on which the evaluation value is based. The second data processing includes calculating an objective index value of the action using an index value in the index value set in the first result data as an explanatory index value, and outputting second result data including the calculated objective index value for the first data processing using the objective index value as at least one of the index value set.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates generally to data processing technology. [Background technology]

[0002] A data processing technology that supports the realization of a desired future image is disclosed in Patent Document 1. According to the technology disclosed in Patent Document 1, it is possible to present to the user intermediate goals, such as what values ​​should be set for which indicators and when, in order to realize the desired future image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] WO2016 / 006101 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to realize a desired future image, determining the measures to be implemented is an important element. Patent Document 1 does not disclose or suggest any technology that supports the determination of the measures to be implemented. [Means for solving the problem]

[0005] The data processing system performs second data processing using first result data of the first data processing as input. In the first data processing, an evaluation value of a target future image is calculated for each time based on a set of index values ​​for each time. The first result data includes the evaluation value for each time and the set of index values ​​on which the evaluation value is based. The second data processing includes calculating a target indicator value of a policy using an index value from the set of index values ​​in the first result data as an explanatory indicator value, and outputting second result data including the calculated target indicator value for the first data processing, with the target indicator value being at least one of the set of index values. [Effects of the Invention]

[0006] According to the present invention, it is possible to support the decision-making of measures for realizing a desired future image. [Brief explanation of the drawings]

[0007] [Figure 1] 1 shows the overall configuration of a system according to an embodiment. [Figure 2] 2 shows the hardware configuration of a first host processing device, a second host processing device, and a terminal device. [Figure 3] An example of a policy tree UI is shown below. [Figure 4] 10 shows the configuration of a policy management table. [Figure 5] 10 shows the configuration of an index management table. [Figure 6] 1 shows the configuration of a node management table. [Figure 7] 1 shows the structure of a link management table. [Figure 8] 10 shows an example of the configuration of a connection structure management table. [Figure 9] 1 shows the overall flow of the support process. [Figure 10] 9 shows the flow of the process of determining whether a measure has been implemented (S902 in FIG. 9). [Figure 11] 9 shows the flow of the process of determining whether a measure can be implemented (S904 in FIG. 9). [Figure 12] 1 shows the flow of an index extraction process. [Figure 13] An example of an area specification UI is shown below. [Figure 14] An example of an indicator UI is shown below. [Figure 15] An example of a simulation result UI is shown below. [Figure 16] 10 illustrates a schematic diagram of the support process. DETAILED DESCRIPTION OF THE INVENTION

[0008] In the following description, an "interface apparatus" may refer to one or more interface devices, which may be at least one of the following: An I / O interface apparatus is one or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices for at least one of an I / O device and a remote display computer. The I / O interface device for the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. A communication interface apparatus that is one or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., an NIC and an HBA (Host Bus Adapter)).

[0009] In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0010] In the following description, a "persistent storage device" may refer to one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and specifically may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

[0011] In the following description, the term "storage device" may refer to at least one of memory and persistent storage device.

[0012] Furthermore, in the following description, a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a processor device in a broader sense, such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (for example, an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0013] In the following description, functions may be described using the expression "yyy unit." However, the functions may be realized by one or more computer programs executed by a processor, by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination thereof. When a function is realized by a program executed by a processor, the specified processing is performed using a storage device and / or an interface device, etc., as appropriate, and therefore the function may be considered to be at least a part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (e.g., a non-transitory storage medium). The description of each function is merely an example; multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0014] In the following description, data that produces an output in response to an input may be described using expressions such as "xxx table," but the data may have any structure (for example, structured data or unstructured data) or may be a model that outputs data in response to data input. Therefore, "xxx table" can be referred to as "xxx data." In the following description, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0015] In the following description, when elements of the same type are described without distinction, common reference symbols are used, and when elements of the same type are described with distinction, reference symbols are used.

[0016] FIG. 1 shows the overall configuration of a system according to an embodiment.

[0017] A terminal device 102 is communicably connected to the host processing system 50 via, for example, a communication network. The host processing system 50 includes a first host processing device 101.

[0018] All or part of the disclosure in WO2016 / 006101 may be incorporated into the first host processing device 101 and the terminal device 102. For example, the first host processing device 101 may have all or part of the functions of the host processing device 101 disclosed in WO2016 / 006101. Similarly, the terminal device 102 may have all or part of the functions of the terminal device 102 disclosed in WO2016 / 006101.

[0019] The host processing system 50 is provided with a second host processing device 191 that cooperates with the first host processing device 101. By cooperation between the first host processing device 101 and the second host processing device 191, measures for realizing the target future image can be proposed to the user instead of or in addition to intermediate goals such as when and what values ​​of which indicators should be achieved in order to realize the user's target future image. Note that, for example, the terminal device 102 may be an input / output console (e.g., a client device) of the host processing system 50. The terminal device 102 may communicate with at least one of the first host processing device 101 and the second host processing device 191. The terminal device 102 may communicate with the second host processing device 191 via or without the first host processing device 101.

[0020] The second host processing device 191 has an interface unit 151, an editing unit 152, an effect simulation unit 153, a policy decision unit 154, map information 155, policy tree information 156, and CLD (Causal Loop Diagram) information 157. The interface unit 151, the editing unit 152, the effect simulation unit 153, and the policy decision unit 154 are functions realized by a processor executing a program in a memory. The map information 155, the policy tree information 156, and the CLD information 157 are stored in a storage device (for example, a memory). Each function and information will be described later.

[0021] FIG. 2 shows the hardware configuration of the first host processing device 101, the second host processing device 191, and the terminal device 102.

[0022] Each of these devices 101, 191, and 102 is a computer, and includes a processor 121, a memory 122, and a network interface 124. The terminal device 102 further includes an input / output interface 123. The host processing devices 101 and / or 191 also include an input / output interface 123 when connected to output devices such as a display and a printer. The network interface 124 and the input / output interface 123 are examples of interface devices.

