Survey plan generation apparatus, survey plan generation method, and program

Abstract

To provide a survey plan generation apparatus, a survey plan generation method, and a program which automatically generate a survey plan according to productivity of a surveyor or the like.SOLUTION: A survey plan generation apparatus, which generates a survey plan for surveying a plurality of buildings by a plurality of surveyors, includes: a condition acquisition step that acquires a condition which is to be satisfied by the survey plan and includes productivity of the plurality of surveyors, or productivity of a plurality of survey teams organized by assigning the respective surveyors to the respective teams; and a survey plan generation step that generates the survey plan based on the acquired condition.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an inspection plan generation device, an inspection plan generation method, and a program, and more particularly to a technique for generating an inspection plan for inspecting a plurality of buildings by a plurality of inspectors. [Background technology]

[0002] There are known techniques for automatically generating plans for work to be performed by workers. For example, Patent Document 1 discloses a water leak detection planning device that creates water leak detection plans for multiple areas divided into water pipe networks. Also, Patent Document 2 discloses a personnel allocation planning device that creates multiple worker allocation plans for each process carried out in a logistics center. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-94665 [Patent Document 2] International Publication No. 2019 / 064379 Summary of the Invention [Problem to be solved by the invention]

[0004] When a home is damaged by a natural disaster such as an earthquake, the local government will conduct a damage assessment survey to determine the extent of damage to the home. When conducting this damage assessment survey, the local government will create an overall survey plan in advance and estimate the expected completion date, required personnel, survey schedule for each area, etc.

[0005] When a severe disaster occurs, the number of houses to be surveyed can reach tens of thousands, and there are various conditions that must be met when creating a survey plan depending on the extent of the damage. Examples of conditions include the target completion date, the number of personnel available, the availability of necessary equipment, the difficulty of surveying the houses (whether they are wooden or non-wooden, etc.), and areas that require priority surveying. It is difficult to manually create a survey plan for tens of thousands of houses while taking these conditions into account, which can lead to delays in survey implementation and reduced survey efficiency.

[0006] Furthermore, in residential damage certification surveys, survey plans vary depending on the productivity of the surveyor or the survey team to which multiple surveyors are assigned. However, the devices described in Patent Documents 1 and 2 have the problem that productivity is not taken into consideration.

[0007] The present invention has been made in view of the above circumstances, and aims to provide a survey plan generation device, a survey plan generation method, and a program that automatically generate a survey plan according to the productivity of surveyors, etc. [Means for solving the problem]

[0008] One aspect of an inspection plan generation device for achieving the above-mentioned object is an inspection plan generation device that generates an inspection plan for inspecting multiple buildings by multiple inspectors, the inspection plan generation device comprising at least one processor and at least one memory that stores instructions to be executed by the at least one processor. The at least one processor acquires conditions that the inspection plan must satisfy, including the productivity of multiple inspectors or the productivity of multiple inspection teams to which the multiple inspectors are assigned, generates an inspection plan based on the acquired conditions, recalculates the productivity of the multiple inspectors or the productivity of the multiple inspection teams based on the inspection performance of the multiple inspectors over a certain period of time in inspections conducted based on the generated inspection plan, and updates the inspection plan based on the recalculated productivity. This aspect allows for automatic generation of inspection plans based on the productivity of the inspectors, etc.

[0009] Preferably, at least one processor generates multiple survey plan candidates, displays summary information of the generated multiple survey plan candidates on a display, and accepts user selection of a survey plan candidate to be adopted as the survey plan from the multiple displayed survey plan candidates, thereby allowing the user to select a desired survey plan candidate from the multiple survey plan candidates.

[0010] The summary information of the survey plan candidates preferably includes at least one of the indicators of the total number of survey days and the forecast of personnel for each fixed period, thereby enabling the user to select a desired survey plan candidate based on at least one of the indicators of the total number of survey days and the forecast of personnel for each fixed period.

[0011] Preferably, the at least one processor generates multiple candidate survey plans according to multiple optimization criteria, thereby allowing a user to select from the multiple candidate survey plans a survey plan that meets a desired optimization criterion.

[0012] The survey preferably includes surveying damage to a plurality of buildings, and the optimization criteria preferably include at least one of prioritizing areas with greater building damage, areas with less building damage, the shortest number of days, and the smallest number of personnel. This makes it possible to select a candidate survey plan that meets an appropriate optimization criteria for the survey of building damage.

[0013] The inspection plan preferably includes at least one of an inspection schedule, a list of buildings to be inspected by day, and assignment information of inspectors or inspection teams, thereby enabling the generation of an appropriate inspection plan.

[0014] Preferably, at least one processor displays a survey schedule for each surveyor or survey team of the survey plan on a display and accepts user editing of the displayed survey schedule, thereby enabling the user to generate an optimal survey plan.

[0015] Preferably, at least one processor accepts editing of the survey schedule by drag-and-drop operation using a pointing device, thereby enabling the user to edit the survey schedule with simple operations.

[0016] The conditions preferably include at least one of a target number of days, a number of equipment, and a priority area, thereby making it possible to generate a survey plan that satisfies at least one of the target number of days, the number of equipment, and the priority area.

[0017] Preferably, the inspection includes inspecting damage to a plurality of buildings, and the at least one processor generates an inspection plan based on at least one of the building information and the damage information by region, thereby generating an appropriate inspection plan for inspecting the damage to the buildings.

[0018] Preferably, the at least one processor generates the search plan using bin packing and / or Dijkstra's shortest path algorithms, which can generate a suitable search plan.

[0019] Preferably, the at least one processor causes the generated survey plan and a map of the area containing the plurality of buildings to be displayed on a display, thereby enabling the generated survey plan to be appropriately presented.

