Intelligent diagnosis method, medium and electronic device for sewage inflow of rainwater pipe network

Abstract

This invention discloses an intelligent diagnostic method, medium, and electronic device for sewage inflow into stormwater drainage networks. The method includes: acquiring sunny-day data, wherein the sunny-day data includes the liquid level data of stormwater wells in the stormwater drainage network on sunny days; calculating a first average liquid level based on the liquid level data, and determining whether the first average liquid level is greater than a preset liquid level threshold; if the first average liquid level is greater than the preset liquid level threshold, determining the liquid level change based on the liquid level data, and determining whether the liquid level change conforms to residents' daily routines; if the liquid level change conforms to residents' daily routines, then diagnosing sewage inflow into the stormwater drainage network. This invention's intelligent diagnostic method for sewage inflow into stormwater drainage networks determines whether sewage is flowing in by comparing peak liquid level values ​​over time. It is economical and convenient, not limited by peak / valley time points, thresholds, or equipment, and the analysis results are accurate and reliable, providing support for identifying and repairing defects in combined stormwater and sewage drainage networks.

Description

Technical Field

[0001] This invention relates to the field of stormwater drainage network technology, and in particular to an intelligent diagnostic method, medium, and electronic equipment for detecting sewage inflow into stormwater drainage networks. Background Technology

[0002] The problem of combined sewer and stormwater pipe network defects is one of the main causes of reduced drainage capacity and water pollution. The investigation of defects in combined sewer and stormwater pipe networks mainly relies on technologies such as QV (Pipe Quick View Inspection), CCTV (Closed Circuit Television Inspection), or manual water sampling and testing. However, these methods are costly in terms of manpower and resources, and are difficult to detect, locate, and resolve, with slow results.

[0003] In related technologies, there are three main methods for analyzing monitoring data to determine the problem of sewage inflow into stormwater pipe networks. The first is manual analysis, which involves manually analyzing whether the changes in the liquid level data from the level gauges in the stormwater wells conform to relevant patterns. The second is the liquid level change rate determination method, which analyzes whether there is an upward trend in the liquid level between low and high water usage periods; see the slope of the fitted curve for water usage periods in Table 1 below. The third is the water quality concentration threshold determination method, which uses threshold alarm lines to determine whether the average monitored concentrations Ci (COD) or Ci (ammonia nitrogen) exceed the set threshold.

[0004] Table 1

[0005]

[0006] Manual analysis of liquid level curve changes is time-consuming, labor-intensive, and inefficient. The liquid level change rate threshold method first requires determining the time points of the peak and trough values, but due to the varying distances of each rainwater well from residential areas, it is difficult to pinpoint the specific peak and trough times for each location. The water quality concentration threshold method faces two main challenges: firstly, the varying sediment content and sewage inflow within the pipe network make threshold setting difficult; secondly, current water quality monitoring equipment is still underdeveloped in complex drainage networks, resulting in insufficient data stability and affecting judgment. Summary of the Invention

[0007] One objective of this invention is to propose an intelligent diagnostic method, medium, and electronic equipment for detecting sewage inflow into stormwater pipe networks. This method determines whether sewage has entered the network by comparing peak liquid level values ​​over time. It is economical and convenient, and is not limited by peak and valley values, thresholds, or equipment. The analysis results are accurate and reliable, providing support for identifying and repairing defects in combined stormwater and sewage pipe networks.

[0008] To achieve the above objectives, a first aspect of the present invention proposes an intelligent diagnostic method for sewage inflow into a stormwater drainage network. The method includes: acquiring sunny-day data, wherein the sunny-day data includes the liquid level data of stormwater wells in the stormwater drainage network on sunny days; calculating a first average liquid level based on the liquid level data, and determining whether the first average liquid level is greater than a preset liquid level threshold; if the first average liquid level is greater than the preset liquid level threshold, determining the liquid level change based on the liquid level data, and determining whether the liquid level change conforms to the residents' daily routines; if the liquid level change conforms to the residents' daily routines, then diagnosing sewage inflow into the stormwater drainage network.

