Digital twin generation method, digital twin generation program, and digital twin search method

Abstract

To provide a digital twin generation device that can shorten search time by optimizing the relationship between digital twins.SOLUTION: A digital twin generation device 100 aggregates a plurality of digital twins that meet a specific condition for aggregating among a plurality of digital twins, generates a single aggregated digital twin 150 that sets meaning of aggregation, and assigns an identification tag to the generated aggregated digital twin 150. This enables search time to be reduced by searching aggregated digital twins that have a reduced number of relationships between a plurality of digital twins.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a digital twin generation method, a digital twin generation program, and a digital twin search method. [Background technology]

[0002] Digital Twin (DT) makes it possible to represent images of things in the real world and the relationships between images. DT is digital data that is generated by sensing and digitizing values ​​(properties) that represent the characteristics of objects, such as the speed of the objects, and relationships between objects, for objects that exist in the real world, such as roads and electric vehicles (EVs).

[0003] The properties and relationships of digital twins such as roads and EVs vary over time. For this reason, many systems that store digital twins have a time-series database (DB) that records the changes at regular intervals. For example, the time-series DB of a DT for EV road driving records, for each EV, the properties of each road that the EV has traveled on, the EV's driving relationships on each road, and the EV's properties while driving over time.

[0004] By using DT, it is possible to check the impact and effectiveness of past measures. To check the effectiveness of a measure, it is possible to check the DT that has an impact on the implemented measure, and to check the situation similar to the real world situation, for example, the relationship between the property values ​​and relationships of multiple digital twins, by searching the DT.

[0005] Prior art related to DT construction and search includes, for example, an exploratory eDT in which an arbitrary clone cDT is generated and combined from a source sDT, which modifies objects at time increments and predicts the development of simulation results. In addition, an exploratory eDT in which an arbitrary clone cDT is generated and combined from a source sDT is calculated to determine the collision risk involving the vehicle itself and the multiple remote vehicles. In addition, in an exploratory eDT in which an arbitrary clone cDT is generated and combined from a source sDT, the route of a bus operation is dynamically updated by other twins of the sDT / cDT in response to an incident. In addition, in control between multiple systems, there is a system that combines and manages each received target area, selects necessary information according to the purpose of the system, and delivers the selected information to a system to be constructed. In addition, there is a system that obtains related information of the behavior performed by a target involved in a waiting event and goal information for evaluation, proposes an action plan to achieve the purpose based on each obtained information, and performs a simulation to optimize the event. In addition, there is a smart parking system that searches for a DT associated with an individual, detects a vehicle entering a parking lot, identifies the individual associated with the vehicle, searches for context information corresponding to the individual, and determines a parking space based on the context information and the parking space. There is also a logistics management system that optimizes the operation process of a logistics sorting terminal by constructing and verifying a DT that can simulate the operation of the sorting terminal (see, for example, Patent Documents 1 to 7 below). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2022-171571 A [Patent Document 2] JP 2020-9428 A [Patent Document 3] Patent Publication No. 2022-65622 [Patent Document 4] JP 2022-143830 A [Patent Document 5] Patent Publication No. 2022-154775 [Patent Document 6] US Patent Application Publication No. 2020 / 0234590 [Patent Document 7] US Patent Application Publication No. 2022 / 0343260 Summary of the Invention [Problem to be solved by the invention]

[0007] In DT, a large number of DTs are intricately related to map real-world objects. There is a trade-off between the amount of information in a DT and search speed. For this reason, when searching a large number of DTs under specific conditions, it takes a long time to search.

[0008] In one aspect, the present invention aims to reduce search time by optimizing relationships between digital twins. [Means for solving the problem]

[0009] According to one embodiment, a requirement is that a computer executes the following processes: aggregating, from among multiple digital twins, multiple digital twins that satisfy specific conditions for aggregatability, generating a single aggregated digital twin that sets the meaning of the aggregation, and assigning an identification tag to the generated aggregated digital twin. Effect of the Invention

[0010] According to one aspect, optimizing relationships between digital twins allows for faster search times. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram illustrating a digital twin generation method according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing a specific example of a digital twin. [Diagram 3] Figure 3 is an explanatory diagram of digital twin time series data. [Figure 4] Figure 4 is an explanatory diagram of an example of the generation and update process of a digital twin in a digital twin system. [Diagram 5] FIG. 5 is an explanatory diagram of an example of a digital twin reading process in a digital twin system. [Figure 6] Figure 6 is an explanatory diagram of an example of a search for measures using digital twins. [Figure 7] Figure 7 is an explanatory diagram of the digital twin search state based on the search conditions of the policy. [Figure 8] FIG. 8 is an explanatory diagram of an example of a search process for digital twins in a digital twin system. [Figure 9A] FIG. 9A is an explanatory diagram of the relationship between the amount of digital twin information and search time. (Part 1) [Figure 9B] FIG. 9B is an explanatory diagram of the relationship between the amount of digital twin information and search time. (Part 2) [Figure 10A] FIG. 10A is an explanatory diagram of a CotoTwin that combines multiple digital twins according to an embodiment. (Part 1) [Figure 10B] FIG. 10B is an explanatory diagram of a CotoTwin that combines a plurality of digital twins according to the embodiment. (Part 2) [Figure 11A] FIG. 11A is an explanatory diagram showing the process of twinning and rewiring relationships. (Part 1) [Figure 11B] FIG. 11B is an explanatory diagram showing the process of twinning and rewiring relationships. (Part 2) [Figure 11C] FIG. 11C is an explanatory diagram showing the process of twinning and rewiring relationships. (Part 3) [Figure 11D] FIG. 11D is an explanatory diagram showing the process of twinning and rewiring relationships. (Part 4) [Figure 12] FIG. 12 is a block diagram illustrating an example of a functional configuration of a digital twin system according to an embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of the hardware configuration of a digital twin generation device. [Figure 14] FIG. 14 is an explanatory diagram of an example of generating a cototwin according to the embodiment. [Figure 15] FIG. 15 is a sequence diagram of an example of generating CotoTwins by implementing a policy. [Figure 16] FIG. 16 is a flowchart showing an outline of the Cototwin generation process. [Figure 17] FIG. 17 is a flowchart showing a detailed example of the Cototwin generation process. [Figure 18] FIG. 18 is a diagram illustrating an example of a digital twin search according to an embodiment. [Figure 19] Figure 19 is a sequence diagram of an example of a digital twin search when a policy is implemented. [Figure 20] FIG. 20 is a flowchart showing an example of a digital twin search process. [Figure 21] FIG. 21 is a diagram illustrating another application example of the cototwinning according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Below, with reference to the drawings, embodiments of the digital twin generation method, digital twin generation program, and digital twin search method according to the present invention will be described in detail.

