An airport water supply pipe network monitoring preliminary positioning system and method

Abstract

The application relates to the technical field of water pressure data processing, and discloses an airport water supply pipe network monitoring initial positioning system and method. The system comprises a data acquisition module, a data transmission module and a data processing module. The method comprises the following steps: finding the number and positions of water pressure monitors of the water supply pipe network by using an intelligent optimization algorithm, each water pressure monitor detecting the water pressure data of the nearby pipe network; transmitting the detected water pressure data of the pipe network to the central server of the airport through a wireless transmission mode; and analyzing the water pressure data of the pipe network by the server to determine whether the water pressure data of the pipe network is abnormal. The application provides the initial positioning function of the water supply pipe network monitoring, can monitor the water pressure condition of the pipe network in real time, and can timely find the abnormality, so that the leakage and waste are reduced, and the operation efficiency and reliability of the pipe network are improved. Through early discovery and processing of the abnormal condition, measures can be taken in time to avoid possible safety hazards and economic losses. Meanwhile, the application provides a scientific basis for the maintenance and management of the water supply pipe network.

Description

Technical Field

[0001] This invention relates to the field of water pressure data processing technology, and in particular to an initial positioning system and method for monitoring airport water supply networks. Background Technology

[0002] Airports are crucial transportation hubs. Due to their unique characteristics, airports consume a large amount of water, and the water supply points are relatively dispersed, resulting in water supply pipelines that can be 5-10 km long. Generally, airport water supply pipelines are buried underground. Various factors, such as pipeline corrosion, aging joints, and leaky valve seals, can lead to leaks in the pipeline network. If these leaks go undetected for a long time, it will result in a significant waste of water resources. Water resources are precious natural resources, and protecting them in water-scarce areas is urgent. However, pipeline leaks are a major cause of this waste. Therefore, it is crucial to quickly and effectively locate and repair pipeline leaks. Traditional pipeline leak detection involves using leak detection instruments such as listening rods to inspect sections of the pipeline. This method is suitable for small-scale pipelines. For large-scale pipelines, this method is not only inefficient but also wastes a significant amount of human and material resources.

[0003] Prior art 1, application number: CN 202010965632.7, discloses an acoustic leakage detection method, including a mechanical listening rod and an electronic amplified leak detector. The electronic amplified leak detector includes a vibration sensor, an amplification circuit, a filtering circuit, a display, a power supply, and an earpiece. The mechanical listening rod includes a sound transmission rod and a resonant cavity. The vibration sensor is connected to the pipe to be tested, or the vibration sensor is connected to the sound transmission rod, and then the sound transmission rod is brought into contact with the pipe. During this process, the stability of the handheld sound transmission rod and the connection stability of the vibration sensor should be maintained. If the pipe to be tested is deeply buried underground, the sound transmission rod should be used at an appropriate valve manhole for contact detection. Although the use of the mechanical listening rod and the electronic amplified leak detector makes the acoustic leakage detection method more diversified, and the combination of mechanical and electronic detection effectively reduces the cost of the detection process, the actual detection range of the pipeline network is limited at one time, making it unsuitable for pipeline network detection within airports, resulting in low pipeline network leak detection efficiency.

[0004] Prior art two, application number CN202410157076.9, discloses a water pipe leak detection device, including a water tank and connecting pipes disposed on both sides inside the water tank. A pressure ring is integrally formed at one end of each connecting pipe that is close to each other. A sealing block is slidably disposed inside each of the two connecting pipes, and a snap-fit ​​assembly is disposed between the two sealing blocks. A driving assembly connected to the sealing block is disposed at one of the connecting pipes. Through the cooperation of the snap-fit ​​assembly and the driving assembly, as the pipe rotates into the water tank, the two sealing blocks can approach each other and compress the gas inside the pipe. The compressed gas can be used to detect leaks in the pipe. Although this device does not require frequent disassembly and installation of the air pump connector compared to the traditional method of injecting gas into the pipe using an air pump, making operation simpler, the equipment requires deployment and installation and is immobile, which is not conducive to effectively improving the efficiency of airport pipeline network inspection.

[0005] Prior art three, application number: CN 202311747688.5, discloses a leak detection device and method for water supply networks based on the Industrial Internet, including a top plate and a bottom plate, with the top plate located above the bottom plate. A connecting assembly is provided between the front and rear side walls of the top and bottom plates, and replacement components are provided on the bottom of the top plate and the top of the bottom plate. The connecting assembly includes a rotating groove, a connecting groove, a rotating shaft, a rotating rod, a sliding cavity, a pressing plate, a pull rod, a clamping plate, a spring, an insertion rod, an insertion hole, and a handle. The rotating groove is located on the bottom plate, and the connecting groove is located on the top plate. The rotating shaft is rotatably connected between the left and right inner walls of the rotating groove. Although the connecting assembly between the top and bottom plates allows for rapid installation and disassembly of the leak detection device, solving the problems caused by traditional bolt fixing and improving work efficiency, its relatively large size is not conducive to the detection of large-area pipe networks within airport areas and increases labor costs.

[0006] Current technologies 1, 2, and 3 suffer from time-consuming and labor-intensive methods for detecting leaks in pipeline networks, resulting in low efficiency. Therefore, this invention provides an initial location system and method for monitoring airport water supply networks. This system can quickly locate leak areas in the network, providing a basis for leak repair. It not only reduces water loss but also saves significant manpower and resources. Summary of the Invention

[0007] The main objective of this invention is to provide an initial location system and method for monitoring airport water supply networks, so as to solve the problems of time-consuming and labor-intensive detection methods and low leakage efficiency in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An airport water supply network monitoring and initial location system, the airport water supply network monitoring and initial location system comprising:

[0010] The data acquisition module is used to detect the water pressure data of the nearby pipe network through the water pressure monitor installed on the water supply network. The detection cycle is preset to be once every 10-30 minutes.

[0011] The data transmission module is used to receive water pressure data detected by the water pressure monitor on the water supply pipeline and transmit it wirelessly to the server in the airport central computer room.

[0012] The data processing module is used to compare and analyze the detected water pressure data with the water pressure data under normal operating conditions.

[0013] As a further improvement of the present invention, the data acquisition module includes:

[0014] The layout acquisition submodule is used to build a geographic information model of the pipeline layout based on the collected data on the pipeline layout during airport construction, including the location, length, diameter and connection information of the pipelines; it is used to analyze the pipeline layout using the spatial analysis function of geographic information software to identify the connection relationships and paths of the pipelines.

[0015] The node analysis submodule is used to import the location, length, diameter, and connection information of pipelines, as well as the connection relationships and paths of pipelines, into geographic information software. Select the buffer analysis tool, create buffers based on buffer distances, and overlay the created buffer layer with the pipeline connection node layer for analysis. Based on the results of the overlay analysis, identify key nodes, confluence points, branch points, and pipeline intersections within the buffer, and filter and label them based on layer attributes or spatial location.

[0016] The location output submodule is used to determine the installation location of water pressure monitors at key nodes based on the key nodes, and to number the key nodes sequentially according to their weights.

