Facility operation support device, method, and program

Abstract

To accomplish task implementation, such as plan creation for a facility, more accurately in line with actual conditions.SOLUTION: A power network restoration plan support device 10 for supporting operation of a power network 2 comprises: a UI part 15 for receiving sensor data 111 regarding operation of the power network 2; a data evaluation part 132 for calculating a degree of certainty of the sensor data 111 which is defined by a combination of a plurality of factors in acquiring the sensor data 111 and indicates certainty of the operation data; a data regeneration part 133 for regenerating the sensor data 111 according to the degree of certainty; and a data storage part for storing the operation data and the degree of certainty of the operation data in association with each other in a storage part. The facility operation support device implements creation of an operation plan of a facility by using the operation data stored in the storage part, in accordance with the degree of certainty stored in the storage part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to data management according to certainty, and more particularly to a technique for supporting business execution using the data. [Background technology]

[0002] Currently, data-based operations are being carried out in a variety of fields. In order to carry out these operations appropriately, it is necessary to consider the reliability of the data. If operations are carried out using data with low reliability, information processing such as analysis based on the actual situation will not be carried out. As a result, the operations carried out will also deviate from reality. For example, the accuracy of plans created for carrying out operations will be low, which may result in inefficient or difficult to implement plans.

[0003] For this reason, the creation of more accurate plans is required when carrying out business. For example, Patent Document 1 states that "in order to make effective decisions during times such as disasters when unpredictable situations may occur, it is necessary to provide tasks to be done and necessary information with appropriate content and at the appropriate time." Here, Patent Document 1 describes "tracing the information sources and their changes (the route taken to collect) for original data and processed data, and conducting a reliability analysis." Then, using the reliability-analyzed data, a material delivery plan with a route is created as a task. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-088829 Summary of the Invention [Problem to be solved by the invention]

[0005] In Patent Document 1, reliability is analyzed based on the information source and its transition. Therefore, in order to ensure the accuracy of the reliability, it is necessary to analyze the analysis factors such as the information source and its transition with high precision. However, Patent Document 1 does not take this into consideration. This makes it difficult to carry out business in a way that is more in line with reality.

[0006] Therefore, an object of the present invention is to realize the execution of work such as planning in facilities more accurately in accordance with the actual situation. [Means for solving the problem]

[0007] To solve the above problems, the present invention evaluates the reliability of operational data, which is defined as a combination of multiple elements in data acquisition and indicates the reliability of the data, and performs business operations based on the evaluation results. Representative elements include acquisition time elements, acquisition location elements, and characteristic elements. The business operations include facility operation support and application service implementation. [Effects of the Invention]

[0008] According to the present invention, it is possible to carry out operations at a facility more accurately in accordance with the actual situation. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a system configuration diagram of a power grid restoration plan creation support system according to a first embodiment. [Figure 2] 1 is a hardware configuration diagram showing an implementation example of a power grid restoration plan support device according to a first embodiment. [Figure 3] 1 is a hardware configuration diagram showing an example of implementation of a utility pole sensor device according to a first embodiment. [Figure 4] FIG. 2 is a hardware configuration diagram showing an example of implementation of a smart meter according to the first embodiment. [Figure 5] FIG. 2 is a diagram for explaining an outline of a process in the first embodiment. [Figure 6]FIG. 4 is a sequence diagram showing the content of processing in the first embodiment. [Figure 7] FIG. 2 is a diagram for explaining the certainty of data and its components in the first embodiment. [Figure 8] FIG. 2 is a diagram illustrating system configuration data used in the first embodiment. [Figure 9] FIG. 4 is a diagram illustrating characteristics included in sensor data used in the first embodiment. [Figure 10] 10 is a flowchart (part 1) showing details of the regeneration process and the storage process in the first embodiment. [Figure 11] 10 is a flowchart (part 2) showing details of the regeneration process and the storage process in the first embodiment. [Figure 12] 10 is a flowchart showing the details of consecutive data missing processing (1) in the first embodiment. [Figure 13] 10 is a flowchart showing details of an inclination check process in the first embodiment. [Figure 14] 10 is a flowchart showing details of consecutive data missing processing (2) in the first embodiment. [Figure 15] FIG. 10 is a diagram showing data bodies in cases 1 to 4 in the first embodiment. [Figure 16] FIG. 10 is a diagram showing the data bodies in cases 11 to 13 in the first embodiment. [Figure 17] FIG. 10 is a diagram showing a summary of the data body of Case 14 in the first embodiment. [Figure 18] 10 is a flowchart illustrating details of a recovery plan creation process in the first embodiment. [Figure 19] FIG. 10 is a diagram for explaining a determination process in creating a recovery plan in the first embodiment. [Figure 20] FIG. 10 is a diagram for explaining a process of creating a detailed recovery plan in the first embodiment. [Figure 21] FIG. 10 is a diagram illustrating a route failure situation in the first embodiment. [Figure 22] FIG. 11 is a diagram illustrating an outline of processing performed by the service delivery support apparatus according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] An embodiment of the present invention will be described below. In this embodiment, a facility having a plurality of pieces of equipment is taken as an example. Furthermore, in this embodiment, the business involves creating an operation plan and carrying out operation work corresponding to the operation plan. Specifically, the facility operation support device for supporting facility operation includes a UI unit that accepts operation data regarding the operation of the facility, a data evaluation unit that calculates a reliability of the operation data, the reliability being defined by a combination of a plurality of elements in acquiring the operation data, and indicating the reliability of the operation data, a data correction unit that corrects the operation data in accordance with the reliability, and a data storage unit that associates the operation data with the reliability of the operation data and stores them in a memory unit, and the facility operation support device creates an operation plan for the facility using the operation data stored in the memory unit in accordance with the reliability stored in the memory unit.

[0011] The present embodiment also includes a facility operations support device for supporting the operation of a facility, the facility operations support device having a communication device that accepts operation data regarding the operation of the facility, a storage device that is connected to the communication device via a communication path and that stores a data management program, and an arithmetic unit that is connected to the communication device and the storage device via the communication path and that calculates, in accordance with the data management program, a degree of certainty of the operation data that is defined by a combination of multiple elements in acquiring the operation data and indicates the reliability of the operation data, regenerates the operation data in accordance with the degree of certainty, associates the operation data with the degree of certainty of the operation data, and stores them in the storage device, and the facility operations support device creates an operation plan for the facility using the operation data stored in the storage device in accordance with the degree of certainty stored in the storage device.

[0012] The present embodiment also includes programs for causing the facility operation support device to function as a computer and storage media storing such programs. Furthermore, the present embodiment also includes a facility operation support method using the facility operation support device. More specific examples of the present embodiment will be described below. [Example]

[0013] In the first embodiment, a restoration operation when a power grid is damaged and a failure occurs in at least a part of the power grid, resulting in a power outage, is taken as an example of the work. In a facility having multiple pieces of equipment, such as a power grid, operation data is acquired from the equipment and used for operation. The equipment in this embodiment includes devices such as utility poles and smart meters.

[0014] If at least a portion of the equipment is damaged by a disaster or other event (a failure occurs), a recovery plan must be created based on the extent of the damage to the equipment. However, when a disaster occurs, it is often initially unclear which equipment has been affected and the extent of the damage. Furthermore, if the equipment from which operational data is acquired is affected, the reliability of that operational data decreases. For example, the communication status of smart meters may be out of order, utility poles may be tilted, or the normal status of the communication network itself may become uncertain. As a result, some operational data may be missing, or data that does not reflect the actual situation may be transmitted, reducing the reliability of the operational data.

[0015] Therefore, in this embodiment, when the power grid 2 suffers a power outage due to a disaster, the reliability of operational data is improved and a power outage restoration plan is created according to the power outage situation. Details thereof are explained below. FIG. 1 is a system configuration diagram of a power grid restoration plan creation support system in embodiment 1. In this embodiment, a power outage restoration plan is created by a power grid restoration plan support device 10 provided in a data center of an electric power company connected to the power grid 2. Then, based on the power outage restoration plan, workers carry out restoration work on the power grid 2. To do this, the workers use worker terminals 50. Here, the power grid restoration plan support device 10 is a type of facility operation support device that supports the operation of facilities related to the power grid 2.