[0009] In addition, the intelligent diagnostic method for sewage inflow into rainwater pipe networks proposed in the above embodiments of the present invention may also have the following additional technical features:

[0010] According to one embodiment of the present invention, obtaining the sunny day data includes: obtaining rainfall data, and recording the rainfall at time point t as... ; Determine within a continuous preset time period Data acquired during periods with a time value of zero represents sunny days.

[0011] According to one embodiment of the present invention, the liquid level data is deburred, and the deburring process includes: removing null values ​​from the liquid level data; calculating a second average liquid level value and standard deviation based on the liquid level data after removing null values; and deburring the liquid level data according to a preset precision, the second average liquid level value, and the standard deviation.

[0012] According to one embodiment of the present invention, determining whether the liquid level change conforms to the residents' daily routine includes: taking data from day I, dividing each day into J time periods on average, where I and J are positive integers greater than 1; and denoting the number of monitoring data points in the j-th time period of day i as... The number of time points corresponding to the highest liquid level is Obtain the time point corresponding to the highest liquid level. ,in, Let x represent the x-th time point corresponding to the highest liquid level in the j-th time period of the i-th day, where 1 ≤ i ≤ I, 1 ≤ j ≤ J, and 1 ≤ x ≤ X; with a unit time of 1, the time point is... Convert to numerical form and denote as ;calculate and The ratio of is denoted as If the ratio If the number of ratios less than the first preset threshold exceeds the first preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First variance Determine the first variance Is it less than the second preset threshold? If the first variance is less than the second preset threshold, then the first variance is less than the second preset threshold. If the number of variances less than the second preset threshold exceeds the second preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First average time point And based on the first average time point Determine whether the changes in the liquid level are consistent with the residents' daily routines.

[0013] According to one embodiment of the present invention, determining whether the liquid level change conforms to the residents' daily routine based on the first average time point includes obtaining the first average time point corresponding to the highest liquid level within J time periods of I days. Record the first average time point corresponding to the highest liquid level in the j-th time period of day I. Given the j-th data set, we obtain J data sets; calculate the second variance of each of the J data sets. According to the second variance Determine whether the changes in the liquid level are consistent with the residents' daily routines.

[0014] According to an embodiment of the present invention, the step of basing the variance on the second variance Determining whether the liquid level change conforms to the residents' daily routine includes: if the second variance If the number of second variances less than the third preset threshold exceeds the third preset number, then the liquid level change is determined to conform to the residents' daily routine.

[0015] According to one embodiment of the present invention, if the first average liquid level is less than or equal to the preset liquid level threshold, the rainwater pipe is diagnosed as operating normally.

[0016] According to an embodiment of the present invention, if the ratio Greater than or equal to the first preset threshold, or, the first variance If the ratio is greater than or equal to the second preset threshold, then a rainwater retention situation is diagnosed in the corresponding time period of the rainwater pipe network; if the ratio is greater than or equal to the second preset threshold, then a rainwater retention situation is diagnosed in the corresponding time period of the rainwater pipe network. The number of ratios less than the first preset threshold does not exceed the first preset number, or the first variance The number of variances less than the second preset threshold does not exceed the second preset number, or the second variance... If the number of second variances less than the third preset threshold does not exceed the third preset number, then the rainwater pipe network is diagnosed as having rainwater retention.

