Drainage pipe network management and control system based on digital backplane

Abstract

The invention discloses a drainage pipe network management and control system based on a digital backplane, and belongs to the technical field of drainage pipe networks, and the system comprises a data collection and sensing module which is used for collecting drainage pipe network monitoring real-time data; the digital base plate construction and integration module is used for constructing a drainage pipe network three-dimensional digital model to form a unified digital base plate; the data analysis and decision support module is used for identifying potential risk points and predicting the running trend of the drainage pipe network; and the intelligent scheduling and emergency response module is used for performing intelligent scheduling and emergency early warning response management and control on the drainage pipe network. The invention solves the problems that the drainage pipe network cannot be monitored in real time, the fault of the drainage pipe network cannot be found in time, and the drainage pipe network cannot be intelligently dispatched and subjected to emergency response management and control in the prior art. The drainage pipe network can be monitored in real time, faults of the drainage pipe network can be found in time, intelligent scheduling and emergency response management and control can be conducted on the drainage pipe network, normal operation of the municipal drainage pipe network can be fully guaranteed, and the management and control effect of the drainage pipe network can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drainage pipe networks, and in particular to a drainage pipe network management and control system based on a digital baseboard. Background Art

[0002] The municipal drainage network is the backbone project for urban water pollution prevention and control and urban drainage and flood control. It is responsible for collecting urban domestic sewage and industrial wastewater and promptly removing rainwater runoff from urban areas. It is an important lifeline to ensure the normal operation of the city. With the process of urbanization, the construction speed of the municipal drainage network system is fast, the scale is large, and the update is fast, and the difficulty of drainage network management and control is increasing.

[0003] The Chinese patent with publication number CN102915474A discloses a municipal drainage network maintenance management method, which implements the network maintenance process through the network maintenance system. The network maintenance system is constructed by combining the C / S mode, the B / S mode and the M / S mode, and specifically includes a C / S maintenance management subsystem, a B / S maintenance management subsystem and an M / S maintenance management subsystem; it can realize the efficient execution of the network maintenance work and the unified supervision of the entire maintenance management work; however, the patent has the following defects: The existing system cannot monitor the drainage network in real time, cannot detect drainage network failures in time, and cannot perform intelligent scheduling and emergency response management of the drainage network. It cannot fully guarantee the normal operation of the municipal drainage network, resulting in poor drainage network management and control effects. Summary of the invention

[0004] The purpose of the present invention is to provide a drainage network management and control system based on a digital baseboard, which can monitor the drainage network in real time, promptly discover drainage network faults and perform intelligent scheduling and emergency response management of the drainage network, fully ensure the normal operation of the municipal drainage network, improve the drainage network management and control effect, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The drainage network management and control system based on the digital baseboard includes: The data collection and perception module is used to use intelligent sensors, RFID tags and drone inspection technology to monitor the water level, flow rate, water quality and pipeline status of the drainage network in real time, determine the real-time monitoring data of the drainage network, and transmit the real-time monitoring data of the drainage network to the data center; Digital baseboard construction and integration module, which is used to build a 3D digital model of the drainage network based on high-precision maps and 3D modeling technology, and seamlessly connect with urban infrastructure data to form a unified digital baseboard with spatial consistency and multi-dimensional visualization; The data analysis and decision support module is used to use big data analysis and artificial intelligence algorithms to conduct in-depth mining of the collected real-time data of drainage network monitoring, identify potential risk points, predict the operation trend of the drainage network, and determine the drainage network risk identification and network prediction results based on the digital baseboard, providing a scientific basis for drainage network maintenance, optimized scheduling, and emergency response; The intelligent scheduling and emergency response module is used to automatically generate maintenance plans, optimize resource allocation, and formulate intelligent scheduling and emergency response management and control plans for the drainage network based on the risk identification and network prediction results of the drainage network. It can perform intelligent scheduling and emergency warning response management and control of the drainage network. When encountering heavy rain and extreme weather, it can quickly launch emergency warning plans and intelligently guide drainage operations.

[0006] Preferably, the data collection and perception module includes: Water level acquisition and sensing unit, used to obtain water level parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water level of the drainage network is monitored and continuously collected in real time to obtain the water level parameters of the drainage network; Flow velocity collection and sensing unit, used to obtain flow velocity parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the flow velocity of the drainage network is monitored and continuously collected in real time to obtain the flow velocity parameters of the drainage network; Water quality collection and sensing unit, used to obtain water quality parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water quality of the drainage network is monitored and continuously collected in real time to obtain the water quality parameters of the drainage network; Pipeline status acquisition and perception unit, used to obtain pipeline status parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the pipeline status of the drainage network is monitored and continuously collected in real time to obtain the pipeline status parameters of the drainage network; Among them, based on the water level parameters, flow rate parameters, water quality parameters and pipeline status parameters of the drainage network, the real-time monitoring data of the drainage network is determined, and the real-time monitoring data of the drainage network is transmitted to the data center.

