A water quality monitoring management system and method based on municipal sewage pipeline

Abstract

The present invention discloses a water quality monitoring and management system and method based on municipal sewage pipelines, which relates to the technical field of water quality monitoring. The system includes a data receiving module, a pipeline evaluation module, a regional division module, and a treatment prediction module. The technical key points are as follows: Before water quality treatment, by comparing the current flow change coefficient of the sewage pipeline with the flow change coefficient after the last repair, the potential blockage risk inside the pipeline can be effectively predicted, and preventive measures can be taken in advance; Based on the calculation of the coefficients of sewage storage, pipeline location, and sewage flow, the influence degree of different factors on environmental interference is comprehensively considered. For example, the coefficient based on sewage storage clearly reflects the proportion of the sewage volume borne by each pipeline in the total storage, helping to judge the size of potential interference; The pipeline location coefficient takes into account the distance from sensitive areas and the situation of crossing special areas, and effectively evaluates the impact of emissions on the surrounding environment.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a water quality monitoring management system and method based on a municipal sewage pipeline. Background Art

[0002] Municipal sewage pipelines are the main arteries of urban sewage discharge, and it is imperative to carry out efficient monitoring and management of their water quality. The collection of various sewage in underground pipes will lead to the collection and reaction of various biochemical substances. Among them, the irritating gases, sediments and new pollutants derived have adverse effects on the surrounding environment of the underground sewage system, the blockage of sewage pipes and the sewage treatment terminal.

[0003] At present, a Chinese patent with the existing patent application number "CN202310138939.3" discloses a sewage monitoring and early warning method for an urban underground sewage system, which belongs to the field of water quality monitoring technology. It includes obtaining urban underground sewage pipeline information, and constructing a physical model of the urban underground sewage pipeline system based on the urban underground sewage pipeline information; obtaining the emission data of the urban sewage source, and obtaining sewage status data based on the physical model of the urban underground sewage pipeline system and the emission data of the urban sewage source; using the sewage status data to obtain sewage intersection information based on the physical model of the urban underground sewage pipeline system; using the sewage intersection information to generate pollutant derivative trends; and obtaining urban underground sewage water quality monitoring and early warning information based on the pollutant derivative trends. The obtained early warning information can be used as guidance information to avoid the generation of derivatives, provide guidance for the time period for sewage discharge from pollution sources, and provide assistance for sewage management of urban underground sewage systems. However, there is a lack of assessment of the current transport capacity of sewage pipes, which may lead to leakage during the re-transport of pollutants (causing sewage to pass through environmentally sensitive areas during transportation, increasing the risk of pollution); secondly, the types of sewage in different regions are different. For example, the composition of industrial wastewater and domestic sewage is significantly different, so they need to be treated separately. However, this application lacks technical solutions in this regard and has certain limitations.

[0004] However, during the implementation of the above technical solution, it was found that there were at least the following technical problems:

[0005] First, the sewage pipes are not cleaned in time. Due to the complexity of urban sewage pipes and the difficulty of cleaning, the sewage pipes can only be cleared and maintained by regular cleaning. In addition, due to the different locations of sewage pipes, some are located in high terrain or remote areas, and the cleaning cycle is difficult to unify, which can easily cause pipe blockage and affect the efficiency of sewage discharge, so that sewage overflow often occurs, polluting the surrounding environment; second, it is impossible to adjust the sewage transportation route according to the real-time status of sewage. For some special pollutants, such as grease and heavy metals in catering wastewater, they will cause corrosion to the inner wall of the pipe they pass through during transportation, aggravating the aging of the pipe; at the same time, during transportation, pollutants and pollutants, or pollutants under specific conditions, may cause chemical reactions to generate harmful substances, further aggravating environmental pollution. To this end, a water quality monitoring management system and method based on municipal sewage pipes are provided. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the deficiencies in the prior art, the present invention provides a water quality monitoring and management system and method based on a municipal sewage pipeline, which solves the problems raised in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A water quality monitoring and management system based on municipal sewage pipelines, the system comprising:

[0011] The data receiving module, based on sensors, collects the original monitoring data of key nodes of sewage pipes, covering water quality, coordinates and flow data. During the collection process, the data is preliminarily screened and divided into abnormal data sets and normal data sets;

[0012] The pipeline assessment module combines the original monitoring data with the municipal sewage pipeline topology, analyzes the transportation stability coefficient of the sewage pipeline, compares the transportation stability coefficient with the stability interval, and executes the corresponding repair strategy based on the comparison result; before the comparison, the threshold of the stability interval is calibrated according to the abnormal data set and the normal data set;

[0013] The regional division module divides the area corresponding to the sewage pipe into a pollutant discharge area according to the pollutant concentration in the water quality data, and automatically triggers the alarm mechanism when the pollutant impact index in the pollutant discharge area exceeds the upper limit of the environmental tolerance;

[0014] After receiving the alarm, the treatment prediction module comprehensively considers the sewage storage in the discharge area and the state parameters of the pipelines between the treatment plants, estimates the comprehensive interference coefficients of different pipelines during the discharge time, constructs a robust optimization model, and solves the model to select the optimal discharge pipeline.

[0015] Preferably, key nodes include the ends of sewage pipes, branch confluences, confluence points of sewage from different functional areas, and areas with variable water quality determined by historical data and pollution risk assessment;

[0016] The initial data screening is carried out by comparing the received original monitoring data with the balance threshold interval of the corresponding parameters. The original monitoring data that exceeds the balance threshold interval is recorded as an abnormal data set; the original monitoring data that does not exceed the balance threshold interval is recorded as a normal data set.

[0017] Preferably, the process of analyzing the stability coefficient of sewage pipeline transportation is as follows:

[0018] Obtain flow data in each sewage pipe, including water level and water flow speed , the flow variation coefficient is calculated based on the acquired flow data. The specific formula is as follows:

[0019] , where is the flow rate variation coefficient of the jth pipeline, , are the water flow velocities at the jth pipe inlet and outlet, respectively. , are the water level heights of the jth pipe inlet and outlet, respectively. , are the preset water flow difference and average water level weights, 0.8 > >0.6;

[0020] Obtain the flow data of the jth pipeline after the last maintenance, and calculate the flow change coefficient of the jth pipeline based on the flow data after the last maintenance, which is recorded as , and then weight the flow change coefficients obtained twice to obtain the transportation stability coefficient of the jth pipeline .

[0021] Preferably, based on the analysis method of the jth pipeline transportation stability coefficient, the transportation stability coefficient of each municipal sewage pipeline is obtained, and the obtained transportation stability coefficient is compared with the preset stability interval, wherein the stability interval includes the upper limit threshold of the deviation and the lower deviation threshold ;

[0022] If the transport stability coefficient is less than the lower limit of the deviation When , no response is made, and the average value of the transport stability coefficients of each sewage pipeline is used as the transport stability coefficient of the municipal sewage pipeline;

[0023] If the transport stability factor is within the upper deviation threshold and the lower deviation threshold When the transmission stability coefficient of municipal sewage pipeline is between 0.04 and 0.10, the secondary maintenance instruction is issued to implement the regular dredging strategy to dredge the municipal pipeline, and the minimum value of the municipal pipeline transmission stability coefficient is used as the transmission stability coefficient of the municipal sewage pipeline;

[0024] If the transport stability coefficient is greater than the upper limit threshold of the deviation When the municipal pipeline is in operation, a first-level maintenance instruction is issued, a deactivation strategy is implemented, and the branch pipeline where the municipal pipeline is located is closed.

