Online intelligent monitoring method for sewage draining exit and intelligent station building

Abstract

The invention discloses an online intelligent monitoring method for a sewage draining exit and an intelligent station building, and relates to the field of intelligent monitoring of the sewage draining exit, and the method comprises the steps: dividing the sewage draining exit into a plurality of edge nodes, setting a four-layer monitoring mode, and improving the monitoring efficiency and precision of the sewage draining exit; hardware detection devices are installed at different edge nodes, and monitoring data of water environments of the different edge nodes are obtained in real time; the water flow rate and the water flow velocity are combined to judge the congestion amount of sludge or solid substances in the blow-off pipeline; on the basis of an image recognition algorithm, the water body color, the solid floating objects and the'secret drainage 'phenomenon of the sewage port terminal are monitored in real time; and establishing an intelligent monitoring and comprehensive evaluation model of the sewage draining exit, and comprehensively evaluating whether the water quality of the sewage draining exit and the pipeline congestion amount are qualified or not. Through a four-layer monitoring mode, source positioning is effectively carried out, and according to the water flow rate, the water flow rate, the water color and the edge image recognition result, the water quality of the sewage draining exit is comprehensively evaluated, so that the working stability of the sewage draining exit is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring of sewage outlets, and particularly to an online intelligent monitoring method and a smart station house for sewage outlets. Background Art

[0002] Intelligent online monitoring of sewage outlets refers to the use of advanced technologies such as the Internet of Things, sensor technology, and data analysis to conduct real-time and automated monitoring of sewage outlets, so as to obtain key information such as the water quality and water volume of sewage outlets, and analyze and process this information through intelligent algorithms, thereby realizing precise supervision and pollution prevention and control of sewage outlets. The existing sewage outlet monitoring adopts the method of manual water sample collection or remote monitoring technology using the Internet of Things. The manual collection method not only consumes manpower and material resources, but also has limited monitoring frequency and is difficult to achieve real-time monitoring. The remote monitoring technology using the Internet of Things has the ability of remote monitoring, conducts real-time data collection and intelligent monitoring of sewage outlets, so as to be able to detect and handle pollution incidents in a timely manner, reduce the impact of pollution on the environment and human health, and improve the efficiency and timeliness of sewage outlet monitoring. Therefore, improving the online intelligent monitoring ability of sewage outlets and ensuring its accuracy is of great significance.

[0003] The existing online intelligent monitoring technology for sewage outlets mainly collects the water quality and water environment of the terminal water area of the sewage outlet by setting sensors, obtains the collected data based on the Internet of Things technology, and summarizes them to the system server through the cloud for comprehensive judgment. However, due to the single-end monitoring method adopted by this kind of technology, the obtained sewage outlet data is not comprehensive enough to reflect the data of the overall sewage pipeline. Especially when there is multi-channel sewage confluence, it is impossible to accurately judge the source of abnormal sewage information, and at the same time, it is also impossible to reflect the accumulation of silt or solid substances in the sewage pipeline, resulting in untimely sewage discharge from the sewage pipeline and causing pipeline congestion. Summary of the Invention

[0004] To solve the above technical problems, an online intelligent monitoring method and a smart station house for sewage outlets are provided. This technical solution solves the problem that the single-end monitoring method mentioned in the above background art will result in insufficiently comprehensive sewage outlet data, unable to reflect the data of the overall sewage pipeline. Especially when there is multi-channel sewage confluence, it is impossible to accurately judge the source of abnormal sewage information, and at the same time, it is also impossible to reflect the accumulation of silt or solid substances in the sewage pipeline, resulting in untimely sewage discharge from the sewage pipeline and causing pipeline congestion.

[0005] To achieve the above purposes, the technical solution adopted by the present invention is as follows:

[0006] An online intelligent monitoring method for sewage outlets, comprising:

[0007] According to the distribution of sewage pipes, the sewage outlets are divided into several edge nodes, and a four-layer monitoring mode is set up to improve the monitoring efficiency and accuracy of the sewage outlets;

[0008] Install hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes;

[0009] According to the four-layer monitoring mode, combined with water flow and water velocity, judge the congestion volume of sludge or solid substances in the sewage pipes;

[0010] Based on the image recognition algorithm, monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet in real time;

[0011] Establish a comprehensive evaluation model for intelligent monitoring of sewage outlets to comprehensively evaluate whether the water quality of the sewage outlets and the congestion volume of the pipes are qualified.

