CN114723139A - Digital pipe network control system based on GIS - Google Patents
Digital pipe network control system based on GIS Download PDFInfo
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- CN114723139A CN114723139A CN202210369872.XA CN202210369872A CN114723139A CN 114723139 A CN114723139 A CN 114723139A CN 202210369872 A CN202210369872 A CN 202210369872A CN 114723139 A CN114723139 A CN 114723139A
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Abstract
The invention relates to a GIS-based digital pipe network control system, which comprises an acquisition module, a data analysis module, a defect detection module, a prediction module and a terminal module, wherein the acquisition module comprises sensor units arranged in each node of a pipe network, the sensor units are used for acquiring monitoring data of each node of the pipe network, the data analysis module is used for guiding the monitoring data of each node into a GIS server to obtain a pipe network distribution diagram of each node, inputting the monitoring data of each node into an abnormal prediction model to obtain a load abnormal node, highlighting the load abnormal node in the pipe network distribution diagram, the defect detection module detects the load abnormal node to obtain defect detection data, the prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model to obtain a risk value of the load abnormal node and highlighting the risk value in the pipe network distribution diagram, risks existing in the pipe network are analyzed and early warned, and therefore control precision is improved.
Description
Technical Field
The invention relates to the field of digital pipe networks, in particular to a GIS-based digital pipe network control system.
Background
Urban pipe networks are an important component of urban infrastructure, which are responsible for the work of transmitting information, energy or transport media, and are the material foundation on which cities live and develop, called the "lifelines" of cities. At present, the pipe network is generally controlled in an empirical judgment and simple reasoning and calculation mode, effective management and scientific evaluation on pipe network data are lacked, and each functional project in the pipe network plays its own role, so that the whole process is lacked with effective linkage.
Disclosure of Invention
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a GIS-based digital pipe network control system, which comprises an acquisition module, a data analysis module, a defect detection module, a prediction module and a terminal module, wherein the acquisition module comprises sensor units arranged in each node of a pipe network, the sensor units are used for acquiring monitoring data of each node of the pipe network, the data analysis module is used for guiding the monitoring data of each node into a GIS server to obtain a pipe network distribution diagram of each node, inputting the monitoring data of each node into an abnormal prediction model to obtain abnormal load nodes, highlighting the abnormal load nodes in the pipe network distribution diagram, the defect detection module detects the abnormal load nodes to obtain defect detection data, the prediction module is used for inputting the monitoring data of the abnormal load nodes and the defect detection data of the abnormal load nodes into a risk prediction model, and the terminal module is used for displaying the pipe network distribution diagram and giving an alarm according to the risk value.
As a preferred embodiment, the sensor unit comprises a flow meter, a level meter, a combustible gas monitor or a rainfall monitor.
In a preferred embodiment, the defect detection module includes a camera, a sonar detector, or a periscope detector, and is configured to detect a functional defect or a structural defect in the abnormal node.
As a preferred embodiment, inputting the monitoring data of each node into the anomaly prediction model to obtain a load anomaly node, includes: carrying out dimensionality reduction analysis on the monitoring data of each node, and determining a correlation factor of each monitoring data; inputting the monitoring data and the association factors of each node into an abnormal prediction model, obtaining the association index of the association factors for the load, if the association index is larger than or equal to a preset index, determining that the node is an abnormal load node, otherwise, determining that the node is not an abnormal load node.
As a preferred embodiment, the abnormality prediction model is obtained by: the method comprises the steps of obtaining historical monitoring data of nodes under normal conditions and historical monitoring data of nodes under fault of a pipe network, extracting characteristic factors of the historical monitoring data, generating an abnormal index according to the characteristic factors, and associating the abnormal index with the historical monitoring data to generate an abnormal prediction model.
As a preferred embodiment, the detecting the load abnormal node by the defect detecting module to obtain the defect detecting data includes: acquiring historical data of normal nodes and historical data of load abnormal nodes, and performing feature extraction on the historical data to obtain a feature map comprising the historical data; classifying the feature map through a defect detection model to obtain a classification result and a classification loss value of the historical data; optimizing parameters of the object defect detection model to obtain a trained defect detection model; and inputting the detection data of the load abnormal node into the trained defect detection model to obtain the defect detection data.
