Intelligent diagnosis and source tracing method for inflow and infiltration of sewage pipe network and related device

Abstract

The invention provides an intelligent diagnosis and source tracing method for inflow and infiltration of a sewage pipe network and a related device. Comprising the steps of performing regional division and historical data analysis on a to-be-diagnosed sewage pipe network, and preliminarily determining problem partitions in the to-be-diagnosed sewage pipe network; obtaining a water body sample in the problem partition, carrying out two-dimensional index detection, and respectively constructing a two-dimensional index database, a fingerprint spectrum and a water body distribution spectrum; then fingerprint comparison and numerical diagnosis are carried out, the proportions of various types of water bodies in the problem subareas are determined, so that the risk levels corresponding to the problem subareas are determined, and a risk level distribution diagram of the sewage pipe network to be diagnosed is generated after rendering is carried out; and identifying the severe risk pipe network subarea, constructing a sewage-external water bidirectional seepage model of the severe risk pipe network subarea based on the rainstorm management model, and realizing traceability processing of the severe risk pipe network subarea so as to obtain a traceability result corresponding to the severe risk pipe network subarea. And the diagnosis accuracy of the water inflow and infiltration problem outside the sewage pipe network is improved.

Description

Technical Field

[0001] The present application relates to the field of drainage pipe networks, and particularly to an intelligent diagnosis and traceability method for the inflow and infiltration of sewage pipe networks and related devices. Background Art

[0002] A sewage pipe network refers to a pipe system used to collect and transport urban sewage and is a core element of urban water environmental protection. However, during the long-term service of urban sewage pipe networks, due to factors such as ground load, corrosion, and erosion, structural defect problems such as misaligned joints and ruptures are common, resulting in a large amount of external water (groundwater, river water, etc.) easily invading the sewage pipes through the above areas, causing sewage overflow and a serious decrease in the biochemical oxygen demand concentration of the influent to the sewage treatment plant. Therefore, it is urgent to comprehensively carry out inspections on the inflow and infiltration of external water.

[0003] Existing precise inspection technologies usually require operations such as pre-blocking, dredging, and dewatering of pipes. This method needs to be implemented on a large scale, resulting in high costs and low efficiency. Then, by constructing a water quality characteristic factor database for different types of water bodies from different sources and combining a numerical partition diagnosis method to identify problem areas, due to the poor stability of water quality characteristic factors, the diagnostic accuracy is insufficient, and the precise inspection cost caused by the low diagnostic accuracy problem (kilometer level) is still relatively high. Further, although the existing hydrodynamic inversion positioning technology uses a lower cost to improve the diagnostic accuracy to the pipe section level (meter level) on the basis of partition diagnosis, it does not consider the two-way seepage process between external water and pipe sewage driven by the head difference in actual situations, resulting in ineffective fitting between the simulated flow rate and the monitored flow rate of the pipe and a large diagnostic error.

[0004] Based on this, in the existing technology, there is a problem of insufficient diagnostic accuracy for the inflow and infiltration of external water in sewage pipe networks. Summary of the Invention

[0005] Embodiments of the present application provide an intelligent diagnosis and traceability method for the inflow and infiltration of sewage pipe networks and related devices to improve the accuracy of the diagnostic results for the inflow and infiltration of urban sewage pipe networks.

[0006] In a first aspect, embodiments of the present application provide an intelligent diagnosis and traceability method for the inflow and infiltration of sewage pipe networks, including:

[0007] Dividing the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network partitions;

[0008] Obtaining the historical data of each sewage pipe network partition, analyzing according to the historical data, and initially determining the problem partitions in the sewage pipe network to be diagnosed;

[0009] Obtaining water body samples in the problem partitions;

[0010] Based on water samples, two-dimensional index detection is carried out, and a two-dimensional index database, a fingerprint map, and a water body distribution map are constructed respectively;

[0011] Based on the two-dimensional index database, the fingerprint map, and the water body distribution map, fingerprint comparison and numerical diagnosis are carried out to determine the proportion of each type of water body in the problem area;

[0012] Based on the proportion of each type of water body, the risk level corresponding to each problem area is determined, and rendering is carried out based on the risk level corresponding to each problem area to generate a risk level distribution map of the sewage pipe network to be diagnosed;

[0013] Identify the severely risky pipe network areas in the risk level distribution map of the sewage pipe network to be diagnosed, and based on the stormwater management model, construct a two-way sewage-exogenous water seepage model for the severely risky pipe network areas;

[0014] Based on the two-way sewage-exogenous water seepage model, traceability processing is carried out on the severely risky pipe network areas to obtain the traceability results corresponding to the severely risky pipe network areas.

[0015] In a possible implementation manner, the water samples include a first water sample and a second water sample. The first water sample is various types of water samples collected at the source in the problem area, and the second water sample is various types of water samples collected at the end in the problem area; then based on the water samples, two-dimensional index detection is carried out, and a two-dimensional index database, a fingerprint map, and a water body distribution map are constructed respectively, including:

[0016] Obtain historical hydrological data;

[0017] Based on the historical data and historical hydrological data of each sewage pipe network area, determine the two-dimensional characteristic indexes of sewage and exogenous water;

[0018] Based on the first water sample, parallel determination is carried out to obtain the two-dimensional characteristic index data in the first water sample;

[0019] Based on the two-dimensional characteristic index data in the first water sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the first water sample;

[0020] Based on the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the first water sample, construct a two-dimensional index database and generate a corresponding fingerprint map;

[0021] Based on the second water sample, parallel determination is carried out to obtain the two-dimensional characteristic index data in the second water sample;

[0022] Based on the two-dimensional characteristic index data in the second water sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the second water sample;

[0023] Generate a corresponding water body distribution map based on the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample within the sampling period.

[0024] In a possible implementation, based on the two-dimensional index database, the fingerprint map, and the water body distribution map, perform fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem area, including:

[0025] Use the data distributions corresponding to the two-dimensional index database and the fingerprint map as the contribution sources, and use the data distribution corresponding to the water body distribution map as the first mixing system;

[0026] Adopt the Markov chain Monte Carlo algorithm to sample the contribution sources to simulate the data distribution of the second mixing system;

[0027] Based on the first mixing system and the second mixing system, perform error analysis to obtain the proportion of each type of water body in the problem area.

