CN112532921A - Water conservancy system intelligent monitoring implementation method and system - Google Patents
Water conservancy system intelligent monitoring implementation method and system Download PDFInfo
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Abstract
The invention provides a method and a system for realizing intelligent monitoring of a water conservancy system, wherein the method comprises the following steps: acquiring a scene video of a water conservancy system based on a 5G network; analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system; and pushing the analysis early warning result of the deep learning neural network to a background. Through 5G communication mode and rear end platform connection, reduced entire system cost, can also simplify wiring and installation greatly through 5G wireless communication. The neural network deep learning algorithm is adopted to carry out safe and intelligent analysis on the water conservancy system, and the identification is carried out in a timing polling mode, so that the system cost is greatly saved, and the intelligent monitoring and early warning on the water conservancy system are realized.
Description
Technical Field
The invention relates to the technical field of intelligent security, in particular to a method and a system for realizing intelligent monitoring of a water conservancy system
Background
The economy which develops too fast and the cities which evolve rapidly destroy the water environment and the water quality greatly, the urban waterlogging is serious, the water safety of people is threatened, and the like, and the traditional manual supervision mode causes the water treatment work efficiency to be very low, so that the water business industry faces serious challenges. The existing monitoring equipment only simply returns a video image, is manually monitored and identified, and has higher false alarm, missing report and higher labor intensity. The high-definition video is large in data volume transmitted back in real time, river monitoring points are wide in distribution range and far away from towns, and a large amount of capital investment is needed for laying a special optical fiber communication network. Therefore, a method and a system for realizing intelligent monitoring of a water conservancy system are needed, so that intelligent monitoring and early warning of the water conservancy system are realized, and meanwhile, the system implementation cost is low.
Disclosure of Invention
The invention mainly aims to provide a method and a system for realizing intelligent monitoring of a water conservancy system, which can realize intelligent monitoring and early warning of the water conservancy system and have lower system implementation cost.
In order to achieve the purpose, all intelligent analysis algorithms are based on videos, all algorithms adopt open-source neural network deep learning algorithms, and all the intelligent analysis algorithms are achieved through intelligent analysis boxes. The intelligent analysis box is connected with a common camera, an original monitoring camera does not need to be replaced, and the cost of the whole system is reduced. Through 5G communication mode and rear end platform connection, greatly reduced wiring and installation cost.
The first aspect of the invention provides a method for realizing intelligent monitoring of a water conservancy system, which comprises the following steps:
acquiring a scene video of a water conservancy system based on a 5G network;
analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system;
and pushing the analysis early warning result of the deep learning neural network to a background.
In order to achieve the above object, a second aspect of the present invention provides a hydraulic system intelligent monitoring implementation system, which includes an acquisition module, a deep learning neural network analysis module, and an analysis early warning module;
the acquisition module is used for acquiring a scene video of the water conservancy system based on a 5G network;
the deep learning neural network analysis module is used for analyzing whether people or objects in the water conservancy system scene video accord with preset water conservancy system scene specifications or not through the deep learning neural network based on the acquired water conservancy system scene video;
the analysis early warning module is used for pushing an analysis early warning result of the deep learning neural network to a background.
The invention provides a method and a system for realizing intelligent monitoring of a water conservancy system, wherein the method comprises the following steps: acquiring a scene video of a water conservancy system based on a 5G network; analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system; and pushing the analysis early warning result of the deep learning neural network to a background. Through 5G communication mode and rear end platform connection, reduced entire system cost, can also simplify wiring and installation greatly through 5G wireless communication. The neural network deep learning algorithm is adopted to carry out safe and intelligent analysis on the water conservancy system, and the identification is carried out in a timing polling mode, so that the system cost is greatly saved, and the intelligent monitoring and early warning on the water conservancy system are realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a hydraulic system intelligent monitoring implementation method according to a first embodiment of the present disclosure;
fig. 2 is a system diagram of an implementation method for intelligent monitoring of a water conservancy system in a second embodiment of the invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Because the existing monitoring equipment only simply returns video images, and is manually monitored and identified, higher false reports, higher missing reports and higher labor intensity exist. The high-definition video is large in data volume transmitted back in real time, river monitoring points are wide in distribution range and far away from towns, and a large amount of capital investment is needed for laying a special optical fiber communication network. Therefore, a method and a system for realizing intelligent monitoring of a water conservancy system are needed, so that intelligent monitoring and early warning of the water conservancy system are realized, and meanwhile, the system implementation cost is low.
