Experimental device and method for simulating leakage, vibration and strain monitoring of comprehensive pipe gallery

Abstract

The invention discloses an experimental device and method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery. The method comprises the following steps: acquiring feature data in a simulation experiment module in a current leakage scene; inputting the feature data into a leakage state recognition model; and correcting and outputting an identification result obtained by the leakage state identification model, wherein the identification result comprises the predicted leakage point position, direction and leakage amount. The device comprises a combined pipe gallery experiment module formed by connecting a plurality of independent simulation experiment module units, a detection device installed in the simulation experiment module and an injection system for injecting a medium into the module. According to the invention, a vivid scene is provided for pipeline and structure simulation in the corridor, and leakage diffusion rules and stress-strain monitoring simulation experiments under the conditions of multiple pipelines, different areas in the corridor space, medium types, leakage rates, medium temperatures and the like can be simulated. Powerful technical support is provided for safe operation of the comprehensive pipe gallery, leakage accidents are effectively prevented and reduced, losses caused by the accidents are reduced, and stable operation of urban infrastructures is guaranteed.

Description

Technical Field

[0001] The present application belongs to the field of technology, specifically to an experimental device and method for simulating leakage, vibration and strain monitoring of a comprehensive pipeline corridor. Background Art

[0002] An underground integrated pipeline corridor refers to a structure and ancillary facilities located below the ground in a city, used to accommodate two or more types of public utility pipelines or specialized pipelines. In modern urban construction, integrated pipeline corridors, as a type of intensive municipal infrastructure, play a vital role in ensuring the stable operation of a city's lifeline. Integrated pipeline corridors usually house a variety of pipelines, such as gas, water, electricity, and communications. The safe operation of these pipelines is directly related to the normal operation of the city. Due to long-term influences from environmental factors, aging wear, and external forces, the pipelines in the pipeline corridors are at risk of leakage. Once a leak occurs, it may cause a serious safety accident, resulting in huge economic losses and social impact.

[0003] Although there are some related gas leakage and diffusion monitoring experimental devices for confined spaces, these devices cannot meet the needs of the real-life construction of integrated pipe corridors. Specifically, the existing technology lacks devices and methods that can be used to conduct simulation experiments on pipeline vibration, leakage, etc. in the corridor based on the real-life construction of the integrated pipe corridor. It is impossible to establish the laws of pipeline vibration, leakage, etc. in the corridor under different working conditions, making it difficult to conduct comprehensive and effective verification of monitoring technology and recognition algorithms. For example, in an actual integrated pipe corridor environment, pipelines of different materials and specifications have significant differences in vibration and leakage characteristics under different pressure, temperature and other working conditions, and existing experimental devices and methods are unable to simulate these complex situations, resulting in problems such as misjudgment and missed judgment in the actual application of monitoring technology and recognition algorithms. Summary of the Invention

[0004] In response to the above-mentioned problems existing in the prior art, this application provides an experimental device and method for simulating leakage, vibration and strain monitoring of a comprehensive pipeline corridor, constructs the laws of pipeline vibration and leakage in the corridor under different working conditions, and verifies the monitoring technology and identification algorithm.

[0005] To achieve the above objectives, the present application provides, in a first aspect, an experimental method for simulating leakage, vibration, and strain monitoring of an integrated pipe gallery, comprising the following steps:

[0006] Acquire characteristic data in the simulation test cabin under the current leakage scenario, including multiple data collected by detection devices arranged along the length of the simulation test cabin at the cabin top, the middle of both sides, and the cabin bottom;

[0007] The characteristic data is input into a leakage state recognition model, the leakage state recognition model comprises a leakage recognition method based on the characteristic data and a multilayer perceptron model used as a synthetic model, the leakage recognition method comprises: determining a leakage point position according to a numerical peak value in the length direction of the detection device; determining a point clock position of the leakage point along the circumferential direction of the pipeline according to a difference value of the detection device at the same length direction position of the collected data; and determining a leakage amount according to a numerical value of the detection device.

[0008] The recognition result obtained by the leakage state recognition model is corrected and output, including a predicted leakage point position, direction and leakage amount.

[0009] The correction of the recognition result comprises the following steps:

[0010] Multiple characteristic data collection is performed on the same leakage scene to obtain multiple recognition results.

