A smart management method and system for water affairs data

Abstract

The present invention discloses a smart management method and system for water data, which relates to the technical field of water management; water data in a target park is obtained, a water supply topology map is established for the water supply system of the target park, and it is determined whether the water supply is abnormal; if the water supply of the water supply topology map is abnormal, the monitoring data in the target area is obtained to determine whether there is a leak in the pipeline; if there is no leak in the pipeline in the target area, the cause of the abnormal water delivery of the water supply system in each target area in the target park is determined through the production plan; a management plan is determined for the cause of the abnormal water delivery of the water supply system. Water data is collected and a water supply topology map is established to monitor the water supply of the water supply system in real time. When the water supply is abnormal, the monitoring data in the target area is obtained to analyze whether there is a leak in the pipeline. If it is confirmed that it is not caused by pipeline leakage, the cause of the abnormal water delivery is determined through the production plan, and a management plan is formulated to ensure stable and efficient water supply, reduce waste and improve the level of water management.

Description

Technical Field

[0001] The present invention belongs to the technical field of water management, and specifically relates to an intelligent management method and system for water data. Background Art

[0002] The rise of smart water management technology is the result of the deep integration of modern information technologies such as the Internet of Things, smart sensors, cloud computing and big data in the water sector. By real-time monitoring of the operating status of water infrastructure such as water sources and pipe networks, it not only ensures water quality safety and water supply stability, but also relies on big data analysis to achieve optimal allocation and efficient use of water resources, reduce operating costs, and improve service efficiency and quality. At the same time, the smart water system can quickly respond to various emergency events, effectively reduce the impact of accidents through intelligent evaluation and plan generation, and ensure urban water safety.

[0003] Existing water data is efficiently managed through an integrated management system, which collects and stores real-time data from various monitoring points, including water supply volume, water quality parameters, etc. It uses data analysis technology to monitor water supply conditions, identify anomalies in a timely manner and trigger early warning mechanisms to ensure the accuracy, completeness and security of data, thus providing assistance for water decision-making and operations.

[0004] In the above technologies, water management is too one-sided and simply considers the abnormal flow of water supply in the pipeline, and the monitoring and detection of the pipeline requires a lot of manpower, which increases the maintenance cost. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that water management is too one-sided and only considers the abnormal flow of water supply in the pipeline, and the monitoring and detection of the pipeline requires a lot of manpower, which increases the maintenance cost, and proposes an intelligent management method and system for water data.

[0006] In a first aspect of the present invention, a method for intelligent management of water service data is first proposed, the method comprising:

[0007] Acquire water service data in the target park, establish a water supply topology map for the water supply system of the target park, and determine whether the water supply volume of the water supply topology map is abnormal based on the water service data; the water service data includes: water consumption and water flow;

[0008] If the water supply volume of the water supply topology is abnormal, the monitoring data in the target area is obtained, and whether there is a leak in the pipeline in the target area is determined according to the monitoring data;

[0009] If there is no leakage in the pipeline in the target area, the abnormal reasons for the water delivery volume of the water supply system in each target area in the target park are determined through the production plan; the abnormal reasons for the water delivery volume include: increased production of industrial equipment and damage to industrial equipment;

[0010] A corresponding management plan is determined based on the cause of the abnormal water delivery volume of the water supply system in the target area.

[0011] Optionally, judging whether the water supply of the topology map is abnormal according to the water service data includes:

[0012] Mapping the water service data to the nodes of the water supply topology map to obtain a target topology map; the nodes in the topology map represent water meters, and the edges in the topology map represent water pipelines;

[0013] Performing water supply anomaly detection on the target topology map, if the deviation value between the water consumption and the water flow in the target area exceeds a deviation threshold, it is determined that there is a water supply anomaly in the pipeline in the target area;

[0014] Leakage determination is performed on the pipelines in the target area.

[0015] Optionally, judging whether there is leakage in the pipeline in the target area according to the monitoring data includes:

[0016] Calculating the leakage probability of each pipeline in the target topology; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value;

[0017] If the pipeline leakage probability is greater than the leakage threshold, it is determined that the target pipeline has leakage;

[0018] If the pipeline leakage probability is less than or equal to the leakage threshold, it is determined that there is an abnormal water supply in the target area;

[0019] Leakage probability formula:

[0020] ;

[0021] in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

[0022] Optionally, if there is an abnormal water supply in the target area, the reasons for the abnormal water supply of the water supply system in each target area in the target park are determined by the production plan, including:

[0023] Obtaining a production plan of industrial equipment in the target area, determining a production volume of a target production cycle according to the production plan, and fitting a first production curve;

[0024] Obtain a historical production plan, and determine a historical target cycle data set for the same period of the target production cycle according to the historical production plan;

[0025] Fitting the historical target cycle data set to obtain a fluctuation curve, and taking the end point data of the previous cycle of the target production cycle as the starting point;

[0026] According to the starting point, the fluctuation curves are connected according to the time period to obtain a second production curve, and the first production curve is matched with the second production curve;

[0027] The difference value of the target production cycle is calculated according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the industrial equipment has abnormal water consumption and a fault is reported.