[0020] One aspect of the survey plan generation method for achieving the above-mentioned object is a survey plan generation method for generating a survey plan for surveying multiple buildings by multiple surveyors, the survey plan generation method comprising: a condition acquisition step for acquiring conditions that the survey plan must satisfy, including the productivity of the multiple surveyors or the productivity of multiple survey teams to which the multiple surveyors are assigned; a survey plan generation step for generating a survey plan based on the acquired conditions; a recalculation step for recalculating the productivity of the multiple surveyors or the productivity of the multiple survey teams based on the survey performance of the multiple surveyors over a certain period of time in surveys conducted based on the generated survey plan; and a survey plan update step for updating the survey plan based on the recalculated productivity. This aspect allows for automatic generation of survey plans based on the productivity of the surveyors, etc.

[0021] One aspect of a program for achieving the above object is a program for causing a computer to execute the above-mentioned survey plan generation method. This aspect may also include a computer-readable non-transitory recording medium on which this program is recorded. According to this aspect, a survey plan can be automatically generated according to the productivity of surveyors, etc. [Effects of the Invention]

[0022] According to the present invention, a survey plan can be automatically generated according to the productivity of surveyors, etc. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 1 is a block diagram of a research plan generator. [Figure 2] FIG. 2 is a functional block diagram of the research plan generating device. [Figure 3] FIG. 3 is a flowchart showing each step of the research plan generation method according to the first embodiment performed by the research plan generation device. [Figure 4] FIG. 4 is a process diagram of each step in the research plan generation method. [Figure 5]FIG. 5 is an example of an input screen displayed on the display. [Figure 6] FIG. 6 shows an example of a plan candidate selection screen displayed on the display. [Figure 7] FIG. 7 shows an example of an editing screen displayed on the display. [Figure 8] FIG. 8 is a diagram showing an example of changing the assigned region of one team and the assigned region of another team. [Figure 9] FIG. 9 is a diagram showing an example of changing the assigned region of one team and the assigned region of another team. [Figure 10] FIG. 10 shows an example of an editing screen in which a survey plan and a map are displayed simultaneously. [Figure 11] FIG. 11 is another example of an editing screen in which a survey plan and a map are simultaneously displayed on the screen. [Figure 12] FIG. 12 is an example of a screen on the display where the Finish button is displayed. [Figure 13] FIG. 13 is a flowchart showing each step of a research plan generating method according to the second embodiment performed by a research plan generating device. [Figure 14] FIG. 14 is a flowchart showing each step of a research plan generating method according to the second embodiment performed by a research plan generating device. [Figure 15] FIG. 15 is a flowchart showing each step of a research plan generating method according to the second embodiment performed by a research plan generating device. [Figure 16] FIG. 16 is a flowchart showing the steps of a research plan generating method according to the second embodiment performed by a research plan generating device. [Figure 17] FIG. 17 is a process diagram showing a research plan generating method according to the third embodiment performed by the research plan generating device. [Figure 18] FIG. 18 is a diagram for explaining the generation of a survey plan candidate. [Figure 19] FIG. 19 is a diagram showing a part of the plan summary display G2. DETAILED DESCRIPTION OF THE INVENTION

[0024] A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings. Here, an example of generating an investigation plan for conducting a damage assessment investigation of a building after a severe disaster will be described. A building refers to a dwelling such as a "detached house" or "apartment building," but may also include buildings in general such as "stores," "offices," and "factories." In the following, a building will be referred to as a "house" without distinguishing between different types.

[0025] [Configuration of the survey plan generation device] 1 is a block diagram of a research plan generation device 10 according to this embodiment. The research plan generation device 10 is implemented by at least one computer. As shown in FIG. 1, the research plan generation device 10 includes a processor 12, a memory 14, a communication interface 16, an input interface 18, and a display 20.

[0026] The processor 12 executes instructions stored in the memory 14. The hardware structure of the processor 12 is various processors as shown below. The various processors include a CPU (Central Processing Unit), which is a general-purpose processor that executes software (programs) and functions as various functional units, a GPU (Graphics Processing Unit), which is a processor specialized for image processing, a PLD (Programmable Logic Device), which is a processor whose circuit configuration can be changed after manufacture such as an FPGA (Field Programmable Gate Array), and a dedicated electrical circuit, such as an ASIC (Application Specific Integrated Circuit), which is a processor having a circuit configuration designed specifically for executing specific processing.

[0027] A single processing unit may be configured with one of these various processors, or may be configured with two or more processors of the same or different types (e.g., multiple FPGAs, a combination of a CPU and an FPGA, or a combination of a CPU and a GPU). Also, multiple functional units may be configured with a single processor. Examples of multiple functional units configured with a single processor include, first, a configuration in which a single processor is configured with a combination of one or more CPUs and software, as typified by a client or server computer, and this processor operates as multiple functional units. Second, a configuration in which a processor is used to realize the functions of an entire system including multiple functional units on a single IC (Integrated Circuit) chip, as typified by an SoC (System On Chip). In this way, the various functional units are configured with one or more of the above-mentioned various processors as a hardware structure.

[0028] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit made up of a combination of circuit elements such as semiconductor elements.

[0029] The memory 14 stores instructions to be executed by the processor 12. The memory 14 includes a RAM (Random Access Memory) and a ROM (Read Only Memory), not shown. The processor 12 uses the RAM as a working area, executes software using various programs and parameters, including the survey plan generation program, stored in the ROM, and also executes various processes of the survey plan generation device 10 by using the parameters stored in the ROM, etc.

[0030] The communication interface 16 controls wired and wireless communications. The research plan generation device 10 is connected to a communication network such as the Internet via the communication interface 16 so as to be able to send and receive data.

[0031] The input interface 18 is an input device that allows the user to input various information and desired instructions to the research plan generation device 10. The input interface 18 includes a pointing device such as a mouse, and an input device such as a keyboard.

[0032] The display 20 is a display device that allows the user to visually confirm information such as the generated survey plan. The display 20 displays a screen required for operations on the input interface 18, and functions as a part that realizes a GUI (Graphical User Interface). A touch panel display that integrates the input interface 18 and the display 20 may also be used.

