Water supply network pressure and flow monitoring and early warning system based on AI model

Abstract

The invention discloses a water supply network pressure and flow monitoring and early warning system based on an AI model, and relates to the technical field of water supply network monitoring, the system comprises the following components: a data acquisition module, a data transmission module, a quantum heuristic AI optimization algorithm processing module, a monitoring and early warning module and a user interaction module; pressure, flow and water quality data in a water supply network are collected in real time through the data collection module, the accuracy and reliability of the data are ensured by adopting a self-adaptive dynamic point distribution strategy and a self-calibration function, the data can be efficiently analyzed and processed through the quantum heuristic AI optimization algorithm processing module, and the accuracy and reliability of the data are ensured. And the monitoring and early-warning module monitors the condition of the pipe network in real time according to a prediction result, immediately sends out an early-warning signal once an abnormality is found, and ensures the timeliness and pertinence of early warning by adopting a multi-dimensional graded early-warning strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply network monitoring, and specifically to a water supply network pressure and flow monitoring and early warning system based on an AI model. Background Art

[0002] With the acceleration of urbanization, the water supply network, as an important part of urban infrastructure, its stability and safety are directly related to the quality of residents' lives and the efficiency of urban operations. The water supply network has a wide coverage and complex structure. It is affected by many factors during operation, all of which may pose a threat to water supply safety.

[0003] Traditional technologies have some significant shortcomings. First, traditional algorithms have high computational complexity when processing large-scale data, resulting in slow response speed and difficulty in meeting the needs of real-time monitoring. Second, traditional algorithms often rely on the statistical laws of historical data when predicting pipeline pressure and flow change trends, but ignore the dynamic changes and uncertainty factors in pipeline operation, thereby reducing the accuracy and reliability of the prediction. In addition, traditional algorithms also have limitations in optimizing scheduling and early warning strategies, making it difficult to achieve comprehensive and accurate control of the operating status of the pipeline network.

[0004] To sum up, the traditional water supply network monitoring technology has certain shortcomings. In order to overcome these shortcomings, it is particularly important to develop a water supply network pressure and flow monitoring and early warning system based on AI model. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a water supply network pressure and flow monitoring and early warning system based on an AI model. It can collect pressure, flow and water quality data in the water supply network in real time through the data acquisition module, and adopt an adaptive dynamic point layout strategy and self-calibration function to ensure the accuracy and reliability of the data. At the same time, a quantum-inspired AI optimization algorithm processing module is introduced to efficiently analyze and process the data and predict the changing trend of the network pressure and flow. The monitoring and early warning module monitors the network status in real time according to the prediction results. Once an abnormality is found, a warning signal is immediately issued, and a multi-dimensional hierarchical early warning strategy is adopted to ensure the timeliness and pertinence of the early warning.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a water supply network pressure and flow monitoring and early warning system based on an AI model, the system comprising the following components: a data acquisition module, a data transmission module, a quantum heuristic AI optimization algorithm processing module, a monitoring and early warning module, and a user interaction module;

[0007] The data acquisition module is used to collect the pressure, flow and water quality data in the water supply network in real time;

[0008] The data transmission module is used to transmit the data collected by the data acquisition module to the quantum heuristic AI optimization algorithm processing module, and receive control instructions from the module;

[0009] The quantum-inspired AI optimization algorithm processing module: introduces the concept of quantum computing, designs a quantum-inspired AI optimization algorithm, analyzes and processes data, and predicts the pressure and flow change trends of the pipeline network;

[0010] The monitoring and early warning module: monitors the pressure and flow conditions of the pipeline network in real time according to the prediction results output by the quantum-inspired AI optimization algorithm processing module, and issues an early warning signal when an abnormality occurs;

[0011] The user interaction module provides an operation interface for users to view real-time data of the pipeline network, early warning information and fault diagnosis results, and set system parameters.