As a preferred embodiment, the risk prediction model is obtained by: acquiring historical monitoring data of load abnormal nodes and historical defect detection data of the load abnormal nodes, generating a fusion map according to the number, position and size of pipe network defects, labeling the fusion map, and generating a sample data set; generating an initial risk prediction model based on a neural network, and training the initial risk prediction according to the sample data set to determine model parameters; and generating a risk prediction model according to the model parameters and the initial defect diagnosis model.
As a preferred embodiment, the predicting module is configured to input the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into the risk prediction model to obtain a risk value of the load abnormal node, and includes: obtaining a predicted value of the pipe network fault according to the risk prediction model, and determining a weight corresponding to the pipe network fault by using an entropy method; and obtaining the risk value of the load abnormal node according to the predicted value of the pipe network fault and the weight corresponding to the pipe network fault.
As a preferred embodiment, the terminal module gives an alarm according to the risk value, and the alarm includes: and judging the risk level according to the risk value, and determining the alarm level according to the risk level.
As a preferred embodiment, the geographic coordinates of each node and the monitoring data of each node are imported into the GIS server to obtain a pipe network distribution map of each node, and then the risk values of the nodes with abnormal load are superimposed and highlighted on the pipe network distribution map.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps that monitoring data of each node of a pipe network are collected through a sensor unit, the monitoring data of each node are led into a GIS server through a data analysis module to obtain a pipe network distribution diagram of each node, and the monitoring data of each node are input into an abnormity prediction model to obtain load abnormity nodes; and then detecting the load abnormal node through a defect detection module to obtain defect detection data, wherein the prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model to obtain a risk value of the load abnormal node, so that a sensor unit and the defect detection module are fused together, the possible risks of the pipe network are analyzed and early warned, and the control precision is greatly improved. Through multi-point monitoring, the operation and maintenance of the pipe network are assisted, and an operation and maintenance scheme, scheduling control and patrol maintenance are formulated. And analyzing the risk and potential safety hazard of the pipe network by using the online monitoring data, and providing a transformation basis. Monitoring liquid level and flow of historical water accumulation points and waterlogging-prone points, and performing real-time early warning and alarming. The operation safety of the pipe network is improved, and the flood drainage and waterlogging prevention cost is reduced. And verifying whether the current drainage facilities are constructed according to the standard. Pipe network inspection, emergency maintenance, pipe network monitoring, liquid level monitoring, water quality monitoring, partition metering, pipe network scheduling, source tracing analysis and rainwater pipe network.
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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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a GIS-based digital pipe network control method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The embodiment of the invention provides a digital pipe network control system based on a GIS (geographic information System), which comprises an acquisition module, a data analysis module, a defect detection module, a prediction module and a terminal module, wherein the acquisition module comprises sensor units arranged in each node of a pipe network, the sensor units are used for acquiring monitoring data of each node of the pipe network, the data analysis module is used for guiding the monitoring data of each node into a GIS server to obtain a pipe network distribution diagram of each node, inputting the monitoring data of each node into an abnormal prediction model to obtain a load abnormal node, highlighting the load abnormal node in the pipe network distribution diagram, the defect detection module detects the load abnormal node to obtain defect detection data, the prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model, and the terminal module is used for displaying the pipe network distribution diagram and giving an alarm according to the risk value.