In order to solve the technical problem, the invention provides a water conservancy system intelligent monitoring implementation method and system.
Fig. 1 is a schematic flow chart of an implementation method for intelligent monitoring of a water conservancy system according to a first embodiment of the present invention. The method comprises the following steps:
step 101: acquiring a scene video of a water conservancy system based on a 5G network; specifically, all intelligent analysis algorithms are based on scene videos of a water conservancy system, all algorithms adopt open-source neural network deep learning algorithms, and all intelligent analysis algorithms are realized through intelligent analysis boxes. The intelligent analysis box is connected with a common camera, an original monitoring camera does not need to be replaced, and the cost of the whole system is reduced. Through 5G communication mode and rear end platform connection, greatly reduced wiring and installation cost.
Step 102: and analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system.
The specific operation comprises the following steps:
step A: and installing a common camera at a place needing intelligent analysis, inserting an SD card, and adjusting the height and the angle of view of the camera.
And B: the method comprises the steps of installing a 5G intelligent analysis box at a nearby place, wherein the intelligent analysis box can adopt a V2722 model analysis box of the British and Fit technologies, Inc., is connected with a camera to be analyzed, and is configured with a detection area and an intelligent analysis type of each camera. And configuring a polling strategy and a timing interval for the non-river channel safety camera.
And C: the back end deploys the wisdom water conservancy platform, inserts intelligent analysis box through 5G wireless mode.
Step 103: and pushing the analysis early warning result of the deep learning neural network to a background.
Specifically, for example, the intelligent analysis box automatically identifies illegal fishing through videos, including illegal ship break-in and illegal fishing identification. Examples are: and detecting a ship target through a neural network deep learning algorithm, tracking, and identifying that the ship intrudes illegally when finding the ship moving towards the camera and entering a detection area range. And identifying illegal fishing when the stay time of the tracked ship exceeds a set threshold value. And sending the alarm information to a monitoring center to remind related workers to effectively supervise, and realizing a neural network deep learning algorithm in an intelligent analysis box, wherein the standard behavior of the scene can be automatically detected according to scene training neural network deep learning.
Specifically, the acquiring a scene video of a water conservancy system based on the 5G network includes:
the 5G network polling is adopted to acquire the scene video of the water conservancy system, the intelligent analysis function considering non-river channel safety is an auxiliary reminding function, and the intelligent analysis box analyzes the access camera according to a timing polling mechanism, so that the number of the intelligent analysis boxes can be reduced, and the system cost is further reduced.
The above embodiment further comprises the following refinement steps:
through the neural network of deep learning, whether people or thing in the analysis water conservancy system scene video accords with preset water conservancy system scene standard includes: training a preset water conservancy system scene standard model through a deep learning neural network, wherein the water conservancy system scene standard model comprises the following conditions: at least one of illegal fishing, illegal intrusion of ships and illegal fishing.
At least one of river floating objects and river chemical pollution identification.
River channel personnel cross border recognition, thermodynamic diagram recognition of personnel around the river channel, and illegal wading recognition of personnel.
And (5) identifying a hydrological scale.
And identifying the position and the area of the ponding.
In deep learning of the neural network, all target detection can adopt a training framework: a caffe training framework may be used, including other open source training frameworks that may implement automatic target detection recognition. Examples are: the cave training framework is used for training the water conservancy system scene standard model, such as illegal fishing, illegal ship break-in and illegal fishing, river floating objects and river chemical pollution identification, the cave training framework can be used for teaching and identifying the situations of illegal fishing, illegal ship break-in, illegal fishing, river floating objects and river chemical pollution from tens of thousands of images, and therefore through deep learning of the neural network, the neural network can automatically identify the situations similar to teaching and identification, and the situations of illegal fishing, illegal ship break-in and illegal fishing, river floating objects, river chemical pollution identification and the like in the cave standard model are taught and identified.