[0017] As a further improvement of the present invention, the position output submodule includes:

[0018] The location acquisition unit records the installation location of each water pressure monitor, using coordinates or geographic location to describe the installation location; each installation location is assigned a weight value, which decreases sequentially from the confluence point, branch point, and pipe network intersection.

[0019] The sorting execution unit is used to sort the installation locations from high to low according to their weight values.

[0020] The location numbering unit is used to number each installation location in a sorted order, using either numeric or alphanumeric codes.

[0021] As a further improvement of the present invention, the data acquisition module also includes: a monitoring equipment layout sub-module for pipeline network operation analysis, pipeline network leakage simulation, pipeline network leakage event determination, and optimized layout of pipeline network water pressure monitors.

[0022] As a further improvement of the present invention, the monitoring equipment layout submodule includes:

[0023] The pipeline operation analysis unit is used to perform hydraulic operation analysis on the airport water supply pipeline network using EPANET software, thereby understanding the water pressure fluctuation range under normal operating conditions.

[0024] The pipeline leakage simulation unit is used to simulate water supply pipeline leakage on EPANET software. A leak point is opened at 50m intervals in each pipeline to obtain water pressure information when pipeline leakage occurs.

[0025] The pipeline leakage event determination unit is used to determine that if the water pressure fluctuation in the water supply network exceeds the normal water pressure fluctuation range under normal conditions, the network is in an abnormal state and a pipeline leakage event may occur.

[0026] The pipeline water pressure monitoring optimization layout unit is used to randomly deploy 20% of the number of water pressure monitoring nodes in the pipeline to monitor pipeline leakage events. Then, based on the principle of marginal benefit, the intelligent optimization algorithm finds the optimal number and location of water pressure monitoring devices to monitor pipeline leakage events.

[0027] As a further improvement of the present invention, the data transmission module includes:

[0028] The target data acquisition submodule is used to collect environmental information of the airport water supply network to obtain target data, and uses the target data as a simulated wireless signal environment.

[0029] The receiving signal generation submodule is used to simulate the wireless signal source in a simulated wireless signal environment, simulate the generation of water pressure data wireless transmission signal, transmit the water pressure data wireless transmission signal to the server, and obtain the signal reception result from the server, which is recorded as the received signal.

[0030] The signal deviation comparison submodule is used to compare the wirelessly transmitted and received water pressure data signals to obtain the signal deviation; and based on the signal deviation, the received signal is processed by the server.

[0031] As a further improvement of the present invention, the signal deviation comparison submodule includes:

[0032] The signal sampling unit is used to sample the wireless transmission signal of water pressure data and convert the continuous wireless transmission signal of water pressure data into discrete wireless transmission signal of water pressure data.

[0033] The signal conversion unit is used to convert the wireless transmission signal of water pressure data from the time domain to the frequency domain using Fourier transform. The frequency domain amplitude characteristics of the signal are represented by the amplitude spectrum, the phase characteristics by the phase spectrum, and the power characteristics by the power spectrum of the signal obtained by Fourier transform.

[0034] The feature extraction unit is used to select and extract features from different signals, compare the spectral features of different signals, and compare the similarity of spectral energy distribution by calculating the correlation coefficient similarity index. Based on the similarity results, the signal comparison deviation is calculated using statistical methods, and a numerical deviation metric is obtained.

[0035] As a further improvement of the present invention, the data processing module includes:

[0036] The water pressure data reading submodule is used to read the detected water pressure data and the water pressure data under normal operating conditions;

[0037] The water pressure data comparison submodule is used to compare the detected water pressure data with the water pressure data under normal operating conditions; by comparing the degree of water pressure drop, it can determine whether the water pressure in the pipeline network is in an abnormal state.

[0038] The data marking submodule is used to calculate the normal water pressure fluctuation range based on the water pressure data under normal operating conditions. If the water pressure drop exceeds the normal water pressure fluctuation range, the current water pressure data will be highlighted in red, and the water pressure monitor number will be displayed. If the water pressure data is within the normal water pressure fluctuation range, the water pressure data will be highlighted in green.

[0039] As a further improvement of the present invention, the water pressure data comparison submodule includes:

[0040] The pipeline network model building unit is used to build a hydraulic model of the pipeline network using EPANET, including the geometry, material and operating parameters of pipes, valves and pumps, as well as initial conditions and boundary conditions; and to set water pressure data under normal operating conditions.

[0041] The model operation calculation unit is used to simulate abnormal conditions in the pipe network by changing certain parameters in the model or introducing leakage conditions; it runs the pipe network hydraulic model to calculate the simulated water pressure data and obtain the water pressure value of each node or pipe segment;

[0042] The comparative analysis unit is used to compare the simulated water pressure data with the water pressure data under normal operating conditions, compare the degree of water pressure drop at each node or pipe section, or compare it with the normal water pressure fluctuation range; based on the results of the comparative analysis, it determines whether the pipe network water pressure is in an abnormal state. If the degree of water pressure drop exceeds the normal range or exceeds the preset threshold, it is determined to be an abnormal state.

[0043] To achieve the above objectives, the present invention also provides the following technical solution:

[0044] A method for initial location monitoring of an airport water supply network, applied to the aforementioned airport water supply network monitoring initial location system, comprising:

[0045] The intelligent optimization algorithm is used to determine the number and location of water pressure monitors in the water supply network, and each water pressure monitor detects the water pressure data of the nearby network.

[0046] The water pressure data of the monitored pipeline network is transmitted wirelessly to the airport's central server.

[0047] The server analyzes the water pressure data of the pipeline network to determine if the data is abnormal. If abnormal, it will display the water pressure monitor number of the current pipeline network. Airport staff will then use the water pressure monitor number to locate the leaking area in the pipeline network, conduct more precise testing in that area, and finally find and repair the leak.

[0048] This invention's data acquisition module detects water pressure data from the water supply network using a water pressure monitor, performing the detection at preset intervals to acquire real-time water pressure data, providing a data foundation for subsequent analysis and processing. The data transmission module wirelessly transmits the data detected by the water pressure monitor to a server in the airport's central computer room, ensuring timely transmission of the collected water pressure data for convenient subsequent data processing and analysis. The data processing module compares and analyzes the detected water pressure data with data under normal operating conditions, identifying abnormal or normal states based on the analysis results. Through comparative analysis, it promptly detects anomalies in the water supply network, such as leaks, allowing for appropriate repair measures to ensure the normal operation of the network. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the functional modules of an embodiment of the airport water supply network monitoring and initial positioning system of the present invention;

[0050] Figure 2 This is a schematic diagram of an embodiment of the airport water supply network monitoring and initial positioning system of the present invention;

[0051] Figure 3 This is a plan view of an airport water supply network, representing an embodiment of the airport water supply network monitoring and initial positioning system of the present invention.

[0052] Figure 4 This is a schematic flowchart of one embodiment of the airport water supply network monitoring and initial location method of the present invention;

[0053] Figure 5 This is a schematic diagram illustrating the steps for determining the number and location of water pressure monitors in an airport water supply network according to an embodiment of the airport water supply network monitoring initial positioning method of the present invention.

