Caisson management system and program

Abstract

To provide a caisson management system and a program capable of flexibly answering questions made by a user.SOLUTION: A caisson management system is provided with: acquisition means for acquiring skeleton information showing various states of a caisson skeleton used for a pneumatic caisson method and question information showing questions; extracting means for extracting similar skeleton information including history information and the skeleton information on the basis of the skeleton information acquired by the acquisition means by referring to a database for storing the skeleton information and the history information showing a work history of the pneumatic caisson method in association with each other; and output means for inputting the question information acquired by the acquisition means to a foundation model, and outputting answer information showing answers to questions on the pneumatic caisson method on the basis of the similar skeleton information extracted with the extraction means.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a caisson management system and a program. [Background technology]

[0002] Caissons are made of concrete or steel and are generally cylindrical in shape. They are used, for example, for underground structures such as shafts and underwater structures such as bridge foundations. When used as underground structures, caissons are sunk into the ground by their own weight or by pressure application while the ground is being excavated. There are two main methods for sunk-caissons into the ground: the open caisson method and the pneumatic caisson method. The open caisson method involves excavating a tube with no lids on either end, while the pneumatic caisson method involves creating an airtight working chamber below the caisson, pumping compressed air into it to prevent groundwater from seeping in, and excavating under conditions similar to those above ground.

[0003] In principle, the air pressure in the workroom of the pneumatic caisson method is set to match the pore water pressure in the ground, so it generally does not lower the groundwater level in the surrounding ground, and is said to be an excellent construction method as there is no need to worry about subsidence in the surrounding ground or wells drying up.

[0004] In the process of excavating a caisson into the ground and submerging a concrete structure using the pneumatic caisson method, it is particularly important to ensure that the concrete structure is submerged vertically and in a straight line. However, the position and orientation of the caisson often change depending on various factors, such as the excavation sequence, friction between the concrete structure and the surrounding ground, ground characteristics, and the shape of the concrete structure. This means that the subsidence direction is not always vertical, and in some cases, the concrete structure may subside obliquely. Therefore, the caisson's position and orientation must be finely adjusted based on these factors as the concrete structure is lowered into the ground. However, achieving this with high precision requires considerable skill and is labor-intensive. Therefore, there has been a need for a technology that can automatically control the submersion of these caisson structures in a vertical direction.

[0005] In order to automatically control the vertical sinking of such a caisson body, it is important to predict the various states of the caisson body at the next point in time from the various states of the caisson body at the current point in time. In other words, if excavation is performed based on a certain excavation method in the various states of the caisson body at the current point in time, such as its position and posture, and it is possible to predict the various states of the position and posture that the caisson body will transition to at the next point in time, it will be possible to find the excavation method necessary for sinking the caisson body in the vertical direction, and ultimately to automatically control the vertical sinking of the caisson body (see, for example, Patent Document 1).

[0006] Patent Document 1 discloses an excavation shape prediction device that, in construction using a caisson, comprises an excavation shape information acquisition unit that acquires excavation shape information indicating the shape of the ground after excavation based on sensor information acquired by a sensor device installed on the caisson's body, a sinking state information acquisition unit that acquires, based on the sensor information, sinking state information indicating information about the sinking state of the caisson after it sinks due to excavation of the ground, and a prediction unit that predicts the excavation shape of the ground in the next excavation from the acquired excavation shape information and sinking state information using a prediction model that has learned the relationship between excavation of the ground and subsidence of the caisson. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2023-70294 Summary of the Invention [Problem to be solved by the invention]

[0008] The settlement of the caisson caused by excavating the ground varies widely depending on various factors, such as the shape and dimensions of the caisson body, the geology of the site, and even the depth and method of excavation. For this reason, users who sink caissons often have questions, such as, "How many minutes will it take for a specific location to settle after excavation?"

[0009] On the other hand, the excavation shape prediction device disclosed in Patent Document 1 is premised on predicting the next excavation information for the user, and is not intended to answer questions from the user. For this reason, the excavation shape prediction device disclosed in Patent Document 1 has the problem of being unable to flexibly answer questions about caissons from users.

[0010] The present invention was devised in consideration of the above-mentioned problems, and its purpose is to provide a caisson management system and program that can flexibly answer users' questions. [Means for solving the problem]

[0011] The caisson management system of the first invention is characterized by comprising: an acquisition means for acquiring structure information indicating various conditions of the caisson structure used in the pneumatic caisson construction method and question information indicating a question; an extraction means for referring to a database that stores structure information linked to history information indicating the work history of the pneumatic caisson construction method, and extracting similar structure information including the history information and the structure information based on the structure information acquired by the acquisition means; and an output means for inputting the question information acquired by the acquisition means into a base model and outputting answer information indicating an answer to the question regarding the pneumatic caisson construction method based on the similar structure information extracted by the extraction means.

[0012] The caisson management system of the second invention is characterized in that, in the first invention, the output means outputs relationship information indicating the relationship between the question information acquired by the acquisition means and the similar structure information extracted by the extraction means, and inputs the question information acquired by the acquisition means into the base model based on the output relationship information and the similar structure information, and outputs the answer information.

[0013] The caisson management system of the third invention is characterized in that, in the first invention, the extraction means extracts similarity information indicating the similarity between the structural information acquired by the acquisition means and the structural information stored in the database, and the output means inputs the question information acquired by the acquisition means into the base model and outputs the answer information based on the similar structural information and similarity information extracted by the extraction means.

[0014] The caisson management system of the fourth invention is characterized in that, in the first invention, the acquisition means acquires the structure information including ground image information indicating the ground elevation within the work chamber of the caisson structure.

[0015] The caisson management system of the fifth invention is characterized in that, in the first invention, the acquisition means acquires the structure information including time series information linked to various states of the caisson structure.

