Water seepage control method and system applied to goaf underground roadway

Abstract

The embodiment of the invention discloses a water seepage control method and system applied to a goaf underground roadway. A specific embodiment of the method comprises the following steps: determining a target roadway network in response to water immersion in a water immersion detection area detected by a water immersion sensor; according to the water flow velocity data, the water level data, the target roadway network and a water feature map extraction model, determining a water feature map sequence for the target roadway network; determining a risk area in the target roadway network according to the water feature map sequence and a risk area positioning model; activating at least one water blocking gate and at least one water collecting well at the upstream of the roadway where the risk area is located in the target roadway network; and responding to activation success, controlling the seepage flow velocity through at least one water blocking gate, and collecting the seepage through at least one water collecting well. According to the implementation mode, the influence of rainwater erosion on the goaf is effectively relieved, so that the problem of differential settlement is relieved, and the structural safety of overground buildings is guaranteed.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology and the field of disaster prevention in underground mined - out areas, and specifically relate to a method and a system for controlling water seepage applied to underground roadways in mined - out areas. Background Art

[0002] A mined - out area refers to the area generated after mining operations. Among them, the mined - out area can cause uneven settlement problems of the above - ground buildings, seriously threatening the structural safety of the buildings. In particular, in rainy and snowy weather, a large amount of rainwater will pour into the underground roadways of the mined - out area, resulting in rainwater erosion and further affecting the structural safety of the above - ground buildings. Currently, the method of grouting and plugging can be used for underground filling of the mined - out area. However, when the scale of the mined - out area is large, the implementation cost of using the grouting and plugging method is huge.

[0003] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept. Therefore, it may include information that does not form the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] This content part of the present disclosure is used to introduce the inventive concept in a brief form, and these inventive concepts will be described in detail in the following detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose a method and a system for controlling water seepage applied to underground roadways in mined - out areas to solve the technical problems mentioned in the above background art section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for controlling water seepage in underground roadways in a goaf area. The method includes: in response to the water immersion sensor detecting water immersion in the water immersion detection area, determining the target roadway network, where the water immersion detection area is the connection area between the shaft and the roadway network, the roadway network represents the underground roadways under the goaf area, and the target roadway network is the local roadway network in the roadway network where the ground depth corresponding to it is greater than the ground depth corresponding to the water immersion sensor that detected the water immersion; according to the water flow velocity data, the water level data, the above-mentioned target roadway network, and the water feature map extraction model, determining a sequence of water feature maps for the above-mentioned target roadway network, where the above-mentioned water flow velocity data is collected in real time by a water flow velocity sensor arranged in the above-mentioned target roadway network, and the above-mentioned water level data is collected in real time by a water level sensor arranged in the above-mentioned target roadway network; according to the above-mentioned sequence of water feature maps and the risk area positioning model, determining the risk areas within the above-mentioned target roadway network, where both the water feature map extraction model and the risk area positioning model are included in the risk identification model; activating at least one water blocking gate and at least one sump upstream of the roadway where the above-mentioned risk area is located in the target roadway network; in response to successful activation, controlling the seepage water flow velocity through the above-mentioned at least one water blocking gate, and collecting the seepage water through the above-mentioned at least one sump.

[0007] In a second aspect, some embodiments of the present disclosure provide a water seepage control system for applying the method in the first aspect. The system includes: at least one water immersion sensor, where the water immersion sensor is arranged in the water immersion detection area, and the water immersion detection area is the connection area between the roadway network and the shaft, and the water immersion sensor is used to detect whether there is water immersion in the water immersion detection area; at least one water flow velocity sensor, where the water flow velocity sensor is arranged in the roadway network, and the water flow velocity sensor is used to detect the water flow velocity; at least one water level sensor, where the water level sensor is arranged in the roadway network, and the water flow velocity sensor is used to detect the water level; at least one water blocking gate, where the water blocking gate is arranged in the roadway network, and the water blocking gate is used to control the seepage water flow velocity, and the water blocking gate includes: at least a pair of flow blocking plates, and the flow blocking plates include: a first flow blocking plate and a second flow blocking plate, where the first flow blocking plate controls the angle of the flow blocking plate through a hydraulic rod, the second flow blocking plate controls the angle of the flow blocking plate through a hydraulic rod, and the first flow blocking plate and the second flow blocking plate are arranged in a V shape; at least one sump, where the sump is arranged in the roadway network, and the sump is used to collect the seepage water.

[0008] In a third aspect, some embodiments of the present disclosure provide a water seepage control device applied to underground roadways in a goaf. The device includes: a first determination unit configured to determine a target roadway network in response to the water immersion sensor detecting water immersion in the water immersion detection area, where the water immersion detection area is the connection area between the shaft and the roadway network, the roadway network represents the underground roadways under the goaf, and the target roadway network is a local roadway network in the roadway network corresponding to a ground depth greater than the ground depth corresponding to the water immersion sensor detecting water immersion; a second determination unit configured to determine a sequence of water feature maps for the target roadway network according to the water flow rate data, the water level data, the target roadway network, and the water feature map extraction model, where the water flow rate data is collected in real time by a water flow rate sensor disposed in the target roadway network, and the water level data is collected in real time by a water level sensor disposed in the target roadway network; a third determination unit configured to determine a risk area within the target roadway network according to the sequence of water feature maps and the risk area positioning model, where both the water feature map extraction model and the risk area positioning model are included in the risk identification model; an activation unit configured to activate at least one water blocking gate and at least one sump upstream of the roadway where the risk area is located in the target roadway network; and a control unit configured to, in response to successful activation, control the seepage water flow rate through the at least one water blocking gate and collect the seepage water through the at least one sump.

[0009] In a fourth aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method described in any implementation manner of the first aspect.