[0017] The intelligent diagnostic method for sewage inflow into rainwater pipes in this embodiment of the invention first acquires sunny day data and deburrs the data. The average liquid level after deburring is then calculated for preliminary judgment. It is determined whether the average liquid level is greater than a preset liquid level threshold. If the average liquid level is less than or equal to the preset liquid level threshold, it indicates that the rainwater pipe network is operating normally. If the average liquid level is greater than the preset liquid level threshold, the liquid level change is further assessed. To determine whether the liquid level change conforms to residents' daily routines, the liquid level data is processed by time period, and the time point corresponding to the highest liquid level within each time period is extracted. The number of time points corresponding to the highest liquid level is considered... With the number of monitoring data ratio If the number of ratios less than a first preset threshold exceeds a first preset number, then the first variance of the time point corresponding to the highest liquid level is calculated. When the number of first variances less than a second preset threshold exceeds a second preset number, the second variance of the average of the highest liquid level time points within the same daily time period is calculated. When the number of second variances less than a third preset threshold exceeds a third preset number, sewage inflow into the stormwater pipe network is diagnosed; conversely, rainwater retention in the stormwater pipe network is diagnosed. This intelligent diagnostic method for sewage inflow into stormwater pipe networks determines whether sewage has entered based on a comparison of peak liquid level time points. It is economical and convenient, not limited by peak and valley time points, thresholds, or equipment. The analysis results are accurate and reliable, and it provides support for identifying and repairing defects in combined stormwater and sewage pipe networks.

[0018] To achieve the above objectives, a second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the intelligent diagnostic method for sewage inflow into rainwater pipe networks as described above.

[0019] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the intelligent diagnostic method for sewage inflow into rainwater pipe networks as described above.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of an intelligent diagnostic method for sewage inflow into a stormwater pipe network according to an embodiment of the present invention;

[0022] Figure 2 This is a flowchart illustrating the acquisition of sunny day data according to an embodiment of the present invention;

[0023] Figure 3This is a flowchart of a deburring process according to an embodiment of the present invention;

[0024] Figure 4 This is a flowchart of an embodiment of the present invention for determining whether changes in liquid level conform to the daily routines of residents;

[0025] Figure 5 This is a flowchart of an embodiment of the present invention for determining whether changes in liquid level conform to the daily routines of residents based on a fitted curve;

[0026] Figure 6 This is a flowchart of rainwater pipe network level analysis according to an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0028] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0029] The intelligent diagnostic method, storage medium, and electronic equipment for sewage inflow into rainwater pipe networks according to embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] Figure 1 This is a flowchart of an intelligent diagnostic method for sewage inflow into a rainwater pipe network according to an embodiment of the present invention.

[0031] In one embodiment of the present invention, such as Figure 1 As shown, the intelligent diagnostic methods for sewage inflow into stormwater pipe networks include:

[0032] S1, Obtain sunny day data, which includes the liquid level data of rainwater wells in the rainwater pipe network on sunny days.

[0033] Specifically, to determine whether rainwater is flowing into the stormwater drainage network, it is first necessary to obtain data on sunny days. Rainwater drainage network data on rainy days is affected by rainfall and cannot provide support for diagnosing sewage inflow. Sunny day data includes the liquid level data of rainwater wells in the stormwater drainage network on sunny days. This invention diagnoses whether sewage is flowing into the stormwater drainage network by analyzing the liquid level of rainwater wells on sunny days.

[0034] More specifically, sunny day data can be obtained by judging rainfall, and rainy day data can be eliminated. Data with zero rainfall over a continuous period can be defined as sunny day data, and other data can be identified as rainy day data and eliminated.

[0035] In one embodiment of the present invention, such as Figure 2 As shown, obtaining sunny day data includes:

[0036] S101, Obtain rainfall data and record the rainfall at time point t as... .

[0037] S102, determine within a continuous preset time period Data acquired during periods with a time value of zero represents sunny days.

[0038] Specifically, rainfall data is acquired, and the rainfall at time point t is recorded as... The preset time period can be set to 3 hours. For example, if the preset time is 3 hours, then data obtained during a period of zero rainfall for 3 consecutive hours is considered sunny data, and other data that does not meet the criteria are considered rainy data. Sunny data is extracted, and rainy data is discarded. The specific algorithm is as follows: Assuming the rainfall is measured in hours, the rainfall at time point t, the time point t-1 1 hour before, and the time point t+1 1 hour after are respectively... , , If both conditions are met , , If the value is 0, then extract the data from time point t-1 to time point t+1. Otherwise, discard the data.

[0039] More specifically, after removing rainy day data and obtaining sunny day data, deburring is also performed on the sunny day data to ensure the accuracy of subsequent data processing.