[0007] Preferably, the digital backplane construction and integration module comprises: A three-dimensional digital model building unit, used to build a three-dimensional digital model of the drainage network; Obtain real-time data for drainage network monitoring; Based on high-precision maps and 3D modeling technology, the real-time data of drainage network monitoring is converted into a format and coordinate system suitable for 3D modeling; Based on the static data association method, the real-time data of drainage network monitoring is associated; Determine the real-time data association set for drainage network monitoring; Select appropriate 3D modeling software based on the real-time data set of drainage network monitoring; Based on 3D modeling software, the real-time data association set of drainage network monitoring is 3D modeled, and the 3D digital model is textured and rendered; Construct a 3D digital model of the drainage network with a one-to-one restoration; Data docking and integration unit, used for seamless docking and integration of the 3D digital model of the drainage network and urban infrastructure data; Collect parameters of roads, bridges and underground spaces of urban infrastructure to obtain urban infrastructure data; Integrate urban infrastructure data into the 3D digital model of the drainage network, so that the 3D digital model of the drainage network and urban infrastructure data can be seamlessly integrated; Determine the digital base for spatial consistency and multi-dimensional visualization.

[0008] Preferably, the data analysis and decision support module includes: Drainage network data processing unit, used to process the real-time data of drainage network monitoring; Obtain the real-time monitoring data of the drainage network and clean the real-time monitoring data of the drainage network; Remove inconsistent data, invalid values ​​and missing values ​​in real-time data of drainage network monitoring; Determine the real-time data of drainage network monitoring that is useful for drainage network management and control; Convert the real-time data of drainage network monitoring that is useful for drainage network management and control; Eliminate the dimensional differences between real-time data of drainage network monitoring that is useful for drainage network management and control; Determine standardized real-time data for drainage network monitoring; Extract features from standardized real-time data of drainage network monitoring; Extract features that can reflect the management and control of drainage networks; Determine the monitoring characteristic data of the drainage network.

[0009] Preferably, the data analysis and decision support module further includes: In the data volume extraction module, after the real-time data of the drainage network monitoring is cleaned, the data volume corresponding to the inconsistent data and invalid values ​​in the real-time data of the drainage network monitoring is extracted; A data anomaly evaluation coefficient acquisition module, used to obtain a data anomaly evaluation coefficient using the data amount corresponding to the inconsistent data and the invalid value; The data anomaly evaluation coefficient is obtained by the following formula:

[0010] Where W represents the data anomaly evaluation coefficient; n represents the number of times the real-time data of drainage network monitoring is collected; C bi represents the amount of inconsistent data in the real-time data of drainage network monitoring collected for the i-th time; C wi represents the amount of invalid values ​​in the real-time data of drainage network monitoring collected for the i-th time; C xi represents the amount of effective value data in the real-time data of drainage network monitoring collected for the i-th time; w represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0011] Where w represents the adjustment coefficient; C bm represents the standard deviation of the inconsistent data in the real-time data of drainage network monitoring collected n times; C wm represents the standard deviation of the amount of invalid values ​​in the real-time data of drainage network monitoring collected n times; C xb represents the standard deviation of the effective value of the real-time data collected from the drainage network monitoring for n times; C zb represents the standard deviation of the total data volume of the real-time data of the drainage network monitoring collected n times; w 01 and w 02 Respectively represent the data type weight values ​​corresponding to the preset inconsistent data and invalid values; A coefficient comparison module, used to compare the data anomaly evaluation coefficient with a preset evaluation coefficient threshold; The missing value filling module is used to fill the missing value data when the data abnormality evaluation coefficient exceeds a preset evaluation coefficient threshold.

[0012] Preferably, the missing value filling module includes: A data anomaly evaluation coefficient extraction module, used to extract the data anomaly evaluation coefficient; A first data information acquisition module is used to extract the valid data adjacent to each missing value data as the first data information; A second data information acquisition module is used to extract all valid data from the real-time data of the drainage network monitoring as the second data information; A filling value acquisition module, used to obtain a filling value corresponding to a missing value by using the first data information and the second data information in combination with a data anomaly evaluation coefficient; The filling value corresponding to the missing value is obtained by the following formula:

[0013] Among them, X t Indicates the filling value corresponding to the missing value; X 01 and X 02 represents the data value of the valid data before and after each missing value data; m represents the total number of all valid data in the real-time data of the drainage network monitoring; X i Indicates the data value corresponding to the i-th valid data; X p represents the data average of m valid data; W represents the data abnormality evaluation coefficient; A filling control module is used to fill the missing values ​​with the filling values ​​corresponding to the missing values.