[0010] In a fifth aspect, some embodiments of the present disclosure provide a computer-readable medium storing a computer program thereon, where the program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0011] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the application of the water seepage control method for underground roadways in goaf areas in some embodiments of the present disclosure, the impact of rain erosion on the goaf area is effectively alleviated, thereby alleviating the problem of uneven settlement, and further ensuring the structural safety of the above-ground buildings. Specifically, first, in response to the water immersion sensor detecting water immersion in the water immersion detection area, a target roadway network is determined. The water immersion detection area is the connection area between the shaft and the roadway network, and the roadway network represents the underground roadways under the goaf area. The target roadway network is a local roadway network in the roadway network corresponding to a ground depth greater than the ground depth corresponding to the water immersion sensor detecting water immersion. In practice, as the main area for the roadway to communicate with the outside world, water immersion sensors are set to detect whether rainwater enters the roadway through the shaft. At the same time, considering that for large-scale goaf areas, the underground often has a complex roadway network. Affected by the terrain and the structure of the roadway network, there are often local water seepage situations. Therefore, the present disclosure extracts the local roadway network that may be affected by water seepage from the roadway network according to the water seepage situation, thereby reducing the subsequent data processing volume and improving the response speed of water seepage control. Second, according to the water flow rate data, the water level data, the above-mentioned target roadway network, and the water feature map extraction model, a water feature map sequence for the above-mentioned target roadway network is determined. The water flow rate data is collected in real time by the water flow rate sensors set in the above-mentioned target roadway network, and the water level data is collected in real time by the water level sensors set in the above-mentioned target roadway network. In this way, the water flow rate sensors and water level sensors automatically collect the changes in the water level and water flow rate of the water seepage in the roadway and generate the corresponding water feature map sequence. Then, according to the above-mentioned water feature map sequence and the risk area positioning model, the risk areas in the above-mentioned target roadway network are determined. The water feature map extraction model and the risk area positioning model are both included in the risk identification model, thereby locating the areas with a higher risk of rain erosion. Immediately afterwards, at least one water blocking gate and at least one sump located upstream of the roadway where the above-mentioned risk area is located in the target roadway network are activated. Finally, in response to the successful activation, the water seepage flow rate is controlled by the above-mentioned at least one water blocking gate, and the water seepage is collected by the above-mentioned at least one sump. The water seepage flow rate is controlled by the water blocking gate and the sump, thereby reducing the erosion of the water seepage on the risk area. In this way, the impact of rain erosion on the goaf area is effectively alleviated, thereby alleviating the problem of uneven settlement, and further ensuring the structural safety of the above-ground buildings. Description of the Drawings

[0012] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more obvious. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flowchart of some embodiments of a method for controlling water seepage in underground roadways in a goaf according to the present disclosure;

[0014] Figure 2 is a schematic diagram of the positional relationship between a roadway network and a shaft;

[0015] Figure 3 is another schematic diagram of the positional relationship between a roadway network and a shaft;

[0016] Figure 4 is a schematic diagram of the network structure of a roadway network;

[0017] Figure 5 is a schematic diagram of a projected grid map, a partial view of a first network diagram, and a partial view of a second network diagram;

[0018] Figure 6 is a schematic diagram of the positional relationship between a starting network node, a sump, a water stop gate, and a risk area;

[0019] Figure 7 is a schematic diagram of the structure of a flow blocking plate;

[0020] Figure 8 is a schematic diagram of the structure of some embodiments of a water seepage control device for underground roadways in a goaf according to the present disclosure;

[0021] Figure 9 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Embodiments

[0022] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0023] In addition, it should be noted that for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0024] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or mutual dependence relationship of the functions performed by these devices, modules or units.

[0025] It should be noted that the modifiers "one" and "many" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0026] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0027] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0028] Reference Figure 1 , which shows the flow 100 of some embodiments of the water seepage control method applied to the underground roadway in the goaf according to the present disclosure. The water seepage control method applied to the underground roadway in the goaf includes the following steps:

[0029] Step 101, in response to the water immersion sensor detecting water immersion in the water immersion detection area, determine the target roadway network.

[0030] In some embodiments, the execution subject (for example, a computing device) of the water seepage control method applied to the underground roadway in the goaf can, in response to the water immersion sensor detecting water immersion in the water immersion detection area, determine the target roadway network. Among them, the water immersion detection area is the connection area between the shaft and the roadway network. The roadway network represents the underground roadway under the goaf. The target roadway network is the local roadway network in the roadway network where the ground depth corresponding to it is greater than the ground depth corresponding to the water immersion sensor that detects water immersion. Among them, the ground depth can represent the distance value of the roadway from the ground. In practice, the shaft can be used for the connection between the underground roadway and the outside world. The roadway network can be the passage dug out from the ore body underground in the goaf for mineral transportation, ventilation, drainage, etc. Specifically, the above execution subject can use the local roadway network that is connected to the water immersion detection area where water immersion is detected and where the ground depth corresponding to it is greater than the ground depth corresponding to the water immersion sensor that detects water immersion as the target roadway network.

[0031] As an example, see Figure 2Schematic diagram of the positional relationship between the roadway network and the shaft shown, where the roadway network can be a passage dug along the distribution of underground ore bodies. Specifically, the roadway network can include multiple roadways. The roadways can be interconnected with each other. Multiple shafts can be vertically arranged above the roadways to connect the roadways with the outside world. In particular, the roadways in the roadway network often follow the distribution of the ore body, showing characteristics such as inclination and bending. For example, the "red" shaft can represent a shaft where rainwater converges, so the local roadway network ("blue" roadway network) that is connected to the waterlogging detection area where waterlogging is detected and whose corresponding ground depth is greater than the corresponding ground depth of the waterlogging sensor detecting waterlogging can be used as the target roadway network starting from the connection area (waterlogging detection area) between the "red" shaft and the roadway network.

[0032] As another example, refer to Figure 3 Schematic diagram of another positional relationship between the roadway network and the shaft shown, where when it is rainy or snowy, rainwater will flow into the roadway from the shaft along the ground. Therefore, a waterlogging detection area is set at the connection area between the shaft and the roadway (network). A waterlogging sensor is arranged in the waterlogging detection area. In particular, to avoid false triggering of the waterlogging sensor, the waterlogging sensor has a certain ground height when arranged in the waterlogging detection area.

[0033] It should be noted that the above computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the above-listed hardware devices. It can be implemented as, for example, multiple software or software modules for providing distributed services, or it can be implemented as a single software or software module. No specific limitation is made here.

[0034] Optionally, the roadway network includes: a set of network nodes and a set of network edges. The network nodes correspond to node information. The node information includes: node type and node location. The node type includes: roadway type and shaft type. The network edge represents the connection between two connected network nodes. The elevation difference between two network nodes connected by the network edge is less than or equal to the first preset elevation difference. For example, the first preset elevation difference can be 10 centimeters (cm). In practice, when using three-dimensional data to represent the roadway network, there is a problem of a large amount of data. In order to reduce the amount of data processing. Therefore, the roadway network is represented in the form of a graph. Specifically, the roadway network is discretized into a set of network nodes, and the connection relationship between the network nodes is represented by network edges. In particular, the setting of network nodes will affect the fineness of subsequent seepage control. Considering that elevation affects the flow of seepage, therefore, the first preset elevation is used as the division granularity to divide the roadway network into a graph structure composed of network nodes and network edges, so as to simplify the positional relationship between roadways and shafts in the roadway network and reduce the amount of corresponding three-dimensional data.

