CN115638850A - Method for flood reduction, manhole cover device, electronic device and storage medium - Google Patents
Method for flood reduction, manhole cover device, electronic device and storage medium Download PDFInfo
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- CN115638850A CN115638850A CN202211151600.9A CN202211151600A CN115638850A CN 115638850 A CN115638850 A CN 115638850A CN 202211151600 A CN202211151600 A CN 202211151600A CN 115638850 A CN115638850 A CN 115638850A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The invention discloses a method, electronic equipment, a storage medium and well lid equipment for flood and disaster reduction.A built convolutional neural network model is utilized to analyze field image information after an obtained field image, so that whether the risk disaster conditions such as a drainage port blocking, an overhigh ground water level, an overlarge rainfall, an underground water level and the like exist can be judged, and when a certain risk disaster condition or a plurality of risk disaster conditions exist, the well lid component is controlled to drain water, so that the well lid does not need to be turned over manually to drain water in heavy water, the labor cost is reduced, and the drainage is accelerated; meanwhile, the system can send risk signals to an upper-level organ for early warning and timely manual treatment of the problem of water channel blockage, can be applied to disaster conditions, can also perform real-time detection on a daily basis, and reduces blind spots to provide convenience for water conservancy workers.
Description
Technical Field
The invention relates to the technical field of urban flood control and waterlogging prevention, in particular to a method for flood control and disaster reduction, well lid equipment, electronic equipment and a storage medium.
Background
With the continuous acceleration of the urbanization process, the urban channel construction is also continuously increased. Urban road drainage pipeline construction is an important part for ensuring urban traffic, sanitation and other fields. When disasters such as rainstorm, flood and the like occur, the urban road drainage pipeline can play an important role. However, when the drainage pipeline on the road surface is blocked by foreign matters such as garbage and fallen leaves, if the foreign matters are not disposed in time, various problems such as water accumulation on the road surface and traffic jam are caused.
At present, the existing flood and disaster reduction is mostly carried out in advance according to the information broadcasted by the weather station. The dredging, flood discharging and water discharging can not be operated in time, and the road traffic and the pedestrian traffic are seriously influenced.
Disclosure of Invention
The invention provides a method for flood reduction, a well cover device, an electronic device and a storage medium, which can automatically control a well cover assembly to drain water and reflect the current situation after confirming the surrounding situation of a drainage port through field image information.
In one aspect of the invention, a method for flood reduction is provided. The method comprises the following steps: acquiring field image information associated with flooding; inputting the live image information to a target convolutional neural network model to generate an output result associated with flooding; and sending a control signal based on the output result to indicate the well lid to drain water, and/or sending an early warning signal based on the output result to the outside to indicate that flood is handled.
In some embodiments, obtaining the live image information associated with flooding comprises: image information associated with at least one of a sewage port, a ground water level, rainfall, and a ground water level is acquired from the camera.
In some embodiments, inputting the live image information to a target convolutional neural network model to generate output results associated with flooding comprises: inputting the live image information to the target convolutional neural network model to generate the output result representing at least one of whether a drain is blocked, whether a ground water level is above a ground threshold level, whether rainfall is greater than a threshold amount or a threshold rate, and whether a ground water level is above a ground threshold level.
In some embodiments, sending a control signal for instructing the well lid to drain based on the output comprises: determining, based on the output, that the ground level is above a ground threshold level, or that the rainfall is greater than a threshold amount or a threshold rate; and sending the control signal to the well lid so that the well lid drains water.
In some embodiments, transmitting an early warning signal indicating that flooding should be handled to the outside based on the output result includes: determining that the drain is blocked or the ground water level is higher than the ground threshold water level based on the output result; and sending the early warning signal to an external monitoring terminal so that the external monitoring terminal presents position information associated with the sewer port or the underground water level.
In some embodiments, the method further comprises: constructing an initial convolutional neural network model; and inputting training image information associated with flooding into the initial convolutional neural network model for training to obtain the target convolutional neural network model.
In some embodiments, constructing the initial convolutional neural network model comprises: creating a plurality of risk branches in a preset convolutional neural network model, wherein the risk branches are respectively used for learning different risk conditions associated with flooding; and respectively adding a logistic regression layer to the tail end of the full connection layer of each risk branch to obtain the initial convolutional neural network model.