[0013] (One Example of a Digital Twin Generation Method According to an Embodiment) 1 is a diagram illustrating an overview of a digital twin generation method according to an embodiment. The digital twin generation method according to the embodiment shortens search time by, for example, reducing the number of DTs and relationships by optimizing the structure of the digital twin DT to be searched. In the optimization, information on multiple DTs that satisfy specific conditions for aggregatability is consolidated (aggregated) into one, and an aggregated DT (called a Cototwin) 150 is generated in which the meaning of the DT is set.

[0014] The digital twin generation method of the embodiment can be realized by a digital twin generation device 100 such as a server executing a program, or by accessing the cloud. The digital twin generation device 100 of the embodiment creates a cototwin 150 by the following process.

[0015] Additionally, in the embodiment described below, a digital twin search method will also be described. For example, the digital twin search device executes a search using a digital twin DT that includes a cototwin 150 generated by the digital twin generation device 100. In this case, a digital twin system including the digital twin generation device 100 and the digital twin search device can also be realized using a single server or the like. In the following explanation, first, the generation of a cototwin by the digital twin generation device 100 will be explained.

[0016] Since the Cototwin 150 has aggregatable conditions for Cototwinning in the property, the DT before Cototwinning and the generated Cototwin 150 have the same information as the search target. As a result, when searching the DT, the DT after Cototwinning can obtain search results similar to those of the DT before Cototwinning.

[0017] Fig. 1(a) shows the DT structure before Cototwinning (corresponding to the conventional technology), and Fig. 1(b) shows the DT structure after Cototwinning according to the embodiment. In the digital twin generation method according to the embodiment, Cototwin 150 shown in Fig. 1(b) is generated from the normal DT structure shown in Fig. 1(a).

[0018] Figure 1 shows the search state of a DT based on a certain measure A. For example, measure A is "promoting use of EV station B" for subjects who normally use EV station (charging station for electric vehicles EV) A. To confirm the effect of implementing this measure A, the DT search performs a search based on the relationship R of the DT of multiple roads on the way from the vehicle EV to EV station B in relation to measure A.

[0019] Each DT, i.e., policy A, target of driving A (People A), EV A driven by People A, multiple adjacent roads A, B, C, EV, and EV stations A and B, each have a property P and a relationship R. Road A is adjacent to (connected to) road B, and road B is adjacent to (connected to) road C, and EV A reaches EV station B via roads A, B, and C. In the following explanation, the multiple relationships R may be distinguished by adding terms such as "driving" relationship and "traveling" relationship.

[0020] Below, an overview of Cototwinning and DT search is given. Details of Cototwinning and DT search are given later. In DT search, EV A, which is a driving relationship R in the same update time period of target relationship R of policy A, is set as the search destination. Below, for multiple DTs, a search is performed to trace the DT from the travel start point to roads A, B, C to the EV station based on relationship R. In Figures 1(a) and (b), the arrows (→) indicate the relationship R of each DT, and the thick lines indicate the search route.

[0021] In the DT search before Cototwin (corresponding to the conventional technology) shown in Fig. 1(a), the number of DTs to be searched is 11. In Fig. 1(a), circles indicate search targets, and there are a total of 11 search targets: the target person, People A, EV A, each road A, B, C, and the EVs that have traveled on each road A, B, C.

[0022] Here, assume that adjacent roads A, B, and C are all congested. In this embodiment, when creating Cototwin, the "congestion" common to the adjacent roads A, B, and C is assigned meaning as property P. Then, the number of travel relationships of the vehicle EV that traveled on each of the roads A, B, and C is set as an aggregation condition, and multiple DTs that satisfy the conditions are combined to generate one Cototwin 150.

[0023] As a result, in this embodiment, as shown in Fig. 1(b), the number of DTs (corresponding to search targets) on the search path after Cototwinning is reduced from 11 to 7. The relationships R of the 11 DTs shown in Fig. 1(a) are replaced (rewired) with relationships R of a total of 7 DTs on the search path shown in Fig. 1(b). Note that the range of DTs included in the generated Cototwin 150 shown in Fig. 1(b) is indicated by a dotted line in Fig. 1(a).

[0024] Kototwinning reduces the number of DTs to be searched and the number of relationships on the search path when searching for DTs, thereby shortening the search time. Kototwin 150 has information about each DT before Kototwinning in its properties, so it can have the same information as before Kototwinning, ensuring the required amount of information.

[0025] (Problems with the prior art) Here, the conventional technology of DT and its problems will be explained.

[0026] Figure 2 is a diagram showing a specific example of a digital twin. Figure 2 shows the digital twin (DT) of a road and an EV in the real world. DT is digital data that is generated by sensing and digitizing values ​​(properties P) that represent the characteristics of objects such as speed, and relationships (relationships R) between objects that exist in the real world, targeting roads, EVs, and other objects.

[0027] In the example of Figure 2, the DT converts the speed, remaining charge, driving speed, etc. of EV A traveling on Road A into data by sensing. The properties of the object "Road A" in the DT include the "name" and "length" of Road A, and the properties of the object "EV A" include the "speed" and "remaining charge" of EV A. The relationship R between the objects "Road A" and "EV A" is "driving".

[0028] Figure 3 is an explanatory diagram of digital twin time series data. The above-mentioned time series data of the DT of roads and EVs will be explained. The properties P and relationships R of the DTs such as roads and EVs vary over time. Therefore, the twin database (DB) 300 of the DT system is a time series DB that records the changes in data at regular intervals in a time series.

[0029] The horizontal axis in Fig. 3 is time. In the example of Fig. 3, the Twin DB 300 records the change in data when the driving relationship R of EV A switches from road A to road B to road C.

[0030] In the example of FIG. 3, the twin DB 300 records, for EV A, a property P of the roads traveled from time t1 to t3, a property P of EV A, and a relationship R between the roads and EV A. The property P of the road A traveled at time t1 is "Name: Road A", "Length: 500 m", and the property P of EV A is "Speed: 50 km / h", and "Remaining charge: 50%". The property P of the road B traveled at time t2 is "Name: Road B", "Length: 700 m", and the property P of EV A is "Speed: 55 km / h", and "Remaining charge: 49%". The property P of the road C traveled at time t3 is "Name: Road C", "Length: 600 m", and the property P of EV A is "Speed: 50 km / h", and "Remaining charge: 48%". For convenience, FIG. 3 shows only the roads that have a relationship R with EV A, but roads A, B, and C exist at all times (times t1 to t3).

[0031] 4 is an explanatory diagram of an example of the generation and update process of a digital twin in a digital twin system. The digital twin system 400 includes a twin generation unit 401, a model DB 402, and a twin DB 300.

[0032] For example, an EV A in the real world collects various sensor values ​​S of the road and the EV A. In the example shown in FIG.

[0033] When creating a new DT, the twin generation unit 401 acquires a sensor value S (step S401) and acquires a corresponding model from the model DB 402 (step S402). The model DB 402 includes a road model and an EV model. The road model and the EV model include their respective properties and relationships. The twin generation unit 401 acquires a model only when creating a new DT, and does not acquire a model when updating the DT.

