Transportation hub engineering foundation pit siltation risk early warning method, computer device and system

Abstract

The application is suitable for the technical field of urban rail transit hub engineering, and provides a traffic hub engineering foundation pit siltation risk early warning method, computer equipment and system. The method comprises the following steps: acquiring a first image sequence collected by a first camera at each preset node of a silt discharge channel; identifying a target image sequence containing siltation images from the first image sequence, determining the siltation form and siltation cumulative speed at the target node corresponding to each target image sequence; determining the blockage speed of the silt discharge channel according to all siltation cumulative speeds, and determining the silt discharge capacity attenuation coefficient of the silt discharge channel when the blockage speed is greater than a preset speed threshold; predicting the potential water level of the silt discharge channel in a preset future period according to the silt discharge capacity attenuation coefficient and the average groundwater collection intensity of the foundation pit in the preset future period; and outputting a dredging prompt information if the potential water level is higher than the safety warning water level corresponding to the silt discharge channel, so that the siltation risk can be predicted in advance, and the safety of the foundation pit operation is improved.

Description

Technical Field

[0001] This application belongs to the technical field of urban rail transit hub engineering, and in particular relates to a method, computer equipment and system for early warning of siltation risk in foundation pits of transportation hub engineering. Background Technology

[0002] Excavation of ultra-large and ultra-deep foundation pits is a crucial step in the construction of large-scale infrastructure projects such as subway station hubs (typically 40 to 50 meters underground, covering an area of ​​tens of thousands of square meters). To keep the excavated pit dry and safe during subsequent formal construction, drainage channels and sump pits are usually constructed at the bottom of the pit. The drainage channels collect water accumulated from various parts of the pit into the sump pits, and then pumps pump the water out of the pit. Due to the complexity and dynamic nature of the pit working environment, siltation is prone to occur in the drainage channels. Siltation reduces the drainage efficiency of the channels, easily leading to large-scale water accumulation in the pit during heavy rainfall; more seriously, when siltation develops into local blockage, it can completely block the water flow in the drainage channels, causing a sudden rise in water level and threatening the structural safety of the pit.

[0003] Currently, the problem of siltation in drainage channels generally relies on the accidental discovery by on-site construction workers during routine operations. However, this method of discovery is prone to delays, missing the optimal time for treatment and potentially leading to problems such as large-scale water accumulation in the foundation pit or threats to the structural safety of the pit. Summary of the Invention

[0004] In view of this, the present application provides a method, computer equipment and system for early warning of siltation risk in foundation pits of transportation hub projects, in order to solve the technical problem that traditional siltation detection methods are prone to missing the best time to deal with siltation due to failure to detect it in time, thereby causing large-scale water accumulation in the foundation pit or threatening the structural safety of the foundation pit.

[0005] In a first aspect, embodiments of this application provide a method for early warning of siltation risk in foundation pits of transportation hub projects. The siltation risk includes water accumulation risk and siltation risk. The method includes:

[0006] The first image sequence is acquired by each first camera in the foundation pit during a first time period; each first camera is respectively set at a preset node of the silt drainage channel of the foundation pit;

[0007] Identify target image sequences containing siltation images from all the first image sequences, and determine the siltation morphology at the target nodes corresponding to each of the target image sequences;

[0008] For each target node, the accumulation velocity at the target node is determined based on the accumulation morphology and all the accumulation images in the corresponding target image sequence.

[0009] Based on the accumulation rate of all the aforementioned silt, determine the blockage rate of the silt drainage channel;

[0010] If the blockage rate is greater than a preset rate threshold, the silt removal capacity attenuation coefficient of the silt removal channel is determined, and the potential water level of the silt removal channel at the end of the preset future period is predicted based on the silt removal capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period.

[0011] If the potential water level is higher than the safety warning water level corresponding to the silt discharge channel, a silt removal prompt message is output; the silt removal prompt message includes the location identifiers of each of the preset nodes.

[0012] In an optional implementation of the first aspect, for each target node, the accumulation velocity at the target node is determined based on the accumulation morphology and all the accumulation images in the corresponding target image sequence, including:

[0013] For each target node, determine the accumulation volume and accumulation cross-sectional area of ​​each accumulation image in the target image sequence corresponding to the target node;

[0014] Based on the siltation pattern at each target node, the blockage risk coefficient at each target node is determined respectively;

[0015] For each target node, the accumulation rate at the target node is calculated using the following formula, based on all the accumulation volumes, all the accumulation cross-sectional areas, and the blockage risk coefficient corresponding to the target node:

[0016] ;

[0017] in, S The accumulation rate at the target node. V ( t n ) is the first in the target image sequence corresponding to the target node. n The congestion volume corresponding to the congestion image in the frame. V ( t 1) is the accumulation volume corresponding to the accumulation image in the first frame of the target image sequence corresponding to the target node. t n The first target image in the target image sequence corresponding to the target node n The acquisition time of the siltation image described in the frame, t 1 represents the acquisition time of the first frame of the siltation image in the target image sequence corresponding to the target node. μThe congestion risk coefficient at the target node. n The total number of congested images contained in the target image sequence corresponding to the target node. A ( t i ) is the first in the target image sequence corresponding to the target node. i The cross-sectional area of ​​the siltation corresponding to the siltation image in the frame. A max The cross-sectional area of ​​the silt drainage channel is given.

[0018] In one optional implementation of the first aspect, the siltation morphology includes localized accumulation, strip-shaped distribution, and uniform distribution; correspondingly, based on the siltation morphology at each target node, a blockage risk coefficient is determined for each target node, including:

[0019] For each target node, if the siltation morphology at the target node is the local accumulation state, the blockage risk coefficient at the target node is determined as a first coefficient;

[0020] For each target node, if the siltation pattern at the target node is a strip-shaped distribution, the blockage risk coefficient at the target node is determined to be a second coefficient; the second coefficient is less than the first coefficient.

[0021] For each target node, if the siltation pattern at the target node is a uniform distribution, the blockage risk coefficient at the target node is determined as a third coefficient; the third coefficient is less than the second coefficient.