In some embodiments, the risk disaster branch includes a sewer inlet blocking branch, a ground water level branch, a rainfall magnitude branch and a ground water level branch, which are respectively used for extracting sewer inlet blocking characteristics, ground water level characteristics, rainfall characteristics and ground water level characteristics, and judging four risk situations, namely whether a sewer inlet is blocked, whether the ground water level is higher than a ground threshold water level, whether the rainfall is greater than a threshold amount or a threshold speed, and whether the ground water level is higher than a ground threshold water level.
In some embodiments, the method further comprises: obtaining raindrop size information according to the scene image information; and inputting the raindrop size information to the target convolutional neural network model together with the live image information to generate the output result.
In some embodiments, the method further comprises: acquiring rainfall duration information; and inputting the rainfall duration information to the target convolutional neural network model together with the live image information to generate the output result.
In another aspect of the invention, a manhole cover apparatus is provided. The manhole cover device comprises a controller configured to perform the above-described method for flood reduction.
In some embodiments, the manhole cover apparatus further comprises: the water feeding cover is provided with a water permeable structure; the drain cover is provided with a sealing structure; and a rotation mechanism having one side attached to the lower water cover and configured to receive a control signal from the controller and separate or combine the upper water cover and the lower water cover based on the control signal.
In some embodiments, the rotating mechanism comprises a hinge and a roller; the first side of the hinge is attached to the lower water cover, and the second side of the hinge is connected with the rolling shaft; the control end of the roller is configured to receive the control signal; the roller is configured to slide up and down to separate or merge the upper water cover and the lower water cover based on the control signal.
In yet another aspect of the present invention, an electronic device is provided. The electronic device includes: a memory configured to store a computer program; and a processor configured to execute the computer program to perform the above-described method for flood reduction.
In yet another aspect of the present invention, a storage medium is provided. The medium has stored thereon a computer program which is executed by a processor to implement the above-described method for flood reduction.
According to the embodiment of the invention, the acquired field image is utilized to analyze the field image information, and whether the conditions of risk disasters such as sewer port blocking, ground water level overhigh, rainfall amount overlarge, underground water level and the like exist can be judged. When there is certain risk disaster condition or multiple risk disaster condition, then carry out the drainage through control well lid subassembly to do not need the manual work to turn over the well lid when the heavy water and carry out the drainage, reduced the human cost, and accelerate the drainage. Simultaneously, can send the risk signal in order to in real time to higher level's office early warning and in time the problem that the manual work handled the water course and blockked up, not only can be applied to the calamity condition, also can real-time detection on a flat day moreover, reduce the blind spot and provide convenience for water conservancy staff.
Drawings
Fig. 1 is a flow chart of a method for flood reduction according to an embodiment of the present invention;
fig. 2 is a schematic structural view of the manhole cover device according to the embodiment of the present invention;
fig. 3 is a flowchart of the method for flood reduction according to the embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
In the prior art, the flood discharge mode is pre-deployed according to the information broadcasted by the weather station, and the flood discharge and drainage can not be dredged in time, so that the road traffic and the pedestrian traffic are seriously influenced.
To address at least the above technical problems, the present disclosure provides a method for flood reduction. According to the method and the device, after the on-site image information of the monitoring area and the flood is obtained, the on-site image is subjected to AI analysis by using the neural network, and when the AI judges that the current on-site has the risk disaster conditions such as blockage of a sewer port, the well lid assembly is controlled to drain water, and/or a risk prompt signal is sent to report the current on-site condition. In this way, according to the embodiment of the disclosure, the well lid does not need to be turned over manually for draining water in heavy water, so that the labor cost is reduced, and the draining can be accelerated; meanwhile, the system can send risk signals to a superior organ for early warning in real time and timely and artificially treat the problem of water channel blockage, can be applied to disaster conditions, can also carry out real-time detection on a daily basis, reduces blind spots for investigation and provides convenience for water conservancy workers.
Hereinafter, the technical solution according to the present disclosure will be described with reference to specific embodiments and with reference to the accompanying drawings.
Fig. 1 is a flow chart illustrating a method 100 for flood mitigation in accordance with an embodiment of the present disclosure. Referring to fig. 1, the method 100 includes the following steps 102 to 106.