[0016] The caisson management system of the sixth invention is characterized in that, in the first invention, the output means inputs the question information acquired by the acquisition means into a large-scale language model (LLM: Large Language Model) and outputs answer information based on the similar structure information extracted by the extraction means.

[0017] The caisson management program of the seventh invention is characterized in that it has a computer execute the following steps: an acquisition step of acquiring structure information indicating various conditions of the caisson structure used in the pneumatic caisson construction method and question information indicating a question; an extraction step of referring to a database that stores structure information linked to history information indicating the work history of the pneumatic caisson construction method, and extracting similar structure information including the history information and the structure information based on the structure information acquired by the acquisition step; and an output step of inputting the question information acquired by the acquisition step into a base model, and outputting answer information indicating an answer to the question regarding the pneumatic caisson construction method based on the similar structure information extracted by the extraction step. [Effects of the Invention]

[0018] According to the first to seventh inventions, the caisson management system and program of the present invention inputs question information into a base model and outputs answer information based on similar structural body information. This allows for setting the generation probability between words based on similar structural body information, making it possible to flexibly answer user questions by taking into account appropriate historical information.

[0019] In particular, according to the second aspect of the present invention, the caisson management system outputs answer information based on the relationship information, question information, and history information. This allows for the consideration of history information that is highly relevant to the question. This makes it possible to answer questions with higher accuracy.

[0020] In particular, according to the third aspect of the present invention, the caisson management system outputs answer information based on question information, history information, and similarity information. This makes it possible to provide answers based on history information linked to structure information similar to the state of the caisson structure at a construction site, for example. This makes it possible to answer questions with higher accuracy.

[0021] In particular, according to the fourth aspect of the present invention, the caisson management system of the present invention acquires structural information including image information. This makes it possible to output answer information taking into account image information such as contour diagrams. This allows for more flexible answers to questions.

[0022] In particular, according to the fifth aspect of the present invention, the caisson management system of the present invention acquires structural information including time-series information. This makes it possible to output answer information taking into account changes in structural information over time, etc. This allows for more flexible answers to questions.

[0023] In particular, according to the sixth aspect of the present invention, the caisson management system of the present invention uses a large-scale language model. This makes it possible to learn the model from a huge amount of data. This allows for more flexible responses to questions. [Brief explanation of the drawings]

[0024] [Figure 1] Figure 1 is a vertical cross-sectional view showing the main equipment of the pneumatic caisson construction method. [Figure 2] FIG. 2 is a vertical cross-sectional view showing the cutting edge. [Figure 3] FIG. 3 is a side view of an excavator, which is an example of a work machine according to the present invention. [Figure 4] FIG. 4 is a block diagram showing a control system in the excavator. [Figure 5] FIG. 5 is a block diagram showing the configuration of a caisson management system to which this embodiment is applied. [Figure 6] FIG. 6 is a flowchart showing the operation of the caisson management system to which this embodiment is applied. [Figure 7] Figure 7 shows the inclination of the caisson. [Figure 8] FIG. 8 is a diagram showing the correlation of the base model. DETAILED DESCRIPTION OF THE INVENTION

[0025] Figure 1 is a diagram showing an example of the main equipment of the pneumatic caisson construction method in which the caisson management system of the present invention is used. Figure 1 shows a caisson 1 in the middle of construction. In detail, the state in which most of the caisson 1 has sunk into the ground 8 and is stationary is shown. The pneumatic caisson construction method is configured to construct an underground structure by sinking a reinforced concrete caisson 1 into the ground using excavation equipment E1, rigging equipment E2, soil removal equipment E3, air supply equipment E4, and standby / safety equipment E5.

[0026] The excavation equipment E1 includes, for example, an excavator 100 (hereinafter referred to as the caisson shovel 100), an automatic earth loading device 11, and a ground remote control room 13. The caisson shovel 100 is installed in a work chamber 2 provided inside the cutting edge portion 7 provided at the bottom of the caisson 1. The automatic earth loading device 11 loads earth excavated by the caisson shovel 100 into a cylindrical earth bucket 31. The ground remote control room 13 includes a remote control device 12 that remotely controls the operation of the caisson shovel 100 from the ground.

[0027] The rigging equipment E2 includes, for example, a man shaft 21, a man lock 22 (airlock), a material shaft 23, and a material lock 24 (airlock). The man shaft 21 is a cylindrical passageway connecting the ground level to the work chamber 2 so that workers can enter and exit the work chamber 2, and is equipped with, for example, a spiral staircase 25. The man lock 22 is an airtight door with a double door structure provided in the man shaft 21 to adjust the pressure difference between the atmospheric pressure on the ground and the pressure inside the work chamber 2. The material shaft 23 is a cylindrical passageway connecting the ground level to the work chamber 2 so that earth buckets 31 loaded with earth and sand by the automatic earth and sand loading device 11 can be transported to the ground. The material lock 24 is an airtight door with a double door structure provided in the material shaft 23 for transporting materials and the like, and adjusts the pressure difference between the atmospheric pressure on the ground and the pressure inside the work chamber 2. The man lock 22 and the material lock 24 are configured to prevent changes in the air pressure inside the work chamber 2, allowing workers and the earth bucket 31 to enter and exit the work chamber 2.

[0028] The earth removal equipment E3 includes, for example, an earth bucket 31, a carrier device 32, and a soil hopper 33. The earth bucket 31 is a cylindrical container with a bottom into which soil excavated by the caisson excavator 100 is loaded. The carrier device 32 is a device that lifts the earth bucket 31 up to the ground via the material shaft 23 and carries it away. The soil hopper 33 is a facility that temporarily stores the soil carried to the ground by the earth bucket 31 and the carrier device 32.