[0016] 1, the power grid 2 includes, as its facilities, smart meter groups 21-24, utility poles 51-53, lower networks 31-34, and upper network 40. Although not shown, the power grid 2 also includes electric wires, substations, etc. Here, the upper network 40 can be realized as a wide area network such as the Internet.

[0017] First, the smart meter groups 21-24 are made up of smart meters 21-1-24-3 (denoted as "Smame" in the figure) installed at each consumer such as a home. Each smart meter group 21-24 is connected to a utility pole 51-53, and is an electricity meter that performs meter reading for each consumer and acquires information on the status of electricity usage. In other words, the smart meters 21-1-24-3 acquire operational status such as communication status as operational data.

[0018] Furthermore, utility poles 51 to 53 are connected to smart meter groups 21 to 24 via lower level networks 31 to 34. Utility poles 51 to 53 are divided into utility poles 51 and 53 with sensors and utility poles 52 and 54 without sensors. Utility poles 51 and 53 are provided with utility pole sensor devices 510 equipped with sensors that detect the inclination of the utility poles themselves as operational data.

[0019] Furthermore, the power grid restoration plan support device 10 is connected to utility poles 51-53 via the upper network 40. As a result, the power grid restoration plan support device 10 collects communication conditions and inclinations from the smart meters 21-1-24-3 and utility poles 51-53. Furthermore, the power grid restoration plan support device 10 can also collect communication conditions of the lower networks 31-34 and the upper network 40. In other words, the power grid restoration plan support device 10 collects operation data from the equipment. Then, in the event of a power outage, the power grid restoration plan support device 10 can create a power outage restoration plan, which is a type of operation plan, based on the communication conditions, inclination, etc. Furthermore, the power grid restoration plan support device 10 outputs the power outage restoration plan.

[0020] For this purpose, the power grid restoration plan support device 10 has a memory unit 11, a restoration plan creation unit 12, a data management unit 13, a power grid management unit 14, and a UI unit 15. The memory unit 11 stores data used for processing in the power grid restoration plan support device 10. The restoration plan creation unit 12 creates a power outage restoration plan based on the communication status, inclination, etc.

[0021] The data management unit 13 manages operational data in order to create a power outage recovery plan. This management includes collecting operational data, evaluating the reliability, etc. For this management, the data management unit 13 has a data collection unit 131, a data evaluation unit 132, a data revision unit 133, and a data storage unit 134.

[0022] Here, the data collection unit 131 collects operation data from the smart meters 21-1 to 24-3 and utility poles 51 to 53 via the higher-level network 40. The data collection unit 131 may actively collect operation data, or may passively collect operation data from each facility. Furthermore, the data evaluation unit 132 evaluates the reliability of the collected operation data. That is, the data evaluation unit 132 calculates the "reliability." Then, it is desirable that the data evaluation unit 132 determine whether the calculated reliability satisfies a predetermined condition.

[0023] Here, the degree of certainty is defined as a combination of multiple elements in the acquisition of operational data, and is an index showing the reliability of the operational data. Therefore, the degree of certainty can be used to confirm to what extent legitimate operational data has been acquired. An example of the degree of certainty is defined as a combination of multiple elements related to the acquisition of operational data. And, It can be defined by a combination of the time element (when) of the operational data acquisition, the location element (where) of the acquisition, and the characteristic element (what) of the operational data or equipment. Details of the certainty will be explained when explaining the calculation process.

[0024] Furthermore, the data revising unit 133 revises the collected operational data according to the evaluation results of the data evaluating unit 132. Here, the revising of operational data refers to processing of the operational data for creating a power outage recovery plan, and includes converting the operational data to improve the reliability and selecting operational data that satisfies predetermined conditions. Furthermore, the revising includes classifying the reliability to determine whether the reliability satisfies predetermined conditions. Then, the data storage unit 134 stores the revisited operational data in the memory unit 11.

[0025] The power grid management unit 14 manages the power grid 2 by acquiring the amount of power used by each consumer, collecting statistics, etc. The UI unit 15 also functions as an interface with the system administrator and other devices. In other words, the UI unit 15 has input / output and communication functions.

[0026] The restoration plan creation unit 12 and the power grid management unit 14 may be realized as a restoration plan creation device, a power grid management device, or a combination thereof, separate from the power grid restoration plan support device 10. Furthermore, the storage unit may also be configured independently, such as a file server.

[0027] Upon receiving the output of the power grid restoration plan support device 10, it becomes possible to display the power outage restoration plan on the worker terminal 50. As a result, the worker can use the worker terminal 50 to carry out the power outage restoration work. Here, the worker terminal 50 is used to manage the power grid 2 and the various facilities that make up the power grid 2, and can be realized by a computer such as a smartphone, mobile phone, tablet, smart speaker, or PC.

[0028] Next, the configuration of each device that constitutes the power grid restoration plan creation support system will be described. Figure 2 is a hardware configuration diagram showing an example of an implementation of the power grid restoration plan support device 10 in Example 1. The power grid restoration plan support device 10 can be realized by a computer, and has an arithmetic unit 101, a storage unit 102, an input unit 103, an output unit 104, and a communication unit 105, which are connected to each other via communication paths.

[0029] First, the calculation device 101 can be realized by a processor such as a CPU (Central Processing Unit), and executes calculations in accordance with a restoration plan creation program 106, a data management program 107, and a power grid management program 108. Each of these programs will be described later.

[0030] The storage device 102 corresponds to the storage unit 11 in FIG. 1 and stores various data. The stored data includes certainty-level data 109, system configuration data 110, and sensor data 111. Each of these data will be described later, with the sensor data 111 being an example of operational data. The storage device 102 can be realized by a temporary storage device such as a memory, and storage devices such as a hard disk drive (HDD), a solid state drive (SSD), and a memory card. Here, it is desirable that in addition to the above-mentioned data, each program is also stored in the storage. When processing is executed in the arithmetic device 101, the related programs and data are loaded from the storage to the temporary storage device. As described above, the programs are stored in the storage medium.

[0031] Here, each of the above-mentioned programs will be explained. First, the recovery plan creation program 106 is a program for realizing the function of the recovery plan creation unit 12 in Fig. 1. Furthermore, the data management program 107 is a program for realizing the function of the data management unit 13 in Fig. 1. Therefore, the data management program 107 has a data collection module 1071, a data evaluation module 1072, a data revision module 1073, and a data storage module 1074.

[0032] These respectively realize the functions of the data collection unit 131, data evaluation unit 132, data revision unit 133, and data storage unit 134 in Fig. 1. Each of these modules may be realized by an independent program, or at least a part of them may be realized by a single module or program.

[0033] The power grid management program 108 is a program for implementing the functions of the power grid management unit 14 in Fig. 1. In this embodiment, each function is implemented by a program, that is, software, but each function may also be implemented by dedicated hardware. This concludes the explanation of each program.

[0034] The input device 103 accepts operations from the system administrator. For this purpose, it can be implemented by input devices such as a keyboard, mouse, or microphone. The output device 104 can be implemented by output devices such as a display monitor or speaker. The input device 103 and the output device 104 can also be implemented as an integrated configuration such as a touch panel. Furthermore, the input device 103 and the output device 104 may be omitted. In this case, input can be accepted and information can be output using a terminal device used by the system administrator. Furthermore, the communication device 105 is connected to the upper network 40 and the worker terminal 50. The input device 103, the output device 104, and the communication device 105 correspond to the UI unit 15 in FIG. 1.

[0035] Next, the utility pole sensor device 510 provided on the utility poles 51 and 53 will be described. FIG. 3 is a hardware configuration diagram showing an example of implementation of the utility pole sensor device 510 in Example 1. The utility pole sensor device 510 has a calculation device 511, a storage device 512, an input device 513, an output device 514, a communication device 515, and a sensor 516, which are connected to each other via a communication path. The calculation device 511 can be realized by a processor such as a CPU, and controls the operation of the utility pole sensor device 510 in accordance with a control program 5111. Note that the calculation device 511 may also be realized by dedicated hardware.

[0036] The storage device 512 stores utility pole sensor data 517 including the details detected by a sensor 516, which will be described later. The utility pole sensor data 517 is a type of operational data, and has the following items: utility pole 5171, characteristics 5172, date and time 5173, and data body 5174. The utility pole sensor data 517 is included in the sensor data 111, and is an example of operational data.