The method comprises the steps that monitoring data of each node of a pipe network are collected through a sensor unit, the monitoring data of each node are led into a GIS server through a data analysis module to obtain a pipe network distribution diagram of each node, and the monitoring data of each node are input into an abnormity prediction model to obtain load abnormity nodes; and then detecting the load abnormal node through a defect detection module to obtain defect detection data, wherein the prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model to obtain a risk value of the load abnormal node, so that a sensor unit and the defect detection module are fused together, the possible risks of the pipe network are analyzed and early warned, and the control precision is greatly improved. Through multi-point monitoring, the operation and maintenance of the pipe network are assisted, and an operation and maintenance scheme, scheduling control and patrol maintenance are formulated. And analyzing the risk and potential safety hazard of the pipe network by using the online monitoring data, and providing a transformation basis. Monitoring the liquid level and the flow of the historical water accumulation points and the water-logging-prone points, and early warning and alarming in real time. The operation safety of a pipe network is improved, and the flood drainage and waterlogging prevention cost is reduced. And verifying whether the current drainage facilities are constructed according to the standard. Pipe network inspection, emergency maintenance, pipe network monitoring, liquid level monitoring, water quality monitoring, partition metering, pipe network scheduling, traceability analysis and rainwater pipe network.
The Geographic Information System (GIS) is a comprehensive technical system for collecting, storing, managing, analyzing and displaying information related to geographic phenomena. The method has the characteristics of spatial distribution, large data volume, multiple information carriers and time sequence, the data types of the method are vector data and grid data, and the basic elements of the data comprise geographic coordinates, plane coordinates and vertical coordinates.
The sensor unit comprises a flowmeter, a liquid level meter, a combustible gas monitor or a rainfall monitor. The defect detection module comprises a camera, a sonar detector or a periscope detector and is used for detecting functional defects or structural defects in abnormal nodes.
During installation, a drop-in liquid level meter or an L-shaped bracket-mounted flowmeter and the like can be adopted. And judging the type, position and number of the defects of the pipeline, wherein the structural defects comprise disjointing, cracking, errors and foreign matter invasion, and the functional defects comprise sludge or slurry deposition in the pipeline.
As a preferred embodiment, inputting the monitoring data of each node into an anomaly prediction model to obtain a load anomaly node, includes: performing dimension reduction analysis on the monitoring data of each node, and determining a correlation factor of each monitoring data; inputting the monitoring data and the association factors of each node into an abnormal prediction model, obtaining the association index of the association factors for the load, if the association index is larger than or equal to a preset index, determining that the node is an abnormal load node, otherwise, determining that the node is not an abnormal load node. The anomaly prediction model is obtained by the following method: the method comprises the steps of obtaining historical monitoring data of nodes under normal conditions and historical monitoring data of nodes under fault of a pipe network, extracting characteristic factors of the historical monitoring data, generating an abnormal index according to the characteristic factors, and associating the abnormal index with the historical monitoring data to generate an abnormal prediction model.
The load abnormal node may be a node with abnormal data such as flow in the pipe network, and may be set according to an empirical value. The abnormal prediction model can be obtained by training through a BP neural network model.
The defect detection module detects the load abnormal node to obtain defect detection data, and the defect detection module comprises: acquiring historical data of normal nodes and historical data of load abnormal nodes, and performing feature extraction on the historical data to obtain a feature map comprising the historical data; classifying the feature map through a defect detection model to obtain a classification result and a classification loss value of the historical data; optimizing parameters of the object defect detection model to obtain a trained defect detection model; and inputting the detection data of the load abnormal node into the trained defect detection model to obtain the defect detection data. Wherein, the defects mainly comprise structural defects and functional defects, the structural defects comprise disjointing, cracking, errors and foreign matter invasion, and the functional defects comprise sludge or slurry deposition in the pipeline and the like.
In this way, defect detection data including defect location, type, etc. can be obtained. And the condition of the defect can influence the operation of the pipe network and even cause the fault. The invention provides the defect detection data and a risk prediction model, so that the failure of the pipe network can be predicted.
As a preferred embodiment, the risk prediction model is obtained by: acquiring historical monitoring data of load abnormal nodes and historical defect detection data of the load abnormal nodes, generating a fusion map according to the number, position and size of pipe network defects, labeling the fusion map, and generating a sample data set; generating an initial risk prediction model based on a neural network, and training the initial risk prediction according to the sample data set to determine model parameters; and generating a risk prediction model according to the model parameters and the initial defect diagnosis model.
The prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into the risk prediction model to obtain the risk value of the load abnormal node, and comprises the following steps: obtaining a predicted value of the pipe network fault according to the risk prediction model, and determining a weight corresponding to the pipe network fault by using an entropy method; and obtaining the risk value of the load abnormal node according to the predicted value of the pipe network fault and the weight corresponding to the pipe network fault.
The terminal module gives an alarm according to the risk value, and the alarm comprises the following steps: and judging the risk level according to the risk value, and determining the alarm level according to the risk level. And importing the geographic coordinates of each node and the monitoring data of each node into the GIS server to obtain a pipe network distribution map of each node, and then overlapping and highlighting the risk values of the nodes with abnormal loads on the pipe network distribution map.
As shown in fig. 1, the present invention also provides a digital pipe network control method based on GIS, which comprises the following steps: the method comprises the steps that an acquisition module is arranged, wherein the acquisition module comprises sensor units arranged in each node of the pipe network, and the sensor units are used for acquiring monitoring data of each node of the pipe network;
the monitoring data of each node is led into a GIS server by using a data analysis module to obtain a pipe network distribution diagram of each node, the monitoring data of each node is input into an abnormity prediction model to obtain load abnormity nodes, and the load abnormity nodes are highlighted in the pipe network distribution diagram;
detecting the load abnormal nodes by using a defect detection module to obtain defect detection data, wherein the prediction module is used for inputting the monitoring data of the load abnormal nodes and the defect detection data of the load abnormal nodes into a risk prediction model to obtain risk values of the load abnormal nodes and brightly displaying the risk values in a pipe network distribution diagram;
and the terminal module is used for displaying the pipe network distribution diagram and giving an alarm according to the risk value.
The method comprises the steps of leading monitoring data of each node into a GIS server to obtain a pipe network distribution diagram of each node, and inputting the monitoring data of each node into an anomaly prediction model to obtain load anomaly nodes; and then detecting the load abnormal node to obtain defect detection data, wherein the prediction module is used for inputting the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model to obtain a risk value of the load abnormal node, so that the sensor unit and the defect detection module are fused together, the possible risks of the pipe network are analyzed and early warned, and the control precision is greatly improved. Through multi-point monitoring, the operation and maintenance of the pipe network are assisted, and an operation and maintenance scheme, scheduling control and patrol maintenance are formulated. And analyzing the risk and potential safety hazard of the pipe network by using the online monitoring data, and providing a transformation basis. Monitoring the liquid level and the flow of the historical water accumulation points and the water-logging-prone points, and early warning and alarming in real time. The operation safety of the pipe network is improved, and the flood drainage and waterlogging prevention cost is reduced. And verifying whether the current drainage facilities are constructed according to the standard. Pipe network inspection, emergency maintenance, pipe network monitoring, liquid level monitoring, water quality monitoring, partition metering, pipe network scheduling, traceability analysis and rainwater pipe network.
The Geographic Information System (GIS) is a comprehensive technical system for collecting, storing, managing, analyzing and displaying information related to geographic phenomena. The method has the characteristics of spatial distribution, large data volume, multiple information carriers and time sequence, the data types of the method are vector data and grid data, and the basic elements of the data comprise geographic coordinates, plane coordinates and vertical coordinates.
The sensor unit comprises a flowmeter, a liquid level meter, a combustible gas monitor or a rainfall monitor. The defect detection module comprises a camera, a sonar detector or a periscope detector and is used for detecting functional defects or structural defects in abnormal nodes.
During installation, a drop-in liquid level meter or an L-shaped bracket-mounted flowmeter and the like can be adopted. And judging the type, position and number of the defects of the pipeline, wherein the structural defects comprise disjointing, cracking, errors and foreign matter invasion, and the functional defects comprise sludge or slurry deposition in the pipeline.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A GIS-based digital pipe network control system is characterized by comprising an acquisition module, a data analysis module, a defect detection module, a prediction module and a terminal module, wherein the acquisition module comprises sensor units arranged in each node of a pipe network, the sensor units are used for acquiring monitoring data of each node of the pipe network, the data analysis module is used for guiding the monitoring data of each node into a GIS server to obtain a pipe network distribution diagram of each node, inputting the monitoring data of each node into an abnormal prediction model to obtain abnormal load nodes, highlighting the abnormal load nodes in the pipe network distribution diagram, the defect detection module is used for detecting the abnormal load nodes to obtain defect detection data, the prediction module is used for inputting the monitoring data of the abnormal load nodes and the defect detection data of the abnormal load nodes into a risk prediction model, and the terminal module is used for displaying the pipe network distribution diagram and giving an alarm according to the risk value.
