Intelligent supervision method and device based on city water supply pipe network isobaric line and readable storage medium

Abstract

The invention provides an intelligent supervision method and device based on an isobaric line of an urban water supply network and a readable storage medium, and relates to the field of urban water supply system management. The technology comprises a system partitioning module, a data acquisition module, an isobaric line generation and analysis prediction module, a target area can be divided, intelligent equipment is accessed to acquire various data, water pressure and longitude and latitude information are combined to generate an isobaric line graph, and the water pressure distribution and the pipe network operation condition are analyzed according to the isobaric line graph. The method comprises the steps of confirming a target area, collecting data, meshing (if necessary), calculating interpolation point pressure, generating a smooth curve, rendering an isobaric graph and the like. According to the technology, the problems of insufficient monitoring points, limited analysis means and the like in existing pipe network pressure management can be solved, accurate pressure management, fault diagnosis, scientific planning design and safety evaluation are achieved, the operation efficiency and service quality of a water supply system are improved, and powerful support is provided for urban water supply management.

Description

Technical Field

[0001] The present invention relates to the field of urban water supply system management, and in particular to an intelligent monitoring method, device and readable storage medium based on isobars of urban water supply pipe networks. Background Art

[0002] Urban water supply network is an important part of urban infrastructure, and its operation status is directly related to the daily life of urban residents and industrial production. In the management of water supply network, water pressure management is one of the core tasks.

[0003] Traditionally, water pressure management in water supply networks faces many challenges. On the one hand, the means of water pressure monitoring are limited. In the early days, it mainly relied on manual inspections and a small number of fixed monitoring points. The monitoring points were sparsely distributed, making it difficult to fully cover the entire network. This leads to inaccurate understanding of the pressure distribution in the network, and it is difficult to find the root cause of the problem in time when water pressure problems occur. For example, at the end of the network or in areas with complex terrain, due to the lack of monitoring points, abnormal water pressure may exist for a long time without being detected, affecting the user's water experience, and may even accelerate pipeline aging and cause leakage due to long-term high-pressure or low-pressure operation.

[0004] On the other hand, data analysis and management capabilities are insufficient. Even if some water pressure data is obtained, there is a lack of effective analysis tools and methods. The overall water pressure distribution trend of the pipe network cannot be presented intuitively, making it difficult for managers to quickly determine areas with too high or too low pressure and take targeted measures in a timely manner. For example, during peak or off-peak water use periods, it is difficult to quickly adjust the pump station operation strategy based on real-time water pressure data, resulting in energy waste or insufficient water supply in some areas.

[0005] In addition, in the planning and design of water supply networks, due to the lack of accurate understanding of water pressure distribution, there is a lack of scientific basis for pipe diameter selection and pump station setting when designing new pipe networks or renovating old pipe networks. Pipe diameters are often too large or too small, and pump station pressure settings are unreasonable, resulting in high energy consumption and uneven pressure in the water supply system, affecting the overall water supply efficiency and stability.

[0006] In view of the defects of the above-mentioned prior art, the present invention aims to provide a city water supply network isobaric line technology. By rationally laying a large number of pressure monitoring equipment in the water supply network and combining advanced data processing and analysis methods, an isobaric line map of the water supply network is generated to intuitively present the water pressure distribution, thereby providing strong support for the pressure management, fault diagnosis, planning and design, and safety assessment of the water supply system, and improving the management level and operation efficiency of the urban water supply system. Summary of the invention

[0007] The embodiments of the present invention provide an intelligent monitoring method, device and readable storage medium based on pressure lines of urban water supply pipe networks. The present invention aims to solve the problems existing in the current technology of urban water supply pipe network pressure management, such as unreasonable layout and insufficient number of monitoring points, lack of data visualization and analysis methods, lack of basis for planning and design, which make it difficult to fully grasp the water pressure conditions, unable to intuitively present distribution trends and anomalies, and unreasonable pipe network design parameters affecting the performance of the water supply system.

[0008] The core technology of the present invention is mainly to achieve accurate description and effective management of the water pressure distribution status of the urban water supply network by dividing the target area, laying pressure monitoring equipment to collect data, combining longitude and latitude information to generate isobaric maps and analyze and predict, so as to improve the operating efficiency and service quality of the water supply system.

[0009] In a first aspect, the present invention provides an intelligent monitoring method based on isobaric lines of an urban water supply network, the method comprising the following steps: S00, selecting an administrative area or a custom planning area as a target area, and obtaining geographic contour information of the target area, including a set of longitude and latitude coordinate points; S10, ensuring that there are enough pressure monitoring points in the target area, and obtaining monitoring data of each pressure monitoring point in real time, including at least water pressure; S20, performing interpolation calculation on all pressure monitoring points or performing interpolation calculation after gridding resampling according to the density of the pressure monitoring points; The pressure values ​​of all unknown interpolation points are calculated by the pressure values ​​of known pressure monitoring points and the distances from the points with known pressure values ​​to the points with unknown pressure data, that is, the pressure values ​​of all pressure monitoring points are obtained; S30, according to the pressure values ​​of all pressure monitoring points, a matrix is ​​constructed in the target area using the pressure values ​​as z values, a threshold point is found using an interpolation algorithm and a set threshold value, and contour lines are generated according to the path based on the position set of the threshold points; S40, assembling the generated threshold position set so as to render a contour effect on the browser-side map; S50, predicting and analyzing the actual water pressure distribution and pipe network operation status according to the rendering results to obtain analysis results.