[0023] The memory 122 stores programs and data. The processor 121 executes the programs in the memory 122 to implement the functions of the device 101, 191, or 102.

[0024] The network interface 124 is a network interface for transmitting and receiving data. The input / output interface 123 is an interface for connecting to output devices such as a display and a printer, as well as a keyboard, a mouse, and a touch panel.

[0025] The devices 101, 191, and 102 may be implemented by different physical devices, or may be implemented by a single physical device. The devices 101, 191, and 102 may be one or more physical computer systems, or may each be a logical computer system based on a physical computer system. A "physical computer system" may be one or more physical computers. A "logical computer system" may be a virtual machine, a cloud computing system, or software executed on a physical computer system.

[0026] FIG. 3 shows an example of a policy tree UI 300.

[0027] The measure tree UI (User Interface) 300 is typically a GUI, displays a measure tree 310, and accepts designation of measures from the user.

[0028] The policy tree 310 is composed of multiple nodes 3 and multiple links 4. In the illustrated example, there is one root node 3A, but there may be multiple root nodes. There is a node 3 for each policy, and a link 4 between each of the policies. Each link 4 represents the order relationship between the policies. For each link 4, the relationship between the connecting policy and the connecting policy means that the connecting policy can be implemented (e.g., started or completed) after the connecting policy is implemented (e.g., started or completed). For example, node 3A is connected to node 3B via link 4B, which means that in order to implement the policy corresponding to node 3B, the corresponding policy must be implemented (e.g., started or completed) in node 3A.

[0029] The significance of the measure tree 310 is, for example, as follows. That is, as a goal adopted as a future vision (so-called final goal), a medium- to long-term goal (for example, a goal that requires several months to several decades) is typically adopted rather than a short-term goal (for example, a goal that can be achieved within a few hours to a few weeks). To realize a future vision as a goal, implementing one measure is generally not sufficient; it is necessary to implement multiple measures in the correct order. Therefore, a measure tree 310 is prepared, which defines measures that can be taken to realize the target future and the order in which those measures are arranged. The measure tree 310 may be defined in advance by a user, may be defined by a computer using a tool such as artificial intelligence (AI), or may be defined by a combination of these (for example, it may be generated by a computer and edited by a user).

[0030] Tree configuration information representing the policy tree 310 is included in the policy tree information 156. Specifically, for example, the tree configuration information may include information representing the policy name for each node, and may include information representing the source node and destination node for each link (edge). The editing unit 152 constructs the policy tree 310 based on the tree configuration information, and provides the policy tree UI 300 of the constructed policy tree 310 to the terminal device 102 via the interface unit 151.

[0031] An example of the policy tree 310 shown in Figure 3 is as follows: Note that, hereinafter, the destination node of a link connected to a node may be referred to as a "child node," and the source node of a link connected to a node may be referred to as a "parent node." Also, hereinafter, "lower node" may be a general term for child nodes and nodes lower than child nodes, and "upper node" may be a general term for parent nodes and nodes higher than parent nodes.

[0032] The nodes 3A and 3B corresponding to the measures that have been implemented are displayed in a display mode (for example, a color or a pattern) that indicates that the measures have been implemented.

[0033] Node 3B, which corresponds to the lowest implemented measure, is connected to nodes 3C1 to 3C5 via links 4C1 to 4C5, and nodes 3C1 to 3C5 are candidate measures for the next implementation. Of these measures, those that can be implemented realistically are those corresponding to nodes 3C2 and 3C5, and nodes 3C2 and 3C5 are displayed in a manner that indicates that they can be implemented realistically. A "realistically implementable measure" is a measure that satisfies predetermined requirements among the candidate measures for implementation that follow the lowest implemented measure. The "predetermined requirements" will be described later.

[0034] When any node in the policy tree 310 is specified, the editing unit 152 may perform the following process. Assume that the specified node is node 3C5 as shown in Fig. 3. In response to node 3C5 being specified by the user, the editing unit 152 may perform at least one of the following, for example. The editing department 152 acquires information about the measure corresponding to the node 3C5 from the measure tree information 156, and displays the measure information 302, which is at least a part of the acquired information, in the measure tree UI 300 (for example, near the specified node 3C5). The editing department 152 acquires information indicating the conditions under which a measure corresponding to node 3C5 can be implemented (for example, information corresponding to a link connected to node 3C5) from the measure tree information 156, and displays the acquired information 301 in the measure tree UI 300 (for example, near the link connected to node 3C5). Note that the information 301 may be at least a part of the index threshold value 402, which is exemplified in FIG. 4 and will be described later. The editing unit 152 identifies all or some of the subordinate nodes of node 3C5 and the links connected to each subordinate node from the policy tree information 156, and displays the identified subordinate nodes 3D1 to 3D3 and 3E1 to 3E3 and links 4D1 to 4D3 and 4E1 to 4E3 below node 3C5 in the policy tree UI 300. In other words, the policy tree UI 300 does not need to initially display the entire configuration of the policy tree 310, and the editing unit 152 may partially display or hide the tree configuration in response to a node designation by the user.

[0035] FIG. 4 shows the configuration of the policy management table 400.

[0036] The policy management table 400 is included in, for example, the policy tree information 156. The policy management table 400 has information for each policy as to whether the policy is a policy that can be realistically implemented. Specifically, for example, the policy management table 400 has an entry for each policy. The entry has information such as a policy 401, an index threshold 402, an implementation condition 403, an implementation flag 404, a target index 405, and a logic ID 406.

[0037] Measure 401 represents the name of the measure. Index threshold 402 represents a threshold that is compared with the target index value to determine whether the measure is a realistically implementable measure. Note that when the target index value is equal to or greater than the threshold, or when the target index value is less than the threshold, the "predetermined requirement" for the measure to be a realistically implementable measure is met. Whether the "predetermined requirement" is equal to or greater than the threshold or less than the threshold may be determined in index threshold 402.

[0038] The implementation condition 403 indicates the condition for a measure to become an implemented measure. The implementation flag 404 indicates whether the measure has been implemented or not ("1" means implemented, "0" means not implemented).