2. The GIS-based digital pipe network control system of claim 1, wherein the sensor unit comprises a flow meter, a level meter, a combustible gas monitor, or a rainfall monitor.
3. The GIS-based digital pipe network control system according to claim 1, wherein the defect detection module comprises a camera, a sonar detector or a periscope detector for detecting functional defects or structural defects in abnormal nodes.
4. The GIS-based digital pipe network control system of claim 1, wherein inputting the monitoring data of each node into an anomaly prediction model to obtain load anomaly nodes comprises: performing dimension reduction analysis on the monitoring data of each node, and determining a correlation factor of each monitoring data; inputting the monitoring data and the association factors of each node into an abnormal prediction model, obtaining the association index of the association factors for the load, if the association index is larger than or equal to a preset index, determining that the node is an abnormal load node, otherwise, determining that the node is not an abnormal load node.
5. The GIS-based digital pipe network control system of claim 4, wherein the anomaly prediction model is obtained by: the method comprises the steps of obtaining historical monitoring data of nodes under normal conditions and historical monitoring data of nodes under fault of a pipe network, extracting characteristic factors of the historical monitoring data, generating an abnormal index according to the characteristic factors, and associating the abnormal index with the historical monitoring data to generate an abnormal prediction model.
6. The GIS-based digital pipe network control system of claim 1, wherein the defect detection module detects the load abnormal node to obtain defect detection data, comprising: acquiring historical data of normal nodes and historical data of load abnormal nodes, and performing feature extraction on the historical data to obtain a feature graph comprising the historical data; classifying the feature map through a defect detection model to obtain a classification result and a classification loss value of the historical data; optimizing parameters of the object defect detection model to obtain a trained defect detection model; and inputting the detection data of the load abnormal node into the trained defect detection model to obtain the defect detection data.
7. The GIS-based digital pipe network control system of claim 1, wherein the risk prediction model is obtained by: acquiring historical monitoring data of load abnormal nodes and historical defect detection data of the load abnormal nodes, generating a fusion map according to the number, position and size of pipe network defects, labeling the fusion map, and generating a sample data set; generating an initial risk prediction model based on a neural network, and training the initial risk prediction according to the sample data set to determine model parameters; and generating a risk prediction model according to the model parameters and the initial defect diagnosis model.
8. The GIS-based digital pipe network control system according to claim 1, wherein the prediction module is configured to input the monitoring data of the load abnormal node and the defect detection data of the load abnormal node into a risk prediction model to obtain a risk value of the load abnormal node, and the risk value includes: obtaining a predicted value of the pipe network fault according to the risk prediction model, and determining a weight corresponding to the pipe network fault by using an entropy method; and obtaining the risk value of the load abnormal node according to the predicted value of the pipe network fault and the weight corresponding to the pipe network fault.
9. The GIS-based digital pipe network control system of claim 8, wherein the terminal module alarms according to the risk value, comprising: and judging the risk level according to the risk value, and determining the alarm level according to the risk level.
10. The GIS-based digital pipe network control system according to claim 1, wherein geographical coordinates of each node and monitoring data of each node are imported into the GIS server to obtain a pipe network distribution map of each node, and then risk values of load abnormal nodes are superimposed and highlighted on the pipe network distribution map.
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| CN117436700A (en) * | 2023-11-14 | 2024-01-23 | 山东和同信息科技股份有限公司 | BIM-based new energy engineering data management system and method |
| CN117436700B (en) * | 2023-11-14 | 2024-04-12 | 山东和同信息科技股份有限公司 | BIM-based new energy engineering data management system and method |
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