Water tunnel leakage in-situ detection system and method
By integrating optical fiber sensing technology and magnetoelectric detection technology and using traceless Kalman filtering technology for data fusion, the problem of difficulty in comprehensively monitoring water transmission tunnel leakage in the existing technology is solved, and efficient and accurate leakage monitoring is achieved.
Patent Information
- Application Number
- CN202510295825.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-13
- Publication Date
- 2025-06-20
AI Technical Summary
Existing water tunnel leakage detection technologies, such as magnetoelectric method and fiber optic sensing methods, are difficult to comprehensively monitor the impact of dynamic changes and climate moisture on measurement results, resulting in problems of incomplete monitoring and large errors.
The fusion method of fiber optic sensing technology and magnetoelectric detection technology is adopted, and through components such as current transmitters, magnetometers, fiber optic temperature sensors and strain sensors, combined with traceless Kalman filtering technology, abnormal magnetic field data, temperature field data and strain field data are fused to generate optimized monitoring data.
It realizes comprehensive monitoring of leaks in water tunnels, reduces monitoring errors, can feedback detection results in real time, and improves monitoring efficiency and accuracy.
Smart Images
Figure CN120176959A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of tunnel leakage, and particularly relates to an in-situ detection system and method for water conveyance tunnel leakage. Background Art
[0002] Water conveyance tunnels are key control nodes of major cross-basin water diversion projects, and play an important role in solving regional water resource imbalance problems, optimizing water resource allocation, etc.
[0003] Existing water conveyance tunnel leakage detection technologies mainly include magnetoelectric method and fiber optic sensing method. The geological environment of water conveyance tunnels is complex, the climate is humid, and they are mostly subjected to long-term dynamic loads. Among them, since the magnetoelectric method is a static detection method, it is difficult to capture dynamic changes such as temperature changes caused by humid climate and load changes caused by dynamic loads, resulting in incomplete monitoring; moreover, the humid climate will cause the measurement results of the magnetoelectric method to be distorted. In addition, due to the large interference of the fiber optic sensing method by temperature gradient, it is easy to have temperature-induced fluctuations in fiber strain, resulting in an increase in monitoring error. Therefore, a single method among the above two methods cannot meet the comprehensive monitoring requirements.
[0004] If the two methods are used separately to monitor the water conveyance tunnel, the differences in data scale, physical dimension, and data acquisition frequency between the two will all affect the monitoring effect, resulting in the inability to fuse the data of the two and unable to obtain an ideal monitoring effect by combining the data of the two.
[0005] Therefore, based on the integration of fiber optic sensing technology and magnetoelectric detection technology, the present solution proposes an in-situ detection system and method for water conveyance tunnel leakage to solve the problem of data fusion between the two and achieve the monitoring effect. Summary of the Invention
[0006] In order to solve the above technical problems, the present invention proposes an in-situ detection system and method for water conveyance tunnel leakage, which can fuse the data of the two and achieve the monitoring effect.
[0007] In order to achieve the above object, the present solution adopts the following technical content: An in-situ detection system for water conveyance tunnel leakage includes the following structures: A current transmitter, whose input end and output end are respectively connected to a grounding electrode through wires, and the two grounding electrodes are located directly above the water conveyance culvert and vertically inserted into the ground; A storage battery for supplying power to the current transmitter; A magnetometer is arranged on the ground surface directly above the water conveyance culvert for monitoring the total magnetic field intensity; and calculating abnormal magnetic field data based on the total magnetic field intensity; Multiple optical fiber temperature sensors and optical fiber strain sensors are arranged in the concrete of the water conveyance tunnel under construction, or are arranged on the inner wall of the concrete of the completed water conveyance tunnel through fixators; they are respectively used to monitor the temperature field data and strain field data of the water conveyance tunnel; A data acquisition module is used to acquire the monitored abnormal magnetic field data, temperature field data and strain field data; A data processing module is used to fuse the abnormal magnetic field data, temperature field data and strain field data by using unscented Kalman filter to generate optimized abnormal magnetic field data, temperature field data and strain field data.
[0008] Furthermore, the two grounding electrodes are arranged along the direction of the water conveyance culvert and are more than 10 m apart.
[0009] The distance between the two grounding electrodes is 10 m, which can avoid magnetic interference between the two grounding electrodes; it can make the current form a wider diffusion electric field in the soil. If the electrode spacing is too close, the current diffusion will be insufficient and the electric field distribution will be uneven, thus affecting the measurement accuracy of the potential difference.