[0025] Preferably, the abnormal monitoring data in the abnormal data set and normal monitoring data in the normal data set Based on abnormal monitoring data Deviation from the stable interval, calculate the adjustment coefficient of the deviation threshold , and then the upper threshold of the deviation and the lower deviation threshold Make adjustments;

[0026] When adjusting the deviation threshold, As the adjusted upper deviation threshold; as the adjusted lower deviation threshold.

[0027] Preferably, the steps for dividing the pollutant emission area are as follows:

[0028] S001. Use cluster analysis methods to divide key nodes into different categories according to the pollutant concentration parameters of each key node. Key nodes with concentration feature differences less than the classification threshold are grouped into one category. Each category can be preliminarily regarded as a potential pollution source area.

[0029] S002. When the pollutant concentration of a key node exceeds a preset concentration threshold, the key node is marked as a polluted point, and the coordinates of the key node are obtained;

[0030] S003. Arrange the pollution points from large to small according to their concentration values, take the key nodes corresponding to the maximum pollutant concentration as the circle point, and then take the average length of the sewage pipe as the initial radius, and the pollution source area covered is the initial pollutant area;

[0031] S004. Obtain the coordinates and concentrations of key nodes in the initial pollutant area, adjust the initial radius based on the change in concentration in the initial pollutant area, and redivide the area to obtain the pollutant area.

[0032] Preferably, based on the original monitoring data collected in the pollutant area, the pollutant impact index of the current area is calculated, and the calculation formula is:

[0033] , where is the impact index of pollutants in the pollutant area, n is the number of pollutant types, h is the number of criterion layers for the impact of pollutants on the pollutant area, is the concentration of the ith pollutant, is the fuzzy membership function, is the relative weight of the ith pollutant relative to the criterion level z, is the weight of the criterion layer z relative to the impact index, P is the population of the region, and A is the area representing the pollutant region.

[0034] Preferably, based on the concentration of each pollutant in the pollutant area, the comprehensive impact index corresponding to the upper limit of the environmental tolerance is calculated, and the calculation formula is:

[0035] , where is the comprehensive impact index corresponding to the upper limit of the pollutant area, is the maximum allowable concentration of the i-th pollutant at the upper limit of environmental tolerance, is the toxicity coefficient of the i-th pollutant, is the maximum population that the pollutant area can accommodate when the environment reaches its upper limit. is the impact weight coefficient of the i-th pollutant; if ≥ , an overload risk alarm is issued.

[0036] Preferably, the state parameters of the pipeline between the sewage storage in the discharge area and the treatment plant are weighted to obtain the interference coefficient of the sewage discharge pipeline to its environment during the discharge time period. ;

[0037] The comprehensive interference factor during the emission period is based on the following formula:

[0038] , where is the comprehensive interference coefficient of the jth pipeline during the discharge time, is the sum of the transport stability coefficients of all pipelines in the jth pipeline, are the weights of the environmental interference coefficient and the transmission stability coefficient respectively;

[0039] Based on the comprehensive interference coefficient of all pipelines during the discharge time According to the uncertainty factors in the calculation process of the comprehensive interference coefficient, a robust optimization model is constructed to solve the model and select the optimal discharge pipeline. The formula is as follows:

[0040] , where For robust tuning parameters, is the number of scenarios corresponding to the uncertainty factors, is the parameter vector corresponding to the uncertainty factor, is the value range of the parameter under the sth uncertainty factor scenario.

[0041] A water quality monitoring and management method based on municipal sewage pipelines comprises the following steps:

[0042] Based on sensors, the original monitoring data of key nodes of sewage pipelines are collected, covering water quality, coordinates and flow data. During the collection process, the data is preliminarily screened and divided into abnormal data sets and normal data sets;

[0043] The original monitoring data is combined with the topological structure of the municipal sewage pipeline, the transportation stability coefficient of the sewage pipeline is analyzed, and then the transportation stability coefficient is compared with the stable interval, and the corresponding repair strategy is implemented based on the comparison result; before the comparison, the threshold of the stable interval is calibrated according to the abnormal data set and the normal data set;

[0044] According to the pollutant concentration in the water quality data, the area corresponding to the sewage pipe is divided into a pollutant discharge area, and when the pollutant impact index in the pollutant discharge area exceeds the upper limit of the environmental tolerance, the alarm mechanism is automatically triggered;

[0045] After receiving the alarm, the sewage storage in the discharge area and the state parameters of the pipelines between the treatment plants are comprehensively considered, the comprehensive interference coefficients of different pipelines during the discharge time are estimated, a robust optimization model is constructed, and the optimal discharge pipeline is selected by solving the model.

[0046] (III) Beneficial effects

[0047] The present invention provides a water quality monitoring management system and method based on municipal sewage pipelines, which has the following beneficial effects:

[0048] Before water treatment, by comparing the current flow change coefficient of the sewage pipeline with the flow change coefficient after the last maintenance, the potential blockage risk inside the pipeline can be effectively predicted, preventive measures can be taken in advance, sudden pipeline failures can be avoided, and the stable operation of the sewage discharge system can be ensured, thereby facilitating the subsequent management of sewage transportation.

[0049] The coefficient calculation based on sewage storage, pipeline location and sewage flow comprehensively considers the impact of different factors on environmental interference. For example, the coefficient based on sewage storage clearly reflects the proportion of sewage volume carried by each pipeline in the total storage, helping to judge the potential interference size; the pipeline location coefficient takes into account the distance from sensitive areas and the situation of crossing special areas, effectively evaluating the impact of discharge on the surrounding environment; the sewage flow coefficient highlights the relationship between flow size and environmental impact. The comprehensive environmental interference coefficient integrates various factors through weight coefficients, which can comprehensively and meticulously quantify the degree of environmental interference of each pipeline, providing a strong basis for reasonable decision-making. In the environmental interference coefficient estimation link during the discharge time, the time period is divided and the coefficient is calculated based on historical data and experience, which can accurately grasp the changes in the interference of sewage discharge on the environment during different periods, and facilitate the adoption of targeted control measures during the period of greater interference to reduce environmental risks. By comparing the environmental interference coefficients of each pipeline during the discharge time, the pipeline with the smallest coefficient is selected, which reduces the negative impact of sewage discharge on the environment from the source, protects the ecological environment, and also helps to improve resource utilization efficiency and avoid resource waste caused by unreasonable discharge. During the actual discharge process, key indicators are continuously monitored. Once a large deviation is found between the actual and estimated values, the data and coefficients are re-evaluated and the calculation model is optimized in a timely manner to continuously improve the accuracy of subsequent pipeline selection, forming a virtuous circle and making the entire sewage discharge management system more scientific and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the overall process of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Municipal sewage pipe networks are responsible for collecting and transporting sewage, and their water quality is related to urban drainage safety, water environment and residents' health. However, many urban sewage pipe networks are currently aging, damaged, and have insufficient capacity. Sewage leakage and overflow often occur, and traditional monitoring methods cannot meet the needs of efficient management and real-time monitoring. Therefore, it is of great significance to develop a water quality monitoring and management system based on municipal sewage pipes.