[0037] Here, utility pole 5171 identifies utility pole 51, which is the target of detection by sensor 516, and indicates the acquisition location element (where) of utility pole sensor data 517. Therefore, utility pole 5171 may be location information of the utility pole 51. Characteristics 5172 indicate the characteristic element (what) of utility pole sensor data 517 itself or its acquisition device, the utility pole sensor device 510 or sensor 516. Furthermore, date and time 5173 indicates the acquisition time element (when) of utility pole sensor data 517. And data body 5174 is detection data indicating the content detected by sensor 516, in this example, the inclination of utility pole 51. Note that a degree of certainty is calculated for utility pole sensor data 517, and details of this will be explained in the explanation of the processing of this embodiment.

[0038] The input device 513 receives operations from workers and the like. For this purpose, it can be realized by input devices such as a keyboard (numeric keypad, etc.) and a microphone. The output device 514 can be realized by output devices such as a display monitor and a speaker. The input device 513 and the output device 514 can also be realized as an integrated configuration such as an operation panel. Furthermore, the input device 513 and the output device 514 may be omitted.

[0039] The communication device 515 also transmits and receives various data such as utility pole sensor data 517. In particular, the communication device 515 transmits the utility pole sensor data 517 to the power grid restoration plan supporting device 10 via the higher-level network 40. For this purpose, the communication device 515 is connected to the lower networks 31 to 34 and the upper network 40. Furthermore, the sensor 516 detects the tilt of the utility pole 51 and outputs detection data indicating this. Furthermore, the utility pole sensor device 510 may have a detachable battery, or may obtain power from the utility pole 51.

[0040] The utility pole sensor device 510 may be realized as a sensor 516 having a communication function. In this case, the detection data detected by the sensor 516 is sequentially transmitted to the power grid restoration plan support device 10.

[0041] Next, the smart meters 21-1 to 24-3 will be described. In the following, the smart meters 21-1 to 24-3 will be collectively referred to as the smart meter 20. Fig. 4 is a hardware configuration diagram showing an example of implementation of the smart meter 20 in the first embodiment.

[0042] 4, the smart meter 20 includes a calculation device 201, a storage device 202, an input device 203, an output device 204, a communication device 205, and a meter reading device 206, which are connected to each other via a communication path. The smart meter 20 further includes a battery 208 as a power source.

[0043] Here, the arithmetic unit 201 can be realized by a processor such as a CPU, and controls the operation of the smart meter 20 in accordance with a control program 2011. Note that the arithmetic unit 201 may also be realized by dedicated hardware.

[0044] The storage device 202 stores smart sensor data 207 including the amount of power usage measured by the meter reading device 206. The smart sensor data 207 is a type of operational data, and has the following items: location 2071, characteristics 2072, date and time 2073, and data body 2074.

[0045] Here, the location 2071 specifies the location where the smart meter 20 is installed, and indicates the location element (where) where the smart sensor data 207 is acquired. In addition, location 2071 is It may also be an item that identifies the relevant consumer. The characteristic 2072 indicates a characteristic element (what) of the smart sensor data 207 itself or the smart meter 20 or meter reading device 206 that is the device that acquires the smart sensor data. The date and time 2073 indicates a time element (when) at which the smart sensor data 207 was acquired. The data body 2074 is the amount of electricity used measured by the meter reading device 206. The smart sensor data 207 is included in the sensor data 111 and is an example of operational data. The degree of certainty is also calculated for this smart sensor data 207, and the details of this calculation will be explained in the explanation of the processing of this embodiment.

[0046] The input device 203 receives operations from workers and the like. For this purpose, it can be realized by input devices such as a keyboard (such as a numeric keypad) and a microphone. The output device 204 can be realized by output devices such as a display monitor and a speaker. The input device 203 and the output device 204 can also be realized as an integrated configuration such as an operation panel. Furthermore, the input device 203 and the output device 204 may be omitted.

[0047] The communication device 205 also transmits and receives various data such as utility pole sensor data 517. In particular, the communication device 515 transmits the smart sensor data 207 to the power grid restoration plan support device 10 via the lower networks 31 to 34 and the upper network 40. For this purpose, the communication device 515 is connected to the lower networks 31 to 34.

[0048] Furthermore, the meter reading device 206 measures the amount of electricity used by the customer and outputs the amount. Furthermore, the battery 208 may be detachable. A power source other than 208 may be used. The smart meter 20 may be realized as a meter reading device 206 having a communication function. In this case, the amount of power used is measured by the meter reading device 206 and then sequentially transmitted to the power grid restoration plan support device 10. This concludes the explanation of the configuration of this embodiment.

[0049] Next, a description will be given of the processing of the first embodiment. First, an outline of the processing of the first embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram for explaining the outline of the processing in the first embodiment. (1) Processing of the data management unit 13 (1)-1: The data collection unit 131 collects utility pole sensor data 517 and smart meter sensor data 207 as sensor data 111 from the utility pole sensor device 510 and the smart meter 20. The data collection unit 131 also collects network sensor data 1113 as sensor data 111 related to the upper network 40 and the lower networks 31 to 34. (1)-2: The data evaluation unit 132 evaluates the reliability of the sensor data 111 through mutual checking, and the data correction unit 133 performs corrections such as improving the reliability. The evaluation of the reliability includes calculating the reliability from the date and time 5173, 2073, which are examples of acquisition time elements included in the sensor data 111, and the utility pole 5171, location 2071, and characteristics 5172, 2072, which are examples of acquisition location elements. (1)-3: The data storage unit 134 associates the certainty degree in (1)-2 with the sensor data 111 and stores them in the memory unit 11. At this time, it is desirable that the data storage unit 134 stores them as data 109 with certainty degree. (2) Processing of the Recovery Plan Creation Unit 12 (2)-1: The recovery plan creation unit 12 receives an instruction to create a recovery plan through an operation from the system administrator. (2)-2: To create a recovery plan, the recovery plan creation unit 12 acquires certainty-level data 109 and system configuration data 110. The certainty-level data 109 may be a certainty level and sensor data 111. The certainty-level data 109 and the system configuration data 110 may also be actively notified to the recovery plan creation unit 12 from the data management unit 13 (particularly, the data storage unit 134). (2)-3: As a result of the above, the recovery plan creation unit 12 creates a recovery plan using the reliability-added data 109 and the system configuration data 110. (3) Processing using the worker terminal 50 (3)-1: The power grid restoration plan support device 10 notifies the worker terminal 50 of the created restoration plan. As a result, the worker can check the restoration plan. The restoration plan may be handed over to the worker by the system administrator on paper or the like. (3)-2: Workers will go to the area and carry out power restoration work based on the restoration plan.

[0050] The following describes the details of the processing of Example 1. Fig. 6 is a sequence diagram showing the processing content in Example 1. In the following description, the power grid restoration plan support device 10 will be described using the configuration shown in Fig. 1 (such as the data management unit 13 and the restoration plan creation unit 12).

[0051] First, in step S11, the arithmetic device 201 of the smart meter 20 determines whether a predetermined time has elapsed. For example, it determines whether 10 minutes (30 minutes) have elapsed since the smart meter 20 was started or since the previous processing. As a result, if the predetermined time has not elapsed (NO), this step is repeated. If the predetermined time has elapsed (YES), the process proceeds to step S12. In this step, the meter reading device 206 detects the amount of electricity used. Then, the arithmetic device 201 creates smart sensor data 207 from the amount of electricity used and stores it in the storage device 202.

[0052] In addition, in step S12, the arithmetic device 201 transmits the smart sensor data 207 in the storage device 202 to the power grid restoration plan support device 10 using the communication device 205. As a result, the smart sensor data 207 created in step S11 is periodically transmitted.

[0053] Next, the processing of the utility pole sensor device 510, which is performed in parallel with the processing of the smart meter 20, will be described. First, in step S21, the sensor 516 of the utility pole sensor device 510 continuously checks the inclination of the utility pole 51. As a result, if an inclination of a predetermined level or more is not detected (NO), this step continues. If an inclination of a predetermined level or more is detected (YES), the process proceeds to step S22. This step continues. In this step, the calculation device 511 creates utility pole sensor data 517 based on the detection result of the sensor 516 and stores it in the storage device 512.