[0028] In a possible implementation, the formula of the sewage-external water two-way seepage model is:

[0029] q gw = A1(d L - h) B1 - A2(h sw - h) B2 + A3d L h sw

[0030] In the formula, q gw is the amount of external water entering the sewage pipe network per unit area per unit time; d L is the depth of the saturated zone; h sw is the water level of the external water infiltration point or sewage leakage point; h is the reference height; A1 is the external water infiltration coefficient; B1 is the external water infiltration index; A2 is the water flow coefficient of the external water infiltration point or sewage leakage point; B2 is the water flow index of the external water infiltration point or sewage leakage point; A3 is the interaction coefficient between sewage and external water within the external water infiltration point or sewage leakage point.

[0031] In a possible implementation, obtain the historical data of each sewage pipe network area, and based on the analysis of the historical data, preliminarily determine the problem areas in the sewage pipe network to be diagnosed, including:

[0032] Obtain the historical data of each sewage pipe network area, where the historical data of each sewage pipe network area includes the biochemical oxygen demand concentration of the effluent water body at the end of each sewage pipe network area;

[0033] Compare the historical data of each sewage pipe network partition with the preset concentration threshold. If the historical data is less than the preset concentration threshold, determine the corresponding sewage pipe network partition as a problem partition.

[0034] In a possible implementation, divide the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network partitions, including:

[0035] Obtain the sewage pipe network data of the sewage pipe network to be diagnosed;

[0036] Based on the sewage pipe network data, establish a drainage geographic information system;

[0037] Based on the drainage geographic information system, divide the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network partitions.

[0038] In a second aspect, the present application proposes an intelligent diagnosis and traceability device for sewage inflow and infiltration in a sewage pipe network, including:

[0039] A partitioning module for dividing the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network partitions;

[0040] A determination module for obtaining the historical data of each sewage pipe network partition, analyzing according to the historical data, and initially determining the problem partitions in the sewage pipe network to be diagnosed;

[0041] An acquisition module for acquiring water body samples in the problem partitions;

[0042] A detection module for performing two-dimensional index detection based on the water body samples, respectively constructing a two-dimensional index database, a fingerprint map, and a water body distribution map;

[0043] A diagnosis module for performing fingerprint comparison and numerical diagnosis based on the two-dimensional index database, the fingerprint map, and the water body distribution map to determine the proportion of each type of water body in the problem partition;

[0044] A generation module for determining the risk level corresponding to each problem partition based on the proportion of each type of water body, and performing rendering based on the risk level corresponding to each problem partition to generate a risk level distribution map of the sewage pipe network to be diagnosed;

[0045] A construction module for identifying the severely risked pipe network partitions in the risk level distribution map of the sewage pipe network to be diagnosed, and constructing a sewage-exogenous water two-way seepage model for the severely risked pipe network partitions based on the stormwater management model;

[0046] An obtaining module for performing traceability processing on the severely risked pipe network partitions based on the sewage-exogenous water two-way seepage model to obtain the traceability result corresponding to the severely risked pipe network partitions.

[0047] In a third aspect, the present application provides an intelligent diagnostic and traceability device for inflow and infiltration of sewage pipelines, including:

[0048] at least one processor; and a memory communicatively connected to the at least one processor;

[0049] wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above first aspect and / or various possible implementation manners of the first aspect.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above first aspect and / or various possible implementation manners of the first aspect when executed by a processor.

[0051] An intelligent diagnostic and traceability method and related device for inflow and infiltration of sewage pipelines provided by an embodiment of the present application analyze historical data of each sewage pipeline partition to initially determine a problem partition in the sewage pipeline to be diagnosed, narrowing the scope of accurate analysis of the inflow and infiltration problem; obtaining water body samples in the problem partition for two-dimensional index detection, respectively constructing a two-dimensional index database, a fingerprint map, and a water body distribution map, and then performing fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem partition, realizing accurate distinction between sewage and external water; determining the risk level corresponding to each problem partition based on the proportion of each type of water body, and rendering based on the risk level corresponding to each problem partition to generate a risk level distribution map of the sewage pipeline to be diagnosed; identifying severely risky pipeline partitions in the risk level distribution map of the sewage pipeline to be diagnosed, and constructing a sewage-external water two-way seepage model for the severely risky pipeline partitions based on a stormwater management model; based on the sewage-external water two-way seepage model, performing traceability processing on the severely risky pipeline partitions to obtain a traceability result corresponding to the severely risky pipeline partitions, thereby improving the detection accuracy of the external water inflow and infiltration problem and achieving accurate traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0053] Figure 1 It is an architecture diagram of an intelligent diagnostic and traceability system for inflow and infiltration of sewage pipelines provided by the present application;

[0054] Figure 2 It is a flowchart of an intelligent diagnostic and traceability method for inflow and infiltration of sewage pipelines provided by the present application Figure 1 ;

[0055] Figure 3Schematic diagram of the regional division of the sewage pipe network to be diagnosed in this application Figure 1 ;

[0056] Figure 4 Schematic diagram of the external water intrusion volume in Zone III provided in this application;

[0057] Figure 5 Risk level map of the sewage pipe network to be diagnosed provided in this application;

[0058] Figure 6 Flow monitoring map of Zone III provided in this application;

[0059] Figure 7 Schematic diagram of the mathematical model of the sewage pipe network provided in this application;

[0060] Figure 8 Schematic diagram of the parameters of the pipe sewage - external water two - way seepage model provided in this application;

[0061] Figure 9 Schematic diagram of the principle of the sewage - external water two - way seepage model provided in this application;

[0062] Figure 10 Flow chart of an intelligent diagnosis and traceability method for sewage inflow and infiltration in the sewage pipe network provided in this application Figure 2 ;

[0063] Figure 11 Schematic diagram of the regional division of the sewage pipe network to be diagnosed provided in this application Figure 2 ;

[0064] Figure 12 Schematic diagram of the regional division of the sewage pipe network to be diagnosed provided in this application Figure 3 ;

[0065] Figure 13 Flow chart of an intelligent diagnosis and traceability method for sewage inflow and infiltration in the sewage pipe network provided in this application Figure 3 ;

[0066] Figure 14 Flow chart of an intelligent diagnosis and traceability method for sewage inflow and infiltration in the sewage pipe network provided in this application Figure 4 ;

[0067] Figure 15 Schematic diagram of a two - dimensional index database provided in this application Figure 1 ;

[0068] Figure 16 Schematic diagram of a fingerprint spectrum provided in this application;

[0069] Figure 17 Schematic diagram of a two - dimensional index database provided in this applicationFigure 2 ;

[0070] Figure 18 Schematic diagram of a water body distribution map provided by this application;

[0071] Figure 19 Flow schematic of an intelligent diagnosis and traceability method for influent and infiltration in a sewage pipe network provided by this application Figure 5 ;

[0072] Figure 20 Schematic diagram of the structure of an intelligent diagnosis and traceability device for influent and infiltration in a sewage pipe network provided by this application;

[0073] Figure 21 Schematic diagram of the structure of an intelligent diagnosis and traceability equipment for influent and infiltration in a sewage pipe network provided by this application.