[0012] Preferably, the step of dividing the sewage outlets into several edge nodes according to the distribution of sewage pipes and setting up a four-layer monitoring mode to improve the monitoring efficiency and accuracy of the sewage outlets specifically includes:

[0013] According to the distribution of the sewage pipes to be monitored, the sewage outlets are divided into several edge nodes. Among them, the edge nodes mainly include: sewage pipe intersections, stable flow areas and sewage terminal outlets;

[0014] Set up a four-layer monitoring mode according to the types of edge nodes;

[0015] The four-layer monitoring mode specifically includes:

[0016] The first layer: the source monitoring mode, which is used to monitor the water chemical components of each sewage pipe at the sewage pipe intersection;

[0017] The second layer: the stable flow congestion mode, which uses the water flow and water velocity at both ends to obtain the congestion volume of sludge or solid substances in the sewage pipes;

[0018] The third layer: the terminal outlet monitoring mode, which is used to monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal in time;

[0019] The fourth layer: the comprehensive evaluation mode, which comprehensively evaluates whether the water quality of the sewage outlet and the congestion volume of the pipe are qualified according to the collected data of each edge node.

[0020] Preferably, the step of installing hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes specifically includes:

[0021] Set up chemical pollutant monitoring devices at the sewage pipe outlets at the sewage pipe intersections;

[0022] Water flow rate and water flow velocity monitoring devices are respectively set at the starting point and the ending point of the steady flow area;

[0023] A high-definition camera acquisition device is set at the sewage discharge terminal port.

[0024] Preferably, the specific steps of judging the congestion amount of sludge or solid substances in the sewage discharge pipeline according to the four-layer monitoring mode in combination with the water flow rate and water flow velocity include:

[0025] Obtain the standard aperture value of the sewage discharge pipeline to be monitored through professional measurement or product description;

[0026] According to the water flow rate and water flow velocity monitoring devices, through synchronous acquisition, obtain the synchronous monitoring data of the water flow rate and water flow velocity at the starting point and the ending point of the steady flow area;

[0027] Perform filtering and normalization processing on the synchronous monitoring data of the water flow rate and water flow velocity collected to eliminate the influence of data peaks and dimensions;

[0028] Calculate the congestion amount of sludge or solid substances in the sewage discharge pipeline according to the synchronous monitoring data of the water flow rate and water flow velocity at both ends.

[0029] Preferably, the specific steps of real-time monitoring of the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal based on the image recognition algorithm include:

[0030] Through the high-definition camera acquisition device, obtain the water body image data of the sewage discharge terminal port in real time;

[0031] Based on big data, extract the component features of water body color, solid floating and human image in the water body image data;

[0032] According to the Gaussian filtering formula, perform filtering processing on each color of the water body image at the sewage discharge terminal port;

[0033] According to the HSV color space formula, extract the water body color of the sewage outlet terminal area, and judge the water quality of the sewage outlet terminal area through the water body color;

[0034] According to the grayscale formula, perform grayscale processing on the environmental image of the sewage discharge terminal port collected in real time;

[0035] According to the Canny operator edge detection algorithm, obtain the external contour information of the environmental image of the sewage discharge terminal port, and judge whether there is a pile-up of solid floating objects and whether there is an illegal discharge behavior of people blocking the monitoring device through the component features of solid floating objects and people.

[0036] Preferably, the specific steps of establishing an intelligent monitoring comprehensive evaluation model for the sewage outlet to comprehensively evaluate whether the water quality of the sewage outlet and the pipeline congestion amount are qualified include:

[0037] Based on big data or test experiments, obtain a sample group of the water quality of the sewage outlet and the pipeline congestion volume, where the sample group includes a target sample group and a training sample group;

[0038] According to the number of sewage pipelines at the sewage pipeline intersection, divide the sewage pipelines at the sewage pipeline intersection into several data clusters, and set the number of model synchronous input matrix groups;

[0039] Among them, the synchronous input matrix refers to the data collected from the sewage pipelines at a single sewage pipeline intersection, which integrates the congestion volume of sludge or solid substances in the sewage pipeline, the water body color, and the determination results of solid floating objects and "illegal discharge";

[0040] The model synchronous input matrix group is composed of multiple synchronous input matrices. Among them, the number of groups of the model synchronous input matrix group is consistent with the number of sewage pipelines at the sewage pipeline intersection;

[0041] Based on machine learning algorithms, establish a comprehensive intelligent monitoring and evaluation model for the sewage outlet. Through the model synchronous input matrix group, comprehensively evaluate whether the water quality of the sewage outlet and the pipeline congestion volume are qualified, and further analyze the abnormal sewage outlets of the sewage pipelines to determine the source points of abnormal sewage discharge.

