Water supply network water quality management and control method based on gridding management and storage medium

Abstract

This application provides a method and storage medium for water quality control of a water supply network based on grid management, relating to the field of refined water quality management technology for water supply networks. The method includes: determining the effective monitoring area corresponding to each online monitoring point based on multiple online monitoring points and network data within the water supply area; dividing the water supply area into multiple management grids based on the effective monitoring areas corresponding to each online monitoring point; identifying substandard water quality points and water quality sensitive points within the management grids based on water quality data from manual and online monitoring points; identifying areas within the management grids that require investigation based on user water quality work orders; and controlling the management grids based on the substandard water quality points, water quality sensitive points, and areas requiring investigation. The technical solution of this application enables water quality monitoring data to effectively reflect the water quality of the corresponding management grid, effectively integrates user water quality work order data, accurately identifies weak points in the network water quality, and guides control measures.

Description

Technical Field

[0001] This application relates to the field of refined water quality management technology for water supply networks, and in particular to a method and storage medium for water quality control of water supply networks based on grid management. Background Technology

[0002] Water quality safety in water supply networks is a key aspect of urban public safety and people's livelihood. As a crucial link in the transmission and distribution process, the network directly affects the quality of water at users' taps, making its water quality control increasingly important.

[0003] To achieve refined management of water supply areas, a few water companies have begun to adopt a grid management approach. In existing technologies, grid management is mostly based on administrative boundaries or physical boundaries of residential areas or communities, dividing a large water supply area into multiple management grids to carry out DMA (district metering area) management, facility management, and user services.

[0004] However, existing water quality control methods suffer from two main problems. First, the division of management grids based on administrative or physical boundaries leads to a mismatch between the management grids and the actual effective monitoring coverage of water quality monitoring points, resulting in insufficient representativeness and coverage of these monitoring points. Second, water quality monitoring data may not reflect the water quality of the entire pipeline network within the management grid. Water quality work orders generated from user complaints and inquiries are the most direct reflection of water supply quality, but water supply companies do not conduct systematic and periodic statistical analysis and source tracing after handling these work orders. This makes it difficult to scientifically identify weak areas in the pipeline network from the user's perspective, leading to insufficient reliability in water quality control of the supply area. Summary of the Invention

[0005] In view of this, this application provides a water quality control method and storage medium for water supply networks based on grid management, which enables the monitoring data of water quality monitoring points to effectively reflect the water quality of the corresponding management grid, while effectively integrating user water quality work order data, accurately identifying weak points in the network water quality and guiding control measures.

[0006] The present application is described below from multiple aspects, and the implementation methods and beneficial effects of these aspects can be referred to each other.

[0007] In a first aspect, this application provides a method for water quality control of a water supply network based on grid management, comprising the following steps:

[0008] Based on data from multiple online monitoring points and the pipeline network in the water supply area, the effective monitoring area corresponding to each online monitoring point is determined. The pipeline network data includes the distribution topology of the pipeline network, and at least one of the following: the roughness of the pipeline inner wall and the pipeline flow rate.

[0009] Based on the effective monitoring area corresponding to each online monitoring point, the water supply area is divided into multiple management grids, and each management grid includes one online monitoring point.

[0010] Based on water quality monitoring data from both manual and online monitoring points within the management grid, locations of substandard water quality and water quality sensitive points within the management grid are identified. Water quality sensitive points are those where there is a risk of water quality failing to meet standards.

[0011] Based on user water quality work orders in the management grid, identify the blocks in the management grid where water quality issues need to be investigated.

[0012] Based on locations with substandard water quality, sensitive water quality locations, and areas requiring investigation of water quality issues, the management grid is controlled.

[0013] According to the scheme, on the one hand, the effective monitoring area corresponding to each online monitoring point is determined based on the pipeline data (distribution topology of the pipeline, roughness of the inner wall of the pipeline, and pipeline flow rate) that can reflect the actual situation of the pipeline network. By using the actual hydraulic operation law of the pipeline network, the actual effective monitoring coverage of the online monitoring point can be accurately measured. Then, the water supply area is divided into corresponding management grids based on the effective monitoring area, and a standardized mapping relationship between online monitoring points, effective monitoring areas, and management grids is established.

[0014] This approach breaks away from the limitations of traditional grid division based on administrative or physical boundaries, transforming grid division into a refined management model driven by monitoring capabilities. This allows online monitoring points to effectively monitor all pipe networks within their corresponding management grids, and water quality monitoring data to effectively reflect the water quality of the corresponding management grids. This avoids the problem of the management grid division being disconnected from the actual monitoring capabilities of online monitoring points. Under the premise of a limited number of online monitoring points or constraints, it can achieve efficient and precise control over the water quality of the entire water supply area, improving the reliability of water quality control in the water supply area.

[0015] On the other hand, water quality monitoring data can include data from both online and manual monitoring points, making the data more comprehensive. Furthermore, by combining water quality monitoring data with user water quality work orders for integrated water quality management, and by integrating data from monitoring point risks and user complaints—two different types and sources—the overall water quality safety situation of the management grid can be comprehensively evaluated. This facilitates the identification of specific types of water quality problems within the management grid, corresponding investigation methods, and specific management measures, improving the sensitivity and reliability of water quality problem identification. It can uncover hidden risks that are difficult to detect using monitoring data alone, avoiding the problems of insufficient overall utilization of user-side data and a single water quality evaluation system. Moreover, it fully utilizes water quality monitoring data, going beyond simply issuing warnings of exceeding standards.

[0016] In one possible implementation of the first aspect above, determining the effective monitoring area corresponding to each online monitoring point based on multiple online monitoring points and pipeline network data in the water supply area includes the following steps:

[0017] Based on the distributed topology of the pipeline network, the pipeline lengths between online monitoring points and multiple nodes on the pipeline network are determined.

[0018] For the pipeline network between online monitoring points and nodes, the equivalent hydraulic length between online monitoring points and nodes is determined based on the pipeline length, the roughness of the pipeline inner wall, and the pipeline flow rate.

[0019] Nodes with equivalent hydraulic lengths less than a preset length threshold are selected and a node set is obtained.

[0020] Based on the node set, a spatial interpolation method is used to determine the boundary of the effective monitoring area, so as to determine the effective monitoring area corresponding to the online monitoring point.

[0021] According to the scheme, the equivalent hydraulic length calculated by comprehensively considering key hydraulic factors (pipeline length, pipe inner wall roughness, and real-time flow rate) can more realistically reflect the monitoring capability of online monitoring points to nodes, thereby ensuring that the division of the management grid is more reasonable and in line with the actual situation.

[0022] In one possible implementation of the first aspect above, the water supply area is divided into multiple management grids based on the effective monitoring area corresponding to each online monitoring point, including the following steps:

[0023] For the overlapping parts between the effective monitoring areas of adjacent online monitoring points, the overlapping parts are divided and assigned to the corresponding effective monitoring areas, so as to divide the water supply area into multiple non-overlapping management grids.

[0024] According to this scheme, after initially obtaining the effective monitoring area of ​​each online monitoring point, a secondary allocation process is performed on the overlapping parts of the effective monitoring areas to ensure that the resulting management grids do not overlap. By clearly defining the area of ​​each management grid, it can be ensured that one online monitoring point corresponds to one management grid, making the responsibility of each management grid clearer and more explicit.

[0025] In one possible implementation of the first aspect above, the water supply area is divided into multiple management grids based on the effective monitoring area corresponding to each online monitoring point, including the following steps:

[0026] Identify whether there are monitoring blind spots in the water supply area that are not covered by the management grid.

[0027] If such a layout exists, the layout of the online monitoring points will be optimized, and the optimal layout scheme will be determined.

[0028] The management grid is updated based on the optimal layout scheme to ensure that there are no monitoring blind spots in the water supply area.

