A pipe network misconnection positioning method and system, a storage medium and an electronic device

Abstract

The application provides a pipe network misconnection positioning method and system, a storage medium and an electronic device. The method comprises the following steps: acquiring basic information of a research area; based on the basic information, a fluorescence mass balance model is established, and the rainwater misconnection amount and the underground seepage water amount of each sub-area in the research area are calculated; based on the rainwater misconnection amount and the underground seepage water amount of each sub-area, the risk level of each sub-area is divided, and according to the risk level and the generalized model, the problem of each sub-area is analyzed in turn, and the optimal internal flow distribution scheme of the detection point of the pipe network in each sub-area is acquired; based on the optimal internal flow distribution scheme, the accurate positioning of the pipe network underground water infiltration point and the rainwater misconnection point is realized. The application constructs an inversion optimization model through the fluorescence characteristics of different pollution sources, the Monte Carlo algorithm and the genetic algorithm core tool, so that the accurate positioning of the municipal pipe network problem point is realized while the cost is low.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of municipal pipe network engineering, and in particular to a pipe network mixed connection positioning method and system, a storage medium and an electronic device. BACKGROUND

[0002] At present, due to the fact that the construction of most municipal pipe networks in China is not synchronized with the urbanization process, the urban drainage pipelines basically have different degrees of damage and mixed connection, which leads to the invasion of external water, especially groundwater infiltration and rainwater and sewage mixed connection. In the municipal pipe network with infiltration points and mixed connection points, the influx of a large amount of external water will cause the municipal pipe network to work overload, reduce the treatment efficiency of the sewage plant, and seriously affect the health of urban water bodies. In view of the problems of low sewage treatment efficiency and poor urban water environment quality caused by groundwater infiltration and rainwater and sewage mixed connection in the municipal pipe network, establishing a precise positioning technology for the groundwater infiltration points and rainwater and sewage mixed connection points in the municipal pipe network has become a great demand in the maintenance work of the municipal pipe network in China.

[0003] The effective investigation of the municipal pipe network lies in whether the precise positioning of the problem points can be achieved. At present, the most widely used method for investigating groundwater infiltration points and rainwater and sewage mixed connection points in China is the physical exploration method, mainly including CCTV detection method, smoke test method, sonar method, etc. Among them, the CCTV detection method directly presents the internal situation of the pipeline through the camera probe placed in the target pipeline, and the result has the characteristics of authenticity and intuitiveness, but it has many usage restrictions, high detection cost, and is affected by the amount of sludge accumulation. The smoke test method has the advantages of low cost and few restrictions compared with the CCTV detection method and the sonar method, and its principle is to blow inert gas into the pipeline to locate and judge the problem points through the discharge position of the gas, but the result has low accuracy and has great uncertainty factors such as pipeline fullness, pipeline structure, flow rate, etc.

[0004] It is a great demand in the maintenance of municipal pipe network in China to construct a positioning method for underground water infiltration points and misconnection points of municipal pipe network with high accuracy, wide application range and low investment cost. Dissolved organic matter (DOM) is the main pollutant category in multi-source water bodies in municipal pipe network. By using three-dimensional fluorescence spectroscopy to characterize DOM, the fluorescence fingerprints of different water bodies are presented, and thus the source identification of external water in municipal pipe network is realized. Now, three-dimensional fluorescence spectroscopy has been widely used in water pollution source tracing field. For example, the patent application (CN115950864A) uses three-dimensional fluorescence spectroscopy to trace the water pollution in the detection area. The whole process is short in time, high in accuracy and reliable in result, and solves the problems of long process cycle and secondary pollution in traditional water quality index tracing process. However, this patent only identifies the pollution source and cannot accurately locate the position of the pollution source. With the continuous maturity of computer technology, some researchers have applied artificial intelligence to the field of pollution source tracing. Zhang et al. invented a pollution source tracing system based on artificial intelligence (CN113947033A). By using the method of machine learning decision tree, the automatic prediction and classification analysis of drainage pipe network are realized, and the factories discharging pollutants are accurately identified. However, this method is not suitable for the positioning of problem points in municipal pipe network.

[0005] Therefore, in view of the above technical problems existing in the prior art, there is an urgent need for a pipe network misconnection positioning method with low cost, simplicity, speed, and high accuracy to accurately locate the problem points of municipal pipe network. SUMMARY

[0006] The purpose of the present application is to provide a pipe network misconnection positioning method, system, storage medium and electronic equipment, which can solve the problems of high investment cost and inaccurate positioning of problem points in municipal pipe network in the prior art.

[0007] In a first aspect, the present application provides a pipe network misconnection positioning method, which comprises: acquiring basic information of a research area; based on the basic information, establishing a fluorescence mass balance model and calculating the rainwater misconnection amount and underground infiltration water amount of each sub-area in the research area; dividing the risk levels of each sub-area based on the rainwater misconnection amount and underground infiltration water amount of each sub-area, and sequentially analyzing the problem points of each sub-area according to the risk levels and generalized model, and acquiring the optimal internal flow distribution scheme of the detection points of the pipe network in each sub-area; and based on the optimal internal flow distribution scheme, accurately positioning the underground water infiltration points and rainwater misconnection points of the pipe network.