[0054] In step S22, the calculation device 511 transmits the utility pole sensor data 517 stored in the storage device 512 to the power grid restoration plan support device 10 using the communication device 515. As a result, the smart sensor data 207 created in step S21 is periodically transmitted. Note that the inclination of the utility pole 51 is merely an example, and other data relating to the operation of the utility pole may also be used. For example, the amount of current passing through the utility pole may be used.

[0055] Next, the processing of the data management unit 13 of the power grid restoration plan support device 10 will be described. First, in step S31, the data collection unit 131 collects the utility pole sensor data 517 and the smart sensor data 207 transmitted in steps S12 and S22. Furthermore, the data collection unit 131 also collects the network sensor data 1113. In this way, the data collection unit 131 collects the sensor data 111.

[0056] In step S32, the data evaluation unit 132 performs an evaluation on the collected sensor data 111. Specifically, the data evaluation unit 132 calculates the degree of certainty by mutual checking. To do this, the data evaluation unit 132 uses the following (Equation 1).

[0057] C=C(when_n)*C(where_n)*C(what_n)...(Number 1) C: Certainty of data, 0≦C(x)≦1, n = 1, ... Here, C(when_n) is the data acquisition time element, C(where_n) is the data acquisition location element, and C(what_n) is the characteristic element of the data or the equipment from which it was acquired.

[0058] The degree of certainty may be calculated using the following (Equation 2).

[0059] C=C(when_n)*C(where_n)*C(what_n)*C(how_n)...(Number 2) C: Certainty of data, 0≦C(x)≦1, n = 1, ... In (Equation 2), a functional element (how) for improving the reliability of data with C(how) is added to (Equation 1).

[0060] The details of the data reliability will be explained below. Fig. 7 is a diagram for explaining the data reliability and its components in the first embodiment. Fig. 7 shows the details of each component of the reliability. In Fig. 7, #1 indicates the acquisition time element (when), #2 indicates the acquisition location element (where), #3 indicates the characteristic element (what), and #4 indicates the high reliability function element (how).

[0061] First, the acquisition time element (when) indicates the degree of certainty related to the acquisition time of data such as operational data. The more recently acquired data is, the higher the degree of certainty of the acquisition time element (when). It is also desirable for the degree of certainty to reflect the hidden time of failures in the facility. For example, the current time is set to 1.0, and it decreases by 0.1 every hour.

[0062] The acquisition location element (where) indicates the degree of certainty related to the location where data such as operational data was acquired. The shorter the distance between the data acquisition location and the location where the data is processed, such as the power grid restoration plan support device 10, the higher the acquisition location element (where). This location and distance include a physical location (position) and distance, as well as a location (position) and distance on the network topology. For example, the acquisition location element (where) of a specific location can be set to 1.0, and can decrease by 0.1 for every 1 km reduction, or by every 1 hop reduction. Furthermore, the acquisition location element (where) may be calculated using these multiple values.

[0063] Furthermore, the characteristic element (what) indicates the degree of certainty related to the characteristics of the equipment and devices (here referred to as unit devices) that make up the facility and the data. The characteristic element (what) is a value that corresponds to the certainty of the device and the characteristics of the data. Here, the certainty of the device is a value that corresponds to the device's function, normality of operation, and certainty. For example, the certainty of the device can use a value that corresponds to the presence or absence of a sensor and the sensitivity of the sensor. Furthermore, the certainty of the device can be calculated using these multiple values.

[0064] The data characteristics are values ​​corresponding to the properties and characteristics of the data. For example, values ​​corresponding to the data transfer time, whether or not retransmission processing is performed in the event of a transfer failure, and the reliability of the transfer route can be used. Furthermore, the data characteristics may be calculated using multiple values.

[0065] Furthermore, the high reliability function element (how) indicates the degree of certainty based on the high reliability function of the data. For example, the high reliability function element (how) can be a value depending on the presence or absence of a mutual check function based on time redundancy, a mutual check function between devices, a weighted majority voting function between devices such as utility poles, and a mutual check function based on route redundancy. It is desirable to set these values ​​higher when there is a high reliability function than when there is not. Furthermore, the high reliability function element (how) may be calculated using these multiple values.

[0066] By using each of the above components as a variable in (Equation 1) or (Equation 2), it is possible to calculate the degree of certainty. This means that the degree of certainty is calculated by combining each component. Furthermore, it may be determined whether the value of the degree of certainty calculated by (Equation 1) or (Equation 2) satisfies a predetermined condition. In other words, the degree of certainty may be compared with a reference value, and this result may be used as the degree of certainty. For example, if the value of the degree of certainty is equal to or greater than the reference value, the degree of certainty is determined to be "stable." If the value is less than the reference value, the degree of certainty is determined to be "stable." It is considered "unstable." Here, the order in which the faults occurred and the hierarchical relationship (connection relationship) of the equipment in the power grid 2 are used to classify the data as "stable" or "unstable." The above process makes it possible to calculate the degree of certainty regarding the quality of the data, such as missing data. If the data is determined to be unstable, it is desirable for the data collection unit 131 to perform end-to-end communication with the utility pole 51 and smart meters 21-1 to 24-3 to detect hidden faults in the power grid 2.

[0067] This concludes the explanation of Fig. 7, and we return to the explanation of Fig. 6. Next, in step S33 of Fig. 6, the data revising unit 133 revises the certainty determined in step S32. Then, the data storage unit 134 associates the certainty with the sensor data 111 to create certainty-added data 109. Here, revising refers to processing of the sensor data 111 for creating a power outage recovery plan, as described above, and includes conversion, selection, and the like. The revising process and storage process in step S32 will be described in detail below.

[0068] In the regeneration process and the storage process, the data regeneration unit 133 also uses the sensor data 111 (particularly, characteristics) and the system configuration data 110. Therefore, each of these data will be described first. First, FIG. 8 is a diagram showing the system configuration data 110 used in the first embodiment. The system configuration data 110 is data indicating the connection relationships of each facility in the power grid 2, which is a facility to be managed. That is, as shown in FIG. 8, the system configuration data 110 indicates the connection relationships from the upper network (network 1) to the smart meter at the end. For example, it indicates that the smart meter 21-1 is connected to the upper network 40 via the lower network 31 and the utility pole 51. Note that the system configuration data 110 may be realized as configuration data separated for each facility, such as a network, utility pole, and smart meter. That is, it can be realized as network configuration data, utility pole configuration data, and smart meter configuration data. In this case, it can be realized as data associating each facility with other facilities connected thereto.

[0069] Next, Fig. 9 is a diagram showing characteristics included in the sensor data 111 used in the first embodiment. Of these, Fig. 9(a) shows characteristics 5172 of utility pole sensor data 517. Fig. 9(a) shows the presence or absence of a sensor (utility pole sensor device) for each utility pole. That is, Fig. 9(a) shows Utility pole equipment The reason why there are sensors and non-sensors is that it is difficult to install sensors (utility pole sensor devices) on all utility poles due to cost, so it is necessary to manage whether or not a sensor is installed for each utility pole.

[0070] Also, Fig. 9(b) shows the characteristics 2072 of the smart sensor data 207. Fig. 9(b) shows the transfer interval (transmission interval) of the smart sensor data 207 for each smart meter. This interval can be set for each smart meter, and the value can be set arbitrarily.

[0071] Next, the contents of the regeneration process and the storage process in step S32 will be described. Figures 10 and 11 are flowcharts showing the details of the regeneration process and the storage process in the first embodiment.

[0072] First, in step S301, the data revising unit 133 determines whether or not a sensor (utility pole sensor device) is present based on the characteristics 5172 of the utility pole sensor data 517. As a result, if a sensor is present (Yes), the process proceeds to step S302. If a sensor is not present (No), the process proceeds to (1) in FIG. 11. Note that in this step, utility pole sensor data 517 for a predetermined period is read from the storage unit 11 and executed. The same applies to the following steps.

