Sewage sampling system and method for drainage pipe network

Abstract

The invention relates to the technical field of water quality sampling, and discloses a drainage pipe network sewage sampling system and method.The drainage pipe network sewage sampling system can adapt to different engineering scenes by customizing a collection frequency value and a sampling frequency value, and blindness of fixed-frequency sampling is avoided; traffic data are collected according to the collection frequency value, fine changes of traffic along with time can be captured, and the defect that in a traditional method, single-point sampling cannot reflect the traffic accumulation effect in a time period is overcome. Meanwhile, the sewage sample is collected according to the sampling frequency value, so that the problem of characteristic factor concentration interruption caused by traditional sparse sampling is avoided; a sampling proportion scheme is generated by utilizing a flow time domain integral mixing proportion algorithm, the influence of flow changes at different moments in the drainage pipe network on the sewage concentration can be comprehensively considered, the collected target sewage sample is more representative, and the sewage concentration of the drainage pipe network to be sampled can be reflected by calculating the concentration of the target sewage sample. And the sampling detection times are greatly reduced on the premise of ensuring the precision.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality sampling, and in particular to a drainage network sewage sampling system and method. Background Art

[0002] Urban drainage systems suffer from issues such as infiltration from external water and misconnected pipes. These issues not only overload sewage treatment plants and reduce treatment efficiency, but also cause overflow pollution during the rainy season and deteriorate the quality of receiving water bodies. Diagnosis and localization of these issues are urgently needed. Geophysical inspection technologies (such as pipeline closed-circuit television) are the primary means of identifying the causes of these issues, but their application requires desilting and precipitation, making large-scale application inefficient and costly.

[0003] Characteristic factor analysis is an effective technique for diagnosing the causes of water-carrying problems. It screens characteristic factors for different source waters and examines these factors for both source waters and mixed water at the end of the pipe network. A mass balance model between the source and sink based on these characteristic factors is constructed to quantify the contribution of different source waters. Localization is achieved through step-by-step zoning diagnosis. However, this method has several drawbacks: Characteristic factor analysis is performed on discrete samples collected multiple times (e.g., eight times) at the end of the pipe network per day. The mixing characteristics of the outflowing water within a single day are characterized based on the mean and standard deviation of the characteristic factors across all samples, resulting in high testing costs. Furthermore, piped water flow fluctuates over time, and discrete sampling effectively uses a single time point (9:00 AM) to represent a time period (9:00 AM to 12:00 PM). This makes it impossible to accurately capture the mixing of water within that period, resulting in information loss and significant deviations between the mass balance model and actual results. While increasing the daily sampling frequency (e.g., 24 times) can improve diagnostic accuracy, the increased testing cost exponentially limits the engineering applicability of characteristic factor analysis. Summary of the Invention

[0004] In view of this, the present invention provides a drainage network sewage sampling system and method to solve the problems of high detection cost and low engineering applicability of the characteristic factor analysis method.

[0005] In a first aspect, the present invention provides a drainage network sewage sampling system, the system comprising: a flow monitoring module, an automatic sampling module and a data analysis terminal module;

[0006] The data analysis terminal module is used to obtain the acquisition frequency value and the sampling frequency value, and send a first control signal to the flow monitoring module based on the acquisition frequency value, and send a second control signal to the automatic sampling module based on the sampling frequency value; the flow monitoring module is used to collect the flow of the monitoring point in the drainage network to be sampled in different time periods based on the first control signal, obtain a flow data set, and send the flow data set in the form of a digital signal to the data analysis terminal module; the automatic sampling module is used to collect sewage from the monitoring point in different time periods based on the second control signal to obtain multiple sewage samples; the data analysis terminal module is also used to generate a sampling ratio scheme based on the flow monitoring data set after processing it with a flow time-domain integral mixing ratio algorithm, and send the sampling ratio scheme to the automatic sampling module; the automatic sampling module is also used to use the sampling ratio scheme to extract and mix multiple sewage samples to obtain target sewage samples, and the water quality characteristics of the target sewage samples are used to characterize the overall mixing situation of the upstream water body of the drainage network to be sampled.

[0007] The drainage network sewage sampling system provided by the present invention can adapt to different engineering scenarios by customizing the collection frequency value and sampling frequency value through the data analysis terminal module, avoiding the blindness of the traditional method of "fixed frequency sampling". Furthermore, through the flow monitoring module, the flow data is collected according to the collection frequency value, which can capture the subtle changes in flow over time, solving the defect that single-point sampling in the traditional method cannot reflect the cumulative effect of flow within a time period. At the same time, through the automatic sampling module, sewage samples are collected according to the sampling frequency value, avoiding the problem of characteristic factor concentration interruption caused by traditional sparse sampling. Furthermore, using the flow time domain integral mixing ratio algorithm, a sampling ratio scheme is generated based on the flow monitoring data set, which can comprehensively consider the impact of flow changes at different times in the drainage network on sewage concentration, making the collected target sewage samples more representative. Then, the water quality characteristics of the target sewage sample can be used to characterize the overall mixing situation of the upstream water body of the drainage network to be sampled, greatly reducing the number of sampling tests while ensuring accuracy, and solving the bottleneck of high cost and low efficiency of engineering application of characteristic factor analysis method. Therefore, by sampling and testing the water quality characteristics of a mixed sample of the outflow water at the end of the sewage network, the overall mixing situation of the water body upstream of the network can be characterized. The sampling and testing costs are orders of magnitude higher, and the efficiency is greatly improved. At the same time, it also effectively guarantees the diagnostic accuracy of the characteristic factor analysis method.