[0011] The predicted leakage point direction is numerically processed based on a sequential digital coding method.

[0012] The average value of the recognition results is calculated as the final corrected recognition result output.

[0013] The predicted leakage point direction is numerically processed based on a sequential digital coding method, which comprises:

[0014] The direction coding of the experimental cabin top is defined as 0, the direction coding of the right side of the experimental cabin is defined as 1, the direction coding of the left side of the experimental cabin is defined as 2, and the direction coding of the bottom of the experimental cabin is defined as 3.

[0015] The corresponding direction coding is determined according to the determined point clock position of the leakage point along the circumferential direction of the pipeline.

[0016] The multilayer perceptron model comprises:

[0017] An input layer for receiving multiple characteristic data extracted from the detection device;

[0018] A hidden layer for performing nonlinear transformation and feature learning on the characteristic data;

[0019] An output layer for outputting a predicted leakage point position, direction and leakage amount.

[0020] The second aspect provides an experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery, which is suitable for the experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery as shown in the first aspect, and comprises:

[0021] A simulation experimental cabin, multiple independent simulation experimental cabin units are connected to form a combined pipe gallery experimental cabin, a simulation pipeline and a flow guide groove are arranged in the simulation experimental cabin, the simulation pipeline is used for simulating a liquid phase and a gas phase medium conveying pipeline, the flow guide groove is used for guiding out in a liquid phase medium leakage simulation experiment, and a flow guide hole is arranged in the groove.

[0022] The detection device comprises a sensor for measuring temperature, vibration and strain parameters, and a flow meter and a pressure meter installed on the simulation pipeline for measuring the flow of medium;

[0023] The injection system comprises a circulating pump, a gas injection compressor and a leakage control valve, the circulating pump and the gas injection compressor are used to inject liquid and gas medium into the simulation pipeline, and the leakage control valve is installed on the simulation pipeline to adjust the simulation leakage amount and change the leakage direction.

[0024] The bottom of the simulation test cabin is provided with a water storage tank for storing liquid medium flowing out of the flow guide hole.

[0025] The installation form is determined based on the material properties of the simulation pipeline, which is support installation or fixed installation, the support installation only has constraints on the simulation pipeline in the direction of gravity, and the support position is not fixed, and the fixed installation fixes the support in the simulation test cabin to constrain the position movement of the simulation pipeline.

[0026] The beneficial effects of the present application are:

[0027] The present application provides an experimental device and method for simulating leakage, vibration and strain monitoring of comprehensive pipe gallery, which provides a realistic scene for pipeline and structure simulation in the gallery, can simulate the leakage diffusion law under the conditions of multiple pipelines, different regions in the gallery space, medium types, leakage amount, medium temperature, etc., and obtain the real-time state of leakage diffusion through sensor measurement and visual capture, simulate leakage by the leakage simulation device, and simulate medium diffusion space domain by the gallery cabin. In combination with the simulation installation of multiple pipelines and the vibration and strain excitation mode, the stress and strain monitoring simulation experiment of the pipeline and structure can also be simulated by the device. This not only provides a comprehensive and reliable verification platform for monitoring technology and identification algorithm, improves the accuracy of monitoring technology and the reliability of identification algorithm, but also provides strong technical support for the safe operation of comprehensive pipe gallery, effectively prevents and reduces the occurrence of leakage accidents, reduces the loss caused by accidents, and ensures the stable operation of urban infrastructure. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flowchart of the experimental method for simulating leakage, vibration and strain monitoring of comprehensive pipe gallery according to the present application is shown;

[0029] Figure 2 The structure diagram of the simulation experiment cabin unit of the experimental device for simulating leakage, vibration and strain monitoring of comprehensive pipe gallery according to the present application is shown;

[0030] Figure 3 The structure diagram of the combined pipe gallery experiment cabin of the experimental device for simulating leakage, vibration and strain monitoring of comprehensive pipe gallery according to the present application is shown;

[0031] Figure 4 Fig. 1 is a structural schematic diagram of a simulated pipeline in an experimental cabin of an experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to the present application;

[0032] Figure 5 Fig. 2 is a schematic diagram of the arrangement of length direction sensors of an experimental cabin of an experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to the present application;

[0033] Figure 6 Fig. 3 is a schematic diagram of the arrangement of monitoring sensors at the same position in the length direction of an experimental cabin of an experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to the present application;