[0042] Furthermore, this solution proposes an online intelligent monitoring and intelligent station house for the sewage outlet, which is used to implement the online intelligent monitoring method for the sewage outlet as described above, including:

[0043] A mode division module, which is used to divide the sewage outlet into several edge nodes according to the distribution of the sewage pipelines and set four-layer monitoring modes to improve the monitoring efficiency and accuracy of the sewage outlet;

[0044] A data collection module, which is used to install hardware detection devices at different edge nodes to obtain the monitoring data of the water environment at different edge nodes in real time;

[0045] A water quality evaluation module, which is used to judge the congestion volume of sludge or solid substances in the sewage pipeline according to the four-layer monitoring mode, combined with the water flow and water flow velocity; based on the image recognition algorithm, monitor the water body color, solid floating object situation and "illegal discharge" phenomenon at the terminal of the sewage outlet in real time; establish a comprehensive intelligent monitoring and evaluation model for the sewage outlet to comprehensively evaluate whether the water quality of the sewage outlet and the pipeline congestion volume are qualified;

[0046] An emergency handling module, which is used to receive and store the monitoring data and warning signals collected by each edge node; automatically start the emergency handling unit according to the type of warning signal; synchronously send encrypted data packets to the supervision platform according to the monitoring results and handling results.

[0047] Preferably, the water quality assessment module includes:

[0048] A congestion unit configured to determine the congestion amount of sludge or solid substances in the sewage pipeline according to a four-layer monitoring mode in combination with water flow and water velocity;

[0049] An image recognition unit configured to monitor the water body color, the situation of solid floating substances, and the "illegal discharge" phenomenon at the sewage outlet terminal in real time based on an image recognition algorithm;

[0050] A comprehensive assessment unit configured to establish a comprehensive assessment model for intelligent monitoring of the sewage outlet, and comprehensively assess whether the water quality of the sewage outlet and the pipeline congestion amount are qualified.

[0051] Preferably, the emergency treatment module includes:

[0052] A signal receiving unit configured to receive and store the monitoring data and early warning signals collected by each edge node;

[0053] An emergency treatment unit configured to automatically start the emergency treatment unit according to the type of early warning signal;

[0054] A data sending unit configured to synchronously send an encrypted data packet to the supervision platform according to the monitoring result and the processing result;

[0055] The automatically started emergency treatment unit includes:

[0056] Obtaining the source point and abnormal information of abnormal sewage discharge according to the evaluation result of the comprehensive assessment model for intelligent monitoring of the sewage outlet;

[0057] Directing the inspection robot to reach the designated position to complete the emergency treatment of the sewage outlet according to the abnormal information prompt.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] According to the distribution of sewage pipes, the sewage outlets are divided into several edge nodes, and a four-layer monitoring mode is set. Among them, the four-layer monitoring mode is respectively: the source monitoring mode, which is used to monitor the water chemical components of each sewage pipe at the confluence of sewage pipes; the steady-flow congestion mode, which uses the water flow rate and water velocity at both ends to obtain the congestion volume of sludge or solid substances in the sewage pipe; the terminal monitoring mode, which is used to timely monitor the water color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet; the comprehensive evaluation mode, which comprehensively evaluates whether the water quality of the sewage outlet and the pipe congestion volume are qualified according to the collected data of each edge node. According to the four-layer monitoring mode, the water quality monitoring of the sewage outlet can be effectively obtained, and the stability of the sewage outlet work can be guaranteed. Secondly, according to the four-layer monitoring mode, a specific analysis is carried out on the steady-flow congestion mode and the terminal monitoring mode. By the water flow rate and water velocity, the congestion volume of sludge or solid substances in the sewage pipe is judged. Based on the image recognition algorithm, the water color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet are monitored in real time, so as to effectively reflect the overall water quality of the sewage outlet and improve the accuracy and stability of on-line monitoring. Finally, by establishing a comprehensive evaluation model for intelligent monitoring of sewage outlets, it is comprehensively evaluated whether the water quality of the sewage outlet and the pipe congestion volume are qualified, so as to effectively locate the source and ensure the stability and efficiency of the sewage outlet work. Brief Description of the Drawings

[0060] Figure 1 It is a flow chart of an on-line intelligent monitoring method for sewage outlets of the present invention;

[0061] Figure 2 It is a flow chart of judging the congestion volume of sludge or solid substances in the sewage pipe according to the four-layer monitoring mode and combining the water flow rate and water velocity of the present invention;

[0062] Figure 3 It is a flow chart of real-time monitoring of the water color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet based on the image recognition algorithm of the present invention;

[0063] Figure 4 It is a flow chart of establishing a comprehensive evaluation model for intelligent monitoring of sewage outlets of the present invention and comprehensively evaluating whether the water quality of the sewage outlet and the pipe congestion volume are qualified. Detailed Embodiment

[0064] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variants.