[0008] In an optional embodiment, the flow monitoring module includes: a signal processing unit and a monitoring unit, the monitoring unit including a pipeline flow sensor, a data transmission line and an L-shaped rod;

[0009] The pipeline flow sensor is connected to the signal processing unit through a data transmission line. The data transmission line is wound and fixed on an L-shaped rod. The L-shaped rod is located in the pipeline of the drainage network to be sampled and is used to support the pipeline flow sensor; the pipeline flow sensor is used to monitor the flow of the monitoring point in different time periods based on the first control signal, obtain a flow monitoring data set, and send the flow monitoring data set to the signal processing unit in the form of an analog signal; the signal processing unit is used to convert the analog signal into a digital signal so that the flow monitoring data set is stored in the form of a digital signal.

[0010] The drainage network sewage sampling system provided by the present invention supports the pipeline flow sensor by means of an L-shaped rod, enabling it to maintain a stable position within the sewage pipe. Furthermore, the data transmission line is wound and fixed to the L-shaped rod, thereby preventing line damage or sensor displacement caused by water flow impact, etc., and ensuring that the pipeline flow sensor can stably monitor the flow at the monitoring point based on the acquisition frequency value included in the first control signal. Furthermore, the signal processing unit converts the flow monitoring data set from analog signals into digital signals and stores them, facilitating subsequent data transmission, analysis, and processing, thereby improving the efficiency and accuracy of data flow within the system.

[0011] In an optional embodiment, the signal processing unit includes: a memory and a signal converter;

[0012] The signal converter is used to convert the analog signal into a digital signal and send the flow monitoring data set to the memory in the form of a digital signal; the memory is used to store the flow monitoring data set in the form of a digital signal.

[0013] The drainage network sewage sampling system provided by the present invention accurately converts analog signals into digital signals through a signal converter, and sends them to a memory so that the flow monitoring data set is stored in the memory in the form of digital signals, thereby ensuring the accuracy and stability of the data conversion and storage process, preventing data loss or errors during the conversion and storage process, and ensuring the integrity and availability of the data.

[0014] In an optional embodiment, the flow monitoring module further includes: a power supply unit, including a battery and a power adapter, for supplying power to the signal processing unit and the monitoring unit.

[0015] The drainage network sewage sampling system provided by the present invention can provide a stable power supply for the signal processing unit and the monitoring unit through batteries and power adapters. Whether when the external power supply is normally connected or when encountering sudden power outages, the battery can be used as a backup power supply to ensure the continuous operation of the equipment, avoiding data collection and processing interruptions caused by power outages, and ensuring that the entire flow monitoring module works stably around the clock.

[0016] In an optional embodiment, the automatic sampling module includes: a sampling unit and a sample collection unit;

[0017] The sampling unit is used to collect sewage from the monitoring point in different time periods based on the second control signal to obtain multiple sewage samples, and transmit the multiple sewage samples to the sample collection unit; the sample collection unit is used to store multiple sewage samples, and use the sampling ratio scheme to extract and mix the multiple sewage samples to obtain the target sewage sample.

[0018] In the drainage network sewage sampling system provided by the present invention, a sampling unit collects sewage samples according to a sampling frequency value under the control of a second control signal, thereby avoiding the characteristic factor concentration gap problem caused by traditional sparse sampling. Furthermore, the multiple collected sewage samples are stored in a sample collection unit, and the multiple sewage samples are extracted and mixed according to a sampling ratio scheme to obtain a target sewage sample and store it, thus avoiding sample loss and providing reliable sample support for subsequent processing based on the sampling ratio scheme.

[0019] In an optional embodiment, the sampling unit includes: a sampling line, a filter head, a peristaltic pump, a motor, a PLC control system, and a signal receiver;

[0020] One end of the sampling pipeline is connected to the filter head, and the other end is connected to the peristaltic pump; the filter head is used to filter impurities in the pipes in the drainage network to be sampled; the signal receiver is used to receive a second control signal and send the second control signal to the PLC control system; the PLC control system is used to start the peristaltic pump by controlling the motor based on the second control signal, so that the sampling pipeline collects sewage from the monitoring point in different time periods to obtain multiple sewage samples; the PLC control system is also used to send a third control signal to the sample collection unit using a sampling ratio scheme.

[0021] The drainage network sewage sampling system provided by the present invention can prevent impurities from entering the sampling pipeline by filtering impurities in the pipeline through a filter head, thereby avoiding clogging of equipment such as peristaltic pumps, thereby ensuring the normal operation of the equipment and extending the service life of the equipment. Furthermore, the signal receiver can quickly and sensitively receive the second control signal and send it to the PLC control system in a timely manner, ensuring the rapid transmission of the control signal, thereby enabling the sampling unit to respond to the instructions of the data analysis terminal module in a timely manner, and realizing precise control of the automated sampling process. Furthermore, under the control of the second control signal, the PLC control system can realize the precise collection of sewage at the monitoring point in different time periods by controlling the motor and starting the peristaltic pump, thereby improving the collection efficiency and sample quality.

[0022] In an optional embodiment, the sample collection unit includes: a rotary arm, a plurality of first vacuum sampling bottles, a second vacuum sampling bottle, and a dry ice chamber, wherein the plurality of first vacuum sampling bottles and the second vacuum sampling bottles are fixed in the dry ice chamber;

[0023] A plurality of first vacuum sampling bottles are used to store a plurality of sewage samples; a rotary arm is used to extract a plurality of sewage samples from the plurality of first vacuum sampling bottles and inject the extracted sewage samples into a second vacuum sampling bottle based on a third control signal; the rotary arm is also used to mix the sewage samples in the second vacuum sampling bottle to form a target sewage sample in the second vacuum sampling bottle; the second vacuum sampling bottle is used to store the target sewage sample; and a dry ice chamber is used to provide a temperature environment for maintaining the sewage samples in the plurality of first vacuum sampling bottles and the second vacuum sampling bottle.

