Intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on digital baseboard

Abstract

The present invention discloses an intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital baseboard, and belongs to the technical field of rainwater and sewage pipe networks. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital baseboard includes a data collection layer, a data processing layer, a data storage layer, an application service layer and a display layer. The present invention solves the problem of low intelligence and refinement in the existing rainwater and sewage pipe network management. By collecting and processing the data of the rainwater and sewage pipe networks in real time, the data can be better understood. The processed data is stored in the data storage layer. The data storage layer adopts big data storage technology and integrates national standards, local standards and foreign standards to realize the storage and management of massive data. The application service layer is based on a microservice architecture and provides data query, analysis and early warning function modules. The display layer uses visualization technology to display and analyze the operation status of the rainwater and sewage pipe networks in real time. Through the above design, management efficiency can be improved and operation and maintenance costs can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of rainwater and sewage pipe networks, and specifically to an intelligent and long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital baseboard. Background Art

[0002] ‌‌The rainwater and sewage pipe network is an important part of the urban drainage system, including rainwater pipes and sewage pipes. ‌ The rainwater and sewage pipe network is responsible for collecting and discharging rainwater and sewage respectively to ensure the normal operation of the urban drainage system. Rainwater pipes are mainly used to collect and discharge rainwater, while sewage pipes are used to collect and transport domestic sewage and industrial wastewater to treatment plants for treatment. The traditional management method of rainwater and sewage pipe networks mainly relies on manual inspections and regular inspections. This method is inefficient and prone to missed inspections. In addition, there are problems such as untimely data collection, inaccurate data processing, and irregular data storage and management, which seriously affect the normal operation and safety of the rainwater and sewage pipe network; therefore, it does not meet the existing needs. For this reason, we propose an intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent and long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane. The operation data and environmental data of the rainwater and sewage pipe networks are collected in real time through the data collection layer, and the collected operation data and environmental data are pre-processed, cleaned and integrated through the data processing layer, so as to provide effective data support for subsequent analysis. The processed data is stored in the data storage layer, and the application service layer, based on the microservice architecture, provides functional modules such as data query, analysis and early warning, and the display layer uses visualization technology to realize real-time display and analysis of the operation status of the rainwater and sewage pipe networks, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: a digital backplane-based intelligent long-term operation analysis platform for rainwater and sewage pipe networks, comprising:

[0005] Data collection layer, used for:

[0006] Real-time collection of operational and environmental data of the rainwater and sewage pipe network through sensors and monitoring equipment, including surveillance cameras and weather stations;

[0007] Data processing layer, used for:

[0008] Preprocess, clean and integrate the collected operation data and environmental data to provide effective data support for subsequent analysis;

[0009] The data storage layer is used to:

[0010] Adopt big data storage technology to realize the storage and management of massive data;

[0011] Application service layer, used for:

[0012] Based on the microservice architecture, it provides various business function modules, including data query, analysis and early warning;

[0013] The presentation layer is used to:

[0014] Through visualization technology, the real-time display and analysis of the operation status of the rainwater and sewage pipe network can be realized;

[0015] The weather station is also used to:

[0016] Determine meteorological data and load data based on the meteorological conditions of the surrounding environment of the rainwater and sewage pipe network monitored in real time;

[0017] Determine monitoring temperature, monitoring humidity and monitoring wind speed according to meteorological data;

[0018] The meteorological indicators of the monitored area are calculated based on the monitored temperature, humidity and wind speed:

[0019]

[0020] Among them, s represents the meteorological index of the monitoring area, T represents the monitoring temperature, D represents the monitoring humidity, W represents the monitoring wind speed, and k represents the rainfall correction coefficient;

[0021] Obtain the standard deviation and covariance of meteorological data and load data, and determine the correlation coefficient between meteorological data and load data based on the standard deviation and covariance:

[0022]

[0023] Where Q represents the correlation coefficient between meteorological data and load data, Expressed as the cumulative effect factor of meteorological development, Expressed as the covariance of meteorological data and load data, Expressed as the standard deviation of meteorological data and load data;

[0024] The potential probability of rainfall is determined based on the correlation coefficient between meteorological data and load data and the meteorological indicators of the monitoring area.