[0040] In one embodiment of the present invention, such as Figure 3 As shown, the intelligent diagnostic method for sewage inflow into the stormwater pipe network also includes deburring the liquid level data. Deburring the liquid level data includes:

[0041] S201, remove null values ​​from the liquid level data.

[0042] S202, calculate the second liquid level average and standard deviation based on the liquid level data after removing null values.

[0043] S203, deburring the liquid level data according to the preset accuracy, the second average liquid level value and the standard deviation.

[0044] Specifically, deburring of liquid level data includes eliminating and filtering outliers. First, null values ​​are removed from the liquid level data. Then, deburring is performed using the average value and standard deviation. For example, if the liquid level data for a certain period deviates significantly from the average value, that segment of data is considered outlier and is removed.

[0045] More specifically, calculate the average value L and standard deviation of the second liquid level after removing blank values. Based on the preset accuracy, the average value L of the second liquid level, and the standard deviation. Deburring of liquid level data, for example, a preset accuracy of [L-3] can be performed. L+3 Between ], with [L-3 L+3 The range is the standard for judging normal values. Level data showing values ​​greater than L+3 indicates a problem. or less than L-3 The numerical probability is very small, therefore it is determined to be greater than L+3. or less than L-3 The liquid level data is abnormal and should be removed.

[0046] After deburring the liquid level data, calculate the first average liquid level value of the deburred liquid level data and determine whether the first average liquid level value is greater than the preset liquid level threshold.

[0047] S2, calculate the first average liquid level based on the liquid level data, and determine whether the first average liquid level is greater than the preset liquid level threshold.

[0048] Specifically, the average first liquid level is calculated based on the liquid level data after deburring. If the average first liquid level is greater than the preset liquid level threshold, it indicates that the stormwater pipe network is operating at a high liquid level on sunny days. A high liquid level on sunny days could be due to rainwater retention or sewage inflow into the stormwater pipe network, requiring further analysis of the liquid level. If the average first liquid level is less than or equal to the preset liquid level threshold, the stormwater pipe network is operating normally.

[0049] In one embodiment of the present invention, if the first average liquid level is less than or equal to a preset liquid level threshold, the rainwater pipe is diagnosed as operating normally.

[0050] Specifically, the preset liquid level threshold can be 0.2m. For example, if the first average liquid level is greater than 0.2m, it indicates that the rainwater pipe network is operating at a high liquid level on sunny days, and the liquid level situation needs further judgment. If the first average liquid level is less than or equal to 0.2m, it is determined that the rainwater pipe network is operating normally.

[0051] S3. If the average value of the first liquid level is greater than the preset liquid level threshold, the liquid level change is determined based on the liquid level data, and it is judged whether the liquid level change conforms to the residents' daily life patterns.

[0052] Specifically, if the average first liquid level is greater than the preset liquid level threshold, it indicates that the stormwater pipe network is operating at a high liquid level on sunny days. High liquid level operation on sunny days could be due to rainwater retention in the stormwater pipe network or sewage inflow. Further analysis of the liquid level is needed to determine whether the liquid level changes conform to residents' daily routines, i.e., whether the liquid level changes exhibit morning, noon, and evening peak patterns.

[0053] In one embodiment of the present invention, such as Figure 4 As shown, determining whether changes in liquid level conform to residents' daily routines includes:

[0054] S301, take data from day I, and divide each day into J time periods on average, where I and J are positive integers greater than 1.

[0055] S302, let the number of monitoring data points in the j-th time period of the i-th day be... The number of time points corresponding to the highest liquid level is Obtain the time point corresponding to the highest liquid level. ,in, Let x represent the x-th time point corresponding to the highest liquid level in the j-th time period on the i-th day, where 1≤i≤I, 1≤j≤J, and 1≤x≤X.

[0056] S303, with a unit time of 1, the time point is... Convert to numerical form and denote as .

[0057] S304, Calculation and The ratio of is denoted as .

[0058] S305, if the ratio If the number of ratios less than the first preset threshold exceeds the first preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First variance .

[0059] S306, Determine the first variance Is it less than the second preset threshold?