[0033] Figure 2 is a functional block diagram of the survey plan generation device 10. As shown in Figure 2, the survey plan generation device 10 includes a plan condition input reception unit 22, a damage information acquisition unit 24, a house information acquisition unit 26, an automatic plan generation unit 28, a plan candidate display unit 30, a plan candidate selection reception unit 32, a map information acquisition unit 34, a plan editing means display unit 36, and a plan editing operation reception unit 38. Each of these functions of the survey plan generation device 10 is realized by a processor 12.

[0034] The planning condition input receiving unit 22 receives input of planning conditions that the generated survey plan must satisfy. When the user inputs planning conditions via the input interface 18, the planning condition input receiving unit 22 receives the input planning conditions. The planning conditions include the expected productivity (number of surveys per day) of each of multiple surveyors, or the expected productivity of each of multiple survey teams. A survey team is a group of one or more surveyors assigned to each of multiple surveyors, and is the smallest unit of a group that conducts a survey. The planning conditions may include factors that affect the schedule of the survey plan, such as the target number of days, number of equipment, and priority areas.

[0035] The damage information acquisition unit 24 acquires damage information for an area that includes a house that is the subject of a damage assessment survey. The damage information acquisition unit 24 may acquire the damage information via the communication interface 16, or may acquire the damage information via the input interface 18. The damage information acquisition unit 24 may acquire the damage information stored in the memory 14.

[0036] The house information acquisition unit 26 acquires house information (an example of "building information"), which is information about houses that are the subject of a damage assessment survey. The house information acquisition unit 26 may acquire the house information via the communication interface 16, or via the input interface 18. The house information acquisition unit 26 may acquire house information stored in the memory 14. The house information includes information on whether the structure is wooden or non-wooden. Wooden structures are structures that use wood for the main parts required for structural strength. Non-wooden structures are structures other than wooden structures, and include reinforced concrete structures, steel-frame structures, etc.

[0037] The automatic plan generation unit 28 generates multiple survey plan candidates in accordance with multiple optimization criteria based on the plan conditions received by the plan condition input reception unit 22, the damage information acquired by the damage information acquisition unit 24, and the house information acquired by the house information acquisition unit 26. The optimization criteria include at least one of prioritizing areas with heavy damage, areas with less damage, the shortest number of days, and the minimum number of personnel. Note that areas with heavy damage and areas with less damage refer to areas with relatively heavy damage and areas with relatively less damage within the area being surveyed, and the determination of whether damage is relatively heavy or light is made based on the number of damaged houses in the area.

[0038] The generated candidate inspection plans each include an inspection schedule, a list of houses to be inspected by each day (an example of a "list of buildings to be inspected"), and assignment information for each inspection team. The candidate inspection plans may be generated using at least one of the shortest path algorithms of bin packing and Dijkstra.

[0039] The plan candidate display unit 30 displays summary information for each of the multiple survey plan candidates generated by the automatic plan generation unit 28 on the display 20. The summary information for the survey plan candidate includes at least one indicator of the total number of survey days and the personnel forecast for each fixed period.

[0040] The plan candidate selection receiving unit 32 receives the selection of one of the multiple survey plan candidates. When the user selects a desired survey plan candidate from the multiple survey plan candidates displayed on the display 20 using the input interface 18, the plan candidate selection receiving unit 32 receives the selected survey plan candidate as the survey plan to be adopted.

[0041] The map information acquisition unit 34 acquires map information of the area including the house that is the target of the damage assessment survey. The map information acquisition unit 34 may acquire the map information via the communication interface 16, or may acquire the map information via the input interface 18. The map information acquisition unit 34 may acquire the map information stored in the memory 14.

[0042] The plan editing means display unit 36 ​​displays the survey plan accepted by the plan candidate selection accepting unit 32 on the display 20 in an editable manner. The plan editing means display unit 36 ​​displays a survey schedule for each surveyor or each survey team in the survey plan on the display 20. The plan editing means display unit 36 ​​may also display map information acquired by the map information acquiring unit 34 on the display 20.

[0043] The plan editing operation receiving unit 38 receives editing operations via the input interface 18 of the survey plan that the plan editing means display unit 36 ​​has displayed on the display 20. The plan editing operation receiving unit 38 receives editing of the survey schedule that the plan editing means display unit 36 ​​has displayed on the display 20, that is, editing of the survey schedule by drag-and-drop operations using a pointing device (not shown).

[0044] [Research plan generation method: First embodiment] FIG. 3 is a flowchart showing each step of the research plan generation method according to the first embodiment performed by the research plan generation device 10. FIG. 4 is a process diagram of each step of the research plan generation method. The research plan generation method is realized by the processor 12 executing a research plan generation program stored in the memory 14. The research plan generation program may be provided by a computer-readable non-transitory recording medium. In this case, the research plan generation device 10 may read the research plan generation program from the non-transitory recording medium and store it in the memory 14.

[0045] In step S1 (an example of a "condition acquisition process"), the planning condition input receiving unit 22 receives input of planning conditions that the survey plan to be generated must satisfy (process P1). Figure 5 is an example of an input screen displayed on the display 20. Figure 5 shows an example in which a target date and equipment are input as planning conditions.

[0046] The target dates include the survey start date and the target completion date. When the survey start date and the target completion date are input, the number of days required is determined. In the example shown in FIG. 5, the survey start date is input as November 4, 2020, and the target completion date is input as December 4, 2020, so the number of days required is 30. The automatic plan generation unit 28 generates a survey plan that fits as closely as possible within the input survey start date and target completion date.

[0047] Equipment refers to the number of devices required for the survey, which in this case refers to the number of survey app terminals. The number of devices affects the upper limit on the number of survey teams and limits the number of areas that can be surveyed.

[0048] Returning to the explanation of FIGS. 3 and 4, in step S2, the damage information acquisition unit 24 acquires damage information including the extent of damage, address, etc. of the area that is the target of the damage assessment survey (process P2).

