Multistage nested drainage pipe network problem diagnosis method, device and equipment and storage medium

Abstract

The invention provides a multi-stage nested drainage pipe network problem diagnosis method, device and equipment and a storage medium, and the method comprises the steps: dividing a target drainage region into at least one first-stage subregion, setting a first-stage monitoring point, and monitoring the drainage data of each first-stage subregion; determining a comprehensive index of each first-level partition according to the drainage data of the first-level partitions; sorting the comprehensive indexes of all the first-level partitions with the comprehensive indexes larger than a preset threshold value from high to low, subdividing and demarcating each first-level partition ranked in a front preset percentage, and determining at least one second-level partition; and so on, until the monitoring point of the last level of subarea monitors the drainage data of the source drainage households; according to the comprehensive index of each stage of subarea, in combination with the drainage data of each subarea, accurate positioning and quantitative evaluation of potential problems in the drainage pipe network are realized, targeted optimization suggestions are proposed, and the overall performance and operation efficiency of a drainage system are improved while the troubleshooting efficiency is greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of drainage systems, and particularly to a method, device, equipment and storage medium for diagnosing problems of a multi-level nested drainage pipe network. Background Art

[0002] With the acceleration of the urbanization process, as an important part of urban infrastructure, the scale of the drainage pipe network is becoming increasingly large and its structure is complex, showing the characteristics of multi-level nesting and mutual connection. However, the drainage pipe network often faces various problems such as blockage, leakage, and excessive water quality during operation. If these problems are not discovered and handled in time, it will seriously affect the urban drainage capacity and even cause disasters such as waterlogging. Traditional drainage pipe network inspection methods mostly adopt a carpet-like census method, which is not only time-consuming and laborious, but also inefficient and difficult to accurately locate the problem area. Therefore, developing an efficient and accurate drainage pipe network problem diagnosis method has become an urgent problem to be solved. Summary of the Invention

[0003] The present disclosure provides a method, device, equipment and storage medium for diagnosing problems of a multi-level nested drainage pipe network to at least solve the above technical problems existing in the prior art.

[0004] According to a first aspect of the present disclosure, there is provided a method for diagnosing problems of a multi-level nested drainage pipe network, the method comprising:

[0005] Dividing the target drainage area into drainage sub-areas to determine at least one first-level sub-area;

[0006] Setting first-level monitoring points in each of the first-level sub-areas;

[0007] Monitoring the drainage data of each of the first-level sub-areas through the first-level monitoring points;

[0008] Determining the comprehensive index of each of the first-level sub-areas according to the drainage data of the first-level sub-areas;

[0009] Sorting all the first-level sub-areas with the comprehensive index greater than the preset threshold from high to low, and subdividing and demarcating each of the first-level sub-areas within the top preset percentage of the ranking to determine at least one second-level sub-area;

[0010] Setting second-level monitoring points in each of the second-level sub-areas;

[0011] Monitoring the drainage data of each of the second-level sub-areas through the second-level monitoring points;

[0012] Determining the comprehensive index of each of the second-level sub-areas according to the drainage data of the second-level sub-areas;

[0013] Sort all the secondary partitions with the comprehensive index greater than the preset threshold in descending order of the comprehensive index, and subdivide each secondary partition within the top preset percentage to determine at least one tertiary partition;

[0014] And so on until the monitoring points of the last-level partition monitor the drainage data of the source drainage households;

[0015] Based on the comprehensive indexes of the first-level partition, the secondary partition until the last-level partition, and combined with the drainage data of each partition, determine the drainage network problems in the target drainage area.

[0016] In an implementable manner, the first-level monitoring points are used to monitor the main pipes in the target drainage area, the monitoring points from the secondary monitoring points to the penultimate-level partition are used to monitor the main pipes and branch pipes in the target drainage area, and the monitoring points of the last-level partition are used to monitor the source drainage households in the target drainage area.

[0017] In an implementable manner, the larger the comprehensive index of each partition, the more serious the drainage network problems in that partition.

[0018] In an implementable manner, the drainage data includes rainfall, flow rate, chemical oxygen demand COD, and ammonia nitrogen index.

[0019] In an implementable manner, the comprehensive index includes the dry-season comprehensive index and the rainy-season comprehensive index;

[0020] Determine the comprehensive index of each partition, including: determine the dry-season comprehensive index and the rainy-season comprehensive index of each partition through the following formula:

[0021]

[0022] Among them, Hs is the dry-season comprehensive index, with a value range of 0-1; Hr is the rainy-season comprehensive index, with a value range of 0-1; a is the weight coefficient of chemical oxygen demand COD, with a value range of 0.1-0.9, and it is required to satisfy a + b = 1; b is the weight coefficient of ammonia nitrogen, with a value range of 0.1-0.9, and it is required to satisfy a + b = 1; Q is the theoretical water production in the dry season, with the unit of m 3 ; T is the rainfall in the rainy season, with the unit of m; A is the area of the research area, with the unit of ㎡; m is the total online monitoring duration of flow and water quality in the dry season, with the unit of h; t is the total online monitoring duration of flow and water quality in the rainy season, with the unit of h; Q i is the instantaneous flow rate at the i-th moment in the dry season, with the unit of m 3 / h; Q j is the instantaneous flow rate at the j-th moment in the rainy season, with the unit of m 3 / h; C 1i is the COD concentration monitored online at the i-th moment in the dry season, with the unit of mg / L; C2i is the ammonia nitrogen concentration monitored online at the i-th moment in the dry season, with the unit of mg / L; C 1j is the COD concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L; C 2j is the ammonia nitrogen concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L; H1: the COD concentration in the 24-hour water quality mixed sample of the dry season water quality sampling, with the unit of mg / L; H2 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample of the dry season water quality sampling, with the unit of mg / L; H3 is the COD concentration in the 24-hour water quality mixed sample of the rainy season water quality sampling, with the unit of mg / L; H4 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample of the rainy season water quality sampling, with the unit of mg / L; C1 is the theoretical COD concentration in the dry season, with the unit of mg / L; C2 is the theoretical ammonia nitrogen concentration in the dry season, with the unit of mg / L; C3 is the theoretical COD concentration in the rainy season, with the unit of mg / L; C4 is the theoretical ammonia nitrogen concentration in the rainy season, with the unit of mg / L; K1, K2, K3, K4, β1, β2, β3, β4 are all undetermined correction parameters.