[0012] Furthermore, the data acquisition module adopts an adaptive dynamic point layout strategy in the sensor layout. For pressure sensors, in areas with large pressure gradients in the pipe network, a dense point layout is adopted to capture more subtle pressure changes. Flow sensors are reasonably arranged according to the pipe diameter and flow fluctuation of the pipe network. High-precision flow sensors are installed in pipes with large diameters and frequent flow changes. Water quality sensors are distributed at key locations of water source inlets and residential water terminals to ensure comprehensive monitoring of water quality. At the same time, the sensor has a self-calibration function and automatically performs calibration operations at regular time intervals. The calibration process is based on a comparative analysis of built-in standard parameters and real-time collected data, and calibration is performed using the following formula:

[0013]

[0014] Where S cal ibrated is the calibrated sensor data, S raw is the original collected data, ΔS is the difference from the standard parameter, S standard are standard parameter values, parameters ΔS and S standard Derived from a large amount of historical calibration data and industry standard specifications, this adaptive dynamic point distribution and self-calibration function greatly improves the accuracy and reliability of data collection, providing a high-quality data foundation for subsequent analysis and processing.

[0015] Furthermore, the data transmission module adopts a hybrid communication protocol optimization strategy. During the data transmission process, the data is divided into critical data and non-critical data according to the importance and real-time requirements of the data. For critical data, a low-latency, high-reliability 5G communication protocol is used for transmission to ensure that the data can accurately reach the quantum-inspired AI optimization algorithm processing module in the shortest time. For non-critical data, in order to reduce communication costs and resource occupation, the LoRa low-power wide area network communication protocol is used for transmission. At the same time, data encryption and error correction coding technology are used in the transmission process. The data encryption adopts a self-created quantum hybrid encryption algorithm, which combines the advantages of quantum key distribution and traditional symmetric encryption algorithms. The encryption formula is:

[0016] E data =Encrypt(S data ,K quantum ⊕K symmetric )

[0017] Where E data is the encrypted data, S data is the original data, K quantum is the quantum key, K symmetric is the symmetric encryption key, ⊕ is the XOR operation, and the error correction coding adopts an improved coding method based on the Hamming code. The anti-interference ability of data transmission is improved by adding redundant bits. The parameter K quantum Generated by quantum key distribution device, K symmetric Regularly updated according to the system's security policy, this hybrid communication protocol optimization strategy and encryption error correction technology ensures efficient, secure and reliable data transmission.

[0018] Furthermore, the quantum-inspired AI optimization algorithm used by the quantum-inspired AI optimization algorithm processing module has a unique structure. The algorithm first performs quantum encoding on the collected multi-source data, and the encoding formula is: where |ψ> is the quantum state, α i is the probability amplitude of the quantum state, |x i > is the data feature vector, probability amplitude α i The determination is based on the importance weight of the data, which is obtained through variance analysis and correlation analysis of historical data. Specifically, a higher weight is given to data features with high correlation and large variance with pressure and flow changes. Then, the quantum state is evolved through quantum gate operations. The evolution formula is: |ψ new >=U evolve |ψ>, where U evolveIt is an evolution operator, which is composed of multiple basic quantum gates. The combination mode is optimized according to the topological structure of the pipeline network and the pattern of historical data. In the evolution process, the quantum annealing mechanism is introduced. By simulating the quantum annealing process, the optimal solution is found in the solution space. The change formula of annealing temperature is: T n+1 =T n ×β, where T n is the annealing temperature of the nth step, β is the annealing coefficient, and the value of β is adjusted according to the convergence speed and accuracy requirements of the algorithm. Through this unique quantum-inspired AI optimization algorithm, it is possible to quickly and accurately search for the optimal solution in a complex pipe network environment and achieve accurate prediction of pressure and flow change trends.

[0019] Furthermore, the monitoring and early warning module adopts a multi-dimensional graded early warning strategy in the early warning mechanism. First, the early warning is divided into four levels according to the abnormal degree of pressure and flow. The first level early warning is a slight abnormality, at which the pressure or flow deviates from the normal range by 5%-10%, the second level early warning is a moderate abnormality, with a deviation range of 10%-20%, the third level early warning is a severe abnormality, with a deviation range of 20%-30%, and the fourth level early warning is an extremely severe abnormality, with a deviation range of more than 30%. Different early warning methods and response measures are adopted for different levels of early warning. The first level early warning uses text messages to remind relevant maintenance personnel to close the Note: In addition to SMS reminders, the second-level warning will also pop up a red warning box on the interface of the user interaction module. The third-level warning will trigger an audible and visual alarm and automatically send an email notification to the heads of relevant departments. In addition to the above measures, the fourth-level warning will automatically activate the emergency response plan. At the same time, the determination of the warning threshold adopts a dynamic adjustment strategy, which is adjusted in real time according to different time periods and seasonal changes, combined with historical data and the prediction results of the quantum-inspired AI optimization algorithm. This multi-dimensional hierarchical warning strategy and dynamic adjustment of thresholds improve the accuracy and pertinence of the warning, and can respond to various pipeline network abnormalities in a timely and effective manner.