[0008] In an implementation form of the first aspect, the obtaining the basic information of the study area comprises: obtaining three-dimensional fluorescence spectrum of each water sample in the study area; extracting three-dimensional fluorescence features of the three-dimensional fluorescence spectrum of each water sample to form a three-dimensional fluorescence database of each water sample; dividing the study area into sub-regions, and taking connecting points between each of the sub-regions as detection nodes of water quality and quantity of the study area; and obtaining water flow and three-dimensional fluorescence data detected by each detection node, and water flow and three-dimensional fluorescence data detected by detection points in each sub-region.

[0009] In an implementation form of the first aspect, the rainwater mixing error refers to mixing of domestic sewage and rainwater, and the establishing the fluorescence mass balance model based on the basic information and calculating the rainwater mixing error quantity and underground seepage water quantity of each sub-region in the study area comprises: extracting fluorescence intensity corresponding to a feature peak region of each water sample in the three-dimensional fluorescence database; the water samples comprise domestic sewage, rainwater and underground water; establishing a fluorescence mass balance model based on the fluorescence intensity; statistically analyzing probability distribution of the fluorescence intensity of each water sample based on the three-dimensional fluorescence database of each water sample, and fitting the statistical result of the probability distribution by using a Monte Carlo algorithm; randomly generating fluorescence intensity values of each water sample in the probability distribution of each water sample that meets fitting accuracy after fitting, and inputting the generated fluorescence intensity values of each water sample into the fluorescence mass balance model to obtain corresponding flow of each water sample, and storing the corresponding flow of each water sample; repeatedly obtaining corresponding flow of multiple groups of water samples, and when the number of obtained corresponding flow groups reaches a preset value, statistically analyzing the corresponding flow of each group of water samples to obtain average values of water flow of each water sample; the average values of each water sample comprise average values of domestic sewage flow, rainwater flow and underground water flow.

[0010] In an implementation form of the first aspect, the fluorescence mass balance model is expressed as:

[0011] D 末 W 末 = D 生 W 生 + D 雨 W 雨 + D 地 W 地

[0012] wherein, D 末 , D 生 , D 雨 , D 地 respectively represent fluorescence intensity of water quality at the end of a pipe network in a sub-region, fluorescence intensity of domestic sewage, fluorescence intensity of rainwater, and fluorescence intensity of underground water, W 末 , W 生 , W 雨 , W地 respectively represent the real-time measured flow at the end of the pipe network in the sub-region, the flow of domestic sewage in the pipe, the flow of mixed and wrongly connected rainwater, and the flow of groundwater infiltration;

[0013] The constraint condition of the fluorescence mass balance model is:

[0014] W 末 = W 生 + W 雨 + W 地

[0015] W 末 , W 生 , W 雨 , W 地 respectively represent the real-time measured flow at the end of the pipe network in the sub-region, the flow of domestic sewage in the pipe, the flow of mixed and wrongly connected rainwater, and the flow of groundwater infiltration.

[0016] In an implementation form of the first aspect, the condition of fitting accuracy is that:

[0017]

[0018] D 末 , D 生 , D 雨 , D 地 respectively represent the fluorescence intensity of the water quality at the end of the pipe network in the sub-region, the fluorescence intensity of the domestic sewage sample, the fluorescence intensity of the rainwater sample, and the fluorescence intensity of the groundwater sample, W 末 , W 生 , W 雨 , W 地 respectively represent the real-time measured flow at the end of the pipe network in the sub-region, the flow of domestic sewage in the pipe, the flow of mixed and wrongly connected rainwater, and the flow of groundwater infiltration.

[0019] In an implementation form of the first aspect, the problem pinpointing analysis of each sub-region is performed in the order from high to low according to the risk level; and the problem pinpointing analysis of each sub-region according to the risk level and the generalized model and the acquisition of the optimal internal flow distribution scheme of the detection point in each sub-region include: establishing a generalized model of the pipe network system of the research region based on the geographical features and the pipe properties of the research region; performing the problem pinpointing analysis of each sub-region based on the generalized model and the genetic algorithm to acquire the internal flow distribution scheme corresponding to the sub-region; and acquiring the internal flow distribution scheme corresponding to the sub-region at the time when the maximum number of iterations and / or the error of the objective function are the smallest as the optimal internal flow distribution scheme of the detection point in the sub-region.

[0020] In an implementation form of the first aspect, the objective function is represented as:

[0021]

[0022] wherein, w 模拟，d is the simulated value at the detection point of the pipe network in the sub-region, w 实测，d is the measured value at the same detection point, d represents the current detection point number, and h represents the total number of detection points.

[0023] Further, the maximum number of iterations is determined according to the time period of simulated detection of the detection point of the pipe network in the sub-region and the time interval of two simulated detections.

[0024] In a second aspect, the present application provides a pipe network misconnection positioning system, which comprises: an acquisition module for acquiring basic information of a research area; a first data processing module for establishing a fluorescence mass balance model based on the basic information, and calculating rainwater misconnection amounts and underground infiltration water amounts of each sub-region in the research area; a second data processing module for dividing each sub-region into a risk level based on the rainwater misconnection amount and the underground infiltration water amount of each sub-region, and sequentially performing problem point analysis on each sub-region according to the risk level and a generalized model, and acquiring an optimal internal flow distribution scheme of a detection point of a pipe network in each sub-region; and an output module for realizing accurate positioning of a pipe network underground water infiltration point and rainwater misconnection point based on the optimal internal flow distribution scheme.