[0035] As an example, refer to Figure 4 the schematic diagram of the network structure of the roadway network shown in Figure 4 The shown roadway network is composed of a set of network nodes and network edges. When the network node is located in the connected area of the roadway and the shaft, the node type of the network node is the shaft type. When the network node is located in the roadway and not in the connected area of the roadway and the shaft, the node type of the network node is the roadway type. When there is a roadway connection between two network nodes, there is a network edge between the network nodes.

[0036] In some optional implementation manners of some embodiments, the above-mentioned execution entity determines the target roadway network in response to the water immersion sensor detecting water immersion in the water immersion detection area, including:

[0037] First step, determine the sensor location corresponding to the water immersion sensor that detects water immersion.

[0038] In practice, for the water immersion sensors, water flow rate sensors, and water level sensors set in the roadway network, a sensor information list can be pre-constructed. Among them, the sensor information list can store the sensor identification, sensor type, sensor location, sensor communication address, etc. corresponding to the sensors. Therefore, when the water immersion sensor triggers water immersion, the sensor location corresponding to the water immersion sensor that triggers water immersion can be obtained by querying the sensor information list according to the sensor identification of the water immersion sensor that triggers water immersion.

[0039] Second step, determine the network node whose corresponding node location satisfies the location condition with the above-mentioned sensor location as the starting network node.

[0040] Among them, the position condition is that the distance value between the sensor position and the node position is the smallest. In practice, since the roadway network includes a large number of graph nodes, the calculation amount is large when calculating the distance value one by one. Therefore, when determining the starting network node, first, the above-mentioned execution entity can construct an initial search box with the sensor position as the center. Among them, the initial search box is a circular search box. Then, when the network nodes are included in the search box, calculate the distance between the network nodes in the initial search box and the sensor position, so as to screen out the network nodes whose corresponding node positions and the above-mentioned sensor positions meet the position condition. Further, when there are no network nodes in the initial search box, increase the initial search box with a preset increment until there are network nodes on the boundary of the initial search box. At this time, the network nodes on the boundary of the initial search box are used as the starting network nodes. In particular, when there are multiple network nodes on the boundary of the initial search box, the above-mentioned execution entity can randomly select a network node as the starting network node.

[0041] In the third step, starting from the above-mentioned starting network node, perform network traversal on the above-mentioned roadway network to obtain a candidate roadway network.

[0042] Among them, the network nodes included in the candidate roadway network are indirectly connected to the above-mentioned starting network node, and the elevation difference between the above-mentioned starting network node and the network nodes corresponding to the candidate roadway network is greater than the second preset elevation difference. In practice, the above-mentioned execution entity can adopt methods such as depth traversal or breadth traversal to perform network traversal on the above-mentioned roadway network starting from the above-mentioned starting network node to obtain a candidate roadway network. Among them, the second preset elevation difference can be 0. Since the roadway is located below the ground, the network nodes included in the selected candidate roadway network have corresponding elevations less than the elevation of the starting network node and are all negative. Therefore, the difference between the elevation of the network nodes included in the selected candidate roadway network and the elevation of the starting network node is a positive number (greater than 0).

[0043] In the fourth step, add the network nodes in the above-mentioned roadway network that are between the above-mentioned starting network node and the network nodes included in the above-mentioned candidate roadway network to the above-mentioned candidate roadway network to obtain the above-mentioned target roadway network.

[0044] In practice, the network nodes in the candidate roadway network screened only by combining the elevation differences may not be directly connected to the starting network node. Therefore, it is necessary to add the network nodes between the above-mentioned starting network node and the network nodes included in the above-mentioned candidate roadway network to the above-mentioned candidate roadway network to obtain the above-mentioned target roadway network. For example, there may be a characteristic of "low-high-low" between network nodes. When water accumulates, it will overflow the "high" network node and flow to the "low" network node. Therefore, it is necessary to add the network nodes between the above-mentioned starting network node and the network nodes included in the above-mentioned candidate roadway network to the above-mentioned candidate roadway network.

[0045] Step 102: Determine the water feature map sequence for the target roadway network according to the water flow velocity data, water level data, target roadway network, and water feature map extraction model.

[0046] In some embodiments, the above-mentioned execution subject can determine the water feature map sequence for the target roadway network according to the water flow velocity data, water level data, target roadway network, and water feature map extraction model. Among them, the above-mentioned water flow velocity data is obtained by real-time collection of water flow velocity sensors arranged in the above-mentioned target roadway network. The above-mentioned water level data is obtained by real-time collection of water level sensors arranged in the above-mentioned target roadway network. In practice, the water flow velocity data can be obtained by real-time collection of multiple water flow sensors arranged in the target roadway network. The water level data is obtained by real-time collection of multiple water level sensors arranged in the above-mentioned target roadway network. Specifically, since the water flow data and water level data change continuously in time series, the water feature map extraction model can adopt a recurrent neural network model (RNN, Recurrent Neural Network) as the backbone network to extract features from the water flow velocity data and water level data to obtain the water feature map sequence.

[0047] Optionally, the water feature maps in the water feature map sequence include: water flow velocity feature maps and water level feature maps, and the above-mentioned water feature map extraction model includes: a variable grid division module, a water flow velocity feature map extraction module, and a water level feature map extraction module.

[0048] In some optional implementation manners of some embodiments, the above-mentioned execution subject determines the water feature map sequence for the above-mentioned target roadway network according to the water flow velocity data, water level data, the above-mentioned target roadway network, and the water feature map extraction model, including:

[0049] First step: According to the above-mentioned water flow velocity data and the above-mentioned water level data, the variable grid division module performs variable grid division on the target roadway network to obtain a first network diagram and a second network diagram.

[0050] Among them, the above-mentioned first network diagram represents the target roadway network after variable grid division for water flow velocity data. The above-mentioned second network diagram represents the target roadway network after variable grid division for water level data.

[0051] As an example, first, the above-mentioned variable grid division module projects the target roadway network diagram onto the grid diagram to obtain the projected grid diagram. Among them, the projected grid diagram includes: K cells. Then, the above-mentioned variable grid division module merges adjacent cells according to the change in water flow velocity in the water flow velocity data. Specifically, when the change in water flow velocity corresponding to adjacent cells is small, the adjacent cells are merged. When the change in water flow velocity corresponding to adjacent cells is large, the adjacent cells are split. In particular, the cells not corresponding to water flow velocity values are initialized to 0, thereby obtaining the first network diagram. Then, the above-mentioned variable grid division module merges adjacent cells according to the change in water level in the water level data. Specifically, when the change in water level corresponding to adjacent cells is small, the adjacent cells are merged. When the change in water level corresponding to adjacent cells is large, the adjacent cells are split. In particular, the cells not corresponding to water level values are initialized to 0. Specifically, the above-mentioned variable grid division module can judge the change trend of water flow velocity or water level change by calculating the second derivative of the water flow velocity value or water level value in adjacent cells.

