Apparatus and method for supporting the decision regarding the introduction of underground buried object location information estimation technology

Abstract

To support the determination of an operator regarding introduction of a technique of estimating positional information of an underground buried object.SOLUTION: There is disclosed a method for supporting a determination regarding introduction of a technique of estimating positional information of an underground buried object. A device stores information on a target area to dig and information on a target underground buried object in the target area. The device performs a pre-treatment on at least one of the information on the target area and the information on a target underground buried object, determines the value of a variable for the target area, calculates the introduction priority of the technique of estimating positional information of an underground buried object for the target area by using the value of the variable, and presents the calculated introduction priority.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] This invention relates to a technology that supports the decision-making process regarding the introduction of a technology for estimating the location information of underground buried objects. [Background technology]

[0002] Businesses that manage underground infrastructure such as water pipes, gas pipes, and cable conduits regularly repair and replace parts of the infrastructure to prevent damage due to aging and disruptions in the supply of lifelines. In recent years, a large amount of infrastructure built during the period of rapid economic growth has become necessitated by the aging of the infrastructure, and there is a need to improve the efficiency of construction.

[0003] When repairing or replacing buried infrastructure, excavation work is carried out by removing soil from the vicinity of the buried object. During this process, drawings containing information such as the location of buried pipes are referred to, but the location of the buried pipes may differ from the recorded location on the drawings. To address this, methods have been proposed to estimate and visualize the location information of buried objects by exploring the ground with radar or other means, with the aim of reducing the process of trial excavation to determine the exact location of buried objects, preventing accidents where heavy machinery accidentally comes into contact with buried objects, and identifying unexpected buried objects in advance. For example, Patent Document 1 describes a technique for extracting the linearity of buried objects based on 3D data obtained by 3D-representing the reflected wave intensity of electromagnetic waves. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-14642 [Overview of the project] [Problems that the invention aims to solve]

[0005] To estimate and visualize the location information of buried objects, it is necessary to scan roads and land over the buried objects using, for example, a vehicle equipped with radar. However, scanning all roads and land where buried objects are assumed to exist is not practical in terms of the amount of work required for scanning. This invention was made in view of this background, and its objective is to provide a technology that supports workers' decisions regarding the introduction of technology for estimating the location information of underground buried objects. [Means for solving the problem]

[0006] One aspect of the present invention for solving the above problems is a device for assisting in the decision regarding the introduction of underground buried object location information estimation technology, comprising one or more processors and one or more storage devices, wherein the one or more storage devices store information of an area to be excavated and information of an underground buried object in the area, the one or more processors preprocess at least one of the information of the area and the information of the underground buried object to determine the value of a variable for the area, calculate the priority for introducing underground buried object location information estimation technology to the area using the value of the variable, and present the calculated introduction priority.

[0007] Further details regarding the problems disclosed in this application, and the means for solving them, will be made clear in the section on embodiments for carrying out the invention and in the drawings. [Effects of the Invention]

[0008] According to the present invention, it is possible to support workers in deciding whether or not to introduce a technology for estimating the location information of underground structures. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the hardware that makes up a priority calculation device. [Figure 2] This figure shows the functions of the priority calculation device in Example 1. [Figure 3] This is a flowchart showing the priority calculation process in Example 1. [Figure 4] It is an activity diagram showing a method for assisting an operator in determining the introduction priority of the underground buried object position information estimation technology in Example 1. [Figure 5] It is a diagram showing the functions of the priority calculation device in Example 2. [Figure 6] It is a flowchart showing the priority calculation process in Example 2. [Figure 7A] It shows a correspondence table between the construction cost prediction error per unit area and the introduction priority in Example 2, which defines the relationship between the construction cost prediction error and the introduction priority. [Figure 7B] It is a diagram showing another example of a method for determining the introduction priority of the teacher data in Example 2 from the prediction error of the environmental load amount input. [Figure 8A] It is a diagram showing an example of the calculation result of the priority prediction distribution P(yi|xi,D) of road i shown in Equation 7 in Example 2. [Figure 8B] It is a diagram showing another example of the calculation result of the priority prediction distribution P(yi|xi,D) of road i shown in Equation 7 in Example 2. [Figure 9] It is an activity diagram showing a method for assisting an operator in determining the introduction priority of the underground buried object position information estimation technology in Example 2. [Figure 10A] It shows the road network indicated by the road network data in Examples 1 and 2. [Figure 10B] It shows the details of a configuration example of the road network data in Examples 1 and 2. [Figure 11A] It shows a configuration example of the past land use data in Examples  1 and 2. [Figure 11B] It schematically shows the relationship between the road network data and the past land use data in Examples 1 and 2. [Figure 11C] It shows a configuration example of the road-land use correspondence data in Examples 1 and 2. [Figure 12A] It shows the pipeline network data represented as a network consisting of connection points and pipelines in Examples 1 and 2. [Figure 12B] Examples of the pipeline network data configuration in Examples 1 and 2 are shown. [Figure 13] Examples of the construction plan data structure in Examples 1 and 2 are shown. [Figure 14A] The relationship between road network data and pipeline network data for two types of underground infrastructure in Examples 1 and 2 is schematically shown. [Figure 14B] Examples of the road-pipeline data structure in Examples 1 and 2 are shown. [Figure 15] This is an example of the priority display screen in the priority calculation device of Example 1 and Example 2. [Figure 16] This figure shows the functions of the priority calculation device in Example 3. [Figure 17A] The land area shown in the land area data for Example 3 is schematically represented. [Figure 17B] This shows an example of the land area data structure in Example 3. [Figure 18A] The relationship between land area data and historical land use data in Example 3 is schematically shown. [Figure 18B] This shows an example of the configuration of land area-land use correspondence data in Example 3. [Figure 19A] The relationship between land area data and pipeline network data for two types of underground infrastructure in Example 3 is schematically shown. [Figure 19B] This shows an example of the configuration of land area-pipeline correspondence data in Example 3. [Figure 20] This is an example of the priority display screen in the priority calculation device of Example 3. [Modes for carrying out the invention]

[0010] The embodiments will be described below with reference to the drawings as appropriate. In the following description, common components will be denoted by the same reference numerals, and redundant explanations may be omitted.

