CN116069071B

CN116069071B - Construction optimization system, method, device and medium based on big data

Info

Publication number: CN116069071B
Application number: CN202310292908.3A
Authority: CN
Inventors: 王燕; 潘长华; 张明海
Original assignee: Guanlu Construction Co Ltd
Current assignee: Guanlu Construction Co Ltd
Priority date: 2023-03-24
Filing date: 2023-03-24
Publication date: 2023-06-30
Anticipated expiration: 2043-03-24
Also published as: CN116069071A

Abstract

The embodiment of the specification provides a construction optimization system, a method, a device and a medium based on big data, wherein the method is realized by the construction optimization system based on the big data, the construction optimization system based on the big data comprises a foundation pit supporting system and an intelligent dewatering system, the method is executed by the intelligent dewatering system, the method comprises the steps of determining a layout management scheme and a work management scheme of a dewatering device based on the big data, and the layout management scheme comprises the layout number and layout positions of the dewatering device; and generating a first control instruction corresponding to the layout management scheme and a second control instruction corresponding to the work management scheme; based on the first control instruction, arranging a precipitation device to carry out precipitation treatment; and controlling the precipitation power of the precipitation device based on the second control instruction.

Description

Construction optimization system, method, device and medium based on big data

Technical Field

The specification relates to the field of construction optimization, in particular to a construction optimization system, method, device and medium based on big data.

Background

In engineering construction, it is generally necessary to dig a foundation pit of a certain size and depth in order to perform building foundation construction. However, the condition of groundwater (aquifer) seepage is usually encountered in the process of digging a foundation pit, and normal construction is affected. It is therefore necessary to arrange a number of precipitation devices (e.g., precipitation wells) around the foundation pit for precipitation treatment so as to lower the level of groundwater within the foundation pit to a prescribed level.

Aiming at the problem of how to reduce the water level of underground water in a foundation pit, CN107989055A provides an intelligent control system and a control method for deep well precipitation of a building engineering. However, since the rainfall condition may be sustained or even aggravated, and the maximum operating power of the precipitation apparatus is limited, it is not effective to stabilize the groundwater level at the bottom of the substrate for a long period of time only by controlling the operating state of the precipitation apparatus.

It would be desirable to provide a system, method, apparatus and medium for optimizing construction based on big data to analyze a variety of conditions to determine more reasonable layout and operating schemes of precipitation apparatus for better precipitation.

Disclosure of Invention

One or more embodiments of the present specification provide a big data based construction optimization system comprising a foundation pit support system and an intelligent precipitation system; the foundation pit supporting system is used for carrying out construction enclosure on construction engineering; the intelligent precipitation system is used for carrying out precipitation treatment on underground water in the foundation pit and comprises a precipitation device management subsystem, a precipitation device layout subsystem and a precipitation device control subsystem; the precipitation device management subsystem is used for: determining a layout management scheme and a work management scheme of the precipitation device based on big data, wherein the layout management scheme comprises the layout number and layout positions of the precipitation device; and generating a first control instruction corresponding to the layout management scheme and a second control instruction corresponding to the work management scheme; the precipitation device layout subsystem is used for: based on the first control instruction, arranging a precipitation device to carry out precipitation treatment; the precipitation device control subsystem is configured to: and controlling the precipitation power of the precipitation device based on the second control instruction.

One or more embodiments of the present specification provide a method for optimizing construction based on big data, the method being implemented by a construction optimization system based on big data, the construction optimization system based on big data including a foundation pit support system and an intelligent precipitation system, the method being performed by the intelligent precipitation system, the method comprising: determining a layout management scheme and a work management scheme of the precipitation device based on big data, wherein the layout management scheme comprises the layout number and layout positions of the precipitation device; and generating a first control instruction corresponding to the layout management scheme and a second control instruction corresponding to the work management scheme; based on the first control instruction, arranging a precipitation device to carry out precipitation treatment; and controlling the precipitation power of the precipitation device based on the second control instruction.

One or more embodiments of the present specification provide a big data based construction optimization apparatus including a processor for performing a big data based construction optimization method.

One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer, perform the big data based construction optimization method according to any of the embodiments described above.

The beneficial effects are that: according to the method disclosed by the embodiments of the specification, the layout management scheme and the work management scheme of the dewatering device are determined through big data, and the layout quantity, layout position, dewatering power and other necessary parameters of the dewatering device are reasonably predicted, so that the water level of groundwater in a foundation pit is effectively reduced, the dewatering cost is controlled, and the normal operation of construction is ensured.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic illustration of an application scenario of a big data based construction optimization system shown in some embodiments of the present description;

FIG. 2 is an exemplary system block diagram of a big data based construction optimization system shown in accordance with some embodiments of the present description;

FIG. 3 is an exemplary flow chart for dewatering groundwater within a foundation pit according to some embodiments of the disclosure;

FIG. 4 is an exemplary flow chart of determining a layout management scheme associated with a plurality of layout sub-areas by a preset algorithm according to some embodiments of the present description;

FIG. 5 is an exemplary schematic diagram of groundwater level data within a target period of time predicted by a groundwater level prediction model according to some embodiments of the description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

Foundation pit excavation is an important part in engineering construction. When the foundation pit is dug, the conditions such as groundwater seepage, precipitation ponding and the like often occur in the foundation pit. If the water level in the foundation pit is too high, normal use of the foundation pit can be affected, so that the construction period progress of construction is dragged slowly.

Effectively reducing the groundwater level in the foundation pit is a key for construction optimization. By adopting manual treatment of the problems, a great deal of labor cost and time cost are required, the precipitation efficiency is difficult to be ensured to be always maintained at a higher level due to the influence of the proficiency of workers, working states and the like. Simply arranging the dewatering device can only control the water level in the foundation pit in real time, and the dewatering effect can be realized in a short time, but the long time can not be maintained. Although the precipitation device with a large number can realize rapid precipitation, the precipitation cost can be increased.