[0022] In one optional implementation of the first aspect, determining the silt discharge capacity attenuation coefficient of the silt discharge channel includes:

[0023] The siltation cross-sectional area corresponding to the siltation image at the current moment in the target image sequence corresponding to each target node is determined as the siltation cross-sectional area at each target node.

[0024] Calculate the average value of the sedimentation cross-sectional area at all the target nodes;

[0025] The siltation capacity attenuation coefficient is calculated using the average of the channel cross-sectional area and the siltation cross-sectional area, according to the following formula:

[0026] β a =1- A s / A max ;

[0027] in, β a The silt removal capacity attenuation coefficient is... A s This represents the average cross-sectional area of ​​the siltation.

[0028] In one optional implementation of the first aspect, predicting the potential water level of the silt discharge channel at the end of the preset future period based on the silt discharge capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit within a preset future period includes:

[0029] Calculate the first product of the average groundwater accumulation intensity of the foundation pit, the preset surface runoff coefficient of the foundation pit, the preset catchment area of ​​the foundation pit, and the duration of the preset future time period, and determine the first product as the effective inflow volume of the silt discharge channel during the preset future time period:

[0030] Calculate the second product of the silt removal capacity attenuation coefficient, the preset silt removal capacity value of the silt removal channel, and the duration, and determine the second product as the effective outflow volume of the silt removal channel in the preset future time period;

[0031] Obtain the current water level of the silt discharge channel and calculate the current water surface area of ​​the silt discharge channel based on the current water level;

[0032] The potential water level is calculated using the following formula based on the effective inflow rate, the effective outflow rate, the current water level, and the current water surface area:

[0033] h final = h initial +max(0, V in - V out ) / A water ;

[0034] in, h final The potential water level, h initial The current water level, V in The effective inflow volume, V out The effective outflow volume is denoted as , and the current water surface area is denoted as .

[0035] In one alternative implementation of the first aspect, determining the blockage rate of the drainage channel based on the accumulation rates of all said silt deposits includes:

[0036] Calculate the average of all the aforementioned sediment accumulation rates;

[0037] The clogging speed is determined by the weighted sum of the maximum value and the average value among all the clogging speeds.

[0038] In one optional implementation of the first aspect, determining the siltation morphology at the target node corresponding to each of the target image sequences includes:

[0039] For each target image sequence, the latest-collected siltation image in the target image sequence is input into the siltation morphology recognition model for processing to obtain the confidence of the latest-collected siltation image relative to each siltation morphology.

[0040] The morphology corresponding to the highest confidence level is determined as the morphology at the target node corresponding to the target image sequence.

[0041] Secondly, embodiments of this application provide a computer device, including:

[0042] The first acquisition unit is used to acquire the first image sequence captured by each first camera in the foundation pit during a first time period; the first cameras are respectively set at each preset node of the sludge discharge channel of the foundation pit;

[0043] The first determining unit is configured to identify target image sequences containing siltation images from all the first image sequences, and determine the siltation morphology at the target nodes corresponding to each of the target image sequences.

[0044] The second determining unit is used to determine the accumulation rate at each target node based on the accumulation morphology and all the accumulation images in the corresponding target image sequence.

[0045] The third determining unit is used to determine the blockage rate of the siltation channel based on the accumulation rate of all the silt.

[0046] The first prediction unit is used to determine the silt removal capacity attenuation coefficient of the silt removal channel if the blockage speed is greater than a preset speed threshold, and to predict the potential water level of the silt removal channel at the end of the preset future period based on the silt removal capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period.

[0047] The risk warning unit is used to output dredging warning information if the potential water level is higher than the safety warning water level corresponding to the dredging channel; the dredging warning information includes the location identifiers of each of the preset nodes.

[0048] Thirdly, embodiments of this application provide another computer device, including a memory and a computer program stored in the memory and executable on a processor, wherein the processor executes the computer program to implement the method as described in any of the optional implementations of the first aspect above.

[0049] Fourthly, embodiments of this application provide a risk warning system for siltation in foundation pits of transportation hub projects, including a computer device and a plurality of first cameras wirelessly connected to the computer device; the computer device is used to execute the method described in any of the optional implementations of the first aspect above.

[0050] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the optional implementations of the first aspect described above.

[0051] In a sixth aspect, embodiments of this application provide a computer program product that, when run on a computer device, causes the computer device to implement the method described in any optional implementation of the first aspect.

[0052] Implementing the method, computer equipment, system, computer-readable storage medium, and computer program product for early warning of siltation risk in foundation pits of transportation hub projects provided in this application has the following beneficial effects:

[0053] The method for early warning of siltation risk in foundation pits of transportation hub projects provided in this application can automatically monitor the overall siltation situation of the siltation channel by setting a first camera at each preset node of the siltation channel. By identifying target image sequences containing siltation images and determining the siltation morphology and siltation rate at the target nodes corresponding to the target image sequences, and determining the blockage rate of the siltation channel based on the siltation rate at all target nodes, the method can quantify the overall blockage rate of the siltation channel. When the detected blockage rate is greater than a preset rate threshold, the method can quantify the current siltation capacity of the siltation channel by determining the siltation capacity attenuation coefficient of the siltation channel. Based on this, combined with the average groundwater accumulation intensity of the foundation pit in a preset future time period, the method can predict the potential water level of the siltation channel at the end of the preset future time period, and output siltation reminder information when the potential water level may be higher than the safety warning water level. This method can not only predict the siltation risk of the foundation pit in advance, reserve sufficient time for siltation, reduce the safety risk caused by missing the best siltation time, and improve the safety of foundation pit operations, but also improve the signal-to-noise ratio of the warning and reduce frequent interference to construction personnel. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A schematic diagram of a risk early warning system for siltation in a foundation pit of a transportation hub project, provided as an embodiment of this application;

[0056] Figure 2 A schematic flowchart illustrating a method for early warning of siltation risk in a foundation pit of a transportation hub project, provided as an embodiment of this application;

[0057] Figure 3 A schematic diagram illustrating the implementation process of S203 in a method for early warning of siltation risk in a foundation pit of a transportation hub project, provided in an embodiment of this application;

[0058] Figure 4 A schematic diagram illustrating the implementation process of S205 in a method for early warning of siltation risk in a foundation pit of a transportation hub project, provided in an embodiment of this application;

[0059] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0060] Figure 6 This is a schematic diagram of the structure of a computer device provided in another embodiment of this application. Detailed Implementation

[0061] The following embodiments are only used to illustrate the technical solutions of this application more clearly, and are therefore only examples and should not be used to limit the scope of protection of this application.