[0034] When creating a new DT, the twin generation unit 401 generates the DT based on the acquired sensor value S and the model, and records it in the time-series twin DB 300 (step S403). The created DT includes the properties "name" and "length" of road A, the properties P "speed" and "remaining charge" of EV A, and the traveling relationship R "roads A, B, C". Note that the value of the property P of road A is a constant, so it does not have time-series data.

[0035] Furthermore, when updating DT over time, the twin generation unit 401 updates the already created property of road A, the property P of EV A, and the traveling relationship R, and further updates the property P of EV A, including the “update time (12:29, 12:30, ...).”

[0036] 5 is an explanatory diagram of an example of a process for reading a digital twin in a digital twin system. The digital twin system 400 includes a twin reference unit 501 for reading the created DT.

[0037] In the DT reading process, for example, a user U requests acquisition of a DT with a specified update time (step S501). In the example of Fig. 5, the user U requests acquisition of all DTs with an update time of 12:30 on January 31 (Sun) 2023 (step S501). In this case, the twin reference unit 501 in the digital twin system 400 refers to the twin DB 402, accesses all DTs in an arbitrary order, and responds with the property P and relationship R of the specified update time (step S502).

[0038] At this time, for Road A, Twin DB 300 responds with the properties P "name" and "length" corresponding to the update time. Note that since Road A has constant property values, Twin DB 300 always returns the same value. Also, for EV A, Twin DB 300 responds with the properties P "update time," "speed," and "remaining charge" corresponding to the update time, and the driving relationship R.

[0039] As a result, the user U obtains the data D corresponding to the update time requested from the digital twin system 400. In the example of Fig. 5, the user U obtains the data D including the properties of road A and EV A and the driving relationship R at the update time of 12:30 on Sunday, January 31, 2023.

[0040] (About searching for digital twins) Next, we will explain DT search. In order to check the effectiveness of measures implemented in the past, we check whether a DT that has an impact on the effectiveness of the measures has the same property P and relationship R as the search conditions. This is called DT search.

[0041] FIG. 6 is an explanatory diagram of an example of searching for measures using a digital twin. The horizontal axis is time from the past to the present, and shows each measure by time. Using FIG. 6, an example of searching for past measures that are the same as a certain measure will be explained. For example, assume that People A, who drive an EV, usually use EV Station A. The property P of measure A is measure: promote use of EV Station B from EV Station A, target: EV Station B, result: success. Note that the digital twin system 400 records measure A itself as measure A twin (DT), and the measure content, target, and result as properties in the twin DB 300.

[0042] When measure A is implemented, the digital twin system 400 searches for roads ABC existing between EV stations A and B, properties P of EVs traveling on these roads ABC and the weather, and their relationships R to see whether they match the search conditions.

[0043] As shown in Fig. 6, the search looks back from the present to the past to search for a measure that is the same as measure A in the past data. The measure implemented at time t3 was measure X, which is not the same as measure A, so digital twin system 400 searches further back in time (step S601). Next, the measure implemented at time t2 was measure Z, which is not the same as measure A, so digital twin system 400 searches further back in time (step S602).

[0044] Then, at time t1, the digital twin system 400 discovers the same measure A (step S603). However, at this point, only the content of measure A is the same, so the digital twin system 400 needs to check whether measure A was implemented under the same circumstances.

[0045] In the embodiment, a search (step S604) is further performed to check "whether the situation when the policy was implemented is the same or not." In step S604, the digital twin system 400 searches, for example, whether a DT exists that has the same property P and relationship R as the situation when policy A was implemented. The search for "whether the situation when the policy was implemented is the same or not" will be described below.

[0046] Fig. 7 is an explanatory diagram of the search state of a digital twin based on the search conditions of a measure. In the example of Fig. 7, the search conditions of measure A are that the weather property P having a relationship R with the road ABC from measure A to EV station B is "sunny" when measure A is implemented. Also, the number of driving relationships R with EVs is "a certain number or more (10 or more)."

[0047] By using this search condition, if all roads ABC are congested on a sunny day for policy A, it is possible to check whether or not the implementation of policy A was effective under the same conditions in the past (all roads ABC are congested on a sunny day).The digital twin system 400 checks whether the conditions that affect the effectiveness of policy A are the same, using the properties P and relationships R of the digital twin.

[0048] In Fig. 7, arrows (→) indicate the relationships R of each DT, and thick lines indicate the search paths. The properties P of the policy A are the policy “Promote the use of EV Station B with coupons (such as discounts)”, the target “EV Station B”, and the effect “Success”.

[0049] In searching for a DT, the digital twin system 400 starts with measure A, and then searches for the next DT by tracing People A, EV A, and Road A, the starting point of the journey. Here, if the weather in property P for Road A is "sunny," it is determined that the search condition is met. Also, if the number of travel relationships R in property P for Road A is "10 or more (corresponding to congestion due to 10 or more EVs traveling on Road A during the same time period)," it is determined that the search condition is met. Thus, if the property P for Road A is in the same situation as measure A, the digital twin system 400 performs a similar search for Road B, which has a relationship R adjacent to Road A, in the next search. Thereafter, a search is performed to trace the relationships R up to EV station B in a similar manner.

[0050] 8 is an explanatory diagram of an example of a search process for a digital twin in a digital twin system. The digital twin system 400 includes a twin search unit 801 for searching for a DT based on a search condition.

[0051] In TWIN DB300, the property P of the DT of the policy A is the update time "11:00, 12:00", the policy "Promote use of EV Station B with coupons", the target "EV Station B", and the result "In progress, successful". The relationship R of the policy A is the target "People A". Furthermore, the property of the DT of People A is the update time "11:30, 11:31, ...", and the driving relationship R is "null, EV A, ...". Furthermore, the property P of the DT of EV A is the update time "11:30, 11:31, ...", and the driving start point relationship is "null, Road A, ...".

[0052] To explain the search process of the DT in accordance with the explanation of Fig. 7, a user U performs a search using predetermined search criteria corresponding to the measure A (step S801). The twin search unit 801 in the digital twin system 400 refers to the twin DB 402 and performs a search using the search criteria of the measure A. First, the twin search unit 801 acquires the DT of the measure A that matches the search criteria "measure" (step S802).

[0053] Next, the twin search unit 801 sets People A, who is in the target relationship R of the policy A, as the next search target (step S803). Next, the twin search unit 801 sets EV A, who is in the driving relationship R of People A in the same update time period, as the next search target (step S804). Thereafter, similar searches are performed in the search order shown in FIG. 7.

[0054] (Digital twin information volume and search time) 9A and 9B are explanatory diagrams of the relationship between the amount of information of a digital twin and search time. When designing (constructing) the DT of the digital twin system 400, an optimal design is made regarding the relationship between the DTs within the assumed range.

[0055] Fig. 9A shows a case where all DTs are stored in the twin DB 300 in order to maximize the amount of information in the DTs. On the other hand, Fig. 9B shows a case where the DTs in Fig. 9A, including road B and EVs related to this road B, etc., are deleted.

