A system and method for real-time monitoring, precise source tracing and in-situ repair of artificial wetland leakage

Abstract

The present invention discloses a system and method for real-time monitoring, precise source tracing and in-situ repair of artificial wetland leakage, which comprises a mobile leak detection device, a data terminal and a server. The water pressure sensor and the conductivity sensor of the mobile leak detection device monitor the leakage of the artificial wetland in real time. If the maximum relative deviation P of the water pressure is less than the standard value α and the maximum relative deviation C of the conductivity is less than the standard value β, it is determined that no leakage occurs in the artificial wetland. Otherwise, the mobile leak detection device is required to accurately trace the leakage. When the total leakage area A t Less than standard value A t1 , the artificial wetland continues to operate, otherwise the artificial wetland needs to be repaired in situ until the measured value A is met t Below standard value A t1 The present invention solves the problems of real-time monitoring, precise tracing and in-situ restoration of artificial wetlands, and has the advantages of fast response, high sensitivity, convenient operation, low investment and environmental friendliness.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy conservation and environmental protection, water treatment and water environment restoration, and specifically provides a system and method for real-time monitoring, precise source tracing and in-situ restoration of artificial wetland leakage. Background Art

[0002] Artificial wetlands have been used in a wider range of treatment in China in recent years, including urban domestic sewage, rural domestic sewage, farmland drainage, aquaculture wastewater, etc., due to their advantages such as low investment and construction costs, low operating costs, low operating technology, and the ability to be built in combination with landscapes. However, since the composition of artificial wetland influent is relatively complex, once leakage occurs, it will cause a sharp increase in the content of organic matter such as ammonia, nitrogen, phosphorus, COD, and toxic and harmful substances such as heavy metals in the surrounding rivers and groundwater, causing damage to the ecological environment around the wetland. Therefore, it is very necessary to establish an accurate and efficient system and method for real-time monitoring, precise tracing, and in-situ repair of artificial wetland leakage during the construction of artificial wetlands.

[0003] Patent application No. 202011124113.4 discloses an artificial wetland water treatment system and its operation method, which includes a functional treatment unit and an artificial wetland unit connected in sequence, wherein the functional treatment unit includes an electro-flocculation and sedimentation unit, and the electro-flocculation and sedimentation unit adopts a mesoporous combined with microporous aeration method. The device only considers the pollutant reduction effect of the artificial wetland, and does not consider the possible leakage of the artificial wetland and its repair after leakage.

[0004] Patent application No. 201320737465.6 discloses an integrated non-clogging artificial wetland, which includes a shell, an inlet pipe and an outlet pipe arranged at the upper part of both sides of the shell, and a plurality of artificial wetland units arranged in series in the shell; the shell is integrally formed, the height of the inlet pipe and the outlet pipe is higher than the height of the water level in the artificial wetland unit, and a partition is arranged between every two artificial wetland units, and a water hole is arranged on the partition to prevent clogging, and the water hole is located at the bottom or the top of the partition. The device only uses engineering means to reduce the frequency of artificial wetland clogging, but does not consider the potential leakage of artificial wetlands.

[0005] Patent application No. 202121800031.7 discloses an online leakage detection device for geomembranes, which includes a metal roller capable of supporting the geomembrane, a metal detector disposed on the upper side of the metal roller, and an alarm component capable of energizing the alarm. The device simply considers the water seepage of the geomembrane, and does not involve the diffusion of pollutants caused by leakage, nor can it detect quickly and accurately.

[0006] Patent application No. 202111137796.1 discloses an environmental pollution source tracing system and method based on edge computing, which includes edge computing equipment, a server and a data acquisition terminal; the data acquisition terminal includes a monitoring device for collecting various types of detection object data and a meteorological acquisition device for collecting meteorological parameters; the edge computing device is used to receive various types of detection object data and meteorological parameters, and uses a threshold judgment method and a quadrant division method to determine potential pollution sources, and uploads the potential pollution sources to the server; the server is used to find the final pollution source among the potential pollution sources. The system and method only provide an algorithm for pollutant tracing, but fail to apply it to actual artificial wetlands, lacking actual engineering verification. Summary of the invention

[0007] Purpose of the invention: In view of the problems that artificial wetland leakage cannot be monitored in real time, the tracing process when leakage occurs is time-consuming, the accuracy is low, it is necessary to shut down for repair, and there is a lack of intelligent judgment methods, the present invention provides a system and method for real-time monitoring, precise tracing and in-situ repair of artificial wetland leakage. The leakage of the artificial wetland is monitored in real time by the water pressure sensor and the conductivity sensor of the mobile leak detection device, and the leakage point is accurately traced when leakage occurs. At the same time, on the basis of evaluating the leakage of the anti-seepage membrane, different in-situ repair technologies are adopted for different leakage situations, and repeated detection is carried out during the repair process until the problem is solved. An intelligent system for artificial wetland leakage monitoring, tracing and in-situ repair with "real-time, rapid, precise and efficient feedback" is constructed, thereby realizing timely and effective control of artificial wetland leakage and reducing the environmental risks of artificial wetland leakage.

[0008] Technical solution: A method for real-time monitoring and precise source tracing of artificial wetland leakage, including the following steps:

[0009] Step 1: Use a mobile leak detection device set at a routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time, and obtain a series of water pressure data P1, P2, ..., P n And the series conductivity data C1, C2, ..., C n , where n is the number of routine leakage monitoring points;

[0010] Based on formula (1) and formula (2), the maximum relative deviation value of real-time water pressure and the maximum relative deviation value of conductivity are calculated;

[0011]

[0012]

[0013] In the formula, is the average water pressure of the ith routine leakage monitoring point in the previous monitoring cycle; is the average conductivity of the ith routine leakage monitoring point in the previous monitoring cycle;

[0014] If P≤α and C≤β, it is determined that the artificial wetland has no leakage; α is the upper tolerance limit of P, and β is the upper tolerance limit of C;

[0015] If P>α or C>β, it is determined that there is potential leakage in the artificial wetland, and step 2 is executed;

[0016] Step 2: Use particle swarm algorithm to determine potential leakage points x1, x2, …, x n+1 , the mobile leak detection device enters the dynamic monitoring mode and moves to the potential leakage point to monitor the leakage situation and achieve accurate tracing.