[0010] Furthermore, in step S10, an abnormal alarm is also configured to report the abnormal situation of each pressure monitoring point by detecting it.

[0011] Furthermore, in step S20, when the density of pressure monitoring points is greater than a set threshold, it is judged to be relatively dense, and the latitude and longitude grid size is determined by the distribution of pressure monitoring points and the scale of the pipeline network to establish a grid framework, and then the grid unit to which the pressure monitoring points belong is determined, and the data in the grid unit is aggregated or representative values ​​are selected, and finally a new data set containing grid information is generated for resampling, thereby facilitating subsequent grid-based interpolation calculations.

[0012] Furthermore, in step S20, the weight of each point with a known pressure value is calculated:

[0013] Among them, w i is the weight of the i-th point; d i is the distance between the point and the position to be interpolated; p is an adjustable parameter with a value of 2 or 3; After obtaining the weight of each point with known pressure value, perform weighted average of the function value to obtain the function value z(x,y) at the position to be interpolated:

[0014] Among them, the number of known sample points is N; the coordinates of the i-th sample point are (x i ,y i ); weight is w i ; Function value z i is the function value of the i-th point, and the function value is the water pressure data of the point; the numerator is the sum of the function values ​​of each point multiplied by the corresponding weight, and the denominator is the sum of the weights. The ratio of the numerator and the denominator is the interpolation result.

[0015] Furthermore, in step S30, the selected interpolation algorithm includes one of linear interpolation, polynomial interpolation, spline interpolation, bilinear interpolation, and bicubic interpolation.

[0016] Furthermore, in step S40, the generated threshold position set is assembled into data that complies with the GeoJSON specification, input into the WEBGIS engine, and a contour effect is rendered on the browser-side map.

[0017] Furthermore, in step S00, the custom planning area complies with the GeoJSON specification.

[0018] In a second aspect, the present invention provides an intelligent monitoring device based on isobaric lines of a city water supply network, comprising: The system partition module is used to select an administrative area or a custom planning area as a target area and obtain the geographic contour information of the target area, including a set of longitude and latitude coordinate points; The data acquisition module is used to access the smart devices of the water supply network in the target area, configure and collect the monitoring data of the smart devices, including voltage, temperature, and abnormal alarm data. The water pressure data can be configured with reporting time period and frequency to obtain the pressure values ​​of each node in the water supply network in the target area at different time periods and real-time reporting data of abnormal conditions; The isobaric line generation module is used to combine the water pressure data reported by each smart device in the target area with the corresponding longitude and latitude location information, and convert the water pressure data into an isobaric line map through interpolation point tiling, unknown point pressure value prediction, and isobaric line drawing functions; The analysis and prediction module is used to analyze the actual water pressure distribution and pipe network operation in the target area by using the line density and color information in the generated isobaric line map in the target area.

[0019] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned intelligent monitoring method based on isobars of urban water supply networks.

[0020] In a fourth aspect, the present invention provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes the above-mentioned intelligent supervision method based on isobars of urban water supply networks.

[0021] The main contributions and innovations of the present invention are as follows: 1. Accurate water pressure monitoring and analysis 1. Comprehensive pressure information acquisition The present invention lays a large number of pressure monitoring devices in the water supply network in the target area. Compared with the traditional technology that only sets a small number of monitoring points at key nodes, it can more comprehensively obtain the pressure values ​​of each node in the network at different time periods. This makes the description of the pressure distribution of the network more accurate, no longer limited to local key positions, but covers the entire network system, including branch pipelines, terminal areas, and complex terrain areas where monitoring was weak in the past. For example, at the end of the network in old urban communities, the problem of unstable water pressure may have existed for a long time without being discovered due to lack of monitoring. The present invention can effectively monitor and solve such problems.

[0022] Combining the water pressure data reported by smart devices with the longitude and latitude location information, the geographical location corresponding to the pressure data can be accurately located, providing a basis for subsequent accurate analysis. In this way, the pressure differences in different regions can be clearly identified. For example, in mountainous areas, the pressure changes caused by changes in altitude can be accurately reflected. Existing technologies are difficult to achieve such detailed pressure monitoring and positioning analysis.