[0029] According to the plan, in cases where there are areas in the water supply area that are not covered by the management grid, the layout of online monitoring points can be optimized to avoid monitoring blind spots.

[0030] In one possible implementation of the first aspect above, the layout of online monitoring points is optimized, and the optimal layout scheme is determined, including the following steps:

[0031] Define the constraints for the layout scheme. The constraints can be either a first constraint or a second constraint. The first constraint maintains the existing locations of online monitoring points in the water supply area, allowing only the addition of new online monitoring points, and limits the total number of online monitoring points corresponding to the layout scheme. The second constraint allows the removal of existing online monitoring points, adjustment of their locations, and the addition of new online monitoring points, and also limits the total number of online monitoring points corresponding to the layout scheme.

[0032] Define evaluation objectives for assessing the performance of the layout scheme. These objectives include a first evaluation objective and a second evaluation objective. The first evaluation objective is to maximize the total water coverage corresponding to the online monitoring points, and the second evaluation objective is to minimize the total number of online monitoring points.

[0033] An evaluation model is defined to quantify the evaluation objectives. The evaluation model includes a monitoring coverage relationship model, a coverage water volume calculation model, and an online monitoring point number calculation model. The monitoring coverage relationship model is used to characterize the node coverage relationship under any layout scheme. The coverage water volume calculation model is used to calculate the total coverage water volume under any layout scheme. The online monitoring point number calculation model is used to calculate the total number of online monitoring points under any layout scheme.

[0034] A multi-objective genetic algorithm is used to determine the optimal layout scheme by taking the evaluation objective as the direction, under the premise of satisfying the constraints.

[0035] According to the plan, by establishing mathematical models and adopting intelligent optimization algorithms, a decision support tool that is scientific, flexible and economical is provided for the layout planning of online monitoring points in water supply networks. This maximizes benefits under limited budget constraints and enhances the refined management and safety early warning capabilities of the water supply system.

[0036] In one possible implementation of the first aspect described above, the water quality monitoring data includes one or more data monitored within a preset time period.

[0037] Based on water quality monitoring data from manual and online monitoring points within the management grid, the locations of water quality non-compliance points within the management grid are identified, including the following steps:

[0038] The water quality monitoring data is compared with the preset compliance threshold.

[0039] If a number of data points in the water quality monitoring data exceed or fall below the compliance threshold, then the corresponding monitoring point is determined to be a non-compliant water quality point.

[0040] According to this plan, locations where water quality has clearly violated standards can be identified, ensuring a rapid response.

[0041] In one possible implementation of the first aspect above, determining the water quality sensitive points in the management grid based on water quality monitoring data from manual and online monitoring points within the management grid includes the following steps:

[0042] The water quality monitoring data were sorted to obtain a water quality monitoring data sequence, and the percentile P of the water quality indicators in the water quality monitoring data sequence was determined. K or P （100-K） Among them, the percentile P of water quality indicators K and P （100-K） These represent water quality monitoring data at the Kth percentile and the 100-Kth percentile, respectively, within the water quality monitoring data sequence.

[0043] The percentile P of water quality indicators K Compared with the preset upper limit indicator, if the water quality indicator percentile P K If the water quality exceeds the upper limit, the monitoring point corresponding to the water quality data is designated as a water quality sensitive point. Alternatively,

[0044] The percentile P of water quality indicators （100-K） Compared with the preset lower limit index, if the water quality index percentile P （100-K） If the water quality is below the lower limit, the location corresponding to the water quality monitoring data is determined to be a water quality sensitive location.

[0045] According to the plan, water quality sensitive points that are hovering on the edge of meeting standards or have not yet exceeded the standards but are at high risk can be accurately identified. Through preventive maintenance, timely warnings can be issued to nip problems in the bud.

[0046] In one possible implementation of the first aspect described above, the management grid comprises one or more blocks.

[0047] Based on user water quality work orders in the management grid, determine the water quality issues that need to be investigated within the management grid, including the following steps:

[0048] Identify the water quality problem category corresponding to the user's water quality work order, and filter out the work orders that meet the requirements of the water quality problem category from the user's water quality work orders.

[0049] The location of work orders that meet the water quality problem category requirements is determined to count the number of work orders that meet the water quality problem category requirements in each block.

[0050] The number of work orders in the block is compared with a preset second threshold. If the number of work orders exceeds the second threshold, the block is determined to be a water quality issue requiring investigation. Alternatively,

[0051] The proportion of work orders in a block to the total number of users in the block is compared with a preset threshold. If the proportion exceeds the threshold, the block is determined to be a water quality issue and needs to be investigated.

[0052] According to this scheme, scattered and occasional user complaints can be filtered out, and geographical blocks with more user feedback that may have systemic water quality problems can be identified, thereby accurately locating areas with high incidence of problems.

[0053] In one possible implementation of the first aspect mentioned above, the management grid is controlled based on locations with substandard water quality, water-sensitive locations, and areas requiring investigation of water quality issues, including the following steps:

[0054] Based on locations with substandard water quality, water-sensitive locations, and areas requiring investigation of water quality issues, the investigation type for the management grid is determined.

[0055] Based on the investigation type of the management grid, the corresponding investigation direction and / or the corresponding control level are indicated.

[0056] According to the plan, based on the type of investigation in the management grid, the corresponding investigation direction or control level will be promptly indicated, which will facilitate relevant personnel to handle water quality issues accurately and efficiently.

[0057] In a second aspect, this application provides a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the method disclosed in the first aspect above and any possible implementation thereof.

[0058] It should be understood that the beneficial effects of the second aspect mentioned above can be referred to the beneficial effects described in the first aspect, and will not be repeated here. Attached Figure Description

[0059] Figure 1 This is a schematic flowchart illustrating a water quality control method for a water supply network according to an embodiment of this application.

[0060] Figure 2 This is a schematic diagram illustrating the division of a water supply area into a management grid, as shown in an embodiment of this application.

[0061] Figure 3This is a schematic diagram illustrating the process of determining the effective monitoring area as shown in the embodiments of this application;

[0062] Figure 4 This is a schematic diagram illustrating the process of updating the management grid as shown in an embodiment of this application;

[0063] Figure 5 This is a flowchart illustrating the process of determining the optimal layout scheme as shown in an embodiment of this application;

[0064] Figure 6 This is a flowchart illustrating the process of determining locations where water quality does not meet standards and locations where water quality is sensitive, as shown in the embodiments of this application.

[0065] Figure 7 This is a flowchart illustrating the process of determining the area to be investigated for water quality problems, as shown in the embodiments of this application.

[0066] Figure 8 This is a schematic diagram illustrating the process of controlling the management grid as shown in an embodiment of this application;

[0067] Figure 9 A block diagram of an electronic device provided in an embodiment of this application;

[0068] Figure 10 This is a block diagram of a SoC (system on chip) provided in an embodiment of this application. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] First, the technical problems to be solved by the embodiments of this application will be explained.

[0071] As mentioned above, in some embodiments, the water supply area is typically divided into grids based on administrative boundaries or physical land boundaries. However, this method suffers from a disconnect between the grid division and the monitoring capabilities of the monitoring points. This results in the monitoring data from the water quality monitoring points failing to accurately reflect the water quality conditions of distant pipe networks, thus reducing the reliability of water quality control in the water supply area. Furthermore, in these embodiments, the water quality monitoring data from online monitoring points is only used for early warning of exceeding standards, failing to fully realize its numerical value.

[0072] In view of this, this application provides a water quality control method for water supply networks based on grid management. When dividing the network into grids, the effective monitoring range corresponding to each online monitoring point is determined based on network data that reflects the actual situation of the network (network topology, roughness of the inner wall of the pipe, and pipe flow rate). The water supply area is divided into corresponding management grids according to the effective monitoring range. After the management grids are divided, the water quality of each management grid can be finely controlled based on its corresponding water quality monitoring data and user water quality work orders.