[0049] The damage information is, for example, the detection results of damaged houses obtained by inputting images taken from a high altitude using a drone or the like into damage assessment AI (Artificial Intelligence), and the results of an automatic assessment of the extent of damage. The damage information may be damaged building information manually entered by local government officials when they discover damaged houses during patrols immediately after the disaster. The damage information may also be information automatically imported as the results of the progress of damage assessment surveys.

[0050] In step S3, the house information acquisition unit 26 acquires house information that is the target of the damage assessment investigation (process P3).

[0051] In step S4 (an example of an "inspection plan generation process"), the automatic plan generation unit 28 generates multiple inspection plan candidates for each optimization criterion that results in a trade-off, based on the information on the planning conditions received by the planning condition input reception unit 22, the damage information acquired by the damage information acquisition unit 24, and the information on the houses to be inspected acquired by the house information acquisition unit 26 (process P4). Here, it is assumed that the automatic plan generation unit 28 has generated three inspection plan candidates: an inspection plan that prioritizes areas with heavy damage, an inspection plan that prioritizes areas with less damage, and an inspection plan that minimizes the number of inspection days.

[0052] FIG. 4 shows an example of a candidate investigation plan 100. The candidate investigation plan 100 includes an investigation schedule, a list of houses to be investigated by day, and assignment information for each investigation team. The assignment information for each investigation team includes information on the area or houses to be assigned to the investigation team. In the example shown in FIG. 4, team T1 is assigned 10 properties (10 houses) in AB1-chome on November 1st, 13 properties in AB2-chome on November 2nd, and 9 properties in AB3-chome on November 3rd.

[0053] Returning to the explanation of Figure 3, in step S5, the plan candidate display unit 30 displays selectable summary information for each of the multiple survey plan candidates generated by the automatic plan generation unit 28 on the display 20. The displayed summary information for the plan candidates is expressed as a set of indicators for comparing the plan candidates, such as the total number of survey days and weekly personnel forecasts.

[0054] Fig. 6 is an example of a plan candidate selection screen that the plan candidate display unit 30 displays on the display 20. In the example shown in Fig. 6, the plan candidate selection screen displays a first plan candidate 104, a second plan candidate 106, and a third plan candidate 108 in a selectable manner.

[0055] The first candidate plan 104 is an investigation plan generated by prioritizing areas with heavy damage. The second candidate plan 106 is an investigation plan generated by prioritizing areas with less damage. The third candidate plan 108 is an investigation plan generated by minimizing the number of investigation days.

[0056] The candidate plan selection screen includes summary information for each candidate plan, as well as indicators for judging the merits of each candidate plan. The indicators displayed here include information on the number of days required for the survey 110, information on the weekly survey progress forecast 112, information on the weekly number of surveyors (number of survey teams) required 114, and information on the order of survey areas 116.

[0057] Required survey days information 110 includes the planned survey start date, planned survey completion date, and the number of days required from survey start to survey completion. Weekly survey progress forecast information 112 includes the number of surveys per day and a line graph showing the remaining number of surveys if the surveys are carried out according to the survey plan. The number of surveys per day is the total number of surveys divided by the required number of days. Weekly required number of surveyors (number of survey teams) forecast information 114 includes the total number of survey teams and a bar graph showing the number of survey teams per week.

[0058] Returning to the explanation of FIG. 3, in step S6, the plan candidate selection receiving unit 32 receives the selection of one of the multiple survey plan candidates. The user selects one desired survey plan candidate from the multiple survey plan candidates using the input interface 18. Here, it is assumed that the first plan candidate 104 has been selected by the user. The plan candidate selection receiving unit 32 receives the first plan candidate 104 as the survey plan to be adopted.

[0059] In step S7, the plan editing means display unit 36 ​​displays the survey plan accepted by the plan candidate selection accepting unit 32 on the display 20 in an editable manner. Fig. 7 shows an example of an editing screen displayed on the display 20. Here, a survey schedule including the weekly assigned areas of teams T1, T2, T3, T4, and T5 is displayed.

[0060] In step S8, the plan editing operation receiving unit 38 receives an editing operation via the input interface 18 of the survey plan that the plan editing means display unit 36 ​​has displayed on the display 20. This allows the user to manually edit the survey plan.

[0061] 8 and 9 are diagrams showing an example of changing (swapping) the assigned region for team T3 for week 3 and the assigned region for team T4 for week 2 on the editing screen shown in FIG. 7. FIG. 8 shows a state in which cell 118, which indicates team T3's assigned region for week 3, is moved by a dragging operation using a pointing device (not shown). FIG. 9 shows a state in which cell 118 is dropped onto cell 120, which indicates team T4's assigned region for week 2. As shown in FIG. 9, when cell 118 is dropped onto cell 120, cell 118 is placed in the position where cell 120 was, and at the same time, cell 120 is moved and placed into the position where cell 118 was. In other words, team T3's assigned region for week 3 and team T4's assigned region for week 2 are swapped.

[0062] The plan editing means display unit 36 ​​can display the map acquired by the map information acquisition unit 34 along with the survey plan on the display 20. FIG. 10 is an example of an editing screen on which the survey plan and map are simultaneously displayed on the display 20. The user can display a map of the area related to the survey plan by selecting a cell of the survey plan with a pointing device (not shown). Here, an example is shown in which a map 124 is displayed as a result of the user selecting cell 122, which indicates the assigned area for team T1 for the first week. The map 124 is a map that includes the area displayed in cell 122, and the area indicated by cell 122 is highlighted.

[0063] FIG. 11 shows another example of an editing screen in which a survey plan and a map are simultaneously displayed on the display 20. In the generated survey plan, all areas requiring surveying are assigned to one of the survey teams, but the user can manually cancel the assignment later. In the example shown in FIG. 11, a map 128 is displayed, including an unassigned area 126 that has not been assigned to any team due to the unassigned area cancellation operation. Here, an example is described in which the user reassigns the unassigned area 126 that has been cancelled by the user. The user can assign the desired area to the survey team and schedule indicated by the cell by dragging and dropping the desired area on the map 128 onto the desired cell position using a pointing device (not shown). Here, the unassigned area 126 is dragged and dropped onto cell 130 for the second week of team T4, thereby editing the unassigned area 126 to be assigned to the survey for the second week of team T4.

