Drainage system inflow infiltration diagnosis method and device and electronic equipment

Abstract

The present disclosure relates to a drainage system inflow infiltration diagnosis method and device, and electronic equipment, belonging to the technical field of drainage system monitoring. The method comprises: determining the flow of each type of inflow source in each catchment area at each collection time according to the water condition data of each catchment area, the first flow proportion of the flow of the inflow source of each type of inflow source in the entire drainage system, and obtaining the first flow proportion time series of each type of inflow source in each catchment area; determining the weight time series corresponding to each first flow proportion time series based on the corresponding first system type and first weather scene of the drainage system and the weight method; determining the score time series corresponding to each first flow proportion time series based on the scoring method; determining the seepage score of each catchment area based on the weight time series and the score time series corresponding to each first flow proportion time series; and determining the key seepage area based on the seepage score. In this way, the accuracy of determining the key seepage area can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of drainage system monitoring technology, and in particular to a method and device for diagnosing inflow and infiltration in drainage systems, as well as electronic equipment. Background Technology

[0002] Drainage systems are a vital urban infrastructure, but current systems suffer from numerous instances of inflow and infiltration due to improper connections and unforeseen circumstances. This hinders the improvement of drainage systems' efficiency and effectiveness, prevents them from fulfilling their designed functions, and makes it difficult to maintain a clean and sustainable water environment.

[0003] Inspecting the drainage system and identifying inflow and infiltration points in a timely and accurate manner is of great significance for ensuring the normal operation of the drainage system, improving water quality management, and reducing maintenance costs.

[0004] Currently, various regions are investing significant financial and material resources in addressing drainage system problems, implementing engineering measures such as drainage system repair and stormwater / sewage system upgrades. However, the timeliness, accuracy, and precision of using related technologies to determine the location of inflows and infiltrations are still lacking. Summary of the Invention

[0005] In view of this, this disclosure proposes a diagnostic scheme for inflow and seepage in drainage systems.

[0006] According to one aspect of this disclosure, a method for diagnosing inflow and seepage in a drainage system is provided, the method comprising:

[0007] Step 1: Based on the water condition data of each catchment area, determine the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source into the entire drainage system, to obtain the time series of the first flow rate percentage of each type of inflow source in each catchment area. The water condition data includes: flow rate time series and time series of various water quality indicators. The drainage system includes multiple catchment areas.

[0008] Step 2: Based on the first system type and first weather scenario corresponding to the drainage system, and the weighting method, determine the weighted time series corresponding to each first flow rate percentage time series. The weighting method represents the correspondence between system type, weather scenario, and weight.

[0009] Step 3: Based on the scoring method, determine the scoring time series corresponding to each of the first traffic percentage time series, wherein the scoring method characterizes the correspondence between the inflow source, traffic percentage and score;

[0010] Step 4: Determine the seepage score for each catchment area based on the weighted time series and the scoring time series corresponding to each of the first flow rate percentage time series;

[0011] Step 5: Based on the seepage score, determine the key seepage zones.

[0012] In one possible implementation, the method further includes:

[0013] Step 6: If the catchment area does not meet the first preset condition, a new catchment area is divided from the key seepage area, and water condition data of each new catchment area is obtained.

[0014] Step 7: Each of the new catchment areas is designated as a catchment area, and steps 1-6 are returned to be executed until the catchment area meets the first preset condition. The key seepage area determined when the catchment area meets the first preset condition is designated as the target key seepage area.

[0015] In one possible implementation, the time series of each water quality indicator in the multiple water quality indicator time series includes: a first water quality indicator time series collected at a first monitoring point, a second water quality indicator time series collected at a second monitoring point, and for the drainage system, the first weather scenario includes: a dry weather scenario. The step of determining, based on the water condition data of each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain the time series of the first flow rate percentage of each type of inflow source in each catchment area, includes: when the first weather scenario is a dry weather scenario, performing the following steps:

[0016] Based on the time series of the first water quality index and the time series of the second water quality index of the same catchment area, the second flow ratio of the flow of each type of inflow source in the same catchment area to the total inflow of the same catchment area at each collection time is determined, and the time series of the second flow ratio of each type of inflow source in the catchment area is obtained.

[0017] Based on the second flow rate percentage time series of various types of inflow sources in the same catchment area and the flow rate time series, the first flow rate of various types of inflow sources in the same catchment area at each collection time is determined, and the first flow rate time series of various types of inflow sources in each catchment area is obtained.

[0018] Based on the time series of the first flow rate of each type of inflow source in each catchment area, determine the time series of the first flow rate percentage of each type of inflow source in each catchment area.

[0019] In one possible implementation, the first weather scenario includes a rainy day scenario. The step of determining, based on water condition data from each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain a time series of the first flow rate percentages of each type of inflow source in each catchment area, further includes:

[0020] If the first weather scenario is a rainy day, perform the following steps:

[0021] Determine at least one dry day prior to the rainy day scenario, and obtain the second flow time series of each catchment area corresponding to the at least one dry day;

[0022] Based on the second flow time series and the flow time series of the same catchment area, the rainfall increment of the same catchment area at each collection time is determined, and the rainfall increment time series of each catchment area is obtained.

[0023] Based on the time series of rainfall increments in each of the aforementioned catchment areas, the time series of the first flow rate percentage for each of the aforementioned catchment areas is determined.

[0024] In one possible implementation, the first flow percentage time series includes: a dry-day wastewater percentage time series, a dry-day clean water percentage time series, and a rainy-day rainwater increment percentage time series; the seepage score includes: a dry-day seepage score and a rainy-day seepage score; the score time series includes: an initial clean water score time series, an initial wastewater score time series, and an initial increment score time series; determining the weight time series corresponding to each of the first flow percentage time series includes: determining the first weight time series of the dry-day wastewater percentage time series, the second weight time series of the dry-day clean water percentage time series, and the third weight time series of the rainy-day rainwater increment percentage time series corresponding to the first system type; determining the weight time series corresponding to each of the first flow percentage time series... The scoring time series includes: scoring the dry-day clean water percentage time series to obtain an initial clean water scoring time series; scoring the dry-day wastewater percentage time series to obtain an initial wastewater scoring time series; and scoring the rainy-day rainwater increment percentage time series to obtain an initial increment scoring time series. The step of determining the seepage score for each catchment area based on the weighted time series and scoring time series corresponding to each first flow percentage time series includes: obtaining the dry-day seepage score based on the first weighted time series, the second weighted time series, the initial clean water scoring time series, and the initial wastewater scoring time series; and obtaining the rainy-day seepage score based on the third weighted time series and the initial increment scoring time series.

[0025] In one possible implementation, the method further includes: determining at least one target inflow source based on the first weather scenario and the first system type; for the key inflow area, determining a first flow time series for each target inflow source based on a second flow rate percentage time series and a flow time series corresponding to each target inflow source; and determining the abnormal period corresponding to the key inflow area and the external water inflow rate corresponding to the abnormal period based on the first flow time series of the target inflow source.

[0026] In one possible implementation, determining the second flow percentage of each category of inflow source relative to the total inflow of the same catchment area at each sampling time, based on the time series of each of the first water quality indicators and the time series of each of the second water quality indicators in the same catchment area, and obtaining the time series of the second flow percentage of each category of inflow source in each catchment area, includes: for any catchment area, performing the following steps:

[0027] Step a: Input the time series of each of the first water quality indicators and each of the second water quality indicators of any one catchment area into the mass balance model to obtain the second flow rate proportion of each category of inflow source in any one catchment area at each time point;

[0028] Step b: If at any given time the proportion of the second flow rate of each type of inflow source in any catchment area does not meet the second preset condition, a global optimization algorithm is used to process the time series of each first water quality indicator and each second water quality indicator to obtain the time series of each first optimized water quality indicator and each second optimized water quality indicator.

[0029] Step c: Take each of the first optimized water quality index time series as the first water quality index time series, take each of the second optimized water quality index time series as the second water quality index time series, and return to execute steps a-b until, at all times, the second flow rate ratio of each type of inflow source in any catchment area meets the second preset condition, and obtain the second flow rate ratio time series of each type of inflow source in any catchment area.

[0030] For each of the aforementioned catchment areas, steps a-c are performed to obtain the second flow rate percentage time series of each type of inflow source in each of the aforementioned catchment areas.

[0031] In one possible implementation, the method further includes: determining the phase difference of water condition data for the first catchment area; adjusting the water condition data for each of the first catchment areas based on the phase difference to obtain first water condition data corresponding to each of the first catchment areas; obtaining the time series of the first flow rate proportion of each type of inflow source in each catchment area includes: determining the first flow rate proportion of each type of inflow source in each catchment area at each collection time, based on each first water condition data, and the first flow rate proportion of the inflow source of the same type flowing into the entire drainage system, to obtain the time series of each first flow rate proportion.

[0032] According to another aspect of this disclosure, a drainage system inflow and seepage diagnostic device is provided, comprising:

[0033] The first flow percentage time series determination unit is used to determine, based on the water condition data of each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow percentage of that type of inflow source into the entire drainage system, to obtain the first flow percentage time series of each type of inflow source in each catchment area. The water condition data includes: flow time series and multiple water quality index time series. The drainage system includes multiple catchment areas.

[0034] The weighted time series determination unit is used to determine the weighted time series corresponding to each of the first flow percentage time series based on the first system type and the first weather scenario corresponding to the drainage system, and the weighting method. The weighting method represents the correspondence between system type, weather scenario, and weight.

[0035] The scoring time series determination unit is used to determine the scoring time series corresponding to each of the first traffic proportion time series based on the scoring method, wherein the scoring method characterizes the correspondence between the inflow source, traffic proportion and score;

[0036] The seepage scoring unit is used to determine the seepage score of each catchment area based on the weighted time series and the scoring time series corresponding to each of the first flow rate percentage time series.

[0037] The key seepage zone determination unit is used to determine the key seepage zone based on the seepage score.

[0038] In one possible implementation, the device further includes:

[0039] The water condition data acquisition unit is used to divide new water catchment areas from the key seepage area and acquire water condition data for each new water catchment area when the water catchment area does not meet the first preset condition.

[0040] The target key seepage area determination unit is used to designate each of the new water catchment areas as the respective water catchment areas, and return to execute steps 1-6 until the water catchment area meets the first preset condition. When the water catchment area meets the first preset condition, the key seepage area determined is designated as the target key seepage area.