[0073] This method achieves two key benefits: First, it enables online monitoring points to effectively monitor all pipe networks within their corresponding management grids. Water quality monitoring data effectively reflects the water quality of the corresponding management grid, achieving efficient and precise control over the water quality of the entire water supply area and improving the reliability of water quality management. Second, it combines water quality monitoring data with user water quality work orders to comprehensively manage the water quality of the management grids, further enhancing the reliability of water quality management in the water supply area. Moreover, it fully utilizes water quality monitoring data, going beyond simply issuing warnings of exceeding standards.

[0074] The following describes in detail the water quality control method for water supply networks based on grid management, with reference to specific embodiments and accompanying drawings.

[0075] It should be noted that the water quality control method for the water supply network can be executed by an electronic device or a processor in an electronic device. For example, the electronic device can be a server or a terminal. This application embodiment does not limit this. The following uses an electronic device as the execution subject to illustrate the method.

[0076] Reference Figure 1 , Figure 1 This is a schematic flowchart illustrating a water quality control method for a water supply network according to an embodiment of this application. Figure 1 As shown, the water quality control method for water supply networks based on grid management includes the following steps S110-S150.

[0077] S110: Based on data from multiple online monitoring points and pipeline networks in the water supply area, determine the effective monitoring area corresponding to each online monitoring point.

[0078] It is understandable that a pipeline network is composed of multiple pipe segments, and pipeline network data can include the distribution topology of the pipeline network, as well as at least one of the roughness of the inner wall of the pipe and the flow rate of the pipe.

[0079] S120: Based on the effective monitoring area corresponding to each online monitoring point, the water supply area is divided into multiple management grids, and each management grid includes one online monitoring point.

[0080] S130: Based on water quality monitoring data from manual and online monitoring points in the management grid, identify the locations of substandard water quality and water quality sensitive points in the management grid.

[0081] Understandably, locations where water quality does not meet standards indicate locations where problems have already occurred, while locations where water quality is sensitive indicate locations where there is a risk of water quality failing to meet standards, meaning that the water quality is hovering on the edge of exceeding standards and there is a risk of problems.

[0082] For ease of understanding, please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the division of a water supply area into a management grid, as shown in an embodiment of this application. Figure 2 As shown, the pipeline network in the water supply area consists of multiple water supply pipe sections. For this water supply area, the water supply area can be divided into multiple management grids according to steps S110-S120, and each management grid includes an online monitoring point.

[0083] Online monitoring points refer to locations where water quality can be automatically monitored using automated instruments such as online sensors, typically without the need for manual water quality testing. Manual monitoring points refer to locations where water quality is monitored manually through sampling, laboratory testing, or other methods. The location of manual monitoring points can be determined based on historical water quality data, pipeline topology, distribution of key users (such as schools and hospitals), and experience. A management grid may have one or more manual monitoring points, or none at all; this application's embodiments do not impose such limitations.

[0084] S140: Based on user water quality work orders in the management grid, determine the water quality problem blocks in the management grid that need to be investigated.

[0085] Understandably, a user water quality work order refers to a work order on the user's side related to water quality issues, such as user water quality complaints and inquiries.

[0086] The "Water Quality Issues Require Investigation" block indicates the area where water quality problems exist. This block can be a residential area or other area identified based on user water quality work orders.

[0087] It is understood that the embodiments of this application do not limit the order of steps S130 and S140. S130 can be executed first and then S140, or S140 can be executed first and then S130.

[0088] S150: Based on locations where water quality does not meet standards, water quality sensitive locations, and areas where water quality issues need to be investigated, the management grid is controlled.

[0089] Based on steps S110-S150, on the one hand, the effective monitoring area corresponding to each online monitoring point is determined based on the pipeline data (distribution topology of the pipeline, roughness of the inner wall of the pipeline, and pipeline flow rate) that can reflect the actual situation of the pipeline network. By using the actual hydraulic operation law of the pipeline network, the actual effective monitoring coverage of the online monitoring point can be accurately measured. Then, the water supply area is divided into corresponding management grids according to the effective monitoring area, and a standardized mapping relationship between online monitoring points, effective monitoring areas, and management grids is established.

[0090] This approach breaks away from the limitations of traditional grid division based on administrative or physical boundaries, transforming grid division into a refined management model driven by monitoring capabilities. This allows online monitoring points to effectively monitor all pipe networks within their corresponding management grids, and water quality monitoring data to effectively reflect the water quality of the corresponding management grids. This avoids the problem of the management grid division being disconnected from the actual monitoring capabilities of online monitoring points. Under the premise of a limited number of online monitoring points or constraints, it can achieve efficient and precise control over the water quality of the entire water supply area, improving the reliability of water quality control in the water supply area.

[0091] On the other hand, water quality monitoring data can include data from both online and manual monitoring points, making the data more comprehensive. Furthermore, by combining water quality monitoring data with user water quality work orders for integrated water quality management, and by integrating data from monitoring point risks and user complaints—two different types and sources—the overall water quality safety situation of the management grid can be comprehensively evaluated. This facilitates the identification of specific types of water quality problems within the management grid, corresponding investigation methods, and specific management measures, improving the sensitivity and reliability of water quality problem identification. It can uncover hidden risks that are difficult to detect using monitoring data alone, avoiding the problems of insufficient overall utilization of user-side data and a single water quality evaluation system. Moreover, it fully utilizes water quality monitoring data, going beyond simply issuing warnings of exceeding standards.

[0092] The following is combined Figures 3-5 The process of dividing the water supply area into multiple management grids based on the effective monitoring area corresponding to each online monitoring point, as mentioned in step S120, will be explained.

[0093] Reference Figure 3 , Figure 3 This is a schematic diagram illustrating the process of determining the effective monitoring area as shown in an embodiment of this application. Figure 3 As shown, in one embodiment, the process of determining the effective monitoring area corresponding to each online monitoring point in step S110 mentioned above, based on multiple online monitoring points and pipeline data in the water supply area, may include the following steps S310-S340.

[0094] S310: Based on the distributed topology of the pipeline network, determine the pipeline length between the online monitoring point and multiple nodes on the pipeline network.

[0095] It is understandable that the distribution topology of a pipeline network represents the distribution of the pipeline network. Since a pipeline network is composed of multiple pipe segments, a node refers to the connection point between two pipe segments.

[0096] S320: For the pipeline network between the online monitoring point and the node, determine the equivalent hydraulic length between the online monitoring point and the node based on the pipeline length, the roughness of the inner wall of the pipeline, and the pipeline flow rate.

[0097] It is understandable that this equivalent hydraulic length characterizes the effective distance between the online monitoring point and the node, taking into account at least one of the following factors: pipe length, pipe inner wall roughness, and pipe flow rate.

[0098] Specifically, the distribution topology of the pipeline network can be abstracted into a topology graph G=(V,E), where V represents a set of nodes consisting of multiple nodes, and E represents a pipe segment in the pipeline network.

[0099] Based on data regarding the pipeline length, inner wall roughness, and flow rate, the equivalent hydraulic length L between the online monitoring point and the node can be calculated using the following formula. p .

[0100]

[0101] In the formula, L represents the pipe length, which can be obtained from a pipeline GIS system (geographic information system), as-built data, or actual measurements. C is the pipe roughness coefficient (Hayzen-Williams coefficient), used to indicate the roughness of the pipe's inner wall; this value is a general range and an empirical coefficient. q p The flow rate is the pipeline flow rate. This value can be obtained based on the hydraulic model of the pipeline network. It can be understood that the hydraulic model of the pipeline network is a model that describes the operating state of the pipeline network based on the basic principles of fluid mechanics. Therefore, the pipeline flow rate can be obtained based on the hydraulic model of the pipeline network. The specific construction of the model will not be elaborated here.