In view of this, in some embodiments of the present disclosure, it is desirable to provide a construction optimization system and method based on big data, so as to analyze multiple situations to determine a more reasonable layout scheme and working scheme of a precipitation device, improve precipitation efficiency, control precipitation cost, and achieve a better precipitation effect, so as to ensure smooth progress of construction progress.

Fig. 1 is a schematic view of an application scenario of a big data based construction optimization system according to some embodiments of the present description.

In some embodiments, the application scenario 100 of the big data based construction optimization system may include a processing device 110, a network 120, a storage device 130, a terminal 140, and a construction project 150. In some embodiments, components in the application scenario 100 of the big data based construction optimization system may be connected and/or communicate with each other via a network 120 (e.g., a wireless connection, a wired connection, or a combination thereof). For example, processing device 110 may be connected to storage device 130 through network 120.

The construction project 150 may include a foundation project, a main body structure project, a roofing project, a decoration project, and the like. Construction work sites often require excavation of foundation pits of a certain size and depth to ensure the construction work. Effectively reducing the groundwater level in the foundation pit is a key for construction optimization. In some embodiments, data required for construction optimization, such as foundation pit conditions, groundwater level data, formation data, and subsurface runoff data, may be obtained through data collection devices (e.g., laser rangefinder, level sensor, impedance analyzer, etc.) disposed at the construction work 150. In some embodiments, the data may also be uploaded to the processing device 110 by manually measuring the data.

In some embodiments, processing device 110 may be directly connected to storage device 130, and terminal 140 may exchange information and/or data via network 120 to access the information and/or data. For example, processing device 110 may obtain big data over network 120. The processing device 110 may acquire rainfall data through the network 120, may also acquire a history of construction record stored by the storage device 130 through the network 120, and the like. The processing device 110 may send information such as the first control instruction and the second control instruction to the terminal 140 through the network 120. In some embodiments, the processing device 110 may process information and/or data related to the application scenario 100 of the big data based construction optimization system to perform one or more functions described in this specification. For example, the processing device 110 may determine a layout management scheme and a work management scheme of the precipitation apparatus based on the big data.

It should be noted that the application scenario 100 of the big data based construction optimization system is provided for illustrative purposes only and is not intended to limit the scope of the present application. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario 100 of the big data based construction optimization system may implement similar or different functions on other devices. However, such changes and modifications do not depart from the scope of the present application.

FIG. 2 is an exemplary system architecture diagram of a big data based construction optimization system, according to some embodiments of the present description.

As shown in fig. 2, big data based construction optimization system 200 may include a foundation pit support system 210 and an intelligent precipitation system 220.

The foundation pit support system 210 is used for performing construction containment for construction works. Construction enclosure may include setting temporary enclosure guardrails, setting signs, etc. The foundation pit supporting system can protect the site of the construction engineering, avoid the ingress and egress of external personnel and ensure the normal and orderly execution of the construction engineering.

The intelligent precipitation system 220 is used for carrying out precipitation treatment on the groundwater in the foundation pit. Wherein, precipitation treatment refers to the step of reducing the groundwater level in the foundation pit to a standard water line or below. The standard water line can be a system default value, an empirical value, a manual preset value, etc. or any combination thereof, and can be set according to actual requirements. In some embodiments, intelligent precipitation system 220 may include a precipitation device management subsystem 220-1, a precipitation device layout subsystem 220-2, and a precipitation device control subsystem 220-3.

The precipitation device management subsystem 220-1 is configured to determine a layout management scheme and a work management scheme of the precipitation device based on the big data, where the layout management scheme includes a layout number and a layout position of the precipitation device; and generating a first control instruction corresponding to the layout management scheme and a second control instruction corresponding to the work management scheme. In some embodiments, the big data includes foundation pit conditions, groundwater level data, and formation data. The precipitation installation management subsystem 220-1 is further configured to determine a layout management scheme based on the foundation pit conditions, groundwater level data, and formation data. In some embodiments, precipitation device management subsystem 220-1 is further configured to divide the perimeter of the foundation pit into a plurality of deployment sub-areas; and determining a layout management scheme related to the plurality of layout subareas through a preset algorithm based on the foundation pit condition, the groundwater level data and the stratum data. In some embodiments, the big data further includes rainfall data and subsurface runoff data. The precipitation device management subsystem 220-1 is also configured to predict groundwater level data within a target time period based on rainfall data, subsurface runoff data, formation data, and foundation pit conditions.

The precipitation device layout subsystem 220-2 is configured to layout the precipitation device for precipitation treatment based on the first control command.

The precipitation device control subsystem 220-3 is configured to control precipitation power of the precipitation device based on the second control command.

It should be noted that the above description of the big data based construction optimization system and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the pit support system and the intelligent dewatering system disclosed in fig. 2 may be different modules in one system, or may be a system that performs the functions of two or more systems described above. For example, each system may share one memory module, or each system may have a respective memory module. Such variations are within the scope of the present description.

Fig. 3 is an exemplary flow chart for dewatering groundwater within a foundation pit according to some embodiments of the description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a precipitation device management subsystem.

Step 310, determining a layout management scheme and a work management scheme of the precipitation device based on the big data. In some embodiments, step 310 may be performed by a precipitation device management subsystem.

Big data may refer to relevant data required for normal construction. In some embodiments, the big data may include data reflecting environmental conditions associated with the excavation of the foundation pit.

In some embodiments, the big data may include foundation pit conditions, groundwater level data, formation data, rainfall data, and subsurface runoff data.