[0054] Figure 6 This is a flowchart illustrating the steps of wirelessly transmitting water pressure data from an airport water supply network monitoring initial location method according to an embodiment of the present invention.

[0055] Figure 7 This is a flowchart illustrating the steps of a server analyzing water pressure data from the pipeline network, as described in one embodiment of the airport water supply network monitoring and initial location method of the present invention.

[0056] Figure 8 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention;

[0057] Figure 9 This is a schematic diagram of the structure of one embodiment of the storage medium of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0060] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0061] like Figure 1 As shown, this embodiment provides an example of an airport water supply network monitoring initial positioning system. In this embodiment, the airport water supply network monitoring initial positioning system includes a data acquisition module 1, a data transmission module 2, and a data processing module 3 that are electrically connected in sequence.

[0062] The data acquisition module 1 is used to detect the water pressure data of the nearby pipe network through the water pressure monitor installed on the water supply network. The detection cycle is preset to be once every 10-30 minutes. The data transmission module 2 is used to receive the water pressure data detected by the water pressure monitor on the water supply network and transmit it to the server in the airport central computer room through wireless transmission. The data processing module 3 is used to compare and analyze the detected water pressure data with the water pressure data under normal operating conditions.

[0063] Preferably, in this embodiment, the data acquisition module 1 detects the water pressure data of the water supply network through a water pressure monitor and performs the detection according to a preset cycle, acquiring the water pressure data of the water supply network in real time to provide a data foundation for subsequent analysis and processing. The data transmission module 2 transmits the data detected by the water pressure monitor to the server in the airport central computer room via wireless transmission, promptly transmitting the collected water pressure data to the server for convenient subsequent data processing and analysis. The data processing module 3 compares and analyzes the detected water pressure data with the water pressure data under normal operating conditions, and identifies abnormal or normal states based on the analysis results. Through comparative analysis, abnormalities in the water supply network, such as network leakage, can be detected in a timely manner so that corresponding measures can be taken for repair to ensure the normal operation of the network; its principle is described in the appendix. Figure 2 .

[0064] In summary, this embodiment provides a preliminary monitoring and positioning function for the water pipe network, enabling real-time monitoring of the network's water pressure and timely detection of anomalies. This reduces leakage and waste, improving the network's operational efficiency and reliability. Early detection and handling of anomalies allow for timely intervention to prevent potential safety hazards and economic losses. Furthermore, the system provides data support to the water pipe network, offering a scientific basis for its maintenance and management.

[0065] This invention combines EPANET software and intelligent optimization algorithms to find the optimal number and location of water pressure monitors based on the principle of marginal benefit when monitoring the number of pipeline leakage events. It also constructs a pipeline monitoring initial location system by combining pressure monitors, wireless transmission equipment, and a central server. This system can quickly locate pipeline leakage areas, providing a basis for pipeline leakage repair, which not only reduces water loss but also saves a lot of human and material resources.

[0066] Furthermore, the data acquisition module 1 specifically includes:

[0067] The layout acquisition submodule is used to build a geographic information model of the pipeline layout based on the collected data on the pipeline layout during airport construction, including information such as the location, length, diameter and connection relationship of the pipelines; it is used to analyze the pipeline layout using the spatial analysis function of geographic information software to identify the connection relationship and path of the pipelines.

[0068] The node analysis submodule is used to import information such as the location, length, diameter, and connection relationships of pipelines, as well as the pipeline connection relationships and paths, into geographic information software. By selecting the buffer analysis tool, a buffer is created based on the buffer distance. The created buffer layer is overlaid and analyzed with the pipeline connection node layer. Based on the results of the overlay analysis, key nodes within the buffer, such as confluence points, branch points, and pipeline intersections, are identified and filtered and marked according to layer attributes or spatial location.

[0069] The location output submodule is used to determine the installation location of water pressure monitors at key nodes based on the key nodes, and to number the key nodes sequentially according to their weights.

[0070] Preferably, the layout acquisition submodule in this embodiment, through a geographic information model, can achieve visualization and spatial analysis of the pipeline network layout, understanding the pipeline's direction, arrangement, and connections, providing basic data for subsequent node analysis. The node analysis submodule, through node analysis, can determine the locations of key nodes in the water supply network, providing specific reference for the subsequent installation of water pressure monitors. The location output submodule, through location output, can determine the specific installation location of the water pressure monitor, enabling the monitoring and control of water pressure in the water supply network.

[0071] In summary, this embodiment improves the accuracy of engineering planning and design: by using a geographic information system (GIS) for layout acquisition, node analysis, and location output, a more accurate understanding of the pipeline network layout and key node locations can be obtained, providing accurate data and reference for engineering planning and design. It also optimizes pipeline network operation and management: analysis of the pipeline network layout and key nodes allows for a better understanding of pipeline connections and routes, providing decision support for pipeline network operation and management. Simultaneously, the installation of water pressure monitors enables real-time monitoring of water pressure, timely detection of problems, and timely implementation of measures, improving the operational efficiency and safety of the pipeline network. Furthermore, it increases work efficiency and reduces costs: spatial and network analysis using GIS software automates the processing of large amounts of geographic data and pipeline network information, improving work efficiency. At the same time, a reasonable pipeline network layout and the identification of key nodes can avoid unnecessary pipeline duplication and redundancy, reducing engineering costs and resource waste.

[0072] Furthermore, the location output submodule specifically includes:

[0073] The location acquisition unit records the installation location of each water pressure monitor, using coordinates or geographic location to describe the installation location; each installation location is assigned a weight value, which decreases sequentially based on the confluence point, branch point, and pipeline intersection, etc.

[0074] The sorting execution unit is used to sort the installation locations from high to low according to their weight values.

[0075] The location numbering unit is used to assign a number to each installation location according to the sorted order. The numbering can use either numbers or letters. For example, suppose there are four key nodes (A, B, C, D), each with a water pressure monitor installed, and the installation locations are sorted by weight values ​​in the order DBCA. Installation location D can be assigned the number D1, installation location B B2, installation location C C3, and installation location A A4. Key nodes with the same weight value can be sorted sequentially from their original numbers using Arabic numerals.

[0076] Preferably, the location acquisition unit in this embodiment accurately records the location information of each monitor, providing accurate data for subsequent sorting and numbering. The sorting execution unit sorts the installation locations according to certain rules, placing locations with higher weights first and locations with lower weights later. The location numbering unit assigns a unique number to each location for easy identification and management later.

[0077] In summary, the module in this embodiment determines the installation location of the water pressure monitor and sorts and numbers the locations according to weight values, achieving the following objectives: Improved monitoring effectiveness: By rationally selecting and sorting installation locations, it ensures that the monitor covers important nodes and key locations, thereby improving the accuracy and effectiveness of monitoring. Optimized resource utilization: Based on the weight values, prioritizing the installation of monitors on important nodes allows for better utilization of limited resources and improves monitoring efficiency. Facilitated management and maintenance: Numbering allows for easy identification and management of each installation location, aiding in subsequent maintenance, troubleshooting, and data analysis. Support for decision-making and planning: The weight values ​​reflect the importance and priority of nodes, providing a reference for decision-makers and supporting the planning and optimization of the pipeline network system. Through rational location acquisition, sorting, and numbering, monitoring effectiveness can be improved, resource utilization optimized, management and maintenance facilitated, and support provided for decision-making and planning.