[0029] The air supply equipment E4 includes, for example, an air compressor 42, an air purifier 43, an air supply pressure regulator 44, and an automatic pressure reducing device 45. The air compressor 42 is a device that sends compressed air into the work chamber 2 through the air supply pipe 41 and the air supply path 3 formed in the caisson 1. The air purifier 43 is a device that purifies the compressed air sent by the air compressor 42. The air supply pressure regulator 44 is a device that adjusts the amount (pressure) of compressed air sent from the air compressor 42 into the work chamber 2 so that the air pressure inside the work chamber 2 is equal to the groundwater pressure. The automatic pressure reducing device 45 is a device that reduces the air pressure inside the manlock 22.

[0030] The standby / safety equipment E5 includes, for example, an emergency air compressor 51 and a hospital lock 53. The emergency air compressor 51 is a device that can send compressed air into the work room 2 in place of the air compressor 42 when the air compressor 42 breaks down or is under inspection. The hospital lock 53 is a decompression chamber into which workers who have worked in the work room 2 enter to gradually acclimate their bodies to atmospheric pressure.

[0031] Next, the cutting edge portion 7 of the present invention will be described with reference to Figure 2. The cutting edge portion 7 is provided at the lower end of the caisson 1. As shown in Figure 2, the cutting edge portion 7 is the portion that penetrates into the ground 8 when the caisson sinks, and is formed in a roughly cylindrical shape. The inner surface 71 of the cutting edge portion 7 is formed in a tapered shape that is inclined so that it approaches the center of the caisson 1 as it extends upward from the cutting edge tip 72. More specifically, the inclination angle of the inner surface 71 at the lowest end of the cutting edge portion 7 is set to be larger than the above-mentioned inclination angle of the inner surface 71 above it.

[0032] During caisson installation, the cutting edge 7 penetrates the remaining soil 80, as shown in FIG. 2 . The remaining soil 80 is left behind near the cutting edge 7 to reduce the ground reaction force acting on the cutting edge 7 and limit the settlement of the caisson 1. When the remaining soil 80 reaches the inner circumferential surface 71, the inner circumferential surface 71 receives the ground reaction force, thereby preventing the settlement of the caisson 1. In particular, in soft ground, increasing the remaining soil 80 reduces the ground reaction force received by the cutting edge 7, thereby preventing the settlement of the caisson 1. The cutting edge boundary 70 is the boundary between the portion of the inner circumferential surface 71 of the cutting edge 7 that is exposed to the work chamber 2 and the portion of the inner circumferential surface 71 that penetrates into the remaining soil 80. By determining the cutting edge boundary 70, the remaining soil width 81 can be calculated. The height H from the cutting edge boundary 70 to the cutting edge tip 72 is the cutting edge depth.

[0033] Next, a caisson excavator 100 according to the present invention will be described with reference to Figs. 3 and 4. As shown in Fig. 3, the caisson excavator 100 includes, for example, a traveling body 110, a boom 130, and a bucket attachment 150. The traveling body 110 is attached to a pair of left and right traveling rails 4 provided on the ceiling of the work chamber 2, and travels along the traveling rails 4 while suspended from the left and right traveling rails 4. The boom 130 is pivotally connected to a rotating frame 121 of the traveling body 110 so as to be swingable up and down. The bucket attachment 150 is attached to the tip of the boom 130.

[0034] The traveling body 110 includes a traveling frame 111, a swivel frame 121, and traveling rollers 113. The swivel frame 121 is provided on the underside of the traveling frame 111 so as to be able to rotate freely. The traveling rollers 113 are four rollers provided on the front, rear, left and right sides of the upper surface of the traveling frame 111. The traveling body 110 is configured to travel along the left and right traveling rails 4 by rotating the front, rear, left and right traveling rollers 113.

[0035] The boom 130 comprises, for example, a base boom 131, a tip boom 132, a telescopic cylinder 133, and a hoisting cylinder 134. The base boom 131 is attached to the swivel frame 121 so that it can be raised and lowered or swing up and down. The tip boom 132 is nested within the base boom 131. The telescopic cylinder 133 is provided inside the base boom 131. Two hoisting cylinders 134 are provided on the left and right sides of the base boom 131. The boom 130 is configured so that when the telescopic cylinder 133 is extended or retracted, the tip boom 132 moves longitudinally relative to the base boom 131, thereby extending or retracting the boom 130. The base ends of the two hoisting cylinders 134 are rotatably attached to the left and right sides of the base boom 131, respectively.

[0036] The bucket attachment 150 includes a base member 151, a bucket 152, and a bucket cylinder 153. The base member 151 is attached to the tip boom 132. The bucket 152 is attached to the tip of the base member 151 so as to be able to swing up and down. The bucket cylinder 153 is configured to swing the bucket 152 up and down relative to the base member 151.

[0037] As shown in FIG. 4, the control unit 165 includes a main controller 165a, a traveling body controller 165b, and a boom / bucket controller 165c. The control unit 165 may be connected to the caisson excavator 100 and the remote control device 12. The control unit 165 may be built into the remote control device 12. The main controller 165a is connected to the traveling body controller 165b and the boom / bucket controller 165c, receives operation signals from the remote control device 12, and outputs drive control signals corresponding to the operation signals to the traveling body controller 165b and the boom / bucket controller 165c. The traveling body controller 165b is configured to drive the traveling body 110 in accordance with the drive control signal output from the main controller 165a. The main controller 165a and the traveling body controller 165b are disposed on the revolving frame 121 of the traveling body 110. The boom bucket controller 165c is configured to drive the boom 130 and the bucket attachment 150 in response to a drive control signal output from the main controller 165a. The boom bucket controller 165c is disposed on the side of the base end boom 131 of the boom 130.