[0062] In the description of the embodiments of this application, the technical terms "comprising," "including," "having," and any variations thereof all mean "including but not limited to," unless otherwise specifically emphasized. In the description of the embodiments of this application, unless otherwise stated, the technical term "multiple" refers to two or more, and the technical terms "at least one" or "one or more" refer to one, two, or more than two. The technical terms "first," "second," etc., are only used to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary / secondary relationship of the indicated technical features. The technical term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0063] For ease of understanding, the terms used in the embodiments of this application are explained below.

[0064] 1. Foundation pit: An underground pit-shaped structure formed by earthwork excavation, used to provide a work site for underground construction of large infrastructure such as subway stations and high-rise buildings.

[0065] 2. Internal sludge drainage system of the foundation pit: This system is located at the bottom of the foundation pit and is used to collect and drain accumulated water inside the pit. The internal sludge drainage system of the foundation pit typically includes sludge drainage channels, sump wells, and water pumps.

[0066] The drainage channels are open trenches laid at the bottom of the foundation pit to guide and collect accumulated water (such as rainwater and groundwater seepage) from various parts of the pit into sump pits. Sump pits are temporary collection pits located at the lowest point of the foundation pit to receive water from the various drainage channels. Water pumps are power equipment installed in the sump pits to pump the collected water out of the foundation pit.

[0067] The technical solutions provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0068] This application first provides a risk warning system for siltation in foundation pits of transportation hub projects. Figure 1 This is a structural schematic diagram of a siltation risk early warning system for a foundation pit in a transportation hub project, provided as an embodiment of this application. Figure 1 As shown, in some embodiments, the early warning system for the risk of siltation in the foundation pit of a transportation hub project may include a computer device 11 and a group of multiple cameras 12 wirelessly connected to the computer device 11.

[0069] For example, multiple camera groups 12 can be respectively set at various preset nodes of the silt drainage channel, that is, one camera group 12 can be set at each preset node. The preset nodes can be locations in the silt drainage channel prone to siltation, such as corners of the silt drainage channel or intersections of multiple silt drainage channels. It should be understood that in practical applications, to support and stabilize the sidewalls of the foundation pit and reduce the risk of inward collapse, support structures are usually erected in the foundation pit. The two ends of these support structures are respectively supported on the two opposite sidewalls of the foundation pit. Based on this, the multiple camera groups 12 can be specifically fixed to the support structures above each preset node using brackets, with each camera group 12 facing the silt drainage channel, and its field of view covering the entire cross-section of the silt drainage channel. In this way, each camera group 12 can acquire a complete cross-sectional image of the silt drainage channel at its respective preset node.

[0070] Optionally, each camera group 12 may include a first camera 121 and a second camera 122. The first camera 121 may be a visible light camera, used to capture visible light images and send the captured visible light images to the computer device 11. The second camera 122 may be a depth camera, used to capture depth images and send the captured depth images to the computer device 11. For example, the first camera 121 and the second camera 122 in each camera group 12 may capture images at preset intervals. The preset interval can be set according to actual needs, for example, it can be 5 minutes.

[0071] In other embodiments, the early warning system for the risk of siltation in the foundation pit may further include multiple level gauges (not shown) wirelessly connected to the computer device 11. The multiple level gauges may be respectively installed at various preset nodes of the siltation drainage channel, i.e., one level gauge may be installed at each preset node. Optionally, the level gauges may be used to collect the current water level at the preset nodes of the siltation drainage channel and send the collected current water level to the computer device 11.

[0072] Optionally, the computer device 11 may be used to perform the various steps in the subsequent method embodiments, or may include the various units in the subsequent device embodiments. For details regarding the specific functions of the computer device 11, please refer to the relevant descriptions in the subsequent method or device embodiments; they will not be detailed here.

[0073] For example, the computer device 11 can be an edge computing server, desktop computer, laptop computer, tablet computer, or mobile phone, etc. This application embodiment does not particularly limit the type of computer device 11.

[0074] This application also provides a method for early warning of siltation risk in foundation pits of transportation hub projects. The subject executing this method can be the aforementioned computer equipment 11. Figure 2 This is a schematic flowchart illustrating a method for early warning of siltation risk in a foundation pit of a transportation hub project, provided as an embodiment of this application. Figure 2 As shown, the method may include S201 to S206, which are detailed below:

[0075] S201, acquire the first image sequence collected by each of the first cameras in the foundation pit during the first time period.

[0076] Each first image sequence may include multiple first images acquired at different times, arranged sequentially according to their acquisition times to form the first image sequence. It should be noted that the first image is the visible light image captured by the first camera.

[0077] For example, the first time period can be the period between a preset historical time and the current time. The preset historical time can be determined based on the duration of the first time period, which can be set according to actual needs. For example, if the duration of the first time period is Δ... t 1. The current time is t dq The time is preset to the historical time. t dq -Δ t At time 1, the first period is t dq -Δ t 1 moment and t dq The time interval between moments.

[0078] It should be understood that the first image sequence includes multiple first images acquired at different times, and these multiple first images are arranged sequentially according to the acquisition time to form the first image sequence.

[0079] S202, identify target image sequences containing siltation images from all first image sequences, and determine the siltation morphology at the target nodes corresponding to each target image sequence.

[0080] Here, a siltation image refers to the first image containing the siltation region (i.e., siltation features).

[0081] It should be understood that the congested region in a congested image typically exhibits a significant grayscale difference from other regions. Based on this, the computer device 11 can process each first image in each first image sequence into a corresponding grayscale image. Optionally, for each grayscale image corresponding to a first image, the computer device 11 can employ a threshold segmentation method to determine pixels with grayscale values ​​less than a preset grayscale threshold as target pixels, and define the region composed of all target pixels as the congested region. Based on this, the computer device 11 can determine the first image corresponding to the grayscale image containing the congested region as the congested image, and can determine the first target image sequence containing at least one congested image as the target image sequence. The preset grayscale threshold can be set according to actual conditions. For example, when the grayscale value range is 0–255, the preset grayscale threshold can be 100.