[0056] There is a trade-off between the amount of information held by the digital twin system 400 (twin DB 300) and search speed. For example, as shown in FIG. 9B, by deleting road B, etc., the number of properties P and relationships R of the DT to be checked during a search decreases, shortening the search time. On the other hand, the DT required for verifying the effectiveness of measures during operation cannot be fully anticipated at the time of design, making it difficult to achieve an optimal design that takes the trade-off into account.

[0057] For example, as shown in Figure 9B, if the DT related to road B is deleted, the property P and relationship R of road B cannot be confirmed during a search, and the search results for measure A cannot confirm the effects of including road B. Furthermore, among roads ABC that are adjacent to each other and have an adjacent relationship R, if the DT of road B is simply deleted, the destination relationship R of the adjacent road A becomes unknown, and the source relationship R of the adjacent road C also becomes unknown. With conventional DTs, it is not possible to obtain the required amount of information while reducing search time, as it is not possible to optimize the design while taking the above trade-off into account.

[0058] (About the embodiment of Kototwin) 10A and 10B are explanatory diagrams of a Cototwin that combines multiple digital twins according to an embodiment. In order to solve the above problem, the digital twin generation device 100 according to the embodiment generates a DT (Cototwin) 150 in which multiple DTs are combined based on specific conditions and then assigned meaning, and re-establishes the relationship R for the generated Cototwin 150. This makes it possible to reduce the number of DTs to be checked during a search, thereby improving search speed.

[0059] Kototwin 150 has meaning and conditions as property P. Having conditions on property P of Kototwin 150 enables lifecycle management of Kototwin 150. In addition, the DT before Kototwin and the DT after Kototwin (Kototwin 150) have the same information as a search target.

[0060] In the example shown in Fig. 10A, multiple DTs "EV" are connected to the DT "Road" before the CotoTwin generation by travel relationships R. The property P of the road has "name" and "length", and the property P of each EV has "speed" and "remaining charge", respectively.

[0061] The digital twin generation device 100 sets "number of driving relationships > 10" as a condition for Cototwin generation. The digital twin generation device 100 sets the condition "number of driving relationships > 10" to property P of the generated Cototwin 150. This condition is based on the fact that the number of EVs traveling on a certain road during the same time period exceeds 10 = "road congestion."

[0062] Figure 10B shows multi-stage Coto Twin generation. The digital twin generation device 100 can also generate one Coto Twin 150' from multiple Coto Twins 150 generated according to Figure 10A.

[0063] For example, suppose three Cototwin ABC (150) are generated for roads ABC as shown in FIG. 10B. The property P of the generated Cototwin A (150) has the meaning "Road A is congested" and the condition "Number of driving relationships on road A > 10". The property P of the generated Cototwin B (150) has the meaning "Road B is congested" and the condition "Number of driving relationships on road B > 10". The property P of the generated Cototwin C (150) has the meaning "Road C is congested" and the condition "Number of driving relationships on road C > 10".

[0064] The digital twin generation device 100 generates one Cototwin 150' from these three Cototwin ABC (150) based on the multi-stage condition and the conditions of each Cototwin ABC. For example, the multi-stage condition is "Roads ABC are adjacent." Furthermore, the condition of each Cototwin ABC is "The number of driving relationships on Road A > 10, and the number of driving relationships on Road B > 10, and the number of driving relationships on Road C > 10."

[0065] The digital twin generation device 100 generates one Cototwin 150' from Cototwin ABC (150) that satisfies both the conditions for multi-stage and the conditions for each Cototwin ABC. Cototwin 150' generated by multi-stage Cototwin is a combination of DTs related to adjacent roads ABC into one DT. The property P of this Cototwin 150' has the meaning "Road ABC is congested" and the condition "The number of travel relationships on Road A > 10, and the number of travel relationships on Road B > 10, and the number of travel relationships on Road C > 10." The meaning of Cototwin 150' is obtained by aggregating (summarizing) the meanings of each of the original Cototwin 150, "Congestion on Road A," "Congestion on Road B," and "Congestion on Road C," into "Congestion on Road ABC."

[0066] (Kototwinning and re-interpreting relationships) Fig. 11A to Fig. 11D are explanatory diagrams showing the process of Cototwinning and rewiring relationships, respectively. These Fig. 11A to Fig. 11D correspond to the details of the processing in Fig. 1(a) and (b). Fig. 11A shows the state before Cototwinning, and similar to Fig. 1(a), multiple DTs, namely, Policy A, EV A, People A, Road ABC, EV, and EV Stations A and B, each have a property P and a relationship R.

[0067] In Cototwin STEP 1 shown in FIG. 11B, the digital twin generation device 100 uses the Cototwinning described above to combine multiple related DTs into one for each road A, B, and C, and assigns meaning to them. Cototwin A (150) combines the DTs related to road A, i.e., road A and the DT of EVs that have traveled on road A, into one. Cototwin B (150) combines road B and the DT of EVs that have traveled on road B, into one. Cototwin C (150) combines road C and the DT of EVs that have traveled on road C, into one. Cototwin A, B, C (150) each include the assignment of meaning to property P, such as "Road X is congested (X = A, B, C)."

[0068] In Coto Twin STEP 2 shown in Fig. 11C, the digital twin generation device 100 performs multi-stage Coto Twin to generate Coto Twin 150' by combining three Coto Twins ABC (150) and assigning meaning to them. Coto Twin ABC (150') includes the meaning "Road ABC is congested" in the property P.

[0069] In the relationship re-establishment shown in Fig. 11D, the digital twin generation device 100 re-establishes the relationship R of the generated Cototwin ABC (150'). After the re-establishment, the relationship R of Cototwin ABC (150') consists only of the relationship R "start point of travel" with RV A and the relationship R "location" with EV station B.

[0070] The digital twin generation device 100 indicates that this Cototwin 150' has been generated as a Cototwin, and adds information that makes it identifiable as a DT, for example, the tag "Cototwin relationship" to the relationship R of the Cototwin 150'.

[0071] By generating the Cototwin 150' shown in Fig. 11D and rearranging the relationships for the generated Cototwin 150', the number of DTs to be searched and the number of relationships can be reduced, as in Fig. 1(b), and the search time can be shortened. Note that the Cototwin 150 in Fig. 1(b) corresponds to the Cototwin 150' after multi-stage Cototwinning in Fig. 11D.

[0072] In the above explanation, the digital twin generation device 100 does not delete the original multiple DTs related to each of the roads ABC that were the subject of Cototwinning, but leaves them behind even after Cototwinning. Then, when searching for a DT, the digital twin generation device 100 searches for the generated Cototwin 150' by referencing the tag of the Cototwin 150', and if a generated Cototwin 150' exists, the generated Cototwin 150' is searched for with priority over the original DT. When searching for the Cototwin 150' with priority, the original DT does not affect the search process.