[0023] 2. Intuitive presentation of pressure distribution The generated isobaric map shows the distribution of water pressure in the pipe network in an intuitive graphical way. Through the density, color and other information of the lines, managers can clearly identify the high-pressure areas, low-pressure areas and pressure transition areas, which is difficult to achieve with existing technologies that rely solely on discrete data. For example, during peak water usage periods, it is possible to quickly see from the isobaric map which areas have a significant drop in water pressure and which areas still have a high water pressure, so that corresponding measures can be taken in a timely manner.

[0024] The analysis and prediction module uses the isobaric line map to further analyze the actual water pressure distribution and the operation of the pipeline network. For example, the pressure change gradient is determined by the density of the lines, providing a quantitative basis for the evaluation of the operation status of the pipeline network. This helps to discover potential problems in advance. For example, abnormal changes in local pressure may indicate pipeline leakage or blockage. The existing technology often detects the problem only after it becomes obvious. The present invention can achieve earlier warning and more accurate fault diagnosis.

[0025] Optimized water supply management strategy 1. Effective stress management Based on the isobar map, water managers can accurately identify areas with excessively high or low pressure, and thus adjust the pump station operation strategy, install pressure reducing valves or boosting equipment locally, etc. For example, if it is found that the water pressure in a certain area is too high for a long time, the pump station output pressure can be adjusted in time or a pressure reducing valve can be installed at an appropriate location to avoid problems such as pipe bursts and increased leakage caused by excessive pressure, thereby improving the safety and stability of the pipe network operation and reducing the waste of water resources and maintenance costs caused by accidents such as pipe bursts.

[0026] It is possible to formulate reasonable pressure adjustment plans in advance according to the trend of isobars in different periods of time. For example, when the peak of summer water consumption is predicted, the pump station operation parameters are optimized in advance to ensure the stability of the pressure of the entire water supply system during the peak period, which effectively improves the quality of water supply services and guarantees the normal water demand of residents and industrial users. Existing technologies cannot achieve such precise forward-looking pressure management.

[0027] 2. Scientific planning and design basis When constructing or renovating a water supply network, designers can use the isobar analysis results provided by the present invention to optimize pipeline layout, pipe diameter selection, and pump station settings. According to the pressure distribution characteristics shown in the isobar diagram, larger diameter pipes are selected in areas with lower pressure to reduce head loss, and pump station layout and operating parameters are rationally planned in areas with excessively high pressure to ensure balanced pressure in the entire water supply system, reduce energy consumption, and improve water supply efficiency. Compared with the prior art that relies on experience or rough estimates for planning and design, the present invention makes planning and design more scientific and reasonable, avoids subsequent operational problems caused by unreasonable design, extends the service life of the network, and reduces long-term operating costs.

[0028] 3. Improved water supply system safety and reliability 1. Rapid fault diagnosis and location Abnormal distribution of isobars can quickly indicate leaks, blockages or other faults in the pipe network. Through dynamic analysis of isobars, such as continuous monitoring of sudden changes in isobars in a certain area, where the lines become abnormally dense or sparse, combined with abnormal alarm data reported by the equipment (such as sudden pressure drops or surges), potential problem areas can be quickly located and repairs can be carried out in a timely manner. This greatly shortens the time for fault discovery and handling, reduces the risk of water supply interruptions, and improves the reliability of the water supply system. Due to the lack of effective pressure distribution visualization tools and system analysis methods in existing technologies, fault diagnosis often requires a lot of time and manpower to troubleshoot.

[0029] 2. Effective assessment and prevention of safety risks Combined with isobars and other operating data, it is possible to monitor the safety risks of pipe network operation and assess potential safety hazards. For example, by analyzing the long-term pressure change trend and the change in the isobars, it is possible to predict problems such as pipeline aging and reduced pressure bearing capacity, and formulate preventive and corrective measures in advance, such as planned pipeline replacement and enhanced monitoring of specific areas. This helps to ensure the long-term stable operation of the water supply system, reduce the probability of safety accidents, and ensure the safety of urban water supply. However, existing technologies lack systematicity and foresight in safety risk assessment.

[0030] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of an intelligent monitoring method based on isobaric lines of a city water supply network according to an embodiment of the present invention; Figure 2 is a schematic diagram of linear interpolation calculation according to an embodiment of the present invention; Figure 3 is a schematic diagram of a landing example according to an embodiment of the present invention; Figure 4 is a schematic diagram of global isobars according to a landing example of an embodiment of the present invention; Figure 5 is a schematic diagram of local isobars according to a landing example of an embodiment of the present invention; Figure 6 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0033] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0034] Embodiment 1 The present invention aims to propose an intelligent monitoring method based on the pressure line of the urban water supply network. Figure 1 , the method comprising: S00. Confirm the scope of the target area: In the current software platform, administrative areas or custom planning areas are manually selected. Administrative areas can support district-level divisions, while custom areas need to automatically select the planning target area. The key to this step is to obtain the geographic contour information data of the target area, that is, the set of latitude and longitude coordinate points that constitute the target area.