[0028] Optionally, if there is a leak in the target pipeline, determining a corresponding management plan for the cause of the abnormal water delivery of the water supply system in the target area includes:

[0029] Acquire historical phase data, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value in the target area, and calculate historical reflection positioning according to the historical phase data;

[0030] The target topology map, the historical reflection location, the historical pipeline pressure, the historical pipeline water flow rate and the historical pipeline vibration peak value are used as inputs of the reverse neural network model for training;

[0031] Using the output of the reverse neural network model as weights and biases, encoding the weights and biases to obtain initial chromosomes, initializing the initial chromosomes and generating an initial population;

[0032] Iteratively optimizing the initial population through genetic operations and calculating the fitness of each chromosome until the chromosome reaches a maximum number of iterations or the fitness of the chromosome no longer decreases, then outputting the optimal chromosome;

[0033] The optimal chromosome is used as an update parameter of the reverse neural network model to update the model, thereby obtaining a leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage position.

[0034] The reflection location, the pressure in the pipeline, the water flow rate in the pipeline and the pipeline vibration peak value are input into the leakage point prediction model to obtain the target pipeline leakage position.

[0035] In a second aspect of the present invention, a smart management system for water service data is proposed, comprising: a topology map establishment module, a pipeline leakage judgment module, an abnormality cause detection module, and a management solution determination module:

[0036] The topology map establishment module is used to obtain water service data in the target park, establish a water supply topology map for the water supply system of the target park, and determine whether the water supply volume of the water supply topology map is abnormal based on the water service data; the water service data includes: water consumption and water flow;

[0037] The pipeline leakage judgment module is used to obtain monitoring data in a target area if the water supply volume of the water supply topology diagram is abnormal, and judge whether there is a leakage in the pipeline in the target area according to the monitoring data;

[0038] The abnormal cause detection module is used to determine the abnormal cause of water delivery of each target area water supply system in the target park through the production plan if there is no leakage in the pipeline in the target area; the abnormal cause of water delivery includes: increased production of industrial equipment and damage of industrial equipment;

[0039] The management scheme determination module is used to determine a corresponding management scheme according to the cause of the abnormal water delivery volume of the water supply system in the target area.

[0040] Optionally, the topology map building module includes: a water affairs data mapping module, a water supply anomaly detection module and a pipeline leakage judgment module:

[0041] The water service data mapping module is used to map the water service data to the nodes of the water supply topology map to obtain a target topology map; the nodes in the topology map represent water meters, and the edges in the topology map represent water pipelines;

[0042] The water supply anomaly detection module is used to perform water supply anomaly detection on the target topology map, and if the deviation value between the water consumption and the water flow in the target area exceeds the deviation threshold, it is determined that there is a water supply anomaly in the pipeline in the target area;

[0043] The pipeline leakage judgment module is used to judge the leakage of the pipeline in the target area.

[0044] Optionally, the pipeline leakage judgment module further includes: a leakage probability calculation module, a first judgment module and a second judgment module:

[0045] The leakage probability calculation module is used to calculate the leakage probability of each pipeline in the target topology map; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value;

[0046] The first judgment module is used to determine that there is leakage in the target pipeline if the pipeline leakage probability is greater than the leakage threshold;

[0047] The second judgment module is used to determine that there is an abnormal water supply in the target area if the pipeline leakage probability is less than or equal to the leakage threshold;

[0048] Leakage probability formula:

[0049] ;

[0050] in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

[0051] Optionally, the second judgment module includes: a first curve fitting module, a data set determination module, a fluctuation curve fitting module, a curve matching module and a fault reporting module:

[0052] The first curve fitting module is used to obtain a production plan of industrial equipment in the target area, determine the production volume of a target production cycle according to the production plan, and obtain a first production curve by fitting;

[0053] The data set determination module is used to obtain a historical production plan and determine a historical target period data set for the same period of the target production cycle according to the historical production plan;

[0054] The fluctuation curve fitting module is used to fit the historical target cycle data set to obtain a fluctuation curve, and use the end point data of the previous cycle of the target production cycle as the starting point;

[0055] The curve matching module is used to connect the fluctuation curves according to the time period based on the starting point to obtain a second production curve, and match the first production curve with the second production curve;

[0056] The fault reporting module is used to calculate the difference value of the target production cycle according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the industrial equipment has abnormal water consumption and a fault is reported.