[0025] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by an electronic device to implement the pipe network misconnection positioning method of any one of the first aspect of the present application.

[0026] In a fourth aspect, the present application provides an electronic device comprising: a processor and a memory; the memory is used to store a computer program; and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the pipe network misconnection positioning method of any one of the first aspect of the present application.

[0027] As described above, the pipe network misconnection positioning method, system, storage medium and electronic device of the present application have the following advantages

[0028] Beneficial effects:

[0029] First, the present application constructs an inversion optimization model by using the fluorescence characteristics of different pollution sources, the Monte Carlo algorithm and the genetic algorithm core tool, so as to realize accurate positioning of the problem points of the municipal pipe network at a low cost.

[0030] Second, the present application has no influence on the normal operation of the target pipe network during the research process; the traceability result can be obtained in the model by acquiring the basic data, and the present application has the characteristics of small workload, high efficiency and simplicity; the positioning result is accurate and has high reliability.

[0031] Thirdly, the application is low in manpower and material resources, and economical and practical. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 A flow chart showing the pipe network misconnection positioning method described in the application in an embodiment.

[0033] Figure 2 A flow chart showing the pipe network misconnection positioning method described in the application in an embodiment.

[0034] Figure 3 A schematic diagram showing the sub-region division and detection node of the research region described in the application in an embodiment.

[0035] Figure 4 A flow chart showing the pipe network misconnection positioning method described in the application in an embodiment.

[0036] Figure 5 A schematic diagram showing the fluorescence characteristic wavelength data of each water sample described in the application in an embodiment.

[0037] Figure 6 A flow chart showing the pipe network misconnection positioning method described in the application in an embodiment.

[0038] Figure 7 A flow chart showing the website misconnection positioning method described in the application in an embodiment.

[0039] Figure 8 A structural schematic diagram of the pipe network misconnection positioning system described in the application in an embodiment.

[0040] Figure 9 A structural schematic diagram of the electronic device described in the embodiment of the application.

[0041] ELEMENT NUMBER EXPLANATION

[0042] 100 pipe network misconnection positioning system

[0043] 10 acquisition module

[0044] 20 first data processing module

[0045] 30 second data processing module

[0046] 40 output module

[0047] 91 processing unit

[0048] 92 memory

[0049] 921 random access memory

[0050] 922 cache memory

[0051] 923 storage system

[0052] 924 program / utility

[0053] 9251 program module

[0054] 93 bus

[0055] 94 input / output interface

[0056] 951 network adapter

[0057] S1-S4 steps

[0058] S11-S14 steps

[0059] S21-S25 steps

[0060] S31-S33 steps DETAILED DESCRIPTION

[0061] The present application is herein described, by way of example only, with the

[0062] It is to be understood that the drawings are designed solely for the purpose of illustration and are not intended to limit the scope of the present application in any way. Further, it should be understood that all features that are common to each of the figures are designated with the same numerals.

[0063] In addition, the terms "first", "second", and the like, as used in the description, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the descriptive terms "first", "second", etc., are to be interpreted, by those skilled in the art, as a structural or positional description and not by a chronological or sequential description. Further, the terms "comprise", "comprising", "comprises", "include", "including", "includes", "contain", "containing", "contains", "characterized by", "characterized into", "have", "having", "has", "may have", "may have", "may have" and the like are used broadly and encompass the terms "consisting of, "consisting essentially of, and "consisting of.

[0064] The following embodiments of the application provide a pipe network misconnection positioning method, system, storage medium and electronic device, which can perform three-dimensional fluorescence characterization on specific DOM of different pollution sources, and then form fluorescence fingerprints of different pollution sources, to calculate the water quantity of misconnected rainwater and infiltrated underground water in the research area by using a fluorescence mass balance model based on the Monte Carlo algorithm. Wherein, the total regional groundwater infiltration amount and the misconnected rainwater amount are used as boundary conditions for the inversion optimization model, and the genetic algorithm is used for pipe network node flow distribution, and the optimal distribution scheme is iterated to realize accurate positioning of municipal pipe network problem points.

[0065] As shown in Figure 1 The embodiment provides a pipe network misconnection positioning method, which comprises steps S1 to S4.

[0066] Step S1, obtaining basic information of a research area.

[0067] As shown in Figure 2 In an embodiment, obtaining basic information of a research area comprises steps S11 to S14.

[0068] Step S11, obtaining three-dimensional fluorescence spectrum of each water sample in the research area. The water sample includes domestic sewage, rainwater and groundwater.

[0069] It should be noted that the three-dimensional fluorescence spectrum (EEM) is a spectrum obtained by projecting the fluorescence intensity in the form of contour lines on the plane with excitation light wavelength and emission light wavelength as longitudinal and transverse coordinates. The image is intuitive and contains rich information. The three-dimensional fluorescence spectrum (EEM) can obtain excitation and emission wavelength information at the same time, and is different due to different types and contents of organic matter, and has the characteristics of one-to-one correspondence with water sample (solution). Just like the uniqueness of human fingerprints, it is called the "fluorescence fingerprint" of water.