[0035] S10, confirm data acquisition module Ensure that sufficient pressure monitoring points have been deployed in the water supply network within the target area. These points may include pump station outlets, key nodes (such as main pipe intersections), user ports, etc., which can reflect the overall pressure distribution of the network. The actual water pressure data of each monitoring point is collected by modifying the configuration of the device reporting information of this system. The reporting period and frequency of the water pressure data of the monitoring point equipment can be configured to achieve the purpose of collecting the real-time water pressure of all equipment at a certain moment. The pressure value can be obtained through the automatic pressure sensor installed at the node of the network equipment.

[0036] In addition, some abnormal alarm data reporting can be configured. For example, if a monitoring point has abnormal conditions such as too high water pressure, too low water pressure, or no water in the pipeline, this type of alarm configuration is turned on on the software platform, and the data will be reported in real time. Based on the above-mentioned real-time monitoring of water pressure under normal conditions and real-time reporting of abnormal conditions at some detection points, the real-time operation of the pipeline network in the target area can be fully grasped through this data acquisition module.

[0037] S20, Target Area Gridding According to the steps S00-S10, the density of data collection points is judged. If the collection points are very dense, directly using all the collection points for interpolation calculations will result in a huge amount of calculations and affect efficiency. The dense point data can be resampled to a coarser grid according to certain rules (such as longitude and latitude grids). This can significantly reduce the number of data points without excessive loss of information. Interpolation is then performed based on this grid, such as inverse distance weighted (IDW), Kriging, etc.

[0038] For example, the method and identification criteria for determining the density of data collection points are as follows: 1. Judgment method 1) Comparison of regional area and number of collection points Calculate the number of collection points per unit area. For example, divide the target area into several small areas of the same area (such as square or circular areas), count the number of collection points in each small area, and then calculate the average value. If the average value exceeds a certain threshold, it can be preliminarily judged that the collection points are relatively dense.

[0039] The distribution characteristics can also be analyzed by calculating the density function of the collection point distribution. If the density function has high values ​​and is relatively uniform in most areas, it indicates that the collection points are dense.

[0040] 2) Analysis of the distance between collection points Measure the distance between any two adjacent collection points and calculate the average spacing. If the average spacing is less than a certain set value, such as a specific length on a map at a certain scale (such as 50 meters, 100 meters, etc., determined according to the scale of the pipeline network and the accuracy requirements), the collection points are considered to be relatively dense.

[0041] The distribution of the distances between the collection points can also be analyzed. If most of the distances are concentrated in a small range and conform to a certain distribution law (such as normal distribution with a small mean), it can also be used as a basis for the density of the collection points.

[0042] 3) Comparison with pipeline network characteristics Compare the distribution of collection points with the topological structure of the water supply network. If the collection points are densely distributed near key pipelines (such as trunk pipes and important branch pipes) and cover the main nodes and areas of the network, and there are also a certain number of collection points in relatively minor pipeline areas, then it can be determined that the collection points are dense overall.

[0043] Consider the service area and user distribution of the pipe network. If the number of collection points is relatively high compared to the number of users or water demand points in the service area (e.g., more than 80% of the water demand points have collection points nearby), it can also be judged that the collection points are dense.

[0044] II. Criteria for identifying dense collection points 1) Standard number of collection points per unit area In a general urban water supply network, if the number of collection points per square kilometer exceeds 10 (this value can be adjusted according to factors such as the size of the city and the complexity of the network), it can be considered as dense collection points. For large cities or areas with complex networks, the threshold may be higher, such as 15 or 20 per square kilometer.

[0045] 2) Average spacing standard of collection points When the average distance between the collection points is less than 100 meters (it may be even smaller, such as 50 meters, for small cities or areas with high pipe network accuracy requirements), the collection points are generally considered dense. This means that in the water supply network, the distance between two adjacent collection points is close, which can reflect the changes in pipe network pressure in more detail.

[0046] 3) Coverage standards If the collection points can cover more than 90% of the key nodes (such as pump stations, valves, main pipe intersections, etc.) and major water use areas (such as residential areas, commercial areas, industrial areas, etc.) in the water supply network, and also have a certain distribution in secondary pipelines and areas, the collection points can be considered dense. This coverage can ensure comprehensive acquisition of network pressure information and accurate depiction of water pressure distribution. In practical applications, the above methods and standards can be combined to determine the density of data collection points to ensure accurate identification of the density of collection points, thereby determining whether subsequent processing steps such as target area gridding are needed to improve data processing efficiency and the accuracy of isobaric line generation.