[0039] The objective index 405 represents an objective index (objective variable) as an index of the effect of a measure calculated to determine whether the measure is a realistically feasible measure. There is one or more objective indexes for each measure. In this embodiment, the entry has objective indexes 405A, 405B, ...

[0040] The logic ID 406 is the ID of the objective index calculation logic that calculates the objective index as an index of the effect of the policy. Any logic may be adopted as the calculation logic. In this embodiment, the calculation logic has a structure that complies with the CLD.

[0041] FIG. 5 shows the configuration of the index management table 500.

[0042] The indicator management table 500 is included in, for example, the measure tree information 156. The indicator management table 500 has information about indicators for each measure. Specifically, for example, the indicator management table 500 has an entry for each measure. The entry has information such as a measure 501, a target indicator 502, a numerical change vector 503, an explanatory indicator 504, a contribution rate 505, a missing flag 507, an occurrence count 508, and a notification flag 509. Note that information such as the "contribution rate" for each explanatory indicator is an example of reference information that serves as the basis for the importance of an explanatory variable with respect to a target variable, and other information may be used as reference information instead of or in addition to the contribution rate as the basis for the importance of an explanatory variable with respect to a target variable.

[0043] Measure 501 represents the measure name. Objective indicator 502 represents the objective indicator as an indicator of the effect of one or more measures for the measure. Numerical change vector 503 represents the trend due to the implementation of the measure for each objective indicator. Since the trend due to the implementation of the measure can be represented for each objective indicator using link type 702 ("1" or "-1") described below in the CLD (causal model), the quantification vector 503 may not be necessary, and similarly, the numerical change vector may not be written for objective indicator 405 in FIG. 4.

[0044] The explanatory indicators 504 represent, for each objective indicator, one or more explanatory indicators (explanatory variables) related to that objective indicator. The contribution rate 505 represents, for each explanatory indicator, the degree of contribution to the objective indicator. The missing flag 507 represents, for each explanatory indicator, whether or not the explanatory indicator is missing in the results of the first host processing ("1" means missing (i.e., no explanatory indicator), and "0" means not missing (i.e., the explanatory indicator is present)). The number of occurrences 508 represents the number of times the explanatory indicator is used to calculate the multiple objective indicators 502 when there are multiple objective indicators 502 for one measure 501 (typically, the number of times the indicator appears as a variable in the formula for calculating the objective indicator 502). Note that instead of or in addition to the number of occurrences 508, a "number of measures used" may be present for each explanatory indicator, and the "number of measures used" represents, for each explanatory indicator, the number of measures that use the explanatory indicator, specifically, the number of measures that use the explanatory indicator to calculate the objective indicator. The notification flag 509 indicates, for each explanatory indicator, whether or not the indicator should be calculated by simulation in the first host processing ("1" means that calculation is required, and "0" means that calculation is not required).

[0045] FIG. 6 shows the configuration of the node management table 600. FIG. 7 shows the configuration of the link management table 700. FIG. 8 shows an example of the configuration of the connection structure management table 800. These tables 600, 700, and 800 exist for each objective index calculation logic (for each logic ID). A set of tables 600, 700, and 800 is identified from the logic ID of the objective index calculation logic. These tables 600, 700, and 800 represent the CLD structure of the objective index calculation logic. In the CLD, nodes correspond to indicators (objective indicators or explanatory indicators), and links correspond to dependencies between indicators.

[0046] 6, the node management table 600 has an entry for each node in the CLD. The entry has a node ID 601, an index name 602, and a purpose flag 603. The node ID 601 indicates the ID of the node. The index name 602 indicates the index name corresponding to the node. The purpose flag 603 indicates whether the index is a purpose index or an explanation index ("1" means a purpose index, and "0" means an explanation index).

[0047] 7, the link management table 700 has an entry for each link in the CLD. The entry has a link ID 701, a link type 702, and a delay flag 703. The link ID 701 indicates the ID of the link.

[0048] The link type 702 indicates the type of link. "1" indicates a positive correlation, and "-1" indicates an inverse correlation. A "positive correlation" means that the cause and result nodes increase or decrease in the same direction, such as when the cause node in a causal relationship increases, the result node also increases, or when the cause node decreases, the result node also decreases. An "inverse correlation" means that the cause and result nodes increase or decrease in opposite directions, such as when the cause node increases, the result node decreases, or when the cause node decreases, the result node increases. Note that a CLD alone does not contain numerical data. A CLD is a qualitative model of events. Using this qualitative model as a framework, a quantitative model is created in which functions are assigned to each link. Creating a quantitative model from a qualitative model makes it possible to calculate indicators. When assigning a function to a link, the function is set while determining whether the link is a positive correlation ("1") or an inverse correlation ("-1").

[0049] The delay flag 703 indicates whether the link corresponds to a delay. "1" means a delay, and "0" means no delay. Note that "delay" means that it takes time from when a cause node changes until it affects a result node (changes with a delay). How long it takes to be considered a "delay" can be determined when designing the CLD. Like the link type 702, the delay flag 703 is referenced when setting a function for the link.

[0050] As shown in FIG. 8, the connection structure management table 800 has an entry for each connection structure in the CLD. The entry has a cause node ID 801, a result node ID 802, and a link ID 803. The cause node ID 801 represents the ID of the cause node. The result node ID 802 represents the ID of the result node. The link ID 803 represents the ID of the link between those nodes. Note that at least one connection structure may not have a causal relationship such as cause-result, and therefore the link may not necessarily be a directed link but may be an undirected link. Furthermore, the link may be associated with a function for calculating an index value of an index corresponding to a result node using an index value of an index corresponding to the cause node, and this function is one element of the objective index calculation logic.

[0051] An example of the processing performed in this embodiment will be described below.

[0052] FIG. 16 shows a schematic diagram of the support process.

[0053] The support processing is a process in which a first host processing 1601 and a second host processing 1602 are repeated. The first host processing 1601 is performed by the first host processing device 101. The second host processing 1602 is performed by the second host processing device 191 using data (hereinafter, first result data) 1651 output as a result of the first host processing 1601 as input. Data (hereinafter, second result data) 1652 output as a result of the second host processing 1602 is used as input to the first host processing 1601, and the first host processing 1601 is performed.