[0010] Furthermore, the wire connecting the two grounding electrodes remains straight.
[0011] A bent wire may cause an increase in inductance, generate self-induced electromotive force when the current fluctuates, and affect the circuit stability. An energized wire will generate a magnetic field (Ampere's law), and its direction is perpendicular to the current direction. If the wire is bent, the local current direction change will cause additional magnetic field superposition, interfering with the magnetic field measurement in the target area. The magnetic field distribution of a straight wire is more uniform and easier to predict, which can reduce the influence of its own magnetic field on the measurement result.
[0012] Furthermore, the magnetometer is connected to an optoelectronic wave intelligent detector. The optoelectronic wave intelligent detector is equipped with a 4G / 5G module. The optoelectronic wave intelligent detector is connected to a cloud platform and can upload the monitored abnormal magnetic field data to the cloud platform.
[0013] It can achieve efficient transmission, real-time feedback of detection results, realize large-scale collaborative detection, and improve efficiency.
[0014] Furthermore, a method for in-situ detection of water leakage in a water conveyance tunnel, based on an in-situ detection system for water leakage in a water conveyance tunnel as claimed in claim 3, is characterized by comprising the following steps: S1: Synchronously collect abnormal magnetic field data, temperature field data and strain field data and preprocess them; S2: Generate sigma points according to the data in S1; The formula is:
[0015] Wherein, is a scaling parameter; is an adjustment parameter; n is the dimension of the state vector; in this embodiment n = 5.
[0016] k represents the step sequence; represents the state vector at the k-th iteration H k mean value; P k represents the state vector at the k-th iteration H k covariance; state vector
[0017] B 1k is the component of the abnormal magnetic field intensity in the horizontal direction of the cross-section of the water conveyance tunnel at the k-th iteration; B 2k represents the component of the abnormal magnetic field intensity along the axis direction of the water conveyance tunnel at the k-th iteration; B 3k represents the component of the abnormal magnetic field intensity in the vertical direction of the cross-section of the water conveyance tunnel at the k-th iteration; represents the strain at the k-th iteration; Tk represents the temperature at the k-th iteration; B 1k , B 2k and B 3k satisfy the following formula:
[0018] In formula (2), B gk represents the abnormal magnetic field intensity at the k-th iteration; S3: Predict the state of each sigma point; The formula is:
[0019] In the formula, f is the state transition function, is the predicted state of the sigma point; S4: Weighted average and sum the predicted states of all sigma points, establish the observation model and update formula, and after iteration, obtain the optimized abnormal magnetic field data, temperature field data and strain field data results, denoted as
[0020] S5: Based on the scoring table and the expert scoring principle, obtain the total score of each data; then, based on the status table of the water conveyance culvert and in combination with the total score, determine the health status of the water conveyance culvert.
[0021] Further, the observation model is divided into the following models: Magnetic field observation model:
[0022] In Equation (1), represents the strain variable at the k-th iteration; T k represents the temperature variable at the k-th iteration; is the strain drift coefficient; is the temperature drift coefficient; is the magnetic field measurement noise; B 1k represents the horizontal component of the abnormal magnetic field intensity in the cross-section of the water conveyance tunnel at the k-th iteration; B 2k represents the component of the abnormal magnetic field intensity along the axis of the water conveyance tunnel at the k-th iteration; B 3k is the vertical component of the abnormal magnetic field intensity in the cross-section of the water conveyance tunnel at the k-th iteration; Temperature observation model:
[0023] In Equation (2), is the temperature measurement noise; Strain observation model:
[0024] In Equation (3), T 0 is the average annual temperature of the water conveyance tunnel; is the strain measurement noise; is the thermal expansion coefficient of the material of the water conveyance tunnel; The formula for obtaining the predicted state mean by weighted average summation of the predicted states is:
[0025] In Equation (4), is the weighted average summation value of the predicted state, c is the weight coefficient; n is the dimension of the state vector; Updated state formula:
[0026] In Equation (5), K k is the weight coefficient, z is the actual observed value, is the predicted observation value, is the state estimation mean. By using the iterative formulas (1)-(5), the optimized abnormal magnetic field data, temperature field data, and strain field data results are obtained. ; Furthermore, the scoring table is as follows: Table 1 Optimized strain , temperature and abnormal magnetic field intensity scoring results
[0027] The state table of the water conveyance culvert is as follows: Table 2 Health status of the water conveyance culvert
[0028] The beneficial effects that this solution can achieve are: This solution uses the unscented Kalman filter to fuse the collected strain data, abnormal magnetic field intensity, and temperature data, obtaining the optimized strain data, abnormal magnetic field intensity, and temperature data. Then, according to the scoring table, the final health status of the water conveyance culvert is obtained through expert scoring. Description of the Drawings
[0029] Figure 1 is the usage state diagram when this system is deployed; Figure 2 The module diagram of this system.