[0082] For example, siltation patterns can include localized siltation, strip-shaped distribution, and uniform distribution. Localized siltation describes a deposition pattern with significant height, which most strongly impedes water flow and is highly prone to causing blockages in a short time. Strip-shaped distribution refers to a band-like deposition pattern extending along the water flow direction; this pattern alters the flow pattern and reduces localized silt removal capacity. Uniform distribution refers to a flat, uniformly sized pattern; this pattern increases flow resistance and slowly reduces silt removal efficiency. In other words, the clogging risk associated with localized siltation is greater than that associated with strip-shaped distribution, and the clogging risk associated with strip-shaped distribution is greater than that associated with uniform distribution.

[0083] Optionally, if there is at least one target image sequence in the first image sequence, for each target image sequence, the computer device 11 can determine the morphology at the target node corresponding to that target image sequence based on the siltation image acquired latest in that target image sequence. Specifically, for each target image sequence, the computer device 11 can input the siltation image acquired latest in that target image sequence into a siltation morphology recognition model for processing, obtain the confidence score of the siltation image relative to each siltation morphology, and determine the siltation morphology with the highest confidence score as the siltation morphology of the target node corresponding to that target image sequence. For example, the siltation morphology recognition model can be trained using a deep learning algorithm with several siltation images labeled with siltation morphologies.

[0084] Optionally, if there is no target image sequence in the first image sequence, it indicates that no siltation has occurred in the siltation channel, and therefore the computer device 11 may not perform subsequent steps.

[0085] S203, for each target node, determine the accumulation velocity at the target node based on the accumulation morphology at the target node and all accumulation images in the corresponding target image sequence.

[0086] Among them, the accumulation rate can be used to measure the dynamic development trend of siltation.

[0087] Optionally, S203 may include, for example, Figure 3 S2031 to S2033 are described in detail below:

[0088] S2031, For each target node, determine the accumulation volume and accumulation cross-sectional area of ​​each accumulation image in the target image sequence corresponding to the target node.

[0089] The siltation cross-sectional area refers to the area occupied by siltation on the cross-section of the siltation channel.

[0090] Optionally, for each siltation image in each target image sequence, the computer device 11 can convert the pixel area of ​​the siltation region in the siltation image into the corresponding siltation cross-sectional area of ​​the siltation image based on the pixel area of ​​the siltation region in the siltation image, combined with the intrinsic and extrinsic parameters of the first camera and the on-site scale. The on-site scale can be calibrated using an on-site calibration plate or a laser rangefinder, etc.

[0091] Optionally, for each congestion image in each target image sequence, the computer device 11 can acquire a depth image from the depth image captured by the depth camera at the same time as the acquisition of the congestion image, determine the depth value of each pixel in the congestion region of the congestion image based on the depth image, and determine the congestion volume corresponding to the congestion image based on the depth value of each pixel in the congestion region and the congestion cross-sectional area corresponding to the congestion image. For example, the computer device 11 can calculate the average depth value of all pixels in the congestion region of the congestion image, and multiply the average value by the congestion cross-sectional area corresponding to the congestion image to determine the congestion volume corresponding to the congestion image.

[0092] S2032, based on the siltation pattern at each target node, determine the blockage risk coefficient at each target node.

[0093] It should be understood that different siltation patterns correspond to different blockage risk coefficients.

[0094] Optionally, the computer device 11 may store a mapping relationship between siltation patterns and blockage risk coefficients. Based on this, for each target node, the computer device can determine the blockage risk coefficient at that target node by querying the above mapping relationship according to the siltation pattern at that target node.

[0095] Specifically, S2032 may include the following steps 1.1 to 1.3:

[0096] Step 1.1: For each target node, if the siltation morphology at the target node is a local accumulation state, determine the blockage risk coefficient at the target node as the first coefficient.

[0097] Step 1.2: For each target node, if the siltation pattern at the target node is a strip-shaped distribution, determine the blockage risk coefficient at the target node as the second coefficient.

[0098] Step 1.3: For each target node, if the siltation pattern at the target node is uniformly distributed, determine the blockage risk coefficient at the target node as the third coefficient.

[0099] Among them, the second coefficient is less than the first coefficient, and the third coefficient is less than the second coefficient.

[0100] For example, the first coefficient, the second coefficient, and the third coefficient can all be set according to the actual situation. For example, the value range of the first coefficient can be [2,3], the value range of the second coefficient can be [0.8,1.5], and the value range of the third coefficient can be [0.1,0.5].

[0101] S2033, for each target node, based on all the sedimentation volumes, all sedimentation cross-sectional areas, and the blockage risk coefficient corresponding to that target node, the sedimentation rate at that target node is calculated using the following formula (1):

[0102] ;Formula (1)

[0103] in, S The accumulation rate at the target node. V ( t n ) is the first in the target image sequence corresponding to the target node. n The congestion volume corresponding to the frame congestion image. V ( t 1) is the accumulation volume corresponding to the first frame of the accumulation image in the target image sequence corresponding to the target node. t n The first image in the target image sequence corresponding to this target node n The acquisition time of the frame congestion image, t 1 represents the acquisition time of the first frame of the congestion image in the target image sequence corresponding to the target node. μ This represents the congestion risk coefficient at the target node. n This represents the total number of congested images contained in the target image sequence corresponding to the target node. A ( t i ) is the first in the target image sequence corresponding to the target node. i The cross-sectional area of ​​the congestion corresponding to the frame congestion image. A max This refers to the cross-sectional area of ​​the silt drainage channel.

[0104] It should be understood that "all the congestion corresponding to the target node" can refer to the congestion volume of all congested images in the target image sequence corresponding to that target node. Similarly, "all the congestion cross-sectional area corresponding to the target node" can refer to the congestion cross-sectional area of ​​all congested images in the target image sequence corresponding to that target node.