[0041] In one possible implementation, the water quality index time series in the multiple water quality index time series includes: a first water quality index time series collected at a first monitoring point, a second water quality index time series collected at a second monitoring point, and for the drainage system, the first weather scenario includes: a dry weather scenario, and the first flow rate proportion time series determination unit is further used for:

[0042] If the first weather scenario is a dry weather scenario, perform the following steps:

[0043] Based on the time series of the first water quality index and the time series of the second water quality index of the same catchment area, the second flow ratio of the flow of each type of inflow source in the same catchment area to the total inflow of the same catchment area at each collection time is determined, and the time series of the second flow ratio of each type of inflow source in the catchment area is obtained.

[0044] Based on the second flow rate percentage time series of various types of inflow sources in the same catchment area and the flow rate time series, the first flow rate of various types of inflow sources in the same catchment area at each collection time is determined, and the first flow rate time series of various types of inflow sources in each catchment area is obtained.

[0045] Based on the time series of the first flow rate of each type of inflow source in each catchment area, determine the time series of the first flow rate percentage of each type of inflow source in each catchment area.

[0046] In one possible implementation, the first weather scenario includes a rainy day scenario, and the first traffic flow percentage time series determination unit is further configured to:

[0047] If the first weather scenario is a rainy day, perform the following steps:

[0048] Determine at least one dry day prior to the rainy day scenario, and obtain the second flow time series of each catchment area corresponding to the at least one dry day;

[0049] Based on the second flow time series and the flow time series of the same catchment area, the rainfall increment of the same catchment area at each collection time is determined, and the rainfall increment time series of each catchment area is obtained.

[0050] Based on the time series of rainfall increments in each of the aforementioned catchment areas, the time series of the first flow rate percentage for each of the aforementioned catchment areas is determined.

[0051] In one possible implementation, the first flow rate proportion time series includes: dry day wastewater proportion time series, dry day clean water proportion time series, and rainy day rainwater increment proportion time series; the seepage score includes: dry day seepage score and rainy day seepage score; and the score time series includes: initial clean water score time series, initial wastewater score time series, and initial increment score time series.

[0052] The weighted time series determination unit is further used for:

[0053] Determine the first weighted time series of the dry-day sewage proportion time series, the second weighted time series of the dry-day clean water proportion time series, and the third weighted time series of the rainy-day rainwater increment proportion time series corresponding to the first system type;

[0054] Determining the scoring time series corresponding to each of the first traffic share time series includes:

[0055] The time series of the proportion of clean water during dry days is scored to obtain an initial clean water score time series; the time series of the proportion of sewage during dry days is scored to obtain an initial sewage score time series; and the time series of the proportion of increased rainwater during rainy days is scored to obtain an initial increased score time series.

[0056] The step of determining the seepage score of each catchment area based on the weighted time series and the scoring time series corresponding to each first flow rate percentage time series includes: obtaining the dry weather seepage score based on the first weighted time series, the second weighted time series, the initial clean water scoring time series, and the initial wastewater scoring time series;

[0057] The rainy day seepage score is obtained based on the third weighted time series and the initial incremental score time series.

[0058] In one possible implementation, the device further includes:

[0059] The target inflow source determination unit is used to determine at least one target inflow source based on the first weather scenario and the first system type.

[0060] The first flow time series determination unit is used to determine the first flow time series of each of the target inflow sources for the key inflow area based on the second flow proportion time series and flow time series corresponding to each of the target inflow sources.

[0061] The abnormal time period and external water inflow determination unit is used to determine the abnormal time period corresponding to the key seepage zone and the external water inflow corresponding to the abnormal time period based on the first flow time series of the target inflow source.

[0062] In one possible implementation, the step of determining the second flow percentage of each category of inflow source in the same catchment area relative to the total inflow of the same catchment area at each sampling time, based on the time series of each of the first water quality indicators and the time series of each of the second water quality indicators in the same catchment area, and obtaining the time series of the second flow percentage of each category of inflow source in each catchment area, includes:

[0063] For any given catchment area, perform the following steps:

[0064] Step a: Input the time series of each of the first water quality indicators and each of the second water quality indicators of any one catchment area into the mass balance model to obtain the second flow rate proportion of each category of inflow source in any one catchment area at each time point;

[0065] Step b: If at any given time the proportion of the second flow rate of each type of inflow source in any catchment area does not meet the second preset condition, a global optimization algorithm is used to process the time series of each first water quality indicator and each second water quality indicator to obtain the time series of each first optimized water quality indicator and each second optimized water quality indicator.

[0066] Step c: Take each of the first optimized water quality index time series as the first water quality index time series, take each of the second optimized water quality index time series as the second water quality index time series, and return to execute steps a-b until, at all times, the second flow rate ratio of each type of inflow source in any catchment area meets the second preset condition, and obtain the second flow rate ratio time series of each type of inflow source in any catchment area.

[0067] For each of the aforementioned catchment areas, steps a-c are performed to obtain the second flow rate percentage time series of each type of inflow source in each of the aforementioned catchment areas.

[0068] In one possible implementation, the device further includes:

[0069] The phase difference determination unit is used to determine the phase difference of the water condition data in the first catchment area;

[0070] The first water condition data determination unit is used to adjust the water condition data of each first catchment area based on the phase difference to obtain the first water condition data corresponding to each first catchment area.

[0071] The process of obtaining the first flow rate percentage time series of each category of inflow source in each of the aforementioned catchment areas includes:

[0072] Based on the first water condition data, the flow rate of each type of inflow source in each catchment area at each collection time is determined, and the first flow rate ratio of the flow rate of the same type of inflow source into the entire drainage system is obtained, thus obtaining the time series of each first flow rate ratio.

[0073] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0074] In this embodiment, a first flow rate percentage time series corresponding to each type of inflow source in each catchment area can be determined. This first flow rate percentage time series reflects the proportion of the flow from each type of inflow source into the catchment area relative to the flow into the entire drainage system. Then, weights and scores are assigned to the first flow rate percentage time series to obtain the seepage score for each catchment area. Based on the seepage score, key seepage areas are then identified. This approach highlights the relationship between the catchment area and the entire drainage system, moving beyond simply considering a single catchment area. Instead, it comprehensively considers each catchment area and its relationship with the entire drainage system, resulting in a more complete evaluation of the seepage score for each catchment area, improving the accuracy of the seepage score, and thus enhancing the reliability of identifying key seepage areas.

[0075] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0076] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0077] Figure 1 This is a schematic flowchart of a drainage system inflow and infiltration diagnosis method provided in an embodiment of this disclosure.

[0078] Figure 2 This is a schematic diagram of the structure of the drainage system inflow and infiltration diagnostic device provided in an embodiment of this disclosure.

[0079] Figure 3 This is a schematic diagram of the structure of an electronic device for inflow and infiltration diagnosis of a drainage system, provided in an embodiment of this disclosure. Detailed Implementation

[0080] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0081] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0082] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0083] In this disclosure, "performing the following steps" refers to the steps required to complete the task in the embodiment. Provided that the task to be completed in the embodiment can be accomplished and the result of completing the task can be achieved, any changes in the order of execution of the steps, the addition of other steps, or equivalent substitutions of the steps are all technical solutions to be protected by this disclosure.

[0084] Figure 1 This is a schematic flowchart illustrating the drainage system inflow and infiltration diagnosis method provided in this embodiment of the disclosure. Figure 1 As shown, the method includes:

[0085] Step 1: Based on the water condition data of each catchment area, determine the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain the time series of the first flow rate percentage of each type of inflow source in each catchment area. The water condition data includes: flow rate time series and time series of various water quality indicators. The drainage system includes multiple catchment areas.

[0086] A drainage system can be divided into multiple catchment areas. The sum of the flow rates of each catchment area can equal the flow rate of the entire drainage system. A catchment area can be a single pipe segment in the drainage system or an area composed of multiple pipe segments. In this embodiment, water condition data of each catchment area can be collected. Water condition data can represent the flow rate information in the pipe segments within the catchment area over a period of time, as well as information on the physical, chemical, and biological characteristics and composition of the water body. For example, water condition data can include: a flow rate time series characterizing the flow rate of the catchment area, and time series of various water quality indicators. Water quality time series can be collected using a spectral sensor. Compared to sampling water quality and then performing laboratory tests, spectral sensor measurement enables online, in-situ, high-frequency, and real-time measurement. For example, the collection frequency can be increased from once a day to 3-60 minutes, preferably 5-30 minutes, particularly preferably 8-20 minutes, and most preferably 10-15 minutes, which is much higher than traditional detection methods. Therefore, water quality data can be obtained at a higher frequency to effectively capture the water quality characteristics of the drainage network. The water quality indicators here can include a variety of parameters such as: chemical oxygen demand (COD), conductivity, ammonia nitrogen, total hardness, temperature, pH, turbidity, total organic carbon (TOC), five-day biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, suspended solids (SSD), total dissolved solids (TOD), petroleum hydrocarbons, anionic surfactants, cyanide, sulfides, fluorides, organophosphorus compounds, sulfates, mercury, chromium, cadmium, arsenic, lead, nickel, beryllium, silver, selenium, copper, zinc, manganese, iron, volatile phenols, benzene compounds, aniline compounds, and nitrobenzene. The types of water quality indicators may be the same or different for each catchment area. Water condition data can be time series, and each time series can contain multiple data points. Therefore, the time when each data point is collected can be named the collection time. For example, if data is collected once per hour, then the duration of a day includes 24 times. Another example: if data is collected four times per hour, then the duration of a day can include 96 times. Water condition data can be collected using high-frequency online monitoring technology. For the first flow rate proportion time series in the water condition data, each moment can correspond to a flow rate proportion (the ratio of the flow rate into a single catchment area from one inflow source to the flow rate into the drainage system). For the flow rate time series in the water condition data, each moment can correspond to a flow rate (the inflow rate into a single catchment area).

[0087] Inflow sources can be categorized in several ways. Based on the nature of the water, its source, and the method of water treatment, inflow sources can be classified as: clean water inflow sources, wastewater inflow sources, and rainwater inflow sources. Each catchment area includes at least one type of inflow source. A category of inflow source represents a type of water source flowing into the catchment area or the drainage system. Clean water inflow sources refer to external water sources such as groundwater and river water flowing into the drainage system.

[0088] In this embodiment, water condition data can be analyzed to select target water quality indicators. Target water quality indicators have two characteristics: specificity and stability. Specificity indicates that the target water quality indicator is one whose content varies significantly among different types of inflow sources in the drainage system. Stability indicates that the target water quality indicator can be a substance whose content in water is relatively stable and which does not undergo significant physical, chemical, or biological reactions. In one example, the target water quality indicator could be hardness and ammonia nitrogen. Water condition data for a single catchment area can include: measurements of the inflow and outflow of water into and out of the single catchment area, obtaining the time series of the target water quality indicator, and the flow time series of the single catchment area.