[0030] 1. Current transmitter; 2. Battery; 3. Magnetometer; 4. Photoelectric wave intelligent detector; 5. Ground electrode. Specific Embodiments
[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0032] Embodiment 1: See Figure 1 and Figure 2 , a system for in-situ detection of water conveyance tunnel leakage, including the following structures: A current transmitter 1, whose input terminal and output terminal are respectively connected to a grounding electrode 5 through wires, and the wires connecting the two grounding electrodes 5 are kept straight. The two grounding electrodes 5 are located directly above the water conveyance culvert and vertically inserted into the ground. The two grounding electrodes 5 are arranged along the direction of the water conveyance culvert and are more than 10 m apart. Based on this, the influence of the magnetic field excited by the current in the wire on the detection result can be reduced. The output terminal and input terminal of the current transmitter 1 form a loop current in the ground, thereby generating an excitation magnetic field.
[0033] A storage battery 2, which is connected to the positive and negative terminals of the current transmitter through wires and is used to supply power to the current transmitter 1.
[0034] A magnetometer 3, which is arranged on the ground surface directly above the water conveyance culvert to receive the total magnetic field intensity. Based on the magnetic field theory, the formula for the total magnetic field intensity is: B = B c +B n + B g ; where B c represents the excitation magnetic field intensity excited by the output current of the current transmitter and is obtained by calculating with the Biot - Savart theorem formula; B n represents the background magnetic field intensity at the location of the water conveyance culvert and is obtained by monitoring a reference point far from the tunnel with a Hall sensor; B g represents the abnormal magnetic field formed due to the existence of water leakage in the water conveyance tunnel; B is the total magnetic field, which is measured by the magnetometer on the ground surface directly above the water conveyance culvert. The magnetometer is connected to an optoelectronic wave intelligent detector 4, and the optoelectronic wave intelligent detector 4 is equipped with a 4G / 5G module, which can receive the data monitored by the magnetometer and upload the collected data to the cloud platform.
[0035] Multiple fiber optic temperature sensors and fiber optic strain sensors. In this embodiment, there are four fiber optic temperature sensors and four fiber optic strain sensors in total. The fiber optic temperature sensors and fiber optic strain sensors are arranged along the direction of the water conveyance tunnel. The above - mentioned fiber optic temperature sensors and fiber optic strain sensors are set in two cases: The first case: The fiber optic temperature sensors and fiber optic strain sensors are implanted into the concrete of the water conveyance tunnel under construction; The second case: The fiber optic temperature sensors and fiber optic strain sensors are arranged on the inner wall of the concrete of the already - built water conveyance tunnel through fixators. The surfaces of the fiber optic temperature sensors and fiber optic strain sensors are covered with protective materials to reduce the temperature influence and avoid external force damage such as water flow scouring and gravel.
[0036] The cloud platform is equipped with a data acquisition module and a data processing module. The data acquisition module includes the aforementioned optoelectronic wave intelligent detector. The data acquisition module uses a GPS timing module (accuracy ±1μs) to synchronize the clocks of all sensors through the NTP protocol, ensuring that the timestamp error of multi-source data is <50ms. The fiber optic sensor and the magnetometer are spatially aligned according to the geographical position coordinates. The data acquisition module simultaneously collects the abnormal magnetic field data, temperature field data, and strain field data monitored by the corresponding sensors above, and sends them to the data processing module; the data processing module uses the unscented Kalman filter to process and fuse the above data to generate the quantities corresponding to the above data, which are represented by the letters respectively, where represents the optimized strain; represents the optimized temperature; represents the abnormal magnetic field intensity. Through the existing scoring table, obtain the respective scoring values, denoted as P1, P2, and P3. Then, according to the evaluation formula: P = 0.5*P1 + 0.2*P2 + 0.3*P3, where P is the total score, and the weights 0.5, 0.2, and 0.3 are obtained by expert scoring according to the analytic hierarchy process, to obtain the total score P; then, according to the existing health status table of the water conveyance culvert, combined with the total score P, compare the total score P with the values in the health status table of the water conveyance culvert to obtain the health status of the water conveyance culvert.