[0025] Furthermore, sensors and monitoring equipment are installed at various locations in the rainwater and sewage pipe network to collect the operation data and environmental data of the rainwater and sewage pipe network in real time. In addition, the sensors and monitoring equipment are connected to the network through wired or wireless means to form a real-time data collection network, and then the data is collected and uploaded in real time.

[0026] Furthermore, sensors, including water level meters, flow meters, pressure gauges and water quality detectors, and monitoring equipment, including surveillance cameras and weather stations, are specifically:

[0027] Water level meter, used to monitor the water level in the rainwater and sewage pipe network in real time;

[0028] Flow meters are used to monitor the flow in the rainwater and sewage pipe network in real time;

[0029] Pressure gauge, used to monitor the pressure in the rainwater and sewage pipe network in real time;

[0030] Water quality detector, used to monitor the water quality in the rainwater and sewage pipe network in real time;

[0031] Surveillance cameras are used to monitor the operation of the rainwater and sewage pipe network in real time;

[0032] The weather station is used to monitor the meteorological conditions in the surrounding environment of the rainwater and sewage pipe network in real time.

[0033] Furthermore, the collected operation data and environmental data are preprocessed, cleaned and integrated, specifically:

[0034] Preprocessing, used to remove noise from operational and environmental data;

[0035] Data cleaning is used to remove duplicate data from the operation data and environmental data after preprocessing, eliminate outliers in the operation data and environmental data, and fill in missing values ​​in the operation data and environmental data;

[0036] Data fusion is used to fuse the processed operation data and environmental data, input the data collected by sensors and monitoring equipment into the neural network for training and learning, and then fuse different types of data.

[0037] Furthermore, the data storage layer includes:

[0038] Repository for:

[0039] Use big data storage technologies to store the collected operation data and environmental data, including Hadoop and Spark;

[0040] Standard library for:

[0041] Provides a standard basis for data storage and management in repositories.

[0042] Furthermore, the standard library includes:

[0043] Local standards, which are used to provide local standards for data storage and management in repositories;

[0044] National standards, which are used to provide national standard basis for data storage and management in repositories;

[0045] Foreign standards are used to provide foreign standard basis for data storage and management in repositories.

[0046] Furthermore, the application service layer includes:

[0047] Data query module, used to:

[0048] Query the massive data stored in the data storage layer. Users can query the required information about the rainwater and sewage pipe network by entering keywords or related conditions;

[0049] Data analysis module for:

[0050] Analyze the collected operation data and environmental data through data mining technology, realize in-depth mining and understanding of the operation data and environmental data, discover the associations and rules between the data, and provide users with targeted optimization suggestions. Among them, data mining technology includes association rule mining and cluster analysis;

[0051] Abnormal warning module, used for:

[0052] Preset thresholds, compare the real-time collected operating data and environmental data with the preset thresholds, and issue an early warning notification when the threshold range is exceeded.

[0053] Furthermore, the display layer includes:

[0054] Map display: Use GIS technology to integrate the geographic spatial data of the location, pipe diameter and flow direction of rainwater and sewage pipe network facilities and display them on the map;

[0055] Chart presentation: Use data visualization tools to summarize and present real-time monitored operation data, environmental data, and analysis results through charts, bar charts, or pie charts. The visualization tools are Tableau or PowerBI.

[0056] Furthermore, after the water quality detector monitors the water quality in the rainwater and sewage pipe network in real time, it also includes:

[0057] Collect water quality data; evaluate water quality distribution based on the collected water quality data by using preset pollution evaluation indicators;

[0058] The water quality distribution is divided into several sewage component matrices, and each sewage component matrix is ​​subjected to corresponding detection processing to obtain the global detection result;

[0059] Determine the sewage labels contained in the water quality distribution according to the global detection results, and screen out the high-temperature sewage component matrix, the low-temperature sewage component matrix, and the ordinary sewage component matrix according to the sewage labels;