[0017] Furthermore, in step 1, a mobile leak detection device is set at a routine leakage monitoring point using a grid method, and the specific operation steps are as follows:

[0018] Set grid lines in the length and width of the constructed wetland according to the following rules 1 to 3;

[0019] Rule 1: If a or b < 20m, draw a grid line at the midpoint;

[0020] Rule 2: If a or b is within the range of 20-40m, draw a grid line at the 1 / 3 and 2 / 3 positions respectively;

[0021] Rule 3: If a or b>40m, start from the midpoint and divide a grid line every 20m;

[0022] Among them, a and b represent the length of the long side and wide side of the artificial wetland, respectively;

[0023] A routine leakage monitoring vertical line is set at the intersection of the grid lines, and a mobile leak detection device is set on the routine leakage monitoring vertical line, with a depth of 0 to 0.5 m from the top of the anti-seepage membrane.

[0024] Furthermore, in step 2, the particle swarm algorithm is used to determine the potential leakage points x1, x2, ..., x n+1 , the specific implementation steps are:

[0025] S210: Setting the initial position of each mobile leak detection device For the routine leakage monitoring point where it is located, represents the distance from the origin in the long side direction at the initial moment of the i-th mobile leak detection device, Indicates the distance of the i-th mobile leak detection device from the origin in the broadside direction at the initial moment; and gives each mobile leak detection device an initial speed is the working speed of the mobile leak detection device, where: represents the moving speed of the i-th mobile leak detection device in the long side direction at the initial moment, represents the moving speed of the i-th mobile leak detection device in the broadside direction at the initial moment;

[0026] S220: When the number of iterations is k, according to formula (3), calculate the current position of each mobile leak detection device The fitness value of

[0027]

[0028] In the formula, a1 is the contribution weight of water pressure in the fitness function; a2 is the contribution weight of conductivity in the fitness function;

[0029] S230: When the number of iterations is k, for each mobile leak detection device, its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained;

[0030] S240: When the number of iterations is k, the maximum fitness value of the current position of all mobile leak detection devices is calculated. With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained;

[0031] S250: when the number of iterations is k, updating the position of each mobile leak detection device;

[0032]

[0033] Simultaneously update the speed of each mobile leak detection device;

[0034]

[0035] Where w is the inertia weight; c1 is the acceleration constant of the historical best position; c2 is the acceleration constant of the global best position;

[0036] S260: If the number of iterations k < kt, then enter the next iteration and go to S220; if the number of iterations k ≥ kt, then the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices The final historical best positions of the n mobile leak detection devices and the final global best position of all mobile leak detection devices are marked as potential leakage points x1, x2, ..., x n+1 .

[0037] The present invention also discloses a real-time monitoring and precise source tracing system for artificial wetland leakage, comprising:

[0038] Several mobile leak detection devices, which have two modes: routine monitoring and dynamic monitoring. When the mobile leak detection device is in the routine monitoring mode, it is located at the routine leakage monitoring point, and is used to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time, and upload them to the data terminal; when the mobile leak detection device is in the dynamic monitoring mode, it is used to move to the potential leakage point according to the moving speed and moving direction sent by the server;

[0039] A data terminal is used to receive the detection data of each mobile leak detection device and upload the detection data to the server;

[0040] The server is used to calculate the maximum relative deviation value P of the real-time water pressure and the maximum relative deviation value C of the conductivity based on equation (1) and equation (2);

[0041] and used to determine whether leakage occurs in the artificial wetland based on the size of P and α, and the size of C and β;

[0042] If P≤α and C≤β, it is determined that the artificial wetland has no leakage; α is the upper tolerance limit of P, and β is the upper tolerance limit of C;

[0043] If P>α or C>β, it is determined that the artificial wetland has potential leakage;

[0044]

[0045]

[0046] In the formula, is the average water pressure of the ith routine leakage monitoring point in the previous monitoring cycle; is the average conductivity of the ith routine leakage monitoring point in the previous monitoring cycle;

[0047] When there is potential leakage in artificial wetlands, the particle swarm algorithm is used to identify the potential leakage points x1, x2, ..., x n+1 Carry out positioning and achieve accurate traceability.

[0048] Further, when the mobile leak detection device is in the routine monitoring mode, the mobile leak detection device is set on the routine leakage monitoring vertical line, and the setting depth is 0 to 0.5 m from the top of the anti-seepage membrane;

[0049] The routine leakage monitoring vertical line is determined according to the intersection of the grid lines corresponding to the artificial wetland;

[0050] The grid lines corresponding to the artificial wetland are obtained by setting the length and width of the artificial wetland according to the following rules;

[0051] Rule 1: If a or b < 20m, draw a grid line at the midpoint;

[0052] Rule 2: If a or b is within the range of 20-40m, draw a grid line at the 1 / 3 and 2 / 3 positions respectively;

[0053] Rule 3: If a or b>40m, start from the midpoint and divide a grid line every 20m;

[0054] Among them, a and b represent the length of the long side and wide side of the artificial wetland respectively.

[0055] Furthermore, when the server is used in the case of potential leakage in artificial wetlands, the particle swarm algorithm is used to identify the potential leakage points x1, x2, …, x n+1 The specific algorithm steps for positioning are:

[0056] S210: Setting the initial position of each mobile leak detection device For the routine leakage monitoring point where it is located, represents the distance from the origin in the long side direction at the initial moment of the i-th mobile leak detection device, Indicates the distance of the i-th mobile leak detection device from the origin in the broadside direction at the initial moment; and gives each mobile leak detection device an initial speed is the working speed of the mobile leak detection device, where: represents the moving speed of the i-th mobile leak detection device in the long side direction at the initial moment, represents the moving speed of the i-th mobile leak detection device in the broadside direction at the initial moment;

[0057] S220: When the number of iterations is k, according to formula (3), calculate the current position of each mobile leak detection device The fitness value of

[0058]

[0059] In the formula, a1 is the contribution weight of water pressure in the fitness function; a2 is the contribution weight of conductivity in the fitness function;

[0060] S230: When the number of iterations is k, for each mobile leak detection device, its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained;

[0061] S240: When the number of iterations is k, the maximum fitness value of the current position of all mobile leak detection devices is calculated. With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained;

[0062] S250: when the number of iterations is k, updating the position of each mobile leak detection device;

[0063]

[0064] Simultaneously update the speed of each mobile leak detection device;

[0065]

[0066] Where w is the inertia weight; c1 is the acceleration constant of the historical best position; c2 is the acceleration constant of the global best position;

[0067] S260: If the number of iterations k < kt, return to S220 and perform the next iteration; if the number of iterations k ≥ kt, the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices The final historical best positions of the n mobile leak detection devices and the final global best position of all mobile leak detection devices are marked as potential leakage points x1, x2, ..., x n+1 , n+1 is the number of potential leakage points.