[0053] On the one hand, real-time monitoring can promptly detect problems such as excessive sewage pollutants, pipe network leakage or blockage. For example, if the concentration of pollutants such as chemical oxygen demand (COD) and ammonia nitrogen is monitored to be abnormally high, it can quickly determine whether industrial wastewater is discharged in violation of regulations or the pipe network is damaged, which is convenient for investigation and repair, preventing sewage from polluting natural water bodies and maintaining water ecological balance; on the other hand, accurate water quality monitoring data helps to optimize sewage treatment processes. Sewage treatment plants can reasonably adjust parameters such as aeration time and dosage of chemicals according to water quality changes in different time periods and regions to ensure that the effluent water quality meets the standards and reduce the impact on the environment. In addition, by monitoring the water quality of the sewage pipe network, the reusability of sewage can be evaluated, providing data support for sewage reuse. Sewage reusability refers to whether sewage can meet the water quality requirements for specific purposes after proper treatment.

[0054] Municipal sewage pipeline water quality monitoring and management system, the overall system process is as follows Figure 1 As shown, the specific implementation plan is as follows:

[0055] 1. Sensor selection

[0056] In the municipal sewage pipeline water quality monitoring and management system, sensors are the core, which can convert the physical and chemical parameters of sewage into electrical signals, providing a basis for data processing. Common sensors include pH, dissolved oxygen, turbidity, and conductivity sensors, each of which accurately monitors water quality parameters based on unique principles.

[0057] The pH sensor measures the acidity and alkalinity of sewage based on electrochemical principles. It consists of a reference electrode and a glass measuring electrode. When immersed in sewage, the glass electrode sensitive membrane reacts with hydrogen ions to produce a potential difference. This potential difference is logarithmically related to the activity of hydrogen ions (pH value). The pH value is calculated by measuring the potential difference. The reference electrode provides a stable reference potential, and the internal preamplifier amplifies the potential signal. For example, a pH sensor is installed at the industrial wastewater discharge port to monitor the pH in real time and prevent environmental pollution.

[0058] The dissolved oxygen sensor uses polarography or fluorescence to monitor the dissolved oxygen content in sewage, which is very important for evaluating the biodegradability of sewage and the living environment of aquatic organisms. The polarography method relies on the cathode, anode and electrolyte. After applying voltage, the dissolved oxygen is reduced at the cathode to produce a diffusion current. The current is proportional to the dissolved oxygen concentration. The fluorescence method uses the quenching effect of fluorescent substances on oxygen and determines the dissolved oxygen concentration by detecting changes in fluorescence intensity. This method has a fast response, does not require polarization, and is simple to maintain. It is widely used in aeration tanks in sewage treatment plants, making it convenient for staff to adjust the aeration intensity.

[0059] The turbidity sensor measures the concentration of suspended particles in sewage based on the principle of light scattering. When light hits suspended particles, it is scattered. The intensity of the scattered light is related to the particle concentration and particle size. The sensor measures the intensity of the scattered light in the direction perpendicular to the incident light and compares it with the internal calibration value to calculate the turbidity. In order to resist interference from ambient light, infrared light sources and filters are often used. It is often used to monitor the turbidity of inlet and outlet water in rivers, lakes and sewage treatment plants. It can detect water quality abnormalities in a timely manner. If the turbidity suddenly increases, the cause needs to be investigated.

[0060] The conductivity sensor reflects the ion concentration of sewage based on the conductive properties of the solution. It consists of two electrodes. When voltage is applied, the solution ions move in a directed manner to form a current. The solution resistance is inversely proportional to the ion concentration. The conductivity is calculated by measuring resistance or conductivity. It can be used to monitor groundwater salinity and industrial wastewater ion concentration, and determine whether emissions exceed the standard. For example, if the conductivity of wastewater from a chemical company increases abnormally, further detection and treatment are required.

[0061] When building a water quality monitoring and management system, it is critical to choose the right sensor. It is necessary to clarify the monitoring needs, such as the type of water quality parameters, measurement range, accuracy and response time requirements. For example, sewage pipes near drinking water sources require high-precision and stable sensors. Industrial wastewater outlets require corrosion-resistant and anti-interference sensors due to the complex composition of sewage, and the measurement range must cover the range of changes in the target parameters.

[0062] The characteristics of sewage also affect the selection of sensors. The pH, temperature, salinity, and suspended matter content of sewage will affect the performance and life of the sensor. For acidic or alkaline sewage, select sensors made of acid- and alkali-resistant materials; for high-temperature environments, select high-temperature resistant sensors; for sewage containing a large amount of suspended matter, select sensors that are not easy to clog and easy to maintain. At the same time, the anti-interference ability must be considered. For example, when measuring dissolved oxygen, if the sewage contains a large amount of reducing substances, it is necessary to select anti-interference sensors or pretreatment.

[0063] To improve the accuracy and stability of sensors, a variety of technical improvement measures can be taken to optimize the sensor structure and materials. For example, high-precision electrode materials and advanced processes can be used to improve the accuracy of pH sensors, and dissolved oxygen sensor membrane materials and structures can be improved to enhance anti-interference capabilities and response speeds. Signal processing technologies such as filtering algorithms for denoising, calibration, and compensation technologies can be used to correct measurement errors. Multi-sensor fusion technology can also be used to combine multiple sensors. Data fusion algorithms can be used to comprehensively analyze data to comprehensively and accurately reflect water quality. Machine learning and artificial intelligence technologies can be combined to deeply mine and analyze data, establish a water quality prediction model, and provide a scientific basis for sewage pipe network management decisions.

[0064] 2. Data Reception

[0065] 2.1 Data Collection

[0066] Sensor collection: original monitoring data at key nodes of sewage pipes (ends of sewage pipes, branch confluences, sewage confluence points in different functional areas, and areas with variable water quality determined by historical data and pollution risk assessment), covering water quality, coordinates and flow data. The specific sensors are selected using the above-mentioned "sensor selection" method.

[0067] Manual collection: It can be set to once a week to obtain sewage concentration data during regular periods; random sampling is conducted 2-3 times a month to prevent enterprises or regions from temporarily adjusting emissions to cope with inspections and to ensure data authenticity. For example, water samples are collected from sewage outlets in industrial parks at a fixed time every Monday morning, and random sampling is conducted on Tuesday and Thursday afternoons every month.

[0068] Use professional water sample collection equipment, such as polyethylene plastic bottles or glass sampling bottles, to ensure that the collection process is pollution-free. For sewage of different depths, use the stratified sampling method to comprehensively analyze the differences in pollutant concentrations at different levels. At discharge outlets with large sewage flows, use automatic samplers to continuously collect water samples at certain time intervals to ensure that the samples are representative. For example, in river-shaped sewage pipes, water samples are collected at 0.2 meters, 0.5 meters, and 0.8 meters below the water surface; install automatic samplers at the sewage outlets of large industrial enterprises, collect water samples every 15 minutes, and mix them as samples for that period.

[0069] Different detection methods are used for different pollutants. For example, high performance liquid chromatography is used to detect the concentration of organic pollutants (such as benzene, toluene, xylene, etc.); atomic absorption spectrometry is used to determine the content of heavy metals (lead, mercury, cadmium, chromium, etc.); chemical analysis is used to detect chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH 3 -N), oil pollutants and other indicators. For example, the concentration of benzene can be obtained by analyzing the sewage samples from the chemical park through high performance liquid chromatography; the COD and BOD values ​​of domestic sewage samples can be determined by chemical analysis.