[0054] The first host process 1601 is a simulation of the time-series change in the evaluation value of the target over a long span of time. On the other hand, the second host process 1602 is a simulation of the effect of implementing measures over that span of time. These simulations are linked together.

[0055] Specifically, for example, the first host processing device 101 may hold an evaluation function. The evaluation function is a function for executing a simulation by calculating an evaluation value using multiple time points and one or more index values ​​(parameter values) for each time point. The first host processing device 101 may calculate an evaluation value for each time point using one or more index values ​​for each of the multiple time points and the evaluation function. The "evaluation value" may be an index value of an index of a target future image, such as a target index value in the first host processing 1601, for example, a KGI (Key Goal Indicator). The first result data 1651 may be data disclosed in WO2016 / 006101, such as the data illustrated in FIG. 5. Specifically, for example, the first result data 1651 may include one or more simulation results. For each simulation result, the simulation result includes, for each of a plurality of times in a future period from the present to a target time, the time, an evaluation value calculated at the time, and an index value (parameter value) of each index (e.g., explanatory index) used to calculate the evaluation value. For example, the first result data 1651 includes, for each time of each simulation result, an evaluation value and each index value of a plurality of indexes (e.g., indexes A to C) (e.g., a1 and a2 are each the index value of index A). For each simulation result, the first host processing device 101 may display the evaluation value for each of the plurality of times (i.e., a time series of the evaluation values) on the terminal device 102.

[0056] The second host processing 1602 supports the determination of measures that can contribute to the realization of an evaluation value time series in a certain simulation result among one or more simulation results represented by the first result data 1651. The "certain simulation result" may be a simulation result that is optimal for realizing a target future image, for example, a simulation result specified by a user. The second host processing 1602 includes a measure presentation 1621, an effect simulation 1622, and a measure decision 1623.

[0057] In the policy presentation 1621 , the editorial department 152 presents a policy tree based on the policy tree information 156 .

[0058] In the effect simulation 1622, the effect simulation unit 153 calculates the effect (one or more purpose indicators) of each candidate action measure based on certain simulation result data in the first result data 1651, using the purpose indicator calculation logic corresponding to the candidate action measure in the CLD information 157 to determine whether the candidate action measure is realistically feasible. For each candidate action measure, the one or more purpose indicator values ​​calculated using the purpose indicator calculation logic and the one or more explanatory indicator values ​​used in the calculation are at least some of the multiple indicators in the "certain simulation result" (processing result in the first host processing 1601). For measures decided in the subsequent policy decision 1623, second result data 1652 including one or more purpose indicator values ​​calculated for the decided policy is output, and these one or more purpose indicator values ​​become the indicator values ​​used in the calculation of the evaluation value in the first host processing 1601. In other words, the user can learn how the index value (target index value) representing the effect of the measures decided in the second host processing 1602 will affect the evaluation value from the results of the first host processing 1601, which is performed using the second result data 1652 as input.

[0059] Incidentally, based on the results of this effect simulation 1622, if one or more calculated objective index values ​​for each candidate measure for implementation satisfy predetermined requirements associated with the candidate measure for implementation (for example, if the objective index value is equal to or greater than the threshold indicated by the indicator threshold 402 or less than the threshold), the editing unit 152 changes the display mode of the node corresponding to the candidate measure for implementation to the display mode of the node corresponding to a measure that can actually be implemented in measure presentation 1621. The results calculated using the objective index calculation logic for each candidate measure for implementation may include at least one of the time of implementation of the measure, the cost of implementing the measure, and the index value at the time of implementation of the measure, in addition to one or more objective index values ​​(i.e., the effect of implementing the measure).

[0060] In the policy decision 1623, the policy decision unit 154 decides a policy from one or more candidate policies for implementation, either automatically or in response to a user's instruction. For the decided policy, the policy decision unit 154 outputs second result data 1652 including one or more objective index values ​​calculated for the policy to the first host processing device 101. The second result data 1652 includes data indicating the implementation timing of the decided policy, in addition to the one or more objective index values.

[0061] The above is an outline of the support process.

[0062] For each candidate policy, the implementation time of the policy may be a time predetermined for the policy (for example, a time predetermined in the policy tree information 156), or may be a time calculated automatically based on a predetermined policy.

[0063] In this embodiment, the unit of "time" may be a precise unit such as year, month, day, hour, minute, and second, but it does not have to be such a precise unit and may be a coarse unit such as only year and month. In other words, this "time" may also be called a "period."

[0064] Furthermore, after a node corresponding to a realistically implementable measure is presented in measure presentation 1621, effect simulation 1622 may be skipped and measure decision 1623 may be performed. That is, when a user designates a realistically implementable measure as the next measure to be implemented via measure tree UI 300, the designation may be confirmed in measure decision 1623, and second result data 1652 may be output.

[0065] In addition, in the effect simulation 1622, instead of each candidate measure for implementation, one or more objective index values ​​may be calculated using objective index calculation logic for at least one candidate measure for implementation (for example, a candidate measure for implementation specified by the user in the measure tree UI300), and / or for each of all or some of the subordinate measures of at least one candidate measure for implementation.

[0066] Furthermore, in effect simulation 1622, the effect simulation unit 153 may calculate a contribution rate for each explanatory indicator value used in calculating the objective index value, and record the calculated contribution rates in the indicator management table 500. Based on the indicator management table 500, for each measure for which an objective index value has been calculated, the editing unit 152 may display the contribution rate for each explanatory indicator value for which the objective index value has been calculated on the measure tree UI 300 or another UI provided to the terminal device 102. In measure decision 1623, the user may decide on a measure to be implemented based on the contribution rate.