[0068] The present invention also discloses an in-situ repair method for artificial wetland leakage, comprising the following steps:

[0069] S10: Using the above-disclosed method for real-time monitoring and precise source tracing of artificial wetland leakage, obtain potential leakage points x1, x2, ..., x n+1 The position of n+1 is the number of potential leakage points;

[0070] S20: Using the threshold judgment method, calculate the leakage area A1, A2, ..., A of each potential leakage point n+1 , and calculate the total leakage area A of the constructed wetland t ;

[0071] S30: If A t ≤A t1 , it is determined that a small leakage has occurred in the artificial wetland, and each mobile leak detection device returns to the initial routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time; if A t1 ≤At ≤A t2 , it is determined that the artificial wetland has a medium leakage, the water distribution to the area around the potential leakage point is stopped, and the potential leakage point is partially repaired, and S40 is executed; if A t ≥A t2 , it is determined that a major leakage has occurred in the artificial wetland, all water inflow to the artificial wetland is interrupted, the artificial wetland is fully repaired, and S40 is executed;

[0072] S40: Determine the repaired detection result by executing S20 and S30 again.

[0073] Furthermore, in S20, the threshold judgment method is used to calculate the leakage areas A1, A2, ..., A of each potential leakage point. n+1 , perform the following steps for each potential leak point:

[0074] The water pressure data P and the conductivity data C of the area around the potential leakage point are obtained by a mobile leakage detection device located at the potential leakage point;

[0075] will satisfy P≤P t or C ≥ C t The area is divided into leakage areas, and the area enclosed by the boundary points of the leakage area is the leakage area of ​​the potential leakage point; where P t is the water pressure threshold, C t is the conductivity threshold.

[0076] The present invention also discloses an artificial wetland leakage in-situ repair system, comprising:

[0077] The potential leakage point positioning module is used to obtain the potential leakage points x1, x2, ..., x based on the real-time monitoring and accurate source tracing method of artificial wetland leakage disclosed above. n+1 The position of n+1 is the number of potential leakage points;

[0078] The leakage area calculation module is used to calculate the leakage area A1, A2, ..., A of each potential leakage point using the threshold judgment method. n+1 , and calculate the total leakage area A of the constructed wetland t ;

[0079] The repair module is used to repair the total leakage area A of the artificial wetland. t , determine the extent of leakage in the artificial wetland, and cooperate with external equipment to implement corresponding repair measures according to the extent of leakage in the artificial wetland;

[0080] The post-repair detection result determination module is used to sequentially call the leakage area calculation module and the repair module after the repair measures are completed to determine the post-repair detection result;

[0081] Among them, in the repair module, if A t ≤A t1 , it is determined that a small leakage has occurred in the artificial wetland, and each mobile leakage detection device is controlled to return to the initial routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time; if A t1 ≤A t ≤A t2 , it is determined that the artificial wetland has a medium leakage, the water distribution to the area around the potential leakage point is stopped, the potential leakage point is partially repaired, and the repaired detection result judgment module is called; if A t ≥A t2 , it is determined that a large leakage has occurred in the artificial wetland, all water inflow to the artificial wetland is interrupted, the artificial wetland is fully repaired, and the detection result judgment module after the repair is called.

[0082] Furthermore, the leakage area calculation module includes:

[0083] The leakage area division module is used to obtain the water pressure data P and conductivity data C of the area around the potential leakage point through a mobile leakage detection device located at the potential leakage point; t or C ≥ C t The area is divided into leakage areas, and the area enclosed by the boundary points of the leakage area is the leakage area of ​​the potential leakage point; where P t is the water pressure threshold, C t is the conductivity threshold;

[0084] Total leakage area of ​​artificial wetland A t The calculation module is used to calculate the total leakage area A of the artificial wetland based on the calculation results of the leakage area division module. t .

[0085] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0086] (1) In the process of monitoring and tracing, the server can receive monitoring data from each mobile leak detection device, and feed back to each mobile leak detection device on the basis of summarizing the monitoring data, thereby realizing group collaboration and achieving rapid response of real-time monitoring compared with traditional methods; at the same time, the tracing process can also obtain the final historical optimal position of each mobile leak detection device and the final global optimal position of all mobile leak detection devices as potential leakage points, thereby guiding the in-situ repair of artificial wetlands, which is suitable for complex situations such as multi-point leakage in artificial wetlands;

[0087] (2) The present invention is highly targeted. Based on the evaluation of the leakage of artificial wetlands, different repair measures are proposed for different leakage situations, achieving three different responses. At the same time, during the in-situ repair process, repeated detection can be performed until the problem is solved. Compared with the previous single monitoring or tracing process, the present invention can realize the real-time monitoring of artificial wetland leakage, accurate tracing and the full life cycle of in-situ repair.

[0088] (3) The present invention has low investment and low operating cost, simple system structure, simple operation and easy implementation, and does not cause environmental pollution. It is highly intelligent and integrated, and can achieve intelligent management of artificial wetland leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 is a plan layout diagram of the present invention;

[0090] Figure 2 It is a cross-sectional view of the present invention.

[0091] Figure 3 is a flow chart of the present invention;

[0092] Figure 4 Provide a flow chart for accurate source tracing of artificial wetland leakage;

[0093] Figure 5 This is a layout diagram of routine leakage monitoring points in Example 1. DETAILED DESCRIPTION

[0094] The technical solution of the present invention is now further described in conjunction with the embodiments and drawings.