[0074] Through the above-mentioned drawings, specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0075] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0076] It should be noted that the information and data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards, and corresponding operation entrances are provided for the user to choose to authorize or reject.

[0077] Urban sewage pipe networks are pipe systems for collecting and transporting urban sewage, which are used to ensure the normal operation of the city and the quality of life of residents. However, during long-term use, urban sewage pipe networks are affected by factors such as ground loads, corrosion, and scouring, resulting in structural defect problems such as misalignment and rupture, leading to the phenomenon of influent and infiltration. Therefore, it is necessary to comprehensively carry out inspections on urban sewage pipe networks for influent and infiltration.

[0078] In the prior art, usually after operations such as pre-blocking, dredging, and dewatering of pipelines, combined with a water quality characteristic factor database constructed based on water bodies of different source types, and adopting a numerical partition diagnosis method based on hydrodynamic inversion positioning technology to identify the inflow and infiltration areas of urban sewage pipe networks; however, due to the instability of water quality characteristic factors and the limitations of hydrodynamic inversion positioning technology in the two-way seepage process of external water and sewage, the accuracy of the diagnosis results is insufficient.

[0079] Based on this, in the prior art, there is a problem of insufficient diagnostic accuracy for the inflow and infiltration of external water into urban sewage pipe networks.

[0080] To solve the above problems, the core concept of this application is as follows: initially determine the problem areas in the sewage pipe network to be diagnosed through the historical data of each sewage pipe network partition, narrow the scope of accurate analysis of the inflow and infiltration problems, and improve the efficiency of inflow and infiltration detection; obtain water body samples in the problem areas for two-dimensional index detection, respectively construct a two-dimensional index database, a fingerprint map, and a water body distribution map, and then perform fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem area, improving the accuracy of distinguishing sewage and external water; based on the proportion of each type of water body, determine the risk level corresponding to each problem area, and perform rendering based on the risk level corresponding to each problem area to generate a risk level distribution map of the sewage pipe network to be diagnosed; identify the severely risky pipe network partitions in the risk level distribution map of the sewage pipe network to be diagnosed, and based on the stormwater management model, construct a sewage-external water two-way seepage model for the severely risky pipe network partitions; based on the sewage-external water two-way seepage model, perform a traceability process on the severely risky pipe network partitions to obtain the traceability results corresponding to the severely risky pipe network partitions, thereby improving the detection accuracy of external water inflow and infiltration problems and achieving accurate traceability.

[0081] Figure 1 Shown is an architecture diagram of an intelligent diagnosis and traceability system for sewage pipe network inflow and infiltration. The intelligent diagnosis and traceability system for sewage pipe network inflow and infiltration is a computer device. In Figure 1 the above framework includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0082] It can be understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the architecture of the intelligent diagnosis and traceability system for sewage pipe network inflow and infiltration. In other feasible embodiments of this application, the above architecture may include more or fewer components than shown, or combine certain components, or split certain components, or have different component arrangements, which can be specifically determined according to the actual application scenario and are not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0083] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0084] The processing device 102 can divide the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network partitions; based on the water body samples, perform two-dimensional index detection, and respectively construct a two-dimensional index database, a fingerprint map, and a water body distribution map; based on the two-dimensional index database, the fingerprint map, and the water body distribution map, perform fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem partition; based on the proportion of each type of water body, determine the risk level corresponding to each problem partition, and perform rendering based on the risk level corresponding to each problem partition to generate a risk level distribution map of the sewage pipe network to be diagnosed; identify the severely risky pipe network partitions in the risk level distribution map of the sewage pipe network to be diagnosed, and construct a sewage-exogenous water two-way seepage model for the severely risky pipe network partitions based on the stormwater management model; based on the sewage-exogenous water two-way seepage model, perform traceability processing on the severely risky pipe network partitions to obtain the traceability results corresponding to the severely risky pipe network partitions, so as to improve the detection accuracy of the problem of exogenous water inflow and infiltration into the sewage pipe network and achieve accurate traceability.

[0085] The display device 103 can also be a touch display screen or the screen of a terminal device, which is used to receive user instructions while displaying the above content to achieve interaction with the user.

[0086] It should be understood that the above processing device can be implemented by a processor reading and executing instructions in a memory or by a chip circuit.

[0087] In addition, the network architecture and service scenarios described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the network architecture and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0088] The following uses specific embodiments to elaborate in detail on the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following will describe the embodiments of the present application with reference to the accompanying drawings.

[0089] Figure 2 Schematic flow of an intelligent diagnosis and traceability method for inflow and infiltration of a sewage pipe network provided by the present application Figure 1 As Figure 2 shown, the method includes:

[0090] S201. Divide the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network sub-regions.

[0091] In this embodiment, by dividing the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network sub-regions, the detection range of inflow and infiltration is significantly reduced, thereby reducing the detection cost and improving the detection efficiency.

[0092] For example, divide the sewage pipe network to be diagnosed into regions to obtain Figure 3 sub-regions I, II, III, and IV as shown.

[0093] S202. Obtain the historical data of each sewage pipe network sub-region, and analyze the historical data to preliminarily determine the problem sub-regions in the sewage pipe network to be diagnosed.

[0094] In this embodiment, the problem sub-regions are preliminarily determined through the historical data of each sewage pipe network sub-region to achieve precise investigation of the inflow and infiltration problems in the problem sub-regions, effectively improving the detection accuracy of external water inflow and infiltration problems.

[0095] S203. Obtain water body samples in the problem sub-regions.

[0096] Optionally, the water body samples include a first water body sample and a second water body sample. The first water body sample is various types of water body samples collected at the source in the problem sub-region, and the second water body sample is various types of water body samples collected at the end in the problem sub-region.