[0014] Preferably, the data analysis and decision support module further includes: A drainage network risk identification model building unit, used to build a drainage network risk identification model; Collect historical data of drainage network monitoring according to the drainage network management and control requirements based on the digital baseboard; Divide the historical data of drainage network monitoring to determine the training set and test set; Select the neural network model framework suitable for digital baseboard-based drainage network management and control; Based on the training set, the selected neural network model framework suitable for the drainage network management and control based on the digital baseboard is trained; Determine the risk identification model of drainage network based on digital baseboard; Among them, based on the test set, the risk identification model of the drainage network based on the digital baseboard was tested; Determine the test results based on the drainage network risk identification model; According to the test results based on the drainage network risk identification model, the parameters of the drainage network risk identification model based on the digital baseboard are adjusted and the structure is optimized; Determine the optimal digital baseboard-based drainage network risk identification model.

[0015] Preferably, the data analysis and decision support module further includes: Risk identification and pipe network prediction unit, used to identify risks and predict the operation trend of the drainage pipe network; Obtain the optimal risk identification model of drainage network based on digital baseboard; Deploy the optimal digital-based drainage network risk identification model in the actual drainage network management and control, and input the drainage network monitoring feature data into the optimal digital-based drainage network risk identification model; Based on the optimal digital baseboard-based drainage network risk identification model, the drainage network monitoring feature data is deeply mined to identify potential risk points and predict the operation trend of the drainage network; Determine the drainage network risk identification and network prediction results based on the digital base plate, and provide a scientific basis for drainage network maintenance, optimized scheduling, and emergency response.

[0016] Preferably, the intelligent dispatching and emergency response module includes: Drainage network control plan formulation unit, used to formulate drainage network intelligent dispatching and emergency response control plans; Obtain drainage network risk identification and network prediction results based on digital baseboard; According to the risk identification and prediction results of the drainage network based on the digital base plate, the drainage network monitoring feature data is mined and analyzed, the maintenance plan is automatically generated, the resource allocation is optimized, and the intelligent scheduling and emergency response management and control plan of the drainage network is formulated.

[0017] Preferably, the intelligent dispatching and emergency response module further includes: Intelligent drainage network control unit, used for intelligent dispatching and emergency warning response control of drainage network; Obtain intelligent dispatching and emergency response management and control solutions for drainage networks; Based on the intelligent dispatching and emergency response control scheme of the drainage network, the drainage network is intelligently dispatched and the emergency warning response is controlled; Among them, when encountering extreme weather such as heavy rain, the emergency warning plan is quickly activated and intelligent guidance is provided for drainage operations.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses intelligent sensors, RFID tags and drone inspection techniques to monitor the water level, flow rate, water quality and pipeline status of the drainage network in real time, determine the real-time monitoring data of the drainage network, and transmit the real-time monitoring data of the drainage network to the data center. Based on high-precision maps and three-dimensional modeling technology, a three-dimensional digital model of the drainage network is constructed, and seamlessly connected with urban infrastructure data to form a unified spatial consistency and multi-dimensional visualized digital base. Big data analysis and artificial intelligence algorithms are used to deeply mine the collected real-time monitoring data of the drainage network, identify potential risk points, predict the operation trend of the drainage network, and determine the risk identification and network prediction results of the drainage network based on the digital base, providing a scientific basis for drainage network maintenance, optimized scheduling, and emergency response.

[0019] 2. The present invention automatically generates a maintenance plan, optimizes resource allocation, and formulates an intelligent scheduling and emergency response management plan for the drainage network based on the risk identification and network prediction results of the drainage network. It performs intelligent scheduling and emergency warning response management on the drainage network. When encountering heavy rain or extreme weather, it quickly activates the emergency warning plan and intelligently guides drainage operations. It can monitor the drainage network in real time, detect drainage network faults in a timely manner, and perform intelligent scheduling and emergency response management on the drainage network. It can fully guarantee the normal operation of the municipal drainage network and improve the management effect of the drainage network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of the drainage network management and control system based on the digital baseboard of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In order to solve the existing problems of being unable to monitor the drainage network in real time, being unable to detect drainage network failures in time, and being unable to intelligently dispatch and control the drainage network in emergency response, and being unable to fully guarantee the normal operation of the municipal drainage network, resulting in poor drainage network control effects, please refer to Figure 1 , this embodiment provides the following technical solutions: The drainage network management and control system based on the digital baseboard includes: The data collection and perception module is used to use intelligent sensors, RFID tags and drone inspection technology to monitor the water level, flow rate, water quality and pipeline status of the drainage network in real time, determine the real-time monitoring data of the drainage network, and transmit the real-time monitoring data of the drainage network to the data center; In this embodiment, the data collection and perception module includes: Water level acquisition and sensing unit, used to obtain water level parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water level of the drainage network is monitored and continuously collected in real time to obtain the water level parameters of the drainage network; Flow velocity collection and sensing unit, used to obtain flow velocity parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the flow velocity of the drainage network is monitored and continuously collected in real time to obtain the flow velocity parameters of the drainage network; Water quality collection and sensing unit, used to obtain water quality parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water quality of the drainage network is monitored and continuously collected in real time to obtain the water quality parameters of the drainage network; Pipeline status acquisition and perception unit, used to obtain pipeline status parameters of the drainage network; Among them, pipeline status includes damage and blockage; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the pipeline status of the drainage network is monitored and continuously collected in real time to obtain the pipeline status parameters of the drainage network; Among them, based on the water level parameters, flow rate parameters, water quality parameters and pipeline status parameters of the drainage network, the real-time monitoring data of the drainage network is determined, and the real-time monitoring data of the drainage network is transmitted to the data center.