[0024] The drainage network sewage sampling system provided by the present invention uses multiple first vacuum sampling bottles to separately store raw sewage samples from each time period, achieving independent sealing of samples from a single time period, avoiding concentration deviations caused by premature mixing of samples from different time periods during storage, and ensuring the integrity and traceability of the original data. Furthermore, the sewage in the sampling pipeline is injected into the multiple first vacuum sampling bottles in sequence according to the sampling frequency through a rotary arm. The mechanical positioning accuracy ensures that each bottle only receives samples from a single time period, solving the problem of time period confusion caused by continuous sampling of a single bottle in traditional methods. Furthermore, the rotary arm extracts corresponding samples from the multiple first sampling bottles into the second vacuum sampling bottle according to the sampling ratio scheme, and mixes the extracted samples in the second vacuum sampling bottle to form a target sewage sample and store it, achieving precise mixing of different samples while avoiding cross-contamination between the original sample and the mixed sample through physical isolation. Furthermore, the dry ice chamber can maintain the sewage samples in the multiple first vacuum sampling bottles and the second vacuum sampling bottles in corresponding temperature environments, thereby inhibiting microbial metabolic activity and chemical reaction rates, maintaining sample stability.

[0025] In an optional embodiment, the data analysis terminal module includes: a data processing unit and a human-computer interaction interface;

[0026] The data processing unit is used to generate a sampling ratio scheme based on the flow monitoring data set after processing it using the flow time-domain integral mixing ratio algorithm; the human-computer interaction interface is used to visualize any data and the operating status of each module during the sampling process.

[0027] The drainage network sewage sampling system provided by the present invention utilizes a flow time-domain integral mixing ratio algorithm to process flow monitoring data sets. This algorithm comprehensively considers the impact of flow changes at different times within the drainage network on sewage concentration, generating a scientific and reasonable sampling ratio scheme. This makes the collected target sewage samples more representative. Furthermore, the water quality characteristics of the target sewage samples can be used to characterize the overall mixing of the upstream water body of the drainage network to be sampled. This significantly reduces the number of sampling times while ensuring accuracy, resolving the high cost and low efficiency bottlenecks of characteristic factor analysis in engineering applications. Furthermore, the human-computer interaction interface can visualize various data from the sampling process and the operating status of each module, allowing operators to intuitively and clearly understand the system's operating status and sampling progress, facilitating timely problem detection and parameter adjustment, thereby improving the system's operability and management efficiency.

[0028] In a first aspect, the present invention provides a drainage network sewage sampling method, which is used in the drainage network sewage sampling system of the first aspect or any corresponding embodiment thereof; the method comprises:

[0029] Obtain a flow data set and multiple sewage samples from a drainage network monitoring point; generate a sampling ratio scheme based on the flow monitoring data set through a flow time-domain integral mixing ratio algorithm; use the sampling ratio scheme to extract multiple sewage samples to obtain a target sewage sample, the water quality characteristics of the target sewage sample being used to characterize the overall mixing condition of the upstream water body of the drainage network to be sampled.

[0030] The drainage network sewage sampling method provided by the present invention utilizes a flow time-domain integral mixing ratio algorithm to generate a sampling ratio scheme based on a flow monitoring data set. It can comprehensively consider the impact of flow changes at different times in the drainage network on sewage concentration, making the collected target sewage samples more representative. Furthermore, the water quality characteristics of the target sewage samples can characterize the overall mixing situation of the upstream water body of the drainage network to be sampled. While ensuring accuracy, the number of sampling times is greatly reduced, solving the engineering application bottleneck of high cost and low efficiency of the characteristic factor analysis method.

[0031] In an optional embodiment, based on the flow monitoring data set, a sampling ratio scheme is generated by processing the flow time-domain integral mixing ratio algorithm, including:

[0032] Based on the flow monitoring data set, multiple flow weight coefficients are obtained through the flow time domain integral mixed ratio algorithm processing; according to the multiple flow weight coefficients, a sampling ratio scheme is generated.

[0033] The sewage sampling method provided by this invention calculates flow weight coefficients using a flow time-domain integral mixing ratio algorithm. This allows the characteristic factor concentration of subsequent target sewage samples to be equivalent to the flow-weighted average concentration, thereby accurately reflecting the true mixing conditions within the pipe network. Furthermore, a sampling ratio scheme is generated based on multiple flow weight coefficients, making subsequent target sewage samples more representative, thus significantly reducing the number of subsequent sampling times while ensuring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 is a structural block diagram of a drainage network sewage sampling system according to an embodiment of the present invention;

[0036] Figure 2 is a structural block diagram of a data analysis terminal module according to an embodiment of the present invention;

[0037] Figure 3 is a structural block diagram of a flow monitoring module according to an embodiment of the present invention;

[0038] Figure 4 is a structural block diagram of an automatic sampling module according to an embodiment of the present invention;

[0039] Figure 5 This is a technical solution flow chart of a drainage network representative sampling intelligent acquisition system based on flow time-domain integration according to an embodiment of the present invention;

[0040] Figure 6 is a schematic diagram of a sewage pipe network topology according to an embodiment of the present invention;

[0041] Figure 7 is a schematic diagram of groundwater infiltration analysis results of a mixed sewage sample according to an embodiment of the present invention;

[0042] Figure 8 1 is a flow chart of a method for sampling sewage in a drainage network according to an embodiment of the present invention;

[0043] Figure 9 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0045] An embodiment of the present invention provides a method for sampling sewage in a drainage network. A sampling ratio scheme is generated through a flow time-domain integral mixing ratio algorithm to achieve the effect of characterizing the overall mixing situation of the water body upstream of the network by sampling and detecting the water quality characteristics of a mixed sample of the outflow water body at the end of the sewage network.