[0078] Furthermore, data acquisition module 1 specifically includes: a monitoring equipment layout submodule for pipeline network operation analysis, pipeline network leakage simulation, pipeline network leakage event determination, and optimized layout of pipeline network water pressure monitors; specifically including:

[0079] The pipeline operation analysis unit is used to perform hydraulic operation analysis on the airport water supply pipeline network using EPANET software, thereby understanding the water pressure fluctuation range under normal operating conditions.

[0080] The pipeline leakage simulation unit is used to simulate water supply pipeline leakage on EPANET software. A leak point is opened at 50m intervals in each pipeline to obtain water pressure information when pipeline leakage occurs.

[0081] The pipeline leakage event determination unit is used to determine that if the water pressure fluctuation in the water supply network exceeds the normal water pressure fluctuation range under normal conditions, the network is in an abnormal state and a pipeline leakage event may occur.

[0082] The pipeline water pressure monitoring optimization layout unit is used to randomly deploy 20% of the number of water pressure monitoring nodes in the pipeline to monitor pipeline leakage events. Then, based on the principle of marginal benefit, the intelligent optimization algorithm finds the optimal number and location of water pressure monitoring devices to monitor pipeline leakage events.

[0083] The marginal benefit is maximized when deploying eight water pressure monitors; for specific placement details, please refer to [link to relevant documentation]. Figure 3. The following steps describe how an intelligent optimization algorithm, based on the principle of marginal benefit, finds the optimal number and location of water pressure monitors to minimize the number of leakage events in the monitoring network: Define the objective function: First, define an objective function to measure the merits of minimizing the number of leakage events in the monitoring network. Initialize the population: Randomly generate a set of initial solutions as the population, each solution representing a possible water pressure monitor layout scheme. Evaluate fitness: Evaluate the fitness of each solution, i.e., the number of leakage events in the monitoring network, according to the objective function. Selection operation: Based on the fitness value, select a certain number of individuals as parents to generate the next generation. Crossover operation: Randomly select two individuals from the selected parents and generate new offspring individuals through crossover. Mutation operation: Perform a certain probability mutation operation on the newly generated offspring individuals to increase the diversity of solutions. Update the population: Merge the parents and offspring into a new population and repeat steps 3-6 until the termination condition is met. Result selection: Select the solution with the best fitness as the optimal solution, i.e., the water pressure monitor layout scheme that minimizes the number of leakage events in the monitoring network.

[0084] Preferably, the pipeline network operation analysis unit in this embodiment uses EPANET software to perform hydraulic operation analysis on the airport water supply network, thereby understanding the water pressure fluctuation range under normal operating conditions and the hydraulic performance of the network, including the water pressure fluctuation range, providing a reference for subsequent network leakage assessment and monitor layout. The network leakage simulation unit simulates water supply network leakage on EPANET software, with a leak point spaced 50m apart on each pipe to obtain water pressure information during network leakage, simulating real leakage conditions and understanding water pressure changes under different locations and degrees of leakage, providing data support for leak event assessment and layout optimization. The network leakage event assessment unit determines whether the network is in an abnormal state and whether a pipeline leakage event may occur based on the water pressure fluctuation range of the water supply network under normal operating conditions; it monitors water pressure fluctuations in the network in real time, promptly detects abnormalities, reduces the occurrence of leakage events, and provides guidance for subsequent maintenance and repair. The pipeline water pressure monitoring optimization layout unit utilizes intelligent optimization algorithms based on the principle of marginal benefit to optimize the number and location of water pressure monitoring devices in order to minimize the number of leakage events in the pipeline network. By rationally arranging water pressure monitoring devices, the detection efficiency of leakage events is improved, leakage losses are reduced, and the efficiency and reliability of pipeline network operation are enhanced.

[0085] In summary, this embodiment provides the capability for monitoring water pipe networks. Through operational analysis, leakage simulation, leakage event determination, and monitor layout optimization, it achieves accurate detection and early warning of pipe network leakage events. This helps to promptly identify and address pipe network leakage problems, reduce water resource waste and economic losses, and improve the sustainable operation capability of the pipe network. In the intelligent optimization algorithm based on the marginal benefit principle, the number of corresponding pipe network leakage events can be calculated according to the location and number of each water pressure monitor, and this number is used as the objective function value for evaluation. Through crossover and mutation operations, new solutions are continuously generated, and solutions with better fitness are retained through selection operations. Finally, a water pressure monitor layout scheme that minimizes the number of pipe network leakage events is found.

[0086] Furthermore, the data transmission module 2 specifically includes:

[0087] The target data acquisition submodule is used to collect environmental information of the airport water supply network to obtain target data, and uses the target data as a simulated wireless signal environment.

[0088] The receiving signal generation submodule is used to simulate the wireless signal source in a simulated wireless signal environment, simulate the generation of water pressure data wireless transmission signal, transmit the water pressure data wireless transmission signal to the server, and obtain the signal reception result from the server, which is recorded as the received signal.

[0089] The signal deviation comparison submodule is used to compare the wirelessly transmitted and received water pressure data signals to obtain the signal deviation; and based on the signal deviation, the received signal is processed by the server.

[0090] Preferably, the target data acquisition submodule in this embodiment acquires accurate environmental information, providing an accurate data foundation for subsequent signal simulation and transmission; establishing a realistic environmental simulation can better simulate the actual wireless signal transmission environment, improving the reliability and accuracy of signal transmission. The receiving signal generation submodule generates a simulated wireless signal based on the target data, wirelessly transmitting the water pressure data to the server, realizing wireless data transmission; simulating the real wireless signal transmission process verifies the feasibility and accuracy of wireless transmission. The signal deviation comparison submodule obtains the deviation during the signal transmission process through comparative analysis, providing a reference for subsequent signal processing and optimization; evaluating the stability and accuracy of wireless transmission, identifying and resolving problems in signal transmission, and improving the reliability and accuracy of data transmission.

[0091] In summary, this embodiment aims to simulate a real wireless signal environment to achieve wireless transmission of water pressure data. By comparing and processing the received signals, it improves the quality and accuracy of data transmission; verifies the feasibility and accuracy of wireless transmission; provides a reference and optimization scheme for data transmission in practical application scenarios; and ensures that data can be transmitted to the server stably and reliably.

[0092] Furthermore, the signal deviation comparison submodule specifically includes:

[0093] The signal sampling unit is used to sample the wireless transmission signal of water pressure data and convert the continuous wireless transmission signal of water pressure data into discrete wireless transmission signal of water pressure data.

[0094] The signal conversion unit is used to convert the wireless transmission signal of water pressure data from the time domain to the frequency domain using Fourier transform, and to represent the frequency domain amplitude characteristics of the signal using amplitude spectrum, phase spectrum, and power spectrum, etc., of the spectrum information obtained by Fourier transform.

[0095] The feature extraction unit is used to select and extract features from different signals, compare the spectral features of different signals, and compare the similarity of spectral energy distribution by calculating the correlation coefficient similarity index. Based on the similarity results, the signal comparison deviation is calculated using statistical methods, and a numerical deviation metric is obtained.