[0070] Step S12, extracting three-dimensional fluorescence characteristics of the three-dimensional fluorescence spectrum of each water sample to form a three-dimensional fluorescence database of each water sample.

[0071] Specifically, the portable pipe flow meter is used to detect the well flow, the water sample of the detection well is collected by the sampler, 500ml of water is collected each time, and the water is stored in a 4℃ environment. Bring back to the laboratory in time for water quality determination, and the determination indexes include total nitrogen, total phosphorus, hardness, conductivity, COD five conventional indexes and determination of dissolved organic matter (DOM).

[0072] Among them, tryptophan (Ex / Em: 280nm / 350nm) is added as the fluorescence fingerprint of new domestic sewage; tyrosine (Ex / Em: 280nm / 325nm) is added as the fluorescence fingerprint of residual domestic sewage; artificial source humus (Ex / Em: 250nm / 388nm) is added as the fluorescence fingerprint of rainwater; and microbial source humus (Ex / Em: 325nm / 375nm) is added as the fluorescence fingerprint of groundwater.

[0073] Step S13, sub-regions are divided in the research region, and the connecting points between the sub-regions are taken as the detection nodes of the water quality and water quantity of the research region.

[0074] It should be noted that the detection nodes include the detection nodes arranged at the connecting points between the regions and the in-pipe detection nodes arranged in the sub-regions.

[0075] As shown in FIG. 1, it is a schematic diagram of the sub-region division and detection nodes of the research region. Figure 3

[0076] In this embodiment, the research region is divided into four sub-regions, two detection nodes are arranged at the connecting point between the sub-region 1 and the sub-region 2, which are respectively the detection node of the sub-region 1 and the detection node of the sub-region 2, and two detection nodes are arranged at the connecting point between the sub-region 3 and the sub-region 4, which are respectively the detection node of the sub-region 3 and the detection node of the sub-region 4.

[0077] It should be noted that the present application is applied to municipal pipe network engineering, and in addition to the detection nodes arranged at the connecting points, in-pipe detection wells are uniformly arranged in the pipes in each sub-region, which are the in-pipe detection nodes of each sub-region.

[0078] Step S14, the water flow and three-dimensional fluorescence data detected by each detection node and the water flow and three-dimensional fluorescence data detected by the detection nodes in each sub-region are obtained.

[0079] Specifically, in this embodiment, each detection node is sampled and flow detected every 30 minutes, 15 times are collected per day, and one time is collected in dry days and rainy days, and the collection in dry days needs to meet no rainfall in the previous 14 days.

[0080] Specifically, in this embodiment, in order to fully meet the sample requirements, each detection node is sampled and flow detected every 30 minutes, 15 times are collected per day, and one time is collected in dry days and rainy days, and the collection in dry days needs to meet no rainfall in the previous 14 days.

[0081] Step S2, based on the basic information, a fluorescence mass balance model is established, and the rainwater mixing error quantity and the underground seepage water quantity of each sub-region in the research region are calculated and obtained.

[0082] ​The rainwater mixed connection refers to the mixing of domestic sewage and rainwater.

[0083] Specifically, the rainwater mixed connection amount and the groundwater infiltration amount of each sub-region are calculated by establishing a fluorescence mass balance model based on the Monte Carlo algorithm. The introduction of the Monte Carlo algorithm greatly ensures the accuracy of the total mixed connection amount and the groundwater infiltration amount calculated for each sub-region.

[0084] As shown in Figure 4 , obtaining the rainwater mixed connection amount and the groundwater infiltration amount of each sub-region in the study area includes steps S21 to S25.

[0085] Step S21, extracting the fluorescence intensity corresponding to the characteristic peak region of each water sample in the three-dimensional fluorescence database; the water sample includes domestic sewage, rainwater and groundwater.

[0086] As shown in Figure 5 , a schematic diagram of the fluorescence characteristic wavelength of each water sample.

[0087] Step S22, establishing a fluorescence mass balance model based on the fluorescence intensity.

[0088] Specifically, the formula of the fluorescence mass balance model is represented as:

[0089] D 末 W 末 = D 生 W 生 + D 雨 W 雨 + D 地 W 地

[0090] The constraint condition of the fluorescence mass balance model is:

[0091] D 末 W 生 = W 雨 + W 地

[0092] The condition for fitting accuracy is:

[0093]

[0094] wherein D 末 , D 生 , D 雨 , D 地 represent the fluorescence intensity of the water quality at the end of the pipe network in the sub-region, the fluorescence intensity of the domestic sewage water sample, the fluorescence intensity of the rainwater water sample, and the fluorescence intensity of the groundwater water sample, respectively, W 末 , W 生 , W 雨 , W地 respectively represent the real-time measured flow at the end of the pipe network in the sub-region, the domestic sewage flow in the pipe, the mixed and wrongly connected rainwater flow, and the groundwater infiltration flow.

[0095] Specifically, the real-time measured flow at the end of the pipe network in the sub-region, the domestic sewage flow in the pipe, the mixed and wrongly connected rainwater flow, and the groundwater infiltration flow are measured by the detection nodes arranged at the connection points of the sub-regions.