[0023] In one implementable manner, the preset threshold is greater than 0.3; the preset percentage is greater than 30%.

[0024] In one implementable manner, sorting all the first-level partitions with the comprehensive index greater than the preset threshold from high to low according to the comprehensive index, and subdividing and delimiting each first-level partition within the top preset percentage to determine at least one second-level partition, including:

[0025] Sorting all the first-level partitions with the dry season comprehensive index greater than the preset threshold from high to low according to the dry season comprehensive index, and subdividing and delimiting each first-level partition within the top preset percentage to determine at least one second-level partition; and / or,

[0026] Sorting all the first-level partitions with the rainy season comprehensive index greater than the preset threshold from high to low according to the rainy season comprehensive index, and subdividing and delimiting each first-level partition within the top preset percentage to determine at least one second-level partition.

[0027] According to the second aspect of the present disclosure, there is provided a multi-level nested drainage pipe network problem diagnosis device, and the device includes:

[0028] A first partitioning unit, configured to perform drainage area partitioning on a target drainage area to determine at least one first-level partition;

[0029] A first setting unit, configured to set first-level monitoring points in each of the first-level partitions;

[0030] A first monitoring unit, configured to monitor the drainage data of each of the first-level partitions through the first-level monitoring points;

[0031] A first determination unit, configured to determine a comprehensive index for each of the first-level partitions according to the drainage data of the first-level partitions;

[0032] A second partitioning unit, configured to sort all the first-level partitions with comprehensive indexes greater than a preset threshold from high to low, and perform a sub-division on each of the first-level partitions within the top preset percentage to determine at least one second-level partition;

[0033] A second setting unit, configured to set second-level monitoring points within each of the second-level partitions;

[0034] A second monitoring unit, configured to monitor the drainage data of each of the second-level partitions through the second-level monitoring points;

[0035] A second determination unit, configured to determine a comprehensive index for each of the second-level partitions according to the drainage data of the second-level partitions;

[0036] A third partitioning unit, configured to sort all the second-level partitions with comprehensive indexes greater than a preset threshold from high to low, and perform a sub-division on each of the second-level partitions within the top preset percentage to determine at least one third-level partition;

[0037] A third monitoring unit, and so on until the monitoring points of the last-level partition monitor the drainage data of the source drainage households;

[0038] A third determination unit, configured to determine the drainage network problems of the target drainage area according to the comprehensive indexes of the first-level partitions, the second-level partitions until the last-level partition, in combination with the drainage data of each partition.

[0039] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0040] At least one processor; and

[0041] A memory communicatively connected to the at least one processor; wherein,

[0042] 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 method described in the present disclosure.

[0043] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, and the computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0044] The multi - level nested drainage pipe network problem diagnosis method, device, equipment and storage medium of the present disclosure divide the drainage pipe network in the target drainage area. First, the primary partitions are determined, and then, according to the comprehensive indicators of the primary partitions, the secondary partitions are divided, and so on until the source is reached. The present disclosure conducts hierarchical partition monitoring through comprehensive indicators to achieve precise positioning and quantitative evaluation of potential problems in the drainage pipe network, thereby being able to prioritize the treatment of problem areas with high risks and high impact degrees, ensuring the stable operation of the urban drainage system, and significantly improving the investigation efficiency.

[0045] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] By reading the following detailed description with reference to the accompanying drawings, the above - mentioned and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become easily understandable. In the drawings, several embodiments of the present disclosure are shown in an exemplary rather than restrictive manner, wherein:

[0047] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0048] Figure 1 is the architecture diagram of the diagnosis system for drainage pipe network problems;

[0049] Figure 2 is the flowchart of the multi - level nested drainage pipe network problem diagnosis method provided by the embodiment of the present disclosure;

[0050] Figure 3 is the detailed process diagram of the multi - level nested drainage pipe network problem diagnosis method provided by the embodiment of the present disclosure;

[0051] Figure 4 is the schematic diagram of the multi - level partition structure of the target drainage area;

[0052] Figure 5 is the layout schematic diagram of the monitoring points in the target drainage area;

[0053] Figure 6 is the structural schematic diagram of the multi - level nested drainage pipe network problem diagnosis device provided by the embodiment of the present disclosure;

[0054] Figure 7 shows the composition structural schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, features, and advantages of the present disclosure more apparent and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present disclosure.

[0056] Figure 1 It is an architecture diagram of a diagnostic system for drainage network problems. As Figure 1 shown, the system includes a perception layer, a basic layer, a data layer, and an application layer. The perception layer can monitor parameters such as rainfall, liquid level, flow rate, and water quality in the target drainage area. After detecting these parameters, the perception layer can upload them to the data layer through the mobile network, server / government cloud, etc. in the basic layer. The data layer calculates these parameters to obtain comprehensive indicators, sorts and intelligently analyzes the comprehensive indicators, and visually displays the intelligent analysis results in the application layer, and proposes optimization suggestions based on the intelligent analysis results to provide decision-making support.

[0057] The embodiments of the present disclosure provide a multi-level nested diagnostic method for drainage network problems. Figure 2 It is a flowchart of the multi-level nested diagnostic method for drainage network problems provided by the embodiments of the present disclosure. Figure 3 It is a detailed process diagram of the multi-level nested diagnostic method for drainage network problems provided by the embodiments of the present disclosure. As Figure 2 and Figure 3 shown, the method includes:

[0058] Step 201, divide the target drainage area into drainage sub-areas to determine at least one first-level sub-area.