[0097] In this embodiment, the various types of water body samples include external water and sewage. Among them, the sewage includes industrial sewage, catering wastewater, and domestic sewage. Industrial sewage is the sewage discharged by enterprises, and domestic sewage is the sewage discharged by residential communities; external water includes groundwater, river water, and lake water.

[0098] For example, when the type of the water body sample is sewage, the sampling duration of the sewage is not less than one complete production and living cycle of the drainage household, and the sampling frequency is not less than once every 3 hours; when the type of the water body sample is external water, the sampling duration of the external water is not less than 1 day, and the sampling frequency is not less than once every 3 hours.

[0099] By obtaining various types of water body samples at the source in the problem sub-region, the initial water quality condition can be reflected. Collecting various types of water body samples at the end of the problem sub-region can reflect the overall water quality change, so as to achieve the minimization of the detection range of external water intrusion in the problem sub-region and further improve the accuracy of inflow and infiltration detection.

[0100] Optionally, during the sampling of the water body samples in the problem sub-region, a handheld pipeline flowmeter is used simultaneously to measure the instantaneous outflow flow rate of the sampling area.

[0101] S204. Based on the water body samples, perform two-dimensional index detection, and respectively construct a two-dimensional index database, a fingerprint map, and a water body distribution map.

[0102] In this embodiment, by performing two-dimensional index detection on the first water body sample and the second water body sample respectively, a two-dimensional index database, a fingerprint map, and a water body distribution map are constructed, and further, the intrusion of external water in the problem area is detected, improving the detection accuracy of the problem of external water inflow and infiltration.

[0103] S205. Based on the two-dimensional index database, the fingerprint map, and the water body distribution map, perform fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem area.

[0104] In this embodiment, based on the two-dimensional index database, the fingerprint map, and the water body distribution map, the proportion of each type of water body in the problem area is obtained, accurately showing the specific situation of external water intrusion in each problem area, and improving the detection accuracy of the problem of external water inflow and infiltration in the problem area.

[0105] S206. Based on the proportion of each type of water body, determine the risk level corresponding to each problem area, and perform rendering based on the risk level corresponding to each problem area to generate a risk level distribution map of the sewage pipe network to be diagnosed.

[0106] In this embodiment, for example, if the proportion of external water is less than or equal to 15%, the risk level corresponding to the problem area is a low risk; if the proportion of external water is greater than 15% and less than or equal to 30%, the risk level corresponding to the problem area is a medium risk; if the proportion of external water is greater than 30%, the risk level corresponding to the problem area is a high risk.

[0107] Further, as Figure 4 shown, if the proportion of external water in Area III is 53%, the risk level corresponding to Area III is a high risk. Take the instantaneous outflow flow corresponding to Area III as the flow mean value within the sampling time interval, calculate the total daily outflow of Area III, and calculate the external water volume in Area III based on the proportion of external water in Area III and the total daily outflow, so as to input the corresponding high-risk file. If the external water volume in Area III is greater than the preset external water volume threshold of 100m 3 , then retain this record.

[0108] Use the xlrd library (a library for reading Excel) in python (a programming language) to read the high-risk file, and call the OGR (vector geographic data reading and writing library) library in python to match the problem area, and perform rendering on the risk level corresponding to Area III to generate as Figure 5Risk level distribution map of the sewage pipe network to be diagnosed, where the red area in Zone III represents that the risk level corresponding to Zone III is a severe risk.

[0109] S207. Identify the severely risky pipe network zones in the risk level distribution map of the sewage pipe network to be diagnosed, and based on the stormwater management model, construct a two-way sewage-exogenous water seepage model for the severely risky pipe network zones.

[0110] In this embodiment, as Figure 6 shown, for node S3 in Zone III with a severe risk level, the instantaneous outflow consistent with the sampling conditions during the real-time detection and sampling period is detected, and based on the instantaneous outflow, a flow hydrograph is plotted to display the full-process flow data of node S3. Using the integral method, the total outflow of node S3 in Zone III during the monitoring period is obtained, and combined with the proportion of exogenous water in Zone III, the actual exogenous water intrusion volume in Zone III during the monitoring period is obtained as 92.7 m 3 , where the sampling conditions include weather, temperature, and the operating conditions of the drainage pipe network. The monitoring duration is 1 day, the monitoring period is from 5:00 to 5:00 the next day, and the monitoring frequency is 5 minutes / time. Figure 6 The abscissa of Figure 6 represents the monitoring period, Figure 6 The ordinate of Figure 6 represents the instantaneous outflow of node S3.

[0111] The full-process flow data of node S3 is used as boundary condition 1, the actual exogenous water intrusion volume of 92.7 m 3 in Zone III during the monitoring period is used as boundary condition 2, and the specific infiltration points and the exogenous water intrusion volume changing with time in Zone III are used as the parameter set to be solved, so as to construct a sewage pipe network mathematical model as Figure 7 shown. Read the identification of each inspection well in Zone III, obtain the inspection wells with the interaction process of pipe sewage and exogenous water driven by the head difference, and set their inspection well identification as 1, and the identification of the remaining inspection wells as 0. On the premise of satisfying boundary condition 2, use a random algorithm to generate the parameters of the two-way sewage-exogenous water seepage model for the pipes with the inspection well identification of 1. The parameters of the two-way sewage-exogenous water seepage model for the pipes with the inspection well identification of 1 are as Figure 8 shown.

[0112] As Figure 8 shown, the parameters of the two-way sewage-exogenous water seepage model for the pipes with the inspection well identification of 1 are input into the simulaton (simulation) in pyswmm (the stormwater management model interface of python) for automatic call and simulation operation to obtain the simulated flow process data of the effluent water at the end of Zone III.

[0113] Obtain the simulated flow process data (also known as the simulated value) of the effluent water body at the end of Sub-region Ⅲ with the same numerical value as the whole-process flow data (also known as the measured value) of node S3, and use the Nash efficiency coefficient index to determine the error level between the measured value and the simulated value. When the error level meets the condition that the Nash efficiency coefficient is greater than 0.75, determine the external water infiltration point and quantity, as well as the sewage leakage point and quantity.