[0020] Furthermore, the user interaction module has intelligent interaction and personalized customization functions. In terms of intelligent interaction, natural language processing technology is adopted. Users can input query instructions through voice or text. The system can automatically recognize and understand the user's intentions and provide relevant information quickly and accurately. At the same time, the system also has an intelligent question-and-answer function. For common questions raised by users, the system will provide detailed answers based on the preset knowledge base and the analysis results of the quantum-inspired AI optimization algorithm. In terms of personalized customization, users can customize the layout and content of the display interface according to their needs and permissions. Users can also set personalized warning receiving methods and time ranges. This intelligent interaction and personalized customization function improves the convenience and satisfaction of users using the system, enabling different users to obtain required information more efficiently.

[0021] Furthermore, the system also includes a data fusion and preprocessing module, which fuses and preprocesses the collected multi-source heterogeneous data before the data enters the quantum-inspired AI optimization algorithm processing module. First, for different types of sensor data, a fusion algorithm based on fuzzy logic is used for fusion. The formula of the algorithm is: Where D fused is the fused data, D i is the i-th sensor data, w i is the weight of the i-th sensor data, weight w i The determination is based on the reliability of the sensor and the relevance of the data. It is calculated through statistical analysis of historical data and machine learning algorithms. Then the fused data is preprocessed, including data cleaning, normalization and feature extraction. Data cleaning uses an outlier detection method based on a sliding window. By comparing the mean and standard deviation of the data in the window, outliers are identified and removed. The normalization formula is: Where D normal ized is the normalized data, D is the original data, and D min and D max are the minimum and maximum values ​​of the data respectively. The feature extraction adopts an improved method of principal component analysis. By calculating the covariance matrix and eigenvector of the data, the principal components that best represent the data characteristics are extracted. This data fusion and preprocessing module improves the quality and availability of the data, and provides a good data foundation for the accurate operation of the quantum-inspired AI optimization algorithm.

[0022] Furthermore, the system also has a fault diagnosis and tracing module, which diagnoses and traces faults in the pipe network based on the analysis results of the quantum-inspired AI optimization algorithm processing module. When abnormal pressure or flow is detected, the fault type is first determined by a fault classification algorithm. The classification algorithm uses a quantum support vector machine algorithm based on an improved support vector machine, and the formula is:

[0023]

[0024] Where f(x) is the classification result, α i is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the quantum kernel function, b is the bias term, and the Lagrange multiplier α iThe and bias term b are solved by the quantum optimization algorithm, and then the fault location is determined by the fault tracing algorithm. The algorithm combines the topological structure of the pipeline network and the propagation model of pressure and flow, and uses the back propagation and path search methods to trace back the possible fault sources from the abnormal point. In the tracing process, the influence of pipeline length, diameter, and material factors on pressure and flow propagation is considered. This fault diagnosis and tracing module can quickly and accurately locate the fault type and location, providing strong support for the repair and maintenance of the pipeline network.

[0025] Furthermore, the system also has an intelligent scheduling and optimization module, which intelligently schedules and optimizes the operation of the water supply network according to the prediction results of the quantum heuristic AI optimization algorithm processing module and the information of the monitoring and early warning module. In terms of water pump scheduling, a quantum genetic algorithm based on the improved genetic algorithm is used to determine the optimal operation combination and speed regulation strategy of the water pump. The algorithm formula is:

[0026]

[0027] Where Fitness is the fitness function, P i is the pressure value of the ith monitoring point, P target is the target pressure value, Q j is the flow value of the jth monitoring point, Q target is the target flow value, w i and w j is the weight coefficient, which is determined according to the importance of pressure and flow and the degree of influence on the operation of the pipeline network. The quantum genetic algorithm is used to continuously iterate and search to find the pump operation combination and speed regulation scheme that minimizes the fitness function. In terms of valve adjustment, the valve opening is dynamically adjusted through the fuzzy control algorithm according to the real-time pressure and flow distribution of the pipeline network. The fuzzy control rules are formulated based on historical data and expert experience. The input variables are pressure deviation and flow deviation, and the output variable is the adjustment amount of the valve opening. This intelligent scheduling and optimization module can improve the operating efficiency of the water supply network, reduce energy consumption, and ensure the stability and reliability of water supply.