[0089] In this embodiment, a mass balance model can be established based on the principle of mass balance of substances in the catchment area. All water condition data are input into the mass balance model to determine the theoretical flow percentage time series for each inflow source in each catchment area. This theoretical flow percentage time series characterizes the proportion of flow from the same inflow source into the catchment area relative to the flow into the entire drainage system over a given period. For a single catchment area, if all values ​​in the theoretical flow percentage time series are less than the theoretical threshold, it indicates that the inflow source corresponding to that theoretical flow percentage time series is not the inflow source for that single catchment area. Therefore, each catchment area can be correlated with the theoretical flow percentage time series of its respective inflow source, thus obtaining the first flow percentage time series for each inflow source in each catchment area.

[0090] In another example, a mass balance model and a global optimization algorithm are used to process the water condition data of a single catchment area to obtain the time series of the first flow percentage of each inflow source in the single catchment area, and then to obtain the time series of the first flow percentage of each catchment area. The global optimization algorithm includes, but is not limited to, differential evolution, genetic algorithm, simulated annealing, gradient descent, and particle swarm optimization, with differential evolution being preferred. The superior performance of this method stems from its effective utilization of parent individuals. By leveraging the differences between parent individuals, the algorithm can generate diverse offspring and grandchildren populations. Compared to the Monte Carlo method, this process not only ensures the objectivity of the algorithm during optimization but also makes it more targeted and stable in solving problems, thus demonstrating significant advantages in continuous optimization.

[0091] The above are merely examples, and the methods for determining the first traffic share time series in this disclosure are not limited.

[0092] Each inflow source in each catchment area can correspond to a first flow percentage time series.

[0093] Step 2: Based on the first system type and first weather scenario corresponding to the drainage system, and the weighting method, determine the weighted time series corresponding to each first flow rate percentage time series. The weighting method represents the correspondence between system type, weather scenario, and weight.

[0094] System types can be categorized according to water type. Typically, system types include: domestic sewage systems, industrial wastewater systems, and stormwater systems. They can also be categorized according to drainage system. Typically, drainage systems include: separate sewage systems, separate stormwater systems, and combined sewer systems. This disclosure does not limit the type of drainage system. Weather scenarios can include: rainy weather scenarios and dry weather scenarios. For ease of description, the system type of the drainage system being diagnosed is designated as the first system type, and the weather scenario targeted by the diagnostic process is designated as the first weather scenario.

[0095] Weights are related to system type and weather scenario; the weights for drainage systems may differ under different system types and weather scenarios. Weighting methods can be determined in advance by statistically analyzing data from various drainage system types under different weather scenarios. The weighting method characterizes the correspondence between system type, weather scenario, and weights. In practice, the primary system type of the drainage system and the primary weather scenario corresponding to the target time period for the study of that drainage system can be determined. For the primary system type and primary weather scenario, the weighting method is used to determine the weighted time series corresponding to each primary flow percentage time series. Within a single catchment area, each flow percentage in a single primary flow percentage time series can correspond to a weight. A single primary flow percentage time series can correspond to a weighted time series.

[0096] Step 3: Based on the scoring method, determine the scoring time series corresponding to each of the first traffic percentage time series. The scoring method represents the correspondence between the inflow source, traffic percentage and score.

[0097] The scores in the scoring method are related to the traffic share and the inflow source. The scores may be different for different inflow sources and different traffic shares.

[0098] The scoring method can be developed based on historical data. It characterizes the correspondence between inflow sources, flow percentages, and scores. In practice, the scoring method can be used to score the flow percentage of each inflow source within a single catchment area, corresponding to a specific time series of the first flow percentage. This yields the scoring time series corresponding to each first flow percentage time series within that single catchment area. Furthermore, it provides the scoring time series for each catchment area. Within a single catchment area, a single first flow percentage time series can correspond to a single scoring time series.

[0099] It should be noted that this application does not impose any restrictions on the order of performing steps 2 and 3.

[0100] Step 4: Determine the seepage score for each catchment area based on the weighted time series and scoring time series corresponding to each first flow rate percentage time series.

[0101] In this embodiment of the disclosure, a weighted time series corresponding to a single first flow rate percentage time series can be used to weight the first flow rate percentage time series to obtain a weighted scoring time series. Based on each weighted scoring time series corresponding to a single catchment area, the score of that catchment area is obtained. Furthermore, the score of each catchment area can be obtained. For ease of description below, this score is named the seepage score of the catchment area. The seepage score can indicate the severity of seepage in the pipes of the catchment area. The seepage score and the severity of seepage can be positively correlated.

[0102] Step 5: Based on the seepage score, determine the key seepage zones.

[0103] In this embodiment of the disclosure, at least one key seepage zone can be identified.

[0104] In one example, the catchment area with the highest seepage score can be designated as the key seepage area. In another example, the catchment area with a seepage score higher than the score threshold can be designated as the key seepage area. The above are merely examples, and the embodiments disclosed herein are not intended to limit the scope of the application.

[0105] In this embodiment, a first flow rate percentage time series corresponding to each type of inflow source in each catchment area can be determined. This first flow rate percentage time series reflects the proportion of the flow from each type of inflow source into the catchment area relative to the flow into the entire drainage system. Then, weights and scores are assigned to the first flow rate percentage time series to obtain the seepage score for each catchment area. Based on the seepage score, key seepage areas are then identified. This approach highlights the relationship between the catchment area and the entire drainage system, moving beyond simply considering a single catchment area. Instead, it comprehensively considers each catchment area and its relationship with the entire drainage system, resulting in a more complete evaluation of the seepage score for each catchment area, improving the accuracy of the seepage score, and thus enhancing the reliability of identifying key seepage areas.

[0106] In one possible implementation, the method further includes: step 6, in the case that the catchment area does not meet the first preset condition, dividing the key seepage area into new catchment areas and obtaining water condition data for each new catchment area; step 7, taking each new catchment area as a separate catchment area, and returning to execute steps 1-6 until the catchment area meets the first preset condition, and taking the key seepage area determined when the catchment area meets the first preset condition as the target key seepage area.

[0107] The first preset condition can be that the spatial density of the catchment area in the drainage system meets the density accuracy requirement, and / or the time frequency of collecting the water condition data meets the frequency accuracy requirement. The density accuracy requirement and frequency accuracy requirement can be set according to the needs of the scenario. The spatial density increment and the collection frequency increment can also be preset. If the catchment area does not meet the first condition, the catchment area can be further divided into at least two new catchment areas according to the spatial density increment; and / or water condition data can be collected from each new catchment area according to the collection frequency increment to improve the precision of water condition data collection for the drainage system.

[0108] In this embodiment of the disclosure, a new catchment area can be delineated from the key seepage area according to the topological relationship of the drainage system.

[0109] If the first preset condition is that the spatial density of the catchment area in the drainage system meets the density accuracy requirements, and the catchment area does not meet the first condition, the catchment area can be further divided into key seepage areas according to the spatial density increment, resulting in at least two new catchment areas. Then, steps 1 to 6 are iteratively executed for the new catchment areas until the first preset condition is met, at which point the iteration stops, and the key seepage area determined when the catchment area meets the first preset condition is taken as the target key seepage area.

[0110] For new catchment areas, the method disclosed herein is used to identify new key seepage areas, thereby improving the positioning accuracy of key seepage areas.

[0111] If the first preset condition is that the spatial density of the catchment area in the drainage system meets the density accuracy requirement and the time frequency of the water condition data collection meets the frequency accuracy requirement, and the catchment area does not meet the first condition, the data collection frequency can be increased according to the collection frequency increment so that the data collection frequency meets the frequency accuracy requirement; and according to the spatial density increment, the catchment area of ​​the key seepage area is further divided into catchment areas to obtain at least two new catchment areas; then, steps 1-6 are iteratively executed for the new catchment areas until the density accuracy requirement is met, the iteration stops, and the key seepage area determined when the catchment area meets the first preset condition is taken as the target key seepage area.

[0112] If the first preset condition is that the time frequency of collecting the water condition data in the drainage system meets the frequency accuracy requirements, and the catchment area does not meet the first condition, the data collection frequency can be increased by the collection frequency increment until the first preset condition is met. Steps 1-6 are executed for each catchment area to determine the key seepage area and the key seepage area is taken as the target key seepage area.

[0113] In this embodiment, steps 1-6 are iteratively executed based on the new water condition data of the catchment area. This gradually narrows down the scope of the key seepage area, ultimately pinpointing it so that staff can conduct targeted investigations, determine the specific location of the seepage pipe section, and improve work efficiency. Furthermore, this method can be refined step-by-step in both spatial and temporal dimensions, improving the accuracy of the key seepage area and contributing to the reliability of inflow and infiltration diagnosis results for the drainage system. Moreover, due to the step-by-step refinement, the water condition data acquisition equipment can be reused in each iteration, increasing its utilization rate. While maintaining accuracy and precision, this reduces the amount of equipment required, saving on equipment investment costs in the inflow and infiltration diagnosis process and improving economic efficiency.

[0114] In one possible implementation, the time series of each water quality indicator in the multiple water quality indicator time series includes: a first water quality indicator time series collected at a first monitoring point, a second water quality indicator time series collected at a second monitoring point, and for the drainage system, the first weather scenario includes: a dry weather scenario. The step of determining, based on the water condition data of each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain the first flow rate percentage time series of each type of inflow source in each catchment area, includes: when the first weather scenario is a dry weather scenario, performing the following steps: based on the time series of each of the first water quality indicators in the same catchment area... By analyzing the time series of each of the second water quality indicators, the second flow percentage of each type of inflow source in the same catchment area is determined at each sampling time, thus obtaining the second flow percentage time series of each type of inflow source in the same catchment area. Based on the second flow percentage time series of each type of inflow source in the same catchment area and the flow time series, the first flow of each type of inflow source in the same catchment area is determined at each sampling time, thus obtaining the first flow time series of each type of inflow source in the same catchment area. Based on the first flow time series of each type of inflow source in the same catchment area, the first flow percentage time series of each type of inflow source in the same catchment area is determined.

[0115] Each inflow source of a single category within a single catchment area can correspond to at least one primary monitoring point. Primary monitoring points can be set at external clean water bodies that may flow into the catchment area (e.g., groundwater, river water, construction runoff, etc.); or at the exit manhole before the drainage user (residential, commercial, or industrial) connects to the municipal pipeline (drainage system). Time series data for the primary water quality index are collected at each primary monitoring point.