[0065] Refer to Figure 1 As shown, an on-line intelligent monitoring method for sewage outlets includes:

[0066] According to the distribution of sewage pipes, the sewage outlets are divided into several edge nodes, and a four-layer monitoring mode is set up to improve the monitoring efficiency and accuracy of the sewage outlets;

[0067] Install hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes;

[0068] According to the four-layer monitoring mode, combined with water flow and water velocity, judge the congestion volume of sludge or solid substances in the sewage pipe;

[0069] Based on the image recognition algorithm, real-time monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet;

[0070] Establish a comprehensive evaluation model for intelligent monitoring of sewage outlets to comprehensively evaluate whether the water quality of the sewage outlets and the pipe congestion volume are qualified.

[0071] It can be explained that the single-end monitoring method will result in incomplete data of the sewage outlet, unable to reflect the data of the overall sewage pipe. Especially when there is multi-channel sewage confluence, it is impossible to accurately judge the source of abnormal sewage information, and it is also impossible to reflect the accumulation of silt or solid substances in the sewage pipe. Therefore, according to the distribution of the sewage pipe, the sewage outlet is divided into several edge nodes, and a four-layer monitoring mode is set up. According to the four-layer monitoring mode, specific analysis is carried out on the steady flow congestion mode and the terminal monitoring mode. Through water flow and water velocity, judge the congestion volume of sludge or solid substances in the sewage pipe. Based on the image recognition algorithm, real-time monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet, so as to effectively reflect the overall water quality of the sewage outlet, improve the accuracy and stability of on-line monitoring. Finally, by establishing a comprehensive evaluation model for intelligent monitoring of sewage outlets, comprehensively evaluate whether the water quality of the sewage outlets and the pipe congestion volume are qualified, so as to effectively locate the source, ensure the stability and efficiency of the sewage outlet work, and solve the problem of incomplete single-end monitoring data.

[0072] The specific steps of dividing the sewage outlets into several edge nodes according to the distribution of the sewage pipes and setting up a four-layer monitoring mode to improve the monitoring efficiency and accuracy of the sewage outlets include:

[0073] According to the distribution of the sewage pipes to be monitored, the sewage outlets are divided into several edge nodes. Among them, the edge nodes mainly include: sewage pipe intersections, steady flow areas and sewage terminal outlets;

[0074] Set up a four-layer monitoring mode according to the types of edge nodes;

[0075] The specific four-layer monitoring mode includes:

[0076] The first layer: is the source monitoring mode, which is used to monitor the water chemical components of each sewage pipeline at the sewage pipeline intersection;

[0077] The second layer: is the steady flow congestion mode, which uses the water flow rate and water flow velocity at both ends to obtain the congestion amount of sludge or solid substances in the sewage pipeline;

[0078] The third layer: is the terminal port monitoring mode, which is used to timely monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal;

[0079] The fourth layer: is the comprehensive evaluation mode, which comprehensively evaluates whether the water quality of the sewage outlet and the pipeline congestion amount are qualified according to the collected data of each edge node.

[0080] It can be explained that through the setting of four-layer monitoring mode, the sewage outlet monitoring is divided into source monitoring mode, steady flow congestion mode, terminal port monitoring mode and comprehensive evaluation mode. By setting the data feedback of different edge nodes, the source of the sewage can be effectively located, and targeted solutions can be taken for different layer monitoring modes, so as to improve the efficiency and precise positioning ability of the online monitoring of the sewage outlet. Among them, the steady flow area refers to that within ten cycles, the variance values of the water flow rate and water flow velocity data at the sampling points are both less than a set threshold. The specific formula is: In the formula, x i is the sampling value of the water flow rate or water flow velocity in the i-th cycle, is the average value of the water flow rate or water flow velocity sampling within ten cycles, σ is the variance value of the water flow rate or water flow velocity data of the sample point, N is the number of sampling cycles, here the value is ten. To ensure that the steady flow area is not affected by water body fluctuations, fixed points can be found under standard conditions according to the above method to fix the position of the steady flow area.