[0064] Also, a Complete button 132 for ending the editing operation of the survey plan is displayed on the display 20. Fig. 12 shows an example of the editing screen of the display 20 displaying the Complete button 132. When the user operates the Complete button 132 via the input interface 18, the survey plan is finalized and the processing of this flowchart ends.

[0065] In this way, the survey plan generation device 10 can automatically generate a survey plan in accordance with the planning conditions. Furthermore, the survey plan generation device 10 generates multiple survey plan candidates in accordance with multiple optimization criteria and displays summary information of the multiple survey plan candidates in a selectable manner, allowing the user to select a survey plan candidate with the desired optimization criterion from the multiple survey plan candidates. Furthermore, the survey plan generation device 10 allows the user to edit the selected survey plan, allowing the user to obtain an optimal survey plan.

[0066] Second Embodiment Figures 13 to 16 are flowcharts showing the steps of the survey plan generation method according to the second embodiment by the survey plan generation device 10. Note that terminal C1 in Figure 13 is connected to terminal C1 in Figure 14, terminal C2 in Figure 13 is connected to terminal C2 in Figure 14 and terminal C2 in Figure 16, terminal C3 in Figure 13 is connected to terminal C3 in Figure 15, terminal C4 in Figure 14 is connected to terminal C4 in Figure 16, and terminal C5 in Figure 15 is connected to terminal C5 in Figure 16, respectively.

[0067] 13 to 16 are data input to the research plan generating device 10. The research plan generating device 10 acquires data D5, and D7 to D9 from the memory 14, and acquires data D1 to D4, D6, and D10 via the communication interface 16 or the input interface 18. Data D11 to D25 are data generated by the research plan generating device 10.

[0068] The processing of steps S11 to S13 shown in Fig. 13 is processing for calculating the total amount of investigation work for the damage certification investigation. In step S11, processor 12 generates a first list of houses to be investigated (data D11) based on the list of houses (data D1), the house tax register (data D2), and the basic resident register (data D3).

[0069] The list of houses includes information on the location, address, and whether the house is wooden or non-wooden. The location of the house includes the latitude and longitude information of the house. The house tax register includes information on a list of houses for which house tax has been paid. The basic resident register includes information on a list of houses that have a history of being occupied. In addition, the first list of houses to be surveyed includes information on the address of each house and whether it is wooden or non-wooden. The houses to be surveyed are all houses within the municipality where the damage certification survey is being conducted, excluding houses for which house tax has not been paid and houses that are not listed in the basic resident register.

[0070] Next, in step S12, the processor 12 calculates the number of walls for each house based on the first list of houses to be surveyed (data D11) and the outer perimeter shape of each house (data D4), and generates a second list of houses to be surveyed (data D12). The second list of houses to be surveyed includes information on the address of each house, whether it is wooden or non-wooden, and the number of walls.

[0071] The perimeter shape of each house is, for example, the perimeter shape data (2D polygon data) of houses published by the Geospatial Information Authority of Japan. The number of walls per house can be calculated using the following formula 1.

[0072] (Number of walls) = (Number of vertices of the house) - 1 ... (Equation 1) The 2D polygon data that makes up the perimeter (walls) of a house has a "closed" vertex structure, and the last element of a house's vertex list always contains the same value as the first element of the list. For example, if the coordinates of the four vertices of a house with four walls are (0,0), (1,0), (1,1), and (0,1), the vertex list of the 2D polygon data will have five elements: (0,0), (1,0), (1,1), (0,1), and (0,0), and the line is closed when the value of the starting point is added to the end. Therefore, the number of walls of a house is obtained by subtracting 1 from the number of vertices in the 2D polygon data. However, walls with a distance of less than one meter between the vertices will be excluded.

[0073] Furthermore, in step S13, the processor 12 calculates the total amount of work required for the investigation (data D13) based on the second list of houses to be investigated (data D12). The total amount of work required for the investigation is the sum of the number of walls of all houses, and has the relationship of the following formula 2.

[0074] (Total amount of work required for the survey) = (total number of walls per house) ... (Equation 2) The total amount of work required for the investigation is expressed as the number of walls because the damage assessment investigation is carried out on a wall-by-wall basis. The processor 12 calculates the total amount of work required for the investigation for wooden and non-wooden houses separately. The total amount of work required for the investigation includes information on the number of walls in wooden houses and the number of walls in non-wooden houses.

[0075] In addition, processor 12 performs steps S14 to S16 in parallel with steps S11 to S13. Steps S14 to S16 are used to calculate the amount of work that can be done in one day by the investigation team of the local government based on the productivity of the investigation team of the local government. The investigation team of the local government refers to an investigation team made up of investigators belonging to the local government that will be conducting the damage certification investigation.

[0076] In step S14, processor 12 generates a first inspection team list (data D14) based on the inspection team profile list (data D5). The inspection team profile list includes information on each inspector of each inspection team in the agency, including a flag indicating whether they are capable of inspecting non-wooden houses, and an inspector ID (Identification). The first inspection team list (data D14) includes information on whether each inspection team has the ability to inspect non-wooden houses. If any one of the inspectors making up the inspection team is an expert capable of inspecting non-wooden houses, the inspection team is considered to have the ability to inspect non-wooden houses.

[0077] In step S15 (an example of a "condition acquisition process"), the processor 12 calculates the productivity of each inspection team based on the first inspection team list (data D14) and at least one of the inspection performance log (data D6) and the first daily productivity target value (data D7), and generates a second inspection team list (data D15). The inspection performance log is the actual number of inspections per day for each inspection team, and can be, for example, the average of the actual number of wall surfaces inspected over the past three days. If the inspection performance log does not exist, the first daily productivity target value for the inspection team entered by the user is used as the default value. The processor 12 calculates the productivity of each inspection team for both wooden and non-wooden buildings. The second inspection team list includes information on whether each inspection team has the ability to inspect non-wooden buildings and their daily productivity.