[0047] Preferably, the method of resampling the densely collected point data into a coarser grid according to the latitude and longitude grid is as follows: 1. Determine the grid size 1) Based on data distribution characteristics Analyze the longitude and latitude distribution range of the collection points and calculate their span. For example, if the longitude range of the collection points in the target area is [113.0, 113.5] and the latitude range is [23.0, 23.3], the longitude span is 0.5 degrees and the latitude span is 0.3 degrees. Determine the appropriate grid size based on the span size and the desired grid sparsity. If you want to reduce the amount of data by about half, you can set the grid size in the longitude and latitude directions to 1 / 2 to 1 / 3 of the span. For example, set the grid size in the longitude direction to 0.2 degrees and the grid size in the latitude direction to 0.1 degrees.

[0048] Observe the changes in the density of collection points in different areas. If the collection points are particularly dense in some local areas and relatively sparse in other areas, an uneven grid size can be used. Set a smaller grid size in dense areas to better preserve data features; set a larger grid size in sparse areas to further reduce the amount of data. For example, in areas with dense collection points in the city center, the grid size in the longitude direction is set to 0.1 degrees and the grid size in the latitude direction is set to 0.05 degrees; in areas with sparse collection points on the edge of the city, the grid size in the longitude direction is set to 0.3 degrees and the grid size in the latitude direction is set to 0.2 degrees.

[0049] 2) Reference network scale and accuracy requirements For large water supply networks, if the overall pressure trend analysis requirements are high and the local detail accuracy requirements are relatively low, a larger grid size can be selected. For example, in a city water supply network covering a large area, in order to quickly obtain the macro pressure distribution, the longitude and latitude grid size can be set to 0.5 degrees or even 1 degree.

[0050] If there are some key areas or areas sensitive to pressure changes in the pipe network, and high-precision data needs to be retained, a smaller size will be used on the grids corresponding to these areas, or a larger grid size will be used overall, and the key areas will be refined separately. For example, in areas close to large industrial water use, the grid size within a certain range around it will be reduced to 1 / 2 or 1 / 3 of the regular size.

[0051] 2. Establishing the Grid Framework 1) Determine the grid origin Select a suitable longitude and latitude point in the target area as the grid origin. Usually, you can select the lower left corner (minimum longitude, minimum latitude) or upper left corner (minimum longitude, maximum latitude) of the area as the origin, which is convenient for subsequent calculations and data processing. For example, longitude 113.0 and latitude 23.0 are used as the grid origin.

[0052] 2) Constructing the grid lines According to the determined grid size, starting from the origin, draw equally spaced straight lines along the longitude and latitude directions to form a grid frame. For example, according to the grid size of 0.2 degrees in the longitude direction and 0.1 degrees in the latitude direction, starting from the origin, draw a longitude line every 0.2 degrees and a latitude line every 0.1 degrees to divide the target area into multiple rectangular grid units.

[0053] 3. Data Resampling 1) Data point attribution judgment For each collection point, determine the grid unit to which it belongs. This is determined by comparing the longitude and latitude of the collection point with the positional relationship of the grid line. For example, the longitude and latitude of a collection point is (113.15, 23.12). Based on the grid size (0.2 degrees longitude, 0.1 degrees latitude) and the origin (113.0, 23.0), it can be calculated that the collection point is located in the third grid in the longitude direction ((113.15-113.0) / 0.2=0.75, rounded up to 1, plus the grid number 0 where the origin is located, which is the first grid. Similarly, it is located in the second grid in the latitude direction), thereby determining the grid unit to which it belongs.

[0054] 2) Data aggregation or selection of representative values In each grid unit, the data of the collection points are processed. If the data aggregation method is used, the statistical values ​​such as the average, median or sum of the data of all the collection points in the grid unit can be calculated as the sampling value of the grid unit. For example, there are 5 collection points in a grid unit, and the pressure values ​​are 0.3MPa, 0.32MPa, 0.28MPa, 0.31MPa, and 0.29MPa, respectively. The average value is (0.3+0.32+0.28+0.31+0.29) / 5=0.3MPa, and 0.3MPa is used as the pressure value after resampling of the grid unit.

[0055] You can also select appropriate representative values ​​based on the characteristics of the data. For example, in areas where pressure changes are relatively stable, select the middle value in the grid unit (such as the pressure value of the middle acquisition point after sorting by acquisition time) as the representative; in areas where pressure fluctuations are large, you can consider the maximum and minimum values ​​comprehensively, or use weighted average to determine the representative value, and the weight can be determined based on the distance between the acquisition point and the grid center or other factors.

[0056] 4. Generate a new dataset 1) Build a new data structure Create a new data structure to store the resampled grid data. This can be in the form of a two-dimensional array or matrix, where the rows and columns correspond to the grid cells in the longitude and latitude directions, respectively, and each element stores the sampled value (such as the pressure value) of the corresponding grid cell. For example, create a matrix (where is the number of grid cells in the longitude direction, is the number of grid cells in the latitude direction), and fill the sampled value of each grid cell into the corresponding position.