[0034] In the figure, 1 is a simulated experimental cabin, 2 is a water storage tank, 3 is an experimental cabin top cover, 4 is a flow guide groove, 5 is a liquid discharge hole, 6 is a medium flow meter, 7 is a pressure gauge, and 8 is a leakage control valve. DETAILED DESCRIPTION

[0035] Embodiments of the present application will be described in more detail by referring to the accompanying drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0036] It should be understood that although the terms "first", "second", "third", etc. are used to describe various information in the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information without departing from the scope of the present application. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0037] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0038] Unless otherwise defined, the terms "mounting", "connected", "connection", "fixed", and the like are to be construed in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0039] Embodiment one:

[0040] As shown in a kind of simulation comprehensive pipe gallery leakage, vibration and strain monitoring experimental device, including multiple independent simulation experiment cabin 1 unit according to experimental demand proportion design, multiple simulation experiment cabin 1 is connected to constitute combined pipe gallery experiment cabin. Cabin is transparent design, pipeline, sensor etc. are arranged in combined pipe gallery experiment cabin, video capture and range measurement can be carried out to leakage diffusion, temperature, vibration, strain and other parameters can be measured by sensor simultaneously. Figure 2 Figure 3 Simulation experiment cabin 1 top is based on experiment cabin top cover 3 and constitutes airtight structure, and the bottom of the cabin is designed with flow guide groove 4, which is convenient for the outflow of liquid phase medium leakage simulation experiment, and the flow guide hole at the bottom flows into the experiment cabin bottom water tank 2. After pipe gallery experiment cabin is connected, pipeline simulation device can be designed according to the length of pipe section and installed in simulation pipe gallery cabin, and the specific connection is as shown in

[0041] Figure 4

[0042] During the experiment, liquid and gas (including steam) medium is injected into the pipe gallery by circulating pump and gas injection compressor, and the medium flows through flow meter and pressure gauge 7 respectively, and the leakage amount is determined by reading before and after the leakage simulation experiment section. During the experiment, the leakage control valve 8 is controlled to adjust the simulated leakage amount. In addition, by changing the direction of the leakage port, the leakage diffusion law of the pipeline in the gallery in different directions can be simulated. The medium in the experimental pipeline is circulated back to the mechanism storage tank outside the gallery for experimental circulation. During the experiment, only the pipeline is placed in the simulation pipe gallery cabin.

[0043] The simulation experimental device can simulate the leakage diffusion law under the conditions of multiple pipelines, different regions in the gallery space, medium type, leakage amount, medium temperature, etc. The real-time state of leakage diffusion can be obtained by sensor measurement and visual capture. The leakage is simulated by leakage simulation device, and the medium diffusion space domain is simulated by gallery cabin. In addition, combined with the installation of multiple pipeline simulation and vibration and strain excitation mode, the stress and strain monitoring simulation experiment of pipeline and structure can also be simulated by the device.

[0044] Simulation sensor identification experiment: after pipeline connection is completed, sensors are arranged according to Figure 5 ​​​Arrangement, on the pipeline, arrangement sensor n1, n2, n3…nx, the number of sensors is determined according to the experimental requirements, arrangement sensor acquisition monitoring data (strain, vibration, etc.), according to the interval L1, L2…Lx-1 between sensors, the relationship between vibration source, external force and sensing data can be established, according to the material properties of the pipeline, installation form (support: only in the direction of gravity constraint, support position is not fixed; Fixed: support is fixed in the corridor cabin, with constraint control position movement), establish monitoring data analysis model, locate and identify vibration and external force.

[0045] Example two:

[0046] An experimental method for simulating comprehensive pipe gallery leakage, vibration and strain monitoring, suitable for the experimental device shown in example one, used for comprehensive pipe gallery leakage simulation analysis. After the corridor cabin is connected, temperature sensors are arranged in the corridor as shown in Figure 6 , distributed temperature sensors A, B / D and C are arranged at the top of the experimental cabin, the middle of the corridor cabin on both sides and the bottom of the corridor, respectively, and the length L is used to represent the length in the length direction.

[0047] As shown in Figure 1 , the experimental method comprises the following steps:

[0048] S1, obtain the characteristic data in the simulation experimental cabin under the current leakage scene.