[0060] The spectral residual matrices of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the common sewage component matrix are obtained respectively, and the local features of the spectrum are focused according to the spectral residual matrices;

[0061] According to the local characteristics of the spectrum, the rainwater-sewage mixing ratios of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the common sewage component matrix are determined;

[0062] According to the ratio of rainwater and sewage mixing, the potential pollutant types of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the ordinary sewage component matrix are determined;

[0063] Determine the connection structure of the rainwater and sewage pipes, construct a directed graph model of the rainwater and sewage pipes according to the connection structure, and determine multiple flow paths of the rainwater and sewage pipes according to the directed graph model based on a depth-first traversal method of the directed graph;

[0064] Obtain the type and size distribution of sewage discharge points on each flow path, and determine the pollution load and pollution source according to the type and size distribution and the potential pollutant types of the high-heat sewage component matrix, the low-heat sewage component matrix, and the common sewage component matrix;

[0065] Determine the load contribution rate of each pollution source based on the corresponding relationship between pollution load and pollution source, and carry out rainwater and sewage source tracing based on the load contribution rate;

[0066] The traceability results will be displayed on the platform.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention collects the operation data and environmental data of the rainwater and sewage pipe network in real time through the data collection layer, and then pre-processes, cleans and integrates the collected operation data and environmental data through the data processing layer, so as to better understand the data, thereby providing effective data support for subsequent analysis. The processed data is stored in the data storage layer. The data storage layer adopts big data storage technology and integrates national standards, local standards and European, American and Japanese standards. It can support all-factor data of the drainage system and support massive dynamic and static data of the drainage system at the city level. The application service layer is based on the microservice architecture and can provide functional modules such as data query, analysis and early warning. Finally, the display layer uses visualization technology to realize real-time display and analysis of the operation status of the rainwater and sewage pipe network, so that users can monitor the status changes of the pipe network in real time. Through the above design, the analysis platform can improve management efficiency, and through real-time monitoring, analysis and early warning, it can realize rapid response and effective processing of the operation status of the rainwater and sewage pipe network, thereby reducing operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a structural schematic diagram of the intelligent long-term operation and analysis platform of the rainwater and sewage pipe network based on the digital baseboard of the present invention. DETAILED DESCRIPTION

[0070] 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.

[0071] In order to solve the existing rainwater and sewage pipe network management method which mainly relies on manual inspection and regular inspection, which is inefficient and prone to missed inspections, and has problems such as untimely data collection, inaccurate data processing, irregular data storage and management, which seriously affects the normal operation and safety of the rainwater and sewage pipe network, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0072] The digital baseboard-based intelligent long-term operation and analysis platform for rainwater and sewage pipe networks includes:

[0073] Data collection layer, used for:

[0074] Through sensors and monitoring equipment, real-time collection of rainwater and sewage pipe network operation data and environmental data;

[0075] Data processing layer, used for:

[0076] Preprocess, clean and integrate the collected operation data and environmental data to provide effective data support for subsequent analysis;

[0077] The data storage layer is used to:

[0078] Adopt big data storage technology to realize the storage and management of massive data;

[0079] Application service layer, used for:

[0080] Based on the microservice architecture, it provides various business function modules, including data query, analysis and early warning;

[0081] The presentation layer is used to:

[0082] Through visualization technology, real-time display and analysis of the operating status of the rainwater and sewage pipe network can be achieved.

[0083] The technical effect of the above content is: the operation data and environmental data of the rainwater and sewage pipe network are collected in real time through the data collection layer, and the collected operation data and environmental data are pre-processed, cleaned and integrated through the data processing layer, so as to better understand the data, thereby providing effective data support for subsequent analysis. The processed data is stored in the data storage layer. The data storage layer adopts big data storage technology and integrates national standards, local standards and European, American and Japanese standards, which can realize the storage and management of massive data. The application service layer is based on the microservice architecture and can provide functional modules such as data query, analysis and early warning. Finally, the display layer realizes the real-time display and analysis of the operation status of the rainwater and sewage pipe network through visualization technology, so that users can monitor the state changes of the pipe network in real time. The rainwater and sewage pipe network intelligent long-term operation analysis platform is composed of the data collection layer, the data processing layer, the data storage layer application service layer and the display layer. The analysis platform can improve management efficiency through real-time monitoring, analysis and early warning, realize rapid response and effective processing of the operation status of the rainwater and sewage pipe network, and reduce operation and maintenance costs. In addition, the analysis platform also improves the operation effect and emission standards of the rainwater and sewage pipe network through data-driven decision support and optimization scheduling strategy.