[0059] First, obtain the basic information of the target drainage area, such as pipe network diagrams, topographic maps, urban planning maps, etc. According to the basic information, conduct a topological structure analysis of the drainage network in the target drainage area, and then investigate the land use and drainage status in the target drainage area. According to the analysis and investigation results, divide the target drainage area into drainage sub-areas. Among them, dividing the target drainage area into drainage sub-areas is Figure 3 the division detection unit in

[0060] Based on the different levels of the drainage network in the target drainage area and the importance and risk levels of key nodes (such as intersection points, pumping stations, inspection wells, etc.), combined with the topological relationship of the drainage network, delimit the drainage sub-areas. First, delimit the drainage sub-areas covering the main pipes as the first-level sub-areas.

[0061] Step 202, set first-level monitoring points in each first-level sub-area;

[0062] Step 203, monitoring the drainage data of each first-level partition through the first-level monitoring point.

[0063] After the first-level partition is divided, a first-level monitoring point needs to be set at the exit of each first-level partition to monitor the drainage data of the first-level partition. The first-level monitoring point can be one or more points.

[0064] In one embodiment, the drainage data includes rainfall, rainfall duration, flow, chemical oxygen demand COD and ammonia nitrogen index. It should be explained that the data monitored for each partition may include not only rainfall, flow, chemical oxygen demand COD and ammonia nitrogen index, but also other data, such as flow rate, liquid level, etc.

[0065] The equipment used to monitor drainage data can be high-precision and high-reliability monitoring equipment, such as flow meters, water level meters, water quality sensors, etc., to ensure the accuracy and real-time nature of the monitoring data.

[0066] In order to improve the representativeness of monitoring data, the flow rate uses an ultrasonic Doppler flowmeter based on the cross-sectional scanning principle. In order to verify the online chemical oxygen demand (COD) and ammonia nitrogen data, the 24-hour water quality mixed sample water quality data proportional to the flow rate is obtained based on the flow data by integral sampling, and the online monitoring water quality data is corrected to eliminate water quality differences.

[0067] And the monitoring equipment should be installed in accordance with the specifications to ensure stable operation and easy maintenance of the equipment. At the same time, the performance requirements of the equipment such as waterproof, dustproof, and explosion-proof should be considered to ensure that it can work normally in harsh environments.

[0068] In order to ensure the temporal representativeness of the monitoring data and prevent accidental interference with the calculation results, it is necessary to monitor the water quantity and quality data for no less than 7 days on dry days and cover three rainfall data of more than 10 mm on rainy days.

[0069] Step 204, determining the comprehensive index of each first-level sub-area based on the drainage data of the first-level sub-area.

[0070] In one embodiment, the comprehensive index includes a dry season comprehensive index and a rainy season comprehensive index;

[0071] Determine the comprehensive index of each sub-zone, including: determine the comprehensive index of dry season and comprehensive index of rainy season of each sub-zone by the following formula:

[0072]

[0073]

[0074] Among them, Hs is the comprehensive index in the dry season, with a value range of 0 - 1; Hr is the comprehensive index in the rainy season, with a value range of 0 - 1; a is the weight coefficient of chemical oxygen demand (COD), with a value range of 0.1 - 0.9, and it needs to satisfy a + b = 1; b is the weight coefficient of ammonia nitrogen, with a value range of 0.1 - 0.9, and it needs to satisfy a + b = 1; Q is the theoretical water production in the dry season, with the unit of m 3 ; T is the rainfall in the rainy season, with the unit of m; A is the area of the research region, with the unit of ㎡; m is the total duration of online monitoring of flow and water quality in the dry season, with the unit of h; t is the total duration of online monitoring of flow and water quality in the rainy season, with the unit of h; Q i is the instantaneous flow at the i-th moment in the dry season, with the unit of m 3 / h; Q j is the instantaneous flow at the j-th moment in the rainy season, with the unit of m 3 / h; C 1i is the COD concentration monitored online at the i-th moment in the dry season, with the unit of mg / L; C 2i is the ammonia nitrogen concentration monitored online at the i-th moment in the dry season, with the unit of mg / L; C 1j is the COD concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L; C 2j is the ammonia nitrogen concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L; H1: the COD concentration in the 24-hour water quality mixed sample of dry season water quality sampling, with the unit of mg / L; H2 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample of dry season water quality sampling, with the unit of mg / L; H3 is the COD concentration in the 24-hour water quality mixed sample of rainy season water quality sampling, with the unit of mg / L; H4 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample of rainy season water quality sampling, with the unit of mg / L; C1 is the theoretical COD concentration in the dry season, with the unit of mg / L; C2 is the theoretical ammonia nitrogen concentration in the dry season, with the unit of mg / L; C3 is the theoretical COD concentration in the rainy season, with the unit of mg / L; C4 is the theoretical ammonia nitrogen concentration in the rainy season, with the unit of mg / L; K1, K2, K3, K4, β1, β2, β3, β4 are all undetermined correction parameters.

[0075] In the embodiments of the present disclosure, in order to eliminate the water quality differences between online monitoring and sampling, the power of the water quality difference is introduced to correct the water quality data. A large amount of data on the average concentration of online monitored water quality and the average concentration of water quality sampling is collected, and the values of the undetermined correction coefficients are determined through experiments, optimization algorithms (such as the least squares method, genetic algorithm, etc.) or experience-based methods.

[0076] Step 205: Sort all the first-level partitions with comprehensive indexes greater than the preset threshold in descending order of the comprehensive index, and subdivide and delimit each first-level partition within the preset percentage of the ranking to determine at least one second-level partition.

[0077] According to the above formula, it can be known that the value ranges of the dry-season comprehensive index and the rainy-season comprehensive index are between 0 and 1. Therefore, in the embodiments of the present disclosure, the preset threshold is set to be greater than 0.3.