[0073] (Example of functional configuration of a digital twin system) Fig. 12 is a block diagram showing an example of the functional configuration of a digital twin system according to an embodiment. A digital twin system 1200 has the function of a digital twin generation device 100 that generates the above-mentioned DT, and the function of a digital twin search device 1220 that uses the generated DT. In the embodiment, in addition to the conventional configuration shown in Fig. 12, it has components shown in bold frames (bold characters).

[0074] The digital twin generation device 100 includes a twin generation unit 1211, a twin control unit 1212, a twin DB 1213, and a model DB 1214.

[0075] The twin generation unit 1211 is a conventional function, similar to the twin generation unit 401 in Fig. 4. The twin generation unit 1211 acquires a sensor value S from an object in the real world, and generates a DT from the acquired sensor value S. The twin generation unit 1211 generates a DT by referring to a model recorded in a model DB 1214, and records the DT in a twin DB 1213. The DT has properties and relationships R. Here, the twin generation unit 1211 generates a time-series DT in which the properties P and relationships R are updated in response to the passage of time (see Fig. 3).

[0076] In an embodiment, the digital twin generation device 100 further has the functions of a coto twin control unit 1212. The coto twin control unit 1212 monitors the twin DB 1213 and generates one coto twin 150 from multiple DTs that satisfy the conditions for generation. The coto twin control unit 1212 records the generated coto twin 150 in the twin DB 1213.

[0077] The digital twin search device 1220 includes a twin search unit 1221. The twin search unit 1221 includes the function of a cototwin priority search unit 1222.

[0078] 8, the twin search unit 1221, using conventional functions, performs a search using search conditions corresponding to a predetermined measure (measure A) by the user U. During this search, the twin search unit 1201 refers to the twin DB 402, acquires a DT that matches the search condition "measure A", performs a search that traces the relationship R between the DTs, and responds with the search results to the user U (see FIG. 1(a) and FIG. 7).

[0079] In the embodiment, the twin search unit 1221 has the function of a Cototwin priority search unit 1222 inside. When a Cototwin 150 created by the Cototwin control unit 1212 is in the twin DB 1213, the Cototwin priority search unit 1222 switches to a search based on the property P and relationship R indicated by the Cototwin 150 (150') (see FIG. 1(b)). Here, the Cototwin priority search unit 1222 prioritizes a search based on the relationship R of the created Cototwin 150 over a search based on the relationship R of the original multiple DTs before the Cototwin was created. The Cototwin priority search unit 1222 determines whether or not a Cototwin 150 exists based on the description of the above tag.

[0080] As a result, the twin search unit 1221 responds to the user U with search results similar to those in the past, but by using the CotoTwin 150, the number of DTs (number of relationships) to be searched can be reduced, thereby shortening the search time.

[0081] (Example of hardware configuration for digital twin generation device) Fig. 13 is a diagram showing an example of the hardware configuration of a digital twin generation device. In Fig. 13, a general-purpose server can be used as the digital twin generation device 100. The digital twin generation device 100 includes a CPU (Central Processing Unit) 1301 such as a processor, a memory 1302, a network IF 1303, a recording medium IF 1304, and a recording medium 1305. In addition, each component is connected to each other by a bus 1300.

[0082] Here, the CPU 1301 is a control unit that is responsible for overall control of the digital twin generation device 100. The CPU 1301 may have multiple cores. The memory 1302 has, for example, a read only memory (ROM), a random access memory (RAM), and a flash ROM. Specifically, for example, the flash ROM stores an OS program, the ROM stores application programs, and the RAM is used as a work area for the CPU 1301. The programs stored in the memory 1302 are loaded into the CPU 1301 to cause the CPU 1301 to execute the coded processing.

[0083] The network IF 1303 is connected to the network NW through a communication line, and can be connected to an external computer via the network NW. The network IF 1303 manages the interface between the network NW and the inside of the device, and controls input and output of data from the external computer. For example, a modem or a LAN adapter can be used as the network IF 1303.

[0084] The recording medium IF 1304 controls reading / writing of data from / to the recording medium 1305 under the control of the CPU 1301. The recording medium 1305 stores the written data. The recording medium 1305 may be a magnetic disk such as an HDD, an optical disk, a solid state drive (SSD), a universal serial bus (USB) memory, or the like.

[0085] In addition to the components described above, the digital twin generation device 100 may also have, for example, an input device, a display, etc.

[0086] Each function of the digital twin generation device 100 shown in Fig. 12 can be realized by executing a program on the CPU 1301 shown in Fig. 13. Each data in the storage unit (twin DB 1213, model DB 1214) shown in Fig. 12 can be stored using the memory 1302 and recording medium 1305 shown in Fig. 13.

[0087] The digital twin search device 1220 shown in Fig. 12 can also be realized using the hardware configuration in Fig. 13. In addition, the digital twin system 1200 including both the digital twin generation device 100 and the digital twin search device 1220 shown in Fig. 12 can also be realized using the hardware configuration in Fig. 13.

[0088] (Example of Cototwin Generation Processing of the Embodiment) Fig. 14 is an explanatory diagram of an example of generating a Cototwin according to an embodiment. Fig. 14 shows a digital twin generation device 100 in a digital twin system 1200, and describes a specific example of generating a Cototwin 150 from the DT described in Figs. 11A to 11D.

[0089] The twin generation unit 1211 acquires a sensor value S from a DT target (such as EV) in the real world (step S1401). The twin generation unit 1211 generates a time-series DT in which the property P and the relationship R are updated based on the sensor value S at each time point, and records the DT in the twin DB 1213 (step S1402).

[0090] The Cototwin control unit 1212 monitors the travel relationships R of all EVs among the DTs in the twin DB 1213 (step S1403). Then, the Cototwin control unit 1212 confirms that the number of travel relationships destined for road A is 10 or more (step S1404). Next, the Cototwin control unit 1212 generates a Cototwin A of a traffic jam at the same update time (e.g., 12:30) (step S1405). Then, the Cototwin control unit 1212 generates a relationship R for the Cototwin based on the relationship R of the DT before Cototwin generation (step S1406).

[0091] In the above process, for example, for the DT of each EV, EV A includes in its property P the traveling relationship R "Road A" at the update time "12:30". Also, EV B includes in its property P the traveling relationship R "Road A" at the update time "12:30". Also, EV C includes in its property P the traveling relationship R "Road A" at the update time "12:30".

[0092] The Cototwin control unit 1212 generates one Cototwin A from three DTs (EVs A-C) that include the Cototwinning condition "Number of travel relationships on Road A > 10", the same update time "12:30", and travel relationship R "Road A". The property P of Cototwin A includes the update time "12:30", the meaning "Road A is congested", the condition "Number of travel relationships on Road A > 10", and the fulfillment condition "True". In addition, the travel relationship R includes "Location: EV Station B".