[0116] For a single catchment area, at least one second monitoring point can exist. When the topology of the catchment area is simple and there is only one second monitoring point, this second monitoring point can characterize the location where water flows out of the catchment area. Typically, this second monitoring point can be set at the end pipe of the single catchment area. A second water quality indicator time series is collected at this second monitoring point. For a single water quality indicator, within the same catchment area, both a first water quality indicator time series and a second water quality indicator time series can be collected.

[0117] When the topology of the catchment area is complex, multiple second monitoring points can be set up within the catchment area. These second monitoring points include inflow and outflow points, with the outflow point downstream of the inflow point. One or more inflow and outflow points can be set up. Time series data for a second water quality indicator are collected at each second monitoring point. For a single water quality indicator, within the same catchment area, both the first and second water quality indicator time series can be collected.

[0118] In this embodiment, time series of each first water quality indicator and each second water quality indicator can be obtained for each catchment area. In a dry weather scenario, the probability and proportion of rainwater infiltration into the drainage system are negligible. Therefore, in this embodiment, the inflow source can include both clean water inflow sources and sewage inflow sources.

[0119] For example, the time series of each first water quality index and each second water quality index of a single catchment area can be input into the mass balance model to obtain the second flow proportion of each category of inflow source in the catchment area at each collection time. For ease of understanding, the process of determining the individual second flow proportion is represented by the mass balance model formula (1).

[0120] (1)

[0121] in, The monitoring value of water quality index i (e.g., ammonia nitrogen, hardness, etc.) at the h-th first monitoring point in the j-th inflow source at a single moment (monitoring data of water quality index i). The proportion of the flow from the h-th first monitoring point of the j-th inflow source into the catchment area at a single moment is the proportion of the total inflow of the catchment area, i.e., the third flow proportion. This can represent the monitored value of water quality index i in a single catchment area at a single moment (monitoring data of water quality index i); n represents the total number of first monitoring points in the catchment area, and t represents the total number of inflow sources of t categories in the catchment area. Where, .

[0122] For each water quality indicator i, an equation can be determined using formula (1). Therefore, for multiple water quality indicators i, multiple equations can be obtained. Then, by simultaneously solving these equations, the third flow percentage of each first monitoring point can be obtained. Classifying and summing these third flow percentages according to their corresponding inflow sources yields the second flow percentage of each category of inflow source. The second flow percentage of each category of inflow source represents the proportion of the flow of a single inflow source in a single catchment area to the total inflow of that catchment area at a single moment. Furthermore, the time series of the second flow percentage corresponding to each category of inflow source can be obtained.

[0123] It should be noted that, given the existence of a second monitoring point in this catchment area, This can be the monitoring value of the second water quality indicator in the time series collected at the second monitoring point. If there is more than one second monitoring point in the catchment area, the multiple second monitoring points in the catchment area are the inflow and outflow points of the catchment area.

[0124] It is necessary to collect flow data at each inflow point in the catchment area to obtain the inflow flow time series for each inflow point; and it is also necessary to collect flow data at each outflow point in the catchment area to obtain the outflow flow time series for each outflow point. Therefore, when there is more than one second monitoring point in the catchment area, the flow rate can be determined based on the time series of each second water quality indicator, the time series of each inflow flow, and the time series of each outflow flow. For ease of understanding, formula (2-1) is used as an example to illustrate the determination of the situation when there is more than one second monitoring point in the catchment area. The method.

[0125] (2-1)

[0126] in, This represents the flow rate at the h-th outflow point at a single time. This represents the flow rate at the c-th inflow point at a single time. This represents the monitored value of water quality index i at the h-th outflow point at a single time point. The value of water quality index i at the c-th inflow point is represented at a single moment. f indicates that there are f two monitoring points in the catchment area that serve as outflow points, and m indicates that there are m two monitoring points in the catchment area that serve as inflow points.

[0127] and All of these can be obtained from the time series of the second water quality index. It can be obtained from the inflow time series. It can be obtained from the outflow time series. The value can be calculated using formula (2-1). In one possible implementation, a chemical mass balance model and a global optimization algorithm can be combined to determine the second flow percentage time series. The combination method is as follows:

[0128] The method, based on the time series of each of the first and second water quality indicators of the same catchment area, determines the second flow percentage of each type of inflow source in the same catchment area relative to the total inflow of the same catchment area at each collection time, and obtains the time series of the second flow percentage of each type of inflow source in each catchment area. This includes: for any catchment area, performing the following steps: Step a, inputting the time series of each of the first and second water quality indicators of the arbitrary catchment area into a mass balance model to obtain the second flow percentage of each type of inflow source in the arbitrary catchment area at each time point; Step b, if at any time the second flow percentage of each type of inflow source in the arbitrary catchment area does not meet the second preset condition, using the full... The local optimization algorithm processes the time series of each first water quality indicator and each second water quality indicator to obtain the time series of each first optimized water quality indicator and each second optimized water quality indicator. In step c, each time series of the first optimized water quality indicator is used as the time series of each first water quality indicator, and each time series of the second optimized water quality indicator is used as the time series of each second water quality indicator. Then, the process returns to steps a-b until, at all times, the second flow proportion of each type of inflow source in any catchment area meets the second preset condition, thus obtaining the time series of the second flow proportion of each type of inflow source in any catchment area. For each catchment area, steps a-c are executed to obtain the time series of the second flow proportion of each type of inflow source in each catchment area.

[0129] The second preset condition here can be that the sum of the second flow proportions of all inflow sources in the same catchment area at the same time is equal to 1. At a single time, if the sum of the second flow proportions of all inflow sources does not meet the second preset condition, the second flow proportion at that time may have an error that affects the accuracy of inflow infiltration diagnosis.

[0130] The method of using the mass balance model has been introduced above. Here, we will focus on the method of using the global optimization algorithm in the embodiments of this disclosure.

[0131] Taking the Differential Evolutionary Algorithm (DEA) as an example, this method determines the second-highest flow proportion of each type of inflow source in each catchment area at each time step. DEA is a method based on iterative evolution of individuals in a population, using mutation, crossover, and selection operations to find the global optimum. Compared to other methods, it has advantages such as fewer input parameters, faster convergence, and better robustness. (The text then repeats the definition of DEA at each time step.) and To initialize the population, where a second monitoring point is included in the catchment area, the data at each time point are... The resulting time series data consists of the time series of the second water quality index collected at the second monitoring point. Therefore, when the catchment area includes one second monitoring point, the initial population can be: the time series of each first water quality index, and the time series of the second water quality index collected at the second monitoring point in the catchment area. When the catchment area includes more than one second monitoring point (at least one inflow point and at least one outflow point in the catchment area), the data at each time point... The resulting time series data can be calculated from the time series of each second water quality indicator, each outflow time series, and each inflow time series. Therefore, when the catchment area includes more than one second monitoring point, the initial population can be: the time series of each first water quality indicator, each second water quality indicator, each outflow time series, and each inflow time series.

[0132] The scaling factor F is set to 0.5, the crossover probability CR to 0.2, the maximum number of iterations to 200, and the convergence threshold to... The calculation continues until the second preset condition is met. The specific steps are as follows:

[0133] A. Initialization: Generate the initial population. Each individual in the population represents a candidate solution in the problem space, which is denoted by X for ease of description.

[0134] B. Mutation: The current individual undergoes a mutation operation. This step typically includes the following sub-steps:

[0135] a. Select 4 individuals from the population and perform a second vector difference operation to generate a difference vector.

[0136] b. Select the optimal individual and sum it with the two difference vectors to generate the mutated individual. For ease of understanding, the process of b is represented by formula (2-2).

[0137] (2-2)

[0138] Where F represents the scaling factor, and F can take any value between [0,2]. , , , This represents the current individual in the g-th iteration. Four different random individuals; Let be the globally optimal individual in the g-th iteration, which is also the basis vector in the g-th iteration; there are two difference vectors, namely . and .

[0139] Two difference vectors can make the perturbation stronger, the randomness better, and the global search capability better. , The difference is calculated, then scaled using F, and then... , The difference between the two vectors is calculated, and then scaled using F. The difference between the scaled vectors is added to the basis vectors to generate the mutated individual obtained after the g-th iteration. .

[0140] C. Crossover: In the g-th iteration, for the current individual ( ) and corresponding variant individuals ( Perform a crossover operation to generate new offspring individuals. For ease of understanding, formula (3) is used to represent the crossing process.

[0141] (3)

[0142] in, Denotes the offspring individuals after the crossover in the g-th iteration. The value in the q-th dimension. rand is a random number between 0 and 1, usually following a uniform distribution; CR is the crossover probability; qrand is a randomly generated integer between 1 and D, where D is the individual gene dimension, i.e., the vector dimension, which means that the value in the q-th dimension must come from V; Denotes the mutated individual in the g-th iteration. The value in the qth dimension; This represents the current individual in the g-th iteration. The value in the q-th dimension.

[0143] D. Selection: In the current individual ( ) and offspring individuals ( The selection process involves choosing between the current individual and its offspring. Both the current individual and its offspring are substituted into the objective function to obtain their respective objective function values. If the objective function value of the offspring is less than that of the current individual, the offspring is considered a candidate solution for the next generation. If the objective function value of the offspring is not less than that of the current individual, the current individual is considered a candidate solution for the next generation. For ease of understanding, formula (4) is used to represent the selection process.

[0144] (4)

[0145] in, This represents the individual obtained after the (g-1)th iteration.

[0146] E. Iteration: The candidate solution includes optimizations at each time step. and optimization That is, to obtain the time series of each first optimized water quality index and the optimized water quality index at each time point. In the case where the catchment area includes a second monitoring point, the optimized [measurement] at each time point... This is the time series of the second optimized water quality index.

[0147] Population updates were performed using time series of the primary optimized water quality indicators, and the optimized water quality indicators at each time point. The time series of each first optimized water quality indicator is used as the new time series of the first water quality indicator. It is then determined whether the new time series of the first water quality indicator meets the second preset condition. If not, the quality balance model and differential evolution algorithm are repeatedly applied to the population until the second preset condition is met. The time series of the first optimized water quality indicator obtained from the last iteration, along with the optimized values ​​at each time point, are then used. Inputting the data into the mass balance model (Formula 1) yields the time series of the second flow rate percentage for each category of inflow source in each catchment area.