[0057] 2) Record grid-related information At the same time, the relevant information of the grid is recorded, such as the longitude and latitude of the grid origin, the grid size, the correspondence between the grid unit number and the actual geographical location, etc. This information is necessary in the subsequent interpolation calculation and isobar generation process to accurately restore the geographical location information of the data and perform spatial analysis. For example, an index table is established to record each grid unit number and its corresponding longitude range and latitude range, so that the grid data can be mapped back to the actual geographical space when needed.

[0058] Through the above steps, the densely collected data can be resampled to a coarser grid according to the latitude and longitude grid rules, which reduces the amount of data while retaining the main characteristics of the pressure distribution in the pipeline network, providing a suitable data basis for subsequent efficient and accurate interpolation calculations and isobaric line generation.

[0059] S30. Calculate the pressure value of the interpolation point (taking inverse distance weighting as an example) The pressure values ​​of all unknown interpolation points are calculated by the pressure values ​​of known pressure monitoring points and the distance from the known pressure value points to the unknown pressure data points, so that the pressure values ​​of all interpolation points in the target area are obtained. (The concepts of point, monitoring point, collection point, sample point, etc. mentioned in the context are all to describe the pressure monitoring point). The specific steps are as follows: 1. Calculate weight For each position to be interpolated, it is necessary to calculate the distance between it and all known sample points and convert the distance into a weight. Generally speaking, the smaller the Euclidean distance between the sample point and the position to be interpolated, the greater the influence of the sample point on the interpolation result. Therefore, the weight is inversely proportional to the distance and can be calculated using the following formula:

[0060] where w i is the weight of the i-th sample point, d i is the distance between the sample point and the position to be interpolated. p is an adjustable parameter, which is generally taken as 2 (Euclidean distance) or 3 (Manhattan distance). The optimal p value can also be determined by cross-validation and other methods.

[0061] 2. Weighted average solution After calculating the weight of each known sample point, we can perform a weighted average of their function values ​​to obtain the function value at the position to be interpolated. Specifically, let the number of known sample points be N, and the coordinates of the i-th sample point be (x i ,y i ), with weight w i , the function value is z i , then the function value z(x,y) at the position to be interpolated can be calculated by the following formula:

[0062] The numerator is the sum of the function values ​​of each sample point multiplied by the corresponding weight, and the denominator is the sum of the weights. Their ratio is the interpolation result.

[0063] S40, generate smooth curves According to step S30, the pressure values ​​of all interpolation points in the target area are obtained, and the pressure z values ​​in the target area are constructed into a matrix. The four adjacent values ​​in the matrix are taken out, and linear interpolation positioning is performed according to the given threshold. The longitude and latitude corresponding to the z value are calculated, which constitutes an interpolated longitude and latitude point coordinate plus the pressure value at the coordinate. These values ​​are pieced together along the path to form an isoline. The specific steps are as follows: 1. Constructing the interpolation matrix According to step S30, the pressure values ​​of all interpolation points in the target area are obtained, and a z value matrix is ​​constructed in the target area using the pressure values ​​as z values.

[0064] [0.12,0.23,0.18,0.20,0.35,0.33,0.5] [0.16,0.56,0.46,0.57,0.6,0.76,0.74] [0.7,0.43,0.67,0.52,0.72,0.89,0.87] [0.72,0.6,0.63,0.83,0.80,0.73,0.92] [0.83,0.9,1.01,0,98,1,0.92,1.3] 2. Select the interpolation function Select or determine the interpolation algorithm that is suitable for the data and requirements. Common interpolation algorithms include linear interpolation, polynomial interpolation, spline interpolation (such as cubic spline interpolation), bilinear interpolation, bicubic interpolation, etc. Using the selected interpolation algorithm and interpolation matrix, the interpolation function can be calculated. This function can generate corresponding output (such as function value) on a given input (such as coordinate point).

[0065] 3. Setting Thresholds After calculating the interpolation function, a threshold needs to be set to determine the location of the threshold point. This threshold can be based on data characteristics, experience, or a specific application scenario. The setting of the threshold is crucial for the subsequent determination of the location of the threshold point. In the current scenario, we can set specific water pressure high and low pressures and some intermediate pressure values ​​as the set thresholds. According to the actual scenario, we can set multiple thresholds. Take the water pressure interval of 0.1MPa as an example: [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6,0.7,0.8,0.9,1.0] 4. Determine the location of the threshold point Once the threshold is set, the location of the threshold point can be determined by interpolating the function and the threshold. This usually involves comparing the output of the interpolation function to the threshold and finding the input points that meet a certain condition (such as the output value equals or exceeds the threshold). These input points are the locations of the threshold points.

[0066] For example, in the case of linear interpolation, Figure 2 As shown in the figure, if we know two points (x1, y1) and (x2, y2), and need to estimate the y value corresponding to a certain x between the two points, we can connect the two points into a straight line and use this straight line to find the value of any point. In our current scenario, we take out the four adjacent values ​​in the matrix constructed by the z value, perform linear interpolation positioning according to the given threshold, and calculate the longitude and latitude corresponding to the z value (threshold), thus forming an interpolated xyz value.