[0096] The process of converting the wireless transmission signal of water pressure data from the time domain to the frequency domain using the Fourier transform method is as follows: The wireless transmission signal of water pressure data is sampled, converting the continuous signal into a discrete signal. A Fourier transform is applied to the sampled signal, using the Fast Fourier Transform (FFT) algorithm for efficient computation. The Fourier transform converts the signal from the time domain to the frequency domain, obtaining the signal's spectral information, which includes frequency components, amplitude characteristics, phase characteristics, and power characteristics. The frequency domain characteristics of the signal are represented using the amplitude spectrum, phase spectrum, or power spectrum. The amplitude spectrum represents the signal's amplitude at different frequencies, the phase spectrum represents the signal's phase at different frequencies, and the power spectrum represents the signal's power at different frequencies.

[0097] The similarity index of correlation coefficient for comparing the similarity of spectral energy distributions can be calculated as follows: Calculate the spectral energy distributions of the two signals using power spectral density (PSD) estimation methods, such as the Welch method and the Bartlett method, to obtain the power spectral density of the two signals; compare the similarity of the spectral energy distributions by calculating the correlation coefficient between the power spectral densities of the two signals. The correlation coefficient measures the linear correlation between the two variables; typically, the Pearson correlation coefficient is used to calculate the similarity of the spectral energy distributions. The Pearson correlation coefficient is calculated by calculating the covariance and standard deviation of the two power spectral density sequences.

[0098] The statistical method for calculating signal contrast deviation can be performed as follows: The correlation coefficient, calculated based on the similarity of the spectral energy distributions, can be converted into a numerical similarity measure, such as mapping the correlation coefficient to an interval or normalizing it. Statistical methods, such as calculating the mean, variance, and standard deviation, can be used to calculate the signal contrast deviation. This can calculate the difference in the spectral energy distributions of two signals, or the deviation of the similarity measure from a reference value. Depending on the specific needs, a suitable statistical method can be selected to calculate the signal contrast deviation, such as calculating the mean absolute error (MAE) or mean squared error (MSE).

[0099] Preferably, the signal sampling unit in this embodiment samples the continuous wireless transmission signal of water pressure data and converts it into a discrete signal. The purpose of sampling is to obtain the numerical values ​​of the signal at discrete time points for subsequent processing and analysis. The signal conversion unit uses Fourier transform to convert the wireless transmission signal of water pressure data from the time domain to the frequency domain. The Fourier transform decomposes the signal into a series of frequency components, obtaining the signal's spectral information. By representing the frequency domain characteristics through amplitude spectrum, phase spectrum, and power spectrum, the frequency components, amplitude characteristics, phase characteristics, and power characteristics of the signal can be analyzed. The feature extraction unit compares the spectral characteristics of different signals and calculates the correlation coefficient similarity index to compare the similarity of the spectral energy distribution. The feature extraction unit selects suitable spectral features for extraction to further analyze the similarity or difference of the signals. By calculating similarity indices such as the correlation coefficient, the similarity of the signal spectral energy distribution can be quantified. Statistical methods are used to calculate the signal comparison deviation. Based on the results of the similarity, statistical methods are used to calculate the signal comparison deviation and obtain a numerical deviation measure. Statistical methods can calculate the deviation of the difference or similarity of the signal spectral energy distribution from the reference value, which is used to quantify the degree of difference of the signal.

[0100] In summary, these modules in this embodiment analyze and compare wireless transmission signals of water pressure data. Through sampling, Fourier transform, and feature extraction, the characteristic information of the signal in the time and frequency domains can be obtained. By calculating similarity indices and using statistical methods, the similarity and deviation of the signals can be quantified, thereby comparing the spectral characteristics of different signals, analyzing the differences and similarities of the signals, which helps to understand the characteristics of the signals, detect signal anomalies, and optimize signal processing methods.

[0101] Furthermore, data processing module 3 specifically includes:

[0102] The water pressure data reading submodule is used to read the detected water pressure data and the water pressure data under normal operating conditions;

[0103] The water pressure data comparison submodule is used to compare the detected water pressure data with the water pressure data under normal operating conditions; by comparing the degree of water pressure drop, it can determine whether the water pressure in the pipeline network is in an abnormal state.

[0104] The data marking submodule is used to calculate the normal water pressure fluctuation range based on the water pressure data under normal operating conditions. If the water pressure drop exceeds the normal water pressure fluctuation range, the current water pressure data will be highlighted in red, and the water pressure monitor number will be displayed. If the water pressure data is within the normal water pressure fluctuation range, the water pressure data will be highlighted in green.

[0105] Preferably, the water pressure data reading submodule of this embodiment can read the detected water pressure data and the water pressure data under normal operating conditions, obtain the required data, and provide input for subsequent comparative analysis. The water pressure data comparison submodule compares the detected water pressure data with the water pressure data under normal operating conditions; by comparing the degree of water pressure drop, it determines whether the pipeline water pressure is in an abnormal state, monitors the pipeline water pressure in real time, promptly detects abnormal phenomena, and provides early warning and judgment basis. The data marking submodule calculates the normal water pressure fluctuation range based on the water pressure data under normal operating conditions. If the water pressure drop exceeds this range, the current water pressure data is marked in red, and the water pressure monitor number is displayed. If the water pressure data is within the normal water pressure fluctuation range, the water pressure data is marked in green, intuitively displaying the pipeline water pressure status and helping users determine whether the pipeline is in an abnormal state and whether there is a possible leakage.

[0106] In summary, the collaborative operation of these modules in this embodiment enables comparative analysis of detected water pressure data, providing users with an intuitive understanding of the pipeline network's water pressure status through labeling and display. This allows for the timely detection of pipeline leaks, improving the safety and stability of the pipeline network and reducing water waste and economic losses. Simultaneously, it provides guidance and decision-making support for maintenance and repair work. The collaborative operation of these functional modules enables comparative analysis of detected water pressure data, and the labeling and display of the results, allowing users to identify whether the pipeline network's water pressure is abnormal or if leaks are possible. Through these functional modules, pipeline leaks can be detected promptly, improving the safety and stability of the pipeline network.

[0107] Furthermore, the water pressure data comparison submodule specifically includes:

[0108] The pipeline network model building unit is used to build a hydraulic model of the pipeline network using EPANET, including the geometry, material and operating parameters of components such as pipes, valves and pumps, as well as initial conditions and boundary conditions; and to set water pressure data under normal operating conditions.

[0109] The model execution calculation unit is used to simulate abnormal conditions in the pipe network, such as pipe leakage and valve failure, by changing certain parameters in the model or introducing leakage. It runs the pipe network hydraulic model and calculates simulated water pressure data. It can obtain the water pressure value for each node or pipe segment.

[0110] The comparative analysis unit is used to compare the simulated water pressure data with the water pressure data under normal operating conditions, compare the degree of water pressure drop at each node or pipe section, or compare it with the normal water pressure fluctuation range; based on the results of the comparative analysis, it is determined whether the pipe network water pressure is in an abnormal state. If the degree of water pressure drop exceeds the normal range or exceeds the preset threshold, it can be determined as an abnormal state.