[0148] Because using only the mass balance model to calculate the second flow proportion time series of various inflow sources in each catchment area may introduce errors that affect the accuracy of inflow infiltration diagnosis, it is necessary to combine the mass balance model with a global optimization algorithm for iterative calculation until the second flow proportion of various inflow sources in the catchment area meets the second preset condition. This reduces the negative impact of such errors on inflow infiltration diagnosis. This approach can address the inaccuracy of diagnostic results caused by objective issues (e.g., spatial differences in water quality indicators at multiple inflow points of the same inflow source, measurement errors in measuring equipment, and the asynchronous nature of water quality indicator time series in different catchment areas, or between inflows and outflows in the same catchment area, or between the first monitoring point at the inflow source or the second monitoring point within the catchment area), thereby improving the reliability of inflow infiltration diagnosis results. Furthermore, although related technologies use methods such as Monte Carlo simulations to address the spatiotemporal differences in water quality index time series, the Monte Carlo method requires the construction of a stochastic model first. The model parameters, model construction, and parameter estimation are subjective, reducing the rationality of model construction and parameter estimation, and thus decreasing the accuracy of the calculation results. Therefore, compared with the Monte Carlo method, the method of the embodiments of this disclosure is more objective and accurate, and the inflow and infiltration diagnosis results are more reliable.

[0149] For a single catchment area, at the same time, multiplying the proportion of the second flow of each type of inflow source by the flow of that catchment area allows us to determine the first flow of each type of inflow source in that single catchment area at that time. Furthermore, we can obtain the first flow of each type of inflow source in each catchment area, thus obtaining the time series of each first flow.

[0150] Based on the first flow time series of each type of inflow source, for the entire drainage system, the first flow of the same type of inflow source at the same time in each catchment area is summed to obtain the flow of that type of inflow source in the entire drainage system at that time. Furthermore, the total flow time series of each type of inflow source in the entire drainage system can be obtained.

[0151] By calculating the ratio of the flow rate of a single type of inflow source in a single catchment area to the total flow rate of that type of inflow source in the entire drainage system, the first flow rate percentage time series for that inflow source in that catchment area is obtained. In this way, the first flow rate percentage time series for each type of inflow source in each catchment area can be obtained. Each type of inflow source in a single catchment area can correspond to one first flow rate percentage time series.

[0152] In this way, by only collecting the time series of the first and second water quality indicators and the flow rate of each catchment area, the time series of the first flow rate proportion of each catchment area under dry weather conditions can be determined. Compared with the survey-based method for drainage systems, this reduces the amount of data collected and the number of devices deployed.

[0153] In one possible implementation, the first weather scenario includes a rainy day scenario. The step of determining, based on water condition data from each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain a time series of the first flow rate percentage of each type of inflow source in each catchment area, further includes: when the first weather scenario is a rainy day scenario, performing the following steps: determining at least one dry day prior to the rainy day scenario, and obtaining a second flow rate time series for each catchment area corresponding to the at least one dry day; based on the second flow rate time series and the flow rate time series of the same catchment area, determining the rainfall increment of the same catchment area at each collection time, and obtaining a rainfall increment time series for each catchment area; and based on the rainfall increment time series of each catchment area, determining the time series of each first flow rate percentage.

[0154] The at least one dry day preceding the rainy scene can be s consecutive dry days adjacent to the rainy scene, for example, s=3. The at least one dry day preceding the rainy scene can also be a dry day every other day within an adjacent dry scene. This disclosure does not specifically limit the at least one dry day preceding the rainy scene.

[0155] In this embodiment of the disclosure, the flow time series of each catchment area can be obtained for at least one dry day prior to the rainy day. For ease of description, the flow time series of each catchment area for at least one dry day prior to the rainy day is named the second flow time series.

[0156] Compared to rainy days, flow data from at least one dry day preceding the rainy day can be used as historical data. For a single catchment area, the values ​​at the same moment in the second flow time series of the dry days preceding the rainy day are calculated, for example, by taking the average or median. The historical flow at each moment can be obtained, i.e., the historical flow time series is obtained. Based on the values ​​in the historical flow time series and the flow time series at each moment, the rainfall increment at each moment is determined, i.e., the rainfall increment time series of the single catchment area is determined. For ease of understanding, formula (5) is used to represent the process of determining the rainfall increment at a single moment.

[0157] (5)

[0158] in, For data in the historical traffic time series, it represents the historical traffic at a single moment (the average or median of the second traffic at that single moment in the dry days before the rainy day scenario). For data in the flow time series, it represents the flow rate at a single moment; t represents the time interval of flow monitoring, that is, the sampling frequency of the flow monitoring device; This represents the increase in rainfall at a single moment. Therefore, the increase in rainfall at each moment in a single catchment area can be determined, resulting in a time series of rainfall increases for that single catchment area. Furthermore, the time series of rainfall increases for each catchment area can be obtained.

[0159] In rainy weather scenarios, the main source of infiltration into the drainage system is rainwater, but for dry weather scenarios, both clean water and sewage have already been considered. Therefore, in this embodiment of the disclosure, the inflow source includes or only includes: rainwater inflow source.

[0160] Based on the time series of rainfall increments in each catchment area, the rainfall increments in each catchment area at the same moment are summed for the entire drainage system to obtain the total rainfall increment of the entire drainage system at that moment. Furthermore, the time series of the total rainfall increment of the entire drainage system can be obtained.

[0161] According to the time frame, the time series of rainfall increment in a single catchment area is compared with the time series of total rainfall increment in the entire drainage system, and the ratio is calculated to obtain the time series of the first flow proportion of the rainwater inflow source for that catchment area. (The calculation method for the first flow proportion of the rainwater inflow source is: at the same time, the ratio of the rainfall increment of a single catchment area to the total rainfall increment of the entire drainage system is taken as the first flow proportion of the rainwater inflow source for that catchment area at that time.) In this way, the time series of the first flow proportion of the rainwater inflow source for each catchment area can be obtained. In rainy weather scenarios, a single catchment area can correspond to a first flow proportion time series (the time series of the first flow proportion of the rainwater inflow source).

[0162] In this way, by collecting only the flow data of each catchment area, the time series of the first flow percentage of each catchment area under rainy weather can be determined. Compared with the survey method of drainage system, this reduces the amount of data collection and the number of devices to be deployed.

[0163] In one possible implementation, the first flow percentage time series includes: a dry-day wastewater percentage time series, a dry-day clean water percentage time series, and a rainy-day rainwater increment percentage time series; the seepage score includes: a dry-day seepage score and a rainy-day seepage score; the score time series includes: an initial clean water score time series, an initial wastewater score time series, and an initial increment score time series; determining the weight time series corresponding to each of the first flow percentage time series includes: determining the first weight time series of the dry-day wastewater percentage time series, the second weight time series of the dry-day clean water percentage time series, and the third weight time series of the rainy-day rainwater increment percentage time series corresponding to the first system type; determining the weight time series corresponding to each of the first flow percentage time series... The scoring time series includes: scoring the dry-day clean water percentage time series to obtain an initial clean water scoring time series; scoring the dry-day wastewater percentage time series to obtain an initial wastewater scoring time series; and scoring the rainy-day rainwater increment percentage time series to obtain an initial increment scoring time series. The step of determining the seepage score for each catchment area based on the weighted time series and scoring time series corresponding to each first flow percentage time series includes: obtaining the dry-day seepage score based on the first weighted time series, the second weighted time series, the initial clean water scoring time series, and the initial wastewater scoring time series; and obtaining the rainy-day seepage score based on the third weighted time series and the initial increment scoring time series.

[0164] Based on whether the first weather scenario of the drainage system is a dry or rainy day, the corresponding first flow percentage time series can be determined. As mentioned earlier, for a dry day scenario, the inflow sources can include clean water inflow sources and sewage inflow sources. Correspondingly, the first flow percentage time series for each catchment area is: dry day sewage percentage time series, dry day clean water percentage time series; for a rainy day scenario, the inflow sources can include rainwater inflow sources, and the first flow percentage time series for each catchment area is: rainwater increase percentage time series.

[0165] In this embodiment of the disclosure, based on the first system type of the drainage system and the first weather scenario, the weight corresponding to each value in the time series of each first flow rate percentage can be determined according to the weighting method.

[0166] For example, in a dry weather scenario, we can determine the first weight of each value in the time series of the proportion of clean water during dry weather, thus obtaining a first-weight time series. Similarly, we can determine the second weight of each value in the time series of the proportion of wastewater during dry weather, thus obtaining a second-weight time series. In a rainy weather scenario, we can determine the third weight of each value in the time series of the proportion of increased rainwater during rainy weather, thus obtaining a third-weight time series.

[0167] As previously described, based on the first weather scenario, the inflow sources corresponding to the drainage system were identified, and the first flow percentage time series for each inflow source was determined. Using a scoring method, the values ​​(flow percentages) in each first flow percentage time series can be scored, resulting in a score time series corresponding to each first flow percentage time series.

[0168] For example, in a dry weather scenario, an initial score can be determined for each value in the time series of the proportion of wastewater during dry weather, based on a scoring method, to obtain an initial clean water score time series. Similarly, an initial score can be determined for each value in the time series of the proportion of clean water during dry weather, to obtain an initial wastewater score time series. In a rainy weather scenario, an initial score can be determined for each value in the time series of the proportion of increased rainwater during rainy weather, to obtain an initial incremental score time series.

[0169] For ease of understanding, Table 1 is used to illustrate the weighting method, and Table 2 is used to illustrate the scoring method.

[0170] surface Weighted methods

[0171]

[0172] The sum of the weights for each system type in Table 1 is 1. Furthermore, the specific weight values ​​can be adjusted according to actual circumstances; the weight values ​​in Table 1 are merely examples.

[0173] surface Scoring Method

[0174]

[0175] As shown in Table 2, there are 10 numerical intervals, each corresponding to a score. The numerical intervals in Table 2, and the correspondence between numerical intervals and scores, can be adjusted according to actual circumstances; the above is just an example.

[0176] When calculating scores, a dry day seepage score can be calculated for a dry day scenario, and a rainy day seepage score can be calculated for a rainy day scenario.

[0177] In this embodiment, the values ​​of the first weighted time series can be used to weight the values ​​at corresponding times in the initial clean water scoring time series to obtain the dry-day clean water scoring time series; the values ​​of the second weighted time series can be used to weight the values ​​at corresponding times in the initial wastewater scoring time series to obtain the dry-day wastewater scoring time series. Then, the dry-day clean water scoring time series and the dry-day wastewater scoring time series are added together according to time to obtain the dry-day seepage scoring time series. The values ​​of the third weighted time series can be used to weight the values ​​at corresponding times in the initial incremental scoring time series to obtain the rainy-day seepage scoring time series. In this way, the dry-day seepage scoring time series and the rainy-day seepage scoring time series of each catchment area can be obtained.

[0178] Furthermore, the dry-day characteristic values ​​of the dry-day seepage score time series can be determined; the rainy-day characteristic values ​​of the rainy-day seepage score time series can be determined, and the dry-day characteristic values ​​can be used as the dry-day seepage score, and the rainy-day characteristic values ​​can be used as the rainy-day seepage score.