[0046] In this embodiment, a drainage network sewage sampling system is provided, such as Figure 1 As shown, the drainage network sewage sampling system 1 includes: a flow monitoring module 11, an automatic sampling module 12 and a data analysis terminal module 13.

[0047] Optionally, the data analysis terminal module 13 is used to obtain the acquisition frequency value and the sampling frequency value, and send a first control signal to the flow monitoring module 11 based on the acquisition frequency value, and send a second control signal to the automatic sampling module 12 based on the sampling frequency value.

[0048] Among them, such as Figure 2 As shown, the data analysis terminal module 13 includes a data processing unit 131 and a human-computer interaction interface 132 .

[0049] Specifically, the user or operator can manually input or preset the collection frequency value (flow monitoring frequency, such as 5 minutes / time) and the sampling frequency value (sewage sampling frequency, such as 24 times / day) through the human-computer interaction interface 132.

[0050] Furthermore, the acquisition frequency value and the sampling frequency value can be converted into corresponding executable time interval instructions through relevant algorithms in the data processing unit 131 and generate corresponding flow monitoring instructions and sampling instructions, namely the first control signal and the second control signal.

[0051] The first control signal includes the time interval corresponding to the sampling frequency value; the second control signal includes the time interval and sampling duration corresponding to the sampling frequency value.

[0052] Furthermore, the data processing unit 131 may send the first control signal to the flow monitoring module 11 , and send the second control signal to the automatic sampling module 12 .

[0053] Optionally, the flow monitoring module 11 is used to collect the flow of the monitoring point in the sampled drainage network in different time periods based on the first control signal, obtain a flow data set, and send the flow data set to the data analysis terminal module 13 in the form of a digital signal.

[0054] Among them, such as Figure 3 As shown, the flow monitoring module 11 includes a monitoring unit 111 and a signal processing unit 112. Furthermore, the monitoring unit 111 includes a pipeline flow sensor 1111, a data transmission line, and an L-shaped rod (the data transmission line and the L-shaped rod are not shown); the signal processing unit 112 includes a memory 1121 and a signal converter 1122.

[0055] Among them, the pipeline flow sensor 1111 is connected to the signal processing unit 112 through a data transmission line; the data transmission line is wound and fixed on the L-shaped rod, avoiding line damage or sensor displacement caused by water flow impact, etc., and ensuring that the pipeline flow sensor can stably monitor the flow of the monitoring point based on the collection frequency value; the L-shaped rod is located in the pipeline of the drainage network to be sampled, and is used to support the pipeline flow sensor 1111.

[0056] First, the pipeline flow sensor 1111 is used to monitor the flow at the monitoring point in different time periods based on the first control signal, obtain a flow monitoring data set, and send the flow monitoring data set to the signal converter 1122 in the form of an analog signal.

[0057] Specifically, by supporting the pipeline flow sensor 1111 with an L-shaped rod, the pipeline flow sensor 1111 can be extended into the sewage pipeline monitoring point, so that it can maintain a stable position in the sewage pipeline, thereby ensuring that the sensor probe is in full contact with the water flow.

[0058] Furthermore, under the control of the first control signal, the pipeline flow sensor 1111 can dynamically monitor the instantaneous flow Q(t) of the monitoring point according to the acquisition frequency value included in the first control signal and form a corresponding flow monitoring data set.

[0059] Furthermore, the obtained flow monitoring data set can be transmitted in real time to the signal converter 1122 in the form of an analog signal through a data transmission line.

[0060] Secondly, the signal converter 1122 is used to convert the analog signal into a digital signal, and send the flow monitoring data set to the memory 1121 in the form of a digital signal.

[0061] Specifically, after receiving the analog signal transmitted by the pipeline flow sensor 1111, the signal converter 1122 can convert it into a digital signal through analog-to-digital conversion (A / D conversion) technology.

[0062] In some optional implementations, an anti-interference filtering algorithm may be used during the signal conversion process to remove abnormal values ​​(such as burst pulse noise) caused by water flow fluctuations, thereby ensuring that the digital signal can accurately reflect the actual flow rate.

[0063] Furthermore, the signal converter 1122 may send the flow monitoring data set to the memory 1121 in the form of a digital signal.

[0064] Finally, the memory 1121 is used to store the flow monitoring data set in the form of digital signals.

[0065] Specifically, after receiving the digital signal, the memory 1121 may store the corresponding flow monitoring data set in the form of the digital signal.

[0066] Furthermore, the memory 1121 can send the stored flow monitoring data set to the data analysis terminal module 13 in the form of digital signals.

[0067] Furthermore, if Figure 3 As shown, the flow monitoring module 11 further includes a power supply unit 113 , including a battery 1131 and a power adapter 1132 , for supplying power to the monitoring unit 111 and the signal processing unit 112 .

[0068] Optionally, the automatic sampling module 12 is configured to collect sewage from the monitoring point in different time periods based on the second control signal to obtain a plurality of sewage samples.

[0069] Among them, such as Figure 4 As shown, the automatic sampling module 12 includes a sampling unit 121 and a sample collecting unit 122 .

[0070] Furthermore, the sampling unit 121 includes a sampling pipeline, a filter head, a peristaltic pump, a motor, a PLC control system, and a signal receiver. One end of the sampling pipeline is connected to the filter head, and the other end is connected to the peristaltic pump.

[0071] The sample collection unit 122 includes a rotary arm, a plurality of first vacuum sampling bottles, a second vacuum sampling bottle, and a dry ice chamber.