[0038] As shown in Fig. 4, the caisson excavator 100 is equipped with a traveling body position sensor 201, a swing angle sensor 202, a boom hoisting angle sensor 203, a boom extension amount sensor 204, a bucket swing angle sensor 205, and an external sensor 206. The traveling body position sensor 201 detects the position of the traveling body 110 on the traveling rail 4. The swing angle sensor 202 detects the swing angle of the swing frame 121 relative to the traveling frame 111. The boom hoisting angle sensor 203 detects the hoisting angle of the boom 130 relative to the swing frame 121. The boom extension amount sensor 204 detects the extension amount of the boom 130. The bucket swing angle sensor 205 detects the swing angle of the bucket 152 relative to the boom 130 or the base member 151 of the bucket attachment 150. The external sensor 206 is provided on the traveling body 110 and acquires information such as the distance to the excavated ground surface in the working chamber 2 and the shape of the ground surface. In addition, the caisson excavator 100 may communicate with the remote control device 12 and the control unit 165 and transmit data acquired by each of the sensors 201 to 206 to the remote control device 12 and the control unit 165.

[0039] The running body position sensor 201 is, for example, a laser sensor disposed on the running frame 111 of the running body 110. The running body position sensor 201 irradiates a laser beam toward the end of the running rail 4 or the wall of the working chamber 2 and measures the time it takes for the beam to reflect off the end of the running rail 4 or the wall of the working chamber 2 and return. Based on this time, the running body position sensor 201 detects the distance from the end of the running rail 4 or the wall of the working chamber 2 to the running body 110. The turning angle sensor 202 is, for example, an optical rotary encoder disposed on the swivel frame 121 of the running body 110. The turning angle sensor 202 converts the amount of turning of the swivel frame 121 relative to the running frame 111 into an electrical signal. The turning angle sensor 202 processes the signal to detect the turning angle, which includes the turning direction and position of the swivel frame 121. Note that the running body position sensor 201 and the turning angle sensor 202 are described as examples, and other sensors that detect the two-dimensional position of the running body 110 and other sensors that detect the turning angle of the turning frame 121 may also be used.

[0040] The boom hoist angle sensor 203 is composed of, for example, a laser sensor disposed on the side of the cylinder bottom of the hoist cylinder 134. The boom hoist angle sensor 203 measures the time it takes for a laser beam to be emitted toward the revolving frame 121 and reflected off the revolving frame 121 before returning. The boom hoist angle sensor 203 detects the amount of extension of the hoist cylinder 134 based on this time, and then detects the hoist angle or hoist position of the boom 130 relative to the revolving frame 121 based on the amount of extension of the hoist cylinder 134. The boom hoist angle sensor 203 is merely an example, and other sensors that directly detect the hoist angle of the boom 130 using an optical rotary encoder, potentiometer, etc. may also be used.

[0041] The boom extension amount sensor 204 is configured by, for example, a laser sensor disposed on the base boom end 131 of the boom 130. The boom extension amount sensor 204 irradiates laser light toward the base member 151 of the bucket attachment 150 attached to the tip of the tip boom 132 and measures the time it takes for the light to be reflected by the base member 151 and return. Based on this time, the boom extension amount sensor 204 detects the extension amount of the tip boom 132 relative to the base boom end 131 as the extension amount of the boom 130. The boom extension amount sensor 204 is also described as one example, and another sensor that directly measures the extension amount of the cable that extends and retracts along with the boom extension and contraction may also be used.

[0042] The bucket swing angle sensor 205 is configured by, for example, a flow rate sensor disposed in an oil passage of the bucket cylinder 153. The bucket swing angle sensor 205 detects the flow rate of hydraulic oil supplied to the bucket cylinder 153 and calculates an integral value of that flow rate. The bucket swing angle sensor 205 determines the extension amount of the piston rod of the bucket cylinder 153 based on this flow rate integral value, and detects the swing angle or swing position of the bucket 152 relative to the base member 151 of the bucket attachment 150 or the boom 130 based on the extension amount of the bucket cylinder 153. The bucket swing angle sensor 205 is also just one example, and other sensors that directly detect the swing angle of the bucket 152 using an optical rotary encoder, potentiometer, etc., or other sensors that determine the extension amount of the bucket cylinder 153 using a laser sensor may also be used.

[0043] The external sensor 206 is configured, for example, by an RGB-D sensor disposed on the revolving frame 121 of the traveling body 110. The external sensor 206 acquires an RGB image or color image of the excavated ground surface, and a distance image or point cloud data, and acquires distance information to the excavated ground surface and shape information of the excavated ground surface based on these images. The external sensor 206 may be a stereo camera, an ultrasonic range finder, a laser sensor, or the like, as other examples of an RGB-D sensor.

[0044] The information detected by the vehicle position sensor 201, swing angle sensor 202, boom hoisting angle sensor 203, boom extension amount sensor 204, bucket swing angle sensor 205, and external sensor 206 is sent to the main controller 165a of the control unit 165. The main controller 165a includes a vehicle position measurement unit 211, a bucket position measurement unit 212, and a ground shape measurement unit 213.

[0045] The running object position measuring unit 211 calculates the position of the running object 110 within the work room 2 using information about the distance from the end of the running rail 4 or the wall of the work room 2 to the running object 110, which is detected by the running object position sensor 201, and information about the position of the running rail 4 within the work room 2. The information about the position of the running rail 4 within the work room 2 is information about the running rail 4 to which the running object 110 is attached, and may be set in the running object position measuring unit 211 when the running object 110 is attached. The two-dimensional position of the running object 110 within the ceiling or the position including the orientation of the running object 110 may be detected by detecting distance information from multiple surrounding locations by the running object position sensor 201.

[0046] The bucket position measurement unit 212 calculates the position of the bucket 152 relative to the traveling frame 111 of the traveling body 110 using the rotation angle including the rotation direction and position of the rotating frame 121 relative to the traveling frame 111 detected by the rotation angle sensor 202, the hoisting angle or hoisting position of the boom 130 relative to the hoisting frame 121 detected by the boom hoisting angle sensor 203, the extension amount of the boom 130 detected by the boom extension amount sensor 204, and the swing angle or swing position of the bucket 152 relative to the boom 130 detected by the bucket swing angle sensor 205.