[0052] As an example, refer to Figure 5 the schematic diagrams of the projected grid diagram, a partial view of the first network diagram, and a partial view of the second network diagram shown in. Among them, the projected grid diagram includes K cells. Network nodes and network edges will fall into the cells, so the water flow velocity values or water level values corresponding to the network nodes and network edges can be projected into the cells they fall into, thereby obtaining the projected grid diagram 501. Further, taking the upper right part of the projected grid diagram 501 as an example, the water flow velocity only involves 2 network nodes and 1 network edge, and the water flow velocity shows a small change in the front section and a large change in the rear section in the network edge (the corresponding roadway). Therefore, the upper right part shows two large cells (initialized to 0) in the upper and lower parts in the partial view 502 of the first network diagram, and the cells in the front half of the middle part are larger, and the cells in the rear half of the middle part are smaller. Then, taking the upper right part of the projected grid diagram 501 as an example, the water level only involves 2 network nodes and 1 network edge, and the water level shows a small change in the front and rear sections and a large change in the middle and rear sections in the network edge (the corresponding roadway). Therefore, the upper right part shows two large cells (initialized to 0) in the upper and lower parts in the partial view 503 of the second network diagram, and the cells in the front and rear sections of the middle part are larger, and the cells in the middle and rear sections of the middle part are smaller.

[0053] In the second step, embed the above water flow velocity data into the above first network diagram in sequence, and embed the above water level data into the above second network diagram in sequence to obtain a third network diagram sequence and a fourth network diagram sequence.

[0054] In practice, due to the fixed positional relationship between the water flow velocity sensor and the water level sensor compared to the network nodes and network edges, the above water flow velocity data can be embedded into the above first network diagram in sequence, and the above water level data can be embedded into the above second network diagram in sequence by projection to obtain a third network diagram sequence and a fourth network diagram sequence. In particular, since the water flow sensor and the water level sensor continuously collect the water flow velocity and the water level, for the same cell, there will be multiple water flow velocity values and water level values corresponding to different times. Therefore, after projecting the water flow velocity values collected at different consecutive times onto the first network diagram, multiple third network diagrams (third network diagram sequence) for different consecutive times can be obtained. For example, project the water flow velocity data at time T1 onto the first network diagram to obtain the third network diagram A1, and project the water flow velocity data at time T2 onto the first network diagram to obtain the third network diagram A2. Similarly, after projecting the water level values collected at different consecutive times onto the second network diagram, multiple fourth network diagrams (fourth network diagram sequence) can also be obtained.

[0055] In the third step, according to the above water flow velocity feature map extraction module, perform water flow velocity feature extraction on each third network diagram in the above third network diagram sequence to obtain the water flow velocity feature maps included in the water feature maps in the above water feature map sequence.

[0056] In the fourth step, according to the above water level feature map extraction module, perform water level feature extraction on each fourth network diagram in the above fourth network diagram sequence to obtain the water level feature maps included in the water feature maps in the above water feature map sequence.

[0057] In practice, due to the obvious temporal characteristics of the water flow velocity value and the water level value, the water flow velocity feature map extraction module and the water level feature map extraction module both adopt the same network structure, that is, the RNN network as the backbone network. In particular, considering that the third network diagram and the fourth network diagram are divided by variable grids and cannot be directly input into the RNN network for feature extraction. If the smallest grid is used as the basic unit to re-divide the third network diagram and the fourth network diagram to obtain the third network diagram and the fourth network diagram in matrix form, it will instead increase the data processing volume. Therefore, the water flow velocity feature map extraction module and the water level feature map extraction module of the present disclosure adopt a strategy of block feature extraction, that is, the unit blocks in the third network diagram sequence that correspond to the same position and have the same adjacent cell size are input into the water flow velocity feature map extraction module for feature extraction, and the unit blocks in the fourth network diagram sequence that correspond to the same position and have the same adjacent cell size are input into the water level feature map extraction module for feature extraction. After extraction, they are projected to the corresponding positions according to the relative positions to obtain multiple water flow velocity feature maps and multiple water level feature maps (water feature map sequence). In particular, in order to further improve the processing speed, the unit blocks containing only 0 are skipped, that is, the water flow velocity feature extraction and the water level feature extraction are not performed on the unit blocks containing only 0.

[0058] In some optional implementation manners of some embodiments, before the above-mentioned execution subject determines the water feature map sequence for the above-mentioned target roadway network according to the water flow velocity data, the water level data, the above-mentioned target roadway network, and the water feature map extraction model, the method further includes:

[0059] First step, determine a sensor information list according to the above-mentioned target roadway network.

[0060] Among them, the sensor information columns in the above-mentioned sensor information list correspond to the water flow sensors or water level sensors arranged in the above-mentioned target roadway network, and the sensor information in the above-mentioned sensor information list includes: sensor type, sensor communication address. In practice, the roadway network can maintain a sensor information list containing the corresponding sensor information of the sensors arranged in the roadway network. Since the target roadway network is a sub-network of the roadway network, that is, the acquisition of the water flow velocity data and the water level data only involves some sensors, the sub-sensor information table corresponding to the target roadway network can be used as the sensor information list.

[0061] Second step, for each sensor information in the above-mentioned sensor information list, perform the following processing steps:

[0062] The first sub-step, activate the water flow sensor or water level sensor corresponding to the above-mentioned sensor information according to the sensor communication address included in the above-mentioned sensor information.

[0063] In practice, a wireless communication or wired communication method can be adopted to send an activation instruction according to the sensor communication address included in the above sensor information, so as to activate the water flow rate sensor or water level sensor corresponding to the above sensor information.

[0064] The second sub-step, in response to successful activation and the sensor type included in the above sensor information being the water flow rate sensor type, collect water flow rate data through the water flow rate sensor corresponding to the above sensor information.

[0065] In practice, after successful activation, the water flow rate data can be collected through the water flow rate sensor according to a preset sampling frequency. In addition, different sampling frequencies can also be set according to the specific scenario to avoid the problem of a large amount of data corresponding to high-frequency collection.