[0096] Step S23, according to the three-dimensional fluorescence database of each water sample, the probability distribution of the fluorescence intensity of each water sample is statistically analyzed, and the Monte Carlo algorithm is used to fit the probability distribution statistical result.

[0097] Specifically, the probability distribution of the fluorescence intensity of the domestic sewage, rainwater and groundwater samples is statistically analyzed by the three-dimensional fluorescence database, and the Monte Carlo algorithm is used to fit the probability distribution statistical result.

[0098] Step S24, in the probability distribution of each water sample after fitting that meets the fitting accuracy, the fluorescence intensity value of each water sample is randomly generated, and the generated fluorescence intensity value of each water sample is input into the fluorescence mass balance model to obtain the corresponding flow of each water sample, and the corresponding flow of each water sample is stored.

[0099] It should be noted that the mixed and wrongly connected rainwater amount includes the domestic sewage flow and the rainwater flow.

[0100] Specifically, in the probability distribution of each water sample, the fluorescence intensity value of each water sample is randomly generated and substituted into the fluorescence mass balance model to determine the flow of domestic sewage, rainwater and groundwater in the municipal pipe network, and when the accuracy of the obtained flow of domestic sewage, rainwater and groundwater is less than 0.05, it is considered that the obtained result is accurate and reliable, and the obtained flow of domestic sewage, rainwater and groundwater is stored in the reliable result library.

[0101] Step S25, repeatedly obtaining the corresponding flow of multiple groups of water samples, when the number of obtained corresponding flow groups reaches a preset value, data statistical analysis is performed on the corresponding flow of each group of water samples to obtain the average value of the water flow of each water sample; the average value of each water sample includes the average value of the domestic sewage flow, the average value of the rainwater flow and the average value of the groundwater flow.

[0102] Specifically, the fluorescence intensity value of each water sample is randomly generated and substituted into the fluorescence mass balance model to obtain the corresponding flow of domestic sewage, rainwater and groundwater, and the accurate and reliable data obtained is stored in the reliable result library, until the number of data stored in the reliable result library reaches a preset value, the data acquisition is stopped.

[0103] The data of the flow of domestic sewage, rainwater and groundwater stored in the trusted result library are statistically analyzed to obtain the average values of the flow of domestic sewage, rainwater and groundwater.

[0104] Preferably, the preset value is set to 10000, that is, when the flow of domestic sewage, rainwater and groundwater stored in the trusted result library reaches 10000 groups, the fluorescence intensity values of each water sample are stopped from being randomly generated, and the stored data is statistically analyzed.

[0105] It should be noted that the above-mentioned preset value is not a unique value, and can be appropriately adjusted according to actual needs.

[0106] In the embodiment, when the Monte Carlo algorithm is not introduced, the maximum relative error of the result calculated by the fluorescence mass balance model is 42.6%, while the maximum relative error of the mixed connection analysis result of the fluorescence mass balance model based on the Monte Carlo algorithm is only 6.3%, which is 36.3% higher in accuracy. The comparison results show that the Monte Carlo algorithm can greatly reduce the uncertainty factors such as the error of the fluorescence intensity value of the fluorescence fingerprint of different pollution sources and the difference of the mixed connection point source space time fluorescence intensity, so that the calculation result is more realistic and has reliability.

[0107] Step S3, based on the rainwater mixing connection amount and the underground infiltration water amount of each sub-region, the risk level of each sub-region is divided, and according to the risk level and the generalization model, the problem is analyzed in each sub-region in turn, and the optimal internal flow distribution scheme of the detection point of the pipe network in each sub-region is obtained.

[0108] Specifically, the problem is analyzed in each sub-region in order from high to low risk level.

[0109] Specifically, according to the average value of the domestic sewage flow, the average value of the rainwater flow and the average value of the groundwater flow of each region obtained in step S2, the risk level of each sub-region is divided, and the greater the flow average value, the higher the risk level of the sub-region.

[0110] As shown in Figure 6 The optimal internal flow distribution scheme of the detection point of the pipe network in each sub-region includes steps S31 to S33.

[0111] Step S31, based on the geographical features and pipe properties of the study area, a generalization model of the pipe network system of the study area is established.

[0112] Specifically, the generalization model of the pipe network system of the study area is established based on the stormwater management model (SWMM), pipe properties and data information such as catchment area land use.

[0113] It should be noted that the SWMM (storm water management model) is a dynamic precipitation-runoff simulation model, which is mainly used for simulating a single precipitation event or long-term water quantity and water quality simulation of a city.

[0114] Specifically, in an embodiment, the municipal pipe network pipe section water balance formula is set as:

[0115] w x，总 = w x，生 + w x，雨 + w x，地

[0116]

[0117]

[0118]

[0119] wherein w x，总 , w x，生 , w x，雨 , w x，地 respectively represent the total flow, domestic sewage flow, mixed wrong rainwater flow and groundwater infiltration flow of the pipe section x, and W 生 , W 雨 , W 地 respectively represent the calculated domestic sewage flow, mixed wrong rainwater flow and groundwater infiltration flow in the research area.

[0120] Step S32, based on the generalized model and the genetic algorithm, the problem is fixed-point analyzed for each of the sub-regions to obtain the corresponding internal flow distribution scheme of the sub-regions.