[0067] Furthermore, in the policy decision 1623, one or more policies may be decided, and at least one policy may not be an implementation candidate policy. The second result data 1652 may represent an implementation time and one or more objective index values ​​for each decided policy. For example, multiple policies with different implementation times may be decided. Based on the second result data 1652 for such multiple policies, the first host processing 1601 may calculate an evaluation value time series based on the order of the different implementation times and multiple index values ​​including one or more objective index values ​​for each implementation time as one or more index values. This makes it possible to perform a simulation that informs the user how multiple policies with different implementation times affect the evaluation value.

[0068] Furthermore, in the second result data 1652 input to the first host processing 1601, the target index value used in the first host processing 1601 may be all or part of the calculated target index value (for example, the target index value of the target index specified by the user).

[0069] Furthermore, in the second host processing 1602, the effect simulation unit 153 may assume that a measure (or other measure) that can actually be implemented is an implemented measure, for example, in response to a user's designation, and may assume that a measure corresponding to each child node of the node corresponding to the measure assumed to be an implemented measure is a candidate measure for implementation, and may calculate one or more objective index values ​​using objective index calculation logic. By repeating the effect simulation 1622 in this manner, the sequential implementation of measures is simulated, and the implementation time and one or more objective index values ​​for each measure in the simulation can be determined by the measure decision unit 154 in the second result data 1652.

[0070] FIG. 9 shows the overall flow of the support process.

[0071] The editing unit 152 determines whether an area designation has been received (S901). For example, the editing unit 152 can provide the terminal device 102 with an area designation UI 1300 illustrated in FIG. 13 and accept designation of a user-desired area 1310 via the area designation UI 1300. The area designation UI 1300 will be described later. The method for designating the area 1310 is not limited to a specific method. For example, an area can be designated not only from a map, but also by selecting from a list.

[0072] If an area is specified (S901: No), the editorial department 152 performs a process of determining whether a policy has been implemented (S902).

[0073] Thereafter, the effect simulation unit 153 determines whether simulation result data has been received from the first host processing device 101 (S903). This "simulation result data" may be all or a part of the first result data 1651. The part of the first result data 1651 may be data representing one or a part of a simulation result out of a plurality of simulation results. The "simulation result data" includes data representing a plurality of times and an evaluation value and an index value set (one or more index values) for each of the plurality of times.

[0074] When the simulation result data is received, the effect simulation unit 153 performs a process to determine feasible measures (S904). "Feasible measures" are measures that can be realistically implemented. The effect simulation unit 153 transmits a re-simulation request associated with the second result data 1652 to the first host processing device 101 (S905). The results of the re-simulation (first host processing) performed in response to the request are visualized by the first host processing device 101 (S906).

[0075] The effect simulation unit 153 determines whether to end the process (S907). For example, if the user does not want to search for measures again after seeing the results of the re-simulation, the process ends (S907: Yes). On the other hand, if the user wants to search for measures again after seeing the results of the re-simulation, for example, if the user specifies a desired simulation result (e.g., an evaluation value time series), the second host processing device 191 receives data of the specified simulation result from the first host processing device 101, so the process does not end (S907: No), and S904 and subsequent steps are performed using the data of the simulation result. In this way, S904 to S907, i.e., the coordination between the long-span simulation by the first host processing device 101 and the simulation of the effect of measures by the second host processing device 191, is repeated.

[0076] FIG. 10 shows the flow of the process of determining whether a measure has been implemented (S902 in FIG. 9).

[0077] The editing department 152 selects one undetermined measure (S1001). An "undetermined measure" is one or more measures to be determined that have not yet been determined in this determination process as to whether they have been implemented. "One or more measures to be determined" are measures that correspond to one or more child nodes of the node corresponding to the lowest-level implemented measure (a measure whose implementation flag 404 is "1"). The number of measures that have been implemented increases in accordance with the order of measures represented by the measure tree. Therefore, the measure to be determined needs to be only the measure that follows the lowest-level implemented measure.

[0078] The editing unit 152 refers to the implementation condition 403 corresponding to the measure selected in S1001 (S1002) and acquires data from the map information 155 (S1003). Here, the map information 155 includes, in addition to map data, various data for each area on the map (e.g., data on population, number of houses, number of passengers getting on and off at stations per unit period or for a specified period). The data acquired in S1003 is data for the area specified by the user. Using the data acquired in S1003, the editing unit 152 determines whether the condition represented by the implementation condition 403 referenced in S1002 is satisfied. If the determination result is true, the editing unit 152 updates the implementation flag 404 corresponding to the measure selected in S1001 to “1” (S1004) and changes the display mode of the node corresponding to the measure in the measure tree to an “implemented” display mode (S1005). Note that the implementation flag 404 may be updated to “1” after determining whether the user has already implemented the measure.

[0079] If there are still undetermined measures (S1006: Yes), the editing department 152 performs S1001. If there are no undetermined measures (S1006: No), the editing department 152 ends this process.

[0080] FIG. 11 shows the flow of the process of determining feasible measures (S904 in FIG. 9).

[0081] The effect simulation unit 153 refers to the policy tree information 156 and identifies all candidate policies for implementation (that is, policies corresponding to all child nodes of the node corresponding to the lowest implemented policy) (S1101).

[0082] The effect simulation unit 153 acquires explanatory index values ​​for each time used in the calculation logic for trial calculation of the objective index value of each candidate implementation measure from the simulation result data from the first host processing device 101 (S1102). For example, for each candidate implementation measure, the effect simulation unit 153 acquires explanatory index values ​​for each time used in the calculation logic corresponding to the logic ID 406 of the measure from the simulation result data based on all explanatory indexes 504 corresponding to the measure.

[0083] The effect simulation unit 153 determines, for each candidate implementation measure, whether all explanatory indicators required for calculating the objective index value of the measure have been acquired from the simulation result data (S1103). That is, not all explanatory indicators are necessarily included in the simulation result data from the first host processing device 101, and therefore S1103 is performed. If the result of the determination in S1103 is true (S1103: Yes), S1104 to S1107 are skipped and the process proceeds to S1108.