[0073] In step S302, the data correcting unit 133 determines whether the utility pole is Determine if it is normal For this purpose, the data body 5174 of the utility pole sensor data 517 is used. If the tilt of the utility pole in this data body 5174 is equal to or less than a predetermined value, it is determined to be normal. Note that data other than tilt may be used to determine whether the utility pole is normal. If the result is that it is not normal (if it is abnormal) (No), the process proceeds to step S303. If it is normal (Yes), the process proceeds to step S306. Note that if there is an abnormality in the utility pole, the data correction unit 133 identifies the time when the tilt of the utility pole became equal to or greater than a predetermined value using the date and time 5173. In other words, the time when the abnormality occurred is identified.

[0074] In step S303, the data correcting unit 133 determines whether a fault occurred in the smart meter before the abnormality occurred in the utility pole. To this end, the data correcting unit 133 uses the data body 2074 and the date and time 2073 to identify the time when the fault occurred in the smart meter. As a result, if no fault has occurred (No), the process proceeds to step S304. If a fault has occurred ( Yes ), the process proceeds to step S308.

[0075] Furthermore, in step S304, the processing of case 3 is executed. That is, the data correction unit 133 performs continuous data missing processing on the target utility pole sensor data 517. The details of this are explained below. The target utility pole sensor data 517 in step S304 is "utility pole sensor present" and "utility pole abnormality." That is, the utility pole has a sensor, and there is a high degree of confidence that the utility pole has fallen. For this reason, the data correction unit 133 specifies the acquisition time element, acquisition location element, and characteristic element as all 1.0. Therefore, the certainty of the target utility pole sensor data 517 is calculated to be utility pole abnormality (C: 1.0).

[0076] In addition, the target smart sensor data 207 is "before the utility pole becomes abnormal, Continuous data loss " In this way, the utility pole is abnormal, but the calculation of the certainty can be divided into the following cases depending on the previous missing state of the smart sensor data 207. First, in case 3-1, there was only one missing data in the smart sensor data 207, at the end. In this case, the missing data is estimated to be random. Then, the characteristic of the data of the characteristic element is reduced. In other words, the characteristic element becomes 0.9. Therefore, the data correction unit 133 calculates the certainty as a smart abnormality (C: 0.9) because the other elements are 1.0.

[0077] In addition, in case 3-2, the utility pole is abnormal, but the data loss in the smart sensor data 207 is continuous. In other words, it can be determined that the loss is regular and the certainty is maintained. Therefore, since the other elements are 1.0, the data correction unit 133 calculates the certainty as smart abnormality (C: 1.0). The above processing will be explained using FIG. 12. The processing flow shown in FIG. 12 is also executed in step S306.

[0078] 12 is a flowchart showing the details of the consecutive data loss processing (1) in the first embodiment. First, in step S3041, the data regenerating unit 133 determines whether loss has occurred in the target utility pole sensor data 517. As a result, if there is consecutive data loss (Yes), the process proceeds to step S3042. If there is not consecutive data loss (No), the process proceeds to step S3043. Here, it is desirable to determine whether there is a loss based on whether a predetermined number of or more data loss has occurred.

[0079] Furthermore, in step S3042, the data correcting unit 133 calculates the degree of certainty by the processing shown in Case 3-2 above. This step is also the same for Case 2-2 in step S306, which will be described later. Furthermore, in step S3043, the data correcting unit 133 calculates the degree of certainty by the processing shown in Case 3-1 above. This step is also the same for Case 2-1 in step S306, which will be described later. This concludes the explanation of step S304.

[0080] In step S305, the data regenerating unit 133 determines whether there is missing data in the target utility pole sensor data 517. As a result, if there is missing data (Yes), the process proceeds to step S306. If there is no missing data (No), the process proceeds to step S307.

[0081] Furthermore, in step S306, as processing for case 2, the data correction unit 133 performs consecutive data missing processing (1) similar to step S304. That is, as shown in FIG. 12, in step S3041, the data correction unit 133 determines whether data is missing. Then, in step S3042, the data correction unit 133 calculates the degree of certainty by the processing shown in case 2-2 above. Note that this step is also similar to case 2-2 in step S306 described later. Furthermore, in step S3043, the data correction unit 133 calculates the degree of certainty by the processing shown in case 2-1 above.

[0082] Here, the processing shown in cases 2-1 and 2-2 will be explained. In case 2-1, there is only one data loss in the smart sensor data 207, at the end. In this case, it is estimated that the loss is random. Then, the characteristic of the data of the characteristic element is reduced. In other words, the characteristic element becomes 0.9. Therefore, since the other elements are 1.0, the data correction unit 133 calculates the certainty as smart abnormality (C: 0.9).

[0083] In addition, in case 2-2, the utility pole is abnormal, but the data loss in the smart sensor data 207 is continuous. In other words, it can be determined that the loss is regular and the certainty is maintained. Therefore, since the other elements are 1.0, the data correction unit 133 calculates the certainty as smart abnormality (C: 1.0). This concludes the explanation of step S306.

[0084] Furthermore, in step S307, the data correction unit 133 executes the processing of case 1. That is, the data correction unit 133 assumes that the smart meter is normal and the utility pole is normal. Then, the data correction unit 133 calculates the reliability of the smart sensor data 207 of the target utility pole sensor data 517 to be 1.0. Furthermore, the data correction unit 133 calculates the reliability of the target utility pole sensor data 517 to be 1.0. At this time, the data correction unit 133 uses the data body shown in FIG. 15. This is also used in the other cases 2 to 4. Note that FIG. 15 will be described later.

[0085] The calculation of the degree of certainty will be explained below. The utility pole sensor data 517 targeted in step S307 is "utility pole sensor present," "utility pole normal," and "no missing data." In other words, the utility pole sensor is present, but there is no notification that the utility pole has fallen. Therefore, the acquisition time element, acquisition location element, and characteristic element are all specified as 1.0. As a result, the data correction unit 133 calculates the degree of certainty of the target utility pole sensor data 517 as 1.0.

[0086] Furthermore, in step S307, the smart sensor data 207 does not have any missing data. Therefore, the acquisition time element, acquisition location element, and characteristic element are all specified as 1.0. Therefore, the acquisition time element, acquisition location element, and characteristic element are all specified as 1.0. As a result, the data correction unit 133 calculates the certainty of the target smart sensor data 207 as 1.0. This concludes the explanation of step S307.

[0087] Furthermore, in step S308, the data correction unit 133 executes the processing of case 4. Here, the target utility pole sensor data 517 in step S308 indicates that the utility pole has a sensor and there is a notification that the utility pole has fallen. In other words, it is "utility pole sensor present" and "utility pole abnormality." Therefore, similar to step S304, the data correction unit 133 calculates the certainty of the target utility pole sensor data 517 as utility pole abnormality (C: 1.0).

[0088] Furthermore, the target smart sensor data 207 is such that "there is no missing data in the smart sensor data 207 before the utility pole becomes abnormal." In this way, after the utility pole abnormality occurs, there is a possibility that a fault will occur in the smart meter. However, this fault cannot be detected. This is called a hidden fault. Therefore, the data reliability of the smart meter is calculated taking this hidden fault into consideration. Specifically, the data reconstructing unit 133 identifies the acquisition time element depending on how much time has passed since the fault. In other words, the time of the hidden fault shown in FIG. 7 is used. The data reconstructing unit 133 then uses this to calculate the reliability of the smart sensor data 207.

[0089] Next, the processing from (1) onwards will be described with reference to Fig. 11. In step S309, the data correction unit 133 reads the relevant smart sensor data 207 from the storage unit 11. Furthermore, in step S310, the data correction unit 133 determines whether there is any missing data in the smart sensor data 207 of each smart meter 21-1 to 24-3. As a result, if there is any missing data (Yes), the process proceeds to step S311. Furthermore, if there is no missing data (No), the process proceeds to step S317.

[0090] Also, in step S311, The data correction unit 133 It is determined whether there is any missing data in the smart sensor data 207 of each of the smart meter groups 21 to 24. As a result, if there is any missing data (Yes), the process proceeds to step S312. On the other hand, if there is no missing data (No), the process proceeds to step S318.

[0091] Furthermore, in step S312, the data correction unit 133 executes tilt check processing for utility poles that do not have utility pole sensors. Details of this tilt check processing will be explained using FIG. 13. FIG. 13 is a flowchart showing details of the tilt check processing in Example 1. First, in step S3121, the data correction unit 133 identifies the target utility pole. Then, the data correction unit 133 extracts utility poles in the vicinity of the identified utility pole. To this end, the data correction unit 133 uses the location 2071 of the system configuration data 110 or the utility pole sensor data 517 to extract surrounding utility poles that have a predetermined relationship, such as a predetermined distance (a radius of 2 km) from the target utility pole.