[0011] Figure 1 shows an example of hardware (hereinafter referred to as the information processing device 101) that constitutes a priority calculation device. The information processing device 101 includes a processor 102, main memory 103, auxiliary memory 104, input device 105, and output device 106. Each part of the information processing device 101 is connected to each other so as to be able to communicate via communication means such as a bus (not shown). Note that the information processing device 101 may be implemented in whole or in part by virtual resources such as a cloud server.

[0012] The processor 102 is composed of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), and the like. The processor 102 reads and executes a program stored in the main memory 103, thereby realizing the functions of the priority calculation device.

[0013] The main memory 103 is a device for storing programs and data, and can be ROM (Read Only Memory), RAM (Random Access Memory), NVRAM (Non-Volatile RAM), etc.

[0014] The auxiliary storage device 104 is, for example, an SSD (Solid State Drive), NVRAM such as an SD memory card, an optical storage device such as a CD (Compact Disc) or DVD (Digital Versatile Disc), an HDD (Hard Disc Drive), or the storage area of ​​a cloud server. Programs and data stored in the auxiliary storage device 104 are read into the main memory device 103 as needed.

[0015] The input device 105 is an interface that accepts information input, such as a keyboard, mouse, touch panel, card reader, or microphone. Alternatively, the information processing device 101 may be configured to accept information input from other devices via some means of communication.

[0016] The output device 106 is an interface for outputting various types of information, such as a screen display device like a liquid crystal monitor, LCD (Liquid Crystal Display), or graphics card, a printing device, or an audio output device like a speaker. Alternatively, the information processing device 101 may be configured to output information to other devices via some means of communication.

[0017] When repairing or replacing buried infrastructure, excavation work is carried out to remove soil from the vicinity of the buried objects. To reduce the need for trial excavation to determine the precise location of buried objects, prevent accidents where heavy machinery mistakenly comes into contact with buried objects, and identify unexpected buried objects in advance, there are methods to explore the ground beforehand using radar and other means to estimate and visualize the location information of buried objects.

[0018] In contrast, the priority calculation device according to one embodiment of this specification calculates the priority for introducing underground buried object location information estimation technology to the target area where excavation is planned, and presents it to the worker. This eliminates the enormous amount of work that would otherwise be required of the worker to determine the priority of all target areas, such as roads and land.

[0019] One embodiment of this specification determines the priority for introducing underground location information estimation technology for target areas such as roads and land, taking into account the construction costs and / or environmental burden that may occur if construction is carried out without introducing underground location information estimation technology. This allows even workers without advanced knowledge to prioritize scanning roads and land where introducing underground location information estimation technology would have a significant effect in reducing construction costs and environmental burden. [Examples]

[0020] Example 1 describes a method for calculating the priority of introducing underground location information estimation technology for each road using a linear sum, with variables that are considered to increase the priority of introducing underground location information estimation technology.

[0021] Figure 2 shows the functions of the priority calculation device 201 in Embodiment 1. The priority calculation device 201 includes the functions of a data storage unit 202 and a calculation processing unit 203. The data storage unit 202 is implemented, for example, by a main memory 103 and / or auxiliary memory 104. The calculation processing unit 203 can be implemented by a processor 102 operating according to a program.

[0022] The data storage unit 202 stores road network data 204, construction plan data 205 for underground infrastructure, historical land use data 206, and pipeline network data 207 for each of the multiple types of underground infrastructure. Details of the data 204 to 207 stored in the data storage unit 202 will be described later.

[0023] The calculation processing unit 203 includes a data preprocessing unit 208 and a priority calculation unit 209. The data preprocessing unit 208 preprocesses each data stored in the data storage unit 202. The priority calculation unit 209 uses the data preprocessed by the data preprocessing unit 208 to perform a priority calculation process to calculate the priority for introducing underground buried object location information estimation technology for each road.

[0024] Figure 3 is a flowchart showing the priority calculation process performed by the priority calculation unit 209 in Example 1. In step 301, the priority calculation unit 209 receives input from the worker who determines the priority for introducing the underground buried object location information estimation technology, regarding the parameters of variables for each element that is considered to increase the priority for introducing the underground buried object location information estimation technology. In step 302, the priority calculation unit 209 performs a process to calculate the priority for introducing the underground buried object location information estimation technology for each target road. The specific method for calculating the introduction priority will be described later.

[0025] In step 303, the priority calculation unit 209 presents the calculated priority to the worker who will determine the priority for introducing the underground buried object location information estimation technology. For example, the calculated priority information is displayed on the display device, which is the output device 106. The worker can then determine the priority for introducing the underground buried object location information estimation technology for each target road by referring to the presented priority. This allows the priority calculation device 201 to assist the worker in determining whether or not to introduce the underground buried object location information estimation technology for each target road.

[0026] In the priority calculation process in Example 1, an example of the priority calculation method in step 302, which calculates road priority, is described below. The calculation method described below calculates the road priority as a linear sum of road variables. In Example 1, the priority calculation device 201 calculates the priority for introducing underground buried object location information estimation technology for each target road as a linear sum, using each element that is considered to increase the priority of introducing underground buried object location information estimation technology as a variable.