[0060] S307, if the first variance If the number of variances less than the second preset threshold exceeds the second preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First average time point And based on the first average time point Determine whether the changes in liquid level are consistent with residents' daily routines.

[0061] Specifically, to determine whether the liquid level changes exhibit a morning, noon, and evening peak pattern, the day is divided into J average time periods, and the liquid level changes are analyzed for each time period. J can be set to 4. The following example illustrates dividing the day into four average time periods. The 24-hour period is divided into four time periods: 0-6:00, 6-12:00, 12-18:00, and 18-24:00. The time point corresponding to the highest liquid level in each time period within day I is obtained. , This represents the x-th time point corresponding to the highest liquid level in the j-th time period of day i, where 1 ≤ i ≤ I, 1 ≤ j ≤ J, and 1 ≤ x ≤ X. I represents the total number of days for the liquid level data, J represents the number of time periods, and X represents the total number of time points corresponding to the highest liquid level in each time period. The time points corresponding to the highest liquid level in each time period can be extracted using software. For example, the time points corresponding to the highest liquid levels in the four time periods of the first day are as follows: , , , The time points corresponding to the highest liquid levels in the four time periods on the second day were as follows: , , , And so on, the time points corresponding to the highest liquid levels in the four time periods of Day I are respectively , , , .

[0062] More specifically, for ease of statistical calculation, this invention can use 1 as the unit of time, converting all time points into numerical form and recording them as follows: For example, convert the time points corresponding to the highest liquid level into numerical form, using minutes as the unit. For instance, 1:00 corresponds to the number 60, 1:30 corresponds to the number 90, and 23:00 corresponds to the number 1380. Convert all time points into numerical form in this way.

[0063] Specifically, the number of monitoring data points within each time period is recorded as follows: The number of time points corresponding to the highest liquid level is Calculate the data for each time period within day I. and The ratio of is denoted as Each time period corresponds to a ratio. For all ratios Make a judgment and determine the ratio. Whether it is less than a first preset threshold, the first preset threshold can be 30%, and the determination is made across all ratios. Whether the number of ratios less than a first preset threshold exceeds a first preset number, where the first preset number can be half of the total number of ratios. For example, if in all ratios... If more than half of the ratios are less than the first preset threshold, it indicates a clear pattern of rising and falling levels in the rainwater well, suggesting potential sewage inflow within the day's data. Further analysis of the distribution of the highest liquid level at each time period is needed. The calculation of the highest liquid level at each time point is performed in numerical form. First variance Each time period corresponds to a first variance. The first variance is then determined. Is it less than the second preset threshold? The second preset threshold can be 225. If the first variance If the number of variances less than the second preset threshold exceeds the second preset number, it indicates that the highest liquid level in the corresponding time period is concentrated at a relatively high time point, which may indicate that there is sewage inflow. Further analysis is needed on the highest liquid level in the same time period of day I.

[0064] More specifically, calculate the average value of the highest liquid level at the corresponding time points within J time periods of day I, and express it in numerical form. This means calculating the time point corresponding to the highest liquid level within the j-th time period of the i-th day. First average time point Based on the first average time point in day I The changes in liquid level are used to determine whether they conform to the residents' daily routines.

[0065] In one embodiment of the present invention, such as Figure 5 As shown, based on the first average time point Determining whether changes in liquid level conform to residents' daily routines includes:

[0066] S401, Obtain the first average time point corresponding to the highest liquid level within the Jth time period of day I. Record the first average time point corresponding to the highest liquid level in the j-th time period of day I. Given the j-th data set, we obtain the J-th data set.

[0067] S402, calculate the second variance of the J groups of data respectively. .

[0068] S403, based on the second variance Determine whether the changes in liquid level are consistent with residents' daily routines.