[0072] Specifically, after the signal receiver receives the second control signal sent by the data analysis terminal module, the received second control signal is sent to the PLC control system.

[0073] Furthermore, upon receiving the second control signal, the PLC control system can determine the next sampling time according to the time interval corresponding to the sampling frequency value contained in the second control signal. When the sampling time is reached, the PLC control system can control the motor to start under the control of the second control signal. Furthermore, the motor can drive the roller assembly of the peristaltic pump to rotate, thereby squeezing the sampling line hose to generate negative pressure, so that the sampling line draws sewage from the corresponding pipe and obtains multiple sewage samples.

[0074] Among them, the peristaltic pump can form a "fluid piston" through the elastic deformation of the hose, and then can extract sewage from the pipe to the sampling line, and the flow rate can be adjusted by the motor speed.

[0075] Furthermore, during the sampling process, a filter at the front end of the sampling line can intercept large particles of impurities (such as leaves, sand, and plastic fragments) in the sewage, preventing them from entering the sampling line and clogging the peristaltic pump or contaminating the sample. The filter can be a stainless steel filter with a pore size of 100 μm, which can be determined based on actual needs and is not specifically limited in this embodiment.

[0076] Furthermore, the end of the sampling line is connected to the mechanical interface of the sample collection unit's rotary arm, so that the rotary arm can rotate to the injection port corresponding to the first vacuum sampling bottle according to the PLC command. For example, the first sampling is corresponding to bottle 1, and the second sampling is corresponding to bottle 2.

[0077] Furthermore, the peristaltic pump runs continuously, injecting the sewage in the sampling line through the rotary arm into the corresponding first vacuum sampling bottle for storage.

[0078] Furthermore, each first vacuum sampling bottle is fixed in a dry ice chamber. The dry ice maintains a low temperature environment (about -20°C to -10°C) by absorbing heat through sublimation, which can inhibit microbial activity and chemical decomposition in the sample and keep the concentration of characteristic factors stable.

[0079] Optionally, the data analysis terminal module 13 is also used to generate a sampling ratio scheme based on the flow monitoring data set through flow time domain integral mixing ratio algorithm processing, and send the sampling ratio scheme to the automatic sampling module 12.

[0080] Among them, the flow time-domain integral mixing ratio algorithm represents an algorithm based on the time accumulation effect of flow and the principle of mass conservation. By simulating the dynamic mixing process of sewage in the pipe network, the flow data of different time periods are converted into sampling weights. It can achieve the goal of "replacing high-frequency discrete sampling with a small amount of mixed samples".

[0081] Specifically, it is assumed that a virtual water tank is connected to the end of the monitoring point, and all sewage flowing through the monitoring section flows into the virtual water tank. The water in the water tank satisfies the complete mixing characteristics and does not consider physical, chemical and biological reactions.

[0082] Furthermore, in the virtual water tank, the characteristic factor index value C at any time n is n Essentially, it is the flow-weighted average of the concentration of the sewage characteristic factors flowing through the monitoring point from time 0 to n, that is, the target concentration value C n This comprehensively reflects the cumulative effect of sewage upstream of the monitoring point in the time dimension, as shown in the following equation (2):

[0083]

[0084] Where: Q(t) represents the instantaneous flow rate at the monitoring point at time t; C(t) represents the instantaneous concentration of the characteristic factor at the monitoring point at time t; N represents the monitoring frequency, that is, the total number of sampling times; C i represents the characteristic factor concentration of the monitoring point in the i-th sampling period; Q i represents the flow rate of the monitoring point in the i-th sampling period; Q 总 Indicates the total flow rate at the monitoring point on that day.

[0085] Furthermore, according to the above relationship (2), when the monitoring frequency N tends to infinity, the total characteristic factor concentration of the virtual water tank at time n is strictly equivalent to the real mixing concentration of the sewage pipe in the period 0 to n.

[0086] Furthermore, the data processing unit 131 can calculate the flow weight coefficient of each sampling period Generate the corresponding sampling ratio plan: according to the flow weight coefficient Determine the fraction of volume extracted from the original sample collected during the i-th sampling period.

[0087] Optionally, the automatic sampling module 12 is further configured to extract and mix a plurality of sewage samples using a sampling ratio scheme to obtain a target sewage sample.

[0088] Specifically, the PLC control system may send a corresponding third control signal according to the received sampling ratio scheme.

[0089] Furthermore, under the control of the third control signal, the rotary arm is controlled to position to different first vacuum sampling bottles, and the rotary arm is controlled to extract samples from different first vacuum sampling bottles according to the corresponding flow weight coefficient based on the sampling ratio scheme and inject all the extracted samples into the second vacuum sampling bottle.

[0090] Furthermore, the rotary arm can be controlled to stir and mix the sample in the second vacuum sampling bottle to form a corresponding target sewage sample.

[0091] The drainage network sewage sampling system provided in this example uses a data analysis terminal module to customize the collection and sampling frequency values, adapting to different engineering scenarios and avoiding the blindness of traditional "fixed-frequency sampling" methods. Furthermore, the flow monitoring module collects flow data according to the collection frequency value, capturing subtle changes in flow over time and resolving the drawback of traditional single-point sampling methods that fail to reflect the cumulative effects of flow over a period of time. Simultaneously, the automatic sampling module collects sewage samples according to the sampling frequency value, avoiding the characteristic factor concentration gaps caused by traditional sparse sampling. Furthermore, using a flow time-domain integral mixing ratio algorithm to generate a sampling ratio scheme based on the flow monitoring dataset, it comprehensively considers the impact of flow changes at different times within the drainage network on sewage concentration, making the collected target sewage samples more representative. Furthermore, the water quality characteristics of the target sewage samples can be used to characterize the overall mixing status of the upstream water body of the drainage network to be sampled. This significantly reduces the number of sampling tests while ensuring accuracy, resolving the high cost and low efficiency bottleneck of characteristic factor analysis methods in engineering applications. Therefore, by sampling and testing the water quality characteristics of a mixed sample of the outflow water at the end of the sewage network, the overall mixing situation of the water body upstream of the network can be characterized. The sampling and testing costs are orders of magnitude higher, and the efficiency is greatly improved. At the same time, it also effectively guarantees the diagnostic accuracy of the characteristic factor analysis method.