[0066] The third sub-step, in response to successful activation and the sensor type included in the above sensor information being the water level sensor type, collect water level data through the water level sensor corresponding to the above sensor information.

[0067] In practice, after successful activation, the water level data can be collected through the water level sensor according to a preset sampling frequency. In addition, different sampling frequencies can also be set according to the specific scenario to avoid the problem of a large amount of data corresponding to high-frequency collection.

[0068] The fourth sub-step, in response to unsuccessful activation and the sensor type included in the above sensor information being the water flow rate sensor type, perform water flow rate data interpolation through two water flow rate sensors adjacent to the water flow rate sensor corresponding to the above sensor information and located within the above target roadway network, so as to achieve the collection of water flow rate data for the water flow rate sensor corresponding to the above sensor information.

[0069] In practice, there may be problems with sensor failures. At this time, if the water flow rate value corresponding to the sensor is directly initialized to 0, it will affect the accuracy of subsequent risk area positioning. Therefore, when activation is unsuccessful, the water flow rate data is supplemented by interpolation.

[0070] The fifth sub-step, in response to unsuccessful activation and the sensor type included in the above sensor information being the water level sensor type, perform water level data interpolation through two water level sensors adjacent to the water level sensor corresponding to the above sensor information and located within the above target roadway network, so as to achieve the collection of water level data for the water level sensor corresponding to the above sensor information.

[0071] In practice, there may be problems with sensor failures. At this time, if the water level value corresponding to the sensor is directly initialized to 0, it will affect the accuracy of subsequent risk area positioning. Therefore, when activation is unsuccessful, the water level data is supplemented by interpolation.

[0072] Step 103: Determine the risk areas within the target roadway network according to the water feature map sequence and the risk area location model.

[0073] In some embodiments, the above-mentioned execution entity may determine the risk areas within the target roadway network according to the water feature map sequence and the risk area location model. Among them, both the water feature map extraction model and the risk area location model are included in the risk identification model (i.e., the risk identification model is composed of the water feature map extraction model and the risk area location model). The risk area represents the area that is easily eroded by seepage water. In practice, since the water feature maps in the water feature map sequence, including the water flow velocity feature map and the water level feature map, still have a time series characteristic, the risk area location model can adopt the Fast R-CNN (Fast Region with CNN features) network. In particular, when the water feature map is input into the risk area location model, the water flow velocity feature map and the water level feature map included in the water feature map need to be stitched together as the input of the risk area location model.

[0074] Optionally, the risk area location model includes: a first region of interest (ROI) location module, a second ROI location model, and an intersection location module. In practice, both the first ROI location module and the second ROI location model can adopt the Fast R-CNN network as the backbone network. In the model training stage, the first ROI location module and the second ROI location model adopt transfer learning for model training to speed up the model training speed. The first ROI location module is used to determine the ROI with the water flow velocity feature map included in the water feature maps in the water feature map sequence as the input. The second ROI location module is used to determine the ROI with the water level feature map included in the water feature maps in the water feature map sequence as the input. The intersection location module is used to score the ROI determined by the first ROI location module and the ROI determined by the second ROI location module, and take the ROI with the highest score as the risk area. The intersection location module can be implemented using a fully connected layer.

[0075] In some optional implementation manners of some embodiments, determining the risk areas within the above-mentioned target roadway network according to the above-mentioned water feature map sequence and the risk area location model includes:

[0076] The first step: Determine the first ROI through the above-mentioned first ROI location module and the water flow velocity feature map included in the water feature maps in the water feature map sequence.

[0077] In practice, to improve the positioning robustness, the first ROI can be a set of the top K ROIs with confidence.

[0078] In the second step, the second region of interest is determined by the above-mentioned second region-of-interest positioning module and the water level feature map included in the water feature maps in the above-mentioned water feature map sequence.

[0079] In practice, to improve the positioning robustness, the second region of interest can be a set of the top K confidence regions of interest.

[0080] In the third step, the above-mentioned risk region is determined by the above-mentioned cross-positioning module, the local water flow velocity feature map corresponding to the above-mentioned first region of interest, and the local water level feature map corresponding to the above-mentioned second region of interest.

[0081] In practice, the cross-positioning module scores the first region of interest and the second region of interest respectively, and thus the region of interest with the highest corresponding score is used as the risk region. In particular, when the first region of interest contains the top K confidence regions of interest, and the second region of interest contains the top K confidence regions of interest, the cross-positioning module can score each of the 2×K regions of interest one by one to screen out the risk region.

[0082] Step 104: Activate at least one water-blocking gate and at least one sump located upstream of the roadway where the risk region is located in the target roadway network.

[0083] In some embodiments, the above-mentioned execution entity can activate at least one water-blocking gate and at least one sump located upstream of the roadway where the risk region is located in the target roadway network. In practice, the sump can include an electrically controlled manhole cover, which can be closed when water collection is not required to prevent sundries from entering the sump and thus affecting the water collection capacity of the sump. The water-blocking gate is arranged in the roadway to reduce the seepage water flow velocity. In practice, the above-mentioned execution entity can send an activation instruction to at least one water-blocking gate and at least one sump located upstream of the roadway where the risk region is located in the target roadway network to activate at least one water-blocking gate and at least one sump. In particular, at least one water-blocking gate and at least one sump are located between the risk region and the starting network node (the network node closest to the water immersion sensor that detects water immersion).

[0084] As an example, refer to Figure 6 the schematic diagram of the positional relationship between the starting network node, the sump, the water-blocking gate, and the risk region shown in the figure. Among them, the water-blocking gate and the sump are arranged upstream of the roadway where the risk region is located, and the water-blocking gate and the sump are arranged between the risk region and the starting network node. The sump is used for water collection to reduce the water flow rate flowing towards the risk region; the water-blocking gate is used to reduce the water flow velocity, thereby reducing the scouring of the risk region.

[0085] Step 105: In response to successful activation, control the seepage water flow velocity through at least one water-blocking gate and collect seepage water through at least one sump.

[0086] In some embodiments, the above-mentioned execution entity may, in response to successful activation, control the seepage flow rate through at least one water-blocking gate and collect seepage water through at least one catch basin.

[0087] Optionally, the water-blocking gate in the at least one water-blocking gate includes: at least one flow-blocking component, and the flow-blocking component includes: a first flow-blocking plate and a second flow-blocking plate. Among them, the first flow-blocking plate controls the angle of the flow-blocking plate through a hydraulic rod, and the second flow-blocking plate controls the angle of the flow-blocking plate through a hydraulic rod. The first flow-blocking plate and the second flow-blocking plate are arranged in a V shape. In practice, the installation position of the water-blocking gate needs to be solidified on the ground accordingly to ensure the water-blocking ability of the water-blocking gate.