[0095] The present invention discloses a system for real-time monitoring, precise source tracing and in-situ repair of artificial wetland leakage, the application scenario of which is composed of an artificial wetland 5 and lower soil 6, wherein the artificial wetland 5 is mainly composed of wetland plants 5-1, wetland fillers 5-2 and impermeable membranes 5-3. Figure 1 and Figure 2As shown, the system of the present invention mainly includes a mobile leak detection device 1, a data terminal 2, a server 3 and a cable 4. Among them, the mobile leak detection device 1 includes a water pressure sensor 1-1 for collecting water pressure data, a conductivity sensor 1-2 for collecting conductivity data and a position sensor 1-3 for receiving and sending positioning signals; the mobile leak detection device 1 has two modes: routine monitoring and dynamic monitoring. The routine monitoring mode is used when there is no leakage in the artificial wetland. The mobile leak detection device obtains the water pressure data and conductivity data of the routine leakage monitoring point in real time at the routine leakage monitoring point. The dynamic monitoring mode is used to trace the leakage and judge the leakage situation when there is potential leakage in the artificial wetland. The range of the water pressure sensor 1-1 is 0-100 kPa, the measurement accuracy is ±0.001-1.5% FS (full scale), and the temperature range is -20-105°C; the range of the conductivity sensor 1-2 is 1-10000 μS / cm, the measurement accuracy is ±0.001-1.5% FS (full scale), and the ambient temperature is normal temperature -20-105°C. The data terminal 2 is used to receive various detection data and upload them to the server 3, wherein the server 3 is used to evaluate the leakage of the artificial wetland, and determine the leakage point and leakage area by using the particle swarm algorithm and the threshold judgment method.

[0096] Based on the above structure, a method for real-time monitoring, precise source tracing and in-situ repair of artificial wetland leakage according to the present invention is now further described.

[0097] Step 1: At each routine leakage monitoring point (M1, M2, …, M n ) Arrange a mobile leak detection device 1, set the mobile leak detection device 1 to routine monitoring mode, use the water pressure sensor 1-1 and the conductivity sensor 1-2 to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time, and obtain a series of water pressure data P1, P2, ..., P n And the series conductivity data C1, C2, ..., C n , where n is the number of routine leakage monitoring points.

[0098] In this step, the grid method can be used to arrange each routine leakage monitoring point. The specific steps are: set grid lines in the length and width directions according to Table 1.

[0099] Table 1 Grid line setting method

[0100] a or b Grid line setting method ＜20m 1 (midpoint) 20-40m 2 (1 / 3, 2 / 3 positions) ＞40m Starting from the midpoint, a grid line is drawn every 20m.

[0101] Among them, a and b represent the length of the long side and wide side of the artificial wetland respectively.

[0102] A routine leakage monitoring vertical line is set at the intersection of the grid lines, and a routine leakage monitoring point is set on it, with a depth of 0 to 0.5 m above the top of the impermeable membrane.

[0103] The above data is transmitted to the server 3 via the data terminal 2 .

[0104] The server calculates the maximum relative deviation value of the real-time water pressure and the maximum relative deviation value of the conductivity based on equations (1) and (2).

[0105]

[0106]

[0107] In the formula, is the average water pressure at the ith routine leakage monitoring point in the past month, kPa; is the average conductivity of the ith routine leakage monitoring point in the past month, μS / cm.

[0108] When P≤α and C≤β, it is determined that no leakage occurs in the artificial wetland, and the mobile leakage detection device 1 maintains the routine monitoring state.

[0109] When P>α or C>β, it is determined that there is potential leakage in the artificial wetland, and further source tracing is required, that is, executing step 2 to accurately trace the leakage of the artificial wetland. The upper tolerance limit α of P and the upper tolerance limit β of C are both in the range of 25% to 50%.

[0110] Step 2: Control the mobile leak detection device 1 to switch to the dynamic monitoring mode, and use the particle swarm algorithm to find the potential leakage points x1, x2, ..., x n+1 Positioning is performed, where n+1 is the number of leakage points. At the same time, the threshold judgment method is used to obtain the leakage area A1, A2, ..., A of each leakage point. n+1 .

[0111] The specific operation steps of the particle swarm algorithm mentioned in this step are:

[0112] S210: Initialization: Setting the initial position of each mobile leak detection device The routine leakage monitoring point where it is located, and the initial speed of each mobile leak detection device It is the operating speed of the mobile leak detection device.

[0113] S220: Calculate fitness value: When the number of iterations is k, calculate the fitness value of each mobile leak detection device at the current position according to the fitness function (Formula 3). The fitness value of

[0114]

[0115] Where a1 is the contribution weight of water pressure in the fitness function, ranging from -1 to 0, dimensionless; a2 is the contribution weight of conductivity in the fitness function, ranging from 0 to 1, dimensionless.

[0116] S230: Find the best individual fitness value: When the number of iterations is k, for each mobile leak detection device, set its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained, as shown in Formula 4:

[0117]

[0118] S240: Find the optimal fitness value of the group: When the number of iterations is k, the current positions of all mobile leak detection devices are The maximum fitness value With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained, as shown in Formula 5:

[0119]

[0120] S250: Update the position and speed of the mobile leak detection device: When the number of iterations is k, update the position of each mobile leak detection device, as shown in Formula 6:

[0121]

[0122] At the same time, the speed of each mobile leak detection device is updated, as shown in Formula 8:

[0123]

[0124] Wherein, w is the inertia weight, which is a non-negative number and is used to adjust the scope of spatial leakage tracing of artificial wetlands. It is 0.9 to 1.2 and dimensionless. c1 is the acceleration constant of the historical best position and is used to adjust the maximum step length of tracing. It is 1 to 3 and dimensionless. c2 is the acceleration constant of the global best position and is used to adjust the maximum step length of tracing. It is 1 to 3 and dimensionless.