[0121] Specifically, in this embodiment, each parameter of the genetic algorithm is set as follows: the initial population size is set to 80, the population size is set to 150, the crossover probability is set to 0.2, the mutation probability is set to 0.003, and the iteration number is set to 10,000 times.

[0122] It should be noted that the above setting of each parameter of the genetic algorithm is not unique, and can be adjusted according to actual application.

[0123] Step S33, the internal flow distribution scheme of the sub-regions obtained when the maximum iteration number or the minimum target function error is reached is taken as the optimal internal flow distribution scheme of the detection points in the sub-regions.

[0124] Specifically, the target function is expressed as:

[0125]

[0126] wherein w模拟，d w represents the simulated value at the detection point of the pipeline network within the sub-region. 实测，d The measured value at the same detection point, d represents the current number of detection points, h represents the total number of detection points, and A is the objective function error.

[0127] The maximum number of iterations is determined based on the time period for simulated testing of the pipeline network detection points within the sub-region and the time interval between two simulated tests. Preferably, in this embodiment, the time period for simulated testing of the pipeline network detection points within the sub-region is set to 24 hours, and the time interval between two simulated tests is 30 minutes.

[0128] It should be noted that, in this embodiment, the selection of measured nodes in the objective function should be representative, with at least four measured nodes selected, and the positions of the simulated nodes in the objective function should correspond to the measured nodes. The objective function value in the model is the calculated average of each selected node and the simulated node.

[0129] Step S4: Based on the optimal internal flow distribution scheme, accurately locate the groundwater infiltration point and the rainwater mixing connection point of the pipeline network.

[0130] Specifically, a density analysis of problem points is conducted on the daily average mixed rainwater flow and groundwater infiltration of each node obtained from the optimal flow allocation scheme, so as to achieve accurate location of groundwater infiltration points and rainwater mixed connection points.

[0131] In this embodiment, the workload of closed-circuit television (CCTV) inspection of pipelines can be reduced by more than 90%, and compared with conventional physical detection methods (such as CCTV, ground-penetrating radar (GPR), pipeline scanning assessment (SSET), and sonar imaging), the workload and capital investment can be reduced by more than 60%, and it has the advantages of accurate results and wide applicability.

[0132] It should be noted that the protection scope of the pipeline misconnection location method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the protection scope of this application.

[0133] like Figure 7 As shown, in one embodiment, the pipeline misconnection location method of this application includes: obtaining basic information within the study area, calculating the total illegal water discharge within the study area based on the fluorescence mass balance model of the Monta Carlo algorithm, and constructing an inversion optimization model based on a genetic algorithm with interaction between Pyswmm and SWMM to further achieve accurate location of groundwater infiltration points and rainwater misconnection points.

[0134] It should be noted that the total amount of illegal water discharge within the study area includes the amount of rainwater mixed with other water in each sub-region and the amount of groundwater seepage.

[0135] Please refer to Figure 8 , which shows a structural schematic diagram of the pipe network misconnection positioning system described in the embodiments of the present application.

[0136] As Figure 8 shown, the pipe network misconnection positioning system 100 comprises an acquisition module 10, a first data processing module 20, a second data processing module 30 and an output module 40.

[0137] The acquisition module 10 is configured to acquire basic information of a study area.

[0138] The first data processing module 20 is configured to establish a fluorescent mass balance model based on the basic information, and calculate rainwater misconnection amounts and underground seepage water amounts of each sub-area in the study area.

[0139] The second data processing module 30 is configured to divide each sub-area into a risk level based on the rainwater misconnection amount and the underground seepage water amount of each sub-area, and sequentially perform problem pinpointing analysis on each sub-area according to the risk level and a generalized model, and acquire an optimal internal flow distribution scheme of a detection point of a pipe network in each sub-area.

[0140] The output module 40 is configured to realize accurate positioning of a pipe network underground water infiltration point and rainwater misconnection point based on the optimal internal flow distribution scheme.

[0141] It should be noted that the functions of each module in the system of the embodiments correspond to the method of the present application one by one, and thus will not be described here.

[0142] It should be noted that the division of each module above is only a logical function division, and all or part of them can be integrated into one physical entity, or can be physically separated. These modules can all be implemented in the form of software called by a processing element; they can all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the x module can be a separately established processing element, or it can be integrated into a chip of the above device, in addition, it can also be stored in the form of program code in the memory of the above device, and the function of the above x module can be called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be independently implemented. The processing element described here can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of hardware or the instruction of software in the processing element.

[0143] For example, the above modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), or one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of a processing element scheduling code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can invoke code. For another example, the modules can be integrated together to implement in the form of a system-on-a-chip (SOC).

[0144] The embodiments of the present application further provide a computer readable storage medium. Those skilled in the art can understand that all or part of the steps of the methods described above can be instructed by a processor through a program, and the program can be stored in a computer readable storage medium. The storage medium is a non-transitory medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, etc. integrated with one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0145] The embodiments of the present application further provide an electronic device, comprising a processor and a memory.

[0146] Specifically, the memory is configured to store a computer program; and the memory includes a ROM, a RAM, a disk, a U disk, a memory card, or an optical disc, and various media that can store program codes.

[0147] The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the pipe network misconnection positioning method described above.