[0088] As an example, refer to Figure 7 the structural schematic diagram of the flow-blocking plate shown in the figure. Among them, the first flow-blocking plate and the second flow-blocking plate are symmetrically arranged in a V shape. A plurality of water-blocking cavities are evenly arranged on the water-blocking sides of the first flow-blocking plate and the second flow-blocking plate. When the seepage water first contacts the flow-blocking component, the seepage water will provide opposite impact forces along the arc-shaped surface of the water-blocking cavity to weaken the impact force of the seepage water on the flow-blocking component when it first contacts the flow-blocking component.

[0089] In some optional implementation manners of some embodiments, the above-mentioned execution entity controls the seepage flow rate through the above-mentioned at least one water-blocking gate and collects seepage water through the above-mentioned at least one catch basin, including:

[0090] The first step is to map to obtain a second target water flow rate according to the first target water flow rate.

[0091] Among them, the above-mentioned first target water flow rate represents the maximum threshold water flow rate at which the seepage water flows to the risk area, and the second target water flow rate represents the water flow rate controlled by the water-blocking gate. From the velocity formula V = V0 + at, it can be obtained that on the premise that the acceleration (a) and duration (t) are fixed, there is a mapping relationship between the water flow rate at which the seepage water flows to the risk area and the water flow rate of the seepage water passing through the flow-blocking component. Therefore, the water flow rate (second target water flow rate) at the flow-blocking component can be mapped in combination with the maximum threshold water flow rate (first target water flow rate) of the risk area.

[0092] The second step is to control the angle of the flow-blocking plate corresponding to the flow-blocking component included in the at least one flow-blocking component included in the water-blocking gate according to the above-mentioned second target water flow rate.

[0093] In practice, there is a correlation between the angle of the flow-blocking plate of the flow-blocking component and the water flow rate. Therefore, the flow-blocking component can pre-calibrate the relationship between the angle of the flow-blocking plate and the water flow rate. On the premise of knowing the second target water flow rate, the angle of the flow-blocking plate can be obtained, so as to drive the first flow-blocking plate and the second flow-blocking plate included in the flow-blocking component to the angle of the flow-blocking plate through a hydraulic rod.

[0094] The above - mentioned various embodiments of the present disclosure have the following beneficial effects: Through the application of the water seepage control method for underground roadways in goafs in some embodiments of the present disclosure, the impact of rain erosion on the goaf is effectively alleviated, thereby alleviating the problem of uneven settlement, and further ensuring the structural safety of above - ground buildings. Specifically, first, in response to the water immersion sensor detecting water immersion in the water immersion detection area, a target roadway network is determined. Here, the water immersion detection area is the connection area between the vertical shaft and the roadway network, and the roadway network represents the underground roadways under the goaf. The target roadway network is a local roadway network in the roadway network where the corresponding ground depth is greater than the ground depth corresponding to the water immersion sensor that detects water immersion. In practice, as the main area for the roadway to communicate with the outside world, water immersion sensors are set to detect whether rainwater pours into the roadway through the vertical shaft. At the same time, considering that for large - scale goafs, the underground often has a complex roadway network, and due to the influence of terrain and roadway network structure, there are often local water seepage situations. Therefore, the present disclosure separates the local roadway network that may be affected by water seepage from the roadway network according to the water seepage situation, thereby reducing the subsequent data processing volume and improving the response speed of water seepage control. Second, according to the water flow velocity data, water level data, the above - mentioned target roadway network, and the water feature map extraction model, a sequence of water feature maps for the above - mentioned target roadway network is determined. Here, the above - mentioned water flow velocity data is collected in real - time by water flow velocity sensors set in the above - mentioned target roadway network, and the above - mentioned water level data is collected in real - time by water level sensors set in the above - mentioned target roadway network. In this way, the water level and water flow velocity changes of water seepage in the roadway are automatically collected by water flow velocity sensors and water level sensors, and a corresponding sequence of water feature maps is generated. Then, according to the above - mentioned sequence of water feature maps and the risk area positioning model, the risk areas within the above - mentioned target roadway network are determined. Here, both the water feature map extraction model and the risk area positioning model are included in the risk identification model, thereby locating the areas with a higher risk of rain erosion. Immediately afterwards, at least one water - blocking gate and at least one sump well upstream of the roadway where the above - mentioned risk area is located in the target roadway network are activated. Finally, in response to successful activation, the seepage flow velocity is controlled by the above - mentioned at least one water - blocking gate, and the water seepage is collected by the above - mentioned at least one sump well. By controlling the seepage flow velocity through water - blocking gates and sump wells, the scouring of the risk area by water seepage is reduced. In this way, the impact of rain erosion on the goaf is effectively alleviated, thereby alleviating the problem of uneven settlement, and further ensuring the structural safety of above - ground buildings.

[0095] Further, the present disclosure provides a water seepage control system, which is applied to the water seepage control method for underground roadways in goafs mentioned above. The water seepage control system includes: at least one water immersion sensor, at least one water flow velocity sensor, at least one water level sensor, at least one water - blocking gate, and at least one sump well, where:

[0096] At least one water immersion sensor, wherein the water immersion sensor is arranged in the water immersion detection area, the water immersion detection area is the connection area of the roadway network and the shaft, and the water immersion sensor is used to detect whether there is water immersion in the water immersion detection area; at least one water flow velocity sensor, wherein the water flow velocity sensor is arranged in the roadway network, and the water flow velocity sensor is used to detect the water flow velocity. At least one water level sensor, wherein the water level sensor is arranged in the roadway network, and the water flow velocity sensor is used to detect the water level. At least one water blocking gate, wherein the water blocking gate is arranged in the roadway network, and the water blocking gate is used to control the seepage water flow velocity. The water blocking gate includes: at least a pair of baffle plates, and the baffle plates include: a first baffle plate and a second baffle plate. Wherein, the first baffle plate controls the angle of the baffle plate through a hydraulic rod, the second baffle plate controls the angle of the baffle plate through a hydraulic rod, and the first baffle plate and the second baffle plate are arranged in a V shape. At least one sump, wherein the sump is arranged in the roadway network, and the sump is used for collecting seepage water.

[0097] Further reference Figure 8 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a seepage control device applied to underground roadways in goaf areas. These device embodiments correspond to Figure 1 the method embodiments shown, and the seepage control device applied to underground roadways in goaf areas can be specifically applied to various electronic devices.