[0037] The scoring table is shown in Table 1.
[0038] Table 1 Optimized strain Temperature and abnormal magnetic field intensity Scoring results
[0039] Calculate the total score P according to the weight distribution (strain 0.5, temperature 0.2, magnetic field 0.3), and then obtain the health status of the water conveyance culvert according to Table 2.
[0040] Table 2 Health status of the water conveyance culvert
[0041] Example 2: A method for in-situ detection of leakage in a water conveyance tunnel, including the following steps: S1: Synchronously collect abnormal magnetic field data B g , temperature field data T and strain field data , and perform preprocessing.
[0042] The preprocessing includes: data cleaning, data normalization, and noise filtering.
[0043] S2: Generate sigma points based on the data in S1.
[0044] The calculation formula for generating sigma points is:
[0045] where is the scaling parameter; is the adjustment parameter; n is the dimension of the state vector; in this embodiment n = 5.
[0046] k represents the step sequence; represents the state vector H k mean at the k-th iteration; P k represents the state vector H k covariance at the k-th iteration; state vector ; B 1k is the component of the abnormal magnetic field intensity in the horizontal direction of the cross-section of the water conveyance tunnel at the k-th iteration; B 2k represents the component of the abnormal magnetic field intensity along the axis direction of the water conveyance tunnel at the k-th iteration; B 3k represents the component of the abnormal magnetic field intensity in the vertical direction of the cross-section of the water conveyance tunnel at the k-th iteration; represents the strain at the k-th iteration; T k represents the temperature at the k-th iteration; the data at the 0-th iteration is the data collected in step S1.
[0047] S3: Predict the state of each sigma point.
[0048]
[0049] In the formula, f is the state transition function, is the predicted state of the sigma point.
[0050] S4: Sum the predicted states of all sigma points by weighted average, and iteratively calculate the optimized results of the abnormal magnetic field data, temperature field data, and strain field data, which are respectively denoted as
[0051] The weighted average sum is:
[0052] is the weighted average summation value of the prediction state ，c is the weight coefficient. ; Update state formula:
[0053] K k is the weight coefficient, z is the actual observed value, is the predicted observed value, is the state estimation mean.
[0054] Among them, the magnetic field observation model is:
[0055] represents the strain variable at the k-th iteration, T k represents the temperature variable at the k-th iteration, is the strain drift coefficient, is the temperature drift coefficient, is the magnetic field measurement noise. B 1k represents the component of the abnormal magnetic field intensity in the horizontal direction of the cross-section of the water conveyance tunnel at the k-th iteration; B 2k represents the component of the abnormal magnetic field intensity along the axis direction of the water conveyance tunnel at the k-th iteration; B 3k is the component of the abnormal magnetic field intensity in the vertical direction of the cross-section of the water conveyance tunnel at the k-th time. B 1k , B 2k and B 3k satisfy the following formula:
[0056] In the formula, B gk represents the abnormal magnetic field intensity at the k-th iteration.
[0057] The temperature observation model is:
[0058] is the temperature measurement noise.
[0059] The strain observation model is:
[0060] In the formula, T 0 is the annual average temperature of the water conveyance tunnel, is the strain measurement noise, is the coefficient of thermal expansion of the material of the water conveyance tunnel.
[0061] Iterate equations (1)-(5) 100 times to obtain the optimized parameters: denoted as
[0062] Substitute the obtained optimized parameters into the corresponding observation models below to obtain the corresponding optimized results.
[0063]
[0064] In equation (3), represents the optimized abnormal magnetic field intensity; Refer to the expert opinions and assign scores to each data according to Table 1.
[0065] Calculate the total score P according to the weight distribution (strain 0.5, temperature 0.2, magnetic field 0.3), and then obtain the health status of the water conveyance culvert according to Table 2.
[0066] Inspired by the ideal embodiments of the present invention described above, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.
Claims
1. A water transfer tunnel leakage in-situ detection system, characterized in that: Includes the following structures: The current transmitter has an input end and an output end connected to a grounding electrode through a wire respectively, and the two grounding electrodes are located just above the water conveyance culvert and vertically inserted into the ground; A storage battery, used to power the current transmitter; A magnetometer, which is placed on the ground just above the water transfer culvert, is used to monitor the total magnetic field strength; And calculate the abnormal magnetic field data according to the total magnetic field strength; A plurality of optical fiber temperature sensors and optical fiber strain sensors are arranged in the concrete of the water conveyance tunnel under construction, or arranged on the inner wall of the concrete of the water conveyance tunnel that has been built through a fixture; They are used to monitor the temperature field data and strain field data of the water transfer tunnel respectively; Data acquisition module, used to collect abnormal magnetic field data, temperature field data and strain field data for monitoring; The data processing module is used to fuse the abnormal magnetic field data, temperature field data and strain field data using unscented Kalman filtering to generate optimized abnormal magnetic field data, temperature field data and strain field data.