[0047] The ground shape measurement unit 213 uses the position of the traveling body 110 in the work chamber 2 determined by the traveling body position measurement unit 211 and the turning angle including the turning direction and position of the turning frame 121 relative to the traveling frame 111 detected by the turning angle sensor 202 to calculate the position of the excavated ground surface using the position of the external sensor 206 provided on the turning frame 121, the direction in which distance information is obtained by the external sensor 206, and the distance information obtained by the external sensor 206. The ground shape measurement unit 213 may also calculate the shape of the leftover soil 80. The shape of the leftover soil 80 includes both the shape of the leftover soil slope 82 and the shape of the horizontal plane where the leftover soil 80 contacts the inner circumferential surface 71.

[0048] The caisson 1 is also equipped with a measuring device 9 that measures structural information indicating various conditions of the caisson 1.

[0049] A first embodiment of the present invention will be described below with reference to the drawings. Figure 5 is a block diagram showing the overall configuration of a caisson management system 6 to which an embodiment of the present invention is applied. The caisson management system 6 answers questions from users of the caisson management system 6. The caisson management system 6 includes the above-mentioned measuring device 9 and a remote control device 12 connected to the measuring device 9.

[0050] The measuring device 9 acquires structural information measured by various sensors and the like provided on the caisson 1. The measuring device 9 transmits the structural information to the remote control device 12.

[0051] The remote control device 12 performs various processes based on the acquired information. The remote control device 12 is configured, for example, with an electronic device such as a personal computer (PC), but may also be embodied with any other electronic device other than a PC, such as a mobile phone, smartphone, tablet terminal, or wearable terminal. The remote control device 12 may be connected to the caisson 1 by wire, but is not limited to this, and may also acquire and process information using wireless communication, etc.

[0052] The remote control device 12 includes an acquisition unit 14 that acquires structure information from the measurement device 9, an extraction unit 15 connected to the acquisition unit 14, and an output unit 16 connected to the extraction unit 15 and the acquisition unit 14. The remote control device 12 also includes a storage unit 17 and a presentation unit 18 that are connected to the acquisition unit 14, the extraction unit 15, and the output unit 16.

[0053] The acquisition unit 14 acquires various types of information. For example, the acquisition unit 14 acquires structure information from the measurement device 9. The acquisition unit 14 may also acquire question information including a question and structure information input by a user via, for example, a keyboard or a microphone (not shown). The acquisition unit 14 outputs the acquired information to the extraction unit 15, the output unit 16, etc.

[0054] The extraction unit 15 extracts similar structural body information including history information indicating the work history of the pneumatic caisson construction method and structural body information, based on the structural body information output from the acquisition unit 14. The extraction unit 15 refers to, for example, a database in the storage unit 17 in which similar structural body information linking structural body information with history information is stored, and extracts similar structural body information based on the structural body information.

[0055] The output unit 16 inputs the question information acquired by the acquisition unit 14 into the base model, and outputs answer information indicating an answer to the question regarding the pneumatic caisson construction method based on the historical information extracted by the extraction unit 15.

[0056] The storage unit 17 stores various information such as body information, question information, similar body information, base model, etc. The storage unit 17 may be, for example, a data storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), an SD card, or a miniSD card.

[0057] The presentation unit 18 outputs various information such as the body information stored in the storage unit 17, or the processing status of the remote control device 12. The presentation unit 18 may be, for example, a display, which may be, for example, a touch panel. The presentation unit 18 may also be, for example, a speaker.

[0058] Next, the operation of the caisson management system 6 to which this embodiment of the present invention is applied will be described. FIG. 6 is a flowchart showing the operation of the caisson management system 6 to which this embodiment is applied. As shown in FIG. 6, in step S1, the acquisition unit 14 acquires various information. The acquisition unit 14 acquires, for example, structural body information measured at the actual site and transmitted from the measuring device 9. The acquisition unit 14 acquires, for example, question information or structural body information input by a user. The acquisition unit 14 outputs the acquired structural body information to the extraction unit 15, and outputs the question information to the output unit 16.

[0059] The question information is information indicating a question and may be information in text or audio format. The question information may also be a question regarding the pneumatic caisson construction method. The question information is, for example, information including a question regarding the construction of the caisson 1 and may be, for example, text-based information, but is not limited to this, and may also be audio information. The question information is information indicating a question or instruction and may be information in text, audio, or image format. The question information may also be a prompt. The question information is, for example, information indicating a question such as "How do I sink point A on the caisson 1?" or "What will happen if I excavate point A on the caisson 1 XX meters?" The question information may also be information obtained by morphologically analyzing the question. When the acquired question information is in audio or image format, the acquisition unit 14 may convert the question information into text format using voice recognition or image recognition. When the acquired question information is in image format, the acquisition unit 14 may calculate features from the image of the question information using R-CNN (Region Based Convolutional Neural Networks), YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), etc., and convert the features into text format such as names of objects, places, etc.

[0060] The structure information includes the position of the caisson 1, its speed, its acceleration, its posture, and the deformation amount of the caisson 1. The position of the caisson 1 indicates the deviation from the target coordinates and is expressed as the reference coordinates (x, y, z) minus the error (x, y, z). The structure posture indicates the inclination θ of the caisson 1, as shown in FIG. 7, and may be displayed as the difference value of the z coordinates of the four corners of the structure, or may be displayed via the inclination angles in the x-axis, y-axis, and z-axis directions. The structure information may also include the cutting edge depth, topographical information inside the caisson, relative ground information, caisson information, fixed ground information, and pressure inside the caisson. The structure information may also include information on the number of caisson excavators 100. The structure information may also be information on an image of the caisson 1, such as a contour map of the ground 8. The structure information may also be a three-dimensional image of the caisson 1. In such cases, the building structure information may be a three-dimensional image generated from multiple two-dimensional images using a known technique such as photogrammetry. Furthermore, the building structure information is not limited to information actually measured by a sensor or the like, but may also be information calculated in a pseudo manner by a simulation or the like.