[0027] Equation 1 below represents the priority y for introducing location information estimation technology for underground structures on road i. i This is a linear formula for calculating [the value].

[0028]

number

[0029] Here, x i n As shown in equation 2 below, the variable vector x consists of N variables associated with road i. i It is an element of.

[0030]

number

[0031] θ n This is a parameter vector containing the parameters for each variable, as shown in equation 3 below.

[0032]

number

[0033] The variables associated with road i could be, for example, 1 if multiple types of underground infrastructure are buried under road i, and 0 if one or fewer types of underground infrastructure are buried under road i. The types of variables and the method for calculating their values ​​are predetermined. Based on the information of each target road stored in the data storage unit 202, the values ​​of each variable for each target road are determined. The specific method for creating the variables will be described later. The linear sum of N variables associated with road i (Equation 1) allows for the calculation of the priority for introducing underground buried object location information estimation technology for each road.

[0034] Here, the value of the parameter vector θ is determined in step 301 of the priority calculation process by receiving parameter input from the worker. In other words, in Example 1, the worker determines which variables related to the road are important and to what extent they should be considered as factors that increase or decrease the priority of introducing the underground buried object location information estimation technology.

[0035] The priority calculated based on the linear sum of Equation 1 eliminates the need for a human to manually determine the priority for introducing location estimation technology for underground structures for all target roads by reviewing the data.

[0036] Figure 4 is an activity diagram showing how, in Example 1, a worker helps determine whether or not to introduce the underground object location information estimation technology to each target road. In step 501, the worker uses the input device 105 to input the parameter values ​​of the parameter vector θ shown in Equation 3.

[0037] In step 502, the priority calculation device 201 receives the parameter values ​​entered by the operator. The entered parameter values ​​are stored in the main memory 103 and / or auxiliary memory 104. In step 503, the priority calculation device 201 calculates the priority based on the received parameter values ​​and formula 1. Specifically, the data preprocessing unit 208 performs preprocessing on the data for each target road and the variable vector x i The value of each variable is determined, and the priority calculation unit 209 calculates the priority for introducing the location information estimation technology for the target road according to the specified parameter vector θ and a prescribed formula. The number of target roads for which the introduction priority is calculated can be one or more.

[0038] In step 504, the priority calculation device 201 presents a priority display screen that shows the calculated priority for introducing location information estimation technology for each target road. For example, the priority display screen is displayed on the display device, which is the output device 106. In step 505, the worker can look at the displayed priority display screen and easily determine the priority for introducing location information estimation technology for underground buried objects for each road. Through this method, the priority calculation device 201 can assist the worker in deciding whether or not to introduce location information estimation technology for underground buried objects for each target road. [Examples]

[0039] Example 2 uses an implementation priority distribution function that represents the distribution of implementation priority for underground buried object location information estimation technology for each target road, with each element considered to increase the priority of implementation of underground buried object location information estimation technology as a variable. The implementation priority distribution function is updated using the difference between planned and actual values ​​associated with the construction. For example, the parameters of the implementation priority distribution function are updated by Bayesian estimation using the difference between planned and actual construction costs and environmental loads per unit area for completed construction work. Construction costs and environmental loads are variables that represent the amount of work involved in the construction. The implementation priority distribution function may also be updated by a method other than Bayesian estimation, such as maximum likelihood estimation.

[0040] Figure 5 shows the functions of the priority calculation device 201 in Embodiment 2. The priority calculation device 201 includes the functions of a data storage unit 202 and a calculation processing unit 203. The data storage unit 202 stores road network data 204, construction plan data 205 for underground infrastructure, past land use data 206, and pipeline network data 207 for each of the multiple types of underground infrastructure.

[0041] The calculation processing unit 203 includes a data preprocessing unit 208, a priority calculation unit 209, and a training data input unit 601. The data preprocessing unit 208 preprocesses each data stored in the data storage unit. The priority calculation unit 209 uses the data preprocessed by the data preprocessing unit 208 to perform a priority calculation process to calculate the priority for introducing underground buried object location information estimation technology for each target road.

[0042] The training data input unit 601 receives input from workers regarding the difference between planned and actual construction costs and environmental load per unit area for completed construction projects. Each time a worker performs construction, they input training data for the completed project. The training data is used to update the introduction priority distribution function. This creates a feedback system where the next priority calculation by the priority calculation unit 209 is performed using the updated introduction priority distribution function. Note that the update of the introduction priority distribution function may be performed using training data from multiple projects.

[0043] Figure 6 is a flowchart showing the process of updating the introduced priority distribution function by repeatedly performing priority calculation processing and inputting training data, as performed by the priority calculation unit 209 and the training data input unit 601 of this embodiment in Example 1.

[0044] In step 701, the operator sets the initial values ​​of the prior distribution of the parameters for the introduction priority distribution function from the input device 105. The introduction priority distribution function is a function that represents the distribution of the introduction priority of underground buried object location information estimation technology for each target road, with each element considered to increase the introduction priority of underground buried object location information estimation technology as a variable. The initial values ​​of the parameter prior distribution set here can be any distribution (for example, a uniform distribution).

[0045] In step 702, the training data input unit 601 receives training data from workers, such as the difference between planned and actual construction costs per unit area and environmental load for completed construction projects. In step 703, the priority calculation unit 209 calculates the posterior distribution of parameters for the introduction priority distribution function, which represents the distribution of the introduction priority of the underground buried object location information estimation technology.