[0069] Specifically, obtain the first average time point corresponding to the highest liquid level within the Jth time period of day I. Let the first average time point corresponding to the highest liquid level in the j-th time period be recorded. For the j-th data set, for example, to obtain the first average time point corresponding to the highest liquid level within the first time period of day I. For the first set of data, the first average time point corresponding to the highest liquid level in the second time period of day I. For the second set of data, and so on, obtain the first average time point corresponding to the highest liquid level in the Jth time period of day I. For the J-th data set, a total of J data sets were obtained. Calculate the second variance of each of the J data sets. For example, if J is set to 4, calculate the second variance of the four sets of data respectively, and judge whether the liquid level change is consistent with the residents' daily life patterns based on the second variance of the four sets of data.

[0070] In one embodiment of the invention, based on the second variance Determining whether changes in liquid level conform to residents' daily routines includes: if the second variance If the number of second variances less than the third preset threshold exceeds the third preset number, then the liquid level change is determined to conform to the residents' daily life patterns.

[0071] Specifically, the third preset threshold can be 225, and the third preset quantity can be half of the total quantity. For example, if the second variance of more than half of the data is less than the third preset threshold, it is determined that the liquid level change is in line with the residents' daily life. For example, if the second variance of three or more of the four sets of data is less than the third preset threshold, it indicates that the highest liquid level of the rainwater well is basically distributed at the same time point (the difference does not exceed 30 minutes), and it can be deduced that there is sewage inflow into the rainwater well. Otherwise, it is determined that there is rainwater stagnation in the rainwater pipe.

[0072] S4. If the liquid level changes are consistent with the residents' daily routines, then it is diagnosed that there is sewage inflow into the rainwater pipe network.

[0073] Specifically, if the number of time points corresponding to the highest liquid level With the number of monitoring data ratio The number of ratios less than the first preset threshold exceeds the first preset number, and the first variance of the time point corresponding to the highest liquid level. When the number of variances less than the second preset threshold exceeds the second preset number, and the first average time point of the highest liquid level... Second variance When the number of second variances less than the third preset threshold exceeds the third preset number, it is determined that the liquid level change is consistent with the residents' daily life pattern, and it is diagnosed that there is sewage inflow into the rainwater pipe network.

[0074] In one embodiment of the present invention, if the ratio Greater than or equal to a first preset threshold, or a first variance If the value is greater than or equal to the second preset threshold, then the corresponding time period is diagnosed as rainwater retention in the rainwater pipe network.

[0075] Specifically, in addition to performing an overall analysis of the data for day I, it is also possible to analyze the changes in liquid level within a certain period of day I, such as the number of time points corresponding to the highest liquid level within a certain period. With the number of monitoring data ratio If the ratio is greater than or equal to a first preset threshold (which can be 30%), it indicates that the liquid level in the rainwater well does not exhibit a clear upward or downward trend, meaning it does not show a morning, noon, or evening peak sewage discharge pattern. Therefore, it is determined that rainwater stagnation exists in the rainwater pipe during the corresponding time period. The first variance of the highest liquid level is less than the first preset threshold. If the value is greater than or equal to the second preset threshold, which can be 225, it indicates that the multiple time points corresponding to the highest liquid level in a certain period are relatively dispersed (greater than 30 minutes), the liquid level fluctuates repeatedly, and there is no pattern of domestic sewage discharge during morning, noon and evening peaks. Similarly, it is determined that there is rainwater stagnation in the rainwater pipe during the corresponding time period.

[0076] In one embodiment of the present invention, if the ratio The number of ratios less than a first preset threshold does not exceed a first preset number, or, the first variance The number of variances less than the second preset threshold does not exceed the second preset number, or the second variance... If the number of second variances less than the third preset threshold does not exceed the third preset number, then the rainwater pipe network is diagnosed as having rainwater retention.

[0077] Specifically, a comprehensive analysis of the data for day I is performed, considering the number of time points corresponding to the highest liquid level. With the number of monitoring data ratio The number of ratios less than a first preset threshold does not exceed a first preset number, or, the first variance If the number of variances less than the second preset threshold does not exceed the second preset number, or if the number of variances less than the third preset threshold does not exceed the third preset number, then rainwater retention in the rainwater pipe network is diagnosed in the data of day I.