[0114] Further, the formula for the sewage-external water two-way seepage model is:

[0115] q gw =A1(d L -h) B1 -A2(h sw -h) B2 +A3d L h sw

[0116] In the formula, q gw is the external water quantity entering the sewage pipe network per unit time through a unit area; d L is the depth of the saturated zone; h sw is the water level at the external water infiltration point or sewage leakage point; h is the reference height; A1 is the external water infiltration coefficient; B1 is the external water infiltration index; A2 is the water flow coefficient at the external water infiltration point or sewage leakage point; B2 is the water flow index at the external water infiltration point or sewage leakage point; A3 is the interaction coefficient between sewage and external water within the external water infiltration point or sewage leakage point.

[0117] As Figure 9 shown, in this embodiment, the external water is groundwater, and q gw reflects the rate of groundwater entering the sewage pipe network, d L is determined by the groundwater level, and h is usually the bottom elevation of the external water infiltration point or sewage leakage point.

[0118] S208. Based on the sewage-external water two-way seepage model, conduct a traceability process on the severely risky pipe network sub-region to obtain the traceability result corresponding to the severely risky pipe network sub-region.

[0119] Through the sewage-external water two-way seepage model, the inflow and infiltration conditions of the external water infiltration point or sewage leakage point can be known in detail, so as to realize the traceability process of the inflow and infiltration of the severely risky pipe network sub-region and obtain the corresponding traceability result.

[0120] Figure 10 is a schematic flow chart of an intelligent diagnosis and traceability method for sewage pipe network inflow and infiltration provided by this application Figure 2 As Figure 10 shown, in this embodiment Figure 2Based on the embodiments, the regional division of the sewage pipe network to be diagnosed in step S201 above is elaborated in detail to obtain multiple sewage pipe network sub - regions, including:

[0121] S1001. Obtain the sewage pipe network data of the sewage pipe network to be diagnosed.

[0122] In this embodiment, the sewage pipe network data of the sewage pipe network to be diagnosed includes sewage treatment plant data, drainage household data, drainage pipeline data, pumping station data, and drainage outlet data.

[0123] S1002. Based on the sewage pipe network data, establish a drainage geographic information system.

[0124] In this embodiment, the sewage pipe network data is input into the corresponding geographic information system to establish the corresponding drainage geographic information system.

[0125] For example, as Figure 11 shown, the drainage household data includes enterprise A and residential communities.

[0126] S1003. Based on the drainage geographic information system, conduct regional division on the sewage pipe network to be diagnosed to obtain multiple sewage pipe network sub - regions.

[0127] In this embodiment, taking the inspection wells at the intersections of the branch pipes and main pipes of the pumping stations and drainage pipelines as nodes, the drainage geographic information system is regionally divided to obtain sub - regions Ⅰ, Ⅱ, Ⅲ, and Ⅳ as Figure 12 shown.

[0128] Optionally, after the step S208 of tracing the severely risked pipe network sub - regions based on the sewage - external water two - way seepage model to obtain the tracing results corresponding to the severely risked pipe network sub - regions, it further includes:

[0129] Mark the tracing results in the drainage geographic information system.

[0130] The intelligent diagnosis and tracing method for sewage pipe network inflow and infiltration provided by this application uses sewage pipe network data to conduct regional division on the sewage pipe network to be diagnosed, avoiding large - area inspections, thus reducing the detection cost and improving the detection efficiency.

[0131] Figure 13 For the flow chart of an intelligent diagnosis and tracing method for sewage pipe network inflow and infiltration provided by this application Figure 3 as Figure 13 shown, in this embodiment, based on the Figure 2 embodiments, the step S202 of obtaining the historical data of each sewage pipe network sub - region and analyzing according to the historical data to preliminarily determine the problem sub - regions in the sewage pipe network to be diagnosed is elaborated in detail, including:

[0132] S1301. Obtain historical data of each sewage pipe network partition, wherein the historical data of each sewage pipe network partition includes the biochemical oxygen demand concentration of the outflow water body at the end of each sewage pipe network partition.

[0133] In this embodiment, since sewage is rich in organic matter and external water is less rich in organic matter, sewage and external water can be distinguished by obtaining the biochemical oxygen demand concentration in the outflow water body at the end of each sewage network partition during the period without precipitation.

[0134] S1302: Compare the historical data of each sewage pipe network partition with a preset concentration threshold. If the historical data is less than the preset concentration threshold, determine the corresponding sewage pipe network partition as a problem partition.

[0135] In this embodiment, for example, the preset concentration threshold may be 50 mg / L. By comparing the biochemical oxygen demand concentrations of the outflow water bodies at the ends of partitions I, II, III and IV respectively obtained with the preset concentration threshold, it is concluded that the biochemical oxygen demand concentration of the outflow water body at the end of partition III is lower than the preset concentration threshold, indicating that partition III has an external water intrusion problem, and partition III is a problem partition.

[0136] The present application provides an intelligent diagnosis and tracing method for inflow and infiltration in a sewage network, which preliminarily determines the problem zone through historical data of each sewage network zone, further reduces the investigation scope of inflow and infiltration in the sewage network to be diagnosed, and improves the investigation efficiency.

[0137] Figure 14 Schematic diagram of the process of intelligent diagnosis and tracing method for inflow and infiltration of sewage pipe network provided in this application Figure 4 ,like Figure 14 As shown, in this embodiment Figure 2 Based on the embodiment, the above step S204 performs two-dimensional index detection based on water samples, constructs a two-dimensional index database and a fingerprint map, and describes the water body distribution map in detail, including:

[0138] S1401. Obtain historical hydrological data.

[0139] In this embodiment, the historical hydrological data include surface water hydrological data and groundwater hydrological data combined with a drainage geographic information system.

[0140] S1402. Determine two-dimensional characteristic indicators of sewage and external water based on historical data and historical hydrological information of each sewage network zone.

[0141] In this embodiment, for example, by combining the historical data of each sewage pipe network partition and the historical hydrological data, it is determined that in the sewage pipe network to be diagnosed, the normal water type admitted to the pipe is sewage, which includes industrial sewage, catering wastewater, and domestic sewage, and the type of infiltrating external water is groundwater. Based on the differences between sewage and groundwater, hydrogen stable isotope δ 2 H and manganese ions are used as the two-dimensional index characteristics of sewage and external water.

[0142] S1403. Based on the first water sample, parallel determinations are carried out to obtain the two-dimensional characteristic index data in the first water sample.