[0057] Optionally, the first judgment module includes: a training data acquisition module, a training data input module, a model parameter encoding module, a population iteration optimization module, a model updating module and a leakage point output module:

[0058] The training data acquisition module is used to acquire historical phase data, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value in the target area, and calculate the historical reflection positioning according to the historical phase data;

[0059] The training data input module is used to train the target topology map, the historical reflection location, the historical pipeline pressure, the historical pipeline water flow rate and the historical pipeline vibration peak value as inputs of the reverse neural network model;

[0060] The model parameter encoding module is used to use the output of the reverse neural network model as weights and biases, encode the weights and biases to obtain initial chromosomes, initialize the initial chromosomes and generate an initial population;

[0061] The population iterative optimization module is used to iteratively optimize the initial population through genetic operations and calculate the fitness of each chromosome until the chromosome reaches a maximum number of iterations or the fitness of the chromosome no longer decreases, and then output the optimal chromosome;

[0062] The model updating module is used to update the model by using the optimal chromosome as an updating parameter of the reverse neural network model to obtain a leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage position;

[0063] The leakage point output module is used to input the reflection positioning, the pressure in the pipeline, the water flow rate in the pipeline and the pipeline vibration peak value into the leakage point prediction model to obtain the target pipeline leakage position.

[0064] Beneficial effects of the present invention:

[0065] The present invention proposes a smart management method for water affairs data. By acquiring water affairs data in a target park, a water supply topology map is established for the water supply system of the target park, and the water supply volume of the water supply topology map is judged according to the water affairs data. If the water supply volume of the water supply topology map is abnormal, the monitoring data in the target area is acquired, and the pipeline in the target area is judged according to the monitoring data. If there is no leakage in the pipeline in the target area, the abnormal water delivery reason of the water supply system in each target area in the target park is judged through the production plan; the corresponding management plan is determined for the abnormal water delivery reason of the water supply system in the target area. Collect water affairs data and establish a water supply topology map, and monitor the water supply of the water supply system in real time to determine whether there is an abnormality. When the water supply volume is abnormal, the monitoring data in the target area is acquired, and the pipeline is accurately analyzed to determine whether there is a leak. If it is confirmed that it is not caused by pipeline leakage, the cause of the abnormal water delivery volume of the water supply system is deeply analyzed through the production plan, and a targeted management plan is formulated, aiming to quickly respond to and solve the problem, ensure the stable operation and efficient water supply of the water supply system, reduce the waste of water resources, and improve the overall water affairs management level of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The present invention will be further described below in conjunction with the accompanying drawings.

[0067] Figure 1 A flow chart of a method for intelligent management of water affairs data is provided for an embodiment of the present invention;

[0068] Figure 2 A framework diagram of a smart management system for water affairs data is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0070] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0071] The embodiment of the present invention provides a method for intelligent management of water affairs data. Figure 1 , Figure 1 A flowchart of a method for intelligent management of water affairs data provided by an embodiment of the present invention. The method comprises the following steps:

[0072] S101, obtaining water service data in the target park, establishing a water supply topology map for the water supply system of the target park, and determining whether the water supply volume of the water supply topology map is abnormal based on the water service data;

[0073] S102, if the water supply in the water supply topology is abnormal, obtain monitoring data in the target area, and determine whether there is a leak in the pipeline in the target area according to the monitoring data;

[0074] S103, if there is no leakage in the pipeline in the target area, determine the cause of abnormal water delivery of the water supply system in each target area in the target park through the production plan;

[0075] S104, determining a corresponding management plan based on the cause of the abnormal water delivery volume of the water supply system in the target area.

[0076] Water service data include: water consumption and water flow; abnormal reasons for water delivery include: increased production and damage of industrial equipment;

[0077] Based on an intelligent management method for water data provided by an embodiment of the present invention, by collecting water data and establishing a water supply topology map, the water supply of the water supply system is monitored in real time to determine whether there is an abnormality. When the water supply is abnormal, the monitoring data in the target area is obtained to accurately analyze whether there is a leak in the pipeline. If it is confirmed that it is not caused by a pipeline leak, the cause of the abnormal water supply of the water supply system is deeply analyzed through the production plan, and a targeted management plan is formulated to quickly respond to and solve the problem, ensure the stable operation and efficient water supply of the water supply system, while reducing water resource waste and improving the overall water management level of the park.

[0078] In one implementation, the target park is a park that includes various factories, the target area is the factory, and the water supply system is a branch pipeline that supplies water to various factories from the water supply main pipeline. For example, a branch main pipeline is extended from the water supply main pipeline, and the branch main pipeline is extended again to connect various industrial parks to supply water to each industrial park.