[0023] Digital baseboard construction and integration module, which is used to build a 3D digital model of the drainage network based on high-precision maps and 3D modeling technology, and seamlessly connect with urban infrastructure data to form a unified digital baseboard with spatial consistency and multi-dimensional visualization; In this embodiment, the digital backplane construction and integration module includes: A three-dimensional digital model building unit, used to build a three-dimensional digital model of the drainage network; Obtain real-time data for drainage network monitoring; Based on high-precision maps and 3D modeling technology, the real-time data of drainage network monitoring is converted into a format and coordinate system suitable for 3D modeling; Based on the static data association method, the real-time data of drainage network monitoring is associated; Determine the real-time data association set for drainage network monitoring; Select appropriate 3D modeling software based on the real-time data set of drainage network monitoring; Based on 3D modeling software, the real-time data association set of drainage network monitoring is 3D modeled, and the 3D digital model is textured and rendered; Construct a 3D digital model of the drainage network with a one-to-one restoration; Data docking and integration unit, used for seamless docking and integration of the 3D digital model of the drainage network and urban infrastructure data; Collect parameters of roads, bridges and underground spaces of urban infrastructure to obtain urban infrastructure data; Integrate urban infrastructure data into the 3D digital model of the drainage network, so that the 3D digital model of the drainage network and urban infrastructure data can be seamlessly integrated; Determine the digital base for spatial consistency and multi-dimensional visualization.

[0024] It should be noted that the digital baseboard refers to a basic data set or database established in a specific field or project to achieve unified management, sharing and application of data; it usually contains the basic data and geospatial data required for the field or project, and provides data support for upper-level applications. The applications of digital baseboards in different fields are as follows: ‌Digital Twin Basin‌: The data base involves sensing technologies such as remote sensing and positioning, information transmission technologies such as the Internet of Things and 5G, cloud computing, edge computing, GIS, BIM and other technologies, which are used to build digital twin basins; ‌Digital Twin City‌: The digital twin city base is a spatial scene with all elements, including spatial elements, temporal elements, semantic elements, and data elements. All static and dynamic data are structured through the spatiotemporal data engine to provide spatiotemporal scene services for other modules; ‌Smart City‌: In the construction of smart cities, the digital base is used to integrate urban spatial data and basic operation data resources to support the construction of "digital government"; ‌Digital Economy‌: The digital base is a general infrastructure for informatization, including 5G networks, cloud computing, etc., which plays a strategic, technical and leading role in economic and social development. Therefore, the importance of the digital base lies in its ability to achieve standardization, integrated management and efficient use of data, support more advanced data processing and analysis needs, and promote digital transformation and intelligent development in various fields.

[0025] The data analysis and decision support module is used to use big data analysis and artificial intelligence algorithms to conduct in-depth mining of the collected real-time data of drainage network monitoring, identify potential risk points, predict the operation trend of the drainage network, and determine the drainage network risk identification and network prediction results based on the digital baseboard, providing a scientific basis for drainage network maintenance, optimized scheduling, and emergency response; In this embodiment, the data analysis and decision support module includes: Drainage network data processing unit, used to process the real-time data of drainage network monitoring; Obtain the real-time monitoring data of the drainage network and clean the real-time monitoring data of the drainage network; Remove inconsistent data, invalid values ​​and missing values ​​in real-time data of drainage network monitoring; Determine the real-time data of drainage network monitoring that is useful for drainage network management and control; It should be noted that the real-time monitoring data of the drainage network is checked for consistency. According to the reasonable value range and mutual relationship of each parameter in the real-time monitoring data of the drainage network, the real-time monitoring data of the drainage network is checked to see whether it meets the requirements, and inconsistent data that exceeds the normal range, is logically unreasonable or contradictory in the real-time monitoring data of the drainage network is removed; It should be noted that the real-time data of drainage network monitoring is checked for invalid values ​​and missing values. According to the data validity and integrity requirements, it is checked whether the real-time data of drainage network monitoring contains invalid values ​​and missing values, and the invalid values ​​and missing values ​​in the real-time data of drainage network monitoring that are useless for drainage network control are removed.