The foundation pit situation may reflect the size of the foundation pit. For example, the size of the pit (e.g., 10 meters in diameter), the depth of the pit (e.g., 3 meters). In some embodiments, the foundation pit situation may be obtained by actual measurements.

The groundwater level data may reflect a water level of groundwater near the foundation pit. For example, the water level at the current time (e.g., 3 meters), the water level at a certain target time period (e.g., 2 hours in the future, one week in the future, etc.). The water level in the target time period refers to the water level at which precipitation is not performed in a certain time period in the future.

In some embodiments, the precipitation device management subsystem may predict groundwater level data over the target period of time based on rainfall data, subsurface runoff data, formation data, and foundation pit conditions.

The formation data may reflect the geological condition of the formation in which the foundation pit is located. For example, formation data may include a geological nature of the formation (e.g., humus layer, sandy pebble layer, etc.), a thickness of the formation (e.g., 73cm, 104cm, etc.). In some embodiments, formation data may be obtained by querying a construction record.

The rainfall data may reflect the rainfall of the construction area or the construction upstream area. For example, the rainfall data may be 49 millimeters of rainfall for the past 24 hours. The rainfall data may also be 67 mm rainfall for the next 4 hours. In some embodiments, the rainfall data may be obtained through weather forecast acquisition.

The subsurface runoff data may reflect the flow of subsurface runoff. The subsurface runoff data may include flow rate of subsurface runoff (e.g., 0.4 meters/second), flow direction of subsurface runoff (e.g., from zone a to zone B), data of runoff flow of subsurface melt water over time (e.g., 2.2 cubic meters/second for summer runoff flow, 0.6 cubic meters/second for winter runoff flow). In some embodiments, the subsurface runoff data may be obtained by actual measurement.

The dewatering device is used for reducing the water level in the foundation pit during construction. Such as dewatering wells, water pumps in dewatering wells, etc.

The layout management scheme may be a scheme indicating how to layout the precipitation device. In some embodiments, the deployment management scheme includes a number of deployments and deployment locations of the precipitation device. For example, the number of precipitation devices may be 3. The layout positions can be circumferentially arranged around the foundation pit, the center of the well hole is 1.50m away from the side line of the foundation pit, and the average well distance is about 5.0m.

In some embodiments, the precipitation device management subsystem may determine the deployment management scheme based on the foundation pit conditions, groundwater level data, and formation data.

In some embodiments, the precipitation device management subsystem may determine the layout management scheme based on foundation pit conditions, groundwater level data, and formation data by setting preset rules, modeling, and employing various other data analysis methods (e.g., vector similarity analysis, cluster analysis, etc.).

In some embodiments, the precipitation device management subsystem may determine the number of deployments based on the foundation pit situation, groundwater level data, and formation data via preset rules. For example, the precipitation device management subsystem may preset a minimum deployment number, a pit threshold, a groundwater level threshold, and a formation threshold. The foundation pit threshold value is the maximum foundation pit size which can finish precipitation treatment by the minimum number of precipitation devices. The ground water level threshold value refers to the highest ground water level of the precipitation treatment which can be completed by the precipitation devices with the minimum layout quantity. The stratum threshold value refers to the minimum stratum thickness of the precipitation treatment which can be completed by the minimum number of precipitation devices. The minimum deployment number, foundation pit threshold, groundwater level threshold, and formation threshold may be empirical values determined based on historical data. If the size of the foundation pit is larger than the threshold value of the foundation pit, the number of the arranged increases is 1; if the condition of the foundation pit is smaller than or equal to the threshold value of the foundation pit, the increment number of the layout number is 0. If the groundwater level data is greater than the groundwater level threshold, the increment of the layout number is 1; if the groundwater level data is less than or equal to the groundwater level threshold, the increment of the layout number is 0. If the thickness of the stratum is smaller than or equal to the stratum threshold value, the increment of the layout number is 1; if the formation data is greater than the formation threshold, the increment of the number of runs is 0. And finally, summarizing the total increment of the layout quantity. The precipitation installation management subsystem may then determine a final number of arrangements based on the minimum number of arrangements and the total number of increases.

In some embodiments, the precipitation device management subsystem may determine the deployment location by a preset rule based on the foundation pit situation, groundwater level data, and formation data. For example, the precipitation device management subsystem may use the historical foundation pit situation, the historical groundwater level data, and the historical stratum data as reference data in advance, and generate a mapping relationship table of the reference data and the corresponding historical actual layout position. And inquiring the reference data which is the same as or similar to the current foundation pit situation, groundwater level data and stratum data in the mapping relation table, and taking the corresponding historical actual layout position as the current layout position.

In some embodiments, the precipitation device management subsystem may also determine the number of deployments by a preset rule based on the precipitation data or the additional displacement.

The additional water discharge means the amount of water that the precipitation device needs to discharge additionally. It will be appreciated that the additional displacement should be higher than would normally be the case. Additional drainage is typically present in rainy seasons, sudden storms, average daily rainfall over one or more days, etc.

In some embodiments, the precipitation device management subsystem may adjust the number of deployments determined in the original deployment management scheme based on the precipitation data or additional water displacement to determine a final deployment number, thereby determining a final deployment management scheme for the precipitation device. The number of layouts determined in the original layout management scheme may refer to the number of layouts determined based on the foundation pit situation, groundwater level data, and formation data.

In some embodiments, when the rainfall data or the additional drainage is within a certain preset range, the rainfall device management subsystem may determine a layout number adjustment value corresponding to the preset range according to the preset range in which the rainfall data or the additional drainage is located. By way of example only, when the rainfall data or additional drainage is between 0-25 mm per 24 hours, the number of runs may remain unchanged based on the original number of runs; when the rainfall data or the extra drainage is 25-50 mm in every 24 hours, the number of the arranged water pipes can be increased by 2 based on the original number of the arranged water pipes; when the rainfall data or the additional drainage is greater than 50mm every 24 hours, the number of the layout can be increased by 4 on the basis of the original number of the layout. It will be appreciated that the greater the rainfall data or additional displacement, the greater the number of precipitation units that need to be deployed, indicating a corresponding increase in groundwater level.