[0079] In one implementation, in water management, water data is collected and a water supply topology is constructed, which can monitor the changes in water supply volume of the water supply system in real time. Once an abnormal water supply volume is found, the monitoring data in the target area is quickly obtained to determine whether there is a pipeline leak. If it is confirmed that there is no leakage problem, the water consumption of the factory is detected, and the cause of the abnormality is explored through in-depth analysis of factors such as production plans. This process not only improves the efficiency and accuracy of problem diagnosis, but also ensures the optimal allocation of resources and the activation of a rapid response mechanism, providing strong support for the formulation of a scientific and reasonable management plan, thereby ensuring the stable operation of the water supply system and the reliability of water use for users.

[0080] In one implementation, a corresponding management plan is determined based on the cause of the abnormal water delivery volume of the water supply system in the target area. That is, a management plan is formulated based on whether there is a pipeline leak or factory equipment damage. If there is a pipeline leak, the corresponding pipeline leak location is first determined, and then the corresponding valve is closed and emergency repairs are carried out; if the factory equipment is damaged, the corresponding person in charge of the factory is notified to arrange the corresponding maintenance work.

[0081] In one embodiment, step S101 includes:

[0082] Step 1: Map the water service data to the nodes of the water supply topology map to obtain the target topology map; the nodes in the topology map represent water meters, and the edges in the topology map represent water pipelines;

[0083] Step 2: Perform water supply anomaly detection on the target topology map. If the deviation between the water consumption and the water flow in the target area exceeds the deviation threshold, it is determined that there is water supply anomaly in the pipeline in the target area.

[0084] Step three: determine if there is leakage in the pipelines within the target area.

[0085] In one implementation, water service data is mapped to the water supply topology map, which makes data intuitive and spatial relationships clear, making it easier for the system and managers to monitor the operation status of the water supply system, quickly discover potential problems such as abnormal water supply, and accurately locate abnormal areas for leakage judgment. This integrated analysis method not only improves the efficiency of troubleshooting and repair, but also provides strong support for the optimization and adjustment of the water supply system and scientific decision-making.

[0086] In one implementation, water consumption refers to the actual water consumption of the factory, which is measured by the factory's water meter. Water flow refers to the total amount of water flowing into the park. The total water volume should be equal to or slightly less than the total water consumption of each factory. When the deviation between the water consumption and the water flow in the target area exceeds the deviation threshold, it means that the pipes supplying water to each factory may be leaking.

[0087] In one embodiment, step three includes:

[0088] Step 1, calculate the leakage probability of each pipeline in the target topology map; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value;

[0089] Step 2: If the pipeline leakage probability is greater than the leakage threshold, it is determined that the target pipeline has leakage;

[0090] Step 3: If the pipeline leakage probability is less than or equal to the leakage threshold, it is determined that there is an abnormal water supply in the target area;

[0091] Leakage probability formula:

[0092] ;

[0093] in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

[0094] In one implementation, phase data is acquired through a φ-OTDR system, which mainly includes a light source, a pulse modulator, a fiber sensor, a photodetector, a signal processor, and other parts. The light source generates a continuous or pulsed light signal, and the pulse modulator modulates the light signal into a pulsed light and injects it into the fiber sensor. The optical fiber in the fiber sensor acts as a sensing element, modulating information such as vibration and strain into the backscattered Rayleigh light. The photodetector receives the backscattered light and converts it into an electrical signal, and the signal processor demodulates the electrical signal, extracts the phase information, and restores the vibration waveform or strain distribution; the pipeline vibration peak is acquired through a vibration sensor, the water flow rate in the pipeline is acquired by a water flow sensor, and the pressure in the pipeline is acquired by a pressure sensor; the φ-OTDR system is an optical fiber installed parallel to the pipeline surface, and the vibration sensor can be a HD-ST series vibration velocity sensor, such as HD-ST-3 and HD-ST-6, etc. The vibration sensor can be installed at the top or bottom of the pipeline, because these locations usually have more obvious vibrations. At the same time, the sensor is in close contact with the pipe surface to reduce interference during signal transmission; the water flow rate in the pipe can be measured by the turbine water flow sensor, and the speed and flow of the water flow are reflected by the rotation speed of the turbine. The water flow impacts the turbine blades to make them rotate. The speed of the turbine is proportional to the water flow speed. The sensor converts the speed of the turbine into an electrical signal output. The turbine water flow sensor is installed on the inner wall of the pipe.

[0095] In one implementation, the phase change rate refers to: the rate of change of the phase of the light wave propagating in the optical fiber with time or space; under ideal conditions, the phase information changes periodically with time, and the periodic change usually has a fixed frequency and amplitude; in practical applications, especially in water pipe leakage detection, the vibrations and sound waves generated by the leakage point are often random and non-periodic. These vibrations and sound waves will cause non-periodic changes in the phase of the light wave in the optical fiber. This non-periodic change does not have a fixed frequency and amplitude, but changes with time and the leakage situation; in φ-OTDR technology, by measuring the phase change rate of each point along the optical fiber, the non-periodic phase change characteristics caused by leakage can be captured. These characteristics include the amplitude, rate and trend of the phase change, etc., which are closely related to the location and degree of leakage. Therefore, by analyzing the above phase change rate data, the precise positioning of the leakage point and the evaluation of the leakage degree can be achieved.