[0028] Compared with the existing technology, this water supply network pressure and flow monitoring and early warning system based on AI model has the following beneficial effects:

[0029] 1. The system collects pressure, flow and water quality data in the water supply network in real time through the data acquisition module, and adopts adaptive dynamic point layout strategy and self-calibration function to ensure the accuracy and reliability of the data. The quantum-inspired AI optimization algorithm processing module can efficiently analyze and process these data and predict the changing trends of network pressure and flow. The monitoring and early warning module monitors the network status in real time according to the prediction results. Once an abnormality is found, it immediately issues an early warning signal and adopts a multi-dimensional hierarchical early warning strategy to ensure the timeliness and pertinence of the early warning. The improvement of this accuracy and timeliness will help managers respond quickly to network problems and prevent the situation from escalating, thereby ensuring the stability and safety of water supply.

[0030] 2. The system also has an intelligent scheduling and optimization module, which can intelligently schedule and optimize the operation of the water supply network according to the prediction results of the quantum heuristic AI optimization algorithm processing module and the information of the monitoring and early warning module. By using the quantum genetic algorithm to determine the optimal operation combination and speed regulation strategy of the water pump, and dynamically adjusting the valve opening through the fuzzy control algorithm, the system can significantly improve the operation efficiency of the water supply network and reduce energy consumption. This intelligent scheduling and optimization not only helps to save resources, but also improves the quality and reliability of water supply services, and meets users' needs for stable and safe water supply. At the same time, the fault diagnosis and tracing module can quickly and accurately locate the type and location of the fault, providing strong support for the repair and maintenance of the network, and further ensuring the stable operation of the water supply network.

[0031] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flowchart for the function implementation of a water supply network pressure and flow monitoring and early warning system based on an AI model;

[0034] Figure 2 This is a flow chart of the overall architecture of a water supply network pressure and flow monitoring and early warning system based on an AI model. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0036] Embodiment 1

[0037] This embodiment describes a large city with a population of one million, where the water supply network is like the "lifeline" of the city, extending intricately to every corner. It connects multiple water plants, pressure stations and water terminals in different areas, covering many residential communities, commercial centers and industrial enterprises. The topography of the city is relatively complex, and the terrain in some areas is undulating, such as areas near mountainous areas. This has a significant impact on the distribution of pipe network pressure. In areas with higher terrain, water flow requires greater pressure to be smoothly transported, while areas with lower terrain are prone to excessive pressure. At the same time, the water demand in different areas varies greatly. For example, commercial centers use a huge amount of water during business hours, while residential communities use a large amount of water during peak water use periods in the morning and evening.

[0038] Strictly follow the adaptive dynamic deployment strategy. In the pipe network areas near mountainous areas with large terrain changes and large pressure gradients, densely deploy pressure sensors to ensure that subtle changes in pressure can be accurately captured. In large-diameter pipes connecting large commercial centers and industrial enterprises with frequent flow changes, high-precision flow sensors are installed to accurately measure flow data. Water quality sensors are reasonably installed at water source entrances and water terminals in major residential areas to comprehensively monitor water quality. The sensors are automatically calibrated every 4 hours. Taking a certain pressure sensor as an example, its original collected data S raw By comparing with the built-in standard parameters, the difference ΔS with the standard parameters is 0.1MPa. The standard parameter value S standard is 2.9MPa, according to the calibration formula S calibrated =S raw × Calculate and calibrate the data S cal ibrated for MPa, so that the calibrated data is more accurate and reliable, providing a solid data foundation for subsequent analysis and processing.

[0039] During the data transmission process, data is divided into critical data and non-critical data according to its importance and real-time requirements. For critical data such as pressure and flow, low-latency and high-reliability 5G communication protocols are used for transmission to ensure that these data can reach the quantum-inspired AI optimization algorithm processing module accurately in the shortest time so that they can be analyzed and processed in a timely manner. For non-critical water quality data, in order to reduce communication costs and resource usage, LoRa low-power wide area network communication protocols are used for transmission. At the same time, in order to ensure the security of data transmission, the self-created quantum hybrid encryption algorithm is used to encrypt the data. The encryption formula is: Among them, the quantum key K quantum The symmetric encryption key K is generated by the quantum key distribution device. symmetric It is updated regularly according to the system's security policy. In addition, the error correction coding adopts an improved coding method based on the Hamming code, which increases the anti-interference ability of data transmission by adding redundant bits, effectively avoiding data errors during transmission.