[0092] In step S3122, the data revising unit 133 executes a weighted majority process. The details of this process are explained below. Note that, like each element, weight can be understood from the perspectives of the acquisition time, acquisition location, characteristics, and high reliability function regarding data acquisition.

[0093] First, the data revising unit 133 identifies a weight using the utility pole sensor data 517 of the target utility pole. Specifically, the data revising unit 133 identifies the weight of the acquisition time from the date and time 5173. For example, if the acquisition date and time of the latest utility pole sensor data 517 is 12:00, the weight of the acquisition time is 1.0. The data revising unit 133 also identifies the weight of the acquisition location from the utility pole 5171. For example, the acquisition location element decreases by 0.1 for every 1 km, to 0.9 within 1 km, 0.8 at 2 km, and so on.

[0094] The data revising unit 133 also identifies the weight of the characteristic from the characteristic 5172. For example, if there is a utility pole sensor device (sensor present), the weight is set to 1.0, and if there is no sensor, the weight is set to 0.9. In addition, the data revising unit 133 sets the weight related to the high reliability function to 1.0 in order to perform majority voting processing.

[0095] Furthermore, the data revising unit 133 calculates the weight of the utility pole sensor data 517 for each utility pole using the weights identified as described above. Here, the weight of the target utility pole and the weights of the extracted surrounding utility poles are calculated. This calculation is performed in the same manner as in (Equation 2) above. For example, the certainty of the target utility pole is set to 0.8, and the certainty of the surrounding utility poles are set to 0.9 and 0.72. Then, the data revising unit 133 executes majority voting processing according to (weight of surrounding utility poles) / (weight of surrounding utility poles+weight of target utility pole) to calculate the adjustment weight. In the above example, the adjustment weight calculated is (0.9+0.72) / (0.9+0.72+0.8)=0.67.

[0096] In step S3123, the data corrector 133 calculates the reliability of the data according to the adjustment weight. That is, if the adjustment weight is 0.9 or more, the reliability is 1.0. If the adjustment weight is 0.7 to 0.89, the reliability is 0.9. If the adjustment weight is 0.51 to 0.69, the reliability is 0.8. In the above example, 0.8 is used as the reliability. be identified Then, the data revising unit 133 identifies the tilt of the target utility pole as having a degree of certainty of 0.8. Note that although the weight of the high reliability function is used here, this can be omitted.

[0097] This concludes the explanation of step S312, and we return to FIG. 11. In step S313, the data correction unit 133 uses the inclination of the utility pole identified in step S312 to determine whether the utility pole is abnormal (for example, collapsed). To do this, the data correction unit 133 determines whether the inclination is equal to or greater than a predetermined value, taking into account the calculated certainty. As a result, if the inclination is abnormal (Yes), the process proceeds to step S314. If the inclination is not abnormal (No), the process proceeds to step S319.

[0098] Then, the processing of Case 13 is executed in the following steps S314 to S316 and S320. First, in step S314, the data corrector 133 uses the utility pole sensor data 517 to identify the time when the abnormality (fault) occurred in step S313.

[0099] Here, in case 13, the target utility pole sensor data 517 is that there is no sensor on the utility pole, and the smart meters 21-1 to 24-3 lack the smart sensor data 207. In this case, two cases, Cases 13-1 and 13-2, are assumed. In order to execute processing along these two cases, the determination process of step S315 is executed. In step S315, the data correction unit 133 uses the smart sensor data 207 to determine whether an abnormality occurred in the smart meter before the occurrence time identified in step S314. As a result, if no abnormality has occurred (single failure), the process proceeds to step S316, and the process of Case 13-1 is executed. On the other hand, if an abnormality has occurred (continuous occurrence), the process proceeds to step S320, and the process of Case 13-2 is executed.

[0100] Then, in step S316, the data correction unit 133 executes the process of case 13-1. In case 13-1, it is assumed that data is lost only once when a failure occurs in each of the smart meters 21-1 to 24-3. Therefore, the data correction unit 133 determines that the abnormality is a utility pole abnormality (C: 1.0) and a smart meter abnormality (C: 0.9). This is executed in the same way as in step S3043.

[0101] Furthermore, in step S320, the data correction unit 133 executes the process of case 13-2. In case 13-2, continuous data is missing at the time when a failure occurs in each of the smart meters 21-1 to 24-3. Therefore, the data correction unit 133 determines that the abnormality is a utility pole (C: 1.0) and a smart meter abnormality (C: 1.0). This is also executed in the same manner as in step S3043.

[0102] Furthermore, in step S317, processing for case 11 is executed. In case 11, "no utility pole sensor" and "no missing data" are present. Therefore, the data reconstructing unit 133 determines that both the smart meter and the utility pole are normal, since there is no missing data. This is executed in the same manner as in step S307. At this time, the data reconstructing unit 133 uses the main data shown in FIG. 16. This is also used in the other cases 12 to 13. Note that FIG. 16 will be described later.

[0103] Furthermore, in step S318, consecutive data loss processing (2) is executed as processing for case 12. FIG. 14 is a flowchart showing the details of consecutive data loss processing (2) in the first embodiment. In case 12, the cases are "no utility pole sensor," "utility pole is normal," and "data loss exists." Furthermore, since smart sensor data 207 is received from some smart meters, it is possible to determine that the utility pole is normal. In this case, case 12 is divided into cases 12-1 and 12-2 depending on whether the data loss is consecutive. Therefore, in step S3181, the data correction unit 133 determines whether the data loss is consecutive. That is, the same processing as in step S3041 is executed. As a result, if the data loss is consecutive (Yes), the process proceeds to step S3183. If the data loss is not consecutive (No), the process proceeds to step S3182.

[0104] In step S3182, the processing of case 12-1 is executed. That is, since "utility pole sensor present" and there is no notification of an abnormality in the utility pole, the data correction unit 133 determines that the utility pole is normal (C: 1.0). Furthermore, since the utility pole is normal and the only data missing in the smart sensor data 207 is the last one, it is insufficient to determine that there is an abnormality in the smart sensor data 207, so the data correction unit 133 determines that there is an abnormality in the smart sensor data 207 (C: 0.9).

[0105] Furthermore, in step S3183, processing for case 12-2 is executed. That is, the data correction unit 133 executes processing based on "utility pole sensor present," "utility pole normal," and "data missing (continuous data missing)." First, the data correction unit 133 determines that the utility pole is normal (C:1.0) because "utility pole sensor present" and there is no notification that the utility pole has fallen. Furthermore, the data correction unit 133 determines that the utility pole is normal and that there are continuous data missing in the smart sensor data 207, so determines that the smart sensor is abnormal (C:1.0).

[0106] Furthermore, in step S319, processing for case 14 is executed. In case 14, "utility pole sensor not present." Therefore, the data revising unit 133 determines the status of the utility pole by majority vote, that is, uses the determination result of normal in step S313. Then, since "utility pole sensor not present," the data revising unit 133 determines that the utility pole is normal (C: 0.9). Then, the data revising unit 133 determines that the utility pole is abnormal (C: 1.0). At this time, the data main body shown in FIG. 17 is used by the data revising unit 133. Note that FIG. 17 will be described later.

[0107] The data storage unit 134 then stores the results determined for each case in the memory unit 11. At this time, the data storage unit 134 stores the corresponding sensor data 111 (smart sensor data 207 and utility pole sensor data 517) in association with the degree of certainty. It is also desirable that the data storage unit 134 associates the sensor data 111 with the degree of certainty to create and store degree-of-certainty-assigned data 109. This degree-of-certainty-assigned data 109 may be configured as data for each facility, such as utility pole degree-of-certainty-assigned data 1091, smart sensor degree-of-certainty-assigned data 1092, and network degree-of-certainty-assigned data 1093.