[0105] It should also be understood that, since not all first images in the target image sequence are necessarily congested images, the first congested image in the target image sequence is not necessarily the original first image in the target image sequence; the first congested image in the target image sequence is not necessarily the first image in the original first frame of the target image sequence. nThe congested frame image is not necessarily the original first frame in the target image sequence. n The first image in frame 1.

[0106] The cross-sectional area of ​​the silt discharge channel can refer to the cross-sectional area of ​​the silt discharge channel. Optionally, the computer device 11 can pre-store the cross-sectional area of ​​the silt discharge channel. Based on this, when it is necessary to calculate the silt accumulation rate at each target node using the above formula (1), the computer device 11 can obtain the pre-stored cross-sectional area of ​​the silt discharge channel.

[0107] S204, determine the blockage rate of the siltation channel based on the siltation accumulation rate at all target nodes.

[0108] Optionally, the computer device 11 may pre-store a first weight corresponding to the maximum siltation accumulation rate of the siltation channel and a second weight corresponding to the average siltation accumulation rate. The first weight may be greater than the second weight, and the sum of the first and second weights is 1. Based on this, when it is necessary to determine the blockage rate of the siltation channel, the computer device 11 can obtain the pre-stored first and second weights and determine the blockage rate of the siltation channel according to steps 2.1 to 2.3:

[0109] Step 2.1: Calculate the average accumulation rate at all target nodes.

[0110] Step 2.2: The maximum value of the siltation accumulation rate at all target nodes is weighted and summed with the above average value to determine the blockage rate of the siltation drainage channel.

[0111] Specifically, computer device 11 can calculate the product of the maximum value of the siltation accumulation rate at all target nodes and the first weight, and calculate the product of the average value of the siltation accumulation rate at all target nodes and the second weight, and determine the sum of these two products as the blockage rate of the siltation drainage channel.

[0112] S205, if the blockage rate of the silt discharge channel is greater than the preset speed threshold, then determine the silt discharge capacity attenuation coefficient of the silt discharge channel, and predict the potential water level of the silt discharge channel at the end of the preset future period based on the silt discharge capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period.

[0113] The preset speed threshold can be set according to the actual situation.

[0114] For example, the duration of the preset future time period can be set according to actual needs. For instance, if the duration of the preset future time period is Δ... t 2. The current time is t dq The time period is preset to the future time period. t dqTime and t dq +Δ t The time interval between two points. The preset end time of the future time interval is... t dq +Δ t 2.

[0115] It should be understood that when the blockage rate of the siltation channel exceeds a preset speed threshold, it indicates that the rate of siltation expansion in the siltation channel has reached a critical level requiring intervention. At this point, the computer device 11 needs to predict whether an overflow hazard will occur in a preset future period. To improve prediction accuracy, the computer device can first determine the drainage capacity attenuation coefficient of the siltation channel. This attenuation coefficient can be used to quantify the current remaining effective drainage capacity of the siltation channel.

[0116] Optionally, the step in S205 to determine the attenuation coefficient of the silt discharge capacity of the silt discharge channel may specifically include, for example: Figure 4 S2051 to S2053 are described in detail below:

[0117] S2051, the siltation cross-sectional area corresponding to the siltation image at the current moment in the target image sequence corresponding to each target node is determined as the siltation cross-sectional area at each target node.

[0118] S2052, calculate the average value of the sedimentation cross-sectional area at all target nodes.

[0119] S2053, based on the cross-sectional area of ​​the siltation channel and the average cross-sectional area of ​​siltation at all target nodes, the siltation capacity attenuation coefficient is calculated using the following formula (2):

[0120] β a =1- A s / A max ;Formula (2)

[0121] in, β a The coefficient for silt removal capacity attenuation. A s This represents the average cross-sectional area of ​​sedimentation at all target nodes. A max This refers to the cross-sectional area of ​​the silt drainage channel.

[0122] For example, the duration of the preset future time period can be set according to actual needs. For instance, if the duration of the preset future time period is Δ... t 2. The current time is t dq The time period is preset to the future time period. tdq Time and t dq +Δ t The time interval between two points. The preset end time of the future time interval is... t dq +Δ t 2.

[0123] Optionally, the average groundwater accumulation intensity in the foundation pit for a future time period can be determined based on weather forecasts.

[0124] Optionally, the step in S205, which predicts the potential water level of the silt discharge channel at the end of the preset future period based on the silt discharge capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit within the preset future period, may specifically include, for example: Figure 4 S2054 to S2057 are described in detail below:

[0125] S2054, calculate the first product of the average groundwater collection intensity of the foundation pit, the preset surface runoff coefficient of the foundation pit, the preset catchment area of ​​the foundation pit and the preset future time period, and determine the first product as the effective inflow volume of the silt discharge channel within the preset future time period.

[0126] The preset surface runoff coefficient can be used to represent the efficiency of the foundation pit surface in converting rainfall into surface runoff. For example, the preset surface runoff coefficient can range from [0,1]. The larger the value of the preset surface runoff coefficient, the less easily the water is absorbed by the foundation pit surface, and the more water will flow into the drainage channel.

[0127] The preset catchment area can be used to describe how much water in the foundation pit will flow into the drainage channel.

[0128] Optionally, the computer device 11 may store a preset surface runoff coefficient and a preset catchment area for the foundation pit. Based on this, when it is necessary to calculate the effective inflow volume of the silt discharge channel within a preset future time period, the computer device 11 can obtain the pre-stored preset surface runoff coefficient and preset catchment area for the foundation pit.

[0129] For example, the preset surface runoff can be determined based on the manual for the materials used in the foundation pit floor. The preset catchment area can be determined based on the foundation pit's siltation design drawings.

[0130] S2055, calculate the second product of the silt discharge capacity attenuation coefficient, the preset silt discharge capacity value of the silt discharge channel and the preset future time period, and determine the second product as the effective outflow volume of the silt discharge channel within the preset future time period.

[0131] The preset silt removal capacity value can be used to represent the maximum amount of water that the silt removal channel can remove per unit time under ideal conditions without siltation.