[0067]

[0068] 5. Generate contour lines According to the position sets corresponding to one or more threshold points calculated in each matrix in the previous step, these position sets are spliced ​​together along the path to generate contour lines.

[0069] S50, Rendering isobars The threshold position set generated in step S40 is assembled into data that conforms to the GeoJSON specification, and finally input into the WEBGIS engine to render the contour effect we need on the browser map. Based on the preset point set obtained in the previous step, the following data can be assembled by looping and processing the data that conforms to the WEBGIS specification: { "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": { "fill-opacity": 1, "fill": "#1a9850", "value": 0.01 }, "geometry": { "type": "MultiLineString", "coordinates": [ [ [ 113.49501800765664, 23.19559463123509 ], [ 113.49510345548515, 23.19551974359861 ], [ 113.49776212170542, 23.19559463123509 ], [ 113.49510345548515, 23.196676496774423 ], [ 113.49501800765664, 23.19559463123509 ] ] ] } }, ] } In this embodiment, a total of 4254 pressure collection points are deployed through two projects, namely, a smart fire hydrant project in a certain development zone (3166) and a district project. The actual landing effect of the equipment monitoring points and isobars in the target area is shown in the figure below: Figure 3 As shown in the figure, the green icon is normal and the red icon is abnormal.

[0070] The generated target area isobar map is as follows: Figure 4 and Figure 5 As shown ( Figure 4 and Figure 5The color mark in the lower left corner changes from dark green to dark red as the number (in MPa) increases, indicating the water pressure and the number of abnormalities). The color of the lines in the generated target area isobaric map, the density of lines in a certain area, and the analysis of the reporting of an abnormal alarm can show which part of the target area has higher water pressure and more abnormalities during a certain period of time. Switching between multiple time periods can control the overall water pressure change trend in the target area. Based on this, water managers can achieve refined management, handle water supply abnormalities in a timely manner, and improve the quality of water supply services.

[0071] Through the analysis of the above-mentioned implementation example, we can know that: 1. There is a big difference in water pressure in the middle area of ​​the target area, and the density of lines is significantly higher than that in other areas; 2. The areas on both sides of the central part of the target area have higher altitudes. After adding the elevation data, the pressure values ​​are significantly higher than those in other areas. 3. The overall water use in the target area is stable, and there is no obvious fluctuation in the overall water use between 2 pm and 10 pm; 4. Water use in the southern and northern parts of the target area is more stable than in the central part, and the overall distribution of isobars is uneven and not dense.

[0072] By comparing the actual intelligent equipment point layout, it can be seen that the locations where the equipment abnormalities occur most frequently are in the middle and north, which corresponds to the relatively high pressure in the middle and northern regions of the isobaric map. By analyzing the geographical location of the central region, it can be seen that there are more peaks and scenic spots in the middle with significantly higher altitudes than the two sides, which corresponds to the relatively higher water pressure in the middle area of ​​the isobaric map than on the two sides, and the isobaric lines in the central region are obviously more dense, which also reflects that the pressure changes are relatively large due to the increase in altitude.

[0073] Embodiment 2 Based on the same concept, the present invention also proposes an intelligent monitoring device based on the isobaric lines of the urban water supply network, comprising: The system partition module is used to select an administrative area or a custom planning area as a target area and obtain the geographic contour information of the target area, including a set of longitude and latitude coordinate points; In this embodiment, if the target area is divided by the existing software platform area management module, the target area can be a standard administrative division or a custom area (a boundary data set is manually planned and customized on the software platform, which complies with the GeoJSON specification).

[0074] The data acquisition module is used to access the smart devices of the water supply network in the target area, configure and collect the monitoring data of the smart devices, including voltage, temperature, and abnormal alarm data. The water pressure data can be configured with reporting time period and frequency to obtain the pressure values ​​of each node in the water supply network in the target area at different time periods and real-time reporting data of abnormal conditions; The isobaric line generation module is used to combine the water pressure data reported by each smart device in the target area with the corresponding longitude and latitude location information, and convert the water pressure data into an isobaric line map through interpolation point tiling, unknown point pressure value prediction, and isobaric line drawing functions; The analysis and prediction module is used to analyze the actual water pressure distribution and pipe network operation in the target area by using the line density and color information in the generated isobaric line map in the target area.

[0075] Embodiment 3 This embodiment also provides an electronic device, referring to Figure 6 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0076] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0077] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0078] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0079] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any intelligent monitoring method based on the isobaric lines of the urban water supply network in the above embodiments.

[0080] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0081] The transmission device 406 can be used to receive or send data via a network. The specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0082] Input / output devices 408 are used to input or output information.