[0049] Characteristic data includes multiple data collected by detection devices arranged at the top, middle of both sides and bottom of the experimental cabin in the length direction of the simulation experimental cabin. There are n groups of A, B / D and C sensors with relatively fixed positions (at the top of the cabin, the middle of both sides of the corridor cabin and the bottom of the corridor) arranged in the length direction of the comprehensive pipe gallery. At the first position in the length direction, the data collected by A sensor is a1, the data collected by B sensor is b1, the data collected by C sensor is c1, and the data collected by D sensor is d1. Similarly, the characteristic data is represented as a two-dimensional list:

[0050] [[a1,b1,c1,d1],[a2,b2,c2,d2],…,[a n ,b n ,c n ,d n ]].

[0051] S2, input the characteristic data into the leakage state recognition model.

[0052] The leakage state recognition model includes a leakage recognition method based on characteristic data and a multi-layer perceptron model used as a synthetic model. The leakage recognition method includes: determining the position of the leakage point according to the numerical peak value of the detection device in the length direction; determining the o'clock position of the leakage point along the circumferential direction of the pipeline according to the difference size of the multi-point detection devices at the same length direction position of the collected data; and determining the leakage amount according to the numerical size of the detection device.

[0053] Sensors along the length of the line can detect temperature variations along the line. When a leak occurs, factors such as fluid flow and heat exchange can cause localized temperature anomalies at the leak site. During the experiment, the sensors continuously collected data. The values ​​from each sensor along the length L were analyzed to identify the location of the peak value. Since the temperature variation is most pronounced at the leak point, this peak location corresponds to the leak point along the length.

[0054] Identify the direction of the leak based on data comparison: At the same location along the length of L, compare the difference in data collected by sensors A, B, C, and D. Due to the different leak directions, sensors at different locations are affected to varying degrees by the leak, resulting in varying temperature changes. Assuming that at a certain location, the value change for sensor A (top of the tank) is the largest, while changes for sensors B, C, and D are relatively small, we can preliminarily determine that the leak is near the top, at 12 o'clock. If the value change for sensor B is significantly greater than that for the other sensors, combined with the right-hand rule along the flow direction, we can determine that the leak is at 3 o'clock. Similarly, based on the size of the data differences between the different sensors, we can determine the leak's o'clock position along the circumference of the pipeline.

[0055] The larger the leak, the greater the impact on the ambient temperature and the greater the change in the sensor value. Through preliminary experimental calibration, a corresponding relationship between sensor values ​​and leak values ​​is established. Based on the current sensor value, the corresponding leak value is determined by comparing it with this established relationship. For example, preliminary experiments have shown that when the sensor value changes by x, the corresponding leak value is y. Therefore, in actual monitoring, if the sensor value changes by x, the leak value can be determined to be y.

[0056] The comprehensive pipeline corridor leakage status recognition model based on multi-layer perceptron includes an input layer for receiving multiple feature data extracted from the detection device, a hidden layer for performing nonlinear transformation and feature learning on the feature data, and an output layer for outputting the predicted leakage point position, direction and leakage amount.

[0057] The input layer contains a two-dimensional list of n features, and the input corresponds to n neurons, each of which corresponds to a single feature value. The input layer passes the received n feature values ​​to the hidden layer, and outputs an n-dimensional vector x.

[0058] The hidden layer is configured with m neurons, which performs weighted summation and nonlinear activation on the output of the input layer, and outputs an m-dimensional vector h. The output layer is configured with 3 neurons, which performs weighted summation on the output of the hidden layer, and outputs a 3-dimensional vector y, representing the predicted leakage point position, direction and leakage amount, respectively.

[0059] In order to enable the model to accurately predict the leakage state, a large amount of training data is required to train the model. The training process adopts the back propagation algorithm, which adjusts the weights and biases of the model by minimizing the error (such as mean square error) between the predicted result and the true value. After randomly initializing all the weights and biases, the training data is input into the model, sequentially passing through the input layer, the hidden layer and the output layer to obtain the predicted result. The error between the predicted result and the true value is calculated using the mean square error, and the gradient is calculated according to the error, and then the weights and biases of the model are updated using optimization algorithms such as gradient descent method, so that the error gradually decreases. The process of forward propagation, error calculation and back propagation is repeatedly performed until the error reaches a satisfactory level or the preset number of training times is reached. Through the above training process, the model can learn the mapping relationship between the sensor data and the leakage state, thereby achieving accurate prediction of the comprehensive pipe gallery leakage state.