[0046] In step 704, the priority calculation unit 209 updates the prior distribution of parameters with the posterior distribution of parameters calculated in step 703. In step 705, the priority calculation unit 209 uses the posterior distribution of parameters calculated in step 703 to calculate the predicted distribution of the priority for introducing the underground buried object location information estimation technology for each target road. The values ​​of the variables are determined by the data preprocessing unit 208 from the information about the target roads in the data storage unit 202.

[0047] In step 706, the priority calculation unit 209, based on the priority prediction distribution calculated in step 705, presents the priority for each target road to the worker who will make the decision regarding the introduction of the underground buried object location information estimation technology. The introduction priority is displayed, for example, on the display device, which is the output device 106. The worker can then use the presented introduction priority as a reference to decide whether or not to introduce the underground buried object location information estimation technology for each target road, and to what priority level.

[0048] As a result, the priority calculation device 201 can assist the operator in determining the introduction priority of the underground buried object position information estimation technology for each road. Each time the operator performs construction and inputs new teacher data, the process returns to step 702, and steps 702 to 706 are repeated, and the introduction priority distribution function is updated.

[0049] In the following, in the priority calculation process in the second embodiment, calculation formulas for the priority calculation methods in step 703 for calculating the posterior distribution of parameters and step 705 for calculating the priority prediction distribution will be described. In the second embodiment, the priority calculation device 201 uses an introduction priority distribution function that represents the distribution of the introduction priority of the underground buried object position information estimation technology for each target road, with each element considered likely to increase the introduction priority of the underground buried object position information estimation technology as a variable. The introduction priority distribution function is updated by Bayesian estimation using the predicted-actual difference in construction cost per unit area and environmental load of the completed construction work.

[0050] The following formula 4 is an introduction priority distribution function representing the distribution P(y i ) of the introduction priority of the underground buried object position information estimation technology for road i. i

[0051]

Equation

[0052] Here, x i is a vector of variables associated with road i, θ is a parameter vector consisting of parameters for each variable, and f is some function with x i as a variable and θ as a parameter. The functional form of f is not particularly specified. For example, a neural network can be mentioned. The value of the priority y i is assumed here to take discrete values such as integers from 1 to 5, but there is no problem even if it is a continuous value.

[0053] Formula 5 below represents training data D, which consists of a variable vector x for a given road and a scalar y representing the implementation priority, associated with the difference between planned and actual construction costs and environmental loads entered by workers after construction of that road. The implementation priority is uniquely determined from the entered difference between planned and actual costs. By having workers input the difference between planned and actual construction costs and environmental loads, rather than the implementation priority itself, it is possible to create training data that excludes the workers' subjectivity. The specific method for providing the training data will be described later.

[0054]

number

[0055] Equation 6 below is used to find the posterior distribution P(θ|D) of the parameter θ given the training data D.

[0056]

number

[0057] Here, P(D|θ) is the conditional probability of the training data D in the parameter prior distribution at that time, and P(θ) is the parameter prior distribution at that time. By Bayes' theorem, the posterior distribution P(θ|D) of the parameter θ can be calculated from equation 6.

[0058] Specifically, the parameter prior distribution P(θ) is the parameter posterior distribution P(θ|D) calculated previously. P(D|θ) represents the probability that the training data D is valid in the parameter distribution P(θ). In other words, it is the probability that the priority y for introducing the training data D is obtained when the variable vector x of the training data D is input to the function f in the parameter distribution P(θ). The integral in the denominator of the right-hand side of equation 6 can be approximately calculated using methods such as the MCMC (Markov Chain Monte Carlo) method.

[0059] Equation 7 is the priority prediction distribution P(y) of road i, based on the posterior distribution P(θ|D) of the parameter θ calculated in Equation 6. i |xi This is the formula for finding D).

[0060]

number

[0061] Here, P(y i |x i θ) is the priority y for a certain parameter value θ based on the posterior distribution P(θ|D). i This is the distribution of . The integral on the right side of equation 7 can be approximately calculated using methods such as the MCMC method. Priority prediction distribution P(y i |x i A specific example of the calculation result for D) will be described later.

[0062] Figure 7A shows an example of a method for determining the implementation priority of training data D in Example 2 based on the difference between the planned and actual construction costs entered. Figure 7A shows a correspondence table between the difference between the planned and actual construction costs per unit area and the implementation priority, defining the relationship between the two.

[0063] The difference between the planned and actual construction costs per unit area for a construction project on a certain target road j is entered by the worker as part of the training data. The priority calculation unit 209 refers to a correspondence table between the planned and actual construction cost difference and the introduction priority, and determines the introduction priority of the training data D from the entered planned and actual difference. At this time, the variable vector x associated with the target road j j This becomes the variable vector x of the training data D.

[0064] In the example shown in Figure 7A, if the actual construction costs are lower than the budget and the construction is completed, a priority y of 1 is assigned to the introduction priority y of the training data D. If the actual construction costs are higher than the budget and the construction is completed, a value from 2 to 5 is assigned to the introduction priority y according to the difference between the budget and the actual cost.

[0065] This means that when there is a large difference between the planned and actual construction costs, i.e., when construction costs are higher than planned, the priority of introducing underground object location estimation technology on that road will be set higher, and training data will be provided accordingly. If construction is carried out without introducing underground object location estimation technology, costs associated with trial excavation, accidents caused by buried objects in locations different from those shown in the drawings, and unexpected buried objects, as well as costs associated with emergency response work, may be incurred. Therefore, the difference between the planned and actual construction costs is input as an indicator to replace the priority of introducing underground object location estimation technology.