[0026] Convert the real-time data of drainage network monitoring that is useful for drainage network management and control; Eliminate the dimensional differences between real-time data of drainage network monitoring that is useful for drainage network management and control; Determine standardized real-time data for drainage network monitoring; It should be noted that the real-time data of drainage network monitoring that is useful for drainage network management and control is converted so that the real-time data of drainage network monitoring is transformed into a unified format, and standardized real-time data of drainage network monitoring is determined.

[0027] Extract features from standardized real-time data of drainage network monitoring; Extract features that can reflect the management and control of drainage networks; Determine the monitoring characteristic data of the drainage network.

[0028] It should be noted that feature extraction refers to the method and process of using computers to extract characteristic information from real-time data of drainage network monitoring. By performing feature extraction on standardized real-time data of drainage network monitoring, the drainage network monitoring characteristic data can be determined, which facilitates subsequent in-depth mining of the drainage network monitoring characteristic data, identification of potential risk points, and prediction of the operation trend of the drainage network.

[0029] Specifically, the data analysis and decision support module also includes: In the data volume extraction module, after the real-time data of the drainage network monitoring is cleaned, the data volume corresponding to the inconsistent data and invalid values ​​in the real-time data of the drainage network monitoring is extracted; A data anomaly evaluation coefficient acquisition module, used to obtain a data anomaly evaluation coefficient using the data amount corresponding to the inconsistent data and the invalid value; The data anomaly evaluation coefficient is obtained by the following formula:

[0030] Where W represents the data anomaly evaluation coefficient; n represents the number of times the real-time data of drainage network monitoring is collected; C bi represents the amount of inconsistent data in the real-time data of drainage network monitoring collected for the i-th time; C wi represents the amount of invalid values ​​in the real-time data of drainage network monitoring collected for the i-th time; C xirepresents the amount of effective value data in the real-time data of drainage network monitoring collected for the i-th time; w represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0031] Where w represents the adjustment coefficient; C bm represents the standard deviation of the inconsistent data in the real-time data of drainage network monitoring collected n times; C wm represents the standard deviation of the amount of invalid values ​​in the real-time data of drainage network monitoring collected n times; C xb represents the standard deviation of the effective value of the real-time data collected from the drainage network monitoring for n times; C zb represents the standard deviation of the total data volume of the real-time data of the drainage network monitoring collected n times; w 01 and w 02 Respectively represent the data type weight values ​​corresponding to the preset inconsistent data and invalid values; A coefficient comparison module, used to compare the data anomaly evaluation coefficient with a preset evaluation coefficient threshold; The missing value filling module is used to fill the missing value data when the data abnormality evaluation coefficient exceeds a preset evaluation coefficient threshold.

[0032] The technical effect of the above technical solution is: through the data volume extraction module, the inconsistent data and invalid values ​​in the real-time data of the drainage network monitoring can be accurately identified and extracted, which helps to improve the data quality of subsequent analysis. The data anomaly evaluation coefficient acquisition module uses the data volume of inconsistent data and invalid values, combined with the data volume of valid values ​​and the adjustment coefficient, to obtain the data anomaly evaluation coefficient. This coefficient can quantify the degree of data anomaly and provide a basis for decision-making. The calculation of the adjustment coefficient takes into account the data volume standard deviation of inconsistent data, invalid values, valid values ​​and the total data volume standard deviation, which enables the data anomaly evaluation coefficient to be adaptively adjusted according to the characteristics of different data sets, thereby improving the accuracy and robustness of the evaluation. Through the preset data type weight values ​​(w01 and w02) corresponding to inconsistent data and invalid values, the influence of different types of data in the anomaly evaluation can be flexibly adjusted, thereby more accurately reflecting the actual situation of the data. When the data anomaly evaluation coefficient exceeds the preset evaluation coefficient threshold, the missing value filling module will fill the missing value data. This intelligent processing can reduce the impact of missing data on subsequent analysis and improve the integrity and availability of data. The entire module provides strong data support for drainage network monitoring by quantifying the degree of data anomalies, identifying missing values ​​and filling them in. This helps to timely discover and solve potential problems and improve the management efficiency and safety of the drainage network.

[0033] In summary, this technical solution achieves comprehensive, accurate and intelligent processing of real-time data from drainage network monitoring through a series of carefully designed sub-modules and algorithms, providing strong data support for decision-making.

[0034] Specifically, the missing value filling module includes: A data anomaly evaluation coefficient extraction module, used to extract the data anomaly evaluation coefficient; A first data information acquisition module is used to extract the valid data adjacent to each missing value data as the first data information; A second data information acquisition module is used to extract all valid data from the real-time data of the drainage network monitoring as the second data information; A filling value acquisition module, used to obtain a filling value corresponding to a missing value by using the first data information and the second data information in combination with a data anomaly evaluation coefficient; The filling value corresponding to the missing value is obtained by the following formula:

[0035] Among them, X t Indicates the filling value corresponding to the missing value; X 01 and X 02 represents the data value of the valid data before and after each missing value data; m represents the total number of all valid data in the real-time data of the drainage network monitoring; X i Indicates the data value corresponding to the i-th valid data; X p represents the data average of m valid data; W represents the data abnormality evaluation coefficient; A filling control module is used to fill the missing values ​​with the filling values ​​corresponding to the missing values.