[0125] S260: Determine whether the algorithm is finished: if the number of iterations k < kt, return to S220 and perform the next iteration; if the number of iterations k ≥ kt, the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices The final historical best positions of the n mobile leak detection devices are marked as potential leakage points x1, x2, ..., x n , and the final global optimal position of all mobile leak detection devices is marked as the potential leakage point x n+1 The value range of kt is 20 to 100.

[0126] The threshold determination method involved in this step is specifically: determine whether the water pressure P and conductivity C around the potential leakage point exceed the threshold P t and C t , for P≤P t or C ≥ C t The area is divided into leakage areas. The area enclosed by the boundary points of the leakage area connected in sequence is the leakage area of ​​each leakage point A1, A2, ..., A n+1 ;Threshold P t The value range is 10~11kPa, and the threshold C t The value range is 1800~2000μS / cm. It should be clear that the water pressure P and conductivity C are obtained by the mobile leak detection device located near the potential leakage point; and when a mobile leak detection device completes the leakage situation of the potential leakage point near itself, it can assist another mobile leak detection device to complete the leakage situation monitoring.

[0127] Step 3: The server evaluates the leakage of the artificial wetland and obtains the total leakage area A of the artificial wetland based on the following formula: t :

[0128]

[0129] In the formula, A i is the leakage area of ​​the i-th leakage point, m 2 .

[0130] When A t ≤A t1 When a small leakage occurs in the artificial wetland, the artificial wetland maintains its current operating state. At the same time, each leak detection device returns to the routine monitoring point and switches back to the routine monitoring mode.

[0131] When A t1 ≤A t ≤A t2 When the artificial wetland is judged to have medium leakage, water distribution to the area around the leakage point is stopped, and local repairs are carried out on the leakage part.

[0132] When A t ≥A t2 When the artificial wetland was found to have a major leak, all water inflow to the artificial wetland was cut off and the artificial wetland was fully repaired.

[0133] Among them, the standard value A t1 The value range is 5-10%A, the standard value is A t2 The value range is 10~25%A, where A is the total area of ​​artificial wetland anti-seepage membrane, m 2 .

[0134] Step 4: After the repair is completed, repeat steps 2 and 3 to determine the repaired test results. If A is satisfied t ≤A t1 , it is determined that the repair is completed, and each mobile leak detection device returns to the routine monitoring point and switches back to the routine monitoring mode; otherwise, steps 2 and 3 are repeated.

[0135] Example 1

[0136] This embodiment will Figure 1 The system shown is applied to a constructed wetland with a long side length a=80m and a short side length b=30m for real-time routine monitoring. The system mainly includes a mobile leak detection device 1, a data terminal 2 and a server 3; each mobile leak detection device 1 is connected to the data terminal 2 via a cable 4, and the data terminal 2 is used to receive various detection data and upload the various detection data to the server 3. Figure 2 As shown, the artificial wetland 5 is mainly composed of wetland plants 5-1, wetland filler 5-2 and impermeable membrane 5-3, and the impermeable membrane 5-3 is used to separate the wetland filler 5-2 from the lower soil 6. The mobile leak detection device 1 of the system is set in the wetland filler 5-2, and the setting depth is 0.3m above the top of the impermeable membrane 5-3. The mobile leak detection device 1 includes a water pressure sensor 1-1 for collecting water pressure data, a conductivity sensor 1-2 for collecting conductivity data, and a position sensor 1-3 for receiving and sending positioning signals; the mobile leak detection device 1 has two modes: routine monitoring and dynamic monitoring, wherein the water pressure sensor 1-1 has a range of 0 to 100 kPa, a measurement accuracy of ±0.001 to 1.5% FS (full scale), and a temperature range of -20 to 105°C; the conductivity sensor 1-2 has a range of 1 to 10000 μS / cm, a measurement accuracy of ±0.001 to 1.5% FS (full scale), and an ambient temperature of normal temperature -20 to 105°C. The server 3 determines the leakage point and leakage area by using a particle swarm algorithm and a threshold judgment method, and obtains the leakage situation of the artificial wetland based on the leakage area. The artificial wetland maintenance personnel repair the leakage according to the leakage situation obtained by the server 3.

[0137] The specific operation steps of this embodiment are as follows:

[0138] First, arrange the routine leakage monitoring points of the mobile leak detection device 1, and set grid lines in the length and width directions. As shown in Table 1, the long side length a=80m>40m, so the long side starts from the midpoint and a grid line is divided every 20m; at the same time, the short side length b=30m is within the range of 20-40m, so a grid line is divided at 10m and 20m respectively. Set a routine leakage monitoring vertical line at the intersection of the grid lines, and set a routine leakage monitoring point on it, and set the depth to 0.3m above the top of the impermeable membrane, such as Figure 5 shown.

[0139] A mobile leak detection device 1 is arranged at each routine leakage monitoring point (M1, M2, ..., M6), and the mobile leak detection device is set to the routine monitoring mode. The water pressure data and conductivity data of the routine leakage monitoring point are obtained in real time by using the water pressure sensor 1-1 and the conductivity sensor 1-2, and a series of water pressure values ​​P1, P2, ..., P6 and a series of conductivity values ​​C1, C2, ..., C6 are obtained. The above data are transmitted to the server through the data acquisition terminal. According to formula (1) and formula (2), in formula (1) and formula (2), n is 6, and the maximum relative deviation value of the real-time water pressure and the maximum relative deviation value of the conductivity are calculated.

[0140] The calculation results are shown in Table 2.

[0141] Table 2 Calculation table of P and C in Example 1

[0142]

[0143] Taking the upper tolerance limit of P α=40%, and the upper tolerance limit of C β=40%, since P=27.63%≤α and C=36.41%≤β, it is determined that no leakage occurs in the artificial wetland, and the mobile leak detection device maintains the routine monitoring state.

[0144] Example 2

[0145] In this embodiment, the method of the present invention is applied to an artificial wetland with a long side length a=80m and a short side length b=30m to perform real-time routine monitoring, precise tracing and in-situ restoration.

[0146] The real-time routine monitoring of artificial wetland leakage in this embodiment is consistent with that in embodiment 1. The difference between this embodiment and embodiment 1 is that: taking the upper tolerance limit of P α=30%, the upper tolerance limit of C β=30%, although P=27.63%≤α but C=36.41%>β, it is necessary to judge whether there is potential leakage in the artificial wetland according to the following Figure 3 The steps shown are used for further tracing.