[0078] In one embodiment, the preset percentage is greater than 30%.

[0079] In one embodiment, all first-level partitions with a comprehensive index greater than the preset threshold are sorted in descending order of the comprehensive index, and each first-level partition within the top preset percentage of the ranking is subdivided to determine at least one second-level partition, including:

[0080] Sort all first-level partitions with a dry-season comprehensive index greater than the preset threshold in descending order of the dry-season comprehensive index, and subdivide each first-level partition within the top preset percentage of the ranking to determine at least one second-level partition; and / or,

[0081] Sort all first-level partitions with a rainy-season comprehensive index greater than the preset threshold in descending order of the rainy-season comprehensive index, and subdivide each first-level partition within the top preset percentage of the ranking to determine at least one second-level partition.

[0082] The comprehensive index of a partition includes the dry-season comprehensive index and the rainy-season comprehensive index. For each partition, the situation may vary according to different seasons. For example, in some partitions, the dry-season comprehensive index may be greater than the preset threshold, but the rainy-season comprehensive index may be less than the preset threshold, indicating that there are problems with the drainage network in the dry season in this partition, but there are no problems in the rainy season; at the same time, if the dry-season comprehensive index of this partition is not only greater than the preset threshold, but also ranks within the top preset percentage among all partitions, then this partition will be subdivided. It can be understood that as long as one of the dry-season comprehensive index and the rainy-season comprehensive index of a partition meets the above rules, this partition will be subdivided.

[0083] It should be explained that this rule is applicable not only to first-level partitions but also to partitions of other levels.

[0084] It should be explained that the preset threshold and the preset percentage can be determined according to the actual situation. For example, in some embodiments, if the requirements for the water volume and water quality of the target drainage area are relatively high, the comprehensive index can be set to 0.3 and the preset percentage can be set to 100%, so that more second-level partitions can be divided to improve the monitoring intensity of the drainage network problems in the target drainage area; in other embodiments, if the requirements for the water volume and water quality of the target drainage area are not too high, for example, the comprehensive index can be set to 0.6 and the preset percentage can be set to 50%.

[0085] Step 206, set second-level monitoring points in each second-level partition;

[0086] Step 207: Monitor the drainage data of each secondary sub-region through secondary monitoring points;

[0087] Step 208: Determine the comprehensive index of each secondary sub-region according to the drainage data of the secondary sub-region;

[0088] Step 209: Sort all secondary sub-regions with comprehensive indexes greater than the preset threshold from high to low, and conduct a detailed delineation of each secondary sub-region within the top preset percentage of the ranking to determine at least one tertiary sub-region.

[0089] The processing procedures of Steps 206 - 209 are the same as those of Steps 202 - 205, and will not be elaborated here.

[0090] Step 210: By analogy, until the monitoring points of the last-level sub-region monitor the drainage data of the source drainage households.

[0091] In the embodiments of the present disclosure, the source drainage household is the starting point of the drainage system, that is, the starting point of the water flow. For example, it can be the point where the drainage household is connected to the municipal drainage pipe network.

[0092] In one embodiment, the primary monitoring points are used to monitor the main pipes of the target drainage area, the monitoring points from the secondary monitoring points to the penultimate-level sub-region are used to monitor the main pipes and branch pipes of the target drainage area, and the monitoring points of the last-level sub-region are used to monitor the source drainage households of the target drainage area.

[0093] The branch pipe refers to a series of interconnected pipes and facilities in the drainage system, which are used to transport the water volume generated at the source to the main pipe; the main pipe is responsible for collecting the water volumes of multiple branch pipes together and then transporting them to the downstream main pipe; the main pipe is the main pipe in the drainage system, which is used to collect the water flow in the main pipe and transport it to the downstream sewage treatment plant or discharge outlet.

[0094] In one embodiment, key monitoring can also be carried out on areas prone to problems such as blockage, leakage, and water quality exceeding the standard, and the density of monitoring points can be increased.

[0095] Figure 4 It is a schematic diagram of the multi-level sub-region structure of the target drainage area. Figure 4 It shows that the target drainage area is divided into three levels of sub-regions, that is, multiple primary sub-regions are divided into multiple secondary sub-regions through the judgment of comprehensive indexes, and multiple secondary sub-regions are also divided into multiple tertiary sub-regions through the judgment of comprehensive indexes.

[0096] In the embodiments of the present disclosure, a multi-level nested model is formed through the multi-level sub-region monitoring method.

[0097] Figure 5 It is a schematic layout diagram of the monitoring points of the target drainage area, Figure 5Only three levels of monitoring points are shown.

[0098] As Figure 5 shown, water flows out of the drainage household. Some of it flows through the three-level monitoring point SM, then converges into the branch pipe, flows through the two-level monitoring point EM, some directly flows through the two-level monitoring point EM, then converges into the main pipe, flows through the one-level monitoring point YM, converges into the main trunk pipe, and is transmitted through the main trunk pipe to the pumping station, and finally enters the sewage treatment plant for treatment.

[0099] Step 211: According to the comprehensive indicators of the first-level partition, the second-level partition until the last-level partition, and combining the drainage data of each partition, determine the drainage pipe network problems in the target drainage area.

[0100] In one embodiment, the greater the comprehensive indicator of each partition, the more serious the drainage pipe network problem in this partition.

[0101] After obtaining the comprehensive indicators of each partition, according to the comprehensive indicators, analyze the drainage data (flow rate, water quality, rainfall, etc.) of each partition to obtain a quantitative evaluation report. The quantitative evaluation report includes problem types, severity, influence scope, and recommended repair measures, etc. At the same time, the current situation of the problem and the possible future development trends are clearly presented in the report, providing strong support for decision-makers.

[0102] Specifically, the following three examples are used for detailed illustration.