[0036] The technical effect of the above technical solution is that by filling missing values, the module can significantly improve the integrity of the data. In drainage network monitoring, data integrity is crucial for accurate analysis and timely detection of problems. The filling value acquisition module combines the first data information (valid data adjacent to the missing value before and after) and the second data information (all valid data), and takes into account the data anomaly evaluation coefficient, so that the missing value can be estimated more accurately. This intelligent filling strategy helps to improve the accuracy and reliability of the filling value. The module not only considers the valid data before and after the missing value, but also utilizes the information of all valid data. This comprehensive data utilization method can more comprehensively reflect the overall characteristics and trends of the data, thereby improving the rationality of the filling value. By introducing the data anomaly evaluation coefficient, the module can make adaptive adjustments according to the characteristics and degree of anomaly of different data sets. This makes the filling value more in line with the actual situation and improves the accuracy and adaptability of the filling. The module realizes an automated and intelligent missing value filling process through a series of sub-modules, reducing the complexity of manual intervention and operation. This helps to improve work efficiency and accuracy. Complete and accurate data is the basis for decision-making. By intelligently filling missing values, this module provides more reliable data support for drainage network monitoring, helping to promptly detect and resolve potential problems and improve management efficiency and safety.

[0037] In summary, this technical solution improves the integrity and accuracy of data by intelligently filling missing values, providing strong data support for drainage network monitoring. At the same time, the module is also easy to operate and highly adaptable, which helps to improve work efficiency and decision-making level.

[0038] In this embodiment, the data analysis and decision support module further includes: A drainage network risk identification model building unit, used to build a drainage network risk identification model; Collect historical data of drainage network monitoring according to the drainage network management and control requirements based on the digital baseboard; Divide the historical data of drainage network monitoring to determine the training set and test set; Select the neural network model framework suitable for digital baseboard-based drainage network management and control; Based on the training set, the selected neural network model framework suitable for the drainage network management and control based on the digital baseboard is trained; Determine the risk identification model of drainage network based on digital baseboard; Among them, based on the test set, the risk identification model of the drainage network based on the digital baseboard was tested; Determine the test results based on the drainage network risk identification model; According to the test results based on the drainage network risk identification model, the parameters of the drainage network risk identification model based on the digital baseboard are adjusted and the structure is optimized; Determine the optimal digital baseboard-based drainage network risk identification model.

[0039] In this embodiment, the data analysis and decision support module further includes: Risk identification and pipe network prediction unit, used to identify risks and predict the operation trend of the drainage pipe network; Obtain the optimal risk identification model of drainage network based on digital baseboard; Deploy the optimal digital-based drainage network risk identification model in the actual drainage network management and control, and input the drainage network monitoring feature data into the optimal digital-based drainage network risk identification model; Based on the optimal digital baseboard-based drainage network risk identification model, the drainage network monitoring feature data is deeply mined to identify potential risk points and predict the operation trend of the drainage network; Determine the drainage network risk identification and network prediction results based on the digital base plate, and provide a scientific basis for drainage network maintenance, optimized scheduling, and emergency response.

[0040] The intelligent scheduling and emergency response module is used to automatically generate maintenance plans, optimize resource allocation, and formulate intelligent scheduling and emergency response management and control plans for the drainage network based on the risk identification and network prediction results of the drainage network. It can perform intelligent scheduling and emergency warning response management and control of the drainage network. When encountering heavy rain and extreme weather, it can quickly launch emergency warning plans and intelligently guide drainage operations.

[0041] In this embodiment, the intelligent dispatching and emergency response module includes: Drainage network control plan formulation unit, used to formulate drainage network intelligent dispatching and emergency response control plans; Obtain drainage network risk identification and network prediction results based on digital baseboard; According to the risk identification and prediction results of the drainage network based on the digital base plate, the drainage network monitoring feature data is mined and analyzed, the maintenance plan is automatically generated, the resource allocation is optimized, and the intelligent scheduling and emergency response management and control plan of the drainage network is formulated.