[0147] Control each leak detection device to switch to dynamic monitoring mode, and use particle swarm algorithm to trace the potential leakage points.

[0148] like Figure 4 As shown, the process of tracing the source using the particle swarm algorithm in this embodiment is as follows:

[0149] S210: Initialization: Setting the initial position of each mobile leak detection device The routine leakage monitoring point where it is located, and the initial speed of each mobile leak detection device It is the operating speed of the mobile leak detection device.

[0150] S220: Calculate fitness value: When the number of iterations is k, calculate the fitness value of each mobile leak detection device at the current position according to the fitness function (Formula 3). The fitness value of the model is: a1 in formula (3) is -0.63; a2 is 0.58.

[0151] S230: Find the best individual fitness value: When the number of iterations is k, for each mobile leak detection device, set its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained, as shown in Formula 4.

[0152] S240: Find the optimal fitness value of the group: When the number of iterations is k, the current positions of all mobile leak detection devices are The maximum fitness value With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained, as shown in Formula 5.

[0153] S250: Update the position and speed of the mobile leak detection device: When the number of iterations is k, update the position of each mobile leak detection device, as shown in Formula 6.

[0154] At the same time, the speed of each mobile leak detection device is updated, as shown in Formula 7, where w is 1.0, c1 is 1.5, and c2 is 1.5.

[0155] S260: Take kt=50 and determine whether the algorithm is finished: if the number of iterations k<kt, return to S220 and perform the next iteration; if the number of iterations k≥kt, the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices Assume that there are potential leakage points x1, x2,…, x7.

[0156] After accurate tracing, it is necessary to determine whether to perform in-situ repair:

[0157] The water pressure value P and conductivity value C around the potential leakage point are compared with the threshold value P t and C t For comparison, for P≤P t or C ≥ C t The area is divided into leakage areas, where P t =10kPa, C t =2000μS / cm. The leakage areas of each leakage point are shown in Table 3.

[0158] Table 3 Leakage area of ​​each leakage point in Example 2

[0159] Leakage point <![CDATA[Leakage area (m 2 )]]> <![CDATA[x1]]> 21.72 <![CDATA[x2]]> 12.31 <![CDATA[x3]]> 52.44 <![CDATA[x4]]> 43.52 <![CDATA[x5]]> 35.64 <![CDATA[x6]]> 33.80 <![CDATA[x7]]> 21.72

[0160] Based on formula (8), where m is 7, the total leakage area A of the constructed wetland is obtained: t =199.44m 2 =8.13%A.

[0161] Take A t1 =10%A, A t2 =25%A, due to A t =199.44m 2 =8.13%A, A t ≤A t1 , it was determined that a small leakage occurred in the artificial wetland, and the artificial wetland maintained its current operating state. At the same time, each leak detection device returned to the routine monitoring point and switched back to the routine monitoring mode.

[0162] Example 3

[0163] In this embodiment, the method of the present invention is applied to an artificial wetland with a long side length a=80m and a short side length b=30m to perform real-time routine monitoring, precise tracing and in-situ restoration.

[0164] The real-time routine monitoring part and the precise source tracing part of artificial wetland leakage in this embodiment are consistent with those in Example 2. The difference between this embodiment and Example 2 is that: t =11kPa, C t =1800μS / cm. The leakage areas of each leakage point are shown in Table 4.

[0165] Table 4 Leakage area of ​​each leakage point in Example 3

[0166] Leakage point <![CDATA[Leakage area (m 2 )]]> <![CDATA[x1]]> 43.05 <![CDATA[x2]]> 24.41 <![CDATA[x3]]> 103.93 <![CDATA[x4]]> 86.25 <![CDATA[x5]]> 70.64 <![CDATA[x6]]> 66.99 <![CDATA[x7]]> 43.05

[0167] Based on formula (8), the total leakage area A of the artificial wetland is obtained: t =395.28m 2 =16.47%A.

[0168] Take At1 =10%A, A t2 =25%A, due to A t1 ≤A t =16.47%A≤A t2 , it was determined that a medium leakage had occurred in the artificial wetland, water distribution to the area around the leakage point was stopped, and local repairs were carried out on the leakage part.

[0169] like Figure 3 As shown in Table 5, after the repair was completed, the leakage of the artificial wetland was traced again, and the leakage points and leakage areas after repair were obtained.

[0170] Table 5 Leakage area of ​​each leakage point after repair in Example 3

[0171]

[0172]

[0173] Based on formula (8), the total leakage area A of the artificial wetland is obtained: t =95.52m 2 =3.98%A.

[0174] Because A t =3.98%A≤A t1 , the repair is determined to be completed, and at the same time, each mobile leak detection device returns to the routine monitoring point and switches back to the routine monitoring mode.

[0175] Example 4

[0176] In this embodiment, the method of the present invention is applied to an artificial wetland with a long side length a=80m and a short side length b=30m to perform real-time routine monitoring, precise tracing and in-situ restoration.

[0177] The real-time routine monitoring part and the precise source tracing part of artificial wetland leakage in this embodiment are consistent with those in embodiment 3. The difference between this embodiment and embodiment 3 is that: t1 =5%A, A t2 =15%A, due to A t =16.47%A≥A t2 , it was determined that a large leak had occurred in the artificial wetland, all water inflow to the artificial wetland was interrupted, and the artificial wetland was fully repaired.

[0178] After the repair was completed, the leakage of the artificial wetland was traced again, and the repaired leakage points and their leakage areas were obtained as shown in Table 6.

[0179] Table 6 Leakage area of ​​each leakage point after restoration of artificial wetland in Example 4

[0180] Leakage point <![CDATA[Leakage area (m 2 )]]> <![CDATA[x1]]> 19.00 <![CDATA[x2]]> 10.77 <![CDATA[x3]]> 45.88 <![CDATA[x4]]> 38.07 <![CDATA[x5]]> 31.18 <![CDATA[x6]]> 29.57 <![CDATA[x7]]> 19.00

[0181] Based on formula (8), the total leakage area A of the artificial wetland is obtained: t =174.48m 2 =7.27%A. Due to A t =7.27%A>A t1 , it was determined that the restoration was not complete, and a second restoration of the artificial wetland was carried out.