[0084] Sensors and monitoring equipment are installed at various locations in the rainwater and sewage pipe network to collect the operation data and environmental data of the rainwater and sewage pipe network in real time. In addition, the sensors and monitoring equipment are connected to the network through wired or wireless means to form a real-time data collection network, and then collect and upload data in real time.

[0085] The technical effect of the above content is: by collecting the operating data and environmental data of the rainwater and sewage pipeline network in real time, the operating status of the pipeline network can be understood in a timely manner, and the efficiency and accuracy of management can be improved. Sensors and monitoring equipment are connected to the network by wired or wireless means, which can realize real-time data collection and uploading, so that users can obtain equipment operating status, environmental parameters and other information anytime and anywhere, thereby improving work efficiency and reducing maintenance costs.

[0086] Sensors, including water level meters, flow meters, pressure gauges and water quality detectors, monitoring equipment, including surveillance cameras and weather stations, specifically:

[0087] Water level meter, used to monitor the water level in the rainwater and sewage pipe network in real time, can be used to monitor the water level changes in the rainwater and sewage pipe network;

[0088] Flow meter, used to monitor the flow in the rainwater and sewage pipe network in real time, can be used to monitor the speed and flow of water in the rainwater and sewage pipe network;

[0089] Pressure gauge, used to monitor the pressure in the rainwater and sewage pipe network in real time, can be used to monitor the pressure changes in the rainwater and sewage pipe network;

[0090] Water quality detector, used to monitor the water quality in the rainwater and sewage pipe network in real time, and can be used to detect the water quality information in the rainwater and sewage pipe network;

[0091] Surveillance cameras are used to monitor the operation of the rainwater and sewage pipe network in real time;

[0092] The weather station is used to monitor the meteorological conditions in the surrounding environment of the rainwater and sewage pipe network in real time.

[0093] The technical effects of the above content are: by real-time monitoring of the water level and flow in the rainwater and sewage pipe network through water level meters and flow meters, the situation of rainwater discharge can be understood; by real-time monitoring of the pressure in the rainwater and sewage pipe network through pressure gauges, the pressure changes of the rainwater pump can be understood; by real-time monitoring of the water quality in the rainwater and sewage pipe network through water quality detectors, the water quality of the discharge can be understood; and the monitoring cameras and weather stations allow us to see the real-time operation of the entire rainwater and sewage pipe network, as well as weather changes in the surrounding environment. This information is very important for urban drainage management and environmental protection. It can timely understand the operation status of the pipe network and improve the efficiency and accuracy of management. At the same time, these sensors and monitoring equipment can also be connected together through the Internet of Things technology to achieve remote monitoring and management, greatly improving our work efficiency.

[0094] Preprocess, clean and integrate the collected operation data and environmental data, specifically:

[0095] Preprocessing, used to remove noise from operational and environmental data;

[0096] Data cleaning is used to remove duplicate data from the operation data and environmental data after preprocessing, eliminate outliers in the operation data and environmental data, and fill in missing values ​​in the operation data and environmental data;

[0097] Data fusion is used to fuse the processed operation data and environmental data, input the data collected by sensors and monitoring equipment into the neural network for training and learning, and then fuse different types of data.

[0098] The technical effects of the above content are as follows: the preprocessing stage is mainly to remove noise in the data and ensure the basis for subsequent analysis; the data cleaning stage is to remove duplicate data and eliminate duplicate and missing values ​​in the data in order to avoid data errors caused by duplication or missing values; and the data fusion stage is to fuse the data from various sensors to achieve better data analysis results. A series of processing is performed on the collected operation data and environmental data to ensure the quality and accuracy of the data.