[0070] 2.2. Data screening

[0071] Since the parameters in the original monitoring data are diverse and two adjacent parameters cannot be directly compared, in order to improve the scalability and accuracy of the system, the data needs to be preliminarily screened and standardized to eliminate outliers and invalid data to ensure data quality. By setting reasonable thresholds and algorithms, values ​​that are obviously deviated from the normal range can be identified and filtered out, and valid data can be retained for subsequent analysis. At the same time, data cleaning rules are established, and they are updated and maintained regularly to adapt to changes in different monitoring scenarios and data characteristics. In this way, there is no need to set separate screening conditions for each parameter, which simplifies the data processing process and improves system efficiency and accuracy.

[0072] Initial screening: mainly by comparing the received raw monitoring data with the balance threshold interval of the corresponding parameters, and dividing the raw monitoring data into different sets according to the comparison results, among which the balance threshold interval includes the balance upper limit threshold and balance lower threshold .

[0073] When the original monitoring data exceeds the balance threshold interval, the corresponding original monitoring data is recorded as abnormal monitoring data. , the abnormal data set is the collection of all abnormal monitoring data; otherwise, the corresponding original monitoring data is recorded as normal monitoring data The normal data set is the collection of all normal monitoring data. Taking pH value as an example, the pH value monitoring data of the water quality at the discharge outlet of a chemical plant exceeded the equilibrium threshold interval of 7.0-8.5 many times and was marked as abnormal data. It needs to be immediately checked and processed on site to prevent environmental pollution. That is, pH=7.0 is the lower limit threshold of the equilibrium corresponding to pH, and pH=8.5 is the upper limit threshold of the equilibrium corresponding to pH.

[0074] 3. Pipeline inspection

[0075] 3.1. Pipeline dredging analysis

[0076] When silt or impurities accumulate inside the pipe, the inner diameter of the pipe will decrease. Based on the principle that the smaller the inner diameter of the pipe is, the faster the water flow rate is, the faster the water flow rate is. By simply detecting the water flow rate at both ends of the sewage pipe, it is possible to estimate whether the sewage pipe is blocked and the degree of blockage. For example, by measuring the flow rate at point A (the outlet of the pipe) to be 1.2 m / s and the flow rate at point B (the inlet of the pipe) to be 0.8 m / s, and the inner diameter of the pipe remains unchanged, it can be inferred that the pipe is slightly blocked in section AB and needs to be cleaned in time to prevent the blockage from worsening and affecting the sewage treatment efficiency.

[0077] However, due to the complexity of urban sewage pipes and the difficulty of cleaning, the only way to dredge and maintain sewage pipes is to use regular cleaning. In addition, due to the different locations of sewage pipes, some are located in high terrain or remote areas, and the cleaning cycle is difficult to unify, which can easily cause pipe blockage and affect sewage discharge efficiency. As a result, sewage overflow often occurs, polluting the surrounding environment. Therefore, in order to effectively solve this problem, we use the collected flow data (water level, flow rate) to estimate the blockage of the pipe. The specific analysis formula is as follows:

[0078]

[0079] In the formula, is the flow variation coefficient of the jth pipeline, , are the water flow velocities at the jth pipe inlet and outlet, respectively. , are the water level heights of the jth pipe inlet and outlet, respectively. , are the preset water flow difference and average water level weights, 0.8 > >0.6. When blockage occurs inside the pipe, the parameters that are directly affected are flow rate and water level. Therefore, a mathematical model is established using the relationship between water level and flow rate to monitor and predict blockage trends in real time, accurately locate pipes that need to be treated first, optimize the cleaning cycle, improve the overall operating efficiency of the sewage treatment system, and reduce environmental pollution risks.

[0080] When the flow rate at the inlet of the sewage pipe is consistent with the water level at the outlet, it means that the sewage pipe is in a relatively flat state (the sludge is deposited below the sewage). The amount of sludge inside the pipe cannot be estimated by the change in water level. However, when the sewage flows, the water flow will impact the sludge deposited inside the pipe, causing a certain deceleration. Therefore, at this time, the amount of sludge deposited inside the pipe can only be estimated by the flow rate of the sewage. When (i.e. When ),Will Recorded as 0, As the calculation formula for the flow variation coefficient of the jth pipeline.

[0081] When sewage moves inside the sewage pipe, it will not only be affected by the resistance of the sewage pipe, but also by factors such as the inclination angle (which is too complicated and its influence is not easy to detect and analyze). Therefore, we ignore the influence of sewage on the sewage flow rate and water level during the flow of sewage in the sewage pipe, and use the sewage pipe inlet and sewage pipe outlet as the data monitoring points. According to the influence coefficient of the unit length (meter) of the sewage pipe on the sewage velocity , and the angle between the sewage pipe and the horizontal plane , get the coordinates of the sewage pipe outlet and the coordinates of the sewage pipe inlet , the angle between the line connecting the two points and the horizontal plane, where the angle , by adjusting the above calculation formula of flow rate variation coefficient, the adjusted formula is as follows:

[0082] , where For the length of the sewage pipe, is the proportional coefficient of the influence of unit tilt angle on water level, is the proportional coefficient of the influence of unit inclination angle on water flow velocity, is the proportional coefficient of the effect of the unit length of the sewage pipe on the sewage velocity; is the angle between the sewage pipe and the horizontal plane, where Refers to the reduction in sewage flow rate per unit pipe length.

[0083] 3.2 Optimization

[0084] It is found during use that when the sludge inside the pipeline is evenly distributed or the pipe inlet and outlet are blocked at the same time, it is impossible to estimate the blockage of the sludge inside the pipeline. Therefore, relying solely on the flow rate variation coefficient to estimate the blockage of the pipeline has certain limitations. In order to solve this problem, this application combines the flow rate variation coefficient under normal use of the pipeline after the last pipeline inspection (that is, after the internal sludge is cleaned up). , and then by comparing the deviation between the two, the overall blockage of the pipeline can be estimated, as follows:

[0085]

[0086]

[0087]

[0088] In the formula, is the historical deviation ratio of the jth pipeline, is the docking deviation ratio between the jth pipeline and the two pipelines before and after it, is the transport stability coefficient of the jth pipeline, , are the average flow rate variation coefficients connected to the j-th pipe outlet and inlet, , are the weights of the preset historical deviation ratio and docking deviation ratio, .

[0089] In the management of municipal sewage pipe networks, the analysis method based on the j-th pipeline transportation stability coefficient is the key to ensuring the efficient and stable operation of the sewage transportation system. Through advanced monitoring equipment to collect flow, pressure, flow velocity and other data, mathematical models and algorithms are used to calculate the transportation stability coefficient of each pipeline.

[0090] After obtaining the coefficient, it needs to be compared with the preset stability range (including upper and lower deviation thresholds). When the coefficient is less than the lower deviation threshold, the system will not respond temporarily, but will calculate the average value of the coefficient of each pipeline as the municipal sewage pipe transportation stability coefficient, so as to avoid unnecessary maintenance due to slight fluctuations in individual pipelines and reduce costs and interference.

[0091] If the coefficient is between the upper and lower thresholds, the system will issue a secondary maintenance instruction and implement a regular dredging strategy. Maintenance personnel will use high-pressure water gun flushing, mechanical dredging and other methods based on the pipeline layout and blockage conditions. After completion, the minimum value of the municipal pipeline coefficient will be used as the municipal sewage pipe transportation stability coefficient to ensure stable operation of the pipeline network.

[0092] Once the coefficient is greater than the upper limit of the deviation, the system will immediately issue a first-level maintenance command, deactivate the relevant pipelines, close the branch pipelines, and organize a professional team to analyze the cause of the failure with the help of advanced equipment. The system will calculate the maintenance time by comprehensively considering factors such as the degree of pipeline damage, material and equipment preparation time, and personnel work efficiency, and evaluate the impact index on the lives of surrounding residents, commercial activities, transportation, etc., to provide a basis for formulating maintenance plans.