[0092] In one example, Figure 5 As shown, a representative sampling intelligent acquisition system for drainage network based on flow time domain integration is provided, which includes three parts: flow monitoring module, automatic sampling module and data analysis terminal module.

[0093] The flow monitoring module includes a signal processing unit, a monitoring unit, and a power supply unit. The signal processing unit includes a memory and a signal converter. It primarily receives data transmitted by the monitoring unit through the signal converter, converts it into a digital signal, stores it in the memory, and uploads it to the data analysis terminal. The monitoring unit includes a pipeline flow sensor, a data transmission line, and an L-shaped rod. The pipeline flow sensor is connected to the signal processing unit via the data transmission line. The data transmission line is wrapped around and fixed to the L-shaped rod, which extends into the sewage pipe. The power supply unit includes a battery and a power adapter, which power the signal processing unit and the monitoring unit.

[0094] The automatic sampling module includes a sampling unit and a sample collection unit. The sampling unit includes a sampling pipeline, a filter head, a peristaltic pump, a motor, a PLC control system, and a signal receiver. One end of the sampling pipeline is connected to the filter head, and the other end is connected to the peristaltic pump. The filter head is used to filter impurities in the sewage pipe. The signal receiver is used to receive the signal from the data analysis terminal module and transmit the signal to the PLC control system, thereby controlling the motor to start the peristaltic pump, so that the sampling pipeline draws water from the sewage pipe. The sample collection unit includes a rotary arm, a vacuum sampling bottle, and a dry ice chamber. The rotary arm is used to inject the sewage in the sampling pipeline into the vacuum sampling bottle. The vacuum sampling bottle is fixed in the dry ice chamber. The dry ice chamber is used to maintain a low-temperature environment for the sample.

[0095] The data analysis terminal module includes a data processing unit and a human-computer interface. The data processing unit receives signal data transmitted by the flow monitoring module and processes the data using a flow time-domain integral mixing ratio algorithm. This generates a sampling solution and transmits it to the automatic sampling module. The human-computer interface provides a visual display of the data and the operating status of each device.

[0096] The principle of the flow time domain integral mixed ratio algorithm is as follows:

[0097] Assume that the monitoring point is connected to a virtual water tank at the end, and all sewage flowing through the monitoring section flows into the virtual water tank, and the water in the water tank satisfies the complete mixing characteristics and does not consider physical, chemical and biological reactions. At this time, in the virtual water tank, the characteristic factor index value C at any time n is n Essentially, it is the flow-weighted average of the concentration of the sewage characteristic factors flowing through the monitoring point from time 0 to n, that is, C n This comprehensively reflects the cumulative effect of wastewater upstream of the monitoring point over time. Therefore, the dynamic monitoring data can be converted into equivalent cumulative concentrations using the virtual water tank model, avoiding the problem of high-frequency sampling detection in traditional methods. According to the principle of conservation of mass, the total characteristic factor concentration of the virtual water tank at time n is shown in the above equation (2).

[0098] Furthermore, according to the above relationship (2), when the monitoring frequency N tends to infinity, the total characteristic factor concentration of the virtual water tank at time n is strictly equivalent to the actual mixed concentration of the sewage pipe in the period 0 to n. The flow rate weight ratio is used to mix the sewage samples in each period, and the deviation (ΔC) between the concentration of the mixed sample and the theoretical value is obeyed The convergence law of .

[0099] By increasing the sampling frequency, the error in mixed concentration can be controlled. Compared with the traditional discrete detection method which requires N independent tests, this example only requires a single test on the mixed sample to obtain an equivalent result, and the detection cost of a single monitoring point is reduced by Taking an average of 24 samples per day as an example, the cost savings rate reaches 95.83%.

[0100] Therefore, through this example, by detecting the water quality characteristics of a mixed sample of the outflow water at the end of the sewage pipe network, the overall mixing situation of the water body upstream of the pipe network can be characterized. The detection cost is reduced by orders of magnitude, the efficiency is greatly improved, and the diagnostic accuracy of the characteristic factor analysis method is effectively guaranteed.

[0101] In some optional embodiments, the above example is introduced by taking external water diagnosis as an example, and the stormwater flood management model (SWMM) is used to simulate the hydrodynamic-water quality process of the sewage pipe network and to verify the effect of external water diagnosis. Figure 6 As shown, the designed discharge flow is 25120m 3 To verify the broad applicability of the above example, a Monte Carlo random sampling method was used to randomly generate 1,000 sets of groundwater infiltration ratio parameters within the interval (0, 0.7), and a multi-scenario numerical simulation was established. Using hydrogen and oxygen isotopes as characteristic factor combinations, the device of the present invention was deployed at the outlet to simultaneously monitor flow and perform automatic sampling. For each scenario, using groundwater and domestic sewage as sources, the hydrogen and oxygen isotope concentrations of the mixed sample at the outlet were inverted based on the system provided by the above example.