[0148] Preferably, the processor can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; also can be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0149] As shown in Figure 9 , the electronic device of the present application is in the form of a general computing device. The components of the electronic device can include, but are not limited to, one or more processors or processing units 91, a memory 92, a bus 93 that connects different system components, including the memory 92 and the processing unit 91.

[0150] The bus 93 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration bus, a processor or local bus using any of a variety of bus architectures. By way of example, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0151] The electronic device typically includes a variety of computer system readable media. These media can be any available media that is accessible by the electronic device and includes both volatile and non-volatile media, removable and non-removable media.

[0152] The memory 92 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 921 and / or cache memory 922. The electronic device can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 923 can be used for reading from and writing to non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 9 (not shown), commonly referred to as a "hard disk drive", for reading from and writing to non-removable, non-volatile magnetic media (e.g., a platter). Although not specifically shown, such computer system typically can further include other removable / non-removable, volatile / non-volatile computer system storage media including, but not limited to, a magnetic floppy disk drive (e.g., to read from and / or write to a removable magnetic floppy disk), a magnetic hard disk drive, or solid state drive (SSD) with suitable non-volatile storage for reading from and writing to non-removable, non-volatile magnetic media (e.g., a platter). Figure 9A disk drive, a floppy disk drive, a CD-ROM drive, a DVD-ROM drive, or other removable media drive, can be provided for reading from and writing to a removable n onvolatile magnetic disk (e.g., a "floppy disk"), and to a removable nonvolatile optical disk (e.g., a CD- ROM, a DVD-ROM, or other optical media). In these instances, each drive can be connected to the bus 93 by one or more data media interfaces. The memory 92 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

[0153] Program / utility 924 having a set (at least one) of program modules 9251 can be stored in memory 92 by way of example, and not limitation, including an operating system, one or more application programs, other program modules, and program data, each or some combination thereof, can include implementation of a networking environment. Program modules 9251 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0154] The electronic device can also communicate with one or more external devices such as a keyboard or a pointing device, a display, etc. through I / O interface 94 and with one or more devices that enable a user to interact with the electronic device and / or one or more devices that enable the electronic device to communicate with one or more other computing devices. Such communication can occur via I / O interface 94. Additionally, the electronic device can communicate with one or more networks such as a local area network (LAN), a wide area network (WAN), and / or the Internet through network adapter 95. As Figure 9 illustrated, network adapter 95 can communicate with the other components of the electronic device through bus 93. It should be understood that although not shown, other hardware and / or software components could be used in conjunction with the electronic device. These include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0155] The descriptions of the flow or structure corresponding to each of the above figures are each focused on a certain aspect, and the parts not described in detail in a certain flow or structure can be referred to the relevant descriptions of other flows or structures.

[0156] In summary, first, the present application constructs an inversion optimization model by the fluorescence characteristics of different pollution sources, Monte Carlo algorithm and genetic algorithm core tools, realizes the accurate positioning of the problem point of the municipal pipe network while the cost is low. Second, the present application has no influence on the normal operation of the target pipe network in the research process; the tracing result can be obtained in the model by obtaining the basic data, and the present application has the characteristics of small workload, high efficiency and simplicity; the positioning result is accurate and has high reliability. Third, the present application has low investment in manpower and material resources, and is economical and practical. Therefore, the present application effectively overcomes various shortcomings in the prior art and has a high industrial utilization value.

[0157] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought disclosed by the present application should be covered by the claims of the present application.

Claims

1. A method for locating misconnected pipe networks, characterized in that, The method includes: Obtain basic information about the study area; Based on the aforementioned basic information, a fluorescence mass balance model was established, and the amount of rainwater mixing and groundwater infiltration in each sub-region of the study area was calculated and obtained. Based on the amount of rainwater mixing and groundwater infiltration in each sub-region, the risk level of each sub-region is classified, and according to the risk level and generalization model, the problem point analysis is carried out in each sub-region in turn, and the optimal internal flow distribution scheme of the pipeline network detection point in each sub-region is obtained. Based on the optimal internal flow distribution scheme, the precise location of groundwater infiltration points and rainwater mixing points in the pipeline network can be achieved. The acquisition of basic information about the study area includes: acquiring three-dimensional fluorescence spectra of each water sample within the study area; extracting three-dimensional fluorescence features from the three-dimensional fluorescence spectra of each water sample to form a three-dimensional fluorescence database for each water sample; dividing the study area into sub-regions and using the connection points between the sub-regions as detection nodes for water quality and quantity in the study area; acquiring the water flow rate and three-dimensional fluorescence data detected at each detection node, as well as the water flow rate and three-dimensional fluorescence data detected at the detection points within each sub-region; The aforementioned rainwater mixing refers to the mixing of domestic sewage and rainwater. Based on this basic information, a fluorescence mass balance model is established, and the amount of rainwater mixing and groundwater infiltration in each sub-region of the study area is calculated. This includes: extracting the fluorescence intensity corresponding to the characteristic peak regions of each water sample in the three-dimensional fluorescence database; the water samples include: domestic sewage, rainwater, and groundwater; establishing a fluorescence mass balance model based on the fluorescence intensity; statistically analyzing the probability distribution of the fluorescence intensity of each water sample according to the three-dimensional fluorescence database of each water sample, and fitting the statistical results of the probability distribution using a Monte Carlo algorithm. In the probability distribution of each water sample that meets the fitting accuracy after fitting, the fluorescence intensity value of each water sample is randomly generated, and the generated fluorescence intensity value of each water sample is input into the fluorescence mass balance model to obtain the flow rate corresponding to each water sample, and the flow rate corresponding to each water sample is stored; the flow rate corresponding to multiple sets of water samples is repeatedly obtained, and when the number of corresponding flow rate sets obtained reaches a preset value, the flow rate corresponding to each set of water samples is statistically analyzed to obtain the average value of the water flow rate of each water sample; the average value of each water sample includes: the average value of domestic sewage flow rate, the average value of rainwater flow rate, and the average value of groundwater flow rate.