[0081] Installing hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes specifically includes:

[0082] Setting chemical pollutant monitoring devices at each sewage pipeline outlet at the sewage pipeline intersection;

[0083] Setting water flow rate and water flow velocity monitoring devices at the starting point and ending point of the steady flow area respectively;

[0084] Setting a high-definition camera acquisition device at the sewage terminal port.

[0085] It can be explained that setting corresponding monitoring devices according to different edge nodes can effectively process the data of different monitoring points, so as to ensure the maximum utilization rate and modular design of each edge node, thus avoiding the problems of data loss and data transmission congestion during data transmission, and effectively improving the efficiency and timeliness of the online monitoring of the sewage outlet.

[0086] Refer to Figure 2 As shown, the specific steps of judging the congestion amount of sludge or solid substances in the sewage discharge pipe according to the four-layer monitoring mode by combining water flow and water flow velocity are as follows:

[0087] Obtain the standard aperture value of the sewage discharge pipe to be monitored through professional measurement or product description;

[0088] According to the water flow and water flow velocity monitoring device, obtain the synchronous monitoring data of the water flow and water flow velocity at the starting point and ending point of the steady flow area through synchronous acquisition;

[0089] Filter and normalize the synchronous monitoring data of the water flow and water flow velocity collected to eliminate the influence of data peaks and dimensions;

[0090] Calculate the congestion amount of sludge or solid substances in the sewage discharge pipe according to the synchronous monitoring data of the water flow and water flow velocity at both ends.

[0091] It can be explained that the on-line monitoring of the sewage outlet is a long-term project. When the sewage is transported in the sewage discharge pipe, it will carry silt or solid substances. Even if the amount is very small, in the case of long-term accumulation, it will cause the blockage of the sewage discharge pipe, resulting in the failure of sewage discharge and the backflow of sewage. Therefore, this solution analyzes the aperture of the sewage discharge pipe by monitoring the values of the water flow and water flow velocity in the steady flow area, so as to judge whether there is congestion in the sewage discharge pipe;

[0092] The expression for analyzing the aperture of the sewage discharge pipe is as follows:

[0093]

[0094] In the formula, ΔD is the reduction amount of the sewage discharge pipe aperture, that is, the congestion amount of sludge or solid substances in the sewage discharge pipe, D is the standard aperture value of the monitored sewage discharge pipe, λ is the friction coefficient, and the expression is: L is the length of the steady flow area, Q is the water flow, g is the acceleration of gravity, h f-max is the maximum head loss, which can be limited according to the test experiment.

[0095] Refer to Figure 3 As shown, the specific steps of real-time monitoring of the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal based on the image recognition algorithm are as follows:

[0096] Obtain the water body image data of the sewage discharge terminal in real time through the high-definition camera acquisition device;

[0097] Extract the component features of the water body color, solid floating and human image in the water body image data based on big data;

[0098] Filter the colors of the water body image at the sewage discharge terminal according to the Gaussian filtering formula;

[0099] Extract the water body color of the sewage outlet terminal area according to the HSV color space formula, and judge the water quality of the sewage outlet terminal area through the water body color;

[0100] Grayscale the environmental image of the sewage discharge terminal collected in real time according to the grayscale formula;

[0101] According to the Canny operator edge detection algorithm, obtain the external contour information of the environmental image of the sewage discharge terminal, and judge whether there is a pile of solid floating objects and whether there is an act of secretly discharging by a person blocking the monitoring device through the component characteristics of the solid floating objects and the person.

[0102] It can be explained that due to the low light brightness in the sewage discharge pipeline, using a high-definition camera acquisition device to collect water body images may not have good results, resulting in low accuracy of image recognition. Therefore, in this solution, chemical substance monitoring of the water body is carried out in the first-layer source monitoring mode, and image data acquisition is carried out in the third-layer terminal monitoring mode to avoid the impact of the dim environment on the picture quality, and the water body color in the sewage discharge terminal area is obtained through the HSV color space formula and the Canny operator edge detection algorithm, as well as whether there is a pile of solid floating objects and whether there is an act of secretly discharging by a person blocking the monitoring device, so as to realize the monitoring of the sewage outlet by observing physical changes and achieve two-way guarantee of chemical monitoring and physical monitoring.