In some embodiments, the precipitation device management subsystem may also determine a backup deployment management scheme based on the additional displacement.

According to the method disclosed by the embodiments of the specification, since the water level of the underground water in the foundation pit generally fluctuates along with the quantity of rainfall and the seasonal variation, by analyzing the rainfall and the additional drainage, the layout management scheme can be further optimized according to the water level rising condition of the underground water possibly occurring, so that the rainfall efficiency in the foundation pit is improved.

According to the method disclosed by the embodiments of the specification, through comprehensive analysis of the condition of the foundation pit, the groundwater level data and the stratum data, a more reasonable layout management scheme can be determined, so that the groundwater level in the foundation pit is effectively reduced.

In some embodiments, the precipitation device management subsystem may divide the foundation pit perimeter into a plurality of deployment sub-areas; and determining a layout management scheme related to the plurality of layout subareas through a preset algorithm based on the foundation pit condition, the groundwater level data and the stratum data.

The work management scheme may be a related scheme of the precipitation device performing precipitation treatment work. In some embodiments, the work management scheme may include an operating parameter of the precipitation device (e.g., precipitation power), an operating time (e.g., 2 hours), and so forth. The precipitation power refers to the amount of work performed by the precipitation device for precipitation treatment in unit time. For example, the precipitation power may be 300 kilowatts, 65 horsepower, etc.

In some embodiments, the precipitation device management subsystem may determine precipitation power of the precipitation device based on a rate of increase of the water level of the groundwater. Wherein, the water level increasing speed of the groundwater refers to the water level increasing amount of the groundwater in unit time. The rate of increase of the groundwater level may be determined by groundwater level data of at least two target time periods (e.g., by calculating a ratio of a difference of groundwater levels of the two target time periods to a time interval of the two target time periods).

In some embodiments, the precipitation device management subsystem may preset a quantitative relationship between a water level increase rate of the groundwater and precipitation power of the precipitation device, and determine the precipitation power of the precipitation device through the preset quantitative relationship. Illustratively, the quantitative relationship may be determined by the following formula:

P=

×/>

+/>

(1)

wherein P is the precipitation power of the precipitation device.

And->

The predetermined constant may be a system default value, an empirical value, a human preset value, or any combination thereof, and may be set according to actual requirements. v represents the water level increasing speed of groundwater, +.>

The increasing function takes the increasing speed of the water level of the underground water as an independent variable. />

Groundwater level data representing a second target period of time,/->

Is an increasing function taking groundwater level data of a second target time period as an independent variable.

And->

The following formula may be exemplarily satisfied:

(2)

wherein, the liquid crystal display device comprises a liquid crystal display device,

the predetermined constant may be a system default value, an empirical value, a human preset value, or any combination thereof, and may be set according to actual requirements.

In some embodiments, the precipitation device management subsystem may temporarily not turn on the precipitation device when the precipitation power is below a power preset threshold, and turn on the precipitation device when the precipitation power reaches the power preset threshold. The power preset threshold value refers to the minimum power value of the precipitation device for completing precipitation treatment. When the precipitation power is higher than the power preset threshold, the precipitation device management subsystem may turn on the precipitation device and take the precipitation power as a target precipitation power of the precipitation device. For example, the power preset threshold is 400 horsepower, and if the calculated precipitation power is 378 horsepower, the precipitation device management subsystem may temporarily not turn on the precipitation device. If the calculated precipitation power is 456 horsepower, the precipitation device management subsystem may turn on the precipitation device and take 456 horsepower as the target precipitation power for the precipitation device.

In some embodiments, the precipitation device management subsystem may also determine the work management scheme by other manners, such as setting a default precipitation efficiency according to historical data, establishing a correspondence between groundwater level and precipitation efficiency and work time, and the like, which are not limited herein.

According to the method, the work management scheme is determined through data such as the water level increasing speed of the groundwater, the groundwater level of target time and the like, and the precipitation power of the precipitation device is reasonably predicted, so that the precipitation device can be fully utilized, waste of manpower and material resources caused by high-power operation of the precipitation device is avoided, and the precipitation cost is controlled.

Step 320 generates a first control command corresponding to the layout management scheme and a second control command corresponding to the job management scheme. In some embodiments, step 320 may be performed by a precipitation device management subsystem.

The first control instruction may be control information corresponding to a layout management scheme. In some embodiments, the first control instructions may include control information related to a number of deployments and deployment locations of the precipitation devices in the deployment management scheme.

The second control instruction may be control information corresponding to a job management scheme. In some embodiments, the second control instruction may include control information related to precipitation power of the precipitation device in the work management scheme.

In some embodiments, the first control instruction and the second control instruction may be a combination of one or more forms including, but not limited to, data instructions, alerts, text messaging, text push, images, video, voice, broadcast, and the like. The precipitation device management subsystem may generate the first control command of the form described above based on the layout management scheme and send it to the precipitation device layout subsystem. The precipitation device layout subsystem may generate and send a second control command of the form described above to the precipitation device control subsystem based on the work management scheme.

Step 330, based on the first control instruction, a precipitation device is deployed to perform precipitation treatment. In some embodiments, step 330 may be performed by a precipitation device layout subsystem.

In some embodiments, the precipitation device may be deployed by an operating machine and/or an associated worker provided in the precipitation device deployment subsystem based on the first control command. For example, the drilling rig may drill the precipitation device based on the received first control command. For another example, the first control command may be sent to a terminal of an associated worker, who lays out the precipitation device based on the layout position and layout number of the precipitation device prompted on the terminal.