[0061] The in-can topography information is the topography inside the can, and is information quantified according to, for example, the amount and location of unexcavated soil. This in-can topography information may be displayed as data showing the height from a reference plane of each grid point when the ground inside the can is divided into equally spaced grids. The in-can topography information may also be information on the geology of the ground 8. The geological information is, for example, information on the amount of moisture contained in the ground 8, constituent materials, etc. The in-can topography information includes ground image information showing the ground elevation inside the working chamber of the caisson body.

[0062] The relative ground information includes the pressure-receiving area of the caisson 1, the opening rate, the peripheral friction force, the cutting edge reaction force, the surrounding ground conditions, and the condition of surrounding structures. The pressure-receiving area is displayed as the cutting edge area + unexcavated area + temporary support area. The opening rate is the ratio of the excavated area [m 2 ] / Structure base area[m 2 ]. The cutting edge reaction force that the cutting edge portion 7 receives from the ground it is in contact with may also be calculated based on the pressure-receiving area. The status of surrounding structures is information such as the position and distance of structures around the caisson 1.

[0063] The pressure inside the caisson is the pressure inside the work chamber 2. The caisson information includes information such as the dimensions, shape, bottom area, shape, and weight of the caisson 1. The fixed ground information includes information such as the geology and groundwater level of the ground 8 to be excavated.

[0064] The above-mentioned structural information is not limited to the examples of the inside-caisson topography information, relative ground information, inside-caisson pressure, caisson information, and fixed ground information, but conceptually includes any parameters included in these. The structural information may be, for example, information on the pressure applied to the caisson 1, the water load, etc. The structural information may also be information indicating the temporal change and change trend of these parameters. The measuring device 9 is embodied by measuring means such as any sensors or devices necessary to detect these various types of information. The structural information is not always fixed, but is information that may change and be updated depending on the progress of excavation by the caisson 1. Therefore, this structural information is acquired and updated as needed via the measuring device 9. The measuring device 9 outputs the measured structural information to the remote control device 12.

[0065] Next, in step S2, the extraction unit 15 extracts similar structural body information. The similar structural body information is information including history information and structural body information. The history information is information indicating the work history of the pneumatic caisson construction method. The history information is information including the content of work performed at past pneumatic caisson construction work sites and the content of events that occurred as a result of the work. The history information includes, for example, information on the content of work such as the position excavated by the caisson shovel 100, the amount of excavation, the time, and the amount of operation, and information on the content of events such as the amount of subsidence of the caisson 1, the subsidence time, or the change over time in the opening rate and the width of the remaining excavation area.

[0066] In step S2, the extraction unit 15 refers to a database in which similar structural body information linking structural body information with historical information is stored, and extracts similar structural body information based on the structural body information acquired by the acquisition unit 14. In this case, the extraction unit 15 searches, for example, a database stored in the storage unit 17 based on the structural body information acquired by the acquisition unit 14, searches for structural body information stored in the database that is similar to the structural body information acquired by the acquisition unit 14, and extracts similar structural body information including historical information that is linked to the searched structural body information. The extraction unit 15 may also search, for example, a database stored in the storage unit 17 based on the structural body information acquired by the acquisition unit 14, to find two or more pieces of structural body information stored in the database whose similarity with the structural body information acquired by the acquisition unit 14 is higher than a reference value, and extract similar structural body information including two or more pieces of history information linked to the two or more pieces of structural body information found. In this case, the extraction unit 15 may also acquire similarity information indicating the similarity between the structural body information acquired by the acquisition unit 14 and the structural body information stored in the database. The extraction unit 15 may also search, for example, a database stored in the storage unit 17 based on the structural body information acquired by the acquisition unit 14, to find the top K pieces of structural body information having the highest similarity with the structural body information acquired by the acquisition unit 14, and extract similar structural body information including the K pieces of history information linked to the K pieces of structural body information found. The extraction unit 15 outputs various pieces of information to the output unit 16.

[0067] In addition, in step S2, the extraction unit 15 may use, for example, maximum inner-product search (MIPS) to search for structure information stored in a database that is similar to the structure information acquired by the acquisition unit 14, and extract similar structure information including historical information stored in association with the searched structure information.

[0068] Furthermore, in step S2, the extraction unit 15 may perform a search not only using the database stored in the storage unit 17 but also using a public communication network to refer to a database stored in a server or the like that is communicable therewith.

[0069] Furthermore, in step S2, the extraction unit 15 may acquire the degree of similarity between the skeleton information acquired by the acquisition unit 14 and the skeleton information stored in the database based on the priority set for each type of information included in the skeleton information. In this case, the extraction unit 15 may set a priority for each type of information included in the skeleton information, such as cutting edge depth, inside-canal topography information, relative ground information, caisson information, fixed ground information, and inside-canal pressure, and set the degree of similarity to be high when the types of information set with high priorities are similar or identical.

[0070] Next, in step S3, the output unit 16 inputs the question information acquired by the acquisition unit 14 into the base model and outputs answer information based on the similar structural body information extracted by the extraction unit 15. The answer information is information indicating an answer regarding the pneumatic caisson construction method to the question included in the question information. For example, the answer information is information indicating an answer regarding the pneumatic caisson construction method, such as "If the opening ratio at point A is reduced by X%, it will sink Y mm" in response to the question, "What should I do to make point A of caisson 1 sink?" The answer information may also include information indicating the basis for the answer. The information indicating the basis for the answer may be, for example, specifications such as the Highway Bridge Specifications and the Concrete Standard Specifications, guidelines for periodic inspection of highway bridges, guidelines issued by the Ministry of Land, Infrastructure, Transport and Tourism or the Road Bureau, bridge longevity repair plans of each prefecture, descriptions in academic papers, patent documents, etc.