2. The method for locating misconnected pipe networks according to claim 1, characterized in that, The formula for the fluorescence mass balance model is expressed as follows: The constraints of the fluorescence mass balance model are: The conditions for meeting the fitting accuracy are: in, , , , The fluorescence intensity of the effluent from the end of the pipe network, the fluorescence intensity of the domestic sewage sample, the fluorescence intensity of the rainwater sample, and the fluorescence intensity of the groundwater sample within the sub-region are represented respectively. , , , These represent the real-time flow rate measured at the end of the pipe network within the sub-region, the flow rate of domestic sewage in the pipe, the flow rate of mixed rainwater, and the infiltration flow rate of groundwater.

3. The method for locating misconnected pipe networks according to claim 1, characterized in that, Problem-specific analysis is performed on each sub-region in descending order of risk level; the step of performing problem-specific analysis on each sub-region according to the risk level and generalization model, and obtaining the optimal internal flow allocation scheme for the detection points of the pipeline network in each sub-region includes: A generalized model of the pipeline network system in the study area is established based on the geographical features and pipeline attributes of the study area. Based on the generalized model and genetic algorithm, problem-specific analysis is performed on each of the sub-regions to obtain the corresponding internal flow allocation scheme for the sub-regions. The internal flow allocation scheme corresponding to the sub-region obtained when the maximum number of iterations or the objective function error is minimized is taken as the optimal internal flow allocation scheme for the detection points within the sub-region.

4. The method for locating misconnected pipe networks according to claim 3, characterized in that, The objective function is expressed as: in, These are simulated values ​​at the monitoring points of the pipeline network within the sub-region. These are the measured values ​​at the same testing point. Indicates the current number of detection points. This indicates the total number of testing sites.

5. The method for locating misconnected pipe networks according to claim 4, characterized in that, The maximum number of iterations is determined based on the time period of the simulated detection of the detection points of the pipeline network within the sub-region and the time interval between two simulated detections.

6. A system for locating misconnected pipe networks, characterized in that, The system includes: The acquisition module is used to acquire basic information about the study area; The first data processing module is used to establish a fluorescence mass balance model based on the basic information, and to calculate and obtain the amount of rainwater mixing and groundwater infiltration in each sub-region of the study area. The second data processing module is used to classify the risk level of each sub-region based on the amount of rainwater mixing and groundwater infiltration in each sub-region, and to perform problem point analysis on each sub-region in sequence according to the risk level and generalization model, and to obtain the optimal internal flow distribution scheme of the detection points of the pipeline network in each sub-region. The output module is used to accurately locate the groundwater infiltration point and the rainwater mixing connection point in the pipeline network based on the optimal internal flow distribution scheme. The acquisition module is used to: acquire the three-dimensional fluorescence spectrum of each water sample in the study area; extract the three-dimensional fluorescence features of each water sample's three-dimensional fluorescence spectrum to form a three-dimensional fluorescence database for each water sample; divide the study area into sub-regions and use the connection points between the sub-regions as detection nodes for water quality and quantity in the study area; acquire the water flow rate and three-dimensional fluorescence data detected by each detection node, as well as the water flow rate and three-dimensional fluorescence data detected by the detection points in each sub-region; The aforementioned rainwater mixing refers to the mixing of domestic sewage and rainwater. The first data processing module is used to: extract the fluorescence intensity corresponding to the characteristic peak regions of each water sample in the three-dimensional fluorescence database; the water samples include: domestic sewage, rainwater, and groundwater; establish a fluorescence mass balance model based on the fluorescence intensity; perform probability distribution statistics on the fluorescence intensity of each water sample according to the three-dimensional fluorescence database of each water sample, and fit the probability distribution statistics results using a Monte Carlo algorithm; randomly generate the fluorescence intensity value of each water sample from the probability distribution of each water sample that meets the fitting accuracy after fitting, and input the generated fluorescence intensity value of each water sample into the fluorescence mass balance model to obtain the flow rate corresponding to each water sample, and store the flow rate corresponding to each water sample; repeatedly obtain the flow rate corresponding to multiple sets of water samples, and when the number of corresponding flow rate sets obtained reaches a preset value, perform data statistical analysis on the flow rate corresponding to each set of water samples to obtain the average value of the water flow rate of each water sample; the average value of each water sample includes: the average flow rate of domestic sewage, the average flow rate of rainwater, and the average flow rate of groundwater.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by an electronic device, the program implements the pipeline misconnection location method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, Including processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the electronic device to perform the pipeline misconnection location method according to any one of claims 1 to 5.

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