[0182] After the second repair was completed, the leakage of the artificial wetland was traced again, and the repaired leakage points and their leakage areas were obtained as shown in Table 7.

[0183] Table 7 Leakage area of ​​each leakage point after the second restoration of the artificial wetland in Example 4

[0184]

[0185]

[0186] Based on formula (8), the total leakage area A of the artificial wetland is obtained: t =52.32m 2 =2.18%A.

[0187] Because A t =2.18%≤A t1 , the repair is determined to be completed, and at the same time, each mobile leak detection device returns to the routine monitoring point and switches back to the routine monitoring mode.

Claims

1. A method for real-time monitoring and precise source tracing of artificial wetland leakage, characterized by: The following steps are involved: Step 1: Use a mobile leak detection device set up at a routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time, and obtain a series of water pressure data P1, P2, K, P n And series conductivity data C1, C2, K, C n , where n is the number of routine leakage monitoring points; Based on formula (1) and formula (2), the maximum relative deviation value of real-time water pressure and the maximum relative deviation value of conductivity are calculated; In the formula, is the average water pressure of the ith routine leakage monitoring point in the previous monitoring cycle; is the average conductivity of the ith routine leakage monitoring point in the previous monitoring cycle; If P≤α and C≤β, it is determined that the artificial wetland has no leakage; α is the upper tolerance limit of P, and β is the upper tolerance limit of C; If P>α or C>β, it is determined that there is potential leakage in the artificial wetland, and step 2 is executed; Step 2: Use particle swarm algorithm to determine potential leakage points x1, x2, ..., x n+1 , the mobile leak detection device enters the dynamic monitoring mode and moves to the potential leakage point to monitor the leakage situation and realize accurate tracing; In step 1, a mobile leak detection device is set up at the routine leakage monitoring point using the grid method. The specific operation steps are as follows: Set grid lines in the length and width of the constructed wetland according to the following rules 1 to 3; Rule 1: If a or b < 20m, draw a grid line at the midpoint; Rule 2: If a or b is within the range of 20-40m, draw a grid line at the 1 / 3 and 2 / 3 positions respectively; Rule 3: If a or b>40m, start from the midpoint and divide a grid line every 20m; Among them, a and b represent the length of the long side and wide side of the artificial wetland, respectively; Set a routine leakage monitoring vertical line at the intersection of the grid lines, set the mobile leak detection device on the routine leakage monitoring vertical line, and set the depth to 0-0.5m from the top of the anti-seepage membrane; In step 2, the particle swarm algorithm is used to determine the potential leakage points x1, x2, ..., x n+1 , the specific implementation steps are: S210: Setting the initial position of each mobile leak detection device For the routine leakage monitoring point where it is located, represents the distance from the origin in the long side direction at the initial moment of the i-th mobile leak detection device, represents the distance of the i-th mobile leak detection device from the origin in the broadside direction at the initial moment; and gives each mobile leak detection device an initial speed is the working speed of the mobile leak detection device, where: represents the moving speed of the i-th mobile leak detection device in the long side direction at the initial moment, represents the moving speed of the i-th mobile leak detection device in the broadside direction at the initial moment; S220: When the number of iterations is k, according to formula (3), calculate the current position of each mobile leak detection device The fitness value of In the formula, a1 is the contribution weight of water pressure in the fitness function; a2 is the contribution weight of conductivity in the fitness function; S230: When the number of iterations is k, for each mobile leak detection device, its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained; S240: When the number of iterations is k, the maximum fitness value of the current position of all mobile leak detection devices is calculated. With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained; S250: when the number of iterations is k, updating the position of each mobile leak detection device; Simultaneously update the speed of each mobile leak detection device; Where w is the inertia weight; c1 is the acceleration constant of the historical best position; c2 is the acceleration constant of the global best position; S260: If the number of iterations k < kt, then enter the next iteration and go to S220; if the number of iterations k ≥ kt, then the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices The final historical best positions of the n mobile leak detection devices and the final global best position of all mobile leak detection devices are marked as potential leakage points x1, x2, ..., x n+1 .