[0132] Optionally, the computer device 11 may store preset silt discharge capacity values ​​for the silt discharge channel. Based on this, when it is necessary to calculate the effective outflow volume of the silt discharge channel within a preset future time period, the computer device 11 can obtain the pre-stored preset silt discharge capacity values. For example, the preset silt discharge capacity values ​​can be calculated using hydraulic formulas (such as Manning's formula) based on the design drawings of the silt discharge channel.

[0133] S2056, obtain the current water level of the silt discharge channel and calculate the current water surface area of ​​the silt discharge channel based on the current water level.

[0134] Optionally, the computer device 11 can obtain the current water level at each preset node from the level gauges set at each preset node of the silt discharge channel, and determine the average value of the current water level at all preset nodes as the current water level of the silt discharge channel.

[0135] It should be understood that the width of the silt discharge channel may differ at different water levels. The computer device 11 can store the correspondence between the water level of the silt discharge channel and its width. Based on this, after obtaining the current water level of the silt discharge channel, the computer device 11 can determine the target width of the silt discharge channel corresponding to the current water level by querying the aforementioned correspondence, and can then determine the current water surface area of ​​the silt discharge channel by multiplying the target width by the length of the silt discharge channel.

[0136] S2057, based on the effective inflow, effective outflow, current water level, and current water surface area, the potential water level is calculated using the following formula (3):

[0137] h final = h initial +max(0, V in - V out ) / A water ;Formula (3)

[0138] in, h final Potential water level, h initial The current water level in the silt drainage channel. V in To ensure effective inflow volume, V out To ensure an effective outflow of water, A water This represents the current water surface area of ​​the silt drainage channel.

[0139] S206 If the potential water level is higher than the safety warning water level corresponding to the silt discharge channel, output silt removal prompt information; the silt removal prompt information includes the location identifier of each preset node.

[0140] For example, the safety warning water level can be set according to actual needs. For instance, the safety warning water level can be 80% of the maximum allowable water level in the siltation channel (i.e., the depth of the siltation channel).

[0141] Optionally, the location identifier of the preset node can be the number of each preset node, or the orientation of each node, etc. This application embodiment does not limit the form of the location identifier.

[0142] Optionally, a communication connection can be established between the computer device 11 and the target terminal device. The target terminal device can be the terminal device of any construction worker. Based on this, when the potential water level is higher than the corresponding safety warning water level of the siltation channel, the computer device 11 can send a siltation reminder message to the target terminal device to prompt the construction workers to clean the silt accumulated at each target node in the siltation channel.

[0143] As can be seen from the above, the method for early warning of siltation risk in the foundation pit of a transportation hub project provided in this application can achieve automated monitoring of the overall siltation situation of the siltation channel by setting a first camera at each preset node of the siltation channel; by identifying a target image sequence containing siltation images and determining the siltation morphology and siltation rate at the target node corresponding to the target image sequence, and determining the blockage rate of the siltation channel based on the siltation rate at all target nodes, it can achieve quantification of the overall blockage rate of the siltation channel; when the blockage rate is detected to be greater than a preset rate threshold, the method can determine the siltation rate of the siltation channel by determining the siltation rate of the siltation channel. The attenuation coefficient of the siltation channel's drainage capacity can quantify the channel's current siltation capacity. Based on this, combined with the average groundwater accumulation intensity of the foundation pit within a preset future time period, the potential water level of the siltation channel at the end of the preset future time period is predicted. If the potential water level may be higher than the safety warning level, a siltation warning message is output. This not only enables early prediction of the risk of foundation pit siltation, allowing sufficient time for siltation and reducing the safety risks caused by missing the optimal siltation time, thus improving the safety of foundation pit operations, but also improves the warning signal-to-noise ratio and reduces frequent interference to construction personnel.

[0144] It is understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0145] Based on the method for early warning of siltation risk in foundation pits of transportation hub projects provided in the above embodiments, this application further provides an embodiment of a computer device for implementing the above method embodiments. Please refer to... Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. Figure 5 As shown, the computer device 50 may include: a first acquisition unit 501, a first determination unit 502, a second determination unit 503, a third determination unit 504, a first prediction unit 505, and a risk warning unit 506. Wherein:

[0146] The first acquisition unit 501 is used to acquire the first image sequence collected by each first camera in the foundation pit during a first time period; the first cameras are respectively set at each preset node of the sludge discharge channel of the foundation pit.

[0147] The first determining unit 502 is used to identify target image sequences containing siltation images from all the first image sequences, and to determine the siltation morphology at the target nodes corresponding to each of the target image sequences.

[0148] The second determining unit 503 is used to determine the accumulation rate at each target node based on the accumulation morphology and all the accumulation images in the corresponding target image sequence.

[0149] The third determining unit 504 is used to determine the blockage rate of the siltation channel based on the accumulation rate of all the silt.

[0150] The first prediction unit 505 is used to determine the silt removal capacity attenuation coefficient of the silt removal channel if the blockage speed is greater than a preset speed threshold, and to predict the potential water level of the silt removal channel at the end of the preset future period based on the silt removal capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period.

[0151] The risk warning unit 506 is used to output dredging warning information if the potential water level is higher than the safety warning water level corresponding to the dredging channel; the dredging warning information includes the location identifiers of each of the preset nodes.

[0152] Optionally, the second determining unit 503 may include a siltation size determining unit, a risk coefficient determining unit, and a first velocity determining unit. Wherein:

[0153] The siltation size determination unit is used to determine the siltation volume and siltation cross-sectional area of ​​each siltation image in the target image sequence corresponding to the target node for each target node.

[0154] The risk coefficient determination unit is used to determine the blockage risk coefficient of each target node according to the siltation morphology at each target node.

[0155] The first velocity determination unit is used to calculate the accumulation velocity at each target node using the following formula, based on all the accumulation volumes, all the accumulation cross-sectional areas, and the blockage risk coefficient corresponding to the target node:

[0156] ;

[0157] in, S The accumulation rate at the target node. V ( t n ) is the first in the target image sequence corresponding to the target node. n The congestion volume corresponding to the congestion image in the frame. V ( t 1) is the accumulation volume corresponding to the accumulation image in the first frame of the target image sequence corresponding to the target node. t n The first target image in the target image sequence corresponding to the target node n The acquisition time of the siltation image described in the frame, t 1 represents the acquisition time of the first frame of the siltation image in the target image sequence corresponding to the target node. μ The congestion risk coefficient at the target node. n The total number of congested images contained in the target image sequence corresponding to the target node. A ( t i ) is the first in the target image sequence corresponding to the target node. i The cross-sectional area of ​​the siltation corresponding to the siltation image in the frame. A max The cross-sectional area of ​​the silt drainage channel is given.