[0084] If the result of the determination in S1103 is false (S1103: No), the effect simulation unit 153 compares all index values ​​contained in the simulation result data from the first host processing device 101 with all explanatory index values ​​required for each candidate measure, and provides the terminal device 102 with a UI that displays whether or not each of all explanatory index values ​​required for each candidate measure is included in the simulation result data (S1104). This UI displays, for example, the index management table 500 illustrated in FIG. 5. For explanatory index values ​​that cannot be obtained from the simulation result data, the effect simulation unit 153 sets the missing flag 507 of the corresponding explanatory index to "1," and for explanatory index values ​​that can be obtained from the simulation result data, the effect simulation unit 153 sets the missing flag 507 of the corresponding explanatory index to "0."

[0085] When the effect simulation unit 153 receives a request from the user to acquire at least one indicator for which the missing flag 507 is set to "1" (for example, "1" is set to the notification flag 509 in the UI provided in S1104) (S1105: Yes), it transmits a simulation request specifying the requested indicator and period to the first host processing device 101 (S1106). The "period" that can be specified here may be at least a partial period (for example, one or more times) between the present and the target time (the time when the targeted future image is realized). Note that in addition to the indicator and period, the simulation request may be associated with the corresponding logic (for example, a CLD of the logic ID corresponding to the corresponding measure or a regression analysis function), and this logic may also be passed to the first host processing device 101 for use.

[0086] In response to the request, if the effect simulation unit 153 receives the simulation results of the index values ​​for each time period from the first host processing device 101 (S1107: Yes), the process proceeds to S1108. On the other hand, if there is no acquisition request from the user for an index whose missing flag 507 is "1" (S1105: No), S1106 and S1107 are skipped and the process proceeds to S1108. Note that even if the first host processing device 101 returns a result that "simulation is not possible," the process may proceed to S1108, in which case the calculation may be performed using only the data possessed by the second host processing device 191.

[0087] The effect simulation unit 153 calculates the target index value for each candidate measure for implementation by year using the target index calculation logic identified from the logic ID 406 corresponding to the candidate measure for implementation (S1108). "Year" is an example of a time interval in the time series of target index values. The period as the range of the time series of target index values ​​and / or the time interval in the time series of target index values ​​may be predetermined for each measure 401 and / or each target index 405 in, for example, the measure management table 400 (for example, the measure management table 400 may include, for each target index 405 of the measure, an expected period until the index value of the target index is obtained (e.g., "1 month," "1 year," etc.)). Furthermore, for each candidate measure for implementation, the start time of the time series of each target index value may be the current time, and the end time may be the last time of multiple times represented by the simulation result data from the first host processing device 101. In this way, a time series of each target index value is obtained for each candidate measure for implementation. Furthermore, if there is an explanatory indicator value for which the missing flag 507 is "1" but no simulation results are obtained from the first host processing device 101, the objective indicator value (the effect of the candidate measure to be implemented) may be calculated without such an explanatory indicator value.

[0088] If, for at least one candidate measure for implementation, a target index value that satisfies the relationship with the threshold value represented by the index threshold value 402 corresponding to the candidate measure for implementation is obtained for at least one year (S1109: Yes), the effect simulation unit 153 determines that the candidate measure for implementation is an implementable measure and changes the display mode of the node corresponding to the candidate measure for implementation (S1110).

[0089] Fig. 12 shows the flow of the index extraction process. This process is possible when the second host processing device 191 has previously obtained from the first host processing device 101 a list of KPIs that can be simulated by the first host processing device 101, and when data related to the area has been previously stored in the map information 155, for example, by obtaining it from an open data portal. In such a case, the process of Fig. 12 is performed after a list of candidate measures to which transition may be possible is obtained by performing S902 of Fig. 9 (processing for determining implemented measures). For example, the process may be performed between S1101 and S1102 of Fig. 11.

[0090] The effect simulation unit 153 acquires, from the map information 155, index values ​​related to the area designated by the user for explanatory indicators linked to a measure (for example, a candidate measure to be implemented) (S1201).

[0091] The effect simulation unit 153 performs a correlation analysis of each explanatory index (S1202). For example, the effect simulation unit 153 calculates the contribution rate for each explanatory index. The effect simulation unit 153 extracts explanatory indexes with high correlation for each objective index 502 (S1203). The explanatory indexes extracted here may be highlighted in the UI provided in S1104, for example.

[0092] A specific example of the processing in FIG. 12 is as follows. For KPIs (which serve as explanatory indicators or objective indicators) acquired from the first host processing device 101 and for which actual data can be acquired from the map information 155, the effect simulation unit 153 maps the KPI names to the actual data. Actual data may not be available for all indicators in the KPI list, and in such cases, no actual data is available. Conversely, there may be data that is not included in the acquired KPI list but is stored independently in the map information 155. This data may be an explanatory indicator or objective indicator, and therefore may be acquired by the effect simulation unit 153. The effect simulation unit 153 refers to the objective indicator of the candidate measure and extracts explanatory indicators with a high contribution rate to explaining the objective indicator from the data corresponding to the KPI acquired from the first host processing device 101 and the data stored independently in the map information 155. The method for calculating the contribution rate of the explanatory indicator may conform to the method for calculating the contribution rate used in multiple regression analysis, etc. The degree of contribution rate to be extracted may be determined by a predetermined rule, such as by setting a threshold value in advance by the user, or by limiting it to the top five, etc. The higher the contribution rate, the greater the explanatory index influence when calculating the target index. Therefore, knowing the numerical value of the explanatory index provides the user with information (support information) for deciding whether to request a re-simulation from the first host processing device 101.

[0093] FIG. 13 shows an example of an area designation UI 1300.

[0094] The area specification UI 1300 displays a region within a range specified by the user based on the map information 155, and an area 1310, which is a range desired by the user, is specified from that region. Note that the "area" may be an example of an object for which a desired future image is to be realized. In the present invention, any object, such as an "organization" such as a company or a local government, can be applied as an object for which a desired future image is to be realized.