[0103] Example 1: The siltation risk level can be divided according to the following criteria. The average flow velocity in the drainage pipe network during the dry season is greater than 0.6 m / s, representing a low siltation risk; greater than 0.1 m / s and less than or equal to 0.6 m / s, representing a certain risk; less than or equal to 0.1 m / s representing a high risk.

[0104] Through the statistical analysis of the effective data of the monitoring points in each partition, the statistical data such as the liquid level and flow velocity at this point are obtained, which are used to evaluate the siltation risk of the drainage pipes in the partition. For example, if the flow velocity at a monitoring point in a certain partition is equal to 0.05 m / s, it indicates that the siltation risk of the pipes in this partition is high. It is recommended to strengthen the daily dredging of the pipes with high siltation risk during the dry season to ensure the smooth conveyance of the water volume in the pipe network.

[0105] Example 2: The overflow risk is the ratio of the maximum liquid level value obtained during the monitoring period to the well depth of the inspection well at the monitoring point. The overflow risk can be divided according to the following criteria: less than 0.7 represents a low risk; between 0.7 and 0.9 represents a certain risk; between 0.9 and 1 represents a high risk; greater than 1 represents an extremely high risk (overflow has occurred).

[0106] Through the statistical analysis of the valid data of each partition monitoring point, statistical data such as the maximum liquid level of this point are obtained to evaluate the overflow risk of the drainage pipes in each partition. It is recommended to timely conduct drainage facility scheduling for the points with high overflow risk to reasonably transfer the high water volume in the area; for the points where overflow has occurred, report to the relevant departments in a timely manner, initiate the emergency response procedure, and take emergency control measures.

[0107] Example 3: In China, the normal range of groundwater infiltration should be 10% - 15% of the total amount of average daily domestic sewage and industrial wastewater. Therefore, taking an infiltration rate of 15% as the standard, when the infiltration rate is higher than 15%, the corresponding detection and repair of the pipe network are required.

[0108] Through the statistical analysis of the valid data of each partition monitoring point, statistical data such as the flow rate and water quality of this point are obtained to evaluate the infiltration situation of the partition drainage pipes.

[0109] Sort the areas with infiltration (infiltration rate > 15%) in descending order of infiltration rate. It is recommended to prioritize the investigation of the partitions with serious infiltration, promptly report the areas with serious problems to the relevant departments, and then repair and transform the pipe network.

[0110] In the embodiments of the present disclosure, the GIS (Geographic Information System) technology can be used to visually display the results of intelligent analysis on the map; combined with the actual layout of the drainage pipe network and the location information of the monitoring points, precise positioning of the problem areas can be achieved.

[0111] After determining the problems, prioritize the potential problem areas according to factors such as the severity of the problems, the affected range, and the repair difficulty. Prioritize the treatment of the problem areas with high risk and high impact to ensure the stable operation of the urban drainage system.

[0112] Targeted optimization suggestions can be put forward according to the quantitative evaluation results and the actual operation conditions of the drainage pipe network, including suggestions on pipe network transformation plans, maintenance plan adjustments, and optimized layout of monitoring points, etc., to improve the overall performance and operation efficiency of the drainage system.

[0113] In the embodiments of the present disclosure, through gradually densifying the monitoring and intelligent analysis, precise positioning and quantitative evaluation of potential problems in the drainage pipe network are achieved, significantly improving the investigation efficiency; avoiding the blindness of traditional carpet - type general surveys, reducing unnecessary resource investment and labor costs; the diagnostic results based on big data analysis and machine learning algorithms are more objective and accurate, providing a scientific and reasonable reference basis for decision - makers; timely discovering and handling the problem areas in the drainage pipe network to ensure the normal operation and drainage capacity of the pipe network system.

[0114] Next, a specific embodiment will be used to introduce the multi - level nested drainage pipe network problem diagnosis method.

[0115] The coverage area of a drainage pipe network system in a certain city is 2 km 2 , including 10 km of drainage pipes, 700 sections of drainage pipes, 780 drainage nodes (i.e., inspection wells), two main pipes connecting to the sewage treatment plant, and 1 sewage treatment plant.

[0116] According to the pipeline connection relationship, the area is initially divided into 2 primary sub - regions based on the number of main pipes. By installing a sensor network at the outlets of the 2 primary sub - regions, real - time monitoring data of each node in the pipe network is collected, including flow rate, water level, water quality parameters, etc., and areas prone to problems such as blockage, leakage, and water quality exceeding the standard are monitored. The monitoring indicators include flow rate, water level, and water quality, etc.

[0117] The collected data is pre - processed and standardized. Then, a multi - level nested model is constructed, and according to the calculation results of the comprehensive indicators, encrypted monitoring and intelligent analysis are carried out step by step.

[0118] Specifically, the dry - season comprehensive index and the rainy - season comprehensive index are calculated according to the calculation formulas of the above - mentioned dry - season comprehensive index and rainy - season comprehensive index.

[0119] Among them, the parameter assumptions for primary sub - region 1 are as follows:

[0120] The weight coefficient a of COD takes a value of 0.7; the weight coefficient b of ammonia nitrogen takes a value of 0.3; the theoretical water production Q in the dry season takes a value of 100 m 3 ; the rainfall T in the rainy season takes a value of 0.3 m; the study area A takes a value of 1000 ㎡; the total online monitoring duration m of flow rate and water quality in the dry season takes a value of 0.1 h; the total online monitoring duration t of flow rate and water quality in the rainy season takes a value of 0.1 h;

[0121] Q i is the instantaneous flow rate at the i - th moment in the dry season, with the unit of m 3 / h, and the total monitoring duration is 0.1 h. The minute - level instantaneous flow rates are 1 m 3 / h, 2 m 3 / h, 2 m 3 / h, 3 m 3 / h, 1 m 3 / h, 1 m 3 / h;