2. A real-time monitoring and precise source tracing system for artificial wetland leakage, characterized by: include: Several mobile leak detection devices, which have two modes: routine monitoring and dynamic monitoring. When the mobile leak detection device is in the routine monitoring mode, it is located at the routine leakage monitoring point, and is used to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time, and upload them to the data terminal; when the mobile leak detection device is in the dynamic monitoring mode, it is used to move to the potential leakage point according to the moving speed and moving direction sent by the server; A data terminal is used to receive the detection data of each mobile leak detection device and upload the detection data to the server; The server is used to calculate the maximum relative deviation value P of the real-time water pressure and the maximum relative deviation value C of the conductivity based on equation (1) and equation (2); and used to determine whether leakage occurs in the artificial wetland based on the size of P and α, and the size of C and β; If P≤α and C≤β, it is determined that the artificial wetland has no leakage; α is the upper tolerance limit of P, and β is the upper tolerance limit of C; If P>α or C>β, it is determined that the artificial wetland has potential leakage; In the formula, is the average water pressure of the ith routine leakage monitoring point in the previous monitoring cycle; is the average conductivity of the ith routine leakage monitoring point in the previous monitoring cycle; When there is potential leakage in artificial wetlands, the particle swarm algorithm is used to identify the potential leakage points x1, x2, ..., x n+1 Conduct positioning and achieve accurate traceability; When the mobile leak detection device is in routine monitoring mode, the mobile leak detection device is set on the routine leakage monitoring vertical line, and the setting depth is 0 to 0.5m from the top of the anti-seepage membrane; The routine leakage monitoring vertical line is determined according to the intersection of the grid lines corresponding to the artificial wetland; The grid lines corresponding to the artificial wetland are obtained by setting the length and width of the artificial wetland according to the following rules; Rule 1: If a or b < 20m, draw a grid line at the midpoint; Rule 2: If a or b is within the range of 20-40m, draw a grid line at the 1 / 3 and 2 / 3 positions respectively; Rule 3: If a or b>40m, start from the midpoint and divide a grid line every 20m; Among them, a and b represent the length of the long side and wide side of the artificial wetland, respectively; When the server is used in artificial wetlands with potential leakage, the particle swarm algorithm is used to find the potential leakage points x1, x2, ..., x n+1 The specific algorithm steps for positioning are: S210: Setting the initial position of each mobile leak detection device For the routine leakage monitoring point where it is located, represents the distance from the origin in the long side direction at the initial moment of the i-th mobile leak detection device, represents the distance of the i-th mobile leak detection device from the origin in the broadside direction at the initial moment; and gives each mobile leak detection device an initial speed is the working speed of the mobile leak detection device, where: represents the moving speed of the i-th mobile leak detection device in the long side direction at the initial moment, represents the moving speed of the i-th mobile leak detection device in the broadside direction at the initial moment; S220: When the number of iterations is k, according to formula (3), calculate the current position of each mobile leak detection device The fitness value of In the formula, a1 is the contribution weight of water pressure in the fitness function; a2 is the contribution weight of conductivity in the fitness function; S230: When the number of iterations is k, for each mobile leak detection device, its current position The fitness value Historical best position with each mobile leak detector The corresponding fitness value By comparison, the updated historical best position of each mobile leak detection device is obtained; S240: When the number of iterations is k, the maximum fitness value of the current position of all mobile leak detection devices is calculated. With the global best position The corresponding fitness value By comparison, the global optimal position of all mobile leak detection devices after update is obtained; S250: when the number of iterations is k, updating the position of each mobile leak detection device; Simultaneously update the speed of each mobile leak detection device; Where w is the inertia weight; c1 is the acceleration constant of the historical best position; c2 is the acceleration constant of the global best position; S260: If the number of iterations k < kt, return to S220 and perform the next iteration; if the number of iterations k ≥ kt, the tracing process ends and the final historical best position of each mobile leak detection device is obtained. and the final global optimal position of all mobile leak detection devices The final historical best positions of the n mobile leak detection devices and the final global best position of all mobile leak detection devices are marked as potential leakage points x1, x2, ..., x n+1 , n+1 is the number of potential leakage points.

3. A method for in-situ repair of artificial wetland leakage, characterized in that: The following steps are involved: S10: Using the method for real-time monitoring and precise source tracing of artificial wetland leakage as claimed in claim 1, to obtain potential leakage points x1, x2, ..., x n+1 The position of n+1 is the number of potential leakage points; S20: Using the threshold judgment method, calculate the leakage area A1, A2, ..., A of each potential leakage point n+1 , and calculate the total leakage area A of the constructed wetland t ; S30: If A t ≤A t1 , it is determined that a small leakage has occurred in the artificial wetland, and each mobile leak detection device returns to the initial routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time; if A t1 ≤A t ≤A t2 , it is determined that the artificial wetland has a medium leakage, the water distribution to the area around the potential leakage point is stopped, and the potential leakage point is partially repaired, and S40 is executed; if A t ≥A t2 , it is determined that a large leakage has occurred in the artificial wetland, all water inflow to the artificial wetland is interrupted, the artificial wetland is fully repaired, and S40 is executed; S40: Determine the repaired detection result by executing S20 and S30 again.

4. The method for in-situ repair of artificial wetland leakage according to claim 3, characterized in that: In S20, the threshold judgment method is used to calculate the leakage areas A1, A2, ..., A of each potential leakage point. n+1 , perform the following steps for each potential leak point: The water pressure data P and the conductivity data C of the area around the potential leakage point are obtained by a mobile leakage detection device located at the potential leakage point; will satisfy P≤P t or C ≥ C t The area is divided into leakage areas, and the area enclosed by the boundary points of the leakage area connected in sequence is the leakage area of ​​the potential leakage point; Among them, P t is the water pressure threshold, C t is the conductivity threshold.

5. An artificial wetland leakage in-situ repair system, characterized by: include: A potential leakage point positioning module is used to obtain potential leakage points x1, x2, ..., x based on the real-time monitoring and precise source tracing method of artificial wetland leakage as claimed in claim 1. n+1 The position of n+1 is the number of potential leakage points; The leakage area calculation module is used to calculate the leakage area A1, A2, ..., A of each potential leakage point using the threshold judgment method. n+1 , and calculate the total leakage area A of the constructed wetland t ; The repair module is used to repair the total leakage area A of the artificial wetland. t , determine the extent of leakage in the artificial wetland, and cooperate with external equipment to implement corresponding repair measures according to the extent of leakage in the artificial wetland; The post-repair detection result determination module is used to sequentially call the leakage area calculation module and the repair module after the repair measures are completed to determine the post-repair detection result; Among them, in the repair module, if A t ≤A t1 , it is determined that a small leakage has occurred in the artificial wetland, and each mobile leakage detection device is controlled to return to the initial routine leakage monitoring point to obtain the water pressure data and conductivity data of the routine leakage monitoring point in real time; if A t1 ≤A t ≤A t2 , it is determined that the artificial wetland has a medium leakage, the water distribution to the area around the potential leakage point is stopped, the potential leakage point is partially repaired, and the repaired detection result judgment module is called; if A t ≥A t2 , it is determined that a large leakage has occurred in the artificial wetland, all water inflow to the artificial wetland is interrupted, the artificial wetland is fully repaired, and the detection result judgment module after the repair is called.

6. The artificial wetland leakage in-situ repair system according to claim 5, characterized in that: The leakage area calculation module comprises: The leakage area division module is used to obtain the water pressure data P and conductivity data C of the area around the potential leakage point through a mobile leakage detection device located at the potential leakage point; t or C ≥ C t The area is divided into leakage areas, and the area enclosed by the boundary points of the leakage area is the leakage area of ​​the potential leakage point; where P t is the water pressure threshold, C t is the conductivity threshold; Total leakage area of ​​artificial wetland A t The calculation module is used to calculate the total leakage area A of the artificial wetland based on the calculation results of the leakage area division module. t .

Images