[0099] The data storage layer includes:

[0100] Repository for:

[0101] Use big data storage technologies to store the collected operation data and environmental data, including Hadoop and Spark;

[0102] Standard library for:

[0103] Provides a standard basis for data storage and management in repositories.

[0104] Among them, the standard library includes:

[0105] Local standards, which are used to provide local standards for data storage and management in repositories;

[0106] National standards, which are used to provide national standard basis for data storage and management in repositories;

[0107] Foreign standards are used to provide foreign standard basis for data storage and management in repositories, including European, American and Japanese standards.

[0108] The technical effect of the above content is: the role of the repository is to store the collected operation data and environmental data, and it uses big data storage technology, such as Hadoop and Spark. Big data storage technology has the ability to process large-scale data and can meet the data storage needs of the analysis platform, thereby realizing the storage and management of massive data. The standard library provides a standard basis for data storage and management. National standards, local standards and foreign standards can all be used as the content of the standard library. Since the drainage system is a large-scale engineering system involving the city level, the full-factor characteristics of its data are also very obvious. This means that our standard library needs to be able to support all data types and characteristics of this system, whether dynamic or static, local or foreign, official or unofficial, all need to be taken into consideration. The standard library integrates national standards, local standards and foreign standards, and can support the full-factor data of the drainage system, and support the dynamic and static data of massive drainage systems at the city level to ensure consistency and accuracy whether used domestically or internationally.

[0109] Application service layer, including:

[0110] Data query module, used to:

[0111] Query the massive data stored in the data storage layer. Users can query the required information about the rainwater and sewage pipe network by entering keywords or related conditions;

[0112] Data analysis module for:

[0113] Analyze the collected operation data and environmental data through data mining technology, realize in-depth mining and understanding of the operation data and environmental data, discover the associations and rules between the data, and provide users with targeted optimization suggestions. Among them, data mining technology includes association rule mining and cluster analysis;

[0114] Association rule mining: mainly to find frequently occurring association rules in operation data and environmental data. Through association rule mining, we can have a deeper understanding of the problems and potential laws in the rainwater and sewage pipe network, thus providing more accurate support for risk warning and response strategy generation;

[0115] Cluster analysis: The main purpose is to classify similar objects in operation data and environmental data into the same cluster. Through cluster analysis, common problems and behavior patterns in the rainwater and sewage pipe network can be discovered, providing more valuable reference for risk warning and response strategy generation;

[0116] Abnormal warning module, used for:

[0117] Preset thresholds, compare the real-time collected operating data and environmental data with the preset thresholds, and issue an early warning notification when the threshold range is exceeded.

[0118] The technical effects of the above content are as follows: the data query module is responsible for processing the user's query request, searching for the corresponding data in the data storage layer according to the user's input keywords or related conditions, and then presenting the results to the user in an easy-to-read form. This function greatly simplifies the process of users obtaining and understanding the required information. The data analysis module uses data mining technology to conduct in-depth analysis of the collected operating data and environmental data to achieve in-depth understanding and utilization of the data. This analysis can not only discover the associations and rules between the data, but also find out possible hidden patterns, further optimize our management and decision-making, so that users can analyze the data. The task of the abnormal warning module is to set thresholds and compare the real-time collected operating data and environmental data with these thresholds. If the data exceeds the set threshold, the warning module will automatically issue a warning notification to remind relevant managers to take measures to prevent possible abnormal situations. These three modules complement each other. Users can choose different functional modules according to their needs to query and analyze data, and can set warning rules to achieve real-time monitoring and warning of the rainwater and sewage pipe network.

[0119] Presentation layer, including:

[0120] Map display: Use GIS technology to integrate the geographic spatial data of the location, pipe diameter and flow direction of rainwater and sewage pipe network facilities and display them on the map;

[0121] Chart presentation: Use data visualization tools to summarize and present real-time monitored operation data, environmental data, and analysis results through charts, bar charts, or pie charts. The visualization tools are Tableau or PowerBI.