[0040] After receiving the data, we first perform quantum coding on the multi-source data. Assuming that six data feature vectors of pressure, flow, and water quality are collected, we perform detailed variance analysis and correlation analysis on the historical data to determine the probability amplitude α corresponding to each data feature vector. i , for data features with high correlation and large variance with pressure and flow changes, higher weights are assigned. For example, after analysis, it is found that the correlation between pressure changes and flow changes is high, and the variance of flow in different time periods is large. Therefore, in quantum encoding, the probability amplitude α of the data feature vector related to flow is assigned a higher value.

[0041] Next, the quantum state is evolved through quantum gate operations, and the evolution operator U evolve It is composed of multiple basic quantum gates. The combination is optimized according to the topological structure of the pipeline network and the pattern of historical data. In the evolution process, the quantum annealing mechanism is introduced to find the optimal solution in the complex solution space by simulating the quantum annealing process. The annealing temperature is determined according to the formula T n+1 =T n ×β changes, assuming the initial annealing temperature T 1 is 100, and the annealing coefficient β is 0.98. With the iteration of the algorithm, the annealing temperature gradually decreases, and the algorithm gradually converges to a better solution, thereby achieving accurate prediction of the changing trend of pipeline network pressure and flow.

[0042] Based on the prediction results of the quantum-inspired AI optimization algorithm processing module, the pressure and flow conditions of the pipeline network are monitored in real time. At 10 a.m. on a certain day, the system detected that the pressure value in a certain area deviated from the normal range by 8%. This situation triggered a level one warning. The system immediately sent a text message to the personnel responsible for the pipeline maintenance in the area. After receiving the text message, the maintenance personnel promptly viewed the detailed data of the area through the user interaction module, including the real-time pressure curve and historical pressure data comparison, so as to conduct preliminary analysis and judgment of the abnormal situation.

[0043] Users can use natural language processing technology to conveniently interact with the system through the user interaction module. For example, if a user wants to know the real-time pressure data of a certain area, he only needs to say to the device: "Query the real-time pressure data of [area name]", and the system can automatically recognize and understand the user's intention, and quickly and accurately display the real-time pressure data of the area. If the user raises some common questions, such as "How to determine whether there is a risk of water leakage in the pipeline network", the system will give detailed answers based on the preset knowledge base and the analysis results of the quantum-inspired AI optimization algorithm, to help users better understand the operation of the pipeline network.

[0044] Users can also customize the display interface according to their own needs and permissions. For example, staff responsible for pipeline maintenance in a specific area can customize the layout and content of the display interface to only display the pressure and flow data of the area they are responsible for, as well as related warning information and fault diagnosis results. This allows them to focus more on the information they are concerned about and improve work efficiency. At the same time, users can also set personalized warning reception methods and time ranges, such as selecting a mobile phone number to receive warnings and a time period to receive warnings, to ensure that they receive warning information in a timely manner within a time that is convenient for them to deal with the problem.

[0045] Embodiment 2

[0046] This embodiment describes a medium-sized town with a population of 300,000. When summer comes, the temperature rises sharply, and residents use air conditioners and shower water equipment more frequently, causing the town's water supply to rise rapidly, reaching nearly 1.5 times the daily water consumption, and the water supply network faces tremendous pressure.

[0047] The data acquisition module in the urban water supply network plays a key role. The pressure sensors, flow sensors and water quality sensors distributed in various areas are always vigilant and continuously collect data. On a certain day during the peak water usage in summer, a pressure sensor located near a large residential area on the edge of the town detected abnormal fluctuations in the water supply pressure. The pressure value, which was originally stable within a certain range, suddenly fluctuated frequently, and the overall pressure level gradually decreased. At the same time, many users in the residential area reported that there was a problem with the water quality of their tap water. The water became turbid and was accompanied by a slight odor. These abnormal conditions were accurately captured by the nearby water quality sensors, and the sensors quickly recorded the detected abnormal water quality data.