[0093] Note that condition satisfaction indicates a value that indicates when the Kototwin existed as time-series data (when it is false, it does not exist). Therefore, when deleting a Kototwin, the condition satisfaction of the Kototwin is set to false, and it is treated as not currently existing (however, it is not deleted from the time-series data). On the other hand, the Kototwin generation process first obtains the model and generates a DT (with condition satisfaction set to True as the initial value), and from the next time onwards, condition satisfaction is set to True when updating.

[0094] Fig. 15 is a sequence diagram of an example of creating a Coto Twin by implementing a policy. Fig. 15 shows the interactions between a user U, the twin generation unit 1211 (twin DB 1213) in the digital twin generation device 100, and the Coto Twin control unit 1212 regarding the creation of a Coto Twin.

[0095] First, the digital twin generation device 100 is notified of the implementation of measure A proposed by user U (step S1501). Measure A of user U is assumed to be "Guide the driver of EV A (People A) from EV Station A to EV Station B." Upon receiving this notification, the twin generation unit 1211 generates a measure A twin (step S1502, see FIG. 11A). Property P of measure A is "Measure: Promote use of EV Station B with coupons," and the result is "Success." The twin generation unit 1211 notifies the event twin control unit 1212 of the generation of the measure A twin (step S1503).

[0096] Next, the Cototwin control unit 1212 acquires DT data related to the measure A twin from the twin DB 1213 (step S1504) and performs the Cototwin processing (step S1505). At this time, when the number of travel relationships of each of the DTs of roads A, B, and C existing between the DT of measure A and EV station B is 10 or more, the Cototwin control unit 1212 generates three Cototwin A, B, and C (150) indicating road congestion (see FIG. 11B).

[0097] Next, the Cototwin control unit 1212 performs multi-stage Cototwin processing (step S1506). At this time, the Cototwin control unit 1212 generates one Cototwin ABC (150') indicating that roads A, B, and C are congested from three Cototwin A, B, and C (150) indicating congestion on roads A, B, and C, respectively (see FIG. 11C). Then, the Cototwin control unit 1212 records the generated Cototwin ABC (150') in the twin DB 1213 (step S1507).

[0098] Next, the Cototwin control unit 1212 reestablishes the relationship (step S1508). At this time, the Cototwin control unit 1212 generates a relationship R for the Cototwin ABC (150').

[0099] Fig. 16 is a flowchart showing an overview of the Cototwin generation process. The process shown in Fig. 16 shows processing related to the life cycle management of the Cototwin through Cototwin control by program execution by the control unit (CPU 1301) of the digital twin generation device 100.

[0100] The digital twin generation device 100 performs the following processing based on the policy DT corresponding to the policy of the user U. First, the digital twin generation device 100 determines whether or not a DT that satisfies the conditions of the policy exists (step S1601). If a DT that satisfies the conditions of the policy exists (step S1601: True), the digital twin generation device 100 generates a Cototwin (step S1602). In addition, the digital twin generation device 100 generates a relationship R for the generated Cototwin (step S1603) and proceeds to the processing of step S1604.

[0101] In step S1601, if there is no DT that satisfies the conditions of the policy (step S1601: False), the digital twin generation device 100 proceeds to the processing of step S1604. In step S1604, the digital twin generation device 100 determines whether there is a coto twin that does not satisfy the conditions (step S1604).

[0102] If a Cototwin exists that does not satisfy the conditions (step S1604: True), the digital twin generation device 100 deletes the relationship R of the corresponding Cototwin (step S1605). The digital twin generation device 100 also deletes this Cototwin (step S1606) and proceeds to the processing of step S1607.

[0103] In step S1604, if there is no Cototwin that does not satisfy the condition (step S1604: False), the digital twin generation device 100 proceeds to processing in step S1607.

[0104] In step S1607, the digital twin generation device 100 determines whether or not a DT for the policy exists (step S1607). For example, if the DT for the policy has not been deleted manually by the user U or at the specified expiration date, the DT for the policy exists. In step S1607, if the DT for the policy exists (step S1607: True), the digital twin generation device 100 returns to the processing of step S1601. On the other hand, if the DT for the policy does not exist (step S1607: False), the digital twin generation device 100 ends the above processing.

[0105] 17 is a flowchart showing a detailed example of the Cototwin generation process. The process in FIG. 17 shows details equivalent to steps S1601 to S1603 in FIG.

[0106] First, the digital twin generation device 100 checks the conditions of the co-twin (step S1701). Next, the digital twin generation device 100 monitors the DT generation and update of the twin DB 1213 (step S1702).

[0107] Next, the digital twin generation device 100 determines whether or not a generated or updated DT has been detected (step S1703). If a generated or updated DT has been detected (step S1703: True), the digital twin generation device 100 proceeds to processing of step S1704. On the other hand, if a generated or updated DT has not been detected (step S1703: False), the digital twin generation device 100 returns to processing of step S1702.

[0108] In step S1704, the digital twin generation device 100 determines whether the detected or updated DT satisfies the conditions of a Cototwin (step S1704). If the detected or updated DT satisfies the conditions of a Cototwin (step S1704: True), the digital twin generation device 100 proceeds to processing of step S1705. On the other hand, if the detected or updated DT does not satisfy the conditions of a Cototwin (step S1704: False), the digital twin generation device 100 returns to processing of step S1702.

[0109] In step S1705, the digital twin generation device 100 checks the update time of the detected DT (step S1705). Next, the digital twin generation device 100 generates a Cototwin with a DT that has the same update time as the update time of the detected DT (step S1706). After this, the digital twin generation device 100 generates a tagged relationship R from the detected DT to the Cototwin (step S1707), and ends the above processing. The tag represents the relationship R to the Cototwin.

[0110] (Digital twin search processing example of embodiment) Fig. 18 is an explanatory diagram of a digital twin search example according to an embodiment. Fig. 18 shows a digital twin search device 1220 in a digital twin system 1200, and describes a specific example of searching for a DT that includes a cototwin.

[0111] The twin search unit 1221 refers to the twin DB 1213 and acquires the DT of measure A that matches the "measure" of the search condition of the user U (step S1801). Next, the twin search unit 801 sets People A of the target relationship R of measure A as the next search destination (step S1802). Next, the twin search unit 801 sets EV A, which is an operating relationship R of People A in the same update time period, as the next search destination (step S1803).

[0112] Here, the Cototwin priority search unit 1222 of the twin search unit 1221 sets Cototwin A, which has Cototwin Relationship R in its tag, as the next search destination among the travel start point relationships R in the same update time period of EV A (step S1804). The Cototwin priority search unit 1222 prioritizes Cototwin A (150), which has Cototwin Relationship R in its tag, as the next search destination over DT(EV) of Relationship R that does not have a tag in the travel start point relationships R in the same update time period of EV A. In this way, the Cototwin priority search unit 1222 determines the next wandering destination based on whether or not a tag has been assigned, and prioritizes Cototwin with a tag as the search destination.