[0060] S3, correcting and outputting the recognition result obtained by the leakage state recognition model, including predicting the leakage point position, direction and leakage amount.

[0061] The correction of the recognition result includes the following steps:

[0062] S31, multiple feature data acquisition is performed on the same leakage scene to obtain multiple recognition results. Based on the model, multiple predictions are made on the same leakage scene, and the prediction results (predicted values of leakage point position, direction and leakage amount) are recorded. Then the average value of these predicted values is calculated as the final corrected prediction result.

[0063] S32, the predicted leakage point direction is numerically processed based on the sequential digital coding method. The direction needs to be reasonably numerically processed to realize the average value calculation, which is realized based on the sequential digital coding method. Including the following steps:

[0064] S321, the direction code of the top of the experimental cabin is 0, the direction code of the right side of the experimental cabin is 1, the direction code of the left side of the experimental cabin is 2, and the direction code of the bottom of the experimental cabin is 3. The coding rule of the sequential digital coding method is to assign consecutive integers to the required coding direction according to a certain order. The coding numbers are consecutive, which facilitates numerical calculation and comparison during model processing, and enables the multilayer perceptron model to more easily learn the relationship between the direction and other features.

[0065] S322、According to the determination of the point clock position of the leakage point along the circumferential direction of the pipeline, the corresponding direction code is determined. For example, when it is determined according to the data of the four sensors that the direction of the leakage point is the bottom, the direction code is input into the model as 2.

[0066] The sequential numerical coding method assigns continuous integers to the four directions, and the operation is simple and direct when calculating the average value. For example, when the direction codes obtained by multiple predictions are 0, 1, and 2 respectively, the average value is (0+1+2) ÷ 3 = 1, and the average direction can be directly obtained as right according to the coding rule. The continuous integer coding makes the distance and order between the values have a clear physical meaning, and the result obtained after calculating the average value can also intuitively reflect the intermediate state of the direction. For example, the average value of the obtained code is 1.5, which is between 1 and 2, and it can be roughly inferred that the average direction is between the right and the bottom. This is helpful for analyzing and understanding the concentration trend of the direction of the leakage point. For the multi-layer perceptron model, the values obtained by the sequential numerical coding can be directly used as input features for processing. The result obtained after calculating the average value can also be used as an effective input, and the model can better learn the relationship between these values and other features (such as the position of the leakage point and the leakage amount).

[0067] S33, calculate the average value of the recognition result as the final corrected recognition result output.

[0068] Accurate leakage point position, direction and leakage amount information can help maintenance personnel quickly locate the leakage position and understand the specific situation of the leakage, so as to formulate more effective maintenance plans. For example, if it is determined that the leakage point is located in a certain area of the pipe gallery and the leakage amount is large, the maintenance personnel can carry the corresponding equipment and materials to the area for emergency repair, avoiding more serious consequences caused by leakage, such as gas leakage may cause explosion, water leakage may cause equipment damage or foundation settlement in the pipe gallery, etc.

[0069] Through accurate identification of the leakage state, the overall safety of the comprehensive pipe gallery can be evaluated. If leakage occurs frequently or the leakage amount is large, it indicates that the pipe gallery may have structural damage or other safety hazards, which needs further inspection and maintenance. For example, if leakage is detected in the same area multiple times, it may mean that the pipes in that area have quality problems or have been affected by external factors such as soil settlement, external impact, etc., which requires detailed inspection and evaluation of that area. Optimize the monitoring system: compare the corrected recognition result with the original data of the monitoring system for analysis, which can find problems in the monitoring system, so as to optimize the monitoring system. For example, if it is found that the measurement data of some sensors deviates greatly from the final corrected recognition result, it may mean that the sensor needs to be calibrated or replaced, or the algorithm of the monitoring system needs to be further improved to improve the accuracy and reliability of the monitoring.

[0070] Accurate leakage state recognition results can provide data support for the management and operation of the comprehensive pipe gallery. For example, through statistical analysis of leakage data, the occurrence regularity of leakage can be understood, such as the high-incidence time period, the high-incidence area, etc., so that corresponding preventive measures can be taken. At the same time, these data can also be used to evaluate the operation efficiency and maintenance cost of the pipe gallery, and provide a reference for the long-term planning and management of the pipe gallery.