[0093] 3.3 Threshold Calibration

[0094] Abnormal monitoring data after initial screening and routine monitoring data Based on the deviation between each detection data and the corresponding threshold, the upper limit threshold of the deviation is adjusted and the lower deviation threshold The adjustment factor is calculated based on the following formula:

[0095]

[0096]

[0097] In the formula, is the adjustment coefficient of the deviation threshold, is the number of abnormal monitoring data in the abnormal data set, is the number of routine monitoring data in the routine data set, is the deviation of the kth parameter in the abnormal data set, is the value of the i-th parameter in the regular data set, is the proportional coefficient corresponding to the kth parameter in the preset abnormal data set, is the proportional coefficient corresponding to the i-th parameter in the preset regular data set, is the value of the kth parameter in the abnormal data set, , The deviation The corresponding upper and lower balance thresholds, , are the weights of the preset abnormal parameter proportion and the average abnormal parameter deviation respectively;

[0098] When adjusting the deviation threshold, As the adjusted upper deviation threshold; as the adjusted lower deviation threshold.

[0099] 4. Pollution impact assessment

[0100] 4.1 Regional Division

[0101] Regional division is crucial for accurately determining the pollution source area. First, the distribution of pollution in space is not uniform. By dividing the potential pollution source area through cluster analysis, we can initially narrow the scope of investigation and focus on key areas. Then, by marking the pollution points and determining the initial pollutant area, we can frame the possible polluted range from a macro perspective. When optimizing the division of the final pollutant area, considering that the diffusion of pollutants will cause the concentration to show a specific change pattern in space, the use of spatial autocorrelation analysis and concentration gradient algorithm can accurately capture the relationship between the concentration change trend and spatial distribution, dynamically adjust the radius and fitting shape, so as to more accurately define the actual pollution area and provide strong support for subsequent governance. The specific steps are as follows:

[0102] S001. Use cluster analysis methods to divide key nodes into different categories according to the pollutant concentration parameters of each key node; classify key nodes whose concentration characteristics have a difference less than the classification threshold into one category. Each category can be preliminarily regarded as a potential pollution source area, and the classification threshold can be set in advance.

[0103] For example, suppose there are 10 key nodes, and their pollutant concentrations are [10mg / L, 12mg / L, 8mg / L, 50mg / L, 54mg / L, 48mg / L, 2mg / L, 3mg / L, 1mg / L, 5mg / L], and the classification threshold is 7mg / L. Through cluster analysis, [10mg / L, 12mg / L, 8mg / L] with similar concentrations will be classified into one category, [50mg / L, 54mg / L, 48mg / L] into another category, and [2mg / L, 3mg / L, 1mg / L, 5mg / L] into the third category. These three categories preliminarily correspond to three potential pollution source areas.

[0104] S002. When the pollutant concentration of a key node exceeds a preset concentration threshold, the key node is marked as a polluted point and the coordinates of the key node are obtained.

[0105] Assume that the preset concentration threshold is 15 mg / L. In the above example, the nodes with concentrations of 50 mg / L, 55 mg / L, and 48 mg / L exceed the threshold. These nodes are marked as contaminated points. If the coordinates of these nodes are (1, 2), (3, 4), and (5, 6), respectively, these coordinates are recorded as the location information of the contaminated points.

[0106] S003. Arrange the pollution points from large to small according to their concentration values, take the key node corresponding to the maximum pollutant concentration as the center of the circle, and then take the average length of the sewage pipe as the initial radius. The resulting area is the initial pollutant area.

[0107] In the above example, the maximum pollutant concentration is 55 mg / L, and the corresponding node coordinates are (3,4). Assuming that the average length of the sewage pipe is 10 meters, then draw a circle with point (3,4) as the center and 10 meters as the radius. This circular area is the initial pollutant area.

[0108] S004. Obtain the coordinates and concentrations of key nodes in the initial pollutant area, use spatial autocorrelation analysis and concentration gradient algorithm, comprehensively consider the relationship between concentration change and spatial distribution, and adjust the initial radius and redivide the area based on this to obtain the final pollutant area.

[0109] Specifically, the Euclidean distance between each key node and the center node in the initial pollutant area is first calculated, and the concentration gradient matrix is ​​constructed based on the concentration difference. For example, in the initial pollutant area, there are key nodes with coordinates (4,5) corresponding to a concentration of 45 mg / L and (2,3) corresponding to a concentration of 40 mg / L. By calculating their Euclidean distances to the center node (3,4) (concentration 55 mg / L), they are: Mihe meters, the concentration differences are 55-45=10mg / L and 55-40=15mg / L respectively, and then the concentration gradient value is constructed. For example, for the (4,5) node, the concentration gradient value is , for the (2,3) node, the concentration gradient value is .

[0110] Then, spatial autocorrelation analysis methods, such as Global Moran's I, are used to determine the spatial distribution characteristics of key node concentrations. If the Moran's index is greater than 0, it indicates that the concentration distribution is positively correlated, that is, high-concentration nodes tend to be clustered. On this basis, the Kriging interpolation method is used to interpolate and simulate the concentration in the region to generate a continuous concentration distribution surface.

[0111] Finally, the initial radius is dynamically adjusted based on the concentration gradient matrix, spatial autocorrelation analysis results and concentration distribution surface. If the concentration gradient is large in a certain direction and the spatial autocorrelation shows high concentration aggregation, the radius in that direction can be appropriately increased. For example, the initial radius of 10 meters can be adjusted to 12 meters in that direction. At the same time, irregular shapes such as ellipses are used to fit high-concentration areas to achieve accurate division of pollutant areas and obtain the final determined pollutant areas.

[0112] 4.2. Estimation of the discharge time required for different pipelines

[0113] Assume that the emission rate of the i-th pollutant discharged from the j-th pipeline is (Unit: mass / time, e.g. g / hour), the total emission is (Unit: mass, e.g. grams).

[0114] 4.2.1. Calculation of total emission: If the concentration of pollutants discharged from the pipeline is known (Unit: mass / volume, e.g. g / m3) and the flow rate of the pipeline (unit: volume / time, e.g. cubic meters / hour), and duration of the emission (Unit: hour), then the total emission .

[0115] Assume that the i-th pollutant in the pipeline is discharged to the maximum allowable concentration corresponding to the environmental tolerance upper limit formula The additional capacity allowed is , can be obtained through the environmental tolerance upper limit formula The actual concentration The difference is calculated by combining factors such as regional volume. Assuming the regional volume is ,but .

[0116] 4.2.2. Calculation of emission time: Emission time The calculation formula is .

[0117] 4.2.3 Pollutant Impact Index: Assume that the comprehensive impact index of multiple pollutants on a certain area is , construct the following formula:

[0118]

[0119] in:

[0120] n is the number of pollutant types; P is the population of the region, reflecting the effect of the size of the affected population on the comprehensive impact index; A represents the area of ​​the region (unit: square kilometers, etc.), which is used to reflect the dilution effect of the size of the regional space on the impact of pollutants;

[0121] About Fuzzy Membership Function

[0122] Assume that the concentration of the i-th pollutant is For different types of pollutants, the corresponding fuzzy membership function is constructed according to their impact on the environment and human health. For example, for a certain type of pollutant in the atmosphere, a descending semi-trapezoidal distribution function can be used:

[0123]

[0124] in, and are the lower and upper limits of the concentration of the i-th pollutant, respectively. These two values ​​can be determined based on environmental quality standards, critical values ​​of pollutants that have significant impacts on ecosystems and human health, etc. Through this function, the actual concentration of pollutants is converted to The membership value mapped to between 0 and 1 reflects the severity of the impact of the pollutant concentration on the environment.