[0078] In step S16, processor 12 calculates the amount of work that can be inspected per day for each inspection team based on the second inspection team list (data D15) and generates the amount of work that can be inspected per day (data D16). Processor 12 calculates the amount of work that can be inspected per day for each inspection team for wooden and non-wooden buildings. The amount of work that can be inspected per day (data D16) includes the sum of the daily productivity of each inspection team.

[0079] The processing in steps S17 to S19 shown in Figure 14 is a process for calculating the amount of investigation work that can be done in one day by the support team that will investigate the portion of the investigation that cannot be handled by the investigation team of the local agency. The support team refers to a team made up of supported investigators other than the local agency investigators, and is made up of supported investigators dispatched in response to a request for support from another local government, for example.

[0080] As shown in Figure 14, in step S17, processor 12 calculates the number of wooden construction support teams and the number of non-wood construction support teams based on the total survey work volume (data D13), the daily survey work volume that can be completed by the agency's survey team (data D16), the second daily productivity target value of the support team (data D8), and the target completion date (data D9), and generates a wooden construction support team list (data D17) and a non-wood construction support team list (data D18).

[0081] The number of wooden support teams is calculated using the following formula 3.

[0082] (Number of wooden support teams) = (Total number of wooden walls - (Number of wooden walls that can be inspected by the agency's inspection team per day x Target number of days)) ÷ (Second daily productivity target value x Target number of days) ... (Equation 3) Similarly, the number of non-wooden support teams is calculated using the following formula 4.

[0083] (Number of non-wooden support teams) = (Total number of non-wooden walls - (Number of non-wooden walls that can be inspected by the agency's inspection team per day x Target number of days)) ÷ (Second daily productivity target value x Target number of days) ... (Equation 4) If the support team's investigation performance log exists, the support team's investigation performance log may be used instead of the second daily productivity target value. Also, the target number of days is the number of days from the investigation start date to the target completion date.

[0084] In step S18, processor 12 calculates the amount of work that the wooden construction support team can inspect in one day based on the wooden construction support team list (data D17), and generates a first amount of work that can be inspected in one day (data D19). The first amount of work that can be inspected in one day (data D19) includes information on the number of wooden wall surfaces.

[0085] Similarly, in step S19, processor 12 calculates the amount of work that can be inspected by the non-wooden support team in one day based on the non-wooden support team list (data D18), and generates a second amount of work that can be inspected in one day (data D20). The second amount of work that can be inspected in one day (data D20) includes information on the number of non-wooden wall surfaces.

[0086] The processing of steps S20 to S22 shown in Figure 15 is processing for sorting houses to be surveyed according to the survey priority. As shown in Figure 15, in step S20, processor 12 calculates the survey priority based on the second list of houses to be surveyed (data D12) and the address list of totally destroyed houses (data D10), and generates a third list of houses to be surveyed (data D21). The third list of houses to be surveyed includes information on the addresses of totally destroyed houses, whether they are wooden or non-wooden structures, and the number of walls.

[0087] For example, when calculating the investigation priority, if "priority is given to areas with the greatest damage," the priority is determined by the "percentage of completely destroyed houses" in the block to which the house belongs. In other words, the investigation priority in this case can be expressed by the following formula 5. The higher the investigation priority value, the higher the priority.

[0088] (Investigation priority) = (number of completely destroyed houses in the block) ÷ (total number of houses in the block) ... (Equation 5) The number of completely destroyed houses in a block can be extracted from a list of addresses of completely destroyed houses included in the damage information acquired by the damage information acquisition unit 24.

[0089] On the other hand, if "priority is given to areas with less damage," the priority is calculated by multiplying the "proportion of completely destroyed houses" in the block to which the house belongs by minus 1. In other words, the investigation priority in this case can be expressed by the following formula 6.

[0090] (Investigation priority) = - (Number of completely destroyed houses in the block) ÷ (Total number of houses in the block) ... (Equation 6) Furthermore, in step S21, processor 12 sorts the houses to be surveyed in order of survey priority based on the third list of houses to be surveyed (data D21) and generates a sorted list of houses to be surveyed (data D22). Here, processor 12 sorts the third list of houses to be surveyed in descending order of house survey priority, and further sorts houses in the same block in order of block number. By sorting in this manner, houses that are close to each other will be assigned to the same survey team in subsequent processing, thereby improving the efficiency of the survey.

[0091] The process of steps S22 to S23 shown in Fig. 16 is a process of assigning a house to be surveyed to an survey team or a support team. Here, there is no particular distinction between the survey team of the own agency and the support team, and they are simply referred to as the survey team.

[0092] 16, in step S22, processor 12 acquires one day's worth of houses to be surveyed based on the daily surveyable work volume (data D16), the first one day's surveyable work volume (data D19), the second one day's surveyable work volume (data D20), and the sorted list of houses to be surveyed (data D22), and generates one day's list of houses (data D23). The one day's list of houses (data D23) includes information on whether the houses are wooden or non-wooden structures.

[0093] In step S23, processor 12 allocates houses to each inspection team based on the one-day house list (data D23) and generates a one-day inspection plan (data D24). The one-day inspection plan (data D24) includes information on the house allocation for each inspection team. Here, processor 12 allocates houses to each inspection team using a bin-packing algorithm. For non-wooden houses, processor 12 allocates only to inspection teams that can inspect non-wooden houses. For wooden houses, processor 12 allocates them preferentially to inspection teams that cannot inspect non-wooden houses, and allocates unallocated wooden houses to inspection teams that can inspect non-wooden houses.

[0094] The processor 12 repeats the processes of steps S22 and S23 until the last house listed in the sorted list of houses to be inspected (data D22) is assigned to one of the inspection teams, and generates an inspection plan (data D25) until the completion date. The inspection plan (data D25) until the completion date includes information on the assignment of houses to each inspection team until the completion date.