[0113] In the above process, for example, for each DT, the policy A includes the policy "Promote use of EV Station B with coupons" in the property P, and the target "People A" in the relationship R. People A includes driving "EV A" in the relationship R. VA includes the starting point of the journey "Road A" and "Tag: Kototwin Relationship" in the relationship R, and "Kototwin A" in the value. Kototwin A (150) includes condition satisfaction "true" in the relationship R, location "Tag: Kototwin Relationship" and value "EV Station B".

[0114] Fig. 19 is a sequence diagram of an example of a digital twin search when implementing a policy. Fig. 19 shows the exchange between a user U and the twin search unit 1221 (twin DB 1213) in the digital twin search device 1220 regarding a DT search including a co-twin.

[0115] It is assumed that the measure that User U wants to search is Measure A, which is to "guide the driver of EV A (People A) from EV Station A to EV Station B." It is assumed that User U has a purpose of checking the effect of Measure A in an environment where roads A, B, and C are congested.

[0116] The user U accesses the digital twin search device 1220 and searches for past cases corresponding to the policy (step S1901). The search condition is "the number of travel relationships on roads A, B, and C is greater than 10."

[0117] The digital twin search device 1220 (twin search unit 1221) moves the search scan from Measure A to People A, who is the next in the target relationship R, as search STEP 1 (step S1902). Next, the twin search unit 1221 moves the scan from People A to EV A, who is the next in the driving relationship R, as search STEP 2 (step S1903).

[0118] Next, in search STEP 3, the twin search unit 1221 moves the scan from EV A to Cototwin ABC (150) beyond the driving start point relationship R (step S1904). In this search STEP 3, the Cototwin priority search unit 1222 in the twin search unit 1221 functions to prioritize the search of Cototwin ABC (150) over DT.

[0119] Next, as search STEP 4, the twin search unit 1221 moves the scan from CotoTwin ABC (150) to EV station B, which is the destination of location relationship R (step S1905). The twin search unit 1221 also checks the conditions of CotoTwin ABC (150) and confirms that it matches the search conditions. After this, the twin search unit 1221 responds to the user U that it matches the search conditions (step S1906).

[0120] As a result, the user U confirms from the search results that the measure A was effective under the same conditions in the past (step S1907). Based on this result, the user can decide to implement the measure A as a reproducible measure. The search state of the DT including the Cototwin 150 described above corresponds to FIG. 11D and FIG. 1(b).

[0121] 20 is a flowchart showing an example of a search process for a digital twin. The process shown in FIG. 20 is performed by executing a program in the control unit (CPU 1301) of the digital twin search device 1220.

[0122] The digital twin search device 1220 searches for a policy DT corresponding to a policy created by a user U (step S2001). Next, the digital twin search device 1220 determines whether the search target (DT) has a tagged relationship R (step S2002).

[0123] If the search target (DT) has a tagged relationship R (step S2002: True), the digital twin search device 1220 proceeds to processing of step S2003. On the other hand, if the search target (DT) does not have a tagged relationship R (step S2002: False), the digital twin search device 1220 proceeds to processing of step S2004.

[0124] In step S2003, the digital twin search device 1220 searches for the destination DT of the relationship R (step S2003), and returns to the processing of step S2002.

[0125] In step S2004, the digital twin search device 1220 determines whether the search target (DT) has a relationship R (step S2004).

[0126] If the search target (DT) has relationship R (step S2004: True), the digital twin search device 1220 proceeds to processing of step S2003. On the other hand, if the search target (DT) does not have relationship R (step S2004: False), the digital twin search device 1220 proceeds to processing of step S2005.

[0127] In step S2005, the digital twin search device 1220 determines whether the search target (DT) is a DT that is a target of a policy (step S2005). If the search target (DT) is a DT that is a target of a policy (step S2005: True), the digital twin search device 1220 ends the above processing. On the other hand, if the search target (DT) is not a DT that is a target of a policy (step S2005: False), the digital twin search device 1220 proceeds to processing in step S2006.

[0128] In step S2006, the digital twin search device 1220 determines whether or not the previous search target has a relationship R addressed to a DT that is not a search target (step S2006). If the previous search target has a relationship R addressed to a DT that is not a search target (step S2006: True), the digital twin search device 1220 searches for the DT that is the destination of the relationship R (step S2007). Then, the digital twin search device 1220 returns to the processing of step S2002. On the other hand, if the previous search target does not have a relationship R addressed to a DT that is not a search target (step S2006: False), the digital twin search device 1220 ends the above processing.

[0129] (Another application example of the Cototwin of the embodiment) Fig. 21 is an explanatory diagram of another application example of Cototwin according to the embodiment. In the example shown in Fig. 21, there are three EV stations A, B, and C, a number of EVs, and a power plant for these in district A. The digital twin generation device 100 generates three Cototwin 150 for each of the EV stations A, B, and C as high-demand EV stations.

[0130] Figure 21 shows even more multi-stage Cototwinning. The digital twin generation device 100 generates a high-demand EV power plant (Cototwin 150') from three high-demand EV stations (Cototwin 150) and a power plant for district A that has relationship R with these three Cototwin 150. The generated high-demand power plant (Cototwin 150') includes a power plant for district A, multiple EV stations, and multiple DTs consisting of multiple EVs.

[0131] The digital twin generation device according to the embodiment can be applied to various applications for generating digital twins of real-world objects. For example, the digital twin generation device can be applied to various digital twin systems that generate digital twins based on sensor values ​​output by various devices such as IoT devices, in addition to the above-mentioned road congestion and power plant demand.

[0132] As described above, the digital twin generation device of the embodiment aggregates, from among multiple digital twins, multiple digital twins that satisfy specific conditions for aggregatability, and generates one aggregated digital twin to which the meaning of the aggregation is set. An identification tag is then assigned to the generated aggregated digital twin. In this way, by generating an aggregated digital twin that aggregates multiple digital twins, it becomes possible to shorten search times.

[0133] Furthermore, the generation process of the digital twin generation device of the embodiment may aggregate multiple digital twins that have the same condition in their properties as a specific condition. This allows the aggregated digital twin to be optimized by aggregating multiple digital twins that have the same condition, making it possible to reduce search time when searching.

[0134] Furthermore, the generation process of the digital twin generation device of the embodiment may aggregate multiple digital twins that have the same update time as a property as a specific condition. This makes it possible to optimize the aggregated digital twin by aggregating multiple digital twins that have the same update time, thereby shortening the search time when searching.

[0135] Furthermore, the generation process of the digital twin generation device of the embodiment may involve aggregating and setting the properties of the aggregated digital twin by aggregating the properties of the multiple digital twins that were aggregated. This results in an aggregated digital twin that is optimized by aggregating the properties of the multiple digital twins, making it possible to shorten search times.

[0136] Furthermore, the generation process of the digital twin generation device of the embodiment may involve aggregating and setting the relationships of the aggregated digital twin by aggregating the relationships of each of the multiple digital twins that were aggregated. As a result, the aggregated digital twin reduces the number of relationships of the multiple digital twins through aggregation, and the reduced number of relationships makes it possible to shorten search times during searches.