[0125] About the weights determined by analytic hierarchy process

[0126] The analytic hierarchy process (AHP) is used to determine the weights of different pollutants under different evaluation criteria, and the impact of pollutants on the region is divided into multiple criterion levels, such as environmental quality impact, ecosystem impact, human health impact, etc., recorded as .

[0127] For each criterion layer z, a judgment matrix is ​​constructed by expert scoring and the relative weight of the i-th pollutant relative to the criterion layer is calculated. , for example, in the judgment matrix middle, It represents the importance ratio of the kth pollutant to the lth pollutant for criterion z, which is calculated by the eigenvector method and other methods. , and satisfies , these weights reflect the differences in the relative importance of different pollutants in different impact dimensions.

[0128] About Criteria Layer Weights

[0129] Similarly, the weight of each criterion level j relative to the comprehensive impact index is determined by the hierarchical analysis method. , construct a judgment matrix for comparing the importance of each criterion layer, and calculate ,and , the weight reflects the relative importance of different impact criteria in the comprehensive assessment of the impact of pollutants on the region.

[0130] Through this formula, combined with the fuzzy comprehensive evaluation method to fuzzy the pollutant concentration, and the hierarchical analysis method to determine the weights of different levels, the comprehensive impact of multiple pollutants on the region in different impact dimensions is comprehensively considered, which can reflect the comprehensive impact index of pollutants in a more detailed and comprehensive manner, and is different from the common simple linear weighted formula.

[0131] 4.2.4. Upper limit: Assume that the comprehensive impact index corresponding to the upper limit of environmental tolerance is , the corresponding parameter values ​​are:

[0132] : The maximum allowable concentration of the ith pollutant at the upper limit of the environmental tolerance. This value can be determined through many factors such as environmental science research, relevant environmental quality standards, and the environmental self-purification capacity reflected by long-term monitoring data.

[0133] :Toxicity coefficient. When analyzing the upper limit of environmental tolerance, its value is consistent with the meaning in the basic formula, because the toxicity coefficient is an inherent property of the pollutant itself and does not change with changes in the environmental carrying state.

[0134] :The maximum number of people a region can accommodate when the environment reaches its upper limit. This value is constrained by many factors, including the available resources (such as water resources, food resources, etc.) in the region, the carrying capacity of infrastructure, and environmental quality standards. For example, in an area with limited water resources, the maximum number of people that can be accommodated while ensuring environmental quality can be calculated based on the reasonable per capita water use and the total amount of local water resources.

[0135] : Impact weight coefficient. When analyzing the upper limit of environmental tolerance, its determination method is consistent with the basic formula, and is determined according to the importance and impact ratio of pollutants in different regional scenarios.

[0136] Then the environmental tolerance upper limit formula can be expressed as:

[0137]

[0138] In practical applications, by monitoring the current concentration of each pollutant , the actual population P, and compare it with the corresponding value in the above environmental bearing upper limit formula to determine the current environmental bearing state. < , indicating that the environment is in a tolerable state; if ≥ , it means that the environment may face the risk of overload and corresponding measures need to be taken, such as controlling pollutant emissions and optimizing population distribution.

[0139] 5. Optimal discharge pipeline selection scheme

[0140] 5.1 Data collection and organization

[0141] Sewage reserves: Accurately measure the sewage reserves at each point in the pollutant area in cubic meters. Through comprehensive analysis of sewage sources such as industrial production and residents' lives in the area, combined with historical data, ensure the accuracy and timeliness of the reserve data, and provide a reliable basis for subsequent analysis.

[0142] Pipeline location: Use advanced surveying and mapping technology to draw a detailed pipeline distribution map, clearly mark the starting point, end point and direction of each pipeline, and accurately present the relative position relationship with surrounding environmental sensitive areas, such as residential areas, schools, hospitals, ecological protection areas, rivers, etc., including key information such as distance and azimuth, laying the foundation for evaluating environmental interference.

[0143] Sewage flow: With the help of high-precision flow meters or other reliable monitoring equipment, the flow data of sewage in each pipeline is collected in real time. The flow unit is unified as cubic meters per hour. At the same time, the time, operating conditions and other information corresponding to the flow data are recorded synchronously to facilitate subsequent in-depth analysis of the flow change pattern and its influencing factors.

[0144] 5.2. Establishment of the calculation model of environmental interference coefficient

[0145] 5.2.1. Coefficient based on sewage storage: Let the sewage storage be V (cubic meters). The sewage storage associated with a certain pipeline is , considering the differences in pollution characteristics of sewage in different regions, a pollution characteristic correction factor is introduced (value range 0-1, determined by the type, concentration and toxicity of pollutants in sewage), defines the environmental interference coefficient based on sewage storage , where m is the total number of pipelines. This coefficient comprehensively reflects the proportion of sewage storage and its pollution characteristics borne by a certain pipeline in the total storage and total pollution characteristics. The larger the proportion, the higher the potential environmental interference.

[0146] 5.2.2 Coefficient based on pipeline location: If the pipeline is close to environmentally sensitive areas such as residential areas, schools, and hospitals, a complex weight model based on distance and sensitivity is constructed. The distance between the pipeline and the sensitive area is d (meters), and the initial sensitivity weight of the sensitive area is (e.g. residential areas =0.6, school =0.7, Hospital = 0.8), the distance attenuation parameter is (determined based on the actual environmental diffusion simulation results), while taking into account the density of human activities in sensitive areas at different times The impact of (person / square meter) on weight, introducing personnel activity correction factor ( is the historical maximum human activity density in the sensitive area), then the weight of the distance sensitive area .

[0147] For pipelines crossing ecological protection areas, rivers and other special areas, the crossing impact range parameter r (meters) is introduced, and the special area impact weight (such as crossing an ecological protection area =1, cross the river =0.6), and the correction factor for the vulnerability of the ecosystem in the crossing area in different seasons (value range 0-1, determined by ecosystem monitoring data). When the pipeline passes through a special area, its weight contribution in the area is ,in is the length of the pipeline in a special area, is the total length of the pipeline, and the comprehensive weight of the pipeline position is , environmental interference coefficient based on pipeline location .

[0148] 5.2.3 Coefficient based on sewage flow: Assume that the sewage flow is Q (m3 / h), and the sewage flow of a pipeline is , considering the impact of flow fluctuation on environmental disturbance, the flow fluctuation coefficient is introduced (obtained by calculating the ratio of the flow standard deviation to the mean), as well as the additional impact of sewage temperature on the environment, introducing a temperature correction factor (value range 0-1, determined by the difference between sewage temperature and ambient temperature and the relevant thermal conductivity coefficient), defines the environmental interference coefficient based on sewage flow The larger the flow rate and the greater the fluctuation, the more significant the temperature impact, and the greater the impact on the environment during the discharge process. is the sum of environmental interference coefficients.

[0149] Comprehensive environmental interference coefficient: For each pipeline j, the comprehensive environmental interference coefficient

[0150] ,in is the weight coefficient, and ,These weight coefficients are determined based on a multi-objective genetic algorithm combined with ,expert experience to better balance the impact of various factors on environmental ,disturbance. At the same time, uncertainty analysis of the weight coefficients is ,performed to evaluate the range of their impact on the ,comprehensive environmental disturbance coefficient.