[0179] The drought characteristic value can be a numerical value representing a drought seepage score time series, or a numerical value extracted from the drought seepage score time series. For example, the median of the drought seepage score time series can be used as the drought characteristic value. Alternatively, a first average value of the drought seepage score time series can be determined, and after removing data in the drought seepage score time series whose difference from the first average value is greater than a first difference, a second average value can be calculated and used as the drought characteristic value. This disclosure does not limit the method for determining the drought characteristic value.

[0180] Rainy day feature values ​​can be numerical values ​​representing a rainy day seepage score time series, or values ​​extracted from the rainy day seepage score time series. For example, the median of the rainy day seepage score time series can be used as the rainy day feature value. Alternatively, a third average value can be determined, and after removing data in the rainy day seepage score time series whose difference from the third average value is greater than a second difference value, a fourth average value can be calculated and used as the rainy day feature value. This disclosure does not limit the method for determining the rainy day feature value.

[0181] For a single catchment area, both dry-day and rainy-day seepage scores can be obtained. If the assessment period only includes dry-day scenarios, the rainy-day seepage score for each catchment area can be 0 and can be disregarded. Similarly, if the assessment period only includes rainy-day scenarios, the dry-day seepage score for each catchment area can be 0 and can be disregarded. The dry-day seepage score characterizes the severity of seepage in the drainage system during dry weather, and is positively correlated with the severity of dry-day seepage. The rainy-day seepage score characterizes the severity of seepage in the drainage system during rainy weather, and is positively correlated with the severity of rainy-day seepage.

[0182] The seepage conditions differ between dry and rainy weather scenarios, and the inflow sources that cause seepage also vary for different types of drainage systems. In this embodiment, the seepage conditions of the drainage system can be evaluated separately for rainy and dry weather scenarios, as well as for different types of drainage systems, to obtain dry weather seepage scores and rainy weather seepage scores. This improves the precision of the drainage system seepage evaluation and thus enhances the accuracy of the evaluation.

[0183] In one possible implementation, the key seepage zone includes a rainy day key seepage zone and a dry day key seepage zone, the seepage score includes a dry day seepage score and a rainy day seepage score, and the step of determining the key seepage zone based on the seepage score includes: determining the catchment area with the highest dry day seepage score as the dry day key seepage zone, and determining the catchment area with the highest rainy day seepage score as the rainy day key seepage zone.

[0184] In this embodiment of the disclosure, the dry weather seepage scores of each catchment area can be compared, and the catchment area with the highest dry weather seepage score, i.e., the catchment area with the most severe dry weather seepage, can be designated as the key dry weather seepage area. Similarly, the rainy weather seepage scores of each catchment area can be compared, and the catchment area with the highest rainy weather seepage score, i.e., the catchment area with the most severe rainy weather seepage, can be designated as the key rainy weather seepage area.

[0185] The catchment areas where seepage occurs may differ in rainy and dry weather scenarios. In this embodiment, the catchment areas with the most severe seepage are determined for both rainy and dry weather scenarios to more effectively pinpoint the seepage location. This improves the accuracy of inflow and seepage diagnosis results for the drainage system.

[0186] In one possible implementation, the method further includes: determining at least one target inflow source based on the first weather scenario and the first system type; for the key inflow area, determining a first flow time series for each target inflow source based on a second flow rate ratio time series and a flow time series corresponding to each target inflow source; and determining the abnormal period corresponding to the key inflow area and the external water inflow rate during the abnormal period based on the first flow time series of the target inflow source.

[0187] In the case that the first weather scenario is a dry weather scenario and the first system type is a separate sewage system, the target inflow sources are: clean water inflow source and sewage inflow source.

[0188] In the case of a dry weather scenario and a combined sewer system, the target inflow sources are: clean water inflow source and sewage inflow source.

[0189] In the case where the first weather scenario is a rainy day and the first system type is a separate sewage system, the target inflow source is: rainwater inflow source.

[0190] When the first weather scenario is a rainy day and the first system type is a combined sewer system, the target inflow source is: rainwater inflow source.

[0191] In the case that the first weather scenario is a dry weather scenario and the first system type is a separate stormwater system, the target inflow sources are: clean water inflow source and sewage inflow source.

[0192] For key seepage areas, the second flow rate percentage time series of a single target inflow source and the flow rate time series of the catchment area at the same time are multiplied to obtain the first flow rate time series of that single target inflow source. In this way, the first flow rate time series of each target inflow source in the key seepage area can be determined.

[0193] In the previous section introducing the method for determining the first flow rate proportion time series, we already explained how to determine the first flow rate time series of each inflow source in each catchment area. Therefore, in determining the first flow rate proportion time series, each first flow rate time series can be stored according to the catchment area and the inflow source. Here, after identifying the target inflow sources in the key seepage area, the first flow rate time series of the target inflow sources in the key seepage area can be directly obtained. For example, if the key seepage area is catchment area A, and the target inflow sources for catchment area A are clean water inflow sources and sewage inflow sources, then the first flow rate time series of the clean water inflow source and the first flow rate time series of the sewage inflow source in catchment area A can be directly obtained.

[0194] Then, from the time series of the first flow rate of the target inflow sources, the abnormal periods and sewage inflow rates corresponding to each target inflow source can be determined. For example, a curve can be plotted based on the time series of the target inflow sources, where the horizontal axis represents time and the vertical axis represents the first flow rate of the target inflow source. By observation, the time period corresponding to the peak in the curve can be identified as the abnormal period. As another example, the time periods corresponding to values ​​greater than the surge threshold in the first flow rate time series of the target inflow sources can be identified as abnormal periods. Furthermore, the sum of the first flow rates within the abnormal periods can be taken as the external water inflow rate corresponding to the target inflow source. Here, the surge threshold is a preset flow rate threshold used to indicate whether the flow rate exceeds the normal level.

[0195] In the scenario where the first weather condition is a dry day and the first system type is a separate sewage system, clear water infiltration and / or sewage infiltration may occur in the key seepage zone. Therefore, abnormal periods and flow rates of clear water infiltration, and / or abnormal periods and flow rates of sewage infiltration can be obtained.

[0196] In the scenario where the first weather condition is a dry day and the first system type is a combined sewer system, the target inflow sources are: clean water inflow and wastewater inflow. Key infiltration areas may experience clean water infiltration and / or wastewater infiltration. Therefore, abnormal periods and volumes of clean water infiltration, and / or abnormal periods and volumes of wastewater infiltration can be obtained.

[0197] In the scenario where the first weather condition is rainy and the first system type is a separate sewage system, the target inflow source is rainwater inflow. Rainwater infiltration may occur in key seepage areas. Therefore, the abnormal periods of rainwater infiltration and the rainwater inflow rate can be determined.

[0198] Given that the first weather scenario is a rainy day and the first system type is a combined sewer system, the target inflow source is rainwater inflow. Rainwater infiltration may occur in key seepage areas. Therefore, abnormal periods of rainwater infiltration and rainwater inflow rates can be determined.

[0199] Under the scenario of a dry weather event and a separate stormwater system, the target inflow sources are: clean water inflow and wastewater inflow. Key infiltration areas may experience clean water infiltration and / or wastewater infiltration. Therefore, abnormal periods and volumes of clean water infiltration, and / or abnormal periods and volumes of wastewater infiltration can be obtained.

[0200] In this embodiment, based on the first weather scenario and the first system type, the inflow rate of external water in key seepage areas can be further determined, and quantitative analysis can be performed on each target inflow source in each key seepage area. This further improves the reliability of inflow and seepage diagnosis, demonstrating the systematic and real-time nature of the online monitoring system. Moreover, abnormal time periods can be identified, and the inflow and seepage situation of the drainage system can be analyzed in the time dimension. This provides reference data for further analysis of the causes of inflow and seepage, and also makes the inflow and seepage inspection more targeted in the time dimension.

[0201] In one possible implementation, the method further includes: determining the phase difference of water condition data for the first catchment area; adjusting the water condition data for each of the first catchment areas based on the phase difference to obtain first water condition data corresponding to each of the first catchment areas; obtaining the time series of the first flow rate proportion of each type of inflow source in each catchment area includes: determining the first flow rate proportion of each type of inflow source in each catchment area at each collection time, based on each first water condition data, and the first flow rate proportion of the inflow source of the same type flowing into the entire drainage system, to obtain the time series of each first flow rate proportion.

[0202] Drainage systems can be divided into multiple catchment areas, which can be adjacent upstream and downstream. For ease of description, catchment areas with adjacent upstream and downstream locations are designated as the first catchment area. When the same water flow passes through the upstream and downstream first catchment areas, the water condition data of each first catchment area can be correlated. However, there will be a time difference when the same water flow passes through each first catchment area. Therefore, if the water condition data of each first catchment area is directly used to determine the time series of the first flow rate proportion, this correlation cannot be accurately reflected, reducing the accuracy of identifying key seepage areas.

[0203] In this embodiment of the disclosure, the water condition data of the first catchment area with upstream and downstream adjacent positions can be aligned in the time dimension so that the correlation of the water condition data of each first catchment area can be reflected in the process of determining the time series of each first flow rate proportion.

[0204] In one example, the time series of water quality indicators in each first catchment area can be aligned along the time dimension. There is no need to align the flow time series.

[0205] In this embodiment of the disclosure, the time series of the first water quality index and the time series of the second water quality index corresponding to the same water quality index in the first catchment area can be aligned in the time dimension so that the correlation of water condition data of each first catchment area can be reflected in the process of determining the time series of each first flow rate proportion.

[0206] This can solve the problem of inaccurate diagnostic results caused by the asynchronous nature of water quality index time series in different catchment areas or inflow and outflow time series in the same catchment area, thus improving the reliability of inflow and infiltration diagnostic results.

[0207] For example, the phase difference of the water condition data for each first catchment area can be determined. To eliminate the phase difference, the water condition data for each first catchment area is adjusted, and the adjusted water condition data is named the first water condition data. Using each first water condition data, the time series of each first flow rate percentage is determined.

[0208] To facilitate understanding, we will take two first catchment areas (catchment area x and catchment area y) as examples and use formula (6) to demonstrate the process of determining the phase difference.

[0209] (6)

[0210] in, This represents the phase difference between the water condition data of catchment area x and catchment area y. This represents the correlation coefficient between the water condition data of catchment area x and catchment area y. This represents the autocorrelation coefficient of water condition data in catchment area x. This represents the autocorrelation coefficient of the water condition data for the catchment area y.

[0211] For example, the phase difference of water condition data for each first catchment area can be determined by observation.