2. The in-situ detection system for water diversion tunnel leakage according to claim 1, characterized in that: The two grounding electrodes are arranged along the direction of the water conveyance culvert and are more than 10m apart.
3. The in-situ detection system for water diversion tunnel leakage according to claim 2 is characterized in that: The wire connecting the two ground electrodes is kept straight.
4. The in-situ detection system for water tunnel leakage according to claim 3 is characterized in that: The magnetometer is connected to a photoelectric wave intelligent detector, which is equipped with a 4G / 5G module. The photoelectric wave intelligent detector is connected to a cloud platform and can upload the monitored abnormal magnetic field data to the cloud platform.
5. A method for in-situ detection of water tunnel leakage, based on the in-situ detection system for water tunnel leakage according to claim 3, characterized in that: The following steps are involved: S1: synchronously collect abnormal magnetic field data, temperature field data and strain field data and preprocess them; S2: Generate sigma points based on the data in S1; The formula is: ; in, is the scaling parameter; To adjust the parameters; n is the dimension of the state vector; in this embodiment n =5; k Indicates the sequence of steps; Indicates that at the kth iteration, the state vector H k Mean; P k Indicates that at the kth iteration, the state vector H k The covariance of State Vector ; B 1k is the component of the abnormal magnetic field intensity in the horizontal direction of the water diversion tunnel cross section at the kth iteration; B 2k It represents the component of abnormal magnetic field intensity along the axis of the water conveyance tunnel at the kth iteration; B 3k It represents the component of abnormal magnetic field intensity in the vertical direction of the cross section of the water conveyance tunnel at the kth iteration; represents the strain at the kth iteration; T k Represents the temperature at the kth iteration; B 1k , B 2k and B 3k Satisfies the following formula: ； In formula (2), B gk represents the abnormal magnetic field intensity at the kth iteration; S3: predict the state of each sigma point; The formula is: ; In the formula, f is the state transfer function, is the predicted state of the sigma point; S4: The predicted states of all sigma points are weighted averaged and summed to establish the observation model and update formula. After iteration, the optimized abnormal magnetic field data, temperature field data and strain field data are obtained, which are recorded as ; S5: According to the scoring table and based on the expert scoring principle, the total score of each data is obtained; then according to the status table of the water transfer culvert and the total score, the health status of the water transfer culvert is obtained.
6. A method for in-situ detection of water tunnel leakage according to claim 5, characterized in that: The observation model is divided into the following models: Magnetic field observation model: ; In formula (1), represents the strain at the kth iteration; T k Represents the temperature at the kth iteration; is the strain drift coefficient; is the temperature drift coefficient; is the magnetic field measurement noise; B 1k It represents the component of abnormal magnetic field intensity in the horizontal direction of the water conveyance tunnel cross section at the kth iteration; B 2k It represents the component of abnormal magnetic field intensity along the axis of the water conveyance tunnel at the kth iteration; B 3k is the component of the abnormal magnetic field intensity in the vertical direction of the water conveyance tunnel cross section at the kth time; Temperature observation model: ; In formula (2), is the temperature measurement noise; Strain Observation Model: ; In formula (3), T 0 is the average annual temperature of the water tunnel; is the strain measurement noise; is the thermal expansion coefficient of the water tunnel material; The formula for the predicted state mean obtained by weighted average summation of the predicted states is: ； In formula (4), is the weighted average sum of the predicted states, c is the weight coefficient; n is the dimension of the state vector; Update status formula: ; In formula (5), K k is the weight coefficient, z is the actual observed value, is the predicted observed value, is the state estimation mean, iterate formulas (1)-(5) to obtain the optimized abnormal magnetic field data, temperature field data and strain field data results .
7. A method for in-situ detection of water tunnel leakage according to claim 6, characterized in that: The scoring table is: Table 1 Optimized strain , Temperature and abnormal magnetic field strength The scoring results ； The status table of the water transfer culvert is: Table 2 Health status of water transfer culverts 。