[0071] By comparing the final correction recognition result with the actual situation, the accuracy and effectiveness of the model can be verified. If it is found that there is a large deviation between the prediction result of the model and the actual situation, the model can be improved and optimized, such as adjusting the parameters of the model, adding more feature data or improving the algorithm of the model, etc., to improve the performance and prediction ability of the model.

[0072] Finally, it should be noted that in this document, relationships such as first and second, and the like, are merely used to distinguish one entity or action from another, and do not necessarily require or imply any such actual relationship or order between or among entities or actions. Moreover, the terms including, including, or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0073] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0074] The above has described the embodiments of the present application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles, practical application or improvement of technology in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. An experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipeline corridor, characterized in that: The following steps are involved: Acquire characteristic data in the simulation experiment cabin under the current leakage scenario, wherein the characteristic data includes a plurality of data collected by detection devices arranged along the length direction of the simulation experiment cabin at the cabin top, the middle of both sides, and the cabin bottom; Inputting the characteristic data into a leakage state identification model, the leakage state identification model including a leakage identification method based on the characteristic data and a multi-layer perceptron model used as a synthesis model, the leakage identification method including: determining the location of the leakage point based on the peak value of the detection device in the longitudinal direction; determining the o'clock position of the leakage point along the circumference of the pipeline based on the difference between the values ​​of multiple detection devices at the same longitudinal position of the collected data; and determining the leakage amount based on the value of the detection device; The identification results obtained by the leakage state identification model are corrected and output, including the predicted leakage point location, direction and leakage amount.

2. The experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to claim 1, characterized in that: Correcting the recognition result includes the following steps: Performing multiple feature data collection on the same leakage scenario and obtaining multiple identification results; Predict the direction of leakage point by numerical processing based on sequential digital coding method; The average value of the recognition results is calculated and output as the final corrected recognition result.

3. The experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to claim 1, characterized in that: The numerical processing based on the sequential digital coding method to predict the direction of the leakage point includes: The direction of the top of the experimental cabin is coded as 0, the direction of the right side of the experimental cabin is coded as 1, the direction of the left side of the experimental cabin is coded as 2, and the direction of the bottom of the experimental cabin is coded as 3; According to the determined o'clock position of the leakage point along the circumferential direction of the pipeline, the corresponding direction code is determined.

4. The experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to claim 1, characterized in that: The multilayer perceptron model includes: An input layer, configured to receive a plurality of feature data extracted from the detection device; A hidden layer, used for performing nonlinear transformation and feature learning on the feature data; The output layer is used to output the predicted leakage point location, direction and leakage amount.

5. An experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery, suitable for the experimental method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery as claimed in claims 1-4, characterized in that: include: A simulation test cabin, wherein multiple independent simulation test cabin units are connected to form a combined pipe gallery test cabin, wherein the simulation test cabin is provided with simulation pipes and guide grooves, wherein the simulation pipes are used to simulate liquid and gas medium conveying pipes, and the guide grooves are used to guide out the liquid medium leakage simulation experiment, and the grooves are provided with guide holes; A detection device, comprising a sensor, a flow meter and a pressure gauge, wherein the sensor is used to measure temperature, vibration and strain parameters, and the flow meter and pressure gauge are installed on the simulated pipeline to measure the amount of medium flowing through; The injection system includes a circulation pump, an air injection compressor and a leakage control valve. The circulation pump and the air injection compressor are used to inject liquid and gaseous media into the simulated pipeline. The leakage control valve is installed on the simulated pipeline to adjust the simulated leakage amount and change the direction of the leakage port.

6. The experimental device for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to claim 5, characterized in that: A water storage tank is provided at the bottom of the simulation test chamber, and the water storage tank is used to store the liquid medium flowing out through the diversion hole.

7. The experimental device and method for simulating leakage, vibration and strain monitoring of a comprehensive pipe gallery according to claim 5, characterized in that: Based on the material properties of the simulated pipeline, the installation form is determined to be support installation or fixed installation. The support installation only constrains the simulated pipeline in the direction of gravity, and the position of the support is not fixed. The fixed installation fixes the support in the simulation experimental cabin to constrain the position movement of the simulated pipeline.

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