[0096] In one implementation, the leakage probability of each pipeline in the target topology is calculated. If the pipeline leakage probability is greater than the leakage threshold, it is determined that the target pipeline has a leak, that is, the pipeline supplying water to the current factory has a leak. The above steps improve the accuracy and timeliness of pipeline leakage detection, and can effectively identify and handle abnormal conditions in the water supply system, reduce water resource waste, ensure water supply safety and efficiency, and lay the foundation for subsequent pipeline positioning.

[0097] In one embodiment, step 3 includes:

[0098] Obtaining a production plan of industrial equipment in a target area, determining a production volume of a target production cycle according to the production plan, and fitting a first production curve;

[0099] Obtain the historical production plan, and determine the historical target cycle data set for the same period of the target production cycle based on the historical production plan;

[0100] The historical target cycle data set is fitted to obtain the fluctuation curve, and the end point data of the previous cycle of the target production cycle is used as the starting point;

[0101] Connecting the fluctuation curves according to the time period based on the starting point to obtain a second production curve, and matching the first production curve with the second production curve;

[0102] The difference value of the target production cycle is calculated according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the industrial equipment has abnormal water consumption and a fault is reported.

[0103] In one implementation, abnormal water consumption in a factory includes two situations. One is that industrial production is at its peak, resulting in a surge in water consumption, which is normal. The second is that there are no large orders in the factory, that is, the factory is in a production trough at this time, but there is abnormal water consumption in industrial equipment. It may be that the energy consumption of the equipment in the factory has increased (water resources), or the industrial equipment is damaged or leaking.

[0104] In one implementation, in the coordinate system of the first production curve and the second production curve, the x-axis is time (unit: day), and the y-axis is production volume (the unit is determined according to actual conditions, for example, tons or quantity, etc.); the difference value of the target production cycle is calculated according to the time scale within the time period, wherein the time period is each month, the time scale is each day, and the difference value is equal to the sum of the daily production volume differences within the target production cycle. For example, if the target production cycle is 30 days and the daily production volume difference is 2, then the difference value is equal to 30×2, which is normalized to 0.6.

[0105] In one embodiment, step 2 includes:

[0106] Obtain historical phase data, historical pipeline pressure, historical pipeline water velocity and historical pipeline vibration peak value in the target area, and calculate historical reflection positioning based on historical phase data;

[0107] The target topology, historical reflection location, historical pipeline pressure, historical pipeline water velocity and historical pipeline vibration peak are used as inputs of the reverse neural network model for training;

[0108] The output of the reverse neural network model is used as weights and biases, the weights and biases are encoded to obtain the initial chromosomes, the initial chromosomes are initialized and the initial population is generated;

[0109] The initial population is iteratively optimized through genetic operations, and the fitness of each chromosome is calculated until the chromosome reaches the maximum number of iterations or the fitness of the chromosome no longer decreases, then the optimal chromosome is output;

[0110] The optimal chromosome is used as the update parameter of the reverse neural network model to update the model and obtain the leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage location.

[0111] The reflection location, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak are input into the leakage point prediction model to obtain the target pipeline leakage location.

[0112] In one implementation, the back propagation neural network (BPNN) is an artificial neural network that updates weights through error feedback. The BP neural network model consists of three components: input layer, hidden layer, and output layer. It has nonlinear mapping capabilities and good self-learning capabilities, but it is easy to be trapped in local optimality and may be limited when fitting complex nonlinear systems. Genetic algorithms (GAs) are used to simulate the natural evolution process to seek to optimize the update method of BP neural network weights and biases; genetic algorithms are used to optimize the weights and biases of neural networks. The physical fitness of each person is calculated, individuals with low fitness are eliminated, and individuals with high fitness are retained. The new population inherits the information of the previous generation and is better than the previous generation. This process is repeated iteratively to find the best solution. The genetic algorithm encodes the initial weights and thresholds, and then genetic operations (selection, crossover, and mutation mechanisms) are used to finally obtain an approximate solution. Then, this optimal solution is used to replace the initial weights and thresholds in the BP neural network, and then the optimized network is trained to obtain the final optimization model.