[0048] After receiving the abnormal pressure and water quality data, the data transmission module immediately activates the emergency transmission mechanism. Since these data are crucial to ensuring the safety of residents' water use and belong to the category of key data, the data transmission module does not hesitate to enable the 5G communication protocol to transmit the data to the quantum-inspired AI optimization algorithm processing module at the fastest speed. During the transmission process, in order to ensure the security and accuracy of the data, the system uses quantum hybrid encryption algorithm and error correction coding technology to prevent data from being tampered with or lost during transmission, and to ensure that the subsequent processing module can obtain complete and reliable abnormal data.

[0049] After receiving the abnormal data, the quantum-inspired AI optimization algorithm processing module quickly conducts in-depth analysis on it. It integrates data on pressure fluctuations and water quality changes, and compares it with a large amount of historical fault data and normal operation data. By simulating the analytical thinking of human experts and combining machine learning algorithms, the data is screened and judged layer by layer. After rapid calculation and analysis, the system preliminarily determines that the abnormality may be due to a leak in a certain section of the pipeline, resulting in a drop in water supply pressure, and the mixing of external impurities, causing water quality problems.

[0050] In order to accurately determine the location of the fault, the system uses the detailed topological structure information of the pipeline network. The pipeline network topology clearly shows the connection relationship, direction and relationship with each monitoring point of each pipeline. The system takes the monitoring point with abnormal pressure and water quality as the starting point, and conducts reverse investigation along the water flow direction and connection path of the pipeline network. Combined with the pressure transmission law in different pipelines and the direction of water flow, the possible scope of the fault is gradually narrowed. After a series of complex reasoning and analysis, the fault location is finally locked on an old cast iron pipe about 2 kilometers away from the residential area.

[0051] Based on the severity of the fault, the monitoring and early warning module determined that the pipeline leakage fault reached the third-level early warning standard. The system immediately activated the full-scale early warning mechanism. At the water supply management center, a shrill alarm sounded instantly. At the same time, the huge electronic display screen displayed the fault information in eye-catching red font, including the fault type (pipeline leakage), approximate location (an old cast iron pipe section 2 kilometers away from the residential area) and the scope of the area that may be affected (involving the residential area and some surrounding small commercial areas). At the same time, the system automatically sent emergency emails and text message notifications containing detailed fault information to the heads of all levels of the water supply department and the main members of the maintenance team to ensure that the relevant personnel can know the situation and take action at the first time.

[0052] After receiving the early warning information, the head of the water supply department responded quickly and immediately organized experienced maintenance personnel to rush to the fault site with professional equipment. On the way, the maintenance personnel could check the detailed information of the fault at any time through the user interaction module on their mobile phones. The user interaction module not only provided a pipeline layout map around the fault point, allowing maintenance personnel to clearly understand the direction of the pipeline and the surrounding environment, but also displayed the recent water supply pressure and flow change trend map in the area, helping maintenance personnel to better analyze the possible impact of the fault. In addition, maintenance personnel used the intelligent question-and-answer function of the interactive module to ask about precautions for repairing this fault, such as underground facilities that may be encountered during excavation and repair materials suitable for the cast iron pipe. The system quickly gave accurate answers based on pre-stored knowledge and past experience, providing strong support for maintenance work.

[0053] After arriving at the fault site, the maintenance personnel quickly started their work. They first used professional tools to close the valves upstream and downstream of the faulty pipeline to cut off the water source, prevent further leakage and reduce the waste of water resources. Then, they used large equipment such as excavators to carefully excavate the area where the fault point was located. During the excavation process, the maintenance personnel paid close attention to the underground situation to avoid damage to other surrounding pipelines and cable facilities. When the leaking pipeline was exposed, the maintenance personnel carefully checked the damage of the pipeline and found that the leakage was caused by corrosion and perforation in some parts of the pipeline due to long-term use. According to the material and degree of damage of the pipeline, they selected suitable repair materials and tools, and repaired the leakage point by welding. After the repair was completed, the repaired part was thoroughly inspected again to ensure the quality of the repair.