[0108] This concludes the explanation of the regeneration process and storage process. In cases 1 to 3, 11, 12, 13-2, and 14, the acquisition time element, acquisition location element, and characteristic element are identified and used to calculate the degree of certainty, but the degree of certainty may also be calculated directly. In other words, if a predetermined situation is met, such as "utility pole sensor present" or "utility pole abnormality," the data regeneration unit 133 may specify the degree of certainty as 1.0.

[0109] Here, the data bodies 5174, 2074 (hereinafter referred to as data bodies) of the sensor data 111 for calculating the degree of certainty in each of the above-mentioned cases will be described with reference to the drawings. FIG. 15 is a diagram summarizing the data bodies in Cases 1 to 4 in Example 1. This data body indicates whether the utility pole and smart meter are normal or abnormal, and the amount of power used, for each case. For the utility pole, whether it is abnormal, such as collapsed, or normal is indicated, and for the smart meter, the amount of power used is recorded. Using these, each of the above-mentioned steps is executed. Note that the smart meter may also be recorded as being normal or abnormal, such as broken. Furthermore, FIG. 15 also shows data for "utility pole sensor present." Note that in FIG. 15, "-" indicates missing data. The same applies to the following FIGS. 16 to 18.

[0110] FIG. 16 is a diagram showing a summary of the data bodies for Cases 11 to 13 in Example 1. Like FIG. 15, FIG. 16 also records, for each case, whether the utility pole and smart meter are normal or abnormal, and the amount of power used. FIG. 16 also shows data for the case "without utility pole sensor." Furthermore, FIG. 17 is a diagram showing a summary of the data bodies for Case 14 in Example 1. Like FIG. 15 and FIG. 16, FIG. 17 also records, for each case, whether the utility pole and smart meter are normal or abnormal, and the amount of power used. Like FIG. 16, FIG. 17 also shows data for the case "without utility pole sensor."

[0111] Returning to FIG. 6, the description of the overall processing of this embodiment will be continued. In step S41, the recovery plan creation unit 12 requests event data to be used for creating a recovery plan from the data management unit 13. Then, in step S34, the data management unit 13 accepts the request for event data from the recovery plan creation unit 12. Here, the event data is data in a format used by the recovery plan creation unit 12 to create a recovery plan. Then, the data management unit 13 (for example, the data storage unit 134) searches for the certainty-leveled data 109 according to the request and converts it into event data.

[0112] Then, in step S35, the data management unit 13 outputs this to the recovery plan creation unit 12. In response to this, in step S42, the recovery plan creation unit 12 accepts the event data. Note that the certainty level data 109 may be used as the event data. In this case, the conversion process can be omitted. Also, the conversion to event data may be performed by the recovery plan creation unit 12.

[0113] In step S43, the restoration plan creation unit 12 executes a restoration plan creation process for the damage to the power grid 2. Here, in this embodiment, it is desirable to recalculate the certainty and create a restoration plan using this. This is because the above-mentioned event data and certainty-associated data 109 do not necessarily satisfy the certainty required by the restoration plan creation unit 12, and verification of this is also difficult.

[0114] Therefore, in this embodiment, the recovery plan creation unit 12 cooperates with the data management unit 13 to update and use the certainty. This enables the recovery plan creation unit 12 to use data with the certainty it requests, allowing it to output more appropriate processing results. For this reason, in the above-mentioned step S42, the recovery plan creation unit 12 outputs a request for event data including the minimum required certainty to the data management unit 13. Then, the data revision unit 133 uses a high-reliability function to improve the certainty of the target certainty-added data 109 or event data so that it satisfies the certainty from the recovery plan creation unit 12. Then, in step S35, the data management unit 13 outputs the event data including the improved certainty. Then, in step S43, the recovery plan creation unit 12 creates a recovery plan using the accepted event data.

[0115] Here, details of the recovery plan creation process will be explained with reference to Fig. 18. Fig. 18 is a flowchart showing details of the recovery plan creation process in the first embodiment. In step S431, the recovery plan creation unit 12 reads a designated area and the reliability of the equipment in that area from the event data. Here, the designated area is an area that needs to be restored due to damage to the power grid 2, and is accepted from the system administrator via the UI unit 15.

[0116] Furthermore, in step S432, the restoration plan creation unit 12 determines whether the certainty read in step S431 satisfies a predetermined condition, for example, whether it is equal to or greater than a threshold. Here, if the specified area is a single facility, it is desirable to use the certainty of that facility (such as a utility pole or a smart meter). Furthermore, if multiple facilities exist, it is desirable to use a representative value (overall evaluation), such as the average or sum of the certainty of the multiple facilities. As a result, if the predetermined condition is satisfied (Yes), the process proceeds to step S433. Furthermore, if the predetermined condition is not satisfied (No), the process proceeds to step S434.

[0117] Here, a specific example of the determination in this step will be described. FIG. 19 is a diagram for explaining the determination process in creating a restoration plan in the first embodiment. In FIG. 19, the reliability of each facility is recorded for each smart meter group. Then, the restoration plan creation unit 12 calculates a representative value of the reliability of each facility and records this as an overall evaluation. Furthermore, the restoration plan creation unit 12 compares the overall evaluation with a preset threshold value (for example, 0.9). As a result, for #1 and #3, which are equal to or greater than the threshold, the process proceeds to step S433. Furthermore, for #2 and #4, which are less than the threshold, the process proceeds to step S434. Note that the content shown in FIG. 19 is preferably stored in the storage unit 11 as reliability data.

[0118] Furthermore, in step S433, the restoration plan creation unit 12 creates a detailed restoration plan using the event data. In creating this detailed restoration plan, route calculation is performed for workers to perform work such as repairs. Details of this process will be described with reference to FIG. 20. FIG. 20 is a diagram for explaining the process of creating a detailed restoration plan in the first embodiment. In this example, it is assumed that a HEMS (Home Energy Management System) is connected to each of the smart meters 21-1 to 21-3 of the smart meter group 21. It is also assumed that the power grid restoration plan support device 10 is realized by cloud computing. It is also assumed that the lower network 31 is connected to the utility pole 51 via a wireless network 31-1 and a wired network 31-2. In other words, the network is also redundant. Utilizing this, the restoration plan creation unit 12 creates routes 1 to 3 as patrol routes for workers. In this embodiment, routes 1 to 3 are set as shown in the figure.

[0119] Then, the restoration plan creation unit 12 compares routes 1 to 3 as follows, and identifies the location of the failure in the facility and the time of the failure. As a result, the restoration plan creation unit 12 verifies these and identifies a travel route. The details are given below.

[0120] The restoration plan creation unit 12 identifies the equipment on each route and the status of the failure. The identified route failure status is shown in FIG. 21. Here, multiple cases are assumed and the respective route failure statuses are shown. In this case, Case 20 , case 21 where a failure has occurred in the wireless network 31-1, case 22 where a failure has occurred in the lower network 31, and case 23 where a failure has occurred in the HEMS. Below, the verification by the restoration plan creation unit 12 will be explained for each failure case. In the figure, ◯ indicates normal, × indicates a failure, and △ indicates that the power grid restoration plan support device 10 has not been able to receive the sensor data 111.

[0121] First, in case 21, the restoration plan creation unit 12 determines that the same failure has occurred based on the comparison result of routes 1 to 3. That is, it can be determined that the failure is in the wireless network 31-1. In addition, in case 22, the restoration plan creation unit 12 can detect a failure in the lower network 31 or the wireless network 31-1 by comparing routes 1 to 3. In addition, in case 23, the restoration plan creation unit 12 can detect a failure in the HEMS by comparing routes 1 to 3. It can also be determined that no failure has occurred in the upper network 40.

[0122] As a result of the above, in the event of a network failure, it is possible to separate the wireless network 31-1 from the lower network 31 or the upper network 40, thereby improving data reliability. Similarly, it is possible to improve the status (data) of other equipment such as a HEMS, such as whether the data is normal or abnormal, and the reliability of that data. In this way, by comparing the results between multiple routes, the location of the failure can be identified, thereby improving data reliability. This concludes the explanation of step S433, and we return to the explanation of FIG. 18.

[0123] In step S434, since the degree of certainty is low, the recovery plan creation unit 12 creates a rough recovery plan. For example, the recovery plan creation unit 12 omits the creation of a detailed route as in step S433, and creates an approximate route that is approximated by the maximum value. The recovery plan created in this manner is output to the system administrator or worker terminal 50 via the UI unit 15. As a result, the worker can carry out recovery work according to the recovery plan. This allows the recovery plan shown in FIG. explanation Now, let us return to the explanation of Figure 6.