At step 102, live image information associated with flooding is acquired. In some embodiments, image information associated with at least one of a gutter inlet, ground level, rainfall, and groundwater level is obtained from a camera. In this way, the scene situation can be comprehensively judged according to a plurality of different scene image information.
At step 104, the live image information is input to a target convolutional neural network model to generate an output result associated with flooding. In some embodiments, the live image information is input to the target convolutional neural network model to generate the output result representing at least one of whether a gully is blocked, whether a ground water level is above a ground threshold level, whether rainfall is greater than a threshold amount or a threshold rate, and whether a ground water level is above a ground threshold level. In this way, the corresponding at least one output result can be output according to different live image information.
In step 106, a control signal is sent based on the output result for instructing the well lid to drain water, and/or a warning signal for instructing the well lid to cope with flooding is sent to the outside based on the output result. In some embodiments, it is determined based on the output that the ground level is above a ground threshold level, or the rainfall is greater than a threshold amount or a threshold rate; and sending the control signal to the well lid so that the well lid drains water. In some embodiments, it is determined that the drain is blocked, or the ground water level is above a ground threshold level, based on the output; and sending the early warning signal to an external monitoring terminal so that the external monitoring terminal presents position information associated with the sewer port or the underground water level. In this way, water level control and flood early warning can be realized according to the output result.
In some embodiments, the method 100 may further include: obtaining raindrop size information according to the on-site image information; and inputting the raindrop size information to the target convolutional neural network model together with the scene image information to generate the output result. In this way, the field situation can be comprehensively judged according to various different data information.
In some embodiments, the method 100 may further include: acquiring rainfall duration information; and inputting the rainfall duration information to the target convolutional neural network model together with the live image information to generate the output result. In this way, the rainfall duration and the scene image information are combined to comprehensively judge the scene condition, and the accuracy of the risk disaster condition judgment is improved.
In some embodiments, the method 100 may further include: constructing an initial convolutional neural network model; and inputting training image information associated with flooding into the initial convolutional neural network model for training to obtain the target convolutional neural network model. In this way, the collected images or video pictures are trained for multiple times through a pre-established neural network training model such as MICNN and the like, and model parameters are adjusted repeatedly until the output state reaches the preset accuracy rate, so that the judgment capability of the model on different scene is improved.
In some embodiments, constructing the initial convolutional neural network model comprises: creating a plurality of risk branches in a preset convolutional neural network model, wherein the risk branches are respectively used for learning different risk conditions associated with flooding; and respectively adding a logistic regression layer to the tail end of the full connection layer of each risk disaster branch to obtain the initial convolutional neural network model. In this way, different risk disaster situations can be learned by creating different risk disaster branches, and the accuracy of the convolutional neural network model in judging different risk disasters can be improved.
In some embodiments, the risk disaster branch includes a sewer inlet blocking branch, a ground water level branch, a rainfall magnitude branch and a ground water level branch, which are respectively used for extracting sewer inlet blocking characteristics, ground water level characteristics, rainfall characteristics and ground water level characteristics, and judging four risk situations, namely whether a sewer inlet is blocked, whether the ground water level is higher than a ground threshold water level, whether the rainfall is greater than a threshold amount or a threshold speed, and whether the ground water level is higher than a ground threshold water level. In this way, the monitoring of four risk disaster conditions, namely, whether the sewer port is blocked, whether the ground water level is higher than the ground threshold water level, whether the rainfall is greater than the threshold amount or the threshold speed and whether the underground water level is higher than the underground threshold water level, is realized, and once one or more risk conditions occur, drainage can be controlled in real time and the conditions can be reported.
According to the embodiment of the disclosure, the controller analyzes the live image information through the trained AI model to determine whether flooding is likely to occur, and sends a control signal to perform a corresponding disaster reduction action. In this way, do not need the manual work to turn over the well lid and carry out the drainage, reduced the human cost to can also in time accelerate the drainage. Meanwhile, the controller sends risk signals to a superior organ to early warn in real time and manually process the problem of water channel blockage in time, so that the system can be applied to disaster conditions, can also detect in real time on a daily basis, reduces blind spots for investigation and provides convenience for water conservancy workers.