[0102] Based on data regarding the pipeline length and the roughness of the pipeline wall, the equivalent hydraulic length L between the online monitoring point and the node can be determined using the following formula. p .

[0103]

[0104] Based on data regarding the pipeline length and flow rate of the pipeline network, and after determining the equivalent hydraulic length between the online monitoring point and the node, the equivalent hydraulic length L can be calculated using the following formula. p .

[0105]

[0106] In addition, after obtaining the equivalent hydraulic length L p Then, the equivalent hydraulic length L can be used. p Replacing the original pipe length allows the pipe network topology to be updated. .

[0107] S330: Filter out nodes whose equivalent hydraulic length is less than the length threshold and obtain the node set.

[0108] For example, the length threshold is preset based on the fact that the water quality monitoring data of the online monitoring point can reflect the water quality of that node, and this value can be an empirical value.

[0109] S340: Based on this set of nodes, the boundary of the effective monitoring area is determined by spatial interpolation, and then the effective monitoring area corresponding to the online monitoring point can be determined.

[0110] Understandably, spatial interpolation, a GIS technology, can be used to determine continuous, well-defined, polygonal effective monitoring areas.

[0111] Furthermore, after determining the effective monitoring area corresponding to each online monitoring point, this area can be spatially overlaid with the base map of the actual water supply area to delineate the effective monitoring area of ​​each online monitoring point and mark it as a management grid. This allows the establishment of a one-to-one mapping table between the IDs of online monitoring points and the IDs of management grids, achieving a fixed binding between water quality monitoring data and management units. When water quality monitoring data is abnormal, the corresponding management grid can be quickly retrieved and located directly through the mapping table, achieving accurate matching between water quality monitoring data and actual management grids. This avoids the problem of data anomalies not being attributed to a management grid and the monitoring system being disconnected from the management grid.

[0112] Therefore, based on steps S310-S340, the equivalent hydraulic length calculated by comprehensively considering key hydraulic factors (pipe length, pipe inner wall roughness, and real-time flow rate) can more realistically reflect the monitoring capability of online monitoring points to nodes, thereby ensuring that the division of the management grid is more reasonable and in line with the actual situation.

[0113] Continue to refer to Figure 3In one embodiment, the process of dividing the water supply area into multiple management grids based on the effective monitoring area corresponding to each online monitoring point in step S120 mentioned above may also include the following step S350.

[0114] S350: For the overlapping parts between the effective monitoring areas corresponding to adjacent online monitoring points, the overlapping parts are divided and assigned to the corresponding effective monitoring areas, so as to divide the water supply area into multiple non-overlapping management grids.

[0115] It is understandable that the overlapping part can be the overlapping part between the effective monitoring areas corresponding to two adjacent online monitoring points, or it can be the overlapping part between the effective monitoring areas corresponding to three or more adjacent online monitoring points.

[0116] For example, when dividing and re-dividing overlapping areas, one approach is to divide the overlapping area equally and then assign it to the corresponding effective monitoring area based on proximity. Another approach is to combine existing administrative or physical boundaries (such as the boundaries of streets, communities, or residential areas) to divide the overlapping area and assign it to the corresponding effective monitoring area based on proximity. A third approach is to refer to existing grid management systems to divide and re-divide the overlapping area based on proximity. A fourth approach is to refer to pre-defined independently measurable areas (such as the zones corresponding to the management areas of each water supply business or pipeline branch company, zones within the business management area, and independent metering areas (DMAs) in a zoning metering system). This embodiment does not limit this approach.

[0117] Based on step S350, after initially obtaining the effective monitoring area of ​​each online monitoring point, a secondary allocation process is performed on the overlapping parts of the effective monitoring areas to ensure that the resulting management grids do not overlap. By clearly defining the area of ​​each management grid, it can be ensured that one online monitoring point corresponds to one management grid, making the responsibility of each management grid clearer and more explicit.

[0118] In one embodiment, after dividing the water supply area into multiple management grids in step S120, if there are areas in the water supply area not covered by the management grids, it indicates that the initial layout of online monitoring points is insufficient or the location is unreasonable. In this case, the layout of the online monitoring points can be optimized by adding online monitoring points or adjusting the existing online monitoring points. Based on the optimized and rearranged online monitoring points, the process corresponding to step S110 is re-executed so that the multiple management grids after division can completely cover the water supply area, avoiding monitoring blind spots. The following is in conjunction with... Figure 4 and Figure 5This process will be explained.

[0119] Figure 4 This is a schematic diagram illustrating the process of updating the management grid as shown in an embodiment of this application. Figure 4 As shown, the process of dividing the water supply area into multiple management grids based on the effective monitoring area corresponding to each of the online monitoring points, as mentioned in step S120 above, may also include steps S410-S430.

[0120] S410: Identify whether there are monitoring blind spots in the water supply area that are not covered by the management grid.

[0121] S420: If it exists, optimize the layout of the online monitoring point and determine the optimal layout scheme.

[0122] S430: Update the management grid based on the optimal layout scheme to ensure that there are no monitoring blind spots in the water supply area.

[0123] Based on steps S410-S430, in cases where there are areas in the water supply area that are not covered by the management grid, the layout of online monitoring points can be optimized to avoid monitoring blind spots.

[0124] Figure 5 This is a schematic diagram illustrating the process of determining the optimal layout scheme as shown in an embodiment of this application. Figure 5 As shown, the process of optimizing the layout of the online monitoring point and determining the optimal layout scheme mentioned in step S420 above may include steps S510-S540.

[0125] S510: Define the constraints of the layout scheme.

[0126] The constraints are either a first constraint or a second constraint. The first constraint maintains the original locations of online monitoring points in the water supply area, allowing only the addition of new online monitoring points, and limiting the total number of online monitoring points corresponding to the layout scheme. The second constraint allows the removal of existing online monitoring points, adjustment of their locations, and the addition of new online monitoring points, and limits the total number of online monitoring points corresponding to the layout scheme.

[0127] It is understandable that the original online monitoring points are the online monitoring points before optimization, and the total number of online monitoring points corresponding to the layout plan is also the total number of online monitoring points in the optimized pipeline network.

[0128] S520: Defines the evaluation objectives used to assess the performance of layout schemes.

[0129] The evaluation objectives include a first evaluation objective and a second evaluation objective. The first evaluation objective is to maximize the total water coverage corresponding to the online monitoring points, and the second evaluation objective is to minimize the total number of online monitoring points.

[0130] S530: Define an evaluation model for quantifying evaluation objectives.

[0131] The evaluation model includes a monitoring coverage relationship model, a coverage water volume calculation model, and an online monitoring point number calculation model. Specifically, the monitoring coverage relationship model is used to characterize the node coverage relationship under any layout scheme. The coverage water volume calculation model is used to calculate the total coverage water volume under any layout scheme. The online monitoring point number calculation model is used to calculate the total number of online monitoring points under any layout scheme.

[0132] S540: Employs a multi-objective genetic algorithm to determine the optimal layout scheme by using an evaluation model, while satisfying the constraints and taking the evaluation objective as the direction.

[0133] For example, this multi-objective genetic algorithm can employ the NSGA-II algorithm (non-dominated sorting genetic algorithm).

[0134] Based on steps S510-S540, by establishing a mathematical model and adopting an intelligent optimization algorithm, a decision support tool that combines scientificity, flexibility, and economy is provided for the layout planning of online monitoring points in water supply networks. This maximizes benefits under limited budget constraints and enhances the refined management and safety early warning capabilities of the water supply system.

[0135] The following section explains the monitoring coverage relationship model, coverage water volume calculation model, and online monitoring point number calculation model in the evaluation model.