[0095] In this way, the survey plan generation device 10 can automatically generate a survey plan according to the productivity of the surveyor or survey team.

[0096] The survey plan generation device 10 may also recalculate the productivity of multiple surveyors or multiple survey teams based on the survey performance of multiple surveyors over a certain period of time in surveys conducted based on the generated survey plan, or the survey performance of multiple survey teams over a certain period of time (an example of a "recalculation process"), and update the survey plan based on the recalculated productivity (an example of a "survey plan update process"). While it is difficult to manually update such a survey plan, the survey plan generation device 10 can automatically generate an updated survey plan.

[0097] When other local governments provide support during a major disaster, it is not realistic to uniformly measure productivity in advance based on efficiency and proficiency, etc., due to the urgency of the situation and differences in standards. For this reason, it is more realistic to measure productivity while actually performing work and reflect this in work from the next day onwards. By generating an investigation plan using productivity that is updated based on daily investigation results, it is possible to generate a highly accurate plan even when a trade-off calculation is made between the number of days and the number of people (or amount of money).

[0098] Each time a survey is completed, the survey team members input the survey results into a survey application terminal (not shown). Therefore, the survey plan generation device 10 acquires the survey results for one day (an example of a fixed period) as a survey performance log (data D6), and can recalculate the daily productivity of each survey team and update the survey plan based on the recalculated productivity. Productivity can be recalculated taking into account the productivity used up to that point, or it can be performed based on the daily performance input without taking into account the productivity up to that point.

[0099] In a situation where completing an investigation quickly would result in the issuance of a disaster damage certificate sooner, and therefore the receipt of disaster relief funds from the local government sooner, it is conceivable that the investigation team would carry out the investigation for the following day ahead of schedule. It is also conceivable that, due to certain circumstances, the investigation may not proceed as planned. By recording these as investigation performance logs, it is possible to appropriately calculate the daily productivity of each investigation team.

[0100] If the survey is not progressing as planned, it is desirable to determine whether the problem is due to the capabilities of the survey team's surveyors or to external factors other than capabilities. For example, if an external factor that affects all survey teams occurs, such as bad weather making the survey impossible, it is possible to not reflect this in the recalculation of productivity. Also, if a specific survey team has a special external factor, it is possible to have that information entered along with the survey results so that it does not affect the productivity estimate.

[0101] Additionally, if an investigation is not progressing as planned, some of the problem may be attributed to the investigator's capabilities, while the rest may be attributed to external factors. For example, if the investigation progress rates of Teams T1, T2, and T3 are 80%, 60%, and 50%, respectively, compared to the previous day, the decrease in Team T2, which is the median, can be determined to be the contribution of external factors affecting all investigation teams. In this case, since the 40% decrease is due to external factors, it can be determined that Team T1's 20% increase is due to capabilities, and Team T3's 10% decrease is due to capabilities.

[0102] Furthermore, the survey plan may be updated by recalculating productivity every half day instead of every day. By generating a survey plan for one day and then updating the survey plan for the remaining half day (afternoon) based on the survey results for the half day (morning) (an example of survey results for a certain period), a survey plan based on more detailed productivity can be generated.

[0103] When updating an investigation plan, it is preferable that the investigation plan generation device 10 newly generates multiple investigation plan candidates based on multiple optimization criteria and displays them again so that they can be selected. This is because, in the event of a major disaster, what should be given top priority may change depending on the needs of residents and changes in the resources that the government can provide, and the optimization criteria may also change each time. When updating an investigation plan, the investigation plan generation device 10 may generate an investigation plan based on the optimization criteria selected previously.

[0104] Furthermore, when updating a research plan, the research plan generation device 10 may reorganize the researchers in each research team.

[0105] Third Embodiment Fig. 17 is a process diagram showing a research plan generation method according to the third embodiment by the research plan generation device 10. Fig. 17 shows the research plan generation method divided into an application layer 140 and a PM (Process Mining) layer 142. The application layer 140 is a program that controls a user interface that displays the reception of input from the user and the generated research plan information. On the other hand, the PM layer 142 is an algorithm unit that generates a research plan based on data input from the application layer 140 and data stored in a database or the like.

[0106] In the application layer 140, the processor 12 displays a condition input form G1 on the display 20. Here, the condition input form G1 is a screen that prompts the user to input the target start date, the target completion date, the number of investigators in the agency, and the target number of investigations per day.

[0107] When the input in the condition input form G1 is completed, the processor 12 generates a list of houses to be surveyed (process P11) based on the house data (data D31) in the PM layer 142. The house data includes information on the location and address of the house.

[0108] Next, the processor 12 calculates the difficulty of the house inspection based on the house attributes (data D32) (process P12). The house attributes include information on the structure of the house and the number of walls.

[0109] The processor 12 also sets inspector skills and productivity based on the inspector attributes (data D33) (process P13). The inspector attributes include information on each inspector's expert attributes and years of experience. The expert attributes include whether or not the inspector has specialized knowledge about construction.

[0110] Next, the processor 12 generates survey plan candidates (process P14). The processor 12 causes the display 20 to display a plan summary display G2, which is a summary of the generated survey plan candidates, in the application layer 140.

[0111] When a survey plan is selected by the user in the plan summary display G2, the processor 12 causes the display 20 to display a plan editor G3 for editing the selected survey plan.

[0112] 18 is a diagram for explaining the generation of research plan candidates in process P14. Here, an example using skill-based bin packing will be explained.

[0113] In Figure 18, each rectangle represents one surveyor. Surveyors marked "Non-wood" are specialist surveyors who can survey non-wood structures, while surveyors marked "Wooden" are general surveyors who can only survey wooden structures.

[0114] In Figure 18, each U-shaped box represents an investigation team. The solid line boxes represent expert investigation teams, and the dashed line boxes represent general investigation teams. Here, teams T1, T2, ..., TN are composed of investigators from the agency, while the other investigation teams are composed of sponsored investigators.