[0158] Optionally, the siltation morphology may include localized accumulation, strip-like distribution, and uniform distribution. Correspondingly, the risk coefficient determination unit is specifically used for:

[0159] For each target node, if the siltation morphology at the target node is the local accumulation state, the blockage risk coefficient at the target node is determined as a first coefficient;

[0160] For each target node, if the siltation pattern at the target node is a strip-shaped distribution, the blockage risk coefficient at the target node is determined to be a second coefficient; the second coefficient is less than the first coefficient.

[0161] For each target node, if the siltation pattern at the target node is a uniform distribution, the blockage risk coefficient at the target node is determined as a third coefficient; the third coefficient is less than the second coefficient.

[0162] Optionally, the first prediction unit 505 is specifically used for:

[0163] The siltation cross-sectional area corresponding to the siltation image at the current moment in the target image sequence corresponding to each target node is determined as the siltation cross-sectional area at each target node.

[0164] Calculate the average value of the sedimentation cross-sectional area at all the target nodes;

[0165] The siltation capacity attenuation coefficient is calculated using the average of the channel cross-sectional area and the siltation cross-sectional area, according to the following formula:

[0166] β a =1- A s / A max ;

[0167] in, β a The silt removal capacity attenuation coefficient is... A s This represents the average cross-sectional area of ​​the siltation.

[0168] Optionally, the first prediction unit 505 is further used for:

[0169] Calculate the first product of the average groundwater accumulation intensity of the foundation pit, the preset surface runoff coefficient of the foundation pit, the preset catchment area of ​​the foundation pit, and the duration of the preset future time period, and determine the first product as the effective inflow volume of the silt discharge channel during the preset future time period:

[0170] Calculate the second product of the silt removal capacity attenuation coefficient, the preset silt removal capacity value of the silt removal channel, and the duration, and determine the second product as the effective outflow volume of the silt removal channel in the preset future time period;

[0171] Obtain the current water level of the silt discharge channel and calculate the current water surface area of ​​the silt discharge channel based on the current water level;

[0172] The potential water level is calculated using the following formula based on the effective inflow rate, the effective outflow rate, the current water level, and the current water surface area:

[0173] h final = h initial +max(0, V in - V out ) / A water ;

[0174] in, h final The potential water level, h initial The current water level, V in The effective inflow volume, V out The effective outflow volume is denoted as , and the current water surface area is denoted as .

[0175] Optionally, the third determining unit 504 is specifically used for:

[0176] Calculate the average of all the aforementioned sediment accumulation rates;

[0177] The clogging speed is determined by the weighted sum of the maximum value and the average value among all the clogging speeds.

[0178] Optionally, the first determining unit 502 is specifically used for:

[0179] For each target image sequence, the latest-collected siltation image in the target image sequence is input into the siltation morphology recognition model for processing to obtain the confidence of the latest-collected siltation image relative to each siltation morphology.

[0180] The morphology corresponding to the highest confidence level is determined as the morphology at the target node corresponding to the target image sequence.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the computer device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of each unit in the above-described computer device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0182] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided for another embodiment of this application. For example... Figure 6 As shown, the computer device 6 provided in this embodiment may include: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as the program corresponding to the method for early warning of siltation risk in a foundation pit of a transportation hub project. When the processor 60 executes the computer program 62, it implements the steps in the above embodiment of the method for early warning of siltation risk in a foundation pit of a transportation hub project, for example... Figure 2 S201~S206 are shown. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each unit in the above-described computer device embodiment.

[0183] For example, computer program 62 can be divided into one or more modules / units, one or more of which are stored in memory 61 and executed by processor 60 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 62 in computer device 6. For example, computer program 62 can be divided into a first acquisition unit, a first determination unit, a second determination unit, a third determination unit, a first prediction unit, and a risk warning unit. For the specific functions of each unit, please refer to [link to relevant documentation]. Figure 5 The relevant descriptions in the corresponding embodiments are not repeated here.

[0184] Those skilled in the art will understand that Figure 6 This is merely an example of computer device 6 and does not constitute a limitation on computer device 6. It may include more or fewer components than shown, or combine certain components, or use different components.

[0185] The processor 60 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0186] The memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or RAM. The memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, or flash card. Furthermore, the memory 61 can include both internal and external storage units of the computer device 6. The memory 61 is used to store computer programs and other programs and data required by the computer device. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0187] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the method for early warning of siltation risk in the foundation pit of a transportation hub project described above.

[0188] This application provides a computer program product that, when run on a computer device, enables the computer device to perform the steps described in the various method embodiments above.

[0189] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

[0190] It should be noted that, unless otherwise specified, all technical terms used in the embodiments of this application have the same meaning as commonly understood by those skilled in the art to which this application belongs. The technical terms used in the embodiments of this application are only used to explain specific embodiments of this application and are not intended to limit this application.

[0191] The term "embodiment" as used in the description of embodiments in this application means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0192] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0193] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for early warning of siltation risk in foundation pits of transportation hub projects, characterized in that, The siltation risk includes the risk of water accumulation and the risk of siltation, and the method includes: The first image sequence is acquired by each first camera in the foundation pit during a first time period; each first camera is respectively set at a preset node of the silt drainage channel of the foundation pit; Identify target image sequences containing siltation images from all the first image sequences, and determine the siltation morphology at the target node corresponding to each target image sequence; the siltation morphology includes local accumulation, strip distribution, and uniform distribution. For each target node, the accumulation velocity at the target node is determined based on the accumulation morphology and all the accumulation images in the corresponding target image sequence. Based on the accumulation rate of all the aforementioned silt, determine the blockage rate of the silt drainage channel; If the blockage rate is greater than a preset rate threshold, the silt removal capacity attenuation coefficient of the silt removal channel is determined, and the potential water level of the silt removal channel at the end of the preset future period is predicted based on the silt removal capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period. If the potential water level is higher than the safety warning water level corresponding to the silt discharge channel, a silt removal prompt message is output; the silt removal prompt message includes the location identifiers of each of the preset nodes.