[0095] Based on the map information 155 and the policy tree information 156, the editing unit 152 may display, for example, a user-specified area 1310 in the area specification UI 1300, at least one of an indicator list 1311 (e.g., a list of explanatory indicators represented by the explanatory indicator 504 in FIG. 5 ), a policy list 1312 (e.g., a list of at least one of implemented policies, candidate policies, and realistically implementable policies), and a KGI list 1313 (e.g., a list of objective indicators represented by the objective indicator 502 in FIG. 5 ). For example, the status of the user-specified area 1310 (e.g., each indicator and KGI) may be displayed on the UI 1300 according to a heat map, people flow, traffic flow, etc. Furthermore, the user can accept policies to be introduced in the area 1310 (i.e., policies to be decided in policy decision 1623) by UI operations such as dragging and dropping from the policy list 1311 onto the map. Policies that are not suitable for a map can be accepted in another UI using check boxes, pull-down menus, etc.

[0096] Note that for KGI, indexes, etc., numerical data may be displayed in the form of graphs as illustrated in Fig. 14. That is, a KGI list 1401 having a list of KGI graphs, or an index list 1402 having a list of index value graphs may be displayed in the area specification UI 1300 or another UI.

[0097] FIG. 15 shows an example of a simulation result UI 1500.

[0098] The simulation result UI 1500 is a UI provided to the terminal device 102 by the first host processing device 101, and displays multiple simulation results. This UI 1500 may be provided to the terminal device 102 from the first host processing device 101 via the second host processing device 191.

[0099] Each simulation result is a time series of evaluation values. Each evaluation value time series is associated with a set of index values ​​(index values ​​for one or more indexes) for each time in the time series period. For example, when a user specifies a desired simulation result and time, the set of index values ​​for that simulation result and time is displayed.

[0100] The data of the simulation result specified from the simulation result UI 1500 may be the data input to the second host processing device 191.

[0101] Furthermore, when the second result data 1652 of the second host processing 1602 is input to the first host processing 1601 and a re-simulation is performed, the evaluation value time series 400a as a result of the re-simulation may be displayed in the simulation result UI 1500 in addition to the evaluation value time series before the re-simulation (#21 to #24 and #10 to #20). This allows the user to know the evaluation value time series when the measure decided in the measure decision 1623 is implemented. For example, by looking at the UI 1500 illustrated in FIG. 15 and comparing the simulation result (evaluation value time series) specified by the user before the re-simulation with the simulation result as a result of the re-simulation, the user can determine whether the desired simulation result can be expected by implementing the decided measure.

[0102] The reason why the re-simulation results showed a significantly higher evaluation value at an earlier time than the user-specified simulation results depends on the start time of the measure decided in measure decision 1623 and the time series of the objective index value of the measure. In other words, the time series of the "start time" and "objective index" (i.e., the estimated result of when and what the objective index will become by taking a certain measure) affects the re-simulation results. The service design details, such as what kind of measure to take (which measure to select from multiple candidates) and the scale of that measure, also affect the re-simulation results.

[0103] Although one embodiment has been described above, this is merely an example for the purpose of explaining the present invention, and the scope of the present invention is not intended to be limited to this embodiment. The present invention can also be implemented in various other forms. For example, measures aimed at reduction, such as "eliminating XX," may be adopted instead of or in addition to measures aimed at increase, such as "building XX." Specifically, if the results of a simulation performed by the second host processing device 191 indicate that the implemented measures will no longer satisfy the index threshold 402 in several years (e.g., due to various social changes, station building revenues will not reach the threshold), an alert can be issued in the UI illustrated in FIG. 13 to suggest that consideration is needed for measures such as downsizing the station building or reducing train and bus routes.

[0104] The above-described embodiment can be summarized as follows: The following summary may include supplementary explanations and explanations of modifications to the above explanations.

[0105] The data processing system (e.g., host processing system 50) includes an interface device (e.g., network interface 124) that communicates with a user terminal (e.g., terminal device 102), a storage device (e.g., memory 122) that stores policy management information (e.g., policy management table 400) including information representing one or more objective indicators and one or more explanatory indicators for each policy, and a processor (e.g., processor 121) connected to the interface device and the storage device. The processor is configured to perform second data processing (e.g., second host processing 1602) using as input first result data (e.g., first result data 1651), which is data resulting from first data processing (e.g., first host processing 1601). The first data processing includes a process of calculating an evaluation value as an index value of a target future image for each of a plurality of times based on input data including data representing a plurality of times and index value sets for each of the plurality of times, and outputting the first result data. The first result data includes data of one or more simulation results. Each of the one or more simulation results includes a plurality of time points, an evaluation value for each of the plurality of time points, and a set of index values ​​on which the evaluation value is based. Each evaluation value set is one or more evaluation values. The second data processing includes the following. For each of one or more measures, identify the objective indicator value and explanatory indicator value of the measure from the measure management information, and calculate one or more objective indicator values ​​as the effect of the measure using at least one indicator value from the set of indicator values ​​in at least one simulation result represented by the first result data as an explanatory indicator value. Outputting second result data including a target indicator value calculated for at least one of the one or more measures for the first data processing to be performed based on input data including at least one target indicator value from the second result data as at least one of the indicator value sets.

[0106] In this way, the first data processing and the second data processing work together to support the decision on measures to realize a desired future image.

[0107] The storage device may store order information (e.g., a part of the action tree information 156) indicating an order relationship between the actions. The aforementioned "at least one action" may be an action determined from one or more actions. The processor may determine that determining (implementing) an action requires that the action immediately before the action has already been implemented (e.g., implementation has started or completed). This allows measures that cannot be implemented to be excluded from the options, thereby supporting the decision on an action. For example, each of the one or more actions may be the action next to the lowest implemented action identified based on the order information. Furthermore, for example, the action management information may associate, with each of the one or more actions, a condition related to at least one objective index value of the action as a transition condition for the action to become implementable after the action immediately before the action. The second data processing may include, for each of the one or more actions, determining the action as an implementable action, that is, a action that is actually implementable, if the calculated objective index value of the action satisfies the transition condition of the action. At least one action may be a action determined to be an implementable action. If at least a part of the description in this paragraph and the next paragraph is considered to be a description according to another aspect, the following can be expressed as an example of a data processing system according to the another aspect. That is, the data processing system may include an interface device that communicates with a user terminal, a storage device that stores order information that indicates an order relationship between measures, and a processor connected to the interface device and the storage device. Based on the order information, the processor may determine (implement) a measure if it is satisfied that the measure immediately before the measure in question has been implemented (for example, implementation has started or implementation has been completed). For example, the processor may determine whether or not to transition the measure based on a measure tree or a threshold value identified from the order information.