[0124] In step S44, the recovery plan creation unit 12 notifies the data management unit 13 of a request to write the created recovery plan. In response to this, in step S36, the data storage unit 134 of the data management unit 13 stores the recovery plan in the memory unit 11 in response to the write request. This concludes the explanation of the first embodiment. According to this embodiment, it is possible to create a more appropriate recovery plan even when a failure occurs in a facility such as the power grid 2. [Example]

[0125] In the first embodiment, a recovery plan for a failure due to a disaster is created, but the present invention can also be provided to support so-called normal operation. In the second embodiment, support for normal operation is targeted. The configuration of the second embodiment is the same as that of the first embodiment, but differs in that a power grid management unit 14 is used. Therefore, at least one of the recovery plan creation unit 12 and the power grid management unit 14 in FIG. 1 may be omitted, or the function of the other may be realized by either one.

[0126] In the second embodiment, the process is performed in the same manner as in the first embodiment up to step S42 in FIG. 6. In step S43, the power grid management unit 14 creates a maintenance plan for maintenance in the same manner as in the first embodiment. Then, in step S44 and thereafter, the process is performed in the same manner as in the first embodiment. According to the second embodiment described above, more appropriate operation management of facility maintenance and the like can be realized. Note that it is also possible to configure so that both the restoration plan of the first embodiment and the normal maintenance plan of the second embodiment are created. According to the second embodiment, the so-called normal maintenance plan can be realized in accordance with the actual situation. [Example]

[0127] In addition to the creation of a restoration plan in the first embodiment, the third embodiment is an example of executing an application service using the sensor data 111 and its reliability as an example of business. The application service includes a monitoring service and a home delivery service. In this embodiment, the reliability is used to determine whether a customer is at home or not, and an appropriate service is provided. The details are explained below.

[0128] FIG. 22 is a diagram illustrating an outline of the processing of the service delivery support device 100 according to the third embodiment. The service delivery support device 100 includes a service support unit in addition to the power grid restoration plan support device 10 according to the first or second embodiment. As a result, in this embodiment, in addition to creating a restoration plan, it also creates routes for monitoring services and home delivery services. Specifically, the data revising unit 133 performs context management on the sensor data 111, such as the utility pole sensor data 517, to identify consumer presence data. The data revising unit 133 calculates the degree of certainty of presence as the degree of certainty. The service support unit then uses the data to create a route. This process preferably follows the processing flow shown in FIG. 18 . The restoration plan and route plan are preferably output via an API (Application Programming Interface). In this embodiment, the creation and output of the restoration plan may be omitted and the process may be limited to service support. According to the third embodiment, it is possible to realize applied services tailored to the presence status of consumers, particularly the creation of route plans. [Explanation of symbols]

[0129] 10...power grid restoration plan support device, 11...storage unit, 12...restoration plan creation unit, 13...data management unit, 131...data collection unit, 132...data evaluation unit, 133...data revision unit, 134...data storage unit, 14...power grid management unit, 15...UI unit, 2...power grid, 21-24...smart meter group, 21-1 to 24-3...smart meters, 31-34...lower network, 40...upper network, 50...worker terminal

Claims

1. A facility operation support device for supporting facility operation, a communication device that receives operational data regarding the operation of the facility; a storage device connected to the communication device via a communication path and storing a data management program; connecting the communication device and the storage device via the communication path; According to the data management program, Calculating the reliability of the operational data, which is defined by a combination of a plurality of elements in the acquisition of the operational data and indicates the reliability of the operational data; Regenerating the operational data according to the certainty; a computing device that associates the operational data with the reliability of the operational data and stores them in the storage device; A facility operation support device that creates an operation plan for the facility using the operation data stored in the storage device in accordance with the certainty stored in the storage device.

2. 2. The facility operation support device according to claim 1, The plurality of elements are an acquisition time element, an acquisition location element, and a characteristic element of the operation data.

3. 3. The facility operation support device according to claim 2, The computing device, in accordance with the data management program, When the facility is affected by a disaster, the operational data is collected via the communication device, and the certainty is calculated at the time of the disaster; A facility operation support device, wherein the facility operation plan is a recovery plan for the facility.

4. 4. The facility operation support device according to claim 3, the storage device stores a recovery plan creation program, The computing device is a facility operation support device that creates a recovery plan for the facility using operational data with a degree of certainty required for creating the recovery plan, among the operational data stored in the storage device, in accordance with the recovery plan creation program.

5. 5. The facility operation support device according to claim 4, The computing device, in accordance with the data management program, Evaluating the reliability as unstable using the order of occurrence of failures due to the disaster and the hierarchical relationship in the facility, which are indicated by the acquisition time of the operational data; When the failure is recovered, end-to-end communication is performed using the communication device; A facility operation support device that detects hidden failures in the facility through the end-to-end communication.

6. A facility operation support method using a facility operation support device for supporting facility operation, receiving operational data regarding the operation of the facility by a communication device; a data management program is stored in a storage device connected to the communication device via a communication path; a computing device connected to the communication device and the storage device via the communication path in accordance with the data management program, Calculating the reliability of the operational data, which is defined by a combination of a plurality of elements in the acquisition of the operational data and indicates the reliability of the operational data; Regenerating the operational data according to the certainty; storing the operational data and the reliability of the operational data in the storage device in association with each other; A facility operation support method for creating an operation plan for the facility using the operation data stored in the storage device in accordance with the certainty stored in the storage device.

7. 7. The facility operation support method according to claim 6, A facility operation support method, wherein the plurality of elements are an acquisition time element, an acquisition location element, and a characteristic element of the operation data.

8. The facility operation support method according to claim 7, The computing device executes the data management program. When the facility is affected by a disaster, the operational data is collected via the communication device, and the certainty is calculated at the time of the disaster; A facility operation support method, wherein the facility operation plan is a recovery plan for the facility.

9. 9. The facility operation support method according to claim 8, a recovery plan creation program is stored in the storage device; A facility operation support method in which the computing device creates a recovery plan for the facility using operational data with a degree of certainty required for creating the recovery plan, from the operational data stored in the storage device, in accordance with the recovery plan creation program.

10. 10. The facility operation support method according to claim 9, The computing device executes the data management program. Evaluating the reliability as unstable using the order of occurrence of failures due to the disaster and the hierarchical relationship in the facility, which are indicated by the acquisition time of the operational data; When the failure is recovered, end-to-end communication is performed using the communication device; A facility operation support method for detecting hidden failures in the facility through the end-to-end communication.

11. A facility operation support device that is a computer for supporting facility operation, a UI unit that receives operation data regarding the operation of the facility; a data evaluation unit that calculates the reliability of the operational data, the reliability being defined by a combination of a plurality of elements in the acquisition of the operational data, and indicating the reliability of the operational data; a data revising unit that revises the operational data in accordance with the certainty; functioning as a data storage unit that associates the operational data with the certainty of the operational data and stores them in a storage unit; A program that creates an operation plan for the facility using the operation data stored in the storage unit in accordance with the certainty stored in the storage unit.

12. The program according to claim 11, The plurality of elements are an acquisition time element, an acquisition location element, and a characteristic element of the operational data.

13. 13. The program according to claim 12, causing the facility operation support device to function as a data collection unit that collects the operation data; the data collection unit collects the operational data via the UI unit when the facility is affected by a disaster; The data evaluation unit calculates the certainty at the time of the disaster, The facility's operational plan is a program that is a recovery plan for the facility.

14. 14. The program according to claim 13, causing the facility operation support device to function as a restoration plan creation unit that creates a restoration plan for the facility; The data storage unit is a program that outputs, to the recovery plan creation unit, operational data with a degree of certainty required for creating the recovery plan, from the operational data stored in the storage unit.

15. 15. The program according to claim 14, the data evaluation unit evaluates the reliability as unstable using the order of occurrence of failures due to the disaster and the hierarchical relationship in the facility, which are indicated by the acquisition time of the operation data; the data collection unit performs end-to-end communication when the failure is recovered, A program that detects hidden faults in the facility through the end-to-end communication.

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