[0103] Refer to Figure 4 As shown, the establishment of an intelligent monitoring comprehensive evaluation model for the sewage outlet, and the comprehensive evaluation of whether the water quality of the sewage outlet and the pipeline congestion volume are qualified specifically includes:

[0104] Based on big data or test experiments, obtain a sample group of the water quality of the sewage outlet and the pipeline congestion volume, where the sample group includes a target sample group and a training sample group;

[0105] According to the number of sewage pipelines at the sewage pipeline intersection, divide the sewage pipelines at the sewage pipeline intersection into several data class sets, and set the number of model synchronous input matrix groups;

[0106] Among them, the synchronous input matrix refers to the data collected from the sewage pipelines at a single sewage pipeline intersection, which fuses the congestion volume of sludge or solid substances in the sewage pipeline, the water body color, and the judgment results of solid floating objects and "secret discharge";

[0107] The model synchronous input matrix group is composed of multiple synchronous input matrices. Among them, the number of groups of the model synchronous input matrix group is consistent with the number of sewage pipelines at the sewage pipeline intersection;

[0108] Based on machine learning algorithms, an intelligent monitoring comprehensive evaluation model for sewage outlets is established. Through the synchronous input of matrix groups into the model, the water quality of the sewage outlets and the qualification of pipeline congestion are comprehensively evaluated, and the abnormal sewage outlets of the sewage pipelines are further analyzed to determine the source points of abnormal sewage discharge.

[0109] It can be explained that since it is difficult to trace the sewage source through single-port monitoring and it is impossible to locate the sewage pipelines with abnormal warnings in a timely manner, resulting in a delay in taking measures and an inability to respond promptly, a large-area discharge of sewage volume occurs, affecting the water quality environment. Therefore, in this solution, according to the number of sewage pipelines at the sewage pipeline intersection, the sewage pipelines at the sewage pipeline intersection are divided into several data sets, and the number of synchronous input matrix groups of the model is set. Through the analysis of each group of synchronous input matrix data, the water quality and pipeline congestion of each sewage pipeline at the sewage pipeline intersection are evaluated, and the comprehensive evaluation of water quality and pipeline congestion is carried out through the synchronous input matrix group, so as to achieve a comprehensive evaluation of the water quality and pipeline congestion of sewage outlets from point to surface, ensuring the accuracy and reliability of intelligent monitoring of sewage outlets. At the same time, it can accurately locate the sewage pipelines with abnormal warnings, take timely treatment measures, and improve the solution efficiency of sewage outlet abnormalities. Among them, the machine learning algorithms that can be used include multi-island genetic algorithms, machine learning algorithms of linear regression, and deep neural networks, etc.

[0110] Furthermore, based on the same inventive concept as the above online intelligent monitoring method for sewage outlets, this solution proposes an online intelligent monitoring intelligent station house for sewage outlets, including:

[0111] A mode division module, which is used to divide the sewage outlets into several edge nodes according to the distribution of sewage pipelines and set four-layer monitoring modes to improve the monitoring efficiency and accuracy of sewage outlets;

[0112] A data acquisition module, which is used to install hardware detection devices at different edge nodes to obtain the monitoring data of the water environment at different edge nodes in real time;

[0113] A water quality evaluation module, which is used to judge the congestion of sludge or solid substances in the sewage pipeline according to the four-layer monitoring mode, combined with water flow and water flow velocity; based on image recognition algorithms, it monitors the water body color, solid floating objects and "illegal discharge" phenomena at the end of the sewage outlet in real time; establishes an intelligent monitoring comprehensive evaluation model for sewage outlets to comprehensively evaluate whether the water quality and pipeline congestion of sewage outlets are qualified;

[0114] Emergency handling module, which is used to receive and store the monitoring data and early warning signals collected by each edge node; automatically start the emergency handling unit according to the type of early warning signal; synchronously send encrypted data packets to the supervision platform according to the monitoring results and processing results;

[0115] The water quality assessment module includes:

[0116] Congestion unit, which is used to judge the congestion amount of sludge or solid substances in the sewage discharge pipe according to the four-layer monitoring mode, combined with water flow and water flow velocity;

[0117] Image recognition unit, which is used to monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal in real time based on the image recognition algorithm;

[0118] Comprehensive evaluation unit, which is used to establish a comprehensive evaluation model for intelligent monitoring of sewage outlets, and comprehensively evaluate whether the water quality of the sewage outlet and the pipe congestion amount are qualified;

[0119] The emergency handling module includes:

[0120] Signal receiving unit, which is used to receive and store the monitoring data and early warning signals collected by each edge node;

[0121] Emergency handling unit, which is used to automatically start the emergency handling unit according to the type of early warning signal;

[0122] Data sending unit, which is used to synchronously send encrypted data packets to the supervision platform according to the monitoring results and processing results;

[0123] The automatic start of the emergency handling unit includes:

[0124] Obtain the source point and abnormal information of abnormal sewage discharge according to the evaluation results of the comprehensive evaluation model for intelligent monitoring of sewage outlets;

[0125] According to the abnormal information prompt, command the inspection robot to reach the designated position to complete the emergency handling of the sewage outlet.