[0136] First, let me explain the process of constructing the monitoring coverage relationship model.

[0137] M is defined using piecewise functions ij , is used to represent the monitoring relationship of nodes, and its meaning is node V j Is it at node V? i Within the effective monitoring area, that is, assuming node V i For online monitoring points, if node V j Can be used by node V i If detected, then node V j At node V i Within the effective monitoring area, M ij It can be represented as:

[0138]

[0139] In the formula, L p Represents node V j and node Vi The equivalent hydraulic length between them, where c represents the length threshold mentioned above.

[0140] Therefore, node V i Effective monitoring area m i It can be represented as:

[0141]

[0142] In the formula, n is the total number of nodes on the water supply network in the water supply area.

[0143] The reachability matrix M of all nodes can be represented as:

[0144]

[0145] Construct a Boolean indicator variable X i , used to represent node V i Whether it is deployed as an online monitoring point is determined by the following formula:

[0146]

[0147] Therefore, based on the monitoring reachability matrix M of all nodes and the Boolean indicator variable X i This allows for the construction of a monitoring coverage relationship model. The total effective monitoring reach matrix for the water supply area is then established. It can be represented as:

[0148]

[0149] Through M ij ×X i It can be determined whether any node in the entire water supply network is covered by the effective monitoring area corresponding to one or more online monitoring points, through... A nonlinear mapping relationship from static physical topology to dynamic monitoring layout has been achieved.

[0150] The following describes the construction process of the water coverage calculation model.

[0151] Coverage water volume refers to the node water volume covered by the effective monitoring area corresponding to an online monitoring point. As mentioned earlier, in the monitoring coverage relationship model, some nodes may be simultaneously covered by the effective monitoring areas corresponding to multiple online monitoring points. Therefore, when calculating the total coverage water volume corresponding to all online monitoring points in the entire water supply network, it is not advisable to simply sum the node water volumes of all nodes covered by the effective monitoring area corresponding to each online monitoring point. Otherwise, duplicate calculations of coverage water volume may occur, leading to redundant evaluation bias.

[0152] To address the above issues, a weight correction function can be constructed. This function determines a node V by... j Frequency of coverage This involves determining the weight of the node's water volume in the coverage water volume calculation, thereby avoiding redundant evaluation bias. The weight correction function is one such function. It can be represented as:

[0153]

[0154] It is understandable that the hydraulic model of a pipeline network is a model that describes the operating state of the pipeline network based on the basic principles of fluid mechanics. Therefore, based on this hydraulic model, the nodal water volume of each node in the pipeline network can be obtained, thereby constructing a nodal water volume vector of the pipeline network. .

[0155] Through weight correction function Correcting the water volume at the network nodes yields the corrected water volume vectors at the network nodes. .

[0156] Matrix of total effective monitoring reach based on water supply area and the corrected water volume vector of the pipeline node This allows the construction of a global monitoring water volume vector Q, leading to a model for calculating the coverage water volume. .

[0157] The following describes the construction process of the online monitoring point number calculation model.

[0158] If the constraint mentioned above is the first constraint, meaning the original locations of online monitoring points in the water supply area remain unchanged, only new online monitoring points are allowed, and the total number of online monitoring points corresponding to the layout scheme is limited, then under this constraint, existing online monitoring points cannot be removed or relocated. Therefore, the objective function can be defined as:

[0159]

[0160] In the formula, D is the maximum preset total number of online monitoring points, and d is the original total number of online monitoring points.

[0161] If the constraint mentioned above is the second constraint, that is, allowing the removal of existing online monitoring points, adjustment of the positions of existing online monitoring points, and addition of new online monitoring points, and limiting the total number of online monitoring points corresponding to the layout scheme, then under this constraint, it means that the removal or adjustment of existing online monitoring points is allowed. Therefore, the objective function can be defined as:

[0162]

[0163] In the formula, The total number of existing online monitoring points after optimization, that is, the total number of existing online monitoring points after removal or relocation.

[0164] Therefore, the online monitoring point number calculation model can be obtained based on the objective function. By inputting the objective function into the NSGA-II algorithm and using the NSGA-II algorithm for optimization, the optimal layout scheme can be found.

[0165] The following is combined Figure 6 The process of determining the locations of substandard water quality and water quality sensitive points in the management grid based on the water quality monitoring data of manual and online monitoring points in the management grid, as mentioned in step S130, is explained.

[0166] Reference Figure 6 , Figure 6 This is a schematic diagram illustrating the process of determining locations where water quality does not meet standards and locations that are sensitive to water quality, as shown in the embodiments of this application. Figure 6 (a) in this application is a schematic diagram of the process for determining locations where water quality does not meet standards, as shown in an embodiment of this application. Figure 6 (b) in this application is a schematic diagram of the process for determining water quality sensitive points as shown in the embodiment of this application.

[0167] In one embodiment, water quality monitoring data may include one or more data monitored within a preset time period. For example, the preset time period may be the past six months, and the water quality monitoring data may include data monitored over the past six months. Based on this, as... Figure 6 As shown in (a), determining the locations of substandard water quality points in the management grid based on water quality monitoring data may include steps S610-S620.

[0168] S610: Compare water quality monitoring data with preset compliance thresholds.

[0169] S620: If there is a first number or more data points in the water quality monitoring data that exceed or fall below the compliance threshold, then the location corresponding to the water quality monitoring data is determined to be a location where the water quality does not meet the standard.

[0170] It is understandable that the threshold for compliance can be set according to the water quality standards (the standards can be referenced from the relevant national standards).

[0171] For example, if the standard requirement for turbidity is below 1 NTU, then the compliance threshold can be set to 1 NTU. When the standard requirement for water quality is below a certain compliance threshold, if a first or more data points in the water quality monitoring data exceed that threshold, it can be determined that the turbidity corresponding to the first or more data points in the water quality monitoring data exceeds the standard requirement of 1 NTU. Therefore, the location corresponding to the water quality monitoring data is a location where the water quality does not meet the standard.

[0172] The first quantity can be set according to the level of stringency. In situations with strict water quality requirements, the first quantity can be set to 0, requiring a 100% compliance rate. That is, if any data exceeds the compliance threshold even once, a problem is identified. Of course, considering the possibility of false positives, the first quantity can also be set to 1, 2, 3, etc., and this embodiment does not limit this.

[0173] Based on the same principle, if the standard requirement for water quality is that it needs to be higher than a certain threshold, and if there is a first number or more data points in the water quality monitoring data that are lower than that threshold, then the corresponding points in the water quality monitoring data can be identified as points where the water quality does not meet the standard. This will not be elaborated further here.

[0174] Based on steps S610-S620, locations where water quality has clearly violated standards can be identified, ensuring a rapid response.

[0175] In one embodiment, reference is made to Figure 6 (b) in the process involves determining water quality sensitive points in the management grid based on water quality monitoring data from manual monitoring points and online monitoring points in the management grid, which may include steps S630-S670.

[0176] S630: Sort the water quality monitoring data to obtain a water quality monitoring data sequence, and determine the percentile P of the water quality indicators in the water quality monitoring data sequence. K or P （100-K） .

[0177] Among them, the percentile P of water quality indicators K and P （100-K） These represent the water quality monitoring data at the Kth percentile and the 100-Kth percentile, respectively, and the percentile P of the water quality index. K and P （100-K） It is a location indicator and boundary value.

[0178] Understandably, the value of K can be set according to the stringency of the water quality requirements. If the water quality requirements are relatively strict, K can be set to a value of 95, 98, 99, etc. If the water quality requirements are relatively lenient, K can be set to a value of 80, 85, etc.