[0098] As Figure 8As shown, the water seepage control device 800 applied to the underground roadway in the goaf includes: a first determination unit 801, a second determination unit 802, a third determination unit 803, an activation unit 804, and a control unit 805. Among them, the first determination unit 801 is configured to determine a target roadway network in response to the water immersion sensor detecting water immersion in the water immersion detection area. The water immersion detection area is the connection area between the vertical shaft and the roadway network, and the roadway network represents the underground roadway under the goaf. The target roadway network is the local roadway network in the roadway network where the ground depth corresponding to it is greater than the ground depth corresponding to the water immersion sensor detecting water immersion; the second determination unit 802 is configured to determine a sequence of water feature maps for the target roadway network according to the water flow rate data, water level data, the above-mentioned target roadway network, and the water feature map extraction model. The water flow rate data is obtained by real-time collection of the water flow rate sensor arranged in the target roadway network, and the water level data is obtained by real-time collection of the water level sensor arranged in the target roadway network; the third determination unit 803 is configured to determine the risk area within the target roadway network according to the sequence of water feature maps and the risk area positioning model. The water feature map extraction model and the risk area positioning model are both included in the risk identification model; the activation unit 804 is configured to activate at least one water blocking gate and at least one water collecting well upstream of the roadway where the risk area is located in the target roadway network; the control unit 805 is configured to control the water seepage flow rate through the at least one water blocking gate and collect the water seepage through the at least one water collecting well in response to successful activation.

[0099] It can be understood that the units described in the water seepage control device 800 applied to the underground roadway in the goaf correspond to the respective steps in the method described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the water seepage control device 800 applied to the underground roadway in the goaf and the units included therein, and will not be repeated here.

[0100] Next, refer to Figure 9 , which shows a schematic structural diagram of an electronic device (for example, a computing device) suitable for implementing some embodiments of the present disclosure. Figure 9 The electronic device shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present disclosure. As Figure 9As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium can store an operating system and computer programs. The computer programs include program instructions, which when executed, can cause the processor to execute any of the above methods. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the operation of the computer programs in the non-volatile storage medium, and when the computer programs are executed by the processor, the processor can be caused to execute any of the above methods. The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some structures related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0101] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be 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. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0102] Wherein, in one embodiment, the above-mentioned processor is used to run a computer program stored in a memory to implement the following steps: in response to the water immersion sensor detecting water immersion in the water immersion detection area, determine the target roadway network, wherein the water immersion detection area is the connection area between the shaft and the roadway network, the roadway network represents the underground roadway under the goaf, and the target roadway network is the local roadway network in the roadway network corresponding to a ground depth greater than the ground depth corresponding to the water immersion sensor detecting water immersion; according to the water flow rate data, the water level data, the above-mentioned target roadway network, and the water feature map extraction model, determine the water feature map sequence for the above-mentioned target roadway network, wherein the above-mentioned water flow rate data is collected in real time by a water flow rate sensor arranged in the above-mentioned target roadway network, and the above-mentioned water level data is collected in real time by a water level sensor arranged in the above-mentioned target roadway network; according to the above-mentioned water feature map sequence and the risk area positioning model, determine the risk area within the above-mentioned target roadway network, wherein both the water feature map extraction model and the risk area positioning model are included in the risk identification model; activate at least one water blocking gate and at least one sump upstream of the roadway where the above-mentioned risk area is located in the target roadway network; in response to successful activation, control the seepage flow rate through the above-mentioned at least one water blocking gate, and collect the seepage water through the above-mentioned at least one sump.

[0103] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, and the computer program includes program instructions, and the method implemented when the program instructions are executed may refer to the various embodiments of the above method of the present disclosure.

[0104] Wherein, the above-mentioned computer-readable storage medium may be the internal storage unit of the above-mentioned computer device in the foregoing embodiment, such as the hard disk or memory of the above-mentioned computer device. The above-mentioned computer-readable storage medium may also be an external storage device of the above-mentioned computer device, such as a plug-in hard disk equipped on the above-mentioned computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0105] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0106] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present disclosure that have similar functions.

Claims

1. A water seepage control method applied to underground tunnels in goaf areas, characterized in that: include: In response to the water flooding sensor detecting the presence of water flooding in the water flooding detection area, determining a target tunnel network, wherein the water flooding detection area is a connection area between the shaft and the tunnel network, the tunnel network represents an underground tunnel under the goaf, and the target tunnel network is a local tunnel network in the tunnel network, the corresponding ground depth of which is greater than the corresponding ground depth of the water flooding sensor detecting the water flooding; Determine a water characteristic map sequence for the target lane network according to water flow rate data, water level data, the target lane network and a water characteristic map extraction model, wherein the water flow rate data is acquired in real time by a water flow rate sensor disposed in the target lane network, and the water level data is acquired in real time by a water level sensor disposed in the target lane network; Determine the risk area in the target lane network according to the water feature map sequence and the risk area positioning model, wherein the water feature map extraction model and the risk area positioning model are both included in the risk identification model; Activate at least one water blocking gate and at least one water collecting well in the target tunnel network, upstream of the tunnel where the risk area is located; In response to successful activation, the seepage water flow rate is controlled by the at least one water blocking gate, and the seepage water is collected by the at least one water collection well.

2. The method according to claim 1, characterized in that The lane network includes: a network node set and a network edge set, the network nodes correspond to node information, the node information includes: node type and node position, the node type includes: lane type and shaft type, the network edge represents the connection between two connected network nodes, and the elevation difference corresponding to the two network nodes connected by the network edge is less than or equal to a first preset elevation difference; and In response to the water immersion sensor detecting that there is water immersion in the water immersion detection area, determining the target lane network includes: Determine a sensor position corresponding to a water immersion sensor that detects water immersion; Determine a network node whose corresponding node position and the sensor position satisfy a position condition as a starting network node, wherein the position condition is: the distance value between the sensor position and the node position is the smallest; Taking the starting network node as the starting point, performing a network traversal on the lane network to obtain a candidate lane network, wherein the network nodes included in the candidate lane network are indirectly connected to the starting network node, and the elevation difference between the starting network node and the network nodes included in the candidate lane network is greater than a second preset elevation difference; The network nodes in the lane network that are located between the starting network node and the network nodes included in the candidate lane network are added to the candidate lane network to obtain the target lane network.