[0108] The storage device may store policy tree information (e.g., policy tree information 156) including order information. The policy tree information may be information about a policy tree (e.g., policy tree 310) composed of multiple nodes and multiple links. In the policy tree, each node may correspond to a policy, and each link may correspond to an order relationship between the policies. The processor may provide a UI (e.g., policy tree UI 300) that displays at least a portion of the policy tree. This allows the user to easily understand the order relationship between the policies.

[0109] The processor may display, on the UI, information indicating transition conditions associated with at least a node in the policy tree that corresponds to a policy designated by the user. The processor may also set the display mode of a node in the policy tree for a policy determined to be an actionable policy to a display mode indicating an actionable policy. These features can assist in policy decision-making.

[0110] The data output by the first data processing has, for example, two aspects. One is the "estimated value of a certain indicator at a certain time in the future." The other is the "recommended value of which indicator needs to be at what value at the time when a branch occurs toward the target future image." For example, in the above-described embodiment, the logic corresponding to logic ID 406 is logic with a concept of time for simulating "timing" (time). In a causal relationship model based on system dynamics, when a cause node changes, it is possible to estimate how many result nodes will be, for example, one year from now. In the above-described embodiment, the candidate values ​​of explanatory indicators to be incorporated into the CLD are narrowed down to a certain extent from the results of correlation analysis and regression analysis, and then the CLD is created. Based on the CLD, a system dynamics-based simulation model incorporating the concept of time is created, and a simulation may be performed based on the simulation model.

[0111] The logic is not limited to CLD logic, and any causal model may be used. Also, it is not necessary to use a causal model; any logic (such as correlation analysis or regression analysis) that can estimate the effect of implementing a policy (that can calculate the target variable from the explanatory variables) may be used. [Explanation of symbols]

[0112] 50: Host processing system 101: First host processing device 102: Terminal device 191: Second host processing device

Claims

1. an interface device for communicating with a user terminal; a storage device that stores policy management information including information representing one or more objective indicators and one or more explanatory indicators for each policy; a processor connected to the interface device and the storage device; Equipped with the processor is configured to perform second data processing using first result data, which is data resulting from the first data processing, as an input; the first data processing includes a process of calculating an evaluation value as an index value of a target future image for each of a plurality of times based on input data including data representing a plurality of times and an index value set for each of the plurality of times, and outputting the first result data; the first result data includes data of one or more simulation results; each of the one or more simulation results includes the plurality of times, an evaluation value for each of the plurality of times, and an index value set on which the evaluation value is based; Each rating value set is one or more ratings, The second data processing includes: - For each of one or more measures, identify an objective indicator value and an explanatory indicator value of the measure from the measure management information, and calculate one or more objective indicator values ​​as the effect of the measure using at least one indicator value of the indicator value set in at least one simulation result represented by the first result data as an explanatory indicator value; and outputting second result data including a target index value calculated for at least one measure among the one or more measures for the first data processing to be performed based on input data including at least one target index value among the second result data as at least one of an index value set; Data processing system.

2. the storage device stores order information representing an order relationship between measures; the at least one measure is a measure determined from the one or more measures, The processor determines that, in order to decide on a measure, it is necessary that a measure immediately before the measure has been implemented.

10. The data processing system of claim 1.

3. Each of the one or more measures is a measure next to a lowest-ranked implemented measure identified based on the order information.

3. The data processing system of claim 2.

4. In the policy management information, a condition regarding at least one objective index value of each of the one or more policies is associated with the policy as a transition condition for the policy to become executable next to a policy immediately before the policy; the second data processing includes determining, for each of the one or more measures, that the measure is an implementable measure that is a measure that can be implemented in reality when the calculated objective index value of the measure satisfies the transition condition of the measure; The at least one measure is a measure determined to be an implementable measure.

3. The data processing system of claim 2.

5. the storage device stores policy tree information including the order information; The policy tree information is information about a policy tree that is configured with a plurality of nodes and a plurality of links, In the policy tree, each node corresponds to a policy, and each link corresponds to an order relationship between policies; the processor provides a UI (User Interface) that displays at least a part of the policy tree; 5. The data processing system of claim 4.

6. the processor displays, on the UI, information indicating a transition condition associated with a node in the policy tree that corresponds to at least a policy designated by a user; 6. The data processing system of claim 5.

7. the processor changes the display mode of a node of the measure determined to be the feasible measure in the measure tree to a display mode that indicates the feasible measure.

6. The data processing system of claim 5.

8. performing second data processing by a computer using first result data as input, the first result data being data resulting from the first data processing; the first data processing includes a process of calculating an evaluation value as an index value of a target future image for each of a plurality of times based on input data including data representing a plurality of times and an index value set for each of the plurality of times, and outputting the first result data; the first result data includes data of one or more simulation results; each of the one or more simulation results includes the plurality of times, an evaluation value for each of the plurality of times, and an index value set on which the evaluation value is based; Each rating value set is one or more ratings, The second data processing includes: - For each of one or more measures, an objective indicator value and an explanatory indicator value of the measure are identified from measure management information including information representing one or more objective indicators and one or more explanatory indicators for each measure, and one or more objective indicator values ​​are calculated as the effect of the measure using at least one indicator value of the indicator value set in at least one simulation result represented by the first result data as an explanatory indicator value; and outputting second result data including a target index value calculated for at least one measure among the one or more measures for the first data processing to be performed based on input data including at least one target index value among the second result data as at least one of an index value set; Data processing methods.

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