[0179] For example, if water quality monitoring data are sorted in ascending order to obtain a water quality monitoring data sequence, and K=95 is set, then the percentile P of the water quality index is... K This indicates that the water quality monitoring data is at the 95th percentile in the water quality monitoring data series. Based on the percentile P of this water quality indicator... K The water quality monitoring data sequence can be divided into two parts, meaning that K% of the data is less than or equal to P. KThere are (100-K)% of the data that are greater than P. K .

[0180] It should be noted that when the amount of data in the water quality monitoring data sequence is n, if K is not a multiple of 1 / (n-1), then linear interpolation can be used to determine P. K The value of .

[0181] According to step S630, determine the percentile P of the water quality index. K or P （100-K） Subsequently, if the water quality standard requires the water to be below a certain threshold, the percentile P of the water quality index can be used as a reference. K Perform steps S640-S650. When the water quality standard requires it to exceed a certain threshold, the percentile P of the water quality index can be used as a reference. （100-K） Perform steps S660-S670.

[0182] S640: Percentile values ​​of water quality indicators (P) K Compare with the preset upper limit indicator.

[0183] S650: If the percentile of water quality index P K If the water quality exceeds the upper limit, the location corresponding to the water quality monitoring data is determined to be a water quality sensitive location.

[0184] Understandably, this upper limit indicator is a value that is below the threshold for meeting the standard.

[0185] For example, if the standard requirement for turbidity is below 1 NTU, and the threshold for compliance is 1 NTU, then the upper limit can be 0.3 NTU. If the percentile of the water quality indicator P... 95 If the turbidity exceeds the upper limit, it means that a certain amount of data corresponds to a turbidity between 0.3 NTU and 1 NTU, and the location corresponding to the water quality monitoring data can be identified as a water quality sensitive location.

[0186] S660: The percentile P of water quality indicators （100-K） Compare with the preset lower limit indicator.

[0187] S670: If the percentile of water quality index P （100-K） If the water quality is below the lower limit, the location corresponding to the water quality monitoring data is determined to be a water quality sensitive location.

[0188] Understandably, the lower limit indicator is a value that is higher than the threshold for meeting the standard.

[0189] For example, if the standard requirement for total chlorine or free chlorine is higher than 0.05 mg / L, and the compliance threshold is 0.05 mg / L, then the lower limit can be 0.1 NTU. If the percentile P5 of the water quality indicator is lower than the lower limit, it means that a certain amount of data corresponds to total chlorine or free chlorine between 0.05 mg / L and 0.1 mg / L, and the location corresponding to the water quality monitoring data can be identified as a water quality sensitive location.

[0190] Based on steps S630-S670, water quality sensitive points that are hovering on the edge of compliance or have not yet exceeded the standards but are at a high risk can be accurately identified. Through preventive maintenance, timely warnings can be issued to nip problems in the bud.

[0191] The following is combined Figure 7 The process of determining the water quality problem areas to be investigated in the management grid based on user water quality work orders in the management grid, as mentioned in step S140, is explained.

[0192] Reference Figure 7 , Figure 7 This is a flowchart illustrating the process of determining the area to be investigated for water quality problems, as shown in the embodiments of this application.

[0193] In one embodiment, the management grid may include one or more blocks. Based on this, as... Figure 7 As shown, based on user water quality work orders in the management grid, determining the water quality problem blocks that need to be investigated in the management grid may include steps S710-S760.

[0194] S710: Identify the water quality problem category corresponding to the user's water quality work order, and filter out the work orders that meet the requirements of the water quality problem category from the user's water quality work orders.

[0195] Understandably, after obtaining user water quality work orders, NLP (Natural Language Processing) models can be used to identify and extract water quality-related information from the work orders, determine their corresponding water quality problem categories, and filter out work orders that meet the requirements of the water quality problem category.

[0196] For example, water quality problem categories can include issues such as yellow water, cloudy water, odor, and red worms. They can also include problems caused by malfunctions in users' internal water facilities, misunderstandings by users regarding scale or white floating matter in the water, and user feedback about the smell of bleach or disinfectant in the water. Work orders for water quality problems can be categorized as yellow water, cloudy water, odor, red worms, etc., and these are work orders that require attention.

[0197] S720: Locate the work orders that meet the requirements of the water quality problem category to count the number of work orders that meet the requirements of the water quality problem category in each block.

[0198] Based on the number of work orders in each block that meet the requirements of the water quality problem category, steps S730-S740 or steps S750-S760 can be executed.

[0199] S730: Compare the number of work orders in the block with the preset second quantity threshold.

[0200] S740: If the number of work orders exceeds the second quantity threshold, the block is determined to be a water quality issue block that needs to be investigated.

[0201] S750: Compare the ratio of the number of work orders in the block to the total number of users in the block with a preset ratio threshold.

[0202] S760: If the ratio exceeds the ratio threshold, the block is determined to be a water quality issue and needs to be investigated.

[0203] Based on steps S710-S760, scattered and occasional user complaints can be filtered out, and geographical blocks with a large number of user feedbacks that may have systemic water quality problems can be identified, thereby accurately locating high-incidence areas of problems.

[0204] The following is combined Figure 8 This section explains the process of controlling the management grid based on the locations of substandard water quality, sensitive water quality locations, and areas requiring investigation of water quality issues, as mentioned in step S150.

[0205] Reference Figure 8 , Figure 8 This is a schematic diagram illustrating the process of controlling the management grid as shown in an embodiment of this application.

[0206] like Figure 8 As shown, the process of controlling the management grid based on water quality non-compliance points, water quality sensitive points, and water quality problem areas can include steps S810-S820.

[0207] S810: Based on locations with substandard water quality, water-sensitive locations, and areas requiring investigation of water quality issues, determine the investigation type for the management grid.

[0208] The investigation types can include comprehensive investigation and block investigation. Comprehensive investigation means that the entire management grid needs to be investigated, while block investigation means that the blocks to be investigated need to be investigated.

[0209] S820: Based on the investigation type of the management grid, it indicates the corresponding investigation direction and / or determines the corresponding control level.

[0210] Based on steps S810-S820, according to the investigation type of the management grid, the corresponding investigation direction or control level is promptly indicated, which facilitates relevant personnel to handle water quality issues accurately and efficiently.

[0211] The following explains how to determine the investigation type of the management grid as described in step S810.

[0212] In one embodiment, the type of investigation for the management grid can be determined in the following way:

[0213] If there are locations in the management grid that do not meet water quality standards or are sensitive to water quality, and there are also areas with water quality problems that need to be investigated, then the management grid is determined to be a comprehensive water quality problem investigation grid, corresponding to the comprehensive investigation type.

[0214] If a management grid contains locations with substandard water quality or sensitive water quality, but does not have any areas requiring investigation due to water quality issues, then the management grid is designated as a special investigation grid for water quality issues, corresponding to a comprehensive investigation type.

[0215] If there are no substandard water quality points or water quality sensitive points in the management grid, but there are water quality issues that need to be investigated, then the management grid is designated as a special investigation grid for block issues, and its corresponding block investigation type is determined.

[0216] If there are no locations with substandard water quality or sensitive water quality in the management grid, and there are no areas with water quality issues that need to be investigated, then the management grid is determined to be a grid without water quality issues.

[0217] In another embodiment, the investigation type of the management grid can be determined by constructing an evaluation index system using methods such as the analytic hierarchy process (AHP) and expert scoring, and then combining it with block characteristics for comprehensive judgment, thereby more accurately determining the investigation type of the management grid.

[0218] The following explains how to prompt the corresponding investigation direction as mentioned in step S820.

[0219] For situations where the management grid is a comprehensive water quality problem investigation grid or a special investigation grid for block-specific problems, i.e., a comprehensive investigation type, the investigation needs to be carried out on the entire management grid in order to more accurately analyze the causes of water quality problems.

[0220] For example, relevant personnel can be advised to refer to the following investigation directions:

[0221] Firstly, it can identify whether there are areas with weak hydraulic conditions within the management grid (such as those located at hydraulic interfaces or at the end of the pipeline network).