3. The method according to claim 2, characterized in that Before determining a water feature map sequence for the target lane network according to the water flow rate data, the water level data, the target lane network and the water feature map extraction model, the method further includes: Determine a sensor information list according to the target lane network, wherein the sensor information in the sensor information list corresponds to a water flow sensor or a water level sensor arranged in the target lane network, and the sensor information in the sensor information list includes: a sensor type and a sensor communication address; For each sensor information in the sensor information list, the following processing steps are performed: According to the sensor communication address included in the sensor information, activating the water flow rate sensor or the water level sensor corresponding to the sensor information; In response to successful activation, and the sensor type included in the sensor information is a water flow rate sensor type, water flow rate data is collected through the water flow rate sensor corresponding to the sensor information; In response to successful activation, and the sensor type included in the sensor information is a water level sensor type, water level data is collected through the water level sensor corresponding to the sensor information; In response to failure to activate successfully, and the sensor type included in the sensor information is a water flow rate sensor type, water flow rate data interpolation is performed through two water flow rate sensors located in the target lane network and adjacent to the water flow rate sensor corresponding to the sensor information, so as to realize water flow rate data collection for the water flow rate sensor corresponding to the sensor information; In response to unsuccessful activation and the sensor type included in the sensor information being a water level sensor type, water level data interpolation is performed through two water level sensors located in the target laneway network and adjacent to the water level sensor corresponding to the sensor information, so as to realize water level data collection for the water level sensor corresponding to the sensor information.

4. The method according to claim 3, characterized in that The water characteristic graphs in the water characteristic graph sequence include: a water flow velocity characteristic graph and a water level characteristic graph, and the water characteristic graph extraction model includes: a variable grid division module, a water flow velocity characteristic graph extraction module, and a water level characteristic graph extraction module; and The step of determining a water characteristic map sequence for the target lane network according to the water flow rate data, the water level data, the target lane network and the water characteristic map extraction model comprises: According to the water flow rate data and the water level data, the target lane network is subjected to variable grid division by the variable grid division module to obtain a first network diagram and a second network diagram, wherein the first network diagram represents the target lane network after variable grid division for the water flow rate data, and the second network diagram represents the target lane network after variable grid division for the water level data; The water flow rate data is respectively embedded into the first network diagram in time sequence, and the water level data is respectively embedded into the second network diagram in time sequence, to obtain a third network diagram sequence and a fourth network diagram sequence; According to the water flow rate characteristic graph extraction module, water flow rate characteristics are extracted for each third network graph in the third network graph sequence to obtain a water flow rate characteristic graph included in the water characteristic graph sequence; According to the water level characteristic graph extraction module, water level characteristic extraction is performed on each fourth network graph in the fourth network graph sequence to obtain a water level characteristic graph included in the water characteristic graph in the water characteristic graph sequence.

5. The method according to claim 4, characterized in that The risk area positioning model includes: a first region of interest positioning module, a second region of interest positioning model, and a cross positioning module; and Determining the risk area in the target tunnel network according to the water characteristic map sequence and the risk area positioning model includes: Determine a first region of interest by using the first region of interest positioning module and a water velocity characteristic map included in the water characteristic map sequence; Determine a second region of interest by using the second region of interest positioning module and the water level characteristic map included in the water characteristic map sequence; The risk area is determined by the cross-location module, the first region of interest and the second region of interest.

6. The method according to claim 5, characterized in that The water blocking gate of the at least one water blocking gate comprises: at least one blocking assembly, the blocking assembly comprises: a first blocking plate and a second blocking plate, wherein the first blocking plate controls the blocking plate angle through a hydraulic rod, the second blocking plate controls the blocking plate angle through a hydraulic rod, and the first blocking plate and the second blocking plate are arranged in a V shape; and The controlling the flow rate of seepage water by the at least one water blocking gate and collecting the seepage water by the at least one water collecting well include: According to the first target water flow rate, a second target water flow rate is mapped, wherein the first target water flow rate represents the maximum threshold water flow rate of the seepage water flowing to the risk area, and the second target water flow rate represents the water flow rate after being controlled by the water blocking gate; According to the second target water flow rate, an angle of a baffle plate corresponding to at least one baffle assembly included in the water blocking gate is controlled.

7. A water seepage control system, applied to the water seepage control method applied to underground tunnels in goaf areas as claimed in any one of claims 1 to 6, characterized in that: include: At least one water immersion sensor, wherein the water immersion sensor is arranged in a water immersion detection area, the water immersion detection area is a connection area between the lane network and the shaft, and the water immersion sensor is used to detect whether there is water immersion in the water immersion detection area; at least one water flow rate sensor, wherein the water flow rate sensor is arranged in the lane network, and the water flow rate sensor is used to detect the water flow rate; at least one water level sensor, wherein the water level sensor is disposed in the lane network and the water flow rate sensor is used to detect the water level; At least one water blocking gate, wherein the water blocking gate is arranged in the lane network, the water blocking gate is used to control the seepage flow rate, the water blocking gate comprises: at least one pair of baffles, the baffles comprise: a first baffle and a second baffle, wherein the first baffle is controlled by a hydraulic rod to control the baffle angle, the second baffle is controlled by a hydraulic rod to control the baffle angle, and the first baffle and the second baffle are arranged in a V shape; At least one water collection well, wherein the water collection well is arranged in the tunnel network, and the water collection well is used for collecting seepage water.

8. A water seepage control device used in underground tunnels in goaf areas, characterized in that: include: The first determination unit is configured to determine a target tunnel network in response to the presence of flooding in a flooding detection area detected by the flooding sensor, wherein the flooding detection area is a connection area between the shaft and the tunnel network, the tunnel network represents an underground tunnel under the goaf, and the target tunnel network is a local tunnel network in the tunnel network, the corresponding depth above the ground is greater than the corresponding depth above the ground of the flooding sensor that detects the flooding; A second determination unit is configured to determine a water characteristic map sequence for the target lane network according to water flow rate data, water level data, the target lane network and a water characteristic map extraction model, wherein the water flow rate data is acquired in real time by a water flow rate sensor disposed in the target lane network, and the water level data is acquired in real time by a water level sensor disposed in the target lane network; A third determination unit is configured to determine the risk area in the target roadway network according to the water feature map sequence and the risk area positioning model, wherein the water feature map extraction model and the risk area positioning model are both included in the risk identification model; An activation unit configured to activate at least one water blocking gate and at least one water collecting well in the target tunnel network and upstream of the tunnel where the risk area is located; The control unit is configured to, in response to successful activation, control the flow rate of the seepage water through the at least one water blocking gate and collect the seepage water through the at least one water collection well.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6 and the system according to claim 7.

10. A computer-readable medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 and the system according to claim 7 are implemented.

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