[0071] As a method for generating the base model, for example, machine learning based on a neural network model may be used to generate the base model. The base model is, for example, an AI neural network. The base model may be trained using machine learning based on a neural network model such as a CNN (Convolution Neural Network), or any other model may be used. Furthermore, as a method for generating the base model, for example, Retrieval-Augmented Generation (RAG), Sequence to Sequence (seq2seq) linear discriminant, support vector machine, k-nearest neighbor method, random forest, deep learning, etc. may be used to generate the base model.

[0072] In such a case, the base model stores weighted associations between question information, which is input data, and answer information, which is output data, as shown in FIG. 8 . Also, in such a case, morphemes or words, etc., included in the question information may be used as input data, and morphemes or words, etc., included in the answer information may be used as output data. The weight indicates the degree of association between the input data and the output data, and it can be determined that the higher the weight, the stronger the association between the data. The weight may be expressed as three or more values or three or more levels, such as a percentage, or may be expressed as two values or two levels. The question information and answer information used to learn the weights are, for example, question information and answer information to be used in previously acquired learning data, but are not limited to this, and information acquired at any time may also be used.

[0073] For example, the association is constructed based on the degree of connection between multiple input data pairs and multiple output data. The association is appropriately updated during the machine learning process and indicates a classifier using a function optimized, for example, based on multiple input data and multiple output data. Note that the association may have multiple weights indicating the degree of connection between each piece of data. For example, when the database is constructed using a neural network, the weights can correspond to weight variables. The association may indicate the degree of connection between multiple input data and multiple output data, as shown in FIG. 8. In this case, by using the association, the degree of relationship between each piece of input data, "Question Information A" to "Question Information C," and multiple output data, "Answer Information A" to "Answer Information C," can be linked and stored for each piece of input data, "Question Information A" to "Question Information C," in FIG. 8. Therefore, for example, multiple input data can be linked to one output data via the association. This enables multifaceted selection of output data for the input data. Furthermore, the input data and output data are not limited to these, and any type of information may be used. Furthermore, the input data may be, for example, text information and / or image information included in the question information.

[0074] The association has, for example, multiple weights that respectively link each input data with each output data. The weights are expressed in three or more levels, such as percentages, 10-level scales, or 5-level scales, and are expressed, for example, by line characteristics (such as thickness). For example, "Question Information A" included in the input data has a weight AA of "73%" between it and "Answer Information A" included in the output data, and a weight AB of "12%" between it and "Answer Information B" included in the output data. In other words, the "weight" indicates the degree of connection between each piece of data; for example, the higher the weight, the stronger the connection between the pieces of data.

[0075] Three or more levels of weights as shown in Fig. 8 are acquired in advance. In other words, when determining the actual solution, past data sets are accumulated to determine which of the input data and output data was adopted and evaluated, and these are analyzed to create the weights shown in Fig. 8.

[0076] For example, suppose that in the past, "answer information B" was judged and evaluated as having the highest relevance to input data "question information B." By collecting and analyzing such data sets, the weighting of input data and output data becomes stronger.

[0077] This analysis may be performed by artificial intelligence. In such a case, if there are many cases where "answer information B" is predicted for input data such as "question information B," the weight connecting "question information B" and "answer information B" is set higher.

[0078] The weights may also be configured by nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of the neural network correspond to the weights described above. Furthermore, the weights are not limited to neural networks, and may be configured by any decision-making factors that constitute artificial intelligence.

[0079] Furthermore, the base model may be configured to have at least one hidden layer between the input data and the output data, and to perform machine learning. The weights described above are set for either the input data or the hidden layer data, or both, and these weight the data, and an output is selected based on this. If the weight exceeds a certain threshold, the output may be selected.

[0080] These weights are what is called learning data in artificial intelligence. Such learning data is learned in advance, and in step S3, the output unit 16 inputs new question information into the base model and outputs answer information. When outputting, the previously acquired weights shown in FIG. 8 are referenced, for example. For example, if the newly acquired question information is identical to or similar to "Question Information A," it is associated with "Answer Information A" via the weights, with a weight AA of 73% and a weight AB of 12%. In this case, "Question Information A," which has the highest weight, is selected as the optimal solution. However, it is not necessary to select the item with the highest weight as the optimal solution; "Answer Information B," which has a lower weight but is recognized as being related, may be selected as the optimal solution. Of course, other output solutions not connected by arrows may also be selected, and any other priority may be selected based on the weights.

[0081] By referring to such weights, it is possible to quantitatively select output data that is suitable for input data, not only when the question information is the same as or similar to the input data, but also when the question information is dissimilar.

[0082] In step S3, the output unit 16 may reset the weights of, for example, the question information and answer information of the base model based on the similar body information. In this case, the weights may be set higher for question information and answer information that include words included in the similar body information or similar words.

[0083] The base model may also be a natural language model. The base model may be a model generated by unsupervised learning. The base model may also be a generative AI. The natural language model may be an interactive, so-called chat-type or conversational model that alternately accepts instruction sentences included in question information and generates response sentences included in answer information. The natural language model may be a large-scale language model (LLM) trained with a large amount of text data, or a model obtained by transfer learning from the large-scale language model.

[0084] A large-scale language model is a deep learning model that pre-trains what is called a language model, which models human spoken language based on its occurrence probability, from a huge amount of data. In other words, a large-scale language model is a natural language processing model trained using a large amount of text data, and takes a sentence as input as question information and outputs a sentence as answer information. When a large-scale language model is applied to a question-and-answer system, when a question sentence is input into the large-scale language model as question information, the LLM outputs an answer sentence as answer information.

[0085] In step S3, when the output unit 16 receives text data (prompt) as question information, it uses a large-scale language model to statistically estimate the probability of generating the next word from the sentence included in the received prompt, and outputs answer information based on the estimation result. As the large-scale language model, for example, known technologies described on internet sites such as "https: / / chatgpt-lab.com / n / n418d3aa56f0b" and "https: / / agirobots.com / chatgpt-mechanism-and-problem / " can be adopted. Alternatively, for example, GPT-4 provided by OpenAI, Inc., USA, can be used as the large-scale language model.