[0111] Preferably, the pipeline network model building unit in this embodiment can use hydraulic simulation software such as EPANET to build a hydraulic model of the pipeline network, including the geometry, materials, and operating parameters of components such as pipes, valves, and pumps, as well as initial and boundary conditions, providing an accurate pipeline network model that can simulate the hydraulic behavior of the pipeline network and provide input for subsequent simulation calculations and comparative analysis. The model operation calculation unit can simulate abnormal conditions of the pipeline network, such as pipe leakage and valve failure, by changing the parameters in the model or introducing leakage. It can simulate water pressure changes in the pipeline network under different abnormal conditions, providing simulation data for subsequent comparative analysis. The comparative analysis unit can compare the simulated water pressure data with water pressure data under normal operating conditions. By comparing the degree of water pressure drop at each node or pipe section, or by comparing it with the normal water pressure fluctuation range, it can determine whether the pipeline network water pressure is in an abnormal state; it can promptly detect abnormal conditions in the pipeline network, provide early warnings and judgment criteria, and help users determine whether there are leaks or other problems in the pipeline network.

[0112] In summary, the collaborative operation of these modules in this embodiment enables comparative analysis of detected water pressure data, determination of whether the pipeline water pressure is in an abnormal state, timely detection of pipeline leaks and other problems, improvement of pipeline safety and stability, and reduction of water waste and economic losses. Simultaneously, it provides guidance and decision-making basis for maintenance and repair work. By establishing an accurate pipeline model, simulating abnormal conditions, and comparing and analyzing water pressure data, the efficiency and quality of pipeline operation and maintenance can be improved. Comparative analysis of the pipeline model allows for more accurate determination of whether the pipeline water pressure is in an abnormal state. Simulation can consider more factors, such as pipeline attributes, pump characteristics, and water supply demand, improving the accuracy of the judgment. Furthermore, model analysis can reveal the causes, scope of impact, and possible solutions for abnormal states, providing support and guidance for pipeline operation and maintenance.

[0113] like Figure 4As shown, this embodiment also provides an embodiment of the airport water supply network monitoring initial location method. In this embodiment, the airport water supply network monitoring initial location method is applied to the airport water supply network monitoring initial location system as described in the above embodiment. The airport water supply network monitoring initial location method specifically includes the following steps:

[0114] Step S1: Use intelligent optimization algorithms to find the number and location of water pressure monitors in the water supply network, and each water pressure monitor detects the water pressure data of the nearby network;

[0115] Step S2: Transmit the detected water pressure data of the pipeline network to the airport's central server via wireless transmission;

[0116] Step S3: The server analyzes the water pressure data of the pipeline network to determine if the water pressure data is abnormal. If abnormal, the water pressure monitor number of the current pipeline network will be displayed. Airport staff will then locate the leaking area of ​​the pipeline network based on the water pressure monitor number, and conduct more precise testing in this area to finally find and repair the leak.

[0117] Preferably, step S1 of this embodiment determines the number and location of water pressure monitors through an intelligent optimization algorithm, enabling effective monitoring of water pressure data in the pipeline network and coverage of key areas of the entire network. By rationally arranging the water pressure monitors, a comprehensive understanding of water pressure changes in the pipeline network can be achieved, allowing for timely detection of anomalies and early warnings of leaks and other problems, facilitating appropriate repair and maintenance. This helps improve the operational efficiency and safety of the pipeline network, reducing resource waste and losses. Step S2 transmits the detected water pressure data from the pipeline network to the airport's central server via wireless transmission, enabling real-time monitoring and remote management of the data. Through real-time data transmission and centralized management, the airport's central server can promptly acquire the water pressure data from the pipeline network, providing foundational data for subsequent analysis and processing. Simultaneously, it facilitates the integration and comparison of data from multiple water pressure monitors, improving data utilization efficiency. Step S3 analyzes and compares the water pressure data of the pipeline network to determine if any abnormalities exist, and displays the abnormal data along with the corresponding water pressure monitor number for later inspection and maintenance. Real-time data analysis and anomaly detection allow for rapid location of leaks in the pipeline network, reducing maintenance time and costs. This helps improve the reliability and safety of the pipeline network, reducing water waste and economic losses. Simultaneously, it also improves the work efficiency of airport staff and the quality of pipeline maintenance.

[0118] Furthermore, such as Figure 5 As shown, the process of determining the number and location of water pressure monitors in the water supply network in step S1 specifically includes the following steps:

[0119] Step S11: Define an objective function to measure the effectiveness of monitoring the number of pipeline leakage events. Define the objective function as minimizing the number of pipeline leakage events.

[0120] Step S12: Randomly generate a set of initial solutions as a population, each solution representing a possible water pressure monitor layout scheme; evaluate the fitness of each solution according to the objective function, i.e., the number of leakage events in the monitoring pipeline network;

[0121] In this scenario, assume there are N leakage areas that need to be monitored, and each leakage area can be covered by a water pressure monitor. The objective is to minimize the number of undetected leakage areas. An example of the objective function could be: minimize the number of undetected leakage areas.

[0122] Suppose that the layout of water pressure monitors is represented by an N-dimensional binary vector, where each dimension has a value of 1 indicating that a water pressure monitor is placed at that location, and a value of 0 indicating that no water pressure monitor is placed at that location. For example, for N=5 leakage areas, a possible layout could be [1,0,1,0,1], indicating that water pressure monitors are placed at locations 1 and 3.

[0123] Based on the objective function, a fitness function can be defined to evaluate the fitness of each solution, which is the number of undetected leakage areas. When evaluating fitness, the detection of each leakage area can be determined by simulating the water pressure transmission process.

[0124] The fitness function can be expressed as:

[0125] fitness=N-(number of undetected leakage areas)

[0126] In the crossover and mutation operations of the genetic algorithm, new individuals can be generated by changing certain positions in the binary vector, thereby increasing the diversity of solutions. By iteratively performing selection, crossover, and mutation operations, the population can be gradually optimized until the termination condition is reached. Finally, the solution with the best fitness is selected as the optimal solution, that is, the water pressure monitor layout scheme that minimizes the number of leakage events in the monitoring pipeline network.

[0127] Step S13: Based on the fitness value, select a certain number of individuals as parents to generate the next generation; randomly select two individuals from the selected parents and generate new offspring individuals through crossover; perform mutation operations on the newly generated offspring individuals with a certain probability to increase the diversity of solutions.

[0128] Step S14: Merge the parent and offspring into a new population, and repeat steps S12-S13 until the termination condition is met; select the solution with the best fitness as the optimal solution, that is, the water pressure monitor layout scheme that can minimize the number of leakage events in the monitoring pipeline.