[0078] Figure 6 This is a flowchart of rainwater pipe network level analysis according to an embodiment of the present invention.

[0079] like Figure 6As shown, if there is no high liquid level in the storm drain network on sunny days, it indicates that the storm drain network is operating normally. If there is a high liquid level in the storm drain network on sunny days, it may indicate that there is rainwater retention or sewage inflow into the storm drain network. It is necessary to determine whether the liquid level change is consistent with residents' daily routines. If it is consistent with residents' daily routines, then sewage inflow into the storm drain network is diagnosed; if it is not consistent with residents' daily routines, then rainwater retention in the storm drain network is diagnosed.

[0080] The intelligent diagnostic method for sewage inflow into stormwater drainage networks according to this invention first acquires sunny day data and deburrs the data. The average liquid level after deburring is then calculated for preliminary judgment. It is determined whether the average liquid level is greater than a preset liquid level threshold. If the average liquid level is greater than the preset threshold, the liquid level change is further assessed. If the average liquid level is less than or equal to the preset threshold, it indicates that the stormwater drainage network is operating normally. To determine whether the liquid level change conforms to residents' daily routines, the liquid level data is processed by time period, and the time point corresponding to the highest liquid level within each time period is extracted. The number of time points corresponding to the highest liquid level is considered... With the number of monitoring data ratio If the number of ratios less than a first preset threshold exceeds a first preset number, then the first variance of the time point corresponding to the highest liquid level is calculated. When the number of first variances less than a second preset threshold exceeds a second preset number, the second variance of the average of the highest liquid level time points within the same daily time period is calculated. When the number of second variances less than a third preset threshold exceeds a third preset number, sewage inflow into the stormwater pipe network is diagnosed; conversely, rainwater retention in the stormwater pipe network is diagnosed. This intelligent diagnostic method for sewage inflow into stormwater pipe networks determines whether sewage has entered based on a comparison of peak liquid level time points. It is economical and convenient, not limited by peak and valley time points, thresholds, or equipment. The analysis results are accurate and reliable, providing support for identifying and repairing defects in combined stormwater and sewage pipe networks.

[0081] The present invention also proposes a computer-readable storage medium.

[0082] In this embodiment, a computer program is stored on a computer-readable storage medium. When the computer program is executed by a processor, it implements the intelligent diagnostic method for sewage inflow into the rainwater pipe network as described above.

[0083] The present invention also proposes an electronic device.

[0084] In this embodiment, such as Figure 7 As shown, the electronic device 100 includes a memory 10 and a processor 20. The memory 10 stores a computer program. When the computer program is executed by the processor 20, it implements the intelligent diagnostic method for sewage inflow into the rainwater pipe network as described above.

[0085] The computer-readable storage medium and electronic device of this invention, through the aforementioned intelligent diagnostic method for sewage inflow into rainwater pipe networks, acquires sunny-day data, deburrs the sunny-day data, calculates the average liquid level after deburring, and makes a preliminary judgment on whether the average liquid level is greater than a preset liquid level threshold. If the average liquid level is greater than the preset liquid level threshold, the liquid level change is further judged. If the average liquid level is less than or equal to the preset liquid level threshold, it indicates that the rainwater pipe network is operating normally. To determine whether the liquid level change conforms to the residents' daily routines, the liquid level data is processed in time periods, and the time point corresponding to the highest liquid level in each time period is extracted. If the number of time points corresponding to the highest liquid level is... With the number of monitoring data ratio If the number of ratios less than a first preset threshold exceeds a first preset number, then the first variance of the time point corresponding to the highest liquid level is calculated. When the number of first variances less than a second preset threshold exceeds a second preset number, the second variance of the average of the highest liquid level time points within the same daily time period is calculated. When the number of second variances less than a third preset threshold exceeds a third preset number, sewage inflow into the stormwater pipe network is diagnosed; conversely, rainwater retention in the stormwater pipe network is diagnosed. This intelligent diagnostic method for sewage inflow into stormwater pipe networks determines whether sewage has entered based on a comparison of peak liquid level time points. It is economical and convenient, not limited by peak and valley time points, thresholds, or equipment. The analysis results are accurate and reliable, and it provides support for identifying and repairing defects in combined stormwater and sewage pipe networks.