[0066] Figure 7B shows another example of how the priority for introducing training data D in Example 2 is determined from the difference between the planned and actual environmental loads input. Figure 7B shows a correspondence table between the difference between the planned and actual CO2 emissions per unit area and the introduction priority, defining the relationship between the two. CO2 emissions are an example of an environmental load. Other environmental loads may be used; for example, waste volume can be used.

[0067] The difference between the planned and actual CO2 emissions per unit area during construction on a certain road j is input by the worker as part of the training data. The priority calculation unit 209 refers to a correspondence table between the planned and actual CO2 emission difference and the introduction priority, and determines the introduction priority y of the training data D from the input planned and actual difference. At this time, the variable vector x associated with the target road j j This becomes the variable vector x of the training data D.

[0068] The example shown in Figure 7B involves setting a higher priority value (y) for introducing location estimation technology for underground structures on a road when there is a large difference between the planned and actual environmental load, i.e., when a greater environmental load than planned occurs, providing training data.

[0069] If construction is carried out without introducing underground location estimation technology, CO2 emissions from the operation of heavy machinery during trial excavations and CO2 emissions due to unexpected delays in the construction period may occur. Therefore, the difference between planned and actual environmental loads will be input as an indicator to substitute for the priority of introducing underground location estimation technology. In addition to construction costs and environmental loads, workers may input values ​​corresponding to the priority of introducing training data using any indicator related to the execution of the work that may result in a difference between planned and actual results if construction is carried out without introducing underground location estimation technology.

[0070] Figure 8A shows the priority prediction distribution P(y) of road i shown in Equation 7 in Example 2. i |x i This figure shows an example of the calculation result of ,D). The variable vector x in construction work on a certain road i. i An example of the predicted distribution of introduction priority given the following is shown. In this case, the calculated predicted distribution of introduction priority P(y i |x i D) represents the probability that the difference between the planned and actual construction costs per unit area when carrying out construction on a certain road i falls into each of the five categories shown in Figure 8A.

[0071] The sum of the probabilities for the five categories is 100%. A high probability of a large difference between planned and actual construction costs indicates a high probability of construction costs exceeding the plan if construction is carried out without introducing underground location estimation technology. Therefore, it can be interpreted that the introduction of underground location estimation technology is a high priority for that road.

[0072] By calculating the expected value of the priority or the priority value with the highest probability from the results of the priority prediction distribution, it is possible to present this as a single priority value to the operator in step 706 of the priority calculation process shown in Figure 6.

[0073] Figure 8B shows the priority prediction distribution P(y) of road i shown in Equation 7 in Example 2. i |x i This figure shows another example of the calculation result of ,D). Variable vector x in construction on a certain road i iAn example of the predicted priority distribution for implementation, given the following, is shown. The calculated priority prediction distribution P(y i |x i D) represents the probability that the difference between the planned and actual environmental load per unit area when carrying out construction on a certain road i falls into each of the five categories shown in Figure 8B.

[0074] A high probability of a large difference between planned and actual environmental load indicates that there is a high probability of an environmental load exceeding the plan occurring if construction is carried out without introducing location estimation technology for underground structures. Therefore, it can be interpreted that the introduction of location estimation technology for underground structures is a high priority for that road.

[0075] As described above, one embodiment of this specification converts the priority prediction distribution calculated by Bayesian estimation into a single priority value by taking the expected value of the priority or the priority value with the highest probability, and presents it to the worker. This eliminates the need for a person to manually determine the priority order for introducing underground buried object location information estimation technology for all roads by looking at the data. Furthermore, it is possible to continuously update the priority order for introducing underground buried object location information estimation technology, taking into account the construction costs and environmental impact that may occur if construction is carried out without introducing underground buried object location information estimation technology, thereby presenting a more accurate priority order and supporting the determination of the priority order for introducing underground buried object location information estimation technology.

[0076] Figure 9 is an activity diagram showing how to assist a worker in determining the priority of introducing underground buried object location information estimation technology in Example 2. In step 1101, the worker inputs initial values ​​of the parameter prior distribution shown in equation 803. In step 1102, the priority calculation device 201 accepts the initial values ​​of the parameter prior distribution input by the worker. In step 1103, the worker inputs training data consisting of an ID that identifies a target road j and the difference between planned and actual construction costs and environmental loads determined after construction of that target road j. As described above, the difference between planned and actual corresponds to the priority of introducing the training data D.

[0077] In step 1104, the training data input unit 601 of the priority calculation device 201 receives the training data values ​​entered by the operator. The training data input unit 601 converts the difference between planned and actual training data entered by the operator into the corresponding introduction priority y. The training data input unit 601 creates a variable vector x from the data storage unit 202 for the road indicated by the road ID specified by the operator. The variable vector x and the introduction priority y constitute the training data D used in the calculation.

[0078] In step 1105, the priority calculation unit 209 of the priority calculation device 201 calculates the parameter posterior distribution based on the parameter prior distribution received as input, the training data D, and equation 6. In step 1106, the priority calculation unit 209 updates the parameter prior distribution with the parameter posterior distribution calculated in step 1105.

[0079] In step 1107, the priority calculation unit 209 calculates the newly input variable vector x of the target road i based on the parameter posterior distribution calculated in step 1105 and equation 7. i Therefore, its priority prediction distribution y i Calculate the result. The number of target roads entered can be one or more arbitrary numbers.