[0151] 5.3. Estimation of environmental interference coefficient during emission period

[0152] 5.3.1. In-depth analysis of sewage flow change patterns: Use machine learning algorithms (such as random forest regression and support vector machine regression) to model historical flow data and related influencing factors (time, date, season, weather conditions, etc.), accurately determine the change patterns of sewage flow in different time periods, and take into account the special impact of weekdays, weekends, holidays, special events, etc. on flow. Conduct targeted training and optimization of the model and divide the discharge time into several fine time periods. , for example, divided into hours.

[0153] 5.3.2. Calculation of time period coefficient: In each time period, calculate the environmental interference coefficient of each pipeline according to the above environmental interference coefficient calculation model. At the same time, considering the impact of emergencies that may occur during the time period (such as sudden flow changes caused by equipment failures and surges in sewage volume caused by heavy rains) on the environmental interference coefficient, an emergency correction factor is introduced (The value range is 0-1, determined by the severity and probability of the emergency).

[0154] 5.3.3. Calculation of total interference coefficient: Estimation of the environmental interference coefficient of a pipeline j during the entire discharge time ,in Emission time period The proportion of the duration of emission to the total emission time, taking into account the differences in environmental self-purification capacity in different time periods, introduces the environmental self-purification correction factor (The value range is 0-1, determined by factors such as ambient temperature, humidity, wind speed, etc. during the time period) The total interference coefficient is further corrected, that is, .

[0155] Combined with the pipeline's transportation stability coefficient, the comprehensive interference coefficient of sewage transportation is analyzed. in, is the sum of the transport stability coefficients of all pipelines in the jth pipeline, m′ is the number of pipelines in the jth pipeline, is the transport stability coefficient of the i-th pipeline in the j-th pipeline, are the weights of the environmental interference coefficient and the transmission stability coefficient respectively.

[0156] 5.4. Optimal discharge pipeline selection

[0157] Calculate the estimated comprehensive interference coefficient of all pipelines during the discharge time In the calculation process, the influence of various uncertain factors (such as data measurement error, model parameter uncertainty) on the coefficients is fully considered. Through multiple simulation calculations (such as Monte Carlo simulation), the probability distribution of the coefficients is obtained to improve the reliability of the coefficient calculation. These coefficients are compared and the pipeline with the smallest coefficient is selected as the optimal discharge pipeline. .

[0158] In order to ensure the robustness of the selection results, a robust optimization model is constructed under the consideration of the uncertainty in the coefficient calculation process, and the optimal discharge pipeline is selected by solving the model, that is,

[0159] ,in, is a robust tuning parameter (set according to the tolerance for uncertainty), is the number of scenarios corresponding to the uncertainty factors, is the parameter vector corresponding to the uncertainty factor, is the range of parameter values ​​under the sth uncertainty factor scenario. At the same time, taking into account factors such as operability, maintenance cost, and potential risks in actual operation, the selection result of the optimal pipeline is comprehensively evaluated and adjusted.

[0160] 5.5. Construction of real-time monitoring system

[0161] 5.5.1. Core monitoring indicators:

[0162] Sewage flow: real-time monitoring by electromagnetic flowmeter, accuracy ±0.5%;

[0163] Water quality parameters: COD, ammonia nitrogen, total phosphorus, etc., using online water quality analyzer (accuracy ±2%);

[0164] Ambient air quality: PM2.5, SO 2 、NO 2 Etc., equipped with a micro air station (accuracy ±5%);

[0165] Ecological indicators: dissolved oxygen in rivers, heavy metal content in soil, using portable detectors;

[0166] Monitoring network deployment: intelligent sensors are installed at key pipeline nodes (every 500 meters), three-layer monitoring circles are set up in environmentally sensitive areas (radius 100m, 500m, 1km), and underwater robots and drones are deployed for inspection in special areas (rivers / protected areas).

[0167] 5.5.2 Data collection and transmission:

[0168] LoRaWAN Internet of Things technology is used to achieve real-time data transmission, establish a cloud platform data center, store data once per minute, and automatically trigger an early warning for abnormal data (threshold ±15%).

[0169] 5.5.3 Deviation analysis method:

[0170] (1) Hypothesis testing:

[0171] Null hypothesis : Estimated interference coefficient = actual interference coefficient;

[0172] Alternative hypothesis : Estimated interference coefficient ≠ actual interference coefficient.

[0173] (2) Selection of inspection method:

[0174] One-way analysis of variance (ANOVA) was used to compare multiple groups of data; chi-square test was used for categorical variable analysis; and regression analysis was used for trend determination.

[0175] 5.5.4. Deviation Quantification Indicators:

[0176] (1) Mean absolute error (MAE): ;

[0177] (2) Root mean square error (RMSE): ;

[0178] (3) Index of consistency (IOA): ;

[0179] In the above formula, n is the number of comprehensive interference coefficients, is the ith estimated comprehensive interference coefficient; is the ith actual comprehensive interference coefficient, is the average value of the actual comprehensive interference coefficient.

[0180] 5.6 Dynamic Adjustment Strategy

[0181] Bayesian Update Algorithm:

[0182] (1) Prior distribution: parameter distribution based on historical data;

[0183] (2) Likelihood function: the degree of match between actual monitoring data and model predictions;

[0184] (3) Posterior distribution: Iteratively update parameters through MCMC method.

[0185] Weight coefficient adjustment:

[0186] in, , where is the adjusted sewage storage weight, is the adjusted pipeline position weight, is the adjusted sewage flow weight, is the initial sewage storage weight, is the initial pipeline position weight, is the initial sewage flow weight; is the mean absolute error between the estimated and actual sewage storage volume, The mean absolute error between the estimated and actual values ​​of the pipeline location coefficient, The mean absolute error between the estimated and actual values ​​of the sewage flow coefficient, is the total comprehensive error.

[0187] The weight coefficient is determined by the coefficient of variation method, which is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of an indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator to obtain the weight of the indicator through calculation, so it is objective.

[0188] In the application, the several formulas involved are all calculated by taking their numerical values ​​after removing the dimensions, and the formulas are established by collecting a large amount of data and performing software simulation to obtain a formula for the most recent actual situation. Some coefficients or weights in the formulas are set by technical personnel in this field according to actual conditions, so they will not be elaborated here.

[0189] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A water quality monitoring and management system based on municipal sewage pipelines, characterized in that: The system includes: The data receiving module, based on sensors, collects the original monitoring data of key nodes of sewage pipes, covering water quality, coordinates and flow data. During the collection process, the data is preliminarily screened and divided into abnormal data sets and normal data sets; The pipeline assessment module combines the original monitoring data with the municipal sewage pipeline topology, analyzes the transportation stability coefficient of the sewage pipeline, compares the transportation stability coefficient with the stability interval, and executes the corresponding repair strategy based on the comparison result; before the comparison, the threshold of the stability interval is calibrated according to the abnormal data set and the normal data set; The regional division module divides the area corresponding to the sewage pipe into a pollutant discharge area according to the pollutant concentration in the water quality data, and automatically triggers the alarm mechanism when the pollutant impact index in the pollutant discharge area exceeds the upper limit of the environmental tolerance; After receiving the alarm, the treatment prediction module comprehensively considers the sewage storage in the discharge area and the state parameters of the pipelines between the treatment plants, estimates the comprehensive interference coefficients of different pipelines during the discharge time, constructs a robust optimization model, and solves the model to select the optimal discharge pipeline.