[0143] In this embodiment, for each sample in the first water sample, the number of parallel determinations is not less than 3 times to improve the accuracy of the two-dimensional characteristic index data in the first water sample.

[0144] S1404. Based on the two-dimensional characteristic index data in the first water sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the first water sample within the sampling period.

[0145] In this embodiment, based on the two-dimensional characteristic index data in the first water sample, the average value and standard deviation of the two-dimensional characteristic index data in the first water sample within the sampling period as shown in Figure 15 are calculated. Among them, Figure 15 the abscissa in represents the manganese ion Figure 15 content, and 2 the ordinate in

[0146] represents the hydrogen stable isotope δ

[0147] In this embodiment, based on the average value and standard deviation of the two-dimensional characteristic index data in the first water sample within the sampling period, a two-dimensional index database is constructed and a corresponding fingerprint spectrum is generated. Figure 15The average value and standard deviation of the two-dimensional characteristic index data in the first water body sample shown during the sampling period are used to construct a two-dimensional index database and generate a fingerprint map as shown in Figure 16 The fingerprint map, where the fingerprint map is divided into a first region and a second region. The first region indicates that there is mixing of sewage and external water, indicating the problem of external water intrusion; the second region indicates that there is no problem of external water intrusion.

[0148] S1406. Based on the second water body sample, parallel measurements are carried out to obtain the two-dimensional characteristic index data in the second water body sample.

[0149] In this embodiment, for example, the sampling duration of the second water body sample is not less than 1 day, the frequency is not less than 3 hours / time, and the number of parallel measurements is not less than 3 times.

[0150] S1407. Based on the two-dimensional characteristic index data in the second water body sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample during the sampling period.

[0151] In this embodiment, based on the two-dimensional characteristic index data in the second water body sample, the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample during the sampling period as shown in Figure 17 are calculated, where Figure 17 the abscissa in represents the content of manganese ions content, Figure 17 the ordinate in represents the hydrogen stable isotope δ 2 H content. Sewage 1, sewage 2, and sewage 3 respectively represent industrial sewage, catering wastewater, and domestic sewage in the first water body sample. Point A represents the average value of the two-dimensional characteristic index data of the external water in the first water body sample during the sampling period. Point B represents the average value of the two-dimensional characteristic index data of the industrial sewage in the first water body sample during the sampling period. Point C represents the average value of the two-dimensional characteristic index data of the catering wastewater in the first water body sample during the sampling period. Point D represents the average value of the two-dimensional characteristic index data of the domestic wastewater in the first water body sample during the sampling period. Region A represents the standard deviation of the two-dimensional characteristic index data of the external water in the first water body sample during the sampling period. Region B represents the standard deviation of the two-dimensional characteristic index data of the industrial sewage in the first water body sample during the sampling period. Region C represents the standard deviation of the two-dimensional characteristic index data of the catering wastewater in the first water body sample during the sampling period. Region D represents the standard deviation of the two-dimensional characteristic index data of the domestic sewage in the first water body sample during the sampling period. Point E represents the average value of the two-dimensional characteristic index data in the second water body sample during the sampling period. Region E represents the standard deviation of the two-dimensional characteristic index data in the second water body sample during the sampling period.

[0152] S1408. Generate a corresponding water body distribution map based on the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample within the sampling period.

[0153] In this embodiment, for example, the water body distribution map is as Figure 18 shown. When point E and area E fall into the first area as shown in Figure 16 shown, it indicates that there is an external water intrusion problem in the second water body sample.

[0154] The intelligent diagnosis and tracing method for influent and infiltration in the sewage pipe network provided by this application determines the two-dimensional characteristic indexes of sewage and external water through the historical data and historical hydrological data of each sewage pipe network partition, simplifies the external water intrusion problem of the sewage pipe network to be diagnosed into a binary contribution ratio solving problem, realizes the accurate determination of the influent and infiltration conditions in the problem partition, and thus realizes the accurate diagnosis of the external water influent and infiltration problem in the sewage pipe network.

[0155] Figure 19 It is a flow schematic of an intelligent diagnosis and tracing method for influent and infiltration in a sewage pipe network provided by this application Figure 5 , as Figure 19 shown. On the basis of the Figure 2 embodiment, the fingerprint comparison and numerical diagnosis are carried out based on the two-dimensional index database, fingerprint map, and water body distribution map in step S205 to determine the proportion of each type of water body in the problem partition, which is described in detail as follows:

[0156] S1901. Take the data distributions corresponding to the two-dimensional index database and fingerprint map as the contribution sources, and take the data distribution corresponding to the water body distribution map as the first mixed system.

[0157] In this embodiment, the first mixed system is a mixed system composed of measured data distributions.

[0158] S1902. Use the Markov chain Monte Carlo algorithm to sample the contribution sources to simulate the data distribution of the second mixed system.

[0159] In this embodiment, the Markov chain Monte Carlo algorithm is used to realize the in-depth analysis and simulation of the data distribution of the second mixed system.

[0160] S1903. Based on the first mixed system and the second mixed system, perform error analysis to obtain the proportion of each type of water body in the problem partition.

[0161] In this embodiment, by analyzing the error between the first mixed system and the second mixed system, the proportion of each type of water body in the problem partition that meets the error requirements is obtained to improve the accuracy and reliability of the proportion of each type of water body in the problem partition.

[0162] The intelligent diagnosis and traceability method for the inflow and infiltration of the sewage pipe network proposed in this application uses the Markov Chain Monte Carlo algorithm to respectively process the data distributions corresponding to the two-dimensional index database, the fingerprint map, and the water body distribution map to simulate the real data distribution and improve the accuracy of the proportion of various types of water bodies in the problem area.

[0163] Figure 20 As shown in the structural schematic diagram of an intelligent diagnosis and traceability device for the inflow and infiltration of the sewage pipe network provided in this application, Figure 20 the intelligent diagnosis and traceability device for the inflow and infiltration of the sewage pipe network provided in this embodiment includes:

[0164] The zoning module 2001 is used to divide the sewage pipe network to be diagnosed into regions to obtain a plurality of sewage pipe network zones.

[0165] The determination module 2002 is used to obtain the historical data of each sewage pipe network zone, analyze according to the historical data, and initially determine the problem zones in the sewage pipe network to be diagnosed.

[0166] The acquisition module 2003 is used to acquire water body samples in the problem zones.