[0080] In step 1108, the priority calculation unit 209 calculates the priority prediction distribution y calculated in step 1107. i Based on this, the priority display screen 1401 is displayed. In step 1109, the worker can look at the displayed priority display screen 1401 and easily determine the priority order for introducing the underground buried object location information estimation technology for each target road. By the above method, the priority calculation device 201 can assist the worker in determining the priority order for introducing the underground buried object location information estimation technology.

[0081] In the following section, we will explain, using diagrams, the data and data preprocessing in the priority calculation device 201, which are common to both Example 1 and Example 2, as well as an example of the priority display screen used for presenting priorities.

[0082] This section describes the various data to be processed stored in the data storage unit 202 of the priority calculation device 201, and an example of data preprocessing in the data preprocessing unit 208. Figures 10A and 10B are diagrams illustrating the road network data 204. Figure 10A shows the road network represented by the road network data 204. Figure 10B shows details of an example configuration of the road network data 204.

[0083] Referring to Figure 10A, the road network data 204 is represented as a road network consisting of intersection 1201 and road 1202. As shown in Figure 10B, the information for intersection 1201 is shown in intersection data 1203. Intersection data 1203 contains information for each intersection, including intersection ID (identifier), latitude, and longitude. The information for road 1202 is shown in road data 1204. Road data 1204 contains information for each road, including road ID (identifier), starting point ID indicating the intersection where the road begins, and ending point ID indicating the intersection where the road ends.

[0084] Figures 11A to 11C illustrate the preprocessing of historical land use data 206 and road network data 204. Figure 11A shows an example of the structure of historical land use data 206. Historical land use data 206 indicates the location and shape of each historical land use area demarcated by historical land use boundaries, as well as the type of historical land use. Historical land use areas are represented as polygon data. Specifically, historical land use data 206 contains various information such as polygon ID, land use type, and geometry information. Geometry information is represented, for example, as a combination of coordinates of each vertex constituting the polygon.

[0085] The data preprocessing unit 208 performs preprocessing to associate historical land use data 206 with road network data 204, creating road-land use correspondence data 1208 as shown in Figure 11C.

[0086] Figure 11B schematically shows the relationship between road network data 204 and historical land use data 206. Road network data 204 shows the road network, and land use data 206 shows past land use areas 1205 where the past land use was residential, and past land use areas 1206 where the past land use was factory.

[0087] The data preprocessing unit 208 determines the corresponding past use area and the land use type of that past use area for each target road. For example, target road 1207 passes through past use area 1205 and past use area 1206. If the midpoint of road 1207 is included in past use area 1206, which was formerly a factory, the data preprocessing unit 208 determines that the road's past land use was a factory and inputs a value of 1, linking it to the road ID. Otherwise, it determines that the past land use was not a factory and inputs a value of 0.

[0088] If the land was previously used for a factory, there is a possibility that unexpected buried objects may be located beneath the road. Therefore, this variable is adopted to increase the priority of introducing the underground object location estimation technology in equations 401 and 801. The correspondence between roads and past land use areas may follow other criteria. For example, if at least a portion of a road passes through a past land use area of ​​a factory, the past land use of that road may be determined to be a factory.

[0089] In addition to the example of factories, other factors related to past land use that are considered to increase the priority of introducing location estimation technology for underground structures may also be introduced as variables. Furthermore, past land use data may be used for a single point in the past, or it may be possible to refer to land use data from multiple points in time and input 1 if the land was used for a factory at any point in the past.

[0090] Next, the pipeline network data 207 will be explained with reference to Figures 12A and 12B. As shown in Figure 12A, the pipeline network data 207 is represented as a network consisting of connection point 1301 and pipeline 1302. As shown in Figure 12B, the information for connection point 1301 is shown in connection point data 1303. Connection point data 1303 contains connection point ID, latitude, and longitude information for each connection point. The information for pipeline 1302 is shown in pipeline data 1304. Pipeline data 1304 contains pipeline ID, starting point ID indicating the starting connection point of the pipeline, ending point ID indicating the ending connection point of the pipeline, year of pipeline burial, and pipeline accident rate information for each pipeline.

[0091] Next, we will explain the construction plan data 205. Figure 13 shows an example of the structure of the construction plan data 205. The construction plan data 205 is linked to the pipeline data 1304. The construction plan data 205 contains the pipeline ID and information on planned construction within one year. For example, a worker enters a value of 1 for pipelines with planned construction within one year, and a value of 0 for pipelines with no planned construction within one year.

[0092] Since older pipelines are more likely to have different locations in drawings than in reality, this is adopted as a variable to increase the priority of introducing underground location estimation technology using Equation 1 in Example 1 or Equation 4 in Example 2. Similarly, if a pipeline has a high accident rate, it indicates a high need for repair, replacement, or other construction work, so this is also adopted as a variable. Furthermore, pipelines with construction planned within one year are also considered to have a high priority for introducing underground location estimation technology, so this is also adopted as a variable.

[0093] Next, we will explain the correspondence between the road network data 204 and the pipeline network data 207 for one or more types of underground infrastructure. Figure 14A schematically shows the relationship between the road network data 204 and the pipeline network data 207 for two types of underground infrastructure. Figure 14B shows an example of the configuration of the road-pipeline correspondence data 1305.

[0094] The data preprocessing unit 208 performs preprocessing to associate the pipeline network data 207 for each of several types of underground infrastructure with the road network data 204, thereby creating road-to-pipeline correspondence data 1305.

[0095] The mapping between roads and pipelines is performed, for example, by referring to the latitude and longitude information of each road and pipeline and mapping each pipeline to the nearest road. Road-pipeline mapping data 1305 contains information such as road IDs, pipeline IDs for infrastructure A associated with each road ID, and pipeline IDs for infrastructure B associated with each road ID, for example, when considering two types of underground infrastructure, infrastructure A and infrastructure B.