[0113] In one implementation, an efficient leakage point prediction model is constructed by comprehensively utilizing multi-dimensional information such as historical phase data, pressure in the pipeline, water flow rate, and vibration peak, combined with the optimization of inverse neural network and genetic algorithm. The advantage of this method is that it can fully explore the hidden patterns in historical data and improve the accuracy of leakage detection. At the same time, the application of genetic algorithm enables the model to automatically adjust parameters during the iterative optimization process, avoiding the tediousness of manual parameter adjustment and enhancing the adaptability and robustness of the model.

[0114] In one implementation, the leak point prediction model can accurately and quickly locate the location of pipeline leaks. In practical applications, this can not only significantly shorten the troubleshooting time, reduce resource losses and environmental impacts caused by leaks, but also improve the maintenance efficiency and safety of the pipeline system.

[0115] Based on the same inventive concept, the present invention also provides an intelligent management system for water affairs data. Figure 2 , Figure 2 A framework diagram of a smart management system for water data provided by an embodiment of the present invention includes: a topology map establishment module, a pipeline leakage judgment module, an abnormal cause detection module, and a management solution determination module:

[0116] The topology map establishment module is used to obtain water service data in the target park, establish a water supply topology map for the water supply system of the target park, and determine whether the water supply volume of the water supply topology map is abnormal based on the water service data; the water service data includes: water consumption and water flow;

[0117] The pipeline leakage judgment module is used to obtain the monitoring data in the target area if the water supply volume in the water supply topology is abnormal, and judge whether there is a leakage in the pipeline in the target area according to the monitoring data;

[0118] The abnormal cause detection module is used to determine the abnormal cause of water delivery of the water supply system in each target area in the target park through the production plan if there is no leakage in the pipeline in the target area; the abnormal cause of water delivery includes: increased production of industrial equipment and damage of industrial equipment;

[0119] The management scheme determination module is used to determine the corresponding management scheme according to the abnormal cause of water delivery of the water supply system in the target area.

[0120] Based on an embodiment of the present invention, a smart management system for water data is provided. By collecting water data and establishing a water supply topology map, the water supply of the water supply system is monitored in real time to determine whether there is an abnormality. When the water supply is abnormal, the monitoring data in the target area is obtained to accurately analyze whether there is a leak in the pipeline. If it is confirmed that it is not caused by a pipeline leak, the cause of the abnormal water supply of the water supply system is deeply analyzed through the production plan, and a targeted management plan is formulated to quickly respond to and solve the problem, ensure the stable operation and efficient water supply of the water supply system, and reduce the waste of water resources and improve the overall water management level of the park.

[0121] In one embodiment, the topology map building module includes: a water service data mapping module, a water supply anomaly detection module and a pipeline leakage judgment module:

[0122] A water affairs data mapping module is used to map water affairs data to nodes of a water supply topology map to obtain a target topology map; nodes in the topology map represent water meters, and edges in the topology map represent water pipelines;

[0123] The water supply anomaly detection module is used to detect water supply anomalies in the target topology. If the deviation between the water consumption and the water flow in the target area exceeds the deviation threshold, it is determined that there is water supply anomaly in the pipeline in the target area.

[0124] The pipeline leakage judgment module is used to judge the leakage of pipelines in the target area.

[0125] In one embodiment, the pipeline leakage judgment module further includes: a leakage probability calculation module, a first judgment module and a second judgment module:

[0126] The leakage probability calculation module is used to calculate the leakage probability of each pipeline in the target topology map; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value;

[0127] The first judgment module is used to judge whether the target pipeline has leakage if the pipeline leakage probability is greater than the leakage threshold;

[0128] The second judgment module is used to determine that there is an abnormal water supply in the target area if the pipeline leakage probability is less than or equal to the leakage threshold;

[0129] Leakage probability formula:

[0130] ;

[0131] in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

[0132] In one embodiment, the second judgment module includes: a first curve fitting module, a data set determination module, a fluctuation curve fitting module, a curve matching module and a fault reporting module:

[0133] A first curve fitting module is used to obtain a production plan of industrial equipment in a target area, determine the production volume of a target production cycle according to the production plan, and obtain a first production curve by fitting;

[0134] A data set determination module is used to obtain the historical production plan and determine the historical target cycle data set for the same period of the target production cycle according to the historical production plan;

[0135] The fluctuation curve fitting module is used to fit the historical target cycle data set to obtain the fluctuation curve, and take the end point data of the previous cycle of the target production cycle as the starting point;

[0136] A curve matching module, used to connect the fluctuation curves according to the time period based on the starting point to obtain a second production curve, and match the first production curve with the second production curve;

[0137] The fault reporting module is used to calculate the difference value of the target production cycle according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the water consumption of the industrial equipment is abnormal and a fault report is performed.