[0212] In this embodiment of the disclosure, the time difference of water condition data for each first catchment area can be determined based on the phase difference. The water condition data for each first catchment area are aligned in the time dimension using the time difference to obtain first water condition data. The time series of each first flow rate percentage is then determined using the first water condition data.

[0213] To make it easier to understand, Equation (7) is used to demonstrate the process of determining the time difference using the phase difference.

[0214] *T (7)

[0215] in, This represents the time difference between water condition data from two or more primary catchment areas. This indicates the periodic frequency of water condition data changes; for example, T can be equal to 24 hours.

[0216] In this embodiment of the disclosure, using Adjusting the water condition data of the first catchment area with upstream and downstream relationships, and aligning the water condition data of the first catchment area with each other in the time dimension, yields the first water condition data. Using the first water condition data to determine the time series of the first flow rate proportion can improve the accuracy of identifying key seepage areas.

[0217] In one possible implementation, the method further includes: acquiring initial water condition data for each catchment area; and preprocessing the initial water condition data using at least one of the following preprocessing methods.

[0218] When two or more preprocessing methods are used in combination, the order of combination is not limited. For ease of description of each preprocessing method, the object of processing is assumed to be the initial water condition data. In practice, the output data of the first preprocessing method executed can be used as the input data of the subsequent preprocessing methods executed. The object of processing for each preprocessing method described below can be the initial water condition data or the output data of the first preprocessing method executed. Furthermore, the output data of the last preprocessing method can be used as the water condition data in this disclosure.

[0219] Method 1: Replace the values ​​collected during the period when the acquisition equipment malfunctioned in the initial water condition data to obtain the second water condition data.

[0220] If the data acquisition equipment malfunctions during the initial water condition data collection, the data collected during the period of equipment failure will be inaccurate. The values ​​collected during the period of equipment failure can be removed from the initial water condition data, and estimation or simulation methods can be used to determine the values ​​during the period of equipment failure. For example, methods such as ARIMA prediction models, smoothing algorithms, and averaging data before and after the period of equipment failure can be used to supplement the data for the period of equipment failure, thus obtaining the second water condition data.

[0221] Method 2: Adjust the initial water condition data so that all values ​​in the second water condition data are within the range of the acquisition device to obtain the third water condition data.

[0222] The initial water condition data collected may contain anomalous data, which may be higher than the upper limit of the acquisition device's measurement range or lower than the detection limit. Anomalous data that is higher than the upper limit of the acquisition device's measurement range can be modified to the upper limit of the acquisition device's measurement range; anomalous data that is lower than the detection limit can be modified to the detection limit to obtain the third water condition data.

[0223] Method 3: Based on the conductivity of the acquisition device, remove the water-free data from the third water condition data to obtain the fourth water condition data.

[0224] Sometimes, the data acquisition equipment may experience water separation issues. When this occurs, the data collected is water separation data, meaning the measured result is an air value and does not reflect the true condition of the object being measured. Therefore, it is necessary to detect and discard water separation data.

[0225] For example, the conductivity of the data acquisition device can be detected based on the data acquisition frequency. Separate conductivity thresholds can be set for dry and rainy weather scenarios. In a dry weather scenario, if the conductivity of the acquisition device is less than the dry weather conductivity threshold, it indicates that the acquisition device has experienced water separation, and the data collected during the period in which water separation occurred should be removed. In a rainy weather scenario, if the conductivity of the acquisition device is less than the rainy weather conductivity threshold, it also indicates that the acquisition device has experienced water separation.

[0226] For example, the liquid level of the water environment under test can be detected according to the data acquisition frequency. The installation elevation of the water quality monitoring equipment can be obtained in advance. The installation elevation is the distance between the installation position of the water quality monitoring equipment and a reference object (e.g., the bottom of a pipe or the ground) when the water quality monitoring equipment is in contact with the water body and can accurately complete water quality monitoring. If the liquid level is higher than the installation elevation, it indicates that the equipment has not left the water. If the liquid level is not higher than the installation elevation, it indicates that the equipment has left the water.

[0227] Furthermore, the data collected during the time period corresponding to the occurrence of water separation by the equipment is removed. In this way, the water separation data can be removed from the initial water condition data to obtain the fourth water condition data.

[0228] Second, third, or fourth water condition data can be used as input data for subsequent preprocessing, or as water condition data in this disclosure.

[0229] In this embodiment, preprocessing the initial water condition data to obtain the final water condition data improves the interpretability and consistency of the data. Furthermore, it reduces the negative impact of errors in the data acquisition equipment itself, malfunctions, or usage errors on the accuracy of the first flow rate percentage time series. This, in turn, improves the interpretability and consistency of the first flow rate percentage time series.

[0230] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0231] Figure 2 A schematic diagram of the structure of a drainage system inflow and infiltration diagnostic device provided in an embodiment of this disclosure. The device 20 includes:

[0232] The first flow percentage time series determination unit 21 is used to determine, based on the water condition data of each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow percentage of the flow rate of that type of inflow source into the entire drainage system, to obtain the first flow percentage time series of each type of inflow source in each catchment area. The water condition data includes: flow time series and multiple water quality index time series. The drainage system includes multiple catchment areas.

[0233] The weighted time series determination unit 22 is used to determine the weighted time series corresponding to each of the first flow percentage time series based on the first system type and the first weather scenario corresponding to the drainage system, and the weighting method. The weighting method represents the correspondence between system type, weather scenario, and weight.

[0234] The scoring time series determination unit 23 is used to determine the scoring time series corresponding to each of the first traffic proportion time series based on the scoring method, wherein the scoring method characterizes the correspondence between the inflow source, traffic proportion and score;

[0235] The seepage scoring determination unit 24 is used to determine the seepage score of each of the catchment areas based on the weighted time series and the scoring time series corresponding to each of the first flow rate percentage time series.

[0236] The key seepage zone determination unit 25 is used to determine the key seepage zone based on the seepage score.

[0237] In one possible implementation, the device 20 further includes:

[0238] The water condition data acquisition unit is used to divide new water catchment areas from the key seepage area and acquire water condition data for each new water catchment area when the water catchment area does not meet the first preset condition.

[0239] The target key seepage area determination unit is used to designate each of the new water catchment areas as the respective water catchment areas, and return to execute steps 1-6 until the water catchment area meets the first preset condition. When the water catchment area meets the first preset condition, the key seepage area determined is designated as the target key seepage area.

[0240] In one possible implementation, the water quality index time series in the multiple water quality index time series includes: a first water quality index time series collected at a first monitoring point, a second water quality index time series collected at a second monitoring point, and for the drainage system, the first weather scenario includes: a dry weather scenario. The first flow rate proportion time series determination unit 21 is further used for:

[0241] If the first weather scenario is a dry weather scenario, perform the following steps:

[0242] Based on the time series of the first water quality index and the time series of the second water quality index of the same catchment area, the second flow ratio of the flow of each type of inflow source in the same catchment area to the total inflow of the same catchment area at each collection time is determined, and the time series of the second flow ratio of each type of inflow source in the catchment area is obtained.

[0243] Based on the second flow rate percentage time series of various types of inflow sources in the same catchment area and the flow rate time series, the first flow rate of various types of inflow sources in the same catchment area at each collection time is determined, and the first flow rate time series of various types of inflow sources in each catchment area is obtained.

[0244] Based on the time series of the first flow rate of each type of inflow source in each catchment area, determine the time series of the first flow rate percentage of each type of inflow source in each catchment area.

[0245] In one possible implementation, the first weather scenario includes a rainy day scenario, and the first traffic flow percentage time series determination unit 21 is further configured to:

[0246] If the first weather scenario is a rainy day, perform the following steps:

[0247] Determine at least one dry day prior to the rainy day scenario, and obtain the second flow time series of each catchment area corresponding to the at least one dry day;

[0248] Based on the second flow time series and the flow time series of the same catchment area, the rainfall increment of the same catchment area at each collection time is determined, and the rainfall increment time series of each catchment area is obtained.

[0249] Based on the time series of rainfall increments in each of the aforementioned catchment areas, the time series of the first flow rate percentage for each of the aforementioned catchment areas is determined.

[0250] In one possible implementation, the first flow rate proportion time series includes: dry day wastewater proportion time series, dry day clean water proportion time series, and rainy day rainwater increment proportion time series; the seepage score includes: dry day seepage score and rainy day seepage score; and the score time series includes: initial clean water score time series, initial wastewater score time series, and initial increment score time series.

[0251] The weighted time series determination unit 22 is further used for:

[0252] Determine the first weighted time series of the dry-day sewage proportion time series, the second weighted time series of the dry-day clean water proportion time series, and the third weighted time series of the rainy-day rainwater increment proportion time series corresponding to the first system type;

[0253] Determining the scoring time series corresponding to each of the first traffic share time series includes:

[0254] The time series of the proportion of clean water during dry days is scored to obtain an initial clean water score time series; the time series of the proportion of sewage during dry days is scored to obtain an initial sewage score time series; and the time series of the proportion of increased rainwater during rainy days is scored to obtain an initial increased score time series.

[0255] The step of determining the seepage score of each catchment area based on the weighted time series and the scoring time series corresponding to each first flow rate percentage time series includes: obtaining the dry weather seepage score based on the first weighted time series, the second weighted time series, the initial clean water scoring time series, and the initial wastewater scoring time series;

[0256] The rainy day seepage score is obtained based on the third weighted time series and the initial incremental score time series.

[0257] In one possible implementation, the device 20 further includes:

[0258] The target inflow source determination unit is used to determine at least one target inflow source based on the first weather scenario and the first system type.

[0259] The first flow time series determination unit is used to determine the first flow time series of each of the target inflow sources for the key inflow area based on the second flow proportion time series and flow time series corresponding to each of the target inflow sources.

[0260] The abnormal time period and external water inflow determination unit is used to determine the abnormal time period corresponding to the key seepage zone and the external water inflow corresponding to the abnormal time period based on the first flow time series of the target inflow source.

[0261] In one possible implementation, the step of determining the second flow percentage of each category of inflow source in the same catchment area relative to the total inflow of the same catchment area at each sampling time, based on the time series of each of the first water quality indicators and the time series of each of the second water quality indicators in the same catchment area, and obtaining the time series of the second flow percentage of each category of inflow source in each catchment area, includes:

[0262] For any given catchment area, perform the following steps:

[0263] Step a: Input the time series of each of the first water quality indicators and each of the second water quality indicators of any one catchment area into the mass balance model to obtain the second flow rate proportion of each category of inflow source in any one catchment area at each time point;

[0264] Step b: If at any given time the proportion of the second flow rate of each type of inflow source in any catchment area does not meet the second preset condition, a global optimization algorithm is used to process the time series of each first water quality indicator and each second water quality indicator to obtain the time series of each first optimized water quality indicator and each second optimized water quality indicator.