2. The method according to claim 1, characterized in that, For each target node, the accumulation velocity at the target node is determined based on the accumulation morphology and all the accumulation images in the corresponding target image sequence, including: For each target node, determine the accumulation volume and accumulation cross-sectional area of ​​each accumulation image in the target image sequence corresponding to the target node; Based on the siltation pattern at each target node, the blockage risk coefficient at each target node is determined respectively; For each target node, the accumulation rate at the target node is calculated using the following formula, based on all the accumulation volumes, all the accumulation cross-sectional areas, and the blockage risk coefficient corresponding to the target node: ； in, S The accumulation rate at the target node. V ( t n ) is the first in the target image sequence corresponding to the target node. n The congestion volume corresponding to the congestion image in the frame. V ( t 1) is the accumulation volume corresponding to the accumulation image in the first frame of the target image sequence corresponding to the target node. t n The first target image in the target image sequence corresponding to the target node n The acquisition time of the siltation image described in the frame, t 1 represents the acquisition time of the first frame of the siltation image in the target image sequence corresponding to the target node. μ The congestion risk coefficient at the target node. n The total number of congested images contained in the target image sequence corresponding to the target node. A ( t i ) is the first in the target image sequence corresponding to the target node. i The cross-sectional area of ​​the siltation corresponding to the siltation image in the frame. A max The cross-sectional area of ​​the silt drainage channel is given.

3. The method according to claim 2, characterized in that, Based on the siltation morphology at each of the target nodes, the blockage risk coefficient at each of the target nodes is determined, including: For each target node, if the siltation morphology at the target node is the local accumulation state, the blockage risk coefficient at the target node is determined as a first coefficient; For each target node, if the siltation pattern at the target node is a strip-shaped distribution, the blockage risk coefficient at the target node is determined to be a second coefficient; the second coefficient is less than the first coefficient. For each target node, if the siltation pattern at the target node is a uniform distribution, the blockage risk coefficient at the target node is determined as a third coefficient; the third coefficient is less than the second coefficient.

4. The method according to claim 2, characterized in that, Determining the attenuation coefficient of the silt discharge capacity of the silt discharge channel includes: The siltation cross-sectional area corresponding to the siltation image at the current moment in the target image sequence corresponding to each target node is determined as the siltation cross-sectional area at each target node. Calculate the average value of the sedimentation cross-sectional area at all the target nodes; The siltation capacity attenuation coefficient is calculated using the average of the channel cross-sectional area and the siltation cross-sectional area, according to the following formula: β a =1- A s / A max ； in, β a The silt removal capacity attenuation coefficient is... A s This represents the average cross-sectional area of ​​the siltation.

5. The method according to claim 2, characterized in that, Based on the siltation capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit within a preset future time period, the potential water level of the siltation channel at the end of the preset future time period is predicted, including: Calculate the first product of the average groundwater accumulation intensity of the foundation pit, the preset surface runoff coefficient of the foundation pit, the preset catchment area of ​​the foundation pit, and the duration of the preset future time period, and determine the first product as the effective inflow volume of the silt discharge channel during the preset future time period: Calculate the second product of the silt removal capacity attenuation coefficient, the preset silt removal capacity value of the silt removal channel, and the duration, and determine the second product as the effective outflow volume of the silt removal channel in the preset future time period; Obtain the current water level of the silt discharge channel and calculate the current water surface area of ​​the silt discharge channel based on the current water level; The potential water level is calculated using the following formula based on the effective inflow rate, the effective outflow rate, the current water level, and the current water surface area: h final = h initial +max(0, V in - V out ) / A water ; in, h final The potential water level, h initial The current water level, V in The effective inflow volume, V out The effective outflow rate is... A water The current water surface area is [area].

6. The method according to any one of claims 1-5, characterized in that, Based on the accumulation rates of all the aforementioned silt deposits, the blockage rate of the drainage channel is determined, including: Calculate the average of all the aforementioned sediment accumulation rates; The clogging speed is determined by the weighted sum of the maximum value and the average value among all the clogging speeds.

7. The method according to any one of claims 1-5, characterized in that, Determining the congestion morphology at the target nodes corresponding to each of the target image sequences includes: For each target image sequence, the latest-collected siltation image in the target image sequence is input into the siltation morphology recognition model for processing to obtain the confidence of the latest-collected siltation image relative to each siltation morphology. The morphology corresponding to the highest confidence level is determined as the morphology at the target node corresponding to the target image sequence.

8. A computer device, characterized in that, include: The first acquisition unit is used to acquire the first image sequence captured by each first camera in the foundation pit during a first time period; the first cameras are respectively set at each preset node of the sludge discharge channel of the foundation pit; The first determining unit is configured to identify target image sequences containing siltation images from all the first image sequences, and determine the siltation morphology at the target nodes corresponding to each of the target image sequences; the siltation morphology includes local accumulation, strip distribution, and uniform distribution. The second determining unit is used to determine the accumulation rate at each target node based on the accumulation morphology and all the accumulation images in the corresponding target image sequence. The third determining unit is used to determine the blockage rate of the siltation channel based on the accumulation rate of all the silt. The first prediction unit is used to determine the silt removal capacity attenuation coefficient of the silt removal channel if the blockage speed is greater than a preset speed threshold, and to predict the potential water level of the silt removal channel at the end of the preset future period based on the silt removal capacity attenuation coefficient and the average groundwater accumulation intensity of the foundation pit in the preset future period. The risk warning unit is used to output dredging warning information if the potential water level is higher than the safety warning water level corresponding to the dredging channel; the dredging warning information includes the location identifiers of each of the preset nodes.

9. A computer device, characterized in that, It includes a memory and a computer program stored in the memory and executable on a processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-7.

10. A risk early warning system for siltation in foundation pits of transportation hub projects, characterized in that, The device includes a computer device and a plurality of first cameras wirelessly connected to the computer device; the computer device is used to perform the method as described in any one of claims 1-7.

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