[0167] Optionally, the water body samples include a first water body sample and a second water body sample. The first water body sample is various types of water body samples collected at the source in the problem zone, and the second water body sample is various types of water body samples collected at the end in the problem zone.

[0168] The detection module 2004 is used to perform two-dimensional index detection based on the water body samples, and respectively construct a two-dimensional index database, a fingerprint map, and a water body distribution map.

[0169] The diagnosis module 2005 is used to perform fingerprint comparison and numerical diagnosis based on the two-dimensional index database, the fingerprint map, and the water body distribution map to determine the proportion of various types of water bodies in the problem zone.

[0170] The generation module 2006 is used to determine the risk level corresponding to each problem zone based on the proportion of various types of water bodies, and perform rendering based on the risk level corresponding to each problem zone to generate a risk level distribution map of the sewage pipe network to be diagnosed.

[0171] The construction module 2007 is used to identify the severely risked pipe network zones in the risk level distribution map of the sewage pipe network to be diagnosed, and construct a sewage-external water two-way seepage model for the severely risked pipe network zones based on the stormwater management model.

[0172] Optionally, the formula for the sewage-external water two-way seepage model is:

[0173] q gw =A1(d L-h) B1 -A2(h sw -h) B2 +A3d L h sw

[0174] In the formula, q gw is the external water volume entering the sewage pipe network per unit area per unit time; d L is the depth of the saturated zone; h sw is the water level at the external water infiltration point or sewage leakage point; h is the reference height; A1 is the external water infiltration coefficient; B1 is the external water infiltration index; A2 is the water flow coefficient at the external water infiltration point or sewage leakage point; B2 is the water flow index at the external water infiltration point or sewage leakage point; A3 is the interaction coefficient between sewage and external water at the external water infiltration point or sewage leakage point.

[0175] Module 2008 is obtained, which is used to perform source tracing processing on the severely risky pipe network partitions based on the sewage-external water two-way seepage model to obtain the source tracing results corresponding to the severely risky pipe network partitions.

[0176] Optionally, the zoning module 2001 can specifically be further used for:

[0177] Obtain the sewage pipe network data of the sewage pipe network to be diagnosed;

[0178] Based on the sewage pipe network data, establish a drainage geographic information system;

[0179] Based on the drainage geographic information system, perform regional division on the sewage pipe network to be diagnosed to obtain multiple sewage pipe network partitions.

[0180] Optionally, after performing source tracing processing on the severely risky pipe network partitions based on the sewage-external water two-way seepage model to obtain the source tracing results corresponding to the severely risky pipe network partitions, it includes:

[0181] Mark the source tracing results in the drainage geographic information system.

[0182] Optionally, the determination module 2002 can specifically be further used for:

[0183] Obtain the historical data of each sewage pipe network partition, where the historical data of each sewage pipe network partition includes the biochemical oxygen demand concentration of the effluent water body at the end of each sewage pipe network partition;

[0184] Compare the historical data of each sewage pipe network partition with a preset concentration threshold. If the historical data is less than the preset concentration threshold, the corresponding sewage pipe network partition is determined as a problem partition.

[0185] Optionally, the detection module 2004 can specifically be further used for:

[0186] Obtain historical hydrological data;

[0187] Based on the historical data and historical hydrological data of each sewage pipe network partition, determine the two-dimensional characteristic indexes of sewage and external water;

[0188] Based on the first water sample, conduct parallel measurements to obtain the two-dimensional characteristic index data in the first water sample;

[0189] Based on the two-dimensional characteristic index data in the first water sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the first water sample;

[0190] Based on the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the first water sample, construct a two-dimensional index database and generate a corresponding fingerprint map;

[0191] Based on the second water sample, conduct parallel measurements to obtain the two-dimensional characteristic index data in the second water sample;

[0192] Based on the two-dimensional characteristic index data in the second water sample, calculate the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the second water sample;

[0193] Based on the average value and standard deviation of the two-dimensional characteristic index data in the sampling period of the second water sample, generate a corresponding water body distribution map.

[0194] Optionally, the diagnosis module 2005 can specifically also be used for:

[0195] Take the data distribution corresponding to the two-dimensional index database and the fingerprint map as the contribution source, and take the data distribution corresponding to the water body distribution map as the first mixing system;

[0196] Adopt the Markov chain Monte Carlo algorithm to sample the contribution source to simulate the data distribution of the second mixing system;

[0197] Based on the first mixing system and the second mixing system, conduct error analysis to obtain the proportion of each type of water body in the problem area.

[0198] An intelligent diagnosis and traceability device for sewage inflow and infiltration in a sewage pipe network provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0199] Figure 21 It is a structural schematic diagram of an intelligent diagnosis and traceability device for sewage inflow and infiltration in a sewage pipe network provided by this application, as Figure 21As shown in the figure, the intelligent diagnosis and traceability device for the influent and infiltration of the sewage pipe network provided in this embodiment includes at least one processor 2101 and a memory 2102. Optionally, the intelligent diagnosis and traceability device for the influent and infiltration of the sewage pipe network further includes a communication component 2103. Among them, the processor 2101, the memory 2102, and the communication component 2103 are connected through a bus 2104.

[0200] In the specific implementation process, at least one processor 2101 executes the computer-executable instructions stored in the memory 2102, so that at least one processor 2101 executes the above-mentioned method.

[0201] For the specific implementation process of the processor 2101, reference can be made to the above method embodiment. The implementation principle and technical effect are similar, and will not be elaborated here in this embodiment.

[0202] In the above embodiment, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, abbreviated as: CPU), or other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated as: DSP), application-specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated as: ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor.

[0203] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0204] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0205] This application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the processor executes the computer-executable instructions, the above-mentioned method is implemented.

[0206] The above-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. The readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0207] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium may also be a component of the processor. The processor and the readable storage medium may be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in a device.

[0208] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed among each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0209] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, in each embodiment of the present invention, the functional units may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0211] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0212] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, and other various media that can store program codes.