[0122] Q j is the instantaneous flow rate at the j - th moment in the rainy season, with the unit of m 3 / h, and the total monitoring duration is 0.1 h. The minute - level instantaneous flow rates are 2 m 3 / h, 4 m 3 / h, 6 m 3 / h, 8 m 3 / h, 6 m 3 / h, 2 m3 / h;

[0123] C 1i is the COD concentration monitored online at the i-th moment in the dry season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the COD concentrations at the minute level are 8 mg / L, 9 mg / L, 10 mg / L, 11 mg / L, 12 mg / L, 12 mg / L in sequence;

[0124] C 2i is the ammonia nitrogen concentration monitored online at the i-th moment in the dry season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the ammonia nitrogen concentrations at the minute level are 18 mg / L, 20 mg / L, 22 mg / L, 22 mg / L, 24 mg / L, 18 mg / L in sequence;

[0125] C 1j : is the COD concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the COD concentrations at the minute level are 18 mg / L, 20 mg / L, 20 mg / L, 22 mg / L, 24 mg / L, 18 mg / L in sequence;

[0126] C 2j : is the ammonia nitrogen concentration monitored online at the j-th moment in the rainy season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the ammonia nitrogen concentrations at the minute level are 43 mg / L, 36 mg / L, 39 mg / L, 42 mg / L, 42 mg / L, 38 mg / L in sequence;

[0127] The value of the COD concentration H1 in the 24-hour water quality mixed sample of the dry season water quality sampling is 10 mg / L;

[0128] The value of the ammonia nitrogen concentration H2 in the 24-hour water quality mixed sample of the dry season water quality sampling is 20 mg / L;

[0129] The value of the COD concentration H3 in the 24-hour water quality mixed sample of the rainy season water quality sampling is 20 mg / L;

[0130] The value of the ammonia nitrogen concentration H4 in the 24-hour water quality mixed sample of the rainy season water quality sampling is 40 mg / L;

[0131] The value of the theoretical COD concentration C1 in the dry season is 10 mg / L;

[0132] The value of the theoretical ammonia nitrogen concentration C2 in the dry season is 15 mg / L;

[0133] The value of the theoretical COD concentration C3 in the rainy season is 12 mg / L;

[0134] The value of the theoretical ammonia nitrogen concentration C4 in the rainy season is 20 mg / L;

[0135] Through the trial-and-error method or optimization algorithms such as the least squares method, the values of K1, K2, K3, K4, β1, β2, β3, and β4 are calculated as follows: K1 = -0.07, K2 = -0.043, K3 = -0.02, K4 = -0.051, β1 = 1.83, β2 = 1.12, β3 = 1.25, and β4 = 1.34.

[0136] After calculation, the comprehensive indexes of the first-level partition 1 are Hs = 0.88 and Hr = 0.82.

[0137] The assumed parameter values of the first-level partition 2 are as follows:

[0138] The weight coefficient a of COD is 0.7; the weight coefficient b of ammonia nitrogen is 0.3; the theoretical water production Q in the dry season is 200 m 3 ; the rainfall T in the rainy season is 0.6 m; the study area A is 500 m 2 ; the total online monitoring duration m of the dry-season flow water quality is 0.1 h; the total online monitoring duration t of the rainy-season flow water quality is 0.1 h;

[0139] Q i is the instantaneous flow at the i-th moment in the dry season, with the unit of m 3 / h, the total monitoring duration is 0.1 h, and the minute-level instantaneous flows are 10 m 3 / h, 30 m 3 / h, 20 m 3 / h, 30 m 3 / h, 10 m 3 / h, 20 m 3 / h;

[0140] Q j is the instantaneous flow at the j-th moment in the rainy season, with the unit of m 3 / h, the total monitoring duration is 0.1 h, and the minute-level instantaneous flows are 20 m 3 / h, 60 m 3 / h, 60 m 3 / h, 40 m 3 / h, 60 m 3 / h, 20 m 3 / h;

[0141] C 1i is the COD concentration monitored online at the i-th moment in the dry season, with the unit of mg / L, the total monitoring duration is 0.1 h, and the minute-level COD concentrations are 18 mg / L, 19 mg / L, 20 mg / L, 21 mg / L, 22 mg / L, 22 mg / L;

[0142] C 2i$C_{i}$ is the ammonia nitrogen concentration monitored online at the $i$-th moment in the dry season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the ammonia nitrogen concentrations at the minute level are 28 mg / L, 30 mg / L, 32 mg / L, 32 mg / L, 24 mg / L, 28 mg / L in sequence;

[0143] C 1j $C_{j}$ is the COD concentration monitored online at the $j$-th moment in the rainy season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the COD concentrations at the minute level are 38 mg / L, 40 mg / L, 40 mg / L, 42 mg / L, 44 mg / L, 38 mg / L in sequence;

[0144] C 2j $C_{j}$ is the ammonia nitrogen concentration monitored online at the $j$-th moment in the rainy season, with the unit of mg / L. The total monitoring duration is 0.1 h, and the ammonia nitrogen concentrations at the minute level are 48 mg / L, 46 mg / L, 49 mg / L, 52 mg / L, 52 mg / L, 48 mg / L in sequence;

[0145] The value of the COD concentration $H_{1}$ in the 24-hour water quality mixed sample of the dry season water quality sampling is 20 mg / L;

[0146] The value of the ammonia nitrogen concentration $H_{2}$ in the 24-hour water quality mixed sample of the dry season water quality sampling is 30 mg / L;

[0147] The value of the COD concentration $H_{3}$ in the 24-hour water quality mixed sample of the rainy season water quality sampling is 40 mg / L;

[0148] The value of the ammonia nitrogen concentration $H_{4}$ in the 24-hour water quality mixed sample of the rainy season water quality sampling is 50 mg / L;

[0149] The value of the theoretical COD concentration $C_{1}$ in the dry season is 18 mg / L;

[0150] The value of the theoretical ammonia nitrogen concentration $C_{2}$ in the dry season is 25 mg / L;

[0151] The value of the theoretical COD concentration $C_{3}$ in the rainy season is 42 mg / L;

[0152] The value of the theoretical ammonia nitrogen concentration $C_{4}$ in the rainy season is 48 mg / L;

[0153] Through the trial-and-error method or optimization algorithms such as the least squares method, the value of $K_{1}$ is calculated to be -0.034, the value of $K_{2}$ is -0.02, the value of $K_{3}$ is -0.03, the value of $K_{4}$ is -0.02, the value of $\beta_{1}$ is 1.38, the value of $\beta_{2}$ is 1.2, the value of $\beta_{3}$ is 1.2, and the value of $\beta_{4}$ is 1.3.