[0138] In one embodiment, the first judgment module includes: a training data acquisition module, a training data input module, a model parameter encoding module, a population iteration optimization module, a model update module and a leakage point output module:

[0139] A training data acquisition module is used to obtain historical phase data, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value in the target area, and calculate the historical reflection positioning according to the historical phase data;

[0140] A training data input module, used for training the target topology map, historical reflection location, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value as inputs of the reverse neural network model;

[0141] A model parameter encoding module is used to use the output of the reverse neural network model as weights and biases, encode the weights and biases to obtain initial chromosomes, initialize the initial chromosomes and generate an initial population;

[0142] The population iteration optimization module is used to iteratively optimize the initial population through genetic operations and calculate the fitness of each chromosome until the chromosome reaches the maximum number of iterations or the fitness of the chromosome no longer decreases, and then output the optimal chromosome;

[0143] A model updating module is used to update the model by taking the optimal chromosome as an updating parameter of the reverse neural network model to obtain a leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage position;

[0144] The leakage point output module is used to input the reflection location, the pressure in the pipeline, the water flow rate in the pipeline and the pipeline vibration peak into the leakage point prediction model to obtain the target pipeline leakage position.

[0145] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A smart management method for water affairs data, characterized in that: The method comprises: Acquire water service data in the target park, establish a water supply topology map for the water supply system of the target park, and determine whether the water supply volume of the water supply topology map is abnormal based on the water service data; the water service data includes: water consumption and water flow; If the water supply volume of the water supply topology is abnormal, the monitoring data in the target area is obtained, and whether there is a leak in the pipeline in the target area is determined according to the monitoring data; If there is no leakage in the pipeline in the target area, the abnormal reasons for the water delivery volume of the water supply system in each target area in the target park are determined through the production plan; the abnormal reasons for the water delivery volume include: increased production of industrial equipment and damage to industrial equipment; Determine a corresponding management plan for the abnormal water delivery of the water supply system in the target area; If the target area has abnormal water supply, the reasons for the abnormal water supply of the water supply system in each target area in the target park are determined by the production plan, including: Obtaining a production plan of industrial equipment in the target area, determining a production volume of a target production cycle according to the production plan, and fitting a first production curve; Obtain a historical production plan, and determine a historical target cycle data set for the same period of the target production cycle according to the historical production plan; Fitting the historical target cycle data set to obtain a fluctuation curve, and taking the end point data of the previous cycle of the target production cycle as the starting point; According to the starting point, the fluctuation curves are connected according to the time period to obtain a second production curve, and the first production curve is matched with the second production curve; The difference value of the target production cycle is calculated according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the industrial equipment has abnormal water consumption and a fault is reported.

2. A smart management method for water affairs data according to claim 1, characterized in that: Judging whether the water supply of the topology diagram is abnormal according to the water service data includes: Mapping the water service data to the nodes of the water supply topology map to obtain a target topology map; the nodes in the topology map represent water meters, and the edges in the topology map represent water pipelines; Performing water supply anomaly detection on the target topology map, if the deviation value between the water consumption and the water flow in the target area exceeds a deviation threshold, it is determined that there is a water supply anomaly in the pipeline in the target area; Leakage determination is performed on the pipelines in the target area.

3. A smart management method for water affairs data according to claim 2, characterized in that: Judging whether there is leakage in the pipeline in the target area according to the monitoring data includes: Calculating the leakage probability of each pipeline in the target topology; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value; If the pipeline leakage probability is greater than the leakage threshold, it is determined that the target pipeline has a leakage; If the pipeline leakage probability is less than or equal to the leakage threshold, it is determined that there is an abnormal water supply in the target area; Leakage probability formula: ； in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

4. A smart management method for water affairs data according to claim 3, characterized in that: If there is a leak in the target pipeline, the corresponding management plan is determined based on the cause of the abnormal water delivery of the water supply system in the target area, including: Acquire historical phase data, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value in the target area, and calculate historical reflection positioning according to the historical phase data; The target topology map, the historical phase data, the historical pipeline pressure, the historical pipeline water flow rate and the historical pipeline vibration peak value are used as inputs of the reverse neural network model for training; Using the output of the reverse neural network model as weights and biases, encoding the weights and biases to obtain initial chromosomes, initializing the initial chromosomes and generating an initial population; Iteratively optimizing the initial population through genetic operations and calculating the fitness of each chromosome until the chromosome reaches a maximum number of iterations or the fitness of the chromosome no longer decreases, then outputting the optimal chromosome; The optimal chromosome is used as an update parameter of the reverse neural network model to update the model, so as to obtain a leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage position; The phase data, the pressure in the pipeline, the water flow rate in the pipeline and the pipeline vibration peak value are input into the leakage point prediction model to obtain the target pipeline leakage position.