Specifically, the hydraulic system scene specification model includes the following situations: identifying river course floating objects and identifying river course chemical pollution. And by calculating the area of the water surface floater, the floater is identified as the river floater when the area of the floater exceeds a threshold value. And detecting the river channel color in the video picture in real time, and identifying that the color changes greatly suddenly or the river channel color exceeds a set threshold (becomes red, black and the like) for a long time as river channel chemical pollution. And sending the identification result to a monitoring center to remind related workers to carry out effective supervision.
River channel safety identification is carried out through videos, and the river channel safety identification comprises river channel personnel border crossing identification, thermodynamic diagram identification of personnel around a river channel and personnel illegal wading identification. The intelligent analysis box is connected with a river channel safety camera, and one or more forbidden lines are drawn on the forbidden border crossing video. And carrying out real-time pedestrian detection, but finding that a pedestrian crosses the forbidden line, carrying out face detection and face recognition on the pedestrian, and judging the identity of the out-of-range person. And configuring a river channel personnel thermal analysis area, detecting the density of the pedestrians in real time, finding that the density value of the personnel exceeds a set threshold value, and triggering alarm when the density value stabilization time exceeds the set threshold value. And (4) configuring an illegal wading area, detecting pedestrians in real time, but finding that pedestrians enter the wading area, detecting faces of the pedestrians, identifying the faces, and judging the identity of the cross-border people. The importance of river channel safety identification is considered, the river channel identification camera does not identify according to a polling mode, and real-time intelligent analysis is adopted.
The intelligent analysis box supports the recognition of the hydrological ruler and uploads the recognition result to the rear-end platform. The intelligent analysis box is connected with a camera needing a hydrological scale, marks the position of the hydrological scale, detects the character information of the hydrological scale in real time through a neural network deep learning algorithm, sends the identification result to a monitoring center, reminds related workers and conducts effective supervision. In order to ensure accuracy, only the numbers and letters related to the hydrological scale are included when the model is trained.
The intelligent analysis box supports automatic ponding recognition through video. And setting a detection area, and marking the specific height and width. And tracking the pedestrians in the area, calculating whether the height of the pedestrians in the area is short or not, calculating short pixels, and obtaining the depth of the water accumulation through calibration calculation. And tracking the vehicle in the area, calculating whether the height of the vehicle wheel in the area is complete or not, calculating incomplete pixels of the vehicle, and obtaining the depth of the water accumulation through calibration calculation. And detecting the color change of the road surface by taking the position of the accumulated water as a starting point to obtain an accumulated water area. The water accumulation condition is divided into four grades of light, medium and heavy, and the alarm can be given immediately when the water is accumulated seriously, so that the early warning manager can drain water and dredge the water, and the safety accidents of people and vehicles are avoided.
Further, the pushing the analysis early warning result of the deep learning neural network to the background comprises: if the judgment result is out of specification, alarming, capturing pictures, prompting by voice and making alarming records are carried out on the platform end in time. The intelligent analysis box is seamlessly integrated with the intelligent water conservancy management platform, and the intelligent analysis box can give an alarm, grab pictures, prompt by voice and inquire and count by alarm records at the platform end in time, so that the leadership and macro-planning are facilitated. Meanwhile, warning information is synchronously pushed to management personnel, and meanwhile, the picture is intercepted and kept as evidence. Considering the 5G bandwidth cost, the rear-end platform only makes alarm video, the front-end camera video is stored in the local SD card, and the rear-end platform is called as required.
Example 2
Fig. 2 is a schematic diagram of a system of an intelligent monitoring implementation method for a water conservancy system according to a second embodiment of the present disclosure; the system comprises an acquisition module, a deep learning neural network analysis module and an analysis early warning module;
the acquisition module is used for acquiring a scene video of the water conservancy system based on a 5G network;
the deep learning neural network analysis module is used for analyzing whether people or objects in the water conservancy system scene video accord with preset water conservancy system scene specifications or not through the deep learning neural network based on the acquired water conservancy system scene video;
the analysis early warning module is used for pushing an analysis early warning result of the deep learning neural network to a background.