[0086] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0087] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0088] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0089] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0090] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0091] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0092] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0093] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An intelligent diagnostic method for sewage inflow into rainwater pipe networks, characterized in that, The method includes: Acquire sunny day data, wherein the sunny day data includes the liquid level data of rainwater wells in the rainwater pipe network on sunny days; Calculate the first average liquid level based on the liquid level data, and determine whether the first average liquid level is greater than a preset liquid level threshold. If the first average liquid level is greater than the preset liquid level threshold, then the liquid level change is determined based on the liquid level data, and it is judged whether the liquid level change conforms to the residents' daily life patterns. If the liquid level changes are consistent with the residents' daily routines, then it is diagnosed that there is sewage inflow into the stormwater drainage network. The determination of whether the liquid level change conforms to the residents' daily routine includes: Take data from day I, and divide each day into J equal time periods, where I and J are positive integers greater than 1; Let the number of monitoring data points in the j-th time period on the i-th day be . The number of time points corresponding to the highest liquid level is Obtain the time point corresponding to the highest liquid level. ,in, Let x represent the x-th time point corresponding to the highest liquid level in the j-th time period of the i-th day, where 1≤i≤I, 1≤j≤J, and 1≤x≤X; Using a unit of time as 1, the time point is... Convert to numerical form and denote as ; calculate and The ratio of is denoted as ; If the ratio If the number of ratios less than the first preset threshold exceeds the first preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First variance ; Determine the first variance Is it less than the second preset threshold? If the first variance If the number of variances less than the second preset threshold exceeds the second preset number, then the numerical form of the time point corresponding to the highest liquid level is calculated. First average time point And based on the first average time point Determine whether the changes in the liquid level are consistent with the residents' daily routines; Specifically, the first average time point corresponding to the highest liquid level within J time periods of I days is obtained. Record the first average time point corresponding to the highest liquid level in the j-th time period of day I. Given the j-th data set, we obtain the J-th data set. Calculate the second variance of the J groups of data respectively. ; If the second variance If the number of second variances less than the third preset threshold exceeds the third preset number, then the liquid level change is determined to conform to the residents' daily routine.

2. The intelligent diagnostic method for sewage inflow into rainwater pipe networks according to claim 1, characterized in that, The acquisition of sunny day data includes: Obtain rainfall data and record the rainfall at time point t as... ; Determine within a continuous preset time period Data acquired during periods with a time value of zero represents sunny days.

3. The intelligent diagnostic method for sewage inflow into rainwater pipe networks according to claim 2, characterized in that, The method further includes: deburring the liquid level data, wherein the deburring of the liquid level data includes: Remove null values ​​from the liquid level data; Calculate the second liquid level average and standard deviation based on the liquid level data after removing null values; The liquid level data is deburred based on the preset accuracy, the second average liquid level value, and the standard deviation.

4. The intelligent diagnostic method for sewage inflow into rainwater pipe networks according to claim 1, characterized in that, If the first average liquid level is less than or equal to the preset liquid level threshold, the rainwater pipe is diagnosed as operating normally.

5. The intelligent diagnostic method for sewage inflow into rainwater pipe networks according to claim 1, characterized in that, If the ratio Greater than or equal to the first preset threshold, or, the first variance If the value is greater than or equal to the second preset threshold, then it is diagnosed that there is rainwater retention in the rainwater pipe network during the corresponding time period. If the ratio The number of ratios less than the first preset threshold does not exceed the first preset number, or the first variance The number of variances less than the second preset threshold does not exceed the second preset number, or the second variance... If the number of second variances less than the third preset threshold does not exceed the third preset number, then the rainwater pipe network is diagnosed as having rainwater retention.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent diagnostic method for sewage inflow into the rainwater pipe network as described in any one of claims 1-5.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent diagnostic method for sewage inflow into the rainwater pipe network as described in any one of claims 1-5.

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