[0102] The specific workflow is as follows: First, the integrated detection equipment is secured inside the manhole and kept horizontal. The data analysis terminal module sets the sampling cycle parameters for the day. The flow monitoring module performs high-precision sewage flow measurement at a frequency of 5 minutes, while the automatic sampling module performs equidistant sampling at a frequency of 24 times per day. The automatic sampling module triggers the peristaltic pump according to the sampling frequency, injecting sewage into the vacuum sampling bottle. After completing the daily sample collection, 24 sets of packaged samples are obtained. The flow monitoring module uploads the monitoring data to the data analysis terminal. During system operation, the data analysis terminal dynamically analyzes the flow monitoring data stream, constructs hourly weight coefficients based on the flow time-domain integration mixing algorithm, generates a mixing scheme, and sends it to the automatic sampling module. After receiving the signal, the automatic sampling module drives the mechanical rotary arm to position itself at the target vacuum sampling bottle. According to the mixing scheme, subsamples are extracted from the 24 original samples according to the weighted subsamples. After vortex mixing, a representative sample for the day is generated. The characteristic factor concentrations of the sample are highly similar to those of the actual sewage characteristic factors under real-time integration.

[0103] Furthermore, if Figure 7As shown in the figure, groundwater infiltration analysis based on the mixed sewage samples generated in the above example shows an average relative error of 1.39% between the inversion results and the true values ​​in 1000 Monte Carlo simulations. ErrorDistribution-24mixSampling represents error distribution - 24 mixed sampling; Frequency represents frequency; KDEFit represents kernel density estimation fitting; Density represents density; and RelativeError represents relative error.

[0104] Furthermore, the accuracy of inversion is compared with the above example using the traditional method. Assuming that the sampling schemes of 24 times / day, 12 times / day, 8 times / day, 6 times / day, 4 times / day and 3 times / day are respectively carried out at the outlet, and 1000 Monte Carlo simulations are performed to obtain the distribution of the relative error of inversion under each sampling scheme. The average relative errors of each sampling scheme are 2.02%, 3.90%, 6.87%, 3.89%, 28.75% and 9.44% respectively. It can be seen that the inversion accuracy of different sampling times under the traditional method varies greatly, and intensive sampling (such as sampling more than 12 times a day) can basically guarantee a higher probability of inversion accuracy. However, the accuracy error under sparse sampling is difficult to control, which is related to the instantaneous concentration change of the characteristic factor of the sampling point, and has a large uncertainty. Therefore, sparse sampling is difficult to guarantee the inversion accuracy under the traditional method, and although high-frequency sampling can guarantee better inversion accuracy, it requires extremely high detection cost.

[0105] This shows that the above example is significantly superior to the traditional method in terms of the accuracy of applying characteristic factors to analyze external water infiltration. Under the same sampling frequency (24 times / day), the above example achieved better inversion error than the traditional method in 779 simulations, verifying the robustness of the above example. Secondly, the traditional method requires testing 24 independent samples one by one, while the above example only requires testing one mixed sample, significantly reducing the testing cost. Moreover, while the cost is reduced, the inversion error is also improved compared to the traditional method, which further proves the effectiveness and superiority of the above example.

[0106] According to an embodiment of the present invention, an embodiment of a method for sampling sewage in a drainage network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0107] In this embodiment, a drainage network sewage sampling method is provided, which can be used in the drainage network sewage sampling system 1 provided in the above embodiment of the present invention. Figure 8 FIG. 1 is a flow chart of a method for sampling sewage in a drainage network according to an embodiment of the present invention. Figure 8As shown, the process includes the following steps:

[0108] Step S101: Obtain a flow data set and multiple sewage samples from a monitoring point in a drainage network.

[0109] For the specific process, please refer to the above functional description of the flow monitoring module 11 and the automatic sampling module 12 in the drainage network sewage sampling system 1, as well as the description of the interaction process between the flow monitoring module 11 and the data analysis terminal module 13, and the automatic sampling module 12 and the data analysis terminal module 13, which will not be repeated here.

[0110] Step S102: Based on the flow monitoring data set, a sampling ratio scheme is generated by processing the flow time domain integral mixing ratio algorithm.

[0111] In some optional implementations, the above step S102 includes:

[0112] In step a1, based on the flow monitoring data set, a plurality of flow weight coefficients are obtained by processing the flow time domain integral mixing ratio algorithm.

[0113] Step a2: Generate a sampling ratio plan based on multiple flow weight coefficients.

[0114] The specific process can refer to the above functional description of the data analysis terminal module 13 in the drainage network sewage sampling system 1, which will not be repeated here.

[0115] Step S103 , extracting multiple sewage samples using a sampling ratio scheme to obtain a target sewage sample, wherein the water quality characteristics of the target sewage sample are used to characterize the overall mixing condition of the upstream water body of the drainage network to be sampled.

[0116] The specific process can refer to the above functional description of the automatic sampling module 12 in the drainage network sewage sampling system 1, which will not be repeated here.

[0117] The drainage network sewage sampling method provided in this embodiment uses a flow time-domain integral mixing ratio algorithm to generate a sampling ratio scheme based on a flow monitoring data set. It can comprehensively consider the impact of flow changes at different times in the drainage network on sewage concentration, making the collected target sewage samples more representative. Furthermore, the water quality characteristics of the target sewage samples can characterize the overall mixing situation of the upstream water body of the drainage network to be sampled. While ensuring accuracy, the number of tests is greatly reduced, solving the bottleneck of high cost and low efficiency in engineering applications of the characteristic factor analysis method.

[0118] The embodiment of the present invention also provides a computer device having the above Figure 9 The sewage sampling method for the drainage network is shown.