Precipitation treatment refers to the associated operation of lowering the water level in the pit. Such as diversion, pumping, etc. In some embodiments, the dewatering device, such as a dewatering well, can enable water in the foundation pit to flow into the dewatering well, and the water can directly flow out of the ground through the drainage channel or be discharged to the ground by the water pump in the dewatering well, so that the purpose of reducing the water level in the foundation pit is achieved.

Step 340, controlling precipitation power of the precipitation device based on the second control command. In some embodiments, step 340 may be performed by a precipitation device control subsystem.

In some embodiments, the precipitation device control subsystem may set and/or adjust precipitation power of the precipitation device based on the second control command. For example, the second control command may be 45 kw of precipitation power, and the precipitation device control subsystem may adjust or maintain the precipitation power of the precipitation device to 45 kw.

According to the method disclosed by the embodiments of the specification, the layout management scheme and the work management scheme of the dewatering device are determined through big data, and the layout quantity, layout position, dewatering power and other necessary parameters of the dewatering device are reasonably predicted, so that the water level of groundwater in a foundation pit is effectively reduced, the dewatering cost is controlled, and the normal operation of construction is ensured.

In some embodiments, the dewatering device management subsystem may divide the periphery of the foundation pit into a plurality of layout sub-areas, and determine a layout management scheme associated with the plurality of layout sub-areas through a preset algorithm based on the foundation pit condition, groundwater level data, and formation data.

By laying sub-areas is meant one or more areas around the foundation pit where precipitation means are laid. For example, a plurality of areas with a radius of 5 meters are centered on the foundation pit.

In some embodiments, the precipitation device management subsystem may cluster the perimeter regions of the foundation pit based on topographical features (e.g., altitude), one or more of which may be a layout sub-region. The clustering number is the maximum layout number of the precipitation devices. The maximum layout number can be a system default value, an empirical value, a manual preset value, etc. or any combination thereof, and can be set according to actual requirements.

In some embodiments, the precipitation device management subsystem may divide the grids directly within the foundation pit, with each grid being a deployment sub-area. For example, the grid may be divided by two sets of parallel lines perpendicular to each other. For another example, the mesh may be divided by concentric circles centered on the foundation pit and the diameters of the concentric circles.

The manner in which the precipitation installation management subsystem divides the plurality of deployment sub-areas includes, but is not limited to, any of the methods described above and combinations thereof. The precipitation device management subsystem may also divide the plurality of layout sub-areas by other means, for example, by sliding a sub-window on the image corresponding to the foundation pit, which is not limited herein.

The layout management scheme associated with the plurality of layout sub-areas may include whether or not a precipitation device is laid out in each layout sub-area.

FIG. 4 is an exemplary flow chart of determining a layout management scheme associated with a plurality of layout sub-areas by a preset algorithm according to some embodiments of the present description. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, the process 400 may be performed by a precipitation device management subsystem.

In step 410, a coding scheme for the layout management scheme is determined. In some embodiments, the precipitation device management subsystem may encode the layout management scheme using a binary encoding method, e.g., if there are 5 layout sub-regions, then scheme encoding 10011 represents that precipitation devices are required to be laid out in the layout management scheme at the 1 st, 4 th, and 5 th layout sub-regions. For another example, if the layout sub-areas are partitioned based on a grid of n×n (e.g., 3×3), the layout management scheme may be represented by a matrix containing elements of 0 or 1, where an element of 1 indicates that the corresponding layout sub-area needs to be laid out of the precipitation device, and an element of 0 indicates that the corresponding layout sub-area does not need to be laid out of the precipitation device.

Step 420, set an initial solution space. The initial solution space may refer to a set of layout management schemes that are initially determined. The initial solution space may contain M initial solutions (i.e., M layout management schemes that are initially determined). The initial solution may be set in advance according to an empirical value, or may be randomly generated. M may be a system default, an empirical, a human preset, etc., or any combination thereof.

Step 430, set fitness function. Each initial solution in the initial solution space may correspond to a return value of an fitness function. The return value of the fitness function of a certain layout management scheme can be the groundwater level determined after the precipitation device performs precipitation treatment according to the standard frequency and the working time after the certain layout management scheme is applied. The higher the return value of the fitness function, namely the better the fitness, the closer the groundwater level to the standard water line after the layout management scheme is applied, which indicates that the layout management scheme is more reasonable. The standard water level line can be preset according to actual requirements. In some embodiments, the precipitation device management subsystem may model or employ other analysis to determine the groundwater level after application of the layout management scheme.

Illustratively, the precipitation device management subsystem may predict the groundwater level after applying the layout management scheme through the first prediction model based on the layout management scheme, the foundation pit situation, the groundwater level data, and the formation data. The first prediction model can be used for processing the layout management scheme, the foundation pit condition, the groundwater level data and the stratum data and determining the groundwater level after the layout management scheme is applied. The first predictive model may be a machine learning model, such as a combination of one or more of a convolutional neural network model, a deep neural network model, and the like. The input of the first predictive model may include a layout management scheme, a foundation pit situation, groundwater level data, and formation data, and the output may be a groundwater level after the layout management scheme is applied.

In some embodiments, the parameters of the first predictive model may be derived by training. The rainfall device management subsystem may train the initial first prediction model based on a plurality of sets of first training samples with labels, where the first training samples may be sample foundation pit conditions, sample groundwater level data, and sample stratum data of the sample foundation pit at a first sample time, and the labels of the first training samples may be groundwater levels at a second sample time (i.e., after applying the sample layout management scheme to perform rainfall treatment according to a standard frequency and a working time). It should be appreciated that the first sample time should be earlier than the second sample time. The tag may be derived by measuring the actual groundwater level at the second sample time.