[0086] Furthermore, when a large-scale language model is used as the base model, in step S3, the output unit 16 may set a generation probability for generating the next word from the sentence included in the received prompt based on the similar body information. In such a case, in step S3, the output unit 16 may set a high probability for generating a base model of a word included in the similar body information or a similar word.

[0087] Furthermore, in step S3, the output unit 16 may output relationship information indicating the relationship between the question information and the similar body information based on the question information acquired in step S1 and the similar body information extracted in step S2, and may input the question information acquired in step S1 to the base model based on the output relationship information and the similar body information, and output answer information. The relationship information is, for example, information indicating the weights of the question information and the similar body information. In such a case, the output unit 16 may, for example, perform morphological analysis on text information indicating a question included in the question information, determine whether information matching or similar to the morphologically analyzed question information is included in the similar body information, and determine the weights of the question information and the similar body information based on the determination result. The output unit 16 may determine the weights of the question information and the answer information of the base model based on the output relationship information and the similar body information. Furthermore, the output unit 16 may refer to a base model trained using training data in which, for example, question information and related information are input data and answer information is output data, and output answer information based on the question information acquired in step S1, the similar body information extracted in step S2, and the output related information. This makes it possible to take into account similar body information that is highly related to the question. This makes it possible to answer the question with higher accuracy.

[0088] In step S3, the output unit 16 may input the question information acquired in step S1 into the foundation model and output answer information based on the history information and similarity information extracted in step S2. The output unit 16 may also determine weights for the question information and answer information of the foundation model based on the extracted similarity information. This makes it possible to provide answers based on history information linked to structure information similar to the condition of the caisson structure at the construction site, for example. This allows questions to be answered with higher accuracy.

[0089] Furthermore, the output unit 16 may output the answer information at any timing.

[0090] This completes the operation of the caisson management system 6 in this embodiment. This makes it possible to flexibly answer questions from users by taking into consideration appropriate historical information.

[0091] Although the embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0092] 1 caisson 2. Workroom 3 Air supply path 4 Running rail 6 Caisson Management System 7 Blade mouth part 8 Ground 9. Measuring equipment 11 Automatic soil loading device 12 Remote control device 13 Ground Remote Control Room 14 Acquisition Department 15 Extraction part 16 Output section 17 Memory section 18 Presentation section 21 Mannschaft 22 Manrock 23 Material Shaft 24 Material Rock 25 spiral staircase 31 Earth Bucket 32 Carrier device 33 Dirt Hopper 41 Air pipe 42 Air Compressor 43 Air Purifier 44 Air supply pressure adjustment device 45 Automatic pressure reducing device 51 Emergency air compressor 53 Hospital Rock 70 Edge boundary 71 Inner peripheral surface 72 Blade tip 80 Leftover soil 81 Width of remaining soil 82 Remaining soil slope 100 Caisson Excavator 110 Running body 111 Running frame 113 Traveling roller 121 Swivel Frame 130 Boom 131 Base boom 132 Tip boom 133 Telescopic Cylinder 134 Elevating Cylinder 150 Bucket Attachment 151 Base material 152 Bucket 153 Bucket cylinder 165 Control Unit 165a Main Controller 165b Vehicle controller 165c Boom Bucket Controller 201 Vehicle position sensor 202 Rotation angle sensor 203 Boom derrick angle sensor 204 Boom extension sensor 205 Bucket swing angle sensor 206 External Sensor 211 Vehicle position measurement unit 212 Bucket position measurement unit 213 Ground shape measurement section

Claims

1. An acquisition means for acquiring skeleton information indicating various states of a caisson skeleton used in a pneumatic caisson construction method and question information indicating a question; an extraction means for referencing a database that stores structural information and historical information indicating the work history of the pneumatic caisson construction method in association with each other, and extracting similar structural information including the historical information and the structural information based on the structural information acquired by the acquisition means; and an output means for inputting the question information acquired by the acquisition means into a base model and outputting answer information indicating an answer to the question regarding the pneumatic caisson construction method based on the similar structure information extracted by the extraction means. A caisson management system featuring:

2. the output means outputs relationship information indicating a relationship between the question information and the similar structural body information based on the question information acquired by the acquisition means and the similar structural body information extracted by the extraction means, inputs the question information acquired by the acquisition means to the base model based on the output relationship information and the similar structural body information, and outputs the answer information. The caisson management system according to claim 1 .

3. the extraction means extracts similarity information indicating a similarity between the building structure information acquired by the acquisition means and the building structure information stored in the database; The output means inputs the question information acquired by the acquisition means into the base model, and outputs the answer information based on the similar body information and similarity information extracted by the extraction means. The caisson management system according to claim 1 .

4. The acquisition means acquires the structure information including ground image information indicating the ground elevation within the work chamber of the caisson structure. The caisson management system according to claim 1 .

5. The acquisition means acquires the body information including time-series information linking time points with various states of the caisson body. The caisson management system according to claim 1 .

6. The output means inputs the question information acquired by the acquisition means into a large language model, and outputs answer information based on the similar body information extracted by the extraction means. The caisson management system according to claim 1 .

7. An acquisition step of acquiring skeleton information indicating various states of a caisson skeleton used in a pneumatic caisson construction method and question information indicating a question; an extraction step of referring to a database that stores structural information and historical information indicating the work history of the pneumatic caisson construction method in association with each other, and extracting similar structural information including the historical information and the structural information based on the structural information acquired by the acquisition step; and an output step of inputting the question information acquired by the acquisition step into a base model, and outputting answer information indicating an answer to the question regarding the pneumatic caisson construction method based on the similar body information extracted by the extraction step. A caisson management program featuring:

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