[0096] Furthermore, if multiple types of underground infrastructure pipeline IDs are associated with a given road ID, a value of 1 is entered, indicating that multiple types of infrastructure are buried beneath that road; otherwise, a value of 0 is entered. When multiple types of infrastructure are buried, there is a possibility of accidents occurring where construction work is carried out without introducing underground infrastructure location estimation technology, potentially leading to accidents where construction work is unknowingly struck by infrastructure other than the target of construction. Therefore, the introduction of underground infrastructure location estimation technology is a high priority, and whether or not multiple types of infrastructure are buried is adopted as a variable.

[0097] The variables described above are just a few examples of factors that are considered to increase the priority of introducing location estimation technology for underground structures. Other factors that are considered to increase the priority of introducing location estimation technology for underground structures may be introduced as variables, and some of the above variables may be omitted.

[0098] Figure 15 shows an example of the priority display screen in the priority presentation in steps 303 and 706 of the priority calculation process in Figures 3 and 6. The priority display screen 1401 displays the road network 1402. In the road network 1402, each road is represented by a different line type, such as lines 1403 to 1407, according to the priority level calculated by the priority calculation device 201.

[0099] By selecting a road on the road network 1402, the operator can display road information 1408. Road information 1408 includes, for example, the road priority, the type of buried infrastructure on the pipeline diagram, the most recent construction date, the accident rate and year of burial for each type of buried infrastructure, and the historical land use at the road location. This allows the operator to understand the priority calculated by the priority calculation device 201 for each road on the road network, and together with the accompanying road information, it can help determine the priority for introducing location information estimation technology for buried objects.

[0100] Furthermore, the types of information displayed on the priority display screen 1401 are not limited to the example shown in Figure 15; other information may be presented, and some information may be omitted. In addition, in the road network 1402, the priority for introducing underground buried object location information estimation technology for each target road can be arbitrarily represented using the shape and color of lines. [Examples]

[0101] Example 3 describes a method for dividing one or more plots of land where underground infrastructure is buried into multiple land areas, and for calculating the priority of introducing location information estimation technology for underground buried objects in each land area. Note that the areas include not only the land areas in this example but also road areas, and the following description can be applied to any area including land and roads.

[0102] Figure 16 shows the functions of the priority calculation device 201 in Example 3. The priority calculation device 201 has a configuration in which the road network data 204 is replaced with land area data 1501, as is the case with the priority calculation device 201 in Example 2 shown in Figure 6. Alternatively, it has a configuration in which the road network data 204 is replaced with land area data 1501, as is the case with the priority calculation device 201 in Example 2 shown in Figure 2, excluding the training data input unit 601.

[0103] The following describes various types of data to be processed stored in the data storage unit 202 of the priority calculation device 201, and examples of data preprocessing in the data preprocessing unit 208. Figures 17A and 17B are diagrams illustrating the land area data 1501. Figure 17A schematically shows the land area represented by the land area data 1501. Figure 17B shows an example of the configuration of the land area data 1501.

[0104] As shown in Figure 17A, the land area data 1501 is represented as data in which multiple plots of land where underground infrastructure is buried are divided into, for example, mesh-like land areas 1601. As shown in Figure 17B, the land area data 1501 contains information for each land area, including a land area ID and geometry information for the land area. The geometry information is represented, for example, as a combination of coordinates of each vertex that makes up the polygon representing the land area.

[0105] The data preprocessing unit 208 performs preprocessing to associate the land area data 1501 with the past land use data 206, as explained with reference to Figure 11A, thereby creating land area-land use correspondence data. Figure 18A schematically shows the relationship between the land area data 1501 and the past land use data 206. Figure 18B shows an example of the configuration of the land area-land use correspondence data 1603.

[0106] The data preprocessing unit 208 determines the corresponding past use area and the land use type of that past use area for each land area. For example, the target land area 1602 partially overlaps with the past use area 1206. For example, if the center point of land area 1602 is included in the past use area 1206, which was formerly a factory, then the area is considered to have been formerly a factory, and a value of 1 is entered into the land area-land use correspondence data 1603, linked to the land area ID. Otherwise, the area is considered not to have been formerly a factory, and a value of 0 is entered into the land area-land use correspondence data 1603.

[0107] The correspondence between land areas and past land use areas may follow other criteria. For example, if at least a portion of a land area passes through a past land use area of ​​a factory, the past land use of that road may be determined to be a factory.

[0108] Next, we will explain the correspondence between the land area data 1501 and the pipeline network data 207 for one or more types of underground infrastructure. Figure 19A schematically shows the relationship between the land area data 1501 and the pipeline network data 207 for two types of underground infrastructure. Figure 19B shows an example of the configuration of the land area-pipeline correspondence data 1701. Note that the pipeline network data 207 is as explained with reference to Figures 12A and 12B.

[0109] The data preprocessing unit 208 performs preprocessing to associate one or more pipeline network data 207 for each of several types of underground infrastructure with the land area data 1501, thereby creating land area-pipeline correspondence data 1701. The association of each target land area with a pipeline is performed, for example, by referring to the latitude and longitude information of each target land area and pipeline, and linking that pipeline to the target land area that contains the midpoint of each pipeline.

[0110] The area-pipeline correspondence data 1701 contains information such as the land area ID, the pipeline ID of infrastructure A associated with each land area ID, and the pipeline ID of infrastructure B associated with each land area ID, for example, when considering two types of underground infrastructure, infrastructure A and infrastructure B. Furthermore, if multiple types of underground infrastructure pipeline IDs are associated with a given land area ID, the data preprocessing unit 208 inputs a value of 1, indicating that multiple types of infrastructure are buried beneath that land area, and a value of 0 otherwise.