5. A smart management system for water affairs data, characterized in that: The system includes: a topology map building module, a pipeline leakage judgment module, an abnormality cause detection module, and a management solution determination module: The topology map establishment module is used to obtain water service data in the target park, establish a water supply topology map for the water supply system of the target park, and determine whether the water supply volume of the water supply topology map is abnormal based on the water service data; the water service data includes: water consumption and water flow; The pipeline leakage judgment module is used to obtain monitoring data in a target area if the water supply volume of the water supply topology diagram is abnormal, and judge whether there is a leakage in the pipeline in the target area according to the monitoring data; The abnormal cause detection module is used to determine the abnormal cause of water delivery of each target area water supply system in the target park through the production plan if there is no leakage in the pipeline in the target area; the abnormal cause of water delivery includes: increased production of industrial equipment and damage of industrial equipment; The management scheme determination module is used to determine a corresponding management scheme according to the cause of the abnormal water delivery volume of the water supply system in the target area; The second judgment module includes: a first curve fitting module, a data set determination module, a fluctuation curve fitting module, a curve matching module and a fault reporting module: The first curve fitting module is used to obtain a production plan of industrial equipment in the target area, determine the production volume of a target production cycle according to the production plan, and obtain a first production curve by fitting; The data set determination module is used to obtain a historical production plan and determine a historical target period data set for the same period of the target production cycle according to the historical production plan; The fluctuation curve fitting module is used to fit the historical target cycle data set to obtain a fluctuation curve, and use the end point data of the previous cycle of the target production cycle as the starting point; The curve matching module is used to connect the fluctuation curves according to the time period based on the starting point to obtain a second production curve, and match the first production curve with the second production curve; The fault reporting module is used to calculate the difference value of the target production cycle according to the time scale within the time period. If the difference value exceeds the difference threshold, it is determined that the industrial equipment has abnormal water consumption and a fault is reported.

6. The intelligent management system for water affairs data according to claim 5, characterized in that: The topology map building module includes: a water affairs data mapping module, a water supply anomaly detection module and a pipeline leakage judgment module: The water service data mapping module is used to map the water service data to the nodes of the water supply topology map to obtain a target topology map; the nodes in the topology map represent water meters, and the edges in the topology map represent water pipelines; The water supply anomaly detection module is used to perform water supply anomaly detection on the target topology map, and if the deviation value between the water consumption and the water flow in the target area exceeds the deviation threshold, it is determined that there is a water supply anomaly in the pipeline in the target area; The pipeline leakage judgment module is used to judge the leakage of the pipeline in the target area.

7. The intelligent management system for water affairs data according to claim 6, characterized in that: The pipeline leakage judgment module also includes: a leakage probability calculation module, a first judgment module and a second judgment module: The leakage probability calculation module is used to calculate the leakage probability of each pipeline in the target topology map; the monitoring data of the pipeline includes: phase data, pressure in the pipeline, water flow rate in the pipeline and pipeline vibration peak value; The first judgment module is used to determine that there is leakage in the target pipeline if the pipeline leakage probability is greater than the leakage threshold; The second judgment module is used to determine that there is an abnormal water supply in the target area if the pipeline leakage probability is less than or equal to the leakage threshold; Leakage probability formula: ； in, represents the probability of pipeline leakage, Represents the phase value change, represents the phase change rate, Represents the peak value of the pipeline vibration data, Represents the preset vibration intensity peak value, Represents the change in water flow rate. represents the flow velocity decrease rate, Represents the pressure drop change, Represents the rate of pressure drop.

8. The intelligent management system for water affairs data according to claim 7, characterized in that: The first judgment module includes: a training data acquisition module, a training data input module, a model parameter encoding module, a population iteration optimization module, a model update module and a leakage point output module: The training data acquisition module is used to acquire historical phase data, historical pipeline pressure, historical pipeline water flow rate and historical pipeline vibration peak value in the target area, and calculate the historical reflection positioning according to the historical phase data; The training data input module is used to train the target topology map, the historical phase data, the historical pipeline pressure, the historical pipeline water flow rate and the historical pipeline vibration peak value as inputs of the reverse neural network model; The model parameter encoding module is used to use the output of the reverse neural network model as weights and biases, encode the weights and biases to obtain initial chromosomes, initialize the initial chromosomes and generate an initial population; The population iterative optimization module is used to iteratively optimize the initial population through genetic operations and calculate the fitness of each chromosome until the chromosome reaches a maximum number of iterations or the fitness of the chromosome no longer decreases, and then output the optimal chromosome; The model updating module is used to update the model by using the optimal chromosome as an updating parameter of the reverse neural network model to obtain a leakage point prediction model; the output of the leakage point prediction model is the pipeline leakage position; The leakage point output module is used to input the phase data, the pressure in the pipeline, the water flow rate in the pipeline and the pipeline vibration peak value into the leakage point prediction model to obtain the target pipeline leakage position.

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