[0122] The technical effects of the above content are: first, by using GIS technology, the geographic spatial data such as the location, diameter and flow direction of rainwater pipe facilities in various places are integrated and successfully displayed on the map. In this way, users can intuitively understand the specific distribution of rainwater pipe facilities, which is conducive to the rational allocation of resources and the improvement of work efficiency. Secondly, using data visualization tools such as Tableau or PowerBI, the real-time monitored rainwater pipe operation data and environmental data are analyzed, and summarized and presented in the form of charts or bar charts, pie charts, etc., so that users can quickly grasp the operation status of rainwater pipe facilities and the changing trends of environmental data, and then formulate targeted response strategies. Through the display layer, users can view the distribution and status of the rainwater and sewage pipe network on the map, and can view the real-time operation data and environmental data and analysis results, so as to monitor the status changes of the pipe network in real time.

[0123] Working principle: The data collection layer collects the operation data and environmental data of the rainwater and sewage pipe network in real time, and then the data processing layer pre-processes, cleans and integrates the collected operation data and environmental data, so as to better understand the data and provide effective data support for subsequent analysis. The processed data is stored in the data storage layer. The data storage layer adopts big data storage technology and integrates national standards, local standards, European, American and Japanese standards. It can support all-factor data of the drainage system and support massive dynamic and static data of the drainage system at the city level to ensure consistency and accuracy whether used domestically or internationally. The application service layer is based on the microservice architecture and can provide functional modules such as data query, analysis and early warning. It can query and analyze data, set early warning rules, and realize real-time monitoring and early warning of the rainwater and sewage pipe network. Finally, the display layer uses visualization technology to realize real-time display and analysis of the operation status of the rainwater and sewage pipe network. You can view real-time operation data and environmental data and analysis results, so as to monitor the status changes of the pipe network in real time.

[0124] In one embodiment, after the water quality detector monitors the water quality in the rainwater and sewage pipe network in real time, the method further includes:

[0125] Collect water quality data; evaluate water quality distribution based on the collected water quality data by using preset pollution evaluation indicators;

[0126] The water quality distribution is divided into several sewage component matrices, and each sewage component matrix is ​​subjected to corresponding detection processing to obtain the global detection result;

[0127] Determine the sewage labels contained in the water quality distribution according to the global detection results, and screen out the high-temperature sewage component matrix, the low-temperature sewage component matrix, and the ordinary sewage component matrix according to the sewage labels;

[0128] The spectral residual matrices of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the common sewage component matrix are obtained respectively, and the local features of the spectrum are focused according to the spectral residual matrices;

[0129] According to the local characteristics of the spectrum, the rainwater-sewage mixing ratios of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the common sewage component matrix are determined;

[0130] According to the ratio of rainwater and sewage mixing, the potential pollutant types of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the ordinary sewage component matrix are determined;

[0131] Determine the connection structure of the rainwater and sewage pipes, construct a directed graph model of the rainwater and sewage pipes according to the connection structure, and determine multiple flow paths of the rainwater and sewage pipes according to the directed graph model based on a depth-first traversal method of the directed graph;

[0132] Obtain the type and size distribution of sewage discharge points on each flow path, and determine the pollution load and pollution source according to the type and size distribution and the potential pollutant types of the high-heat sewage component matrix, the low-heat sewage component matrix, and the common sewage component matrix;

[0133] Determine the load contribution rate of each pollution source based on the corresponding relationship between pollution load and pollution source, and carry out rainwater and sewage source tracing based on the load contribution rate;

[0134] The traceability results will be displayed on the platform.

[0135] The beneficial effects of the above technical solution are: it can intuitively and accurately trace the source of water pollution to determine the emission source, lay a reference for subsequent rectification and operation, and reasonably understand the sewage status of the rainwater and sewage pipes to ensure long-term operation.