[0126] In summary, the advantages of the present invention are as follows: through the four-layer monitoring mode, the source can be effectively located, and the water quality of the sewage outlet can be comprehensively evaluated according to the water flow, water flow velocity, water body color and edge image recognition results, ensuring the stability of the sewage outlet operation.

[0127] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. An online intelligent monitoring method for sewage outlets, characterized in that Including: According to the distribution of sewage pipes, divide the sewage outlets into several edge nodes and set up a four-layer monitoring mode to improve the monitoring efficiency and accuracy of sewage outlets; Install hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes; According to the four-layer monitoring mode, combined with water flow and water velocity, judge the congestion volume of sludge or solid substances in the sewage pipe; Based on the image recognition algorithm, monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet in real time; Establish a comprehensive evaluation model for intelligent monitoring of sewage outlets to comprehensively evaluate whether the water quality of sewage outlets and the congestion volume of pipes are qualified.

2. The on-line intelligent monitoring method for a sewage outlet according to claim 1, characterized in that, The step of dividing the sewage outlets into several edge nodes according to the distribution of sewage pipes and setting up a four-layer monitoring mode to improve the monitoring efficiency and accuracy of sewage outlets specifically includes: According to the distribution of sewage pipes to be monitored, divide the sewage outlets into several edge nodes. Among them, the edge nodes mainly include: sewage pipe intersections, stable flow areas and sewage terminal outlets; Set up a four-layer monitoring mode according to the types of edge nodes; The four-layer monitoring mode specifically includes: The first layer: the source monitoring mode, which is used to monitor the water chemical components of each sewage pipe at the sewage pipe intersection; The second layer: the stable flow congestion mode, which uses the water flow and water velocity at both ends to obtain the congestion volume of sludge or solid substances in the sewage pipe; The third layer: the terminal outlet monitoring mode, which is used to monitor the water body color, solid floating objects and "illegal discharge" phenomenon at the sewage outlet terminal in time; The fourth layer: the comprehensive evaluation mode, which comprehensively evaluates whether the water quality of the sewage outlet and the congestion volume of the pipe are qualified according to the collected data of each edge node.

3. The on-line intelligent monitoring method for a sewage outlet according to claim 2, characterized in that, The step of installing hardware detection devices at different edge nodes to obtain real-time monitoring data of the water environment at different edge nodes specifically includes: Set up chemical pollutant monitoring devices at the sewage pipe outlets at the sewage pipe intersections; Set up water flow and water velocity monitoring devices at the starting point and the ending point of the stable flow area respectively; Set up a high-definition camera acquisition device at the sewage terminal outlet.

4. The on-line intelligent monitoring method for a sewage outlet according to claim 3, characterized in that, The step of judging the congestion volume of sludge or solid substances in the sewage pipe according to the four-layer monitoring mode, combined with water flow and water velocity specifically includes: Obtain the standard aperture value of the sewage pipe to be monitored through professional measurement or product description; According to the water flow and water velocity monitoring devices, obtain the synchronous monitoring data of the water flow and water velocity at the starting point and the ending point of the stable flow area through synchronous acquisition; Filter and normalize the synchronous monitoring data of the collected water flow and water velocity to eliminate the influence of data peaks and dimensions; Calculate the congestion volume of sludge or solid substances in the sewage pipe according to the synchronous monitoring data of the water flow and water velocity at both ends.

5. The on-line intelligent monitoring method for a sewage outlet according to claim 4, characterized in that, The step of monitoring the water body color, solid floating objects and "illegal discharge" phenomenon at the terminal of the sewage outlet in real time based on the image recognition algorithm specifically includes: Obtain the water body image data of the sewage terminal outlet in real time through the high-definition camera acquisition device; Extract the component features of water body color, solid floating and human image in the water body image data based on big data; Filter each color of the water body image at the sewage terminal outlet according to the Gaussian filtering formula. According to the HSV color space formula, extract the water body color of the sewage outlet terminal area, and judge the water quality of the sewage outlet terminal area through the water body color; According to the grayscale formula, perform grayscale processing on the environmental image of the sewage discharge terminal collected in real time; According to the Canny operator edge detection algorithm, obtain the external contour information of the environmental image of the sewage discharge terminal, and judge whether there is a pile of solid floating objects and whether there is an act of illegal discharge by a person covering the monitoring device through the component characteristics of the solid floating objects and the person.