Inputting a plurality of first training samples into an initial first prediction model, constructing a loss function based on the output of the initial first prediction model and the labels of the first training samples, iteratively updating parameters of the initial first prediction model based on the loss function, and obtaining a trained first prediction model after training is finished when the trained model meets a first preset condition. The first preset condition may include, but is not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold, etc.

Step 440, a selection operation is performed. The precipitation device management subsystem may select the first several solutions with optimal fitness in the initial solution space by a selection operator, where the selection operator may be a roulette selection operator or any other selection operator.

At step 450, a crossover operation is performed. The precipitation device management subsystem may select a partial solution from the plurality of solutions using the crossover operator according to the crossover probability, and perform crossover operation on the partial solution to obtain at least one new solution. The cross probability may be a system default value, an empirical value, an artificial preset value, or any combination thereof, and may be set according to actual requirements. For example, the crossover probability may be 0.4-0.99, i.e., the number of partial solutions selected for crossover operations may be 40% -99% of the number of solutions. In the interleaving operation, the interleaving method is not limited. Taking a single-point crossover operator as an example, elements at one position are randomly selected on two solutions to exchange with each other to obtain two new solutions. For example, the selected solution includes 01101 and 11001, and the element in the third position is selected for exchange, so that two new solutions are 01001 and 11101.

Step 460, a mutation operation is performed. The precipitation device management subsystem can continuously select a plurality of solutions from the new solutions obtained in the cross operation according to the mutation probability to carry out mutation operation. The variation probability may be a system default value, an empirical value, a human preset value, or any combination thereof, and may be set according to actual requirements. For example, the variation probability may be 0.001-0.1, i.e., the number of solutions selected for the variation operation is 0.1% -10% of the number of new solutions obtained in the crossover operation. Taking a basic bit mutation operator as an example, performing mutation operation on elements of a certain bit or a plurality of bits on a certain solution by using mutation probability. If the second and third positions on the solution 10101 are mutated to change 1 to 0 and 0 to 1, the new solution is 11001. The original solution can be replaced by the mutated solution.

Step 470, update the original initial solution space. The precipitation device management subsystem may put the new solutions obtained through the crossover operation and the mutation operation into the original initial solution space, and remove the initial solutions (for example, solutions with lower return values of the fitness function) in the original initial solution space, which are the same as the number of the new solutions, correspondingly. The precipitation device management subsystem can calculate the return values of the adaptation functions of all new and old solutions in the current solution space, and the return values are ordered according to the size relation.

Step 480, it is determined whether the end condition of the preset algorithm is currently satisfied. The ending condition may be preset, and the ending condition may include, but is not limited to, at least one of that a return value of the fitness function of the solution with a preset number of solutions obtained by the present iteration is higher than a preset fitness threshold, a maximum value of the return values of the fitness functions of the solutions obtained by at least two consecutive iterations is the same or a difference between the maximum values is smaller than a difference threshold, and a preset maximum iteration number is completed. And responding to the meeting of the ending condition, the rainfall device management subsystem can output the layout management scheme with the largest return value of the fitness function, namely the optimal layout management scheme. In response to not satisfying the end condition, the precipitation device management subsystem may re-enter the selection operation of step 440 and iterate therewith.

In some embodiments, the precipitation device management subsystem may also determine an additional displacement based on the precipitation data and determine a backup deployment management scheme based on the additional displacement.

In some embodiments, the precipitation device management subsystem may select a maximum daily precipitation in the past year as the additional water displacement. In some embodiments, the precipitation device management subsystem may also multiply the aforementioned maximum daily precipitation by some preset factor (e.g., 1.1) as an additional water displacement. For example, the maximum daily rainfall is 600mm, and the additional drainage may be 600×1.1=660 mm.

In some embodiments, the precipitation device management subsystem may determine a backup deployment management scheme. The standby layout management scheme may be implemented simultaneously with the original layout management scheme. The standby layout management scheme is a supplementary scheme of the original layout management scheme, and the final layout management scheme is obtained by comprehensive addition. In the case of additional displacement, the two are implemented simultaneously to achieve the optimal precipitation effect. Wherein the alternate deployment management scheme may be an increase in alternate precipitation devices in one or more deployment sub-areas. For example, the code of the original layout management scheme may be 10111, the standby layout management scheme may be 00100, and the third element "1" indicates that a standby precipitation device is added to the third layout subarea, and then the precipitation devices of the layout subareas are added to two.

In some embodiments, the precipitation device management subsystem may determine whether a backup deployment management scheme is viable. In some embodiments, the precipitation installation management subsystem may predict the groundwater level after application of the alternate deployment management plan, and if the water level is below a water level threshold, the alternate deployment management plan may be viable. For example, the precipitation device management subsystem may set the water level threshold to 50cm. If the groundwater level after applying a certain standby layout management scheme is 43cm, the application of the standby layout management scheme is feasible. If the groundwater level after applying a certain standby layout management scheme is 67cm, the application of the standby layout management scheme is not feasible.

In some embodiments, the precipitation installation management subsystem may model or employ other analysis to determine the groundwater level after application of the alternate layout management scheme.

For example, the precipitation device management subsystem may predict the groundwater level after applying the layout management scheme based on the first predicted water level, the additional displacement, the original layout management scheme, the standby layout management scheme, and by the second prediction model. The first predicted water level refers to the groundwater level after the original layout management scheme is applied. The first predicted water level may be determined by the aforementioned first predictive model. The second prediction model can be used for processing the first predicted water level, the additional drainage volume, the original layout management scheme and the standby layout management scheme and determining the groundwater level after the standby layout management scheme is applied. The second predictive model may be a machine learning model, such as a combination of one or more of a convolutional neural network model, a deep neural network model, and the like. The input of the second prediction model may include the first predicted water level, the additional displacement, the original layout management scheme, the standby layout management scheme, and the output may be the groundwater level after the standby layout management scheme is applied.