[0154] After calculation, the comprehensive indexes of the first-level partition 2 are $H_{s}=0.30$ and $H_{r}=0.13$.

[0155] According to the calculation results of the comprehensive indicators, assuming that the preset threshold is 0.3, the comprehensive indicator of the first-level partition 1 is greater than 0.3, and the comprehensive indicator of the first-level partition 2 is less than or equal to 0.3. Since there is only one first-level partition exceeding the preset threshold, the preset percentage is set to 100%. Therefore, it is necessary to perform secondary partitioning on the first-level partition 1, conduct more intensive monitoring and analysis, and encrypt step by step to finally locate the specific problem drainage pipe section.

[0156] Finally, generate a detailed diagnosis report and repair suggestions according to the diagnosis results and submit them to the relevant departments for handling.

[0157] The embodiment of the present disclosure also provides a multi-level nested drainage pipe network problem diagnosis device. Figure 6 As shown in Figure 6 the structural schematic diagram of the multi-level nested drainage pipe network problem diagnosis device provided by the embodiment of the present disclosure, the device includes:

[0158] A first partitioning unit 601, configured to partition a target drainage area to determine at least one first-level partition;

[0159] A first setting unit 602, configured to set first-level monitoring points in each first-level partition;

[0160] A first monitoring unit 603, configured to monitor the drainage data of each first-level partition through the first-level monitoring points;

[0161] A first determining unit 604, configured to determine the comprehensive indicator of each first-level partition according to the drainage data of the first-level partition;

[0162] A second partitioning unit 605, configured to sort all the first-level partitions with comprehensive indicators greater than the preset threshold from high to low, and subdivide and delimit each first-level partition within the top preset percentage to determine at least one second-level partition;

[0163] A second setting unit 606, configured to set second-level monitoring points in each second-level partition;

[0164] A second monitoring unit 607, configured to monitor the drainage data of each second-level partition through the second-level monitoring points;

[0165] A second determining unit 608, configured to determine the comprehensive indicator of each second-level partition according to the drainage data of the second-level partition;

[0166] A third partitioning unit 609, configured to sort all the second-level partitions with comprehensive indicators greater than the preset threshold from high to low, and subdivide and delimit each second-level partition within the top preset percentage to determine at least one third-level partition;

[0167] The third monitoring unit 610 is used to monitor the drainage data of the source by analogy until the monitoring point of the last level of partition;

[0168] The third determination unit 611 is used to determine the drainage network problem of the target drainage area according to the comprehensive indicators of the first-level partition, the second-level partition and the last-level partition in combination with the drainage data of each partition.

[0169] It should be pointed out here that the above description of the embodiment of the multi-level nested drainage network problem diagnosis device is similar to the description of the embodiment of the multi-level nested drainage network problem diagnosis method shown above, and has similar beneficial effects as the embodiment of the multi-level nested drainage network problem diagnosis method, so it will not be repeated. For technical details not disclosed in the embodiment of the multi-level nested drainage network problem diagnosis device of the present invention, please refer to the description of the embodiment of the multi-level nested drainage network problem diagnosis method of the present invention, and it will not be repeated to save space.

[0170] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0171] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0172] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0173] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as a keyboard, mouse, etc.; output unit 707, such as various types of displays, speakers, etc.; storage unit 708, such as a disk, optical disc, etc.; and communication unit 709, such as a network card, modem, wireless communication transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0174] Computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 701 executes the various methods and processes described above, such as the multi-level nested drainage pipe network problem diagnosis method. For example, in some embodiments, the multi-level nested drainage pipe network problem diagnosis method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by computing unit 701, one or more steps of the multi-level nested drainage pipe network problem diagnosis method described above can be executed. Alternatively, in other embodiments, computing unit 701 can be configured to execute the multi-level nested drainage pipe network problem diagnosis method in any other suitable manner (e.g., by means of firmware).

[0175] The various embodiments of the systems and technologies described above in this article can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0176] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on the remote machine or server.

[0177] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0178] In order to provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0179] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0180] A computer system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0181] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this is not limited herein.

[0182] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of this disclosure, "a plurality" means two or more, unless otherwise specifically defined.

[0183] As described above, the above are only specific embodiments of this disclosure, but the protection scope of this disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed in this disclosure can easily think of changes or substitutions, which should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be subject to the protection scope of the claims.

Claims

1. A method for diagnosing problems in a multi-level nested drainage pipe network, characterized in that, The method includes: Dividing the target drainage area into drainage sub - areas to determine at least one primary sub - area; Setting primary monitoring points in each of the primary sub - areas; Monitoring the drainage data of each primary sub - area through the primary monitoring points; Determining the comprehensive index of each primary sub - area according to the drainage data of the primary sub - area; Sorting all primary sub - areas with comprehensive indexes greater than the preset threshold from high to low, and subdividing each primary sub - area within the top preset percentage to determine at least one secondary sub - area; Setting secondary monitoring points in each of the secondary sub - areas; Monitoring the drainage data of each secondary sub - area through the secondary monitoring points; Determining the comprehensive index of each secondary sub - area according to the drainage data of the secondary sub - area; Sorting all secondary sub - areas with comprehensive indexes greater than the preset threshold from high to low, and subdividing each secondary sub - area within the top preset percentage to determine at least one tertiary sub - area; And so on until the monitoring points of the last - level sub - area monitor the drainage data of the source drainage households; Determining the drainage network problems of the target drainage area according to the comprehensive indexes of the primary sub - areas, secondary sub - areas until the last - level sub - area, in combination with the drainage data of each sub - area.