[0213] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention. These variations, uses, or adaptations follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. An intelligent diagnosis and tracing method for the inflow and infiltration of sewage pipe networks, characterized in that, Including: Dividing the sewage pipe network to be diagnosed into regions to obtain multiple sewage pipe network sub-regions; Obtaining the historical data of each sewage pipe network sub-region, analyzing according to the historical data, and initially determining the problem sub-regions in the sewage pipe network to be diagnosed; Obtaining water body samples in the problem sub-regions; Based on the water body samples, performing two-dimensional index detection, respectively constructing a two-dimensional index database, a fingerprint map, and a water body distribution map; Based on the two-dimensional index database, the fingerprint map, and the water body distribution map, performing fingerprint comparison and numerical diagnosis to determine the proportion of each type of water body in the problem sub-region; Based on the proportion of each type of water body, determining the risk level corresponding to each problem sub-region, and rendering based on the risk level corresponding to each problem sub-region to generate a risk level distribution map of the sewage pipe network to be diagnosed; Identifying the severely risky pipe network sub-regions in the risk level distribution map of the sewage pipe network to be diagnosed, and constructing a sewage-exogenous water two-way seepage model for the severely risky pipe network sub-regions based on the stormwater management model; Based on the sewage-exogenous water two-way seepage model, performing a traceability process on the severely risky pipe network sub-region to obtain the traceability result corresponding to the severely risky pipe network sub-region.

2. The method according to claim 1, wherein The water body samples include a first water body sample and a second water body sample. The first water body sample is various types of water body samples collected at the source in the problem sub-region, and the second water body sample is various types of water body samples collected at the end in the problem sub-region; then the performing two-dimensional index detection based on the water body samples, respectively constructing a two-dimensional index database, a fingerprint map, and a water body distribution map includes: Obtaining historical hydrological data; Based on the historical data of each sewage pipe network sub-region and the historical hydrological data, determining the two-dimensional characteristic indexes of sewage and exogenous water; Based on the first water body sample, performing parallel determination to obtain the two-dimensional characteristic index data in the first water body sample; Based on the two-dimensional characteristic index data in the first water body sample, calculating the average value and standard deviation of the two-dimensional characteristic index data in the first water body sample within the sampling period; Based on the average value and standard deviation of the two-dimensional characteristic index data in the first water body sample within the sampling period, constructing a two-dimensional index database and generating a corresponding fingerprint map; Based on the second water body sample, performing parallel determination to obtain the two-dimensional characteristic index data in the second water body sample; Based on the two-dimensional characteristic index data in the second water body sample, calculating the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample within the sampling period; Based on the average value and standard deviation of the two-dimensional characteristic index data in the second water body sample within the sampling period, generating a corresponding water body distribution map.

3. The method according to claim 1, characterized in that The performing fingerprint comparison and numerical diagnosis based on the two-dimensional index database, the fingerprint map, and the water body distribution map to determine the proportion of each type of water body in the problem sub-region includes: Regarding the data distribution corresponding to the two-dimensional index database and the fingerprint map as the contribution source, and regarding the data distribution corresponding to the water body distribution map as the first mixing system; Using the Markov chain Monte Carlo algorithm, sample the contribution sources to simulate the data distribution of the second mixed system; Based on the first mixed system and the second mixed system, perform error analysis to obtain the proportion of each type of water body in the problem area.

4. The method according to claim 1, wherein The formula of the sewage-external water two-way seepage model is: q gw = A1(d L - h) B1 - A2(h sw - h) B2 + A3d L h sw where q gw is the external water volume entering the sewage pipe network per unit area per unit time; d L is the depth of the saturated zone; h sw is the water level at the external water infiltration point or sewage leakage point; h is the reference height; A1 is the external water infiltration coefficient; B1 is the external water infiltration index; A2 is the water flow coefficient at the external water infiltration point or sewage leakage point; B2 is the water flow index at the external water infiltration point or sewage leakage point; A3 is the interaction coefficient between the sewage and external water at the external water infiltration point or sewage leakage point.

5. The method according to claim 1, wherein The steps of obtaining the historical data of each sewage pipe network area and analyzing the historical data to preliminarily determine the problem areas in the sewage pipe network to be diagnosed include: Obtain the historical data of each sewage pipe network area, where the historical data of each sewage pipe network area includes the biochemical oxygen demand concentration of the effluent water body at the end of each sewage pipe network area; Compare the historical data of each sewage pipe network area with a preset concentration threshold. If the historical data is less than the preset concentration threshold, determine the corresponding sewage pipe network area as a problem area.

6. The method according to claim 1, wherein The steps of dividing the sewage pipe network to be diagnosed into multiple sewage pipe network areas include: Obtain the sewage pipe network data of the sewage pipe network to be diagnosed; Based on the sewage pipe network data, establish a drainage geographic information system; Based on the drainage geographic information system, divide the sewage pipe network to be diagnosed into multiple sewage pipe network areas.

7. The method according to claim 6, characterized in that After obtaining the tracing result corresponding to the severe risk pipe network area by tracing the severe risk pipe network area based on the sewage-external water two-way seepage model, the steps include: Mark the tracing result in the drainage geographic information system.

8. An intelligent diagnosis and traceability device for the inflow and infiltration of a sewage pipe network, characterized in that, It includes: A zoning module for dividing the sewage pipe network to be diagnosed into multiple sewage pipe network areas; A determination module for obtaining the historical data of each sewage pipe network area and analyzing the historical data to preliminarily determine the problem areas in the sewage pipe network to be diagnosed; An acquisition module for acquiring water body samples in the problem areas; A detection module for performing two-dimensional index detection based on the water body samples, respectively constructing a two-dimensional index database, a fingerprint map, and a water body distribution map; A diagnosis module for performing fingerprint comparison and numerical diagnosis based on the two-dimensional index database, the fingerprint map, and the water body distribution map to determine the proportion of each type of water body in the problem area; A generation module for determining the risk level corresponding to each problem area based on the proportion of each type of water body and performing rendering based on the risk level corresponding to each problem area to generate a risk level distribution map of the sewage pipe network to be diagnosed; A construction module for identifying the severe risk pipe network areas in the risk level distribution map of the sewage pipe network to be diagnosed and constructing a sewage-external water two-way seepage model for the severe risk pipe network areas based on the stormwater management model; A obtaining module for tracing the severe risk pipe network areas based on the sewage-external water two-way seepage model to obtain the tracing result corresponding to the severe risk pipe network areas.

9. An intelligent diagnostic and traceability device for the inflow and infiltration of a sewage pipe network, characterized in that, It includes: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent diagnosis and traceability method for influent and infiltration of sewage pipe networks as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used to implement the intelligent diagnosis and traceability method for influent and infiltration of sewage pipe networks as described in any one of claims 1 to 7 when executed by a processor.

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