[0129] Preferably, in step S11 of this embodiment, the problem is transformed into an optimization problem by defining an objective function, which facilitates the use of optimization algorithms to solve it. The definition of the objective function allows the optimization algorithm to find the optimal water pressure monitor layout scheme, thereby minimizing the number of leakage events in the monitoring pipeline network. This can improve the efficiency and accuracy of leakage detection and reduce the losses and waste caused by leakage. Step S12 forms a population by randomly generating initial solutions, where each solution represents a possible water pressure monitor layout scheme. The fitness of each solution is evaluated according to the objective function to determine the quality of the solution. By evaluating the fitness of the solutions, various water pressure monitor layout schemes can be compared to find the solution with higher fitness. This helps to determine a better water pressure monitor layout scheme and improve the effectiveness of leakage detection. Step S13 generates new offspring individuals according to selection operations and genetic operators (crossover and mutation operations). The crossover operation combines the information of two parent individuals to generate a new individual; the mutation operation randomly modifies an individual to increase the diversity of solutions. Through selection, crossover, and mutation operations, excellent solutions can be retained and new solutions can be introduced to increase solution diversity and explore better water pressure monitor layout schemes. This helps improve the search capability of the optimization algorithm and find better solutions. Step S14 iteratively performs selection, crossover, and mutation operations to gradually optimize the population until the termination condition is reached, selecting the solution with the best fitness as the optimal solution, that is, the water pressure monitor layout scheme that minimizes the number of monitoring pipeline leakage events. Through the iterative process of the optimization algorithm, the water pressure monitor layout scheme can be continuously optimized to minimize the number of monitoring pipeline leakage events, which helps improve the detection efficiency of pipeline leakage, reduce economic and resource waste, and improve the operational safety and reliability of the pipeline network.

[0130] Furthermore, such as Figure 6 As shown, the process of wirelessly transmitting water pressure data from the pipeline network in step S2 specifically includes the following steps:

[0131] Step S21: Collect environmental information of the airport water supply network to obtain target data, and use the target data as a simulated wireless signal environment;

[0132] Step S22: Simulate wireless signal source In simulated wireless signal environment, simulate the generation of water pressure data wireless transmission signal, and transmit the water pressure data wireless transmission signal to server, and obtain the signal reception result of server, which is recorded as the received signal;

[0133] Step S23: Compare the wirelessly transmitted and received water pressure data signals to obtain the signal comparison deviation; and based on the signal comparison deviation, process the received signal through the server.

[0134] Preferably, in step S21 of this embodiment, by collecting environmental information of the airport's water supply network, actual water pressure data can be obtained as target data for simulating the wireless signal environment. The purpose of this step is to obtain real water pressure data for subsequent wireless signal simulation and transmission. The collected actual data allows for a more accurate simulation of the wireless signal transmission environment, improving the reliability of the simulation results. Step S22, under the simulated wireless signal environment, generates a wireless transmission signal for water pressure data using a simulated wireless signal source and transmits it to the server to obtain the server's signal reception result. The purpose of this step is to simulate the wireless signal transmission process by transmitting the generated wireless transmission signal for water pressure data to the server to simulate the actual wireless transmission scenario. The received signal obtained through simulation can be further analyzed and processed to evaluate the transmission performance and accuracy of the water pressure data. Step S23, by comparing the wireless transmission signal and the received signal for water pressure data, calculates the signal comparison deviation and processes the received signal accordingly. The purpose of this step is to analyze the difference between the wireless transmission signal and the received signal for water pressure data, i.e., the signal comparison deviation, which can evaluate the reliability and accuracy of the signal transmission. By processing the received signal, errors in the signal can be corrected, thereby improving the transmission quality and accuracy of water pressure data.

[0135] In summary, the purpose of these steps in this embodiment is to acquire and process water pressure data from the airport water supply network by simulating the wireless transmission process, thereby achieving reliable transmission and accurate analysis of the water pressure data from the wireless transmission network. Its significance lies in providing a method for wirelessly transmitting water pressure data from a network, enabling real-time monitoring and analysis of water pressure conditions, and providing support for the maintenance and management of the network.

[0136] Furthermore, such as Figure 7 As shown, the process of the server analyzing the water pressure data of the pipeline network in step S3 specifically includes the following steps:

[0137] Step S31: Use EPANET to build a hydraulic model of the pipeline network, including the geometry, materials and operating parameters of components such as pipes, valves and pumps, as well as initial conditions and boundary conditions; set the water pressure data under normal operating conditions;

[0138] Step S32: Simulate abnormal conditions in the pipe network, such as pipe leakage or valve failure, by changing certain parameters in the model or introducing leakage scenarios; run the pipe network hydraulic model to calculate the simulated water pressure data. The water pressure value for each node or pipe segment can be obtained.

[0139] Step S33: Compare the simulated water pressure data with the water pressure data under normal operating conditions, compare the degree of water pressure drop at each node or pipe section, or compare it with the normal water pressure fluctuation range; based on the results of the comparison and analysis, determine whether the water pressure of the pipeline network is in an abnormal state. If the degree of water pressure drop exceeds the normal range or exceeds the preset threshold, it can be determined as an abnormal state.

[0140] Preferably, in step S31 of this embodiment, a hydraulic model of the pipeline network is established, including the geometry, materials, and operating parameters of each component, and water pressure data under normal operating conditions is set. By establishing the hydraulic model of the pipeline network, the working state of the pipeline network can be simulated, and water pressure data under normal operating conditions can be set, providing a basis for subsequent simulation of abnormal conditions and water pressure analysis, and serving as a reference for comparison. Step S32, based on the established hydraulic model of the pipeline network, simulates abnormal conditions of the pipeline network by changing the model parameters or introducing abnormal conditions, and calculates the simulated water pressure data, including the water pressure value of each node or pipe segment. The purpose is to simulate abnormal conditions of the pipeline network, such as pipe leakage, valve failure, etc., to obtain the corresponding water pressure data. Through the simulated water pressure data, the water pressure changes of the pipeline network under abnormal conditions can be analyzed, providing a basis for subsequent anomaly detection and diagnosis. Step S33 compares the simulated water pressure data with the water pressure data under normal operating conditions to determine the degree of water pressure change and whether the pipeline water pressure is in an abnormal state. The purpose is to determine whether the pipeline water pressure is abnormal based on the comparative analysis results. By comparing the simulated water pressure data with the water pressure data under normal operating conditions, the water pressure changes in the pipeline can be assessed, and it can be determined whether there are any abnormalities such as leakage or valve malfunction. This is crucial for the maintenance and management of the pipeline network, allowing for timely detection and repair of problems, ensuring the normal operation of the pipeline network and the quality of water supply.

[0141] In summary, the purpose of the steps in this embodiment is to simulate abnormal conditions in the pipeline network based on the established pipeline network hydraulic model, and to determine whether the pipeline network is in an abnormal state by comparing and analyzing water pressure data; it provides a method based on numerical simulation and comparative analysis to help monitor and diagnose abnormal water pressure in the pipeline network, and to provide support for the maintenance and management of the pipeline network.

[0142] like Figure 8 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0143] The memory 42 stores program instructions for implementing the airport water supply network monitoring initial location method of any of the above embodiments.

[0144] The processor 41 is used to execute program instructions stored in the memory 42 for initial location monitoring of the airport water supply network.