[0136] In one embodiment, the weather station is also used to:

[0137] Determine meteorological data and load data based on the meteorological conditions of the surrounding environment of the rainwater and sewage pipe network monitored in real time;

[0138] Determine monitoring temperature, monitoring humidity and monitoring wind speed according to meteorological data;

[0139] The meteorological indicators of the monitored area are calculated based on the monitored temperature, humidity and wind speed:

[0140]

[0141] Among them, s represents the meteorological index of the monitoring area, T represents the monitoring temperature, D represents the monitoring humidity, W represents the monitoring wind speed, and k represents the rainfall correction coefficient;

[0142] Obtain the standard deviation and covariance of meteorological data and load data, and determine the correlation coefficient between meteorological data and load data based on the standard deviation and covariance:

[0143]

[0144] Where Q represents the correlation coefficient between meteorological data and load data, ϑ represents the cumulative effect factor of meteorological development, cov(X,Y) represents the covariance of meteorological data and load data, σ X σ Y Expressed as the standard deviation of meteorological data and load data;

[0145] The potential probability of rainfall is determined based on the correlation coefficient between meteorological data and load data and the meteorological indicators of the monitoring area.

[0146] The beneficial effects of the above technical solution are: by calculating the meteorological index of the monitoring area, the potential meteorological index of the monitoring area can be determined according to the monitored meteorological data, thereby improving the evaluation efficiency and accuracy. Furthermore, by determining the potential probability of rainfall according to the correlation coefficient between the meteorological data and the load data and the meteorological index of the monitoring area, the rainfall probability can be accurately predicted based on the correlation index of the regional load and the meteorology. Compared with the traditional meteorological forecast, it is more accurate and improves the practicality and forecast reliability.

[0147] 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.

[0148] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The intelligent long-term operation and analysis platform of rainwater and sewage pipe network based on digital baseboard is characterized by: include: Data collection layer, used for: Real-time collection of operational and environmental data of the rainwater and sewage pipe network through sensors and monitoring equipment. Sensors include water level meters, flow meters, pressure gauges and water quality testers, and monitoring equipment includes surveillance cameras and weather stations. Among them, after the water quality detector monitors the water quality in the rainwater and sewage pipe network in real time, it also includes: Collect water quality data; evaluate water quality distribution based on the collected water quality data by using preset pollution evaluation indicators; The water quality distribution is divided into several sewage component matrices, and each sewage component matrix is ​​subjected to corresponding detection processing to obtain the global detection result; Determine the sewage labels contained in the water quality distribution according to the global detection results, and screen out the high-temperature sewage component matrix, the low-temperature sewage component matrix, and the ordinary sewage component matrix according to the sewage labels; The spectral residual matrices of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the common sewage component matrix are obtained respectively, and the local features of the spectrum are focused according to the spectral residual matrices; According to the local characteristics of the spectrum, the rainwater-sewage mixing ratio of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the ordinary sewage component matrix is ​​determined; According to the ratio of rainwater and sewage mixing, the potential pollutant types of the high-temperature sewage component matrix, the low-temperature sewage component matrix and the ordinary sewage component matrix are determined; Determine the connection structure of the rainwater and sewage pipes, construct a directed graph model of the rainwater and sewage pipes according to the connection structure, and determine multiple flow paths of the rainwater and sewage pipes according to the directed graph model based on a depth-first traversal method of the directed graph; Obtain the type and size distribution of sewage discharge points on each flow path, and determine the pollution load and pollution source according to the type and size distribution and the potential pollutant types of the high-heat sewage component matrix, the low-heat sewage component matrix, and the common sewage component matrix; Determine the load contribution rate of each pollution source based on the corresponding relationship between pollution load and pollution source, and carry out rainwater and sewage source tracing based on the load contribution rate; Display the traceability results on the platform; Data processing layer, used for: Preprocess, clean and integrate the collected operation data and environmental data to provide effective data support for subsequent analysis; The data storage layer is used to: Adopt big data storage technology to realize the storage and management of massive data; Application service layer, used for: Based on the microservice architecture, it provides various business function modules, including data query, analysis and early warning; The presentation layer is used to: Through visualization technology, the real-time display and analysis of the operation status of the rainwater and sewage pipe network can be realized; The weather station is also used to: Determine meteorological data and load data based on the meteorological conditions of the surrounding environment of the rainwater and sewage pipe network monitored in real time; Determine monitoring temperature, monitoring humidity and monitoring wind speed according to meteorological data; The meteorological indicators of the monitored area are calculated based on the monitored temperature, humidity and wind speed: ； Among them, s represents the meteorological index of the monitoring area, T represents the monitoring temperature, D represents the monitoring humidity, W represents the monitoring wind speed, and k represents the rainfall correction coefficient; Obtain the standard deviation and covariance of meteorological data and load data, and determine the correlation coefficient between meteorological data and load data based on the standard deviation and covariance: ； Where Q represents the correlation coefficient between meteorological data and load data, Expressed as the cumulative effect factor of meteorological development, Expressed as the covariance of meteorological data and load data, Expressed as the standard deviation of meteorological data and load data; The potential probability of rainfall is determined based on the correlation coefficient between meteorological data and load data and the meteorological indicators of the monitoring area.

2. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 1 is characterized by: Sensors and monitoring equipment are installed at various locations in the rainwater and sewage pipe network to collect the operation data and environmental data of the rainwater and sewage pipe network in real time. In addition, the sensors and monitoring equipment are connected to the network through wired or wireless means to form a real-time data collection network, and then collect and upload data in real time.

3. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 1 is characterized in that: Sensors include water level meters, flow meters, pressure gauges and water quality detectors, and monitoring equipment includes surveillance cameras and weather stations, specifically: Water level meter, used to monitor the water level in the rainwater and sewage pipe network in real time; Flow meters are used to monitor the flow in the rainwater and sewage pipe network in real time; Pressure gauge, used to monitor the pressure in the rainwater and sewage pipe network in real time; Water quality detector, used to monitor the water quality in the rainwater and sewage pipe network in real time; Surveillance cameras are used to monitor the operation of the rainwater and sewage pipe network in real time; The weather station is used to monitor the meteorological conditions in the surrounding environment of the rainwater and sewage pipe network in real time.

4. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 1 is characterized by: Preprocess, clean and integrate the collected operation data and environmental data, specifically: Preprocessing, used to remove noise from operational and environmental data; Data cleaning is used to remove duplicate data from the operation data and environmental data after preprocessing, eliminate outliers in the operation data and environmental data, and fill in missing values ​​in the operation data and environmental data; Data fusion is used to fuse the processed operation data and environmental data, input the data collected by sensors and monitoring equipment into the neural network for training and learning, and then fuse different types of data.

5. According to the digital backplane-based intelligent long-term operation and analysis platform for rainwater and sewage pipe networks according to claim 1, it is characterized by: The data storage layer includes: Repository for: Use big data storage technologies to store the collected operation data and environmental data, including Hadoop and Spark; Standard library for: Provides a standard basis for data storage and management in repositories.

6. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 5 is characterized by: The standard library includes: Local standards, which are used to provide local standards for data storage and management in repositories; National standards, which provide national standards for data storage and management in repositories; Foreign standards are used to provide foreign standard basis for data storage and management in repositories.

7. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 1 is characterized by: The application service layer includes: Data query module, used to: Query the massive data stored in the data storage layer. Users can query the required information about the rainwater and sewage pipe network by entering keywords or related conditions; Data analysis module for: Analyze the collected operation data and environmental data through data mining technology, realize in-depth mining and understanding of the operation data and environmental data, discover the associations and rules between the data, and provide users with targeted optimization suggestions. Among them, data mining technology includes association rule mining and cluster analysis; Abnormal warning module, used for: Preset thresholds, compare the real-time collected operating data and environmental data with the preset thresholds, and issue an early warning notification when the threshold range is exceeded.

8. The intelligent long-term operation and analysis platform for rainwater and sewage pipe networks based on a digital backplane according to claim 1 is characterized by: The presentation layer includes: Map display: Use GIS technology to integrate the geographic spatial data of the location, pipe diameter and flow direction of rainwater and sewage pipe network facilities and display them on the map; Chart presentation: Use data visualization tools to summarize and present real-time monitored operation data, environmental data, and analysis results through charts, bar charts, or pie charts. The visualization tools are Tableau or PowerBI.

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