[0115] Team T1 is a specialist team consisting of four non-wooden surveyors. Team T2 is a specialist team consisting of two non-wooden surveyors and one wooden surveyor. Team TN is a general survey team consisting of four wooden surveyors.

[0116] Each team has a variable productivity, and is assigned a number of tasks depending on their productivity. Tasks are assigned to each team so that the total number of tasks assigned to all teams meets the daily target number of tasks.

[0117] Figure 19 shows a portion of the plan summary display G2. Here, the number of investigators for each week is displayed, with a distinction between in-house investigators and supported investigators. Furthermore, for supported investigators, the display also distinguishes between specialist investigators and general investigators. For example, in the first week, it can be seen that there were 40 in-house investigators, 400 specialist investigators among the supported investigators, and 100 general investigators among the supported investigators.

[0118] In this way, by displaying the number of investigators per week for each of multiple survey plan candidates, users can compare the plan candidates.

[0119] 〔others〕 The technical scope of the present invention is not limited to the scope described in the above embodiments. The configurations and the like in each embodiment can be appropriately combined with each other within the scope that does not deviate from the spirit of the present invention. [Explanation of symbols]

[0120] 10...Survey plan generation device 12...Processor 14...Memory 16...Communication interface 18...Input interface 20...Display 22...Planning condition input reception section 24... Damage Information Acquisition Department 26…House information acquisition department 28...Automatic plan generation unit 30...Plan candidate display section 32...Plan candidate selection reception section 34...Map information acquisition section 36...Plan editing means display section 38...Plan editing operation reception section 100...Survey plan candidates 104...First plan candidate 106...Second Plan Candidate 108...Third Plan Candidate 110...Information on required investigation days 112...Investigation progress forecast information 114...Forecast information on the number of investigators required (number of investigation teams) 116…Order information 118...Cell 120…cell 122...Cell 124...Map 126...Unallocated area 128...Map 130...cell 132...Finish button 140...Application layer 142…PM layer C1~C5…terminals D1~D25, D31~D33...Data G1...Condition input form G2...Plan summary display G3…Plan Editor P1~P4, P11~P14...Process T1~T5, TN...Team S1 to S8, S11 to S30: Each step in the survey plan generation method

Claims

1. An inspection plan generation device that generates an inspection plan for inspecting a plurality of buildings by a plurality of inspectors, at least one processor; at least one memory storing instructions for execution by said at least one processor; Equipped with The at least one processor Obtaining conditions that the survey plan must satisfy, including the productivity of the plurality of surveyors or the productivity of a plurality of survey teams to which the plurality of surveyors are respectively assigned; generating the research plan based on the acquired conditions; Survey plan generator.

2. The at least one processor Generate multiple candidate survey plans; Displaying summary information of the generated plurality of survey plan candidates on a display; Accepting a user's selection of a survey plan candidate to be adopted as a survey plan from the displayed plurality of survey plan candidates; The research plan generation device of claim 1 .

3. The summary information of the candidate investigation plan includes at least one indicator of the total number of investigation days and a personnel forecast for each fixed period; The research plan generation device according to claim 2 .

4. the at least one processor generates the plurality of candidate survey plans along a plurality of optimization criteria. The survey plan generation device according to claim 2 or 3.

5. the investigation includes an investigation of damage to a plurality of buildings; The optimization criteria include at least one of prioritizing areas with heavy building damage, prioritizing areas with less building damage, a minimum number of days, and a minimum number of personnel. The research plan generation device according to claim 4 .

6. The inspection plan includes at least one of an inspection schedule, a list of buildings to be inspected by day, and assignment information of the inspector or the inspection team. The survey plan generation device according to any one of claims 1 to 5.

7. The at least one processor Displaying a survey schedule for each of the surveyors or survey teams in the survey plan on a display; Accepting editing of the displayed survey schedule by the user; The survey plan generation device according to any one of claims 1 to 6.

8. The at least one processor accepts editing of the survey schedule by a drag-and-drop operation using a pointing device. The research plan generation device of claim 7.

9. The conditions include at least one of a target number of days, a number of equipment, and a priority area. The survey plan generation device according to any one of claims 1 to 8.

10. the investigation includes an investigation of damage to a plurality of buildings; The at least one processor generating the survey plan based on at least one of building information and damage information for each area; The survey plan generation device according to any one of claims 1 to 9.

11. the at least one processor generates the search plan using at least one of bin-packing and Dijkstra's shortest path algorithms. The survey plan generation device according to any one of claims 1 to 10.

12. the at least one processor causes a display to display the generated survey plan and a map of an area including the plurality of buildings.

12. A survey plan generation device according to any one of claims 1 to 11.

13. 1. A survey plan generation method for generating a survey plan for surveying a plurality of buildings by a plurality of surveyors, comprising: a condition acquisition step of acquiring conditions that the survey plan must satisfy, including the productivity of the plurality of surveyors or the productivity of a plurality of survey teams to which the plurality of surveyors are respectively assigned; a survey plan generation step of generating the survey plan based on the acquired conditions; A survey plan generation method comprising:

14. a recalculation step of recalculating the productivity of the plurality of investigators or the productivity of the plurality of investigation teams based on the investigation performance of the plurality of investigators over a certain period of time in investigations conducted based on the generated investigation plan; a survey plan updating step of updating the survey plan based on the recalculated productivity; The research plan generation method of claim 13, comprising:

15. Calculating the total number of walls of the building calculated from the outer periphery of the building; The survey plan generating method according to claim 13 or 14, further comprising the step of calculating a total amount of work for the survey based on the total number of wall surfaces.

16. generating the survey plan based on damage information indicating the extent of damage to the building determined from the photographed images of the building; 15. A method for generating a research plan according to claim 13 or 14.

17. A program for causing a computer to execute the survey plan generation method according to any one of claims 13 to 16.

18. A non-transitory computer-readable recording medium having the program according to claim 17 recorded thereon.

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