2. The method according to claim 1, wherein: The primary monitoring points are used to monitor the main pipes of the target drainage area, the monitoring points of the secondary sub - areas to the penultimate - level sub - areas are used to monitor the main pipes and branch pipes of the target drainage area, and the monitoring points of the last - level sub - area are used to monitor the source drainage households of the target drainage area.

3. The method according to claim 1, wherein: The larger the comprehensive index of each sub - area, the more serious the drainage network problems of this sub - area.

4. The method according to claim 1, wherein: The drainage data includes rainfall, rainfall duration, flow rate, chemical oxygen demand (COD) and ammonia nitrogen index.

5. The method according to claim 4, wherein: The comprehensive index includes the dry - season comprehensive index and the rainy - season comprehensive index; Determining the comprehensive index of each sub - area includes: determining the dry - season comprehensive index and the rainy - season comprehensive index of each sub - area through the following formula: Among them, Hs is the comprehensive index for the dry season, with a value range of 0 - 1; Hr is the comprehensive index for the rainy season, with a value range of 0 - 1; a is the weight coefficient of chemical oxygen demand (COD), with a value range of 0.1 - 0.9, and it needs to satisfy a + b = 1; b is the weight coefficient of ammonia nitrogen, with a value range of 0.1 - 0.9, and it needs to satisfy a + b = 1; Q is the theoretical water production in the dry season, with the unit of m 3 ; T is the rainfall in the rainy season, with the unit of m; A is the area of the sub-region, with the unit of ㎡; m is the total duration of online monitoring of flow and water quality in the dry season, with the unit of h; t is the total duration of online monitoring of flow and water quality in the rainy season, with the unit of h; Q i is the instantaneous flow at the i-th moment in the dry season, with the unit of m 3 / h; Q j is the instantaneous flow at the j-th moment in the rainy season, with the unit of m 3 / h; C 1i is the COD concentration measured online at the i-th moment in the dry season, with the unit of mg / L; C 2i is the ammonia nitrogen concentration measured online at the i-th moment in the dry season, with the unit of mg / L; C 1j is the COD concentration measured online at the j-th moment in the rainy season, with the unit of mg / L; C 2j is the ammonia nitrogen concentration measured online at the j-th moment in the rainy season, with the unit of mg / L; H1: COD concentration in the 24-hour water quality mixed sample collected during dry season water quality sampling, with the unit of mg / L; H2 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample collected during dry season water quality sampling, with the unit of mg / L; H3 is the COD concentration in the 24-hour water quality mixed sample collected during rainy season water quality sampling, with the unit of mg / L; H4 is the ammonia nitrogen concentration in the 24-hour water quality mixed sample collected during rainy season water quality sampling, with the unit of mg / L; C1 is the theoretical COD concentration in the dry season, with the unit of mg / L; C2 is the theoretical ammonia nitrogen concentration in the dry season, with the unit of mg / L; C3 is the theoretical COD concentration in the rainy season, with the unit of mg / L; C4 is the theoretical ammonia nitrogen concentration in the rainy season, with the unit of mg / L; K1, K2, K3, K4, β1, β2, β3, β4 are all undetermined correction parameters.

6. The method according to claim 5, wherein: The preset threshold is greater than 0.3; The preset percentage is greater than 30%.

7. The method according to claim 5, wherein: The step of sorting all primary sub - areas with comprehensive indexes greater than the preset threshold from high to low, and subdividing each primary sub - area within the top preset percentage to determine at least one secondary sub - area includes: Sorting all primary sub - areas with dry - season comprehensive indexes greater than the preset threshold from high to low, and subdividing each primary sub - area within the top preset percentage to determine at least one secondary sub - area; and / or, All first-level subareas whose rainy season comprehensive index is greater than a preset threshold are sorted from high to low in terms of rainy season comprehensive index, and each first-level subarea ranked within the top preset percentage is subdivided to determine at least one second-level subarea.

8. A multi-level nested drainage pipe network problem diagnosis device, characterized in that, The device comprises: A first division unit is used to divide the target drainage area into drainage zones and determine at least one primary zone; A first setting unit is used to set a first-level monitoring point in each of the first-level partitions; A first monitoring unit, used for monitoring the drainage data of each of the first-level partitions through the first-level monitoring points; A first determination unit, configured to determine a comprehensive index of each of the first-level sub-areas according to the drainage data of the first-level sub-areas; A second partitioning unit is used to sort all the first-level partitions whose comprehensive index is greater than a preset threshold from high to low by comprehensive index, and to subdivide each first-level partition ranked within a preset percentage to determine at least one second-level partition; A second setting unit is used to set a secondary monitoring point in each of the secondary partitions; A second monitoring unit, used for monitoring the drainage data of each of the secondary partitions through the secondary monitoring points; A second determination unit is used to determine the comprehensive index of each of the secondary sub-areas according to the drainage data of the secondary sub-areas; A third partitioning unit is used to sort all the secondary partitions whose comprehensive index is greater than a preset threshold value from high to low by comprehensive index, and to subdivide each secondary partition ranked within a preset percentage to determine at least one third-level partition; The third monitoring unit is used to monitor the drainage data of the source drainage households by analogy until the monitoring point of the last level of sub-area; The third determination unit is used to determine the drainage network problem of the target drainage area according to the comprehensive indicators of the first-level partition, the second-level partition and the last-level partition, combined with the drainage data of each partition.

9. An electronic device, characterized in that, include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to make a computer execute the method according to any one of claims 1-7.

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