A third embodiment of the present invention provides an electronic device, which implements the steps of any one of the above methods when executing a computer program.
The above-mentioned refining steps are already described in the foregoing specific steps, and are not repeated here.
In the embodiment of the invention, the invention provides a method and a system for realizing intelligent monitoring of a water conservancy system, wherein the method comprises the following steps: acquiring a scene video of a water conservancy system based on a 5G network; analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system; and pushing the analysis early warning result of the deep learning neural network to a background. Through 5G communication mode and rear end platform connection, reduce entire system cost, can also simplify wiring and installation greatly through 5G wireless communication. The neural network deep learning algorithm is adopted to intelligently analyze the safety of the river channel, and the river channel is identified in a timed polling mode, so that the system cost is greatly saved, and the intelligent monitoring and early warning of a water conservancy system are realized.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (10)
1. An intelligent monitoring implementation method for a water conservancy system is characterized by comprising the following steps:
acquiring a scene video of a water conservancy system based on a 5G network;
analyzing whether people or objects in the scene video of the water conservancy system accord with preset scene specifications of the water conservancy system or not through a deep learning neural network based on the scene video of the water conservancy system;
and pushing the analysis early warning result of the deep learning neural network to a background.
2. The method according to claim 1, wherein the acquiring of the scene video of the water conservancy system based on the 5G network comprises:
and polling by adopting a 5G network to obtain a scene video of the water conservancy system.
3. The method of claim 1, wherein analyzing whether the person or thing in the hydraulic system scene video meets a preset hydraulic system scene specification through the deep learning neural network comprises: training a preset water conservancy system scene standard model through a deep learning neural network, wherein the water conservancy system scene standard model comprises the following conditions: at least one of illegal fishing, illegal intrusion of ships and illegal fishing.
4. The method of claim 3, wherein the hydraulic system scenario specification model further comprises the following: at least one of river floating objects and river chemical pollution identification.
5. The method of claim 3, wherein the hydraulic system scenario specification model further comprises the following: river channel personnel cross border recognition, thermodynamic diagram recognition of personnel around the river channel, and illegal wading recognition of personnel.
6. The method of claim 3, wherein the hydraulic system scenario specification model further comprises the following: and (5) identifying a hydrological scale.
7. The method of claim 3, wherein the hydraulic system scenario specification model further comprises the following: and identifying the position and the area of the ponding.
8. The method of claim 1, wherein pushing the analysis pre-warning results of the deep-learned neural network to the background comprises: if the judgment result is out of specification, alarming, capturing pictures, prompting by voice and making alarming records are carried out on the platform end in time.
9. An intelligent monitoring implementation system for a water conservancy system is characterized by comprising an acquisition module, a deep learning neural network analysis module and an analysis early warning module;
the acquisition module is used for acquiring a scene video of the water conservancy system based on a 5G network;
the deep learning neural network analysis module is used for analyzing whether people or objects in the water conservancy system scene video accord with preset water conservancy system scene specifications or not through the deep learning neural network based on the acquired water conservancy system scene video;
the analysis early warning module is used for pushing an analysis early warning result of the deep learning neural network to a background.
10. An electronic device, wherein the electronic device implements the steps of the method of any one of claims 1 to 8 when executing the computer program.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113159005A (en) * | 2021-05-28 | 2021-07-23 | 青海中水数易信息科技有限责任公司 | Machine learning-based water level and foreign matter identification integrated monitoring system and method |
| CN113658394A (en) * | 2021-08-12 | 2021-11-16 | 中冶京诚工程技术有限公司 | River channel monitoring method and device |
| CN113903007A (en) * | 2021-12-10 | 2022-01-07 | 宁波弘泰水利信息科技有限公司 | Intelligent scene analysis system for water conservancy industry |
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