6. The on-line intelligent monitoring method for a sewage outlet according to claim 5, characterized in that, The establishment of the intelligent monitoring comprehensive evaluation model for the sewage outlet, and the comprehensive evaluation of whether the water quality of the sewage outlet and the pipeline congestion volume are qualified specifically includes: Based on big data or test experiments, obtain the sample groups of the water quality of the sewage outlet and the pipeline congestion volume, where the sample groups include the target sample group and the training sample group; According to the number of sewage pipes at the sewage pipe intersection, divide the sewage pipes at the sewage pipe intersection into several data class sets, and set the number of model synchronous input matrix groups; Among them, the synchronous input matrix refers to the data collected from the sewage pipes at a single sewage pipe intersection, which fuses the congestion volume of sludge or solid substances in the sewage pipe, the water body color, and the judgment results of solid floating objects and "illegal discharge"; The model synchronous input matrix group is composed of multiple synchronous input matrices. Among them, the number of groups of the model synchronous input matrix group is consistent with the number of sewage pipes at the sewage pipe intersection; Based on the machine learning algorithm, establish an intelligent monitoring comprehensive evaluation model for the sewage outlet. Through the model synchronous input matrix group, comprehensively evaluate whether the water quality of the sewage outlet and the pipeline congestion volume are qualified, and further analyze the abnormal sewage outlets of the sewage pipes to determine the source points of abnormal sewage discharge.

7. An online intelligent monitoring and smart station house for sewage outlets, characterized in that, For implementing the online intelligent monitoring method for the sewage outlet as described in any one of claims 1-6, including: A mode division module, which is used to divide the sewage outlet into several edge nodes according to the distribution of the sewage pipes, and set four layers of monitoring modes to improve the monitoring efficiency and accuracy of the sewage outlet; A data acquisition module, which is used to install hardware detection devices at different edge nodes to obtain the monitoring data of the water environment at different edge nodes in real time; A water quality evaluation module, which is used to judge the congestion volume of sludge or solid substances in the sewage pipe according to the four-layer monitoring mode, combined with the water flow and water flow velocity; based on the image recognition algorithm, monitor the water body color, solid floating object situation and "illegal discharge" phenomenon at the sewage outlet terminal in real time; establish an intelligent monitoring comprehensive evaluation model for the sewage outlet, and comprehensively evaluate whether the water quality of the sewage outlet and the pipeline congestion volume are qualified; An emergency treatment module, which is used to receive and store the monitoring data and warning signals collected by each edge node; automatically start the emergency treatment unit according to the type of warning signal; synchronously send encrypted data packets to the supervision platform according to the monitoring results and treatment results.

8. An online intelligent monitoring and intelligent station house for a sewage outlet according to claim 7, characterized in that, The water quality evaluation module includes: A congestion unit, which is used to judge the congestion volume of sludge or solid substances in the sewage pipe according to the four-layer monitoring mode, combined with the water flow and water flow velocity; An image recognition unit, which is used to monitor the water body color, solid floating objects and "illegal discharge" phenomena at the sewage outlet terminal in real time based on image recognition algorithms; A comprehensive evaluation unit, which is used to establish a comprehensive evaluation model for intelligent monitoring of sewage outlets, and comprehensively evaluate whether the water quality of sewage outlets and the pipeline congestion volume are qualified.

9. The on-line intelligent monitoring intelligent station house for a sewage outlet according to claim 8, characterized in that, The emergency treatment module includes: A signal receiving unit, which is used to receive and store the monitoring data and early warning signals collected by each edge node; An emergency treatment unit, which is used to automatically start the emergency treatment unit according to the type of early warning signal; A data sending unit, which is used to synchronously send encrypted data packets to the supervision platform according to the monitoring results and treatment results; The automatically started emergency treatment unit includes: Obtaining the source points and abnormal information of abnormal sewage discharge according to the evaluation results of the comprehensive evaluation model for intelligent monitoring of sewage outlets; According to the abnormal information prompt, commanding the inspection robot to reach the designated position to complete the emergency treatment of the sewage outlet.

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