According to another aspect of the present invention, the present disclosure provides a well lid apparatus. The manhole cover device comprises a controller configured to perform the method for flood reduction as described above.
Fig. 2 is a block diagram illustrating a manhole cover device 200 according to an embodiment of the present invention. Referring to fig. 2, the manhole cover apparatus 200 includes an upper cover 202, a lower cover 204, and a rotating mechanism 206, in addition to a controller, which is not shown. The upper water cover 202 has a water permeable structure. The drain cover 204 has a hermetic structure. The rotating mechanism 206 has a side that is attached to the lower water cover 204. The rotation mechanism 206 is configured to receive a control signal from the controller and to separate or merge the upper water cap 202 and the lower water cap 204 based on the control signal.
In some embodiments, the rotation mechanism 206 includes a hinge 2062 and a roller 2064. The first side of the hinge 2062 is connected to the drain cover 204, and the second side of the hinge 2062 is connected to the roller 2064. The roller 2064 may be coupled to the controller. The control end of the roller 2064 is configured to receive the control signal. The roller 2064 is configured to slide up and down to separate or combine the upper water cover 202 and the lower water cover 204 based on the control signal.
Hereinafter, an application scenario of the method for flood mitigation according to an embodiment of the present invention will be described by way of example.
Fig. 3 is a flowchart illustrating steps of a method for flood mitigation according to an embodiment of the present invention, applied in a specific scenario, and includes the following steps 302 to 318. In some embodiments, the controller comprises a chip. In some embodiments, the chip may be a RK1808 model chip.
In step 302, the camera acquires a live image and transmits the live image to the chip.
In step 304, the chip judges whether the situations of drainage port blockage, overhigh ground water level, overlarge rainfall and overhigh underground water level exist in the scene according to the scene image information of the target convolutional neural network model.
In step 306, the chip determines whether there is an excessive rain/water condition in the field.
In step 308, the chip determines whether there is an excessive ground level condition in the field.
In step 310, the chip determines whether a drain is blocked.
In step 312, the chip determines whether there is an excessive groundwater level in the field.
In step 314, when it is determined that there is an excessive water amount and/or an excessive ground water level, the chip slides through the control roller to drive the hinge to control the lower water cover to turn over downward, i.e., the upper water cover is separated from the lower water cover, so that the water on the ground flows into the sewer.
In step 316, when it is determined that the drain is blocked and/or the groundwater level is too high, the chip sends the current site information to the monitoring end.
In step 318, after the monitoring end receives the reported disaster dangerous case, the monitoring end allocates manpower to process the dangerous case according to the actual situation. In some embodiments, the chip instructs the well lid device to implement drainage control.
According to yet another aspect of the invention, FIG. 4 is a schematic diagram illustrating an electronic device 400 according to an embodiment of the invention. Referring to fig. 4, the electronic device 400 comprises a memory 402, a processor 404 and a computer program stored on said memory and executable on the processor, said processor implementing the steps of the method for flood mitigation as described above when executing said computer program.
According to yet another aspect of the invention, a computer-readable medium is provided. The computer readable medium has stored thereon a computer program which is executed by a processor to implement the method for flood reduction as described above.
In summary, according to the method for flood and disaster reduction, the well lid device, the electronic device and the storage medium provided by the invention, after the acquired field image is obtained, the field image information is analyzed by using the established convolutional neural network model, whether the risk disaster situations such as a drainage port blocking, an excessively high ground water level, an excessively large rainfall, an excessively high underground water level and the like exist can be judged, and when a certain risk disaster situation or multiple risk disaster situations exist, the well lid component is controlled to drain, so that the well lid does not need to be manually opened to drain in heavy water, the labor cost is reduced, the drainage can be accelerated, and the disaster relief work can be rapidly carried out; meanwhile, the system can send risk signals to an upper-level office for early warning and timely and manually treating the problem of water channel blockage, can be applied to disaster conditions, can also carry out real-time detection on the day, and reduces blind spots to provide convenience for water conservancy workers.
The above description is only an embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent modifications made by the present invention and the contents of the accompanying drawings, which are directly or indirectly applied to the related technical fields, are included in the scope of the present invention.