In some embodiments, the parameters of the second predictive model may be derived by training. The rainfall device management subsystem can train the initial second prediction model based on a plurality of groups of second training samples with labels, the second training samples can be sample water level, extra water displacement of the samples, a first sample layout management scheme and a second sample layout management scheme, the labels of the second training samples can be application of the second sample layout management scheme, and actual groundwater water level after rainfall treatment is carried out according to standard frequency and working time. It should be appreciated that the tag may be derived by measuring the actual groundwater level.

Inputting a plurality of second training samples into an initial second prediction model, constructing a loss function based on the output of the initial second prediction model and the labels of the second training samples, iteratively updating the parameters of the initial second prediction model based on the loss function, and obtaining a trained second prediction model after training is finished when the trained model meets a second preset condition. The second preset condition may include, but is not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold, etc.

According to some embodiments of the present specification, by determining the additional drainage amount to determine the standby layout management scheme, a large amount of accumulated water in the rainy season, sudden heavy rain and other situations can be handled, thereby realizing foundation pit dewatering and ensuring normal work of the foundation pit.

According to some embodiments of the specification, the optimal layout management scheme can be determined through a preset algorithm, so that the dewatering effect of the dewatering device on the foundation pit is optimized, and the dewatering efficiency is improved.

The target time period refers to a time period in which groundwater level data needs to be determined. The target time period may be at least a period of time in the future. For example, 18 for 2 hours in the future, 1 day in the future, 6 months in 2023, 30 days: 00-19: 00, etc.

In some embodiments, the precipitation device management subsystem may employ various data analysis algorithms to analyze rainfall data, subsurface runoff data, formation data, and foundation pit conditions to predict groundwater level data within a target time period.

For example only, the precipitation device management subsystem may determine historical data vectors corresponding to the historical rainfall data, the historical underground runoff data, the historical formation data, and the historical pit conditions in advance based on the historical rainfall data, the historical underground runoff data, the historical formation data, and the historical pit conditions for a first historical time period, and generate a mapping relationship of the historical data vectors and the historical groundwater level data for a second historical time period based on the historical groundwater level data for each of the historical data vectors for the second historical time period, wherein the first historical time period is earlier than the second historical time period.

It can be appreciated that the precipitation device management subsystem can determine the corresponding data vector to be predicted based on the current rainfall data, the underground runoff data, the stratum data and the foundation pit condition. Further, the precipitation device management subsystem may determine at least one target historical data vector of the historical data vector that has a minimum vector distance from the data vector to be predicted based on a vector distance (e.g., euclidean distance) of the data vector to be predicted from the historical data vector. The precipitation device management subsystem may use, according to the mapping relationship, historical groundwater level data of the second historical time period corresponding to the at least one target historical data vector as groundwater level data of the current target time period.

In some embodiments, the precipitation device management subsystem may predict groundwater level data within the target period of time based on the rainfall data, the groundwater runoff data, the formation data, and the foundation pit conditions via a groundwater level prediction model.

The groundwater level prediction model may be used to process rainfall data, groundwater runoff data, formation data, and foundation pit conditions to determine groundwater level data within a target time period. The groundwater level prediction model may be a machine learning model, such as a combination of one or more of a convolutional neural network model, a deep neural network model, and the like. The input of the groundwater level prediction model may include rainfall data, groundwater runoff data, formation data, and foundation pit conditions, and the output may be groundwater level data within a target period of time.

As shown in FIG. 5, groundwater level prediction model 560 includes a rainfall embedment layer 560-1, a runoff embedment layer 560-2, a formation embedment layer 560-3, and a groundwater level prediction layer 560-4.

The rainfall embedding layer can process rainfall data and determine rainfall characteristics. The rainfall characteristic is a characteristic obtained after characteristic extraction of rainfall data. In some embodiments, the rainfall embedding layer may be a convolutional neural network. As shown in FIG. 5, the input to the rain embedding layer 560-1 may be rainfall data 510 and the output of the rain embedding layer 560-1 may be a rainfall signature 561.

The runoff embedding layer can process the underground runoff data and determine the characteristics of the underground runoff. The underground runoff characteristics are characteristics obtained after characteristic extraction of underground runoff data. In some embodiments, the radial flow embedded layer may be a convolutional neural network. As shown in FIG. 5, the input to the runoff embedding layer 560-2 may be the subsurface runoff data 520 and the output of the runoff embedding layer 560-2 may be the subsurface runoff feature 562.

The formation insert may process formation data to determine formation characteristics. The stratum features are features obtained after feature extraction of stratum data. In some embodiments, the formation-embedded layer may be a convolutional neural network. As shown in FIG. 5, the input of formation embedded layer 560-3 may be formation data 530 and the output of formation embedded layer 560-3 may be formation characteristics 563.

The groundwater level prediction layer can analyze and process rainfall characteristics, underground runoff characteristics, stratum characteristics, target time periods and foundation pit conditions, and determine groundwater level in the target time periods. As shown in fig. 5, the inputs to the groundwater level prediction layer 560-4 may be a rainfall signature 561 based on the rainfall embedment layer 560-1, an underground runoff signature 562 based on the runoff embedment layer 560-2, a formation signature 563 based on the formation embedment layer 560-3, a foundation pit situation 540, and a target time period 550, and the output may be a groundwater level 570 for the target time period. In some embodiments, the groundwater level prediction layer may be a combination of one or more of a convolutional neural network, a deep neural network, and the like.