[0222] Secondly, the existence of old or outdated pipes can be identified based on the management grid and the pipe attributes (such as pipe age, pipe material, and pipe diameter) of the pipes upstream.

[0223] Third, the time corresponding to the work order in the water quality problem investigation area can be matched and analyzed with the time of emergency repairs or valve opening and closing records in the surrounding area to identify whether there are problems with improper emergency repairs or maintenance management.

[0224] Fourth, by combining water quality monitoring data from online monitoring points located upstream of the management grid with water quality monitoring data from manual monitoring points, we can conduct water quality change analysis along the route and multi-indicator collaborative analysis to identify whether there are problems with unreasonable disinfectant dosing.

[0225] For management grids that are designated as block-based problem investigation grids, i.e., block-based investigation types, the investigation only needs to focus on the blocks where water quality issues need to be investigated, which allows for a faster analysis of the causes of water quality problems.

[0226] For example, in addition to the four directions mentioned above, the suggested investigation directions can also include investigating the secondary water supply facilities in the area where the water quality problem needs to be investigated. For instance, water quality monitoring data from online monitoring points at the secondary water supply facilities, water quality monitoring data from manual monitoring points, and operation and maintenance records can be obtained to identify whether there are problems with the water quality and operation and maintenance of the secondary water supply facilities. If necessary, water quality sampling and retesting can also be conducted in the area where the water quality problem needs to be investigated.

[0227] The following explains how to determine the corresponding control level as mentioned in step S820.

[0228] In one embodiment, the control level can be indicated based on the type of investigation within the management grid. For example, a comprehensive investigation corresponds to a level one control level, while a block-based investigation corresponds to a level two control level.

[0229] In another embodiment, a more granular control level can be determined based on the type of investigation within the management grid, combined with other information. Referring to Table 1 below, one method for indicating the control level is illustrated in an embodiment of this application.

[0230]

[0231] In addition, based on the control levels shown in Table 1, the corresponding control measures can be implemented with reference to Table 2.

[0232]

[0233] This application provides a control system, which includes a monitoring area determination module, a management grid division module, a problem location determination module, a problem block determination module, and a control module.

[0234] The monitoring area determination module is used to determine the effective monitoring area corresponding to each online monitoring point based on multiple online monitoring points and pipeline network data in the water supply area. The pipeline network data includes the pipeline network topology, and at least one of the following: the roughness of the pipeline inner wall and the pipeline flow rate.

[0235] The management grid division module is used to divide the water supply area into multiple management grids based on the effective monitoring area corresponding to each online monitoring point, and each management grid includes one online monitoring point.

[0236] The problem location identification module is used to identify water quality non-compliant and water quality sensitive locations within the management grid based on water quality monitoring data from manual and online monitoring points. Water quality sensitive locations are those where there is a risk of water quality non-compliance.

[0237] The Problem Block Identification Module is used to identify water quality problem blocks in the management grid that need to be investigated based on user water quality work orders in the management grid.

[0238] The control module is used to manage the management grid based on locations where water quality does not meet standards, water quality sensitive locations, and areas where water quality issues need to be investigated.

[0239] It is understood that each module in this control system executes the methods described in the above embodiments, and the specific functions and corresponding technical effects can be found in the above embodiments. Figures 1-8 The methods explained will not be elaborated here.

[0240] This application also provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction or at least one program segment. The processor loads and executes the instruction or program segment to implement the grid-based water supply network water quality control method described in the above embodiments. Its specific functions and corresponding technical effects can be found in the above embodiments. Figures 1-8 The methods explained will not be elaborated here.

[0241] Now for reference Figure 9 The diagram shown is a block diagram of an electronic device 1200 according to an embodiment of this application. The electronic device 1200 may include one or more processors (corresponding to...) coupled to a controller hub 1203. Figure 9The first processor 1201 is described above. In at least one embodiment, the controller hub 1203 communicates with the first processor 1201 via a multi-branch bus such as a front-side bus (FSB), a point-to-point interface such as a quick path interconnect (QPI), or a similar connection. The first processor 1201 executes instructions that control general types of data processing operations. In one embodiment, the controller hub 1203 includes, but is not limited to, a graphics memory controller hub (GMCH) (not shown) and an input / output hub (IOH) (which may be on a separate chip) (not shown), wherein the GMCH includes memory and a graphics controller and is coupled to the IOH.

[0242] Electronic device 1200 may also include a coprocessor coupled to controller hub 1203 (corresponding to...) Figure 9 The processor 1202 and memory 1204 are integrated within the processor (as described in this application). Alternatively, one or both of the memory and GMCH can be integrated within the processor (as described in this application), with memory 1204 and the first coprocessor 1202 directly coupled to the first processor 1201 and the controller hub 1203, which is located on a single chip with the IOH. Memory 1204 can be, for example, dynamic random access memory (DRAM), phase change memory (PCM), or a combination of both. In one embodiment, the first coprocessor 1202 is a dedicated processor, such as a high-throughput MIC processor (many integerized core, MIC), a network or communication processor, a compression engine, a graphics processor, a general-purpose computing on GPU (GPGPU), or an embedded processor, etc. Optional properties of the first coprocessor 1202 are indicated by dashed lines. Figure 9 middle.

[0243] As a computer-readable storage medium, memory 1204 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. For example, memory 1204 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device such as one or more hard-disk drives (HDDs), one or more compact disc (CD) drives, and / or one or more digital versatile disc (DVD) drives.

[0244] In one embodiment, electronic device 1200 may further include a network interface controller (NIC) 1206. Network interface 1206 may include a transceiver for providing a radio interface for electronic device 1200 to communicate with any other suitable device, such as a front-end module, antenna, etc. In various embodiments, network interface 1206 may be integrated with other components of electronic device 1200. Network interface 1206 can implement the functions of the communication unit in the above embodiments.

[0245] Electronic device 1200 may further include input / output (I / O) device 1205. I / O device 1205 may include: a user interface designed to enable a user to interact with electronic device 1200; a peripheral component interface designed to enable peripheral components to also interact with electronic device 1200; and / or sensors designed to determine environmental conditions and / or location information related to electronic device 1200.

[0246] It is worth noting that, Figure 9 This is merely an example. That is, although... Figure 9 The electronic device 1200 shown includes multiple devices such as a first processor 1201, a first coprocessor 1202, a controller hub 1203, and a memory 1204. However, in actual applications, devices using the methods of this application may include only a portion of the devices in the electronic device 1200. For example, it may include only the first processor 1201 and the network interface 1206. Figure 9 The properties of the optional devices are shown in dashed lines. According to some embodiments of this application, the memory 1204, which is a computer-readable storage medium, stores instructions that, when executed on a computer, cause the electronic device 1200 to perform the water quality control method for a grid-based water supply network as described in the above embodiments. Specific details can be found in the methods described in the above embodiments, and will not be repeated here.

[0247] Now for reference Figure 10The diagram shown is a block diagram of a SoC (system on chip) 1300 according to an embodiment of this application. Figure 10 In the diagram, similar components share the same reference numerals. Additionally, dashed boxes are an optional feature for more advanced SoCs. Figure 10 In the SoC1300, interconnect unit 1350 is coupled to the processor (corresponding to...). Figure 10 The system includes a second processor 1310, a system agent unit 1380, a bus controller unit 1390, an integrated memory controller unit 1340, and one or more coprocessors (corresponding to...). Figure 10 The second coprocessor 1320 may include integrated graphics logic, an image processor, an audio processor, and a video processor; a static random access memory (SRAM) unit 1330; and a direct memory access (DMA) unit 1360. In one embodiment, the second coprocessor 1320 includes a dedicated processor, such as a network or communication processor, a compression engine, a GPGPU, a high-throughput MIC processor, or an embedded processor.