[0083] Embodiment 4 This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes an intelligent monitoring method based on isobars of an urban water supply network according to the first embodiment.

[0084] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0085] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0086] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0087] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. An intelligent monitoring method based on isobaric lines of urban water supply networks, characterized in that: The following steps are involved: S00, selecting an administrative area or a custom planning area as a target area, and obtaining geographic contour information of the target area, including a set of longitude and latitude coordinate points; S10, ensuring that there are enough pressure monitoring points in the target area, and obtaining monitoring data of each pressure monitoring point in real time, including at least water pressure; S20, performing interpolation calculation on all pressure monitoring points or performing interpolation calculation after gridding resampling according to the density of the pressure monitoring points; The pressure values ​​of all unknown interpolation points are calculated by the pressure values ​​of known pressure monitoring points and the distances from the points with known pressure values ​​to the points with unknown pressure data, that is, the pressure values ​​of all pressure monitoring points are obtained; S30, according to the pressure values ​​of all pressure monitoring points, a matrix is ​​constructed in the target area using the pressure values ​​as z values, a threshold point is found using an interpolation algorithm and a set threshold value, and contour lines are generated according to the path based on the position set of the threshold points; S40, assembling the generated threshold position set so as to render a contour effect on the browser-side map; S50, predicting and analyzing the actual water pressure distribution and pipe network operation status according to the rendering results to obtain analysis results.

2. The intelligent monitoring method based on the isobaric line of the urban water supply network according to claim 1, characterized in that: In step S10, an abnormal alarm is also configured to report the abnormal situation of each pressure monitoring point by detecting it.

3. The intelligent monitoring method based on the isobaric line of the urban water supply network according to claim 1, characterized in that: In step S20, when the density of pressure monitoring points is greater than the set threshold, it is judged to be relatively dense. The latitude and longitude grid size is determined by the distribution of pressure monitoring points and the scale of the pipeline network to establish a grid framework. The grid unit to which the pressure monitoring points belong is then determined. The data in the grid unit is aggregated or representative values ​​are selected. Finally, a new data set containing grid information is generated for resampling, thereby facilitating subsequent grid-based interpolation calculations.

4. The intelligent monitoring method based on the isobaric line of the urban water supply network according to claim 1, characterized in that: In step S20, the weight of each point with a known pressure value is calculated: ； Among them, w i is the weight of the i-th point; d i is the distance between the point and the position to be interpolated; p is an adjustable parameter with a value of 2 or 3; After obtaining the weight of each point with known pressure value, perform weighted average of the function value to obtain the function value z(x,y) at the position to be interpolated: ； Among them, the number of known sample points is N; the coordinates of the i-th sample point are (x i ,y i ); weight is w i ; Function value z i is the function value of the i-th point, and the function value is the water pressure data of the point; the numerator is the sum of the function values ​​of each point multiplied by the corresponding weight, and the denominator is the sum of the weights. The ratio of the numerator and the denominator is the interpolation result.

5. The intelligent monitoring method based on the isobaric line of the urban water supply network according to claim 1, characterized in that: In step S30, the selected interpolation algorithm includes one of linear interpolation, polynomial interpolation, spline interpolation, bilinear interpolation, and bicubic interpolation.

6. The intelligent monitoring method based on the isobaric line of the urban water supply network according to claim 1, characterized in that: In step S40, the generated threshold position set is assembled into data that complies with the GeoJSON specification, input into the WEBGIS engine, and the contour effect is rendered on the browser-side map.

7. The intelligent monitoring method based on the isobaric line of the urban water supply network according to any one of claims 1 to 6, characterized in that: In step S00, the custom planning area complies with the GeoJSON specification.

8. An intelligent monitoring device based on the pressure line of the urban water supply network, characterized in that: include: The system partition module is used to select an administrative area or a custom planning area as a target area and obtain the geographic contour information of the target area, including a set of longitude and latitude coordinate points; The data acquisition module is used to access the smart devices of the water supply network in the target area, configure and collect the monitoring data of the smart devices, including voltage, temperature, and abnormal alarm data. The water pressure data can be configured with reporting time period and frequency to obtain the pressure values ​​of each node in the water supply network in the target area at different time periods and real-time reporting data of abnormal conditions; The isobaric line generation module is used to combine the water pressure data reported by each smart device in the target area with the corresponding longitude and latitude location information, and convert the water pressure data into an isobaric line map through interpolation point tiling, unknown point pressure value prediction, and isobaric line drawing functions; The analysis and prediction module is used to analyze the actual water pressure distribution and pipe network operation in the target area by using the line density and color information in the generated isobaric line map in the target area.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the intelligent monitoring method based on isobars of urban water supply pipe networks as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: A computer program is stored in the readable storage medium, and the computer program includes a program code for controlling a process to execute a process, and the process includes the intelligent supervision method based on the isobaric lines of the urban water supply network according to any one of claims 1 to 7.

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