[0042] In this embodiment, the intelligent dispatching and emergency response module further includes: Intelligent drainage network control unit, used for intelligent dispatching and emergency warning response control of drainage network; Obtain intelligent dispatching and emergency response management and control solutions for drainage networks; Based on the intelligent dispatching and emergency response control scheme of the drainage network, the drainage network is intelligently dispatched and the emergency warning response is controlled; Among them, when encountering extreme weather such as heavy rain, the emergency warning plan is quickly activated and intelligent guidance is provided for drainage operations.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0044] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The drainage network management and control system based on the digital baseboard is characterized by: include: The data collection and perception module is used to use intelligent sensors, RFID tags and drone inspection technology to monitor the water level, flow rate, water quality and pipeline status of the drainage network in real time, determine the real-time monitoring data of the drainage network, and transmit the real-time monitoring data of the drainage network to the data center; Digital baseboard construction and integration module, which is used to build a 3D digital model of the drainage network based on high-precision maps and 3D modeling technology, and seamlessly connect with urban infrastructure data to form a unified digital baseboard with spatial consistency and multi-dimensional visualization; The data analysis and decision support module is used to use big data analysis and artificial intelligence algorithms to conduct in-depth mining of the collected real-time data of drainage network monitoring, identify potential risk points, predict the operation trend of the drainage network, and determine the drainage network risk identification and network prediction results based on the digital baseboard, providing a scientific basis for drainage network maintenance, optimized scheduling, and emergency response; The intelligent dispatching and emergency response module is used to automatically generate maintenance plans, optimize resource allocation, and formulate intelligent dispatching and emergency response management plans for the drainage network based on the risk identification and network prediction results of the drainage network. It can also conduct intelligent dispatching and emergency warning response management for the drainage network. In the event of heavy rain or extreme weather, it can quickly launch emergency warning plans and provide intelligent guidance for drainage operations. The data analysis and decision support module includes: Drainage network data processing unit, used to process the real-time data of drainage network monitoring; Obtain the real-time monitoring data of the drainage network and clean the real-time monitoring data of the drainage network; Remove inconsistent data, invalid values ​​and missing values ​​in real-time data of drainage network monitoring; Determine the real-time data of drainage network monitoring that is useful for drainage network management and control; Convert the real-time data of drainage network monitoring that is useful for drainage network management and control; Eliminate the dimensional differences between real-time data of drainage network monitoring that is useful for drainage network management and control; Determine standardized real-time data for drainage network monitoring; Extract features from standardized real-time data of drainage network monitoring; Extract features that can reflect the management and control of drainage networks; Determine the monitoring characteristic data of the drainage network; The data analysis and decision support module also includes: In the data volume extraction module, after cleaning the real-time data of the drainage network monitoring, the data volume corresponding to the inconsistent data and invalid values ​​in the real-time data of the drainage network monitoring is extracted; A data anomaly evaluation coefficient acquisition module, used to obtain a data anomaly evaluation coefficient using the data amount corresponding to the inconsistent data and the invalid value; The data anomaly evaluation coefficient is obtained by the following formula: ； Where W represents the data anomaly evaluation coefficient; n represents the number of times the real-time data of drainage network monitoring is collected; C bi represents the amount of inconsistent data in the real-time data of drainage network monitoring collected for the i-th time; C wi represents the amount of invalid values ​​in the real-time data of drainage network monitoring collected for the i-th time; C xi represents the amount of effective value data in the real-time data of drainage network monitoring collected for the i-th time; w represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: ； Where w represents the adjustment coefficient; C bm represents the standard deviation of the inconsistent data in the real-time data of drainage network monitoring collected n times; C wm represents the standard deviation of the amount of invalid values ​​in the real-time data of drainage network monitoring collected n times; C xb represents the standard deviation of the effective value of the real-time data collected from the drainage network monitoring for n times; C zb represents the standard deviation of the total data volume of the real-time data of the drainage network monitoring collected n times; w 01 and w 02 Respectively represent the data type weight values ​​corresponding to the preset inconsistent data and invalid values; A coefficient comparison module, used to compare the data anomaly evaluation coefficient with a preset evaluation coefficient threshold; A missing value filling module is used to fill the missing value data when the data abnormality evaluation coefficient exceeds a preset evaluation coefficient threshold; The missing value filling module comprises: A data anomaly evaluation coefficient extraction module, used to extract the data anomaly evaluation coefficient; A first data information acquisition module, used to extract valid data adjacent to and before each missing value data as first data information; A second data information acquisition module is used to extract all valid data from the real-time data of the drainage network monitoring as the second data information; A filling value acquisition module, used to obtain a filling value corresponding to a missing value by using the first data information and the second data information in combination with a data anomaly evaluation coefficient; The filling value corresponding to the missing value is obtained by the following formula: ； Among them, X t Indicates the filling value corresponding to the missing value; X 01 and X 02 represents the data value of the valid data before and after each missing value data; m represents the total number of all valid data in the real-time data of the drainage network monitoring; X i Indicates the data value corresponding to the i-th valid data; X p represents the data average of m valid data; W represents the data abnormality evaluation coefficient; A filling control module is used to fill the missing values ​​with the filling values ​​corresponding to the missing values.