[0119] See also Figure 9, Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0120] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0121] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0122] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0124] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0125] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0126] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0127] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A drainage network sewage sampling system, characterized in that: The system includes: a flow monitoring module, an automatic sampling module and a data analysis terminal module; The data analysis terminal module is configured to obtain an acquisition frequency value and a sampling frequency value, and send a first control signal to the flow monitoring module based on the acquisition frequency value, and send a second control signal to the automatic sampling module based on the sampling frequency value; The flow monitoring module is configured to collect the flow of the monitoring point in the drainage network to be sampled at different time periods based on the first control signal to obtain a flow data set, and send the flow data set in the form of a digital signal to the data analysis terminal module; The automatic sampling module is configured to collect sewage from the monitoring point in different time periods based on the second control signal to obtain a plurality of sewage samples; The data analysis terminal module is further configured to generate a sampling ratio scheme based on the flow monitoring data set through a flow time-domain integral mixing ratio algorithm, and send the sampling ratio scheme to the automatic sampling module; The automatic sampling module is also used to extract and mix the multiple sewage samples using the sampling ratio scheme to obtain a target sewage sample. The water quality characteristics of the target sewage sample are used to characterize the overall mixing condition of the upstream water body of the drainage network to be sampled.

2. The system according to claim 1, wherein: The flow monitoring module includes: a signal processing unit and a monitoring unit, wherein the monitoring unit includes a pipeline flow sensor, a data transmission line and an L-shaped rod; The pipeline flow sensor is connected to the signal processing unit via the data transmission line, and the data transmission line is wound and fixed on the L-shaped rod. The L-shaped rod is located in the pipeline of the drainage network to be sampled and is used to support the pipeline flow sensor; The pipeline flow sensor is configured to monitor the flow at the monitoring point in different time periods based on the first control signal, obtain the flow monitoring data set, and send the flow monitoring data set to the signal processing unit in the form of an analog signal; The signal processing unit is used to convert the analog signal into a digital signal, so that the flow monitoring data set is stored in the form of the digital signal.

3. The system according to claim 2, characterized in that The signal processing unit includes: a memory and a signal converter; The signal converter is used to convert the analog signal into a digital signal, and send the flow monitoring data set to the memory in the form of the digital signal; The memory is used to store the flow monitoring data set in the form of the digital signal.

4. The system according to claim 2, wherein: The flow monitoring module further includes: The power supply unit includes a battery and a power adapter, and is used to supply power to the signal processing unit and the monitoring unit.

5. The system according to claim 1, wherein: The automatic sampling module includes: a sampling unit and a sample collection unit; The sampling unit is configured to collect sewage from the monitoring point in different time periods based on the second control signal to obtain the plurality of sewage samples, and transmit the plurality of sewage samples to the sample collection unit; The sample collection unit is used to store the multiple sewage samples, and use the sampling ratio scheme to extract and mix the multiple sewage samples to obtain the target sewage sample.

6. The system according to claim 5, characterized in that The sampling unit includes: a sampling pipeline, a filter head, a peristaltic pump, a motor, a PLC control system, and a signal receiver; One end of the sampling line is connected to the filter head, and the other end is connected to the peristaltic pump; The filter head is used to filter impurities in the pipes of the drainage network to be sampled; The signal receiver is configured to receive the second control signal and send the second control signal to the PLC control system; The PLC control system is configured to control the motor to start the peristaltic pump based on the second control signal, so that the sampling pipeline collects sewage from the monitoring point at different time periods to obtain the multiple sewage samples; The PLC control system is further configured to send a third control signal to the sample collection unit using the sampling ratio scheme.

7. The system according to claim 6, characterized in that The sample collection unit includes: a rotary arm, a plurality of first vacuum sampling bottles, a second vacuum sampling bottle and a dry ice chamber, wherein the plurality of first vacuum sampling bottles and the second vacuum sampling bottle are fixed in the dry ice chamber; The rotary arm is used to inject the multiple sewage samples in the sampling pipeline into the multiple first vacuum sampling bottles for storage; The rotary arm is further configured to extract the plurality of sewage samples from the plurality of first vacuum sampling bottles and inject the extracted sewage samples into the second vacuum sampling bottles based on the third control signal; The rotary arm is further used to mix the sewage sample in the second vacuum sampling bottle, so that the target sewage sample is formed in the second vacuum sampling bottle and stored; The dry ice chamber is used to provide a temperature environment for maintaining the sewage samples in the plurality of first vacuum sampling bottles and the second vacuum sampling bottles.

8. The system according to claim 1, wherein: The data analysis terminal module includes: a data processing unit and a human-computer interaction interface; The data processing unit is used to generate the sampling ratio scheme based on the flow monitoring data set through the flow time domain integral mixing ratio algorithm; The human-computer interaction interface is used to visually display any data and the operating status of each module during the sampling process.

9. A method for sampling sewage from a drainage network, characterized in that: Used in a drainage network sewage sampling system according to any one of claims 1 to 8; the method comprising: Obtain flow data sets and multiple sewage samples from monitoring points in the drainage network; Based on the flow monitoring data set, a sampling ratio scheme is generated by processing the flow time domain integral mixing ratio algorithm; The generated sampling ratio scheme is used to extract the multiple sewage samples to obtain target sewage samples. The water quality characteristics of the target sewage samples are used to characterize the overall mixing conditions of the upstream water body of the drainage network to be sampled.

10. The method according to claim 9, characterized in that Based on the flow monitoring data set, a sampling ratio scheme is generated by processing the flow time domain integral mixing ratio algorithm, including: Based on the flow monitoring data set, a plurality of flow weight coefficients are obtained by processing the flow time domain integral mixing ratio algorithm; The sampling ratio scheme is generated according to the multiple flow weight coefficients.

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