[0265] Step c: Take each of the first optimized water quality index time series as the first water quality index time series, take each of the second optimized water quality index time series as the second water quality index time series, and return to execute steps a-b until, at all times, the second flow rate ratio of each type of inflow source in any catchment area meets the second preset condition, and obtain the second flow rate ratio time series of each type of inflow source in any catchment area.

[0266] For each of the aforementioned catchment areas, steps a-c are performed to obtain the second flow rate percentage time series of each type of inflow source in each of the aforementioned catchment areas.

[0267] In one possible implementation, the device 20 further includes:

[0268] The phase difference determination unit is used to determine the phase difference of the water condition data in the first catchment area;

[0269] The first water condition data determination unit is used to adjust the water condition data of each first catchment area based on the phase difference to obtain the first water condition data corresponding to each first catchment area.

[0270] The process of obtaining the first flow rate percentage time series of each category of inflow source in each of the aforementioned catchment areas includes:

[0271] Based on the first water condition data, the flow rate of each type of inflow source in each catchment area at each collection time is determined, and the first flow rate ratio of the flow rate of the same type of inflow source into the entire drainage system is obtained, thus obtaining the time series of each first flow rate ratio.

[0272] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0273] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0274] Figure 3 This is a schematic diagram of an electronic device for inflow and infiltration diagnosis in a drainage system, provided as an embodiment of this disclosure. For example, the electronic device 1900 can be provided as a server or terminal device. (Refer to...) Figure 3 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0275] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0276] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0277] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0278] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0279] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0280] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0281] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0282] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0283] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0284] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0285] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for diagnosing inflow and seepage in a drainage system, characterized in that, include: Step 1: Based on the water condition data of each catchment area, determine the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source into the entire drainage system, to obtain the time series of the first flow rate percentage of each type of inflow source in each catchment area. The water condition data includes: flow rate time series and time series of various water quality indicators. The drainage system includes multiple catchment areas. Step 2: Based on the first system type and first weather scenario corresponding to the drainage system, and the weighting method, determine the weighted time series corresponding to each first flow rate percentage time series. The weighting method represents the correspondence between system type, weather scenario, and weight. Step 3: Based on the scoring method, determine the scoring time series corresponding to each of the first traffic percentage time series, wherein the scoring method characterizes the correspondence between the inflow source, traffic percentage and score; Step 4: Determine the seepage score for each catchment area based on the weighted time series and the scoring time series corresponding to each of the first flow rate percentage time series; Step 5: Based on the seepage score, determine the key seepage zones; The first flow rate proportion time series includes: dry day sewage proportion time series, dry day clean water proportion time series, and rainy day rainwater increment proportion time series. The seepage score includes: dry day seepage score and rainy day seepage score. The score time series includes: initial clean water score time series, initial sewage score time series, and initial increment score time series. Determining the weighted time series corresponding to each of the first traffic share time series includes: Determine the first weighted time series of the dry-day sewage proportion time series, the second weighted time series of the dry-day clean water proportion time series, and the third weighted time series of the rainy-day rainwater increment proportion time series corresponding to the first system type; Determining the scoring time series corresponding to each of the first traffic share time series includes: The time series of the proportion of clean water during dry days is scored to obtain an initial clean water score time series; the time series of the proportion of sewage during dry days is scored to obtain an initial sewage score time series; and the time series of the proportion of increased rainwater during rainy days is scored to obtain an initial increased score time series. The step of determining the seepage score of each catchment area based on the weighted time series and the scoring time series corresponding to each first flow rate percentage time series includes: obtaining the dry weather seepage score based on the first weighted time series, the second weighted time series, the initial clean water scoring time series, and the initial wastewater scoring time series; The rainy day seepage score is obtained based on the third weighted time series and the initial incremental score time series.

2. The method according to claim 1, characterized in that, The method further includes: Step 6: If the catchment area does not meet the first preset condition, a new catchment area is divided from the key seepage area, and water condition data of each new catchment area is obtained. Step 7: Each of the new catchment areas is designated as a catchment area, and steps 1-6 are returned to be executed until the catchment area meets the first preset condition. The key seepage area determined when the catchment area meets the first preset condition is designated as the target key seepage area.

3. The method according to claim 1, characterized in that, The time series of various water quality indicators includes: a first water quality indicator time series collected at a first monitoring point, and a second water quality indicator time series collected at a second monitoring point. For the drainage system, the first weather scenario includes a dry weather scenario. The step of determining the flow rate of each type of inflow source in each catchment area at each collection time, based on water condition data from each catchment area, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain the first flow rate percentage time series of each type of inflow source in each catchment area includes: If the first weather scenario is a dry weather scenario, perform the following steps: Based on the time series of the first water quality index and the time series of the second water quality index of the same catchment area, the second flow ratio of the flow of each type of inflow source in the same catchment area to the total inflow of the same catchment area at each collection time is determined, and the time series of the second flow ratio of each type of inflow source in the catchment area is obtained. Based on the second flow rate percentage time series of various types of inflow sources in the same catchment area and the flow rate time series, the first flow rate of various types of inflow sources in the same catchment area at each collection time is determined, and the first flow rate time series of various types of inflow sources in each catchment area is obtained. Based on the time series of the first flow rate of each type of inflow source in each catchment area, determine the time series of the first flow rate percentage of each type of inflow source in each catchment area.

4. The method according to claim 1, characterized in that, The first weather scenario includes a rainy day scenario. The step of determining, based on water condition data from each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow rate percentage of that type of inflow source flowing into the entire drainage system, to obtain a time series of the first flow rate percentage of each type of inflow source in each catchment area, further includes: If the first weather scenario is a rainy day, perform the following steps: Determine at least one dry day prior to the rainy day scenario, and obtain the second flow time series of each catchment area corresponding to the at least one dry day; Based on the second flow time series and the flow time series of the same catchment area, the rainfall increment of the same catchment area at each collection time is determined, and the rainfall increment time series of each catchment area is obtained. Based on the time series of rainfall increments in each of the aforementioned catchment areas, the time series of the first flow rate percentage for each of the aforementioned catchment areas is determined.

5. The method according to claim 3, characterized in that, The method further includes: Based on the first weather scenario and the first system type, at least one target inflow source is identified; For the key seepage zone, the first flow time series of each target inflow source is determined based on the second flow rate percentage time series and flow time series corresponding to each target inflow source; Based on the first flow time series of the target inflow source, the abnormal time period corresponding to the key seepage zone and the external water inflow rate corresponding to the abnormal time period are determined.

6. The method according to claim 3, characterized in that, Based on the time series of each of the first water quality indicators and each of the second water quality indicators in the same catchment area, the second flow percentage of each type of inflow source in the same catchment area is determined at each collection time, relative to the total inflow of the same catchment area. This yields the time series of the second flow percentage of each type of inflow source in each catchment area, including: For any given catchment area, perform the following steps: Step a: Input the time series of each of the first water quality indicators and each of the second water quality indicators of any one catchment area into the mass balance model to obtain the second flow rate proportion of each category of inflow source in any one catchment area at each time point; Step b: If at any given time the proportion of the second flow rate of each type of inflow source in any catchment area does not meet the second preset condition, a global optimization algorithm is used to process the time series of each first water quality indicator and each second water quality indicator to obtain the time series of each first optimized water quality indicator and each second optimized water quality indicator. Step c: Take each of the first optimized water quality index time series as the first water quality index time series, take each of the second optimized water quality index time series as the second water quality index time series, and return to execute steps a-b until, at all times, the second flow rate ratio of each type of inflow source in any catchment area meets the second preset condition, and obtain the second flow rate ratio time series of each type of inflow source in any catchment area. For each of the aforementioned catchment areas, steps a-c are performed to obtain the second flow rate percentage time series of each type of inflow source in each of the aforementioned catchment areas.

7. The method according to claim 1, characterized in that, The method further includes: Determine the phase difference of the water condition data for the first catchment area; Based on the phase difference, the water condition data of each first catchment area are adjusted to obtain the first water condition data corresponding to each first catchment area; The process of obtaining the first flow rate percentage time series of each category of inflow source in each of the aforementioned catchment areas includes: Based on the first water condition data, the flow rate of each type of inflow source in each catchment area at each collection time is determined, and the first flow rate ratio of the flow rate of the same type of inflow source into the entire drainage system is obtained, thus obtaining the time series of each first flow rate ratio.

8. A device for diagnosing inflow and seepage in a drainage system, characterized in that, include: The first flow percentage time series determination unit is used to determine, based on the water condition data of each catchment area, the flow rate of each type of inflow source in each catchment area at each collection time, and the first flow percentage of that type of inflow source into the entire drainage system, to obtain the first flow percentage time series of each type of inflow source in each catchment area. The water condition data includes: flow time series and multiple water quality index time series. The drainage system includes multiple catchment areas. The weighted time series determination unit is used to determine the weighted time series corresponding to each of the first flow percentage time series based on the first system type and the first weather scenario corresponding to the drainage system, and the weighting method. The weighting method represents the correspondence between system type, weather scenario, and weight. The scoring time series determination unit is used to determine the scoring time series corresponding to each of the first traffic proportion time series based on the scoring method, wherein the scoring method characterizes the correspondence between the inflow source, traffic proportion and score; The seepage scoring unit is used to determine the seepage score of each catchment area based on the weighted time series and the scoring time series corresponding to each of the first flow rate percentage time series. The key seepage zone determination unit is used to determine the key seepage zone based on the seepage score; The first flow rate proportion time series includes: dry day sewage proportion time series, dry day clean water proportion time series, and rainy day rainwater increment proportion time series. The seepage score includes: dry day seepage score and rainy day seepage score. The score time series includes: initial clean water score time series, initial sewage score time series, and initial increment score time series. The weighted time series determination unit is further used for: Determine the first weighted time series of the dry-day sewage proportion time series, the second weighted time series of the dry-day clean water proportion time series, and the third weighted time series of the rainy-day rainwater increment proportion time series corresponding to the first system type; The scoring time series determination unit is further used for: The time series of the proportion of clean water during dry days is scored to obtain an initial clean water score time series; the time series of the proportion of sewage during dry days is scored to obtain an initial sewage score time series; and the time series of the proportion of increased rainwater during rainy days is scored to obtain an initial increased score time series. The seepage scoring and determination unit is further used for: The dry weather seepage score is obtained based on the first weighted time series, the second weighted time series, the initial clean water score time series, and the initial wastewater score time series. The rainy day seepage score is obtained based on the third weighted time series and the initial incremental score time series.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing instructions stored in the memory.

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