[0137] Furthermore, the generation process of the digital twin generation device of the embodiment may aggregate the meanings of the properties of the multiple source digital twins as aggregated meanings, and set these as properties of the aggregated digital twin. This makes it possible to optimize the aggregated digital twin by preserving the meanings of the multiple source digital twins in the properties, thereby shortening search times.

[0138] Furthermore, the digital twin generation device of the embodiment may perform the generation process by aggregating multiple aggregated digital twins that satisfy specific conditions from among the multiple aggregated digital twins generated, and generating one aggregated digital twin to which the meaning of the aggregation is set. By generating such multi-stage aggregated digital twins, the number of digital twins and the number of relationships can be reduced, further shortening search time.

[0139] Furthermore, the digital twin generation device may perform the generation process by monitoring a twin database that records and holds digital twins, and upon detection of multiple digital twins that satisfy certain conditions, add the generated aggregated digital twin to the twin database and record it. This makes it possible to hold data on both the aggregated digital twin and the digital twin before aggregation, and, for example, makes it possible to generate an aggregated digital twin that is an optimized digital twin each time search conditions corresponding to different measures are met.

[0140] The method for generating and searching for digital twins described in this embodiment can be realized by executing a prepared program on a computer such as a PC or a workstation. The program for generating and searching for digital twins described in this embodiment is recorded on a computer-readable recording medium, and is executed by the computer by reading it from the recording medium. The program for generating and searching for digital twins described in this embodiment may also be distributed via a network such as the Internet.

[0141] The following supplementary notes are further disclosed regarding the above-described embodiment.

[0142] (Appendix 1) Among multiple digital twins, multiple digital twins that satisfy certain conditions for aggregation are aggregated to generate one aggregated digital twin with the meaning of the aggregation set, Adding an identification tag to the generated aggregated digital twin; A method for generating a digital twin, characterized in that the processing is executed by a computer.

[0143] (Appendix 2) The generation process is aggregating a plurality of the digital twins having the same condition in a property as the specific condition; 2. A digital twin generation method as described in claim 1.

[0144] (Appendix 3) The generation process is As the specific condition, aggregating a plurality of the digital twins having the same update time in a property; 3. A digital twin generation method as described in claim 2.

[0145] (Additional Note 4) The generation process is The properties of the aggregated digital twin are aggregated and set as the properties of the aggregated digital twin. 2. A digital twin generation method as described in claim 1.

[0146] (Appendix 5) The generation process is The relationships of the aggregated digital twin are set by aggregating the relationships of each of the multiple digital twins that are the source of the aggregation; 2. A digital twin generation method as described in claim 1.

[0147] (Appendix 6) The generation process is As the aggregated meaning, the meaning of each property of the multiple digital twins that are the source of aggregation is aggregated and set as a property of the aggregated digital twin. 2. A digital twin generation method as described in claim 1.

[0148] (Appendix 7) The generation process is Among the generated aggregated digital twins, aggregating a plurality of aggregated digital twins that satisfy specific conditions to generate a single aggregated digital twin for which the meaning of the aggregation is set. 2. A digital twin generation method as described in claim 1.

[0149] (Appendix 8) The generation process is monitor a twin database that maintains records of said digital twins; Based on the detection of multiple digital twins that satisfy a certain condition, adding and recording the generated aggregated digital twin to the twin database; 2. A digital twin generation method as described in claim 1.

[0150] (Appendix 9) Among multiple digital twins, multiple digital twins that satisfy certain conditions for aggregation are aggregated to generate one aggregated digital twin with the meaning of the aggregation set, Adding an identification tag to the generated aggregated digital twin; A digital twin generation program that causes a computer to execute processing.

[0151] (Appendix 10) A digital twin search method for searching to trace relationships between multiple digital twins corresponding to specified search conditions, aggregating a number of digital twins that satisfy certain conditions for aggregating among the digital twins, and determining the existence of one aggregated digital twin that has been generated by assigning an aggregated meaning and relationship and an identification tag based on the presence or absence of the tag; If the aggregated digital twin exists, a search based on the relationships of the aggregated digital twin is performed with priority. A digital twin search method characterized in that the processing is performed by a computer. [Explanation of symbols]

[0152] 100 Digital Twin Generator 150,150' Koto Twin 1200 Digital Twin System 1201 Twin Search Department 1211 Twin Generation Department 1212 Kototwin Control Unit 1213 Twin DB 1214 Model DB 1220 Digital Twin Search Device 1221 Twin Search Department 1222 Kototwin Priority Search Department 1301 CPU 1302 Memory 1304 Recording media 1305 Recording media DT Digital Twin EV Vehicle NW Network P Property R Relationships U User

Claims

1. Among the multiple digital twins, multiple digital twins that satisfy specific conditions for aggregation are aggregated to generate a single aggregated digital twin for which the meaning of the aggregation has been set; Adding an identification tag to the generated aggregated digital twin; A method for generating a digital twin, characterized in that the processing is executed by a computer.

2. The generation process includes: aggregating a plurality of the digital twins having the same condition in a property as the specific condition; The digital twin generation method according to claim 1 .

3. The generation process includes: As the specific condition, aggregating a plurality of the digital twins having the same update time in a property; The digital twin generation method according to claim 2 .

4. The generation process includes: The properties of the aggregated digital twin are aggregated and set as the properties of the aggregated digital twin. The digital twin generation method according to claim 1 .

5. The generation process includes: The relationships of the aggregated digital twin are set by aggregating the relationships of each of the multiple digital twins that are the source of the aggregation; The digital twin generation method according to claim 1 .

6. The generation process includes: As the aggregated meaning, the meaning of each property of the multiple digital twins that are the source of aggregation is aggregated and set as a property of the aggregated digital twin. The digital twin generation method according to claim 1 .

7. The generation process includes: Among the generated aggregated digital twins, aggregating a plurality of aggregated digital twins that satisfy a specific condition to generate a single aggregated digital twin in which the meaning of the aggregation is set. The digital twin generation method according to claim 1 .

8. Among the multiple digital twins, multiple digital twins that satisfy specific conditions for aggregation are aggregated to generate a single aggregated digital twin for which the meaning of the aggregation has been set; Adding an identification tag to the generated aggregated digital twin; A digital twin generation program that causes a computer to execute processing.

9. A digital twin search method for performing a search to trace relationships between multiple digital twins corresponding to specified search conditions, aggregating a plurality of digital twins that satisfy specific conditions for aggregatability among the digital twins, and determining the existence of one aggregated digital twin that has been generated by assigning an aggregated meaning and relationship and an identification tag based on the presence or absence of the tag; If the aggregated digital twin exists, a search based on the relationships of the aggregated digital twin is performed with priority. A digital twin search method characterized in that the processing is performed by a computer.

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