Claims (15)
1. A method for flood mitigation, comprising:
acquiring field image information associated with flooding;
inputting the live image information to a target convolutional neural network model to generate an output result associated with flooding; and
and sending a control signal based on the output result to indicate the well lid to drain water, and/or sending an early warning signal based on the output result to the outside to indicate that the flood is dealt with.
2. The method of claim 1, wherein obtaining the live image information associated with flooding comprises:
image information associated with at least one of a drain, a ground water level, rainfall, and a groundwater level is obtained from the camera.
3. The method of claim 1, wherein inputting the live image information to a target convolutional neural network model to generate output results associated with flooding comprises:
inputting the live image information to the target convolutional neural network model to generate the output result representing at least one of whether a drain is blocked, whether a ground water level is above a ground threshold water level, whether rainfall is greater than a threshold amount or a threshold rate, and whether a ground water level is above a ground threshold water level.
4. The method of claim 1, wherein sending a control signal for instructing the well lid to drain based on the output comprises:
determining, based on the output, that the ground water level is above a ground threshold water level, or that the rainfall is greater than a threshold amount or a threshold rate; and
and sending the control signal to the well lid to enable the well lid to drain water.
5. The method of claim 1, wherein transmitting an early warning signal indicating that flooding should be dealt with to the outside based on the output result comprises:
determining that the drainage port is blocked or the underground water level is higher than an underground threshold water level based on the output result; and
sending the early warning signal to an external monitoring terminal so that the external monitoring terminal presents position information associated with the sewer port or the ground water level.
6. The method of claim 1, further comprising:
constructing an initial convolutional neural network model;
and inputting training image information associated with flooding into the initial convolutional neural network model for training to obtain the target convolutional neural network model.
7. The method of claim 6, wherein constructing the initial convolutional neural network model comprises:
creating a plurality of risk branches in a preset convolutional neural network model, wherein the risk branches are respectively used for learning different risk conditions associated with flooding;
and respectively adding a logistic regression layer to the tail end of the full connection layer of each risk branch to obtain the initial convolutional neural network model.
8. The method of claim 7, wherein the risk branches comprise a sewer inlet blockage branch, a ground water level branch, a rainfall magnitude branch and a ground water level branch, which are respectively used for extracting sewer inlet blockage characteristics, ground water level characteristics, rainfall characteristics and ground water level characteristics, and judging whether a sewer inlet is blocked, whether the ground water level is higher than a ground threshold water level, whether the rainfall is greater than a threshold amount or a threshold speed, and whether the ground water level is higher than a ground threshold water level.
9. The method of claim 1, further comprising:
obtaining raindrop size information according to the on-site image information; and
inputting the raindrop size information to the target convolutional neural network model along with the scene image information to generate the output result.
10. The method of claim 1, further comprising:
acquiring rainfall duration information; and
inputting the rainfall duration information to the target convolutional neural network model along with the live image information to generate the output result.
11. A manhole cover apparatus, comprising:
a controller configured to perform the method of any one of claims 1 to 10.
12. The manhole cover apparatus of claim 11, further comprising:
the water feeding cover is provided with a water permeable structure;
the drain cover is provided with a sealing structure; and
and a rotating mechanism having one side attached to the lower water cover and configured to receive a control signal from the controller and separate or combine the upper water cover and the lower water cover based on the control signal.
13. The manhole cover apparatus of claim 12, wherein the rotating mechanism comprises a hinge and a roller;
the first side of the hinge is attached to the lower water cover, and the second side of the hinge is connected with the rolling shaft;
the control end of the roller is configured to receive the control signal;
the roller is configured to slide up and down to separate or combine the upper water cover and the lower water cover based on the control signal.
14. An electronic device, comprising:
a memory configured to store a computer program; and
a processor configured to execute the computer program to perform the method of any of claims 1 to 10.
15. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program is executed to implement the method according to any one of claims 1 to 10.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117314704A (en) * | 2023-09-28 | 2023-12-29 | 光谷技术有限公司 | Emergency event management method, electronic device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117314704A (en) * | 2023-09-28 | 2023-12-29 | 光谷技术有限公司 | Emergency event management method, electronic device and storage medium |
| CN117314704B (en) * | 2023-09-28 | 2024-04-19 | 光谷技术有限公司 | Emergency event management method, electronic device and storage medium |
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