2. A water quality monitoring and management system based on municipal sewage pipeline according to claim 1, characterized in that: Key nodes include the ends of sewage pipes, branch confluences, confluence points of sewage from different functional areas, and areas with variable water quality determined by historical data and pollution risk assessment; The initial data screening is performed by comparing the received raw monitoring data with the balance threshold interval of the corresponding parameters, and the raw monitoring data exceeding the balance threshold interval is recorded as an abnormal data set; The original monitoring data that does not exceed the balance threshold interval is recorded as the normal data set.

3. A water quality monitoring and management system based on municipal sewage pipeline according to claim 2, characterized in that: The process of analyzing the stability coefficient of sewage pipeline transportation is as follows: Obtain flow data in each sewage pipe, including water level and water flow speed , the flow variation coefficient is calculated based on the acquired flow data. The specific formula is as follows: , where is the flow variation coefficient of the jth pipeline, , are the water flow velocities at the jth pipe inlet and outlet, respectively. , are the water level heights of the jth pipe inlet and outlet, respectively. , are the preset water flow difference and average water level weights, 0.8 > >0.6; Obtain the flow data of the jth pipeline after the last maintenance, and calculate the flow change coefficient of the jth pipeline based on the flow data after the last maintenance, which is recorded as , and then weight the flow change coefficients obtained twice to obtain the transportation stability coefficient of the jth pipeline .

4. A water quality monitoring and management system based on municipal sewage pipeline according to claim 3, characterized in that: Based on the analysis method of the jth pipeline transportation stability coefficient, the transportation stability coefficient of each municipal sewage pipeline is obtained, and the obtained transportation stability coefficient is compared with the preset stability interval, where the stability interval includes the upper limit threshold of the deviation and the lower deviation threshold ; If the transport stability coefficient is less than the lower limit of the deviation When , no response is made, and the average value of the transport stability coefficients of each sewage pipeline is used as the transport stability coefficient of the municipal sewage pipeline; If the transport stability factor is within the upper deviation threshold and the lower deviation threshold When the transmission stability coefficient of municipal sewage pipeline is between 0.1% and 0.2%, a secondary maintenance instruction is issued to implement the regular dredging strategy to dredge the municipal pipeline, and the minimum value of the municipal pipeline transmission stability coefficient is used as the transmission stability coefficient of the municipal sewage pipeline; If the transport stability coefficient is greater than the upper limit threshold of the deviation When the municipal pipeline is in operation, a first-level maintenance instruction is issued, a deactivation strategy is implemented, and the branch pipeline where the municipal pipeline is located is closed.

5. A water quality monitoring and management system based on municipal sewage pipelines according to claim 4, characterized in that: Anomaly monitoring data in anomaly dataset and normal monitoring data in the normal data set Based on abnormal monitoring data Deviation from the stable interval, calculate the adjustment coefficient of the deviation threshold , and then the upper limit threshold of the deviation and the lower deviation threshold Make adjustments; When adjusting the deviation threshold, as the adjusted upper deviation threshold; by as the adjusted lower deviation threshold.

6. A water quality monitoring and management system based on municipal sewage pipelines according to claim 5, characterized in that: The steps for demarcating pollutant emission zones are as follows: S001. Use cluster analysis methods to divide key nodes into different categories according to the pollutant concentration parameters of each key node. Key nodes with concentration feature differences less than the classification threshold are grouped into one category. Each category can be preliminarily regarded as a potential pollution source area. S002. When the pollutant concentration of a key node exceeds a preset concentration threshold, the key node is marked as a polluted point, and the coordinates of the key node are obtained; S003. Arrange the pollution points from large to small according to their concentration values, take the key nodes corresponding to the maximum pollutant concentration as the circle point, and then take the average length of the sewage pipe as the initial radius, and the pollution source area covered is the initial pollutant area; S004. Obtain the coordinates and concentrations of key nodes in the initial pollutant area, adjust the initial radius based on the change in concentration in the initial pollutant area, and redivide the area to obtain the pollutant area.

7. A water quality monitoring and management system based on municipal sewage pipelines according to claim 6, characterized in that: Based on the original monitoring data collected in the pollutant area, the pollutant impact index of the current area is calculated using the following formula: , where is the impact index of pollutants in the pollutant area, n is the number of pollutant types, h is the number of criterion layers for the impact of pollutants on the pollutant area, is the concentration of the ith pollutant, is the fuzzy membership function, is the relative weight of the ith pollutant relative to the criterion level z, is the weight of the criterion layer z relative to the impact index, P is the population of the region, and A is the area representing the pollutant region.

8. A water quality monitoring and management system based on municipal sewage pipelines according to claim 7, characterized in that: Based on the concentration of each pollutant in the pollutant area, the comprehensive impact index corresponding to the upper limit of the environmental tolerance is calculated. The calculation formula is: , where is the comprehensive impact index corresponding to the upper limit of the pollutant area, is the maximum allowable concentration of the i-th pollutant at the upper limit of environmental tolerance, is the toxicity coefficient of the i-th pollutant, is the maximum population that the pollutant area can accommodate when the environment reaches its upper limit. is the impact weight coefficient of the i-th pollutant; if ≥ , an overload risk alarm is issued.

9. A water quality monitoring and management system based on municipal sewage pipelines according to claim 8, characterized in that: The state parameters of the pipeline between the sewage storage in the discharge area and the treatment plant are weighted to obtain the interference coefficient of the sewage discharge pipeline on its environment during the discharge period. ; The comprehensive interference factor during the emission period is based on the following formula: , where is the comprehensive interference coefficient of the jth pipeline during the discharge time, is the sum of the transport stability coefficients of all pipelines in the jth pipeline, are the weights of the environmental interference coefficient and the transmission stability coefficient respectively; Based on the comprehensive interference coefficient of all pipelines during the discharge time According to the uncertainty factors in the calculation process of the comprehensive interference coefficient, a robust optimization model is constructed to solve the model and select the optimal discharge pipeline. The formula is as follows: , where For robust tuning parameters, is the number of scenarios corresponding to the uncertainty factors, is the parameter vector corresponding to the uncertainty factor, is the value range of the parameter under the sth uncertainty factor scenario.

10. A water quality monitoring and management method based on a municipal sewage pipeline, using the water quality monitoring and management system according to any one of claims 1 to 9, characterized in that: The steps include: Based on sensors, the original monitoring data of key nodes of sewage pipelines are collected, covering water quality, coordinates and flow data. During the collection process, the data is preliminarily screened and divided into abnormal data sets and normal data sets; The original monitoring data is combined with the topological structure of the municipal sewage pipeline, the transportation stability coefficient of the sewage pipeline is analyzed, and then the transportation stability coefficient is compared with the stable interval, and the corresponding repair strategy is implemented based on the comparison result; before the comparison, the threshold of the stable interval is calibrated according to the abnormal data set and the normal data set; According to the pollutant concentration in the water quality data, the area corresponding to the sewage pipe is divided into a pollutant discharge area, and when the pollutant impact index in the pollutant discharge area exceeds the upper limit of the environmental tolerance, the alarm mechanism is automatically triggered; After receiving the alarm, the sewage storage in the discharge area and the state parameters of the pipelines between the treatment plants are comprehensively considered, the comprehensive interference coefficients of different pipelines during the discharge time are estimated, a robust optimization model is constructed, and the optimal discharge pipeline is selected by solving the model.

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