2. The drainage network management and control system based on digital baseboard according to claim 1 is characterized in that: The data collection and perception module includes: Water level acquisition and sensing unit, used to obtain water level parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water level of the drainage network is monitored and continuously collected in real time to obtain the water level parameters of the drainage network; Flow velocity collection and sensing unit, used to obtain flow velocity parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the flow velocity of the drainage network is monitored and continuously collected in real time to obtain the flow velocity parameters of the drainage network; Water quality collection and sensing unit, used to obtain water quality parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the water quality of the drainage network is monitored and continuously collected in real time to obtain the water quality parameters of the drainage network; Pipeline status acquisition and perception unit, used to obtain pipeline status parameters of the drainage network; Among them, based on the technical means of intelligent sensors, RFID tags and drone inspections, the pipeline status of the drainage network is monitored and continuously collected in real time to obtain the pipeline status parameters of the drainage network; Among them, based on the water level parameters, flow rate parameters, water quality parameters and pipeline status parameters of the drainage network, the real-time monitoring data of the drainage network is determined, and the real-time monitoring data of the drainage network is transmitted to the data center.

3. The drainage network management and control system based on digital baseboard according to claim 2 is characterized in that: The digital backplane construction and integration module includes: A three-dimensional digital model building unit, used to build a three-dimensional digital model of the drainage network; Obtain real-time data for drainage network monitoring; Based on high-precision maps and 3D modeling technology, the real-time data of drainage network monitoring is converted into a format and coordinate system suitable for 3D modeling; Based on the static data association method, the real-time data of drainage network monitoring is associated; Determine the real-time data association set for drainage network monitoring; Select appropriate 3D modeling software based on the real-time data set of drainage network monitoring; Based on 3D modeling software, the real-time data association set of drainage network monitoring is 3D modeled, and the 3D digital model is textured and rendered; Construct a 3D digital model of the drainage network with a one-to-one restoration; Data docking and integration unit, used for seamless docking and integration of the 3D digital model of the drainage network and urban infrastructure data; Collect parameters of roads, bridges and underground spaces of urban infrastructure to obtain urban infrastructure data; Integrate urban infrastructure data into the 3D digital model of the drainage network, so that the 3D digital model of the drainage network and urban infrastructure data can be seamlessly integrated; Determine the digital base for spatial consistency and multi-dimensional visualization.

4. The drainage network management and control system based on digital baseboard according to claim 3 is characterized in that: The data analysis and decision support module also includes: A drainage network risk identification model building unit, used to build a drainage network risk identification model; Collect historical data of drainage network monitoring according to the drainage network management and control requirements based on the digital baseboard; Divide the historical data of drainage network monitoring to determine the training set and test set; Select the neural network model framework suitable for digital baseboard-based drainage network management and control; Based on the training set, the selected neural network model framework suitable for the drainage network management and control based on the digital baseboard is trained; Determine the risk identification model of drainage network based on digital baseboard; Among them, based on the test set, the risk identification model of the drainage network based on the digital baseboard was tested; Determine the test results based on the drainage network risk identification model; According to the test results based on the drainage network risk identification model, the parameters of the drainage network risk identification model based on the digital baseboard are adjusted and the structure is optimized; Determine the optimal digital baseboard-based drainage network risk identification model.

5. The drainage network management and control system based on digital baseboard according to claim 4 is characterized in that: The data analysis and decision support module also includes: Risk identification and pipe network prediction unit, used to identify risks and predict the operation trend of the drainage pipe network; Obtain the optimal risk identification model of drainage network based on digital baseboard; Deploy the optimal digital-based drainage network risk identification model in the actual drainage network management and control, and input the drainage network monitoring feature data into the optimal digital-based drainage network risk identification model; Based on the optimal digital baseboard-based drainage network risk identification model, the drainage network monitoring feature data is deeply mined to identify potential risk points and predict the operation trend of the drainage network; Determine the drainage network risk identification and network prediction results based on the digital base plate, and provide a scientific basis for drainage network maintenance, optimized scheduling, and emergency response.

6. The drainage network management and control system based on digital baseboard according to claim 5 is characterized in that: The intelligent dispatching and emergency response module includes: Drainage network control plan formulation unit, used to formulate drainage network intelligent dispatching and emergency response control plans; Obtain drainage network risk identification and network prediction results based on digital baseboard; According to the risk identification and prediction results of the drainage network based on the digital base plate, the drainage network monitoring feature data is mined and analyzed, the maintenance plan is automatically generated, the resource allocation is optimized, and the intelligent scheduling and emergency response management and control plan of the drainage network is formulated.

7. The drainage network management and control system based on digital baseboard according to claim 6 is characterized in that: The intelligent dispatching and emergency response module further includes: Intelligent drainage network control unit, used for intelligent dispatching and emergency warning response control of drainage network; Obtain intelligent dispatching and emergency response management and control solutions for drainage networks; Based on the intelligent dispatching and emergency response control scheme of the drainage network, the drainage network is intelligently dispatched and the emergency warning response is controlled; Among them, when encountering extreme weather such as heavy rain, the emergency warning plan is quickly activated and intelligent guidance is provided for drainage operations.

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