[0054] After completing the maintenance, the maintenance personnel gradually opened the valves to restore the water supply. During the process of restoring the water supply, the data acquisition module continued to monitor the pressure, flow and water quality data of the area at a high frequency, and transmitted them to the monitoring and early warning module in real time. The monitoring and early warning module analyzed these data in real time and compared them with the normal operation data. After several hours of close monitoring and confirming that the pressure has returned to stability, the flow is normal, and the water quality meets the standards, the system officially lifted the early warning. At the same time, the system recorded the entire handling process of the fault in detail, including the time of the fault, the troubleshooting process, the maintenance measures and the recovery time information. These records will be stored in the system database to provide valuable data support for subsequent pipe network maintenance and fault analysis, so as to timely discover potential problems, take preventive measures in advance, and ensure the long-term stable operation of the water supply network.

[0055] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A water supply network pressure and flow monitoring and early warning system based on AI model, characterized in that: The system includes the following components: data acquisition module, data transmission module, quantum-inspired AI optimization algorithm processing module, monitoring and early warning module, and user interaction module; The data acquisition module is used to collect the pressure, flow and water quality data in the water supply network in real time; The data transmission module is used to transmit the data collected by the data acquisition module to the quantum heuristic AI optimization algorithm processing module, and receive control instructions from the module; The quantum-inspired AI optimization algorithm processing module: introduces the concept of quantum computing, designs a quantum-inspired AI optimization algorithm, analyzes and processes data, and predicts the pressure and flow change trends of the pipeline network; The monitoring and early warning module: monitors the pressure and flow conditions of the pipeline network in real time according to the prediction results output by the quantum-inspired AI optimization algorithm processing module, and issues an early warning signal when an abnormality occurs; The user interaction module provides an operation interface for users to view real-time data of the pipeline network, early warning information and fault diagnosis results, and set system parameters.

2. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The data acquisition module adopts an adaptive dynamic point layout strategy in the sensor layout. For pressure sensors, in areas with large pressure gradients in the pipe network, a dense point layout is adopted to capture more subtle pressure changes. Flow sensors are reasonably arranged according to the pipe diameter size and flow fluctuation of the pipe network. High-precision flow sensors are installed in pipes with large diameters and frequent flow changes. Water quality sensors are distributed at key locations such as water source entrances and residential water terminals. At the same time, the sensors have a self-calibration function and will automatically perform calibration operations at regular time intervals. The calibration process is based on a comparative analysis of built-in standard parameters and real-time collected data, and calibration is performed using the following formula: Where S calibrated is the calibrated sensor data, S raw is the original collected data, ΔS is the difference from the standard parameter, S standard is the standard parameter value.

3. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The data transmission module adopts a hybrid communication protocol optimization strategy. During the data transmission process, the data is divided into critical data and non-critical data according to the importance and real-time requirements of the data. For critical data, the low-latency, high-reliability 5G communication protocol is used for transmission. For non-critical data, in order to reduce communication costs and resource occupation, the LoRa low-power wide area network communication protocol is used for transmission. At the same time, data encryption and error correction coding technology are used in the transmission process. The data encryption adopts the self-created quantum hybrid encryption algorithm, which combines the advantages of quantum key distribution and traditional symmetric encryption algorithms. The encryption formula is: Where E data is the encrypted data, S data is the original data, K quantum is the quantum key, K symmetric is the symmetric encryption key, The error correction code uses an improved coding method based on the Hamming code, which increases the anti-interference ability of data transmission by adding redundant bits. The parameter K quantum Generated by quantum key distribution device, K symmetric Update regularly according to the system's security policy.

4. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The quantum-inspired AI optimization algorithm used by the quantum-inspired AI optimization algorithm processing module has a unique structure. The algorithm first performs quantum encoding on the collected multi-source data. The encoding formula is: where |ψ> is the quantum state, α i is the probability amplitude of the quantum state, |x i > is the data feature vector, probability amplitude α i The determination is based on the importance weight of the data. Then, the quantum state is evolved through quantum gate operations. The evolution formula is: |ψ new >=U evolve |ψ>, where U evolve It is an evolution operator, which is composed of multiple basic quantum gates. The combination mode is optimized according to the topological structure of the pipeline network and the pattern of historical data. In the evolution process, the quantum annealing mechanism is introduced. By simulating the quantum annealing process, the optimal solution is found in the solution space. The change formula of annealing temperature is: T n+1 =T n ×β, where T n is the annealing temperature of the nth step, β is the annealing coefficient, and the value of β is adjusted according to the convergence speed and accuracy requirements of the algorithm.

5. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The monitoring and early warning module adopts a multi-dimensional graded early warning strategy in the early warning mechanism. First, the early warning is divided into four levels according to the degree of abnormality of pressure and flow. The first level early warning is a slight abnormality, the second level early warning is a moderate abnormality, the third level early warning is a severe abnormality, and the fourth level early warning is an extremely severe abnormality. Different early warning methods and response measures are adopted for early warnings of different levels. The first level early warning uses text messages to remind relevant maintenance personnel to pay attention. In addition to text message reminders, a red warning box will pop up on the interface of the user interaction module for the second level early warning. The third level early warning will trigger an audible and visual alarm and automatically send an email notification to the head of the relevant department. In addition to the above measures, the fourth level early warning will automatically activate the emergency response plan. At the same time, the determination of the early warning threshold adopts a dynamic adjustment strategy, which is adjusted in real time according to different time periods and seasonal changes, combined with historical data and the prediction results of the quantum-inspired AI optimization algorithm.

6. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The user interaction module has intelligent interaction and personalized customization functions. In terms of intelligent interaction, natural language processing technology is adopted. Users can input query instructions through voice or text. The system can automatically identify and understand the user's intentions and provide relevant information quickly and accurately. At the same time, the system also has an intelligent question-and-answer function. For common questions raised by users, the system will provide detailed answers based on the preset knowledge base and the analysis results of the quantum-inspired AI optimization algorithm. In terms of personalized customization, users can customize the layout and content of the display interface according to their needs and permissions. Users can also set personalized warning reception methods and time ranges.

7. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The system also includes a data fusion and preprocessing module, which fuses and preprocesses the collected multi-source heterogeneous data before the data enters the quantum-inspired AI optimization algorithm processing module. First, for different types of sensor data, a fusion algorithm based on fuzzy logic is used for fusion. The formula of the algorithm is: Where D fused is the fused data, D i is the i-th sensor data, w i is the weight of the i-th sensor data, and then the fused data is preprocessed, including data cleaning, normalization and feature extraction. Data cleaning adopts an outlier detection method based on a sliding window. By comparing the mean and standard deviation of the data in the window, outliers are identified and removed. The normalization formula is: Where D normalized is the normalized data, D is the original data, and D min and D max are the minimum and maximum values ​​of the data respectively. The feature extraction adopts an improved method of principal component analysis. By calculating the covariance matrix and eigenvector of the data, the principal component that best represents the data characteristics is extracted.

8. According to claim 1, a water supply network pressure and flow monitoring and early warning system based on an AI model is characterized in that: The system also has a fault diagnosis and tracing module, which diagnoses and traces faults in the pipe network based on the analysis results of the quantum heuristic AI optimization algorithm processing module. When abnormal pressure or flow is detected, the fault type is first determined by a fault classification algorithm. The classification algorithm uses a quantum support vector machine algorithm based on an improved support vector machine, and the formula is: Where f(x) is the classification result, α i is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the quantum kernel function, b is the bias term, and the Lagrange multiplier α i The and bias term b are solved by the quantum optimization algorithm, and then the fault location is determined by the fault tracing algorithm. The algorithm combines the topological structure of the pipeline network and the propagation model of pressure and flow, and uses the back propagation and path search methods to trace back the possible fault sources from the abnormal point. In the tracing process, the influence of pipeline length, diameter, and material factors on pressure and flow propagation is considered.

9. The water supply network pressure and flow monitoring and early warning system based on AI model according to claim 1 is characterized in that: The system also has an intelligent scheduling and optimization module, which intelligently schedules and optimizes the operation of the water supply network according to the prediction results of the quantum heuristic AI optimization algorithm processing module and the information of the monitoring and early warning module. In terms of water pump scheduling, a quantum genetic algorithm based on the improved genetic algorithm is used to determine the optimal operation combination and speed regulation strategy of the water pump. The algorithm formula is: Where Fitness is the fitness function, P i is the pressure value of the ith monitoring point, P target is the target pressure value, Q j is the flow value of the jth monitoring point, Q target is the target flow value, w i and w j is the weight coefficient. The quantum genetic algorithm is used to iteratively search and find the pump operation combination and speed regulation scheme that minimizes the fitness function. In terms of valve regulation, the valve opening is dynamically adjusted according to the real-time pressure and flow distribution of the pipeline network through the fuzzy control algorithm. The input variables are pressure deviation and flow deviation, and the output variable is the adjustment amount of the valve opening.

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