In some embodiments, the rainfall embedment layer, the runoff embedment layer, the formation embedment layer, and the groundwater level prediction layer may be obtained through joint training. The management platform may jointly train the initial groundwater level prediction model based on a plurality of sets of labeled third training samples. The third training sample may include sample rainfall data, sample subsurface runoff data, sample formation data, sample foundation pit conditions, and a second sample time for the first sample time. The tag may be actual groundwater level data at the second sample time. It should be appreciated that the first sample time should be earlier than the second sample time.

In the joint training, the management platform may input sample rainfall data at a first sample time in the third training sample into the initial rainfall embedding layer, sample underground runoff data at the first sample time in the third training sample into the initial runoff embedding layer, and sample stratum data at the first sample time in the third training sample into the initial stratum embedding layer. And then, inputting the output of the initial rainfall embedded layer, the output of the initial runoff embedded layer, the output of the initial stratum embedded layer, the second sample time and the sample foundation pit condition into the initial groundwater level prediction layer together, and constructing a loss function based on the output of the initial groundwater level prediction layer and the label. And iteratively updating parameters of each layer in the initial groundwater level prediction model based on the loss function so that the loss function of the model meets preset conditions to obtain a trained groundwater level prediction model. The preset conditions may include, but are not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold.

In some embodiments, the precipitation device management subsystem may determine the rate of increase of the water level of the groundwater based on the predicted first groundwater level data for the first target time period and the second groundwater level data for the second target time period. Illustratively, the rate of increase of the water level of groundwater satisfies the following quantitative relationship:

(3)

for increasing the speed of the water level of the groundwater, +.>

First groundwater level data for a first target period of time,

second groundwater level data for a second target period of time,/for example>

Representing the time interval of the first target time period and the second target time period. For example, a->

=56cm、/>

=86cm，/>

=3 hours, then v= (86-56)/3=10 cm/hour.

The method disclosed by some embodiments of the specification can predict the water level increasing speed, so that basis is provided for determining the precipitation power of the precipitation device in the work management scheme.

According to the method disclosed by the embodiments of the specification, the data (such as rainfall data, underground runoff data, stratum data, foundation pit conditions and the like) affecting the groundwater level is comprehensively analyzed and processed through the model, so that the groundwater level data in a target time period can be rapidly and accurately predicted.

One or more embodiments of the present specification provide a big data based construction optimization apparatus, including a processor for performing any one of the big data based construction optimization methods as provided in the embodiments of the present specification.

The embodiments of the present specification also provide a computer-readable storage medium storing computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes any one of the big data-based construction optimization methods as provided in the embodiments of the present specification.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. The construction optimization system based on big data is characterized by comprising a foundation pit supporting system and an intelligent precipitation system;

The foundation pit supporting system is used for carrying out construction enclosure on construction engineering;

the intelligent precipitation system is used for carrying out precipitation treatment on underground water in the foundation pit and comprises a precipitation device management subsystem, a precipitation device layout subsystem and a precipitation device control subsystem;

the precipitation device management subsystem is used for:

determining a layout management scheme and a work management scheme of the precipitation device based on big data, wherein the layout management scheme comprises the layout quantity and the layout positions of the precipitation device; the big data comprise rainfall data, underground runoff data, groundwater level data, stratum data and foundation pit conditions;

predicting groundwater level data within a target time period based on the rainfall data, the subsurface runoff data, the formation data, and the foundation pit conditions,

the groundwater level data in the target time period is determined through a groundwater level prediction model, and the groundwater level prediction model comprises: the rainfall embedded layer, the runoff embedded layer, the stratum embedded layer and the groundwater level prediction layer;

the rainfall embedding layer is used for processing the rainfall data and determining rainfall characteristics;

The runoff embedding layer is used for processing the underground runoff data and determining the characteristics of the underground runoff;

the stratum embedding layer is used for processing the stratum data and determining stratum characteristics;

the groundwater level prediction layer is used for analyzing and processing the rainfall characteristic, the underground runoff characteristic, the stratum characteristic, the target time period and the foundation pit condition, and determining groundwater level data in the target time period;

predicting groundwater level data in at least two target time periods, and determining groundwater level increasing speed;

determining precipitation power of the precipitation device based on the groundwater level increasing speed; and

generating a first control instruction corresponding to the layout management scheme and a second control instruction corresponding to the work management scheme;

the precipitation device layout subsystem is used for:

based on the first control instruction, laying out the precipitation device to perform precipitation treatment;

the precipitation device control subsystem is configured to:

and controlling the precipitation power of the precipitation device based on the second control instruction.

2. The big data based construction optimization system of claim 1,

The precipitation device management subsystem is further configured to:

and determining the layout management scheme based on the foundation pit condition, the groundwater level data and the stratum data.

3. The big data based construction optimization system of claim 2, the precipitation device management subsystem further configured to:

dividing the periphery of the foundation pit into a plurality of layout subareas;

and determining the layout management scheme related to the plurality of layout subareas through a preset algorithm based on the foundation pit condition, the groundwater level data and the stratum data.

4. A method of optimizing construction based on big data, the method being implemented by a big data based construction optimization system comprising a foundation pit support system and an intelligent precipitation system, the method being performed by the intelligent precipitation system, the method comprising:

5. The method for optimizing construction based on big data according to claim 4,

the layout management scheme for determining the precipitation device based on the big data comprises the following steps:

6. The big data based construction optimization method of claim 5, wherein the determining the layout management scheme based on the foundation pit situation, the groundwater level data, and the formation data comprises:

7. A big data based construction optimization apparatus comprising a processor for executing the big data based construction optimization method of any one of claims 1 to 3.

8. A computer-readable storage medium storing computer instructions that, when read by a computer, perform the big data-based construction optimization method according to any one of claims 1 to 3.