[0111] Through the above process, the functions of the priority calculation device 201 shown in Example 1 and Example 2 can also be performed on land areas that are divided into one or more plots of land where underground infrastructure is buried, rather than roads. This makes it possible to calculate the priority for introducing location information estimation technology for underground buried objects for each land area.

[0112] Figure 20 shows an example of the priority display screen in the priority calculation device 201 of Embodiment 3. The priority display screen 1801 displays the land area division diagram 1802. In the land area division diagram 1802, each land area is represented by a different pattern, such as rectangles 1803 to 1807, according to the priority level calculated by the priority calculation device 201. The operator can display land area information 1808 by selecting a land area on the land area division diagram 1802. Note that the method of representing the introduction priority of land areas is not limited to the example in Figure 20. For example, different priorities may be represented by different colors.

[0113] The land area information 1808 includes, for example, the priority of the land area, the type of buried infrastructure on the drawing, the most recent construction schedule, the accident rate and year of burial for each type of buried infrastructure, and the past land use at the area location. This allows workers to understand the priority calculated by the priority calculation device 201 for each land area on the land area division diagram, and together with the accompanying area information, it can support the determination of the priority for introducing location information estimation technology for buried objects.

[0114] As described above, the priority calculation device 201 of this embodiment can calculate the priority for introducing underground location information estimation technology for each area, such as a road or land area, taking into account the construction costs and environmental burden that may occur if construction is carried out without introducing underground location information estimation technology. This helps workers who repair or renew underground infrastructure to determine the priority for introducing underground location information estimation technology.

[0115] Furthermore, the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are explained in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Also, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of an embodiment with other configurations.

[0116] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, a recording device such as a hard disk or SSD, or a recording medium such as an IC card, SD card, or DVD.

[0117] Furthermore, in each diagram, only control lines and information lines deemed necessary for explanation are shown, and not necessarily all control lines and information lines in the actual implementation are represented. For example, it can be assumed that almost all components are interconnected in practice. Also, the arrangement of the various functional units, processing units, and storage units of the information processing device described above is merely an example. The arrangement of the various functional units, processing units, and storage units can be changed to the optimal arrangement for each information processing device from the perspective of hardware and software performance, processing efficiency, communication efficiency, etc. [Explanation of Symbols]

[0118] 101 Information processing device, 201 Priority calculation device, 202 Data storage unit, 203 Calculation processing unit, 204 Road network data, 205 Construction plan data, 206 Past land use data, 207 Pipeline network data, 208 Data preprocessing unit, 209 Priority calculation unit, 601 Training data input unit

Claims

1. A device that assists in making decisions regarding the introduction of underground buried object location information estimation technology, One or more processors, Includes one or more storage devices, The one or more processors mentioned above are: From at least one of the information of the target area and the information of the target underground structures in the target area, the values ​​of the variables of a predetermined function for the target area are determined according to a predetermined calculation method. The information of the aforementioned target area includes information on past land use types in the aforementioned target area. The information on the underground structures in question includes at least one of the following: the number of types of underground structures, the year of burial, the accident rate of the underground structures, and the planned construction date for the underground structures. The function includes parameters and the variables, The one or more processors mentioned above are: The determined values ​​of the variables are substituted into the function to calculate the priority for introducing underground buried object location information estimation technology to the target area. The calculated priority for implementation is presented, A device that updates the parameters of the function based on the difference between the actual value and the predicted value of a predetermined type of numerical value associated with construction work in an area where construction has been completed.

2. The apparatus according to claim 1, The aforementioned target area is a road area, and the device is such that...

3. The apparatus according to claim 1, The aforementioned target area is a land area, and the device is also a device.

4. The apparatus according to claim 1, The aforementioned underground structure is the target pipeline. The one or more storage devices include pipeline network data indicating a pipeline network including the target pipeline, The aforementioned pipeline network data indicates the location of each pipeline in the pipeline network. The one or more processors mentioned above are: A device that identifies the target pipeline buried in the target area by comparing the location information of the target area with the pipeline network data.

5. The apparatus according to claim 1, The one or more storage devices mentioned above are Area location information indicating the location of multiple areas including the aforementioned target area, The system stores underground buried object location information indicating the locations of multiple underground buried objects, including the aforementioned target underground buried object, The one or more processors mentioned above are devices that compare the area location information with the underground buried object location information to identify underground buried objects in each of the multiple target areas.

6. The apparatus according to claim 1, The figures associated with the aforementioned construction work are the construction costs and equipment.

7. The apparatus according to claim 1, The numerical values ​​associated with the aforementioned construction work represent the environmental impact of the equipment.

8. A method for assisting in the introduction of underground buried object location information estimation technology, the device comprising: The device determines the value of a variable in a pre-set function for the target area from at least one of the information of the target area and the information of the target underground structures in the target area, according to a predetermined calculation method. The information of the aforementioned target area includes information on past land use types in the aforementioned target area. The information on the underground structures in question includes at least one of the following: the number of types of underground structures, the year of burial, the accident rate of the underground structures, and the planned construction date for the underground structures. The function includes parameters and the variables, The above method is performed by the apparatus, The determined values ​​of the variables are substituted into the function to calculate the priority for introducing underground buried object location information estimation technology to the target area. The calculated priority for implementation is presented, A method for updating the parameters of the function based on the difference between the actual value and the predicted value of a predetermined type of numerical value associated with construction work in an area where construction work has been completed.

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