[0145] The processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0146] Furthermore, Figure 9 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 5 of this embodiment stores program instructions 51 capable of implementing all the above methods. These program instructions 51 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0147] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0148] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0149] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A preliminary positioning system for monitoring airport water supply networks, characterized in that, The airport water supply network monitoring and initial location system includes: The data acquisition module is used to detect the water pressure data of the nearby pipe network through the water pressure monitor installed on the water supply network. The detection cycle is preset to be once every 10-30 minutes. The data transmission module is used to receive water pressure data detected by the water pressure monitor on the water supply pipeline and transmit it wirelessly to the server in the airport central computer room. The data processing module is used to compare and analyze the detected water pressure data with the water pressure data under normal operating conditions. The data transmission module includes: The target data acquisition submodule is used to collect environmental information of the airport water supply network to obtain target data, and uses the target data as a simulated wireless signal environment. The receiving signal generation submodule is used to simulate the wireless signal source in a simulated wireless signal environment, simulate the generation of water pressure data wireless transmission signal, transmit the water pressure data wireless transmission signal to the server, and obtain the signal reception result from the server, which is recorded as the received signal. The signal deviation comparison submodule is used to compare the wirelessly transmitted and received water pressure data signals to obtain the signal deviation; and based on the signal deviation, the received signal is processed by the server. The signal deviation comparison submodule includes: The signal sampling unit is used to sample the wireless transmission signal of water pressure data and convert the continuous wireless transmission signal of water pressure data into discrete wireless transmission signal of water pressure data. The signal conversion unit is used to convert the wireless transmission signal of water pressure data from the time domain to the frequency domain using Fourier transform. The frequency domain amplitude characteristics of the signal are represented by the amplitude spectrum, the phase characteristics by the phase spectrum, and the power characteristics by the power spectrum of the signal obtained by Fourier transform. The feature extraction unit is used to select and extract features from different signals, compare the spectral features of different signals, and compare the similarity of spectral energy distribution by calculating the correlation coefficient similarity index. Based on the similarity results, the signal comparison deviation is calculated using statistical methods, and a numerical deviation metric is obtained.

2. The airport water supply network monitoring and initial positioning system according to claim 1, characterized in that, The data acquisition module includes: The layout acquisition submodule is used to build a geographic information model of the pipeline layout based on the collected data on the pipeline layout during airport construction, including the location, length, diameter and connection information of the pipelines; it is used to analyze the pipeline layout using the spatial analysis function of geographic information software to identify the connection relationships and paths of the pipelines. The node analysis submodule is used to import the location, length, diameter, and connection information of pipelines, as well as the connection relationships and paths of pipelines, into the geographic information software. Select the buffer analysis tool, create buffers based on buffer distances, and overlay the created buffer layer with the pipeline connection node layer for analysis. Based on the results of the overlay analysis, identify the key nodes within the buffer. Key nodes include confluence points, branch points, and pipeline intersections. They are filtered and identified based on layer attributes or spatial location. The location output submodule is used to determine the installation location of water pressure monitors at key nodes based on the key nodes, and to number the key nodes sequentially according to their weights.

3. The airport water supply network monitoring and initial positioning system according to claim 2, characterized in that, The position output submodule includes: The location acquisition unit records the installation location of each water pressure monitor, using coordinates or geographic location to describe the installation location; each installation location is assigned a weight value, which decreases sequentially from the confluence point, branch point, and pipe network intersection. The sorting execution unit is used to sort the installation locations from high to low according to their weight values. The location numbering unit is used to number each installation location in a sorted order, using either numeric or alphanumeric codes.

4. The airport water supply network monitoring and initial positioning system according to claim 1, characterized in that, The data acquisition module also includes a monitoring equipment layout submodule for pipeline network operation analysis, pipeline network leakage simulation, pipeline network leakage event determination, and optimized layout of pipeline network water pressure monitors.

5. The airport water supply network monitoring and initial positioning system according to claim 4, characterized in that, The monitoring equipment layout submodule includes: The pipeline operation analysis unit is used to perform hydraulic operation analysis on the airport water supply pipeline network using EPANET software, thereby understanding the water pressure fluctuation range under normal operating conditions. The pipeline leakage simulation unit is used to simulate water supply pipeline leakage on EPANET software. A leak point is opened at 50m intervals in each pipeline to obtain water pressure information when pipeline leakage occurs. The pipeline leakage event determination unit is used to determine that if the water pressure fluctuation in the water supply network exceeds the normal water pressure fluctuation range under normal conditions, the network is in an abnormal state and a pipeline leakage event may occur. The pipeline water pressure monitoring optimization layout unit is used to randomly deploy 20% of the number of water pressure monitoring nodes in the pipeline network to monitor pipeline leakage events. Then, based on the principle of marginal benefit, the intelligent optimization algorithm finds the optimal number and location of water pressure monitoring devices to monitor pipeline leakage events.

6. The airport water supply network monitoring and initial positioning system according to claim 1, characterized in that, The data processing module includes: The water pressure data reading submodule is used to read the detected water pressure data and the water pressure data under normal operating conditions; The water pressure data comparison submodule is used to compare the detected water pressure data with the water pressure data under normal operating conditions; by comparing the degree of water pressure drop, it can determine whether the water pressure in the pipeline network is in an abnormal state. The data marking submodule is used to calculate the normal water pressure fluctuation range based on the water pressure data under normal operating conditions. If the water pressure drop exceeds the normal water pressure fluctuation range, the current water pressure data will be highlighted in red, and the water pressure monitor number will be displayed. If the water pressure data is within the normal water pressure fluctuation range, the water pressure data will be highlighted in green.

7. The airport water supply network monitoring and initial positioning system according to claim 6, characterized in that, The water pressure data comparison submodule includes: The pipeline network model building unit is used to build a hydraulic model of the pipeline network using EPANET, including the geometry, material and operating parameters of pipes, valves and pumps, as well as initial conditions and boundary conditions; and to set water pressure data under normal operating conditions. The model operation calculation unit is used to simulate abnormal conditions in the pipe network by changing certain parameters in the model or introducing leakage conditions; it runs the pipe network hydraulic model to calculate the simulated water pressure data and obtain the water pressure value of each node or pipe segment; The comparative analysis unit is used to compare the simulated water pressure data with the water pressure data under normal operating conditions, compare the degree of water pressure drop at each node or pipe section, or compare it with the normal water pressure fluctuation range; based on the results of the comparative analysis, it determines whether the pipe network water pressure is in an abnormal state. If the degree of water pressure drop exceeds the normal range or exceeds the preset threshold, it is determined to be an abnormal state.

8. A method for initial location monitoring of an airport water supply network, applied to the airport water supply network initial location monitoring system as described in any one of claims 1 to 7, characterized in that, The initial location method for monitoring the airport water supply network includes: The intelligent optimization algorithm is used to determine the number and location of water pressure monitors in the water supply network, and each water pressure monitor detects the water pressure data of the nearby network. The water pressure data of the monitored pipeline network is transmitted wirelessly to the airport's central server. The server analyzes the water pressure data of the pipeline network to determine if the data is abnormal. If abnormal, it will display the water pressure monitor number of the current pipeline network. Airport staff will then use the water pressure monitor number to locate the leaking area in the pipeline network, conduct more precise testing in that area, and finally find and repair the leak.

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