[0248] The static random access memory (SRAM) cell 1330 may include one or more computer-readable media for storing data and / or instructions. The computer-readable storage medium may store instructions, specifically, temporary and permanent copies of those instructions. These instructions may include, when executed by at least one unit in the processor, causing the SoC 1300 to perform the water quality control method for a grid-based water supply network as described in the above embodiments, specifically referring to the methods in the above embodiments, which will not be repeated here.

[0249] Various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or combinations of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0250] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as a digital signal processor (DSP), microcontroller, application-specific integrated circuit (ASIC), or microprocessor.

[0251] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0252] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, compact disc read-only memory (CD-ROMs), magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other forms of propagated signals. Therefore, machine-readable media include any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0253] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the accompanying drawings. Furthermore, including structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0254] This application also provides a computer-readable storage medium storing at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by a processor to implement the grid-based water supply network water quality control method described in the above embodiments. Its specific functions and corresponding technical effects can be referred to the above embodiments. Figures 1-8 The methods explained will not be elaborated here.

[0255] This application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are loaded and executed by a processor, they implement the water quality control method for water supply networks based on grid management described in the above embodiments. The specific functions and corresponding technical effects can be found in the above embodiments. Figures 1-8 The methods explained will not be elaborated here.

[0256] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0257] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0258] It should be noted that in the examples and description of this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0259] Although this application has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art should understand that various changes in form and detail may be made thereto without departing from the spirit and scope of this application.

Claims

1. A method for water quality control of a water supply network based on grid management, characterized in that, include: Based on multiple online monitoring points and pipeline network data in the water supply area, the effective monitoring area corresponding to each of the online monitoring points is determined; wherein, the pipeline network data includes the distribution topology of the pipeline network, and at least one of the roughness of the inner wall of the pipeline and the pipeline flow rate; Based on the effective monitoring area corresponding to each of the online monitoring points, the water supply area is divided into multiple management grids, and each management grid includes one of the online monitoring points; Based on the water quality monitoring data from the manual monitoring points and the online monitoring points in the management grid, the locations of substandard water quality and water quality sensitive points in the management grid are determined; wherein, the water quality sensitive points are points where there is a risk of water quality failing to meet standards. Based on the user water quality work orders in the management grid, the water quality problem areas of the management grid that need to be investigated are determined; Based on the locations where water quality does not meet standards, the locations where water quality is sensitive, and the areas where water quality problems need to be investigated, the management grid is controlled.

2. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, The determination of the effective monitoring area corresponding to each online monitoring point based on multiple online monitoring points and pipeline network data in the water supply area includes: Based on the distribution topology of the pipeline network, the pipeline length between the online monitoring point and multiple nodes on the pipeline network is determined; For the pipeline network between the online monitoring point and the node, the equivalent hydraulic length between the online monitoring point and the node is determined based on at least one of the pipeline length, the roughness of the inner wall of the pipeline and the pipeline flow rate. Nodes whose equivalent hydraulic length is less than a preset length threshold are selected, and a node set is obtained; Based on the node set, a spatial interpolation method is used to determine the boundary of the effective monitoring area, thereby determining the effective monitoring area corresponding to the online monitoring point.

3. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, Based on the effective monitoring area corresponding to each of the online monitoring points, the water supply area is divided into multiple management grids, including: For the overlapping portion between the effective monitoring areas corresponding to adjacent online monitoring points, the overlapping portion is divided and assigned to the corresponding effective monitoring areas, so as to divide the water supply area into multiple non-overlapping management grids.

4. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, Based on the effective monitoring area corresponding to each of the online monitoring points, the water supply area is divided into multiple management grids, including: Identify whether there are any monitoring blind spots in the water supply area that are not covered by the management grid; If they exist, the layout of the online monitoring points will be optimized, and the optimal layout scheme will be determined. The management grid is updated based on the optimal layout scheme to ensure that there are no monitoring blind spots in the water supply area.

5. The water quality control method for water supply networks based on grid management according to claim 4, characterized in that, The optimization of the layout of the online monitoring points and the determination of the optimal layout scheme include: Define the constraints of the layout scheme; the constraints are either a first constraint or a second constraint. The first constraint is to keep the original online monitoring points in the water supply area unchanged, only allow the addition of new online monitoring points, and limit the total number of online monitoring points corresponding to the layout scheme. The second constraint is to allow the removal of the original online monitoring points, the adjustment of the original online monitoring points, and the addition of new online monitoring points, and limit the total number of online monitoring points corresponding to the layout scheme. Define evaluation objectives for assessing the performance of the layout scheme; the evaluation objectives include a first evaluation objective and a second evaluation objective, wherein the first evaluation objective is to maximize the total coverage water volume corresponding to the online monitoring points, and the second evaluation objective is to minimize the total number of online monitoring points; An evaluation model is defined to quantify the evaluation target; the evaluation model includes a monitoring coverage relationship model, a coverage water volume calculation model, and an online monitoring point number calculation model. The monitoring coverage relationship model is used to characterize the node coverage relationship under any layout scheme. The coverage water volume calculation model is used to calculate the total coverage water volume under any layout scheme. The online monitoring point number calculation model is used to calculate the total number of online monitoring points under any layout scheme. Using a multi-objective genetic algorithm, and under the premise of satisfying the constraints, the optimal layout scheme is determined by the evaluation model with the evaluation objective as the direction.

6. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, The water quality monitoring data includes one or more data points monitored within a preset time period; Based on water quality monitoring data from both manual and online monitoring points within the management grid, the locations of water quality non-compliance points within the management grid are determined, including: The water quality monitoring data is compared with the preset compliance threshold. If a first number or more of the water quality monitoring data exceed or fall below the compliance threshold, then the location corresponding to the water quality monitoring data is determined to be the location where the water quality does not meet the standard.

7. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, Based on water quality monitoring data from manual monitoring points and online monitoring points within the management grid, the water quality sensitive points of the management grid are determined, including: The water quality monitoring data is sorted to obtain a water quality monitoring data sequence, and the percentile P of the water quality index in the water quality monitoring data sequence is determined. K or P （100-K） ; whereby the percentile P of the water quality index K and P （100-K） These represent the water quality monitoring data at the Kth percentile and the 100-Kth percentile, respectively, in the water quality monitoring data sequence; The percentile P of the water quality index K Compared with the preset upper limit indicator, if the percentile P of the water quality indicator is... K If the water quality exceeds the upper limit, the location corresponding to the water quality monitoring data is determined to be the water quality sensitive location; or, The percentile P of the water quality index （100-K） Compared with the preset lower limit index, if the percentile P of the water quality index is... （100-K） If the water quality is below the lower limit, the location corresponding to the water quality monitoring data is determined to be the water quality sensitive location.

8. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, The management grid includes one or more blocks; The process of determining the water quality problem areas requiring investigation within the management grid based on user water quality work orders in the management grid includes: Identify the water quality problem category corresponding to the user's water quality work order, and filter out work orders that meet the requirements of the water quality problem category from the user's water quality work orders; The location of the work orders that meet the requirements of the water quality problem category is located to count the number of work orders that meet the requirements of the water quality problem category in each block; The number of work orders in the block is compared with a preset second threshold. If the number of work orders exceeds the second threshold, the block is determined to be the block where the water quality problem needs to be investigated; or... The proportion of the number of work orders in the block to the total number of users in the block is compared with a preset proportion threshold. If the proportion exceeds the proportion threshold, the block is determined to be the block where the water quality problem needs to be investigated.

9. The water quality control method for water supply networks based on grid management according to claim 1, characterized in that, The management grid is controlled based on the locations where water quality does not meet standards, the locations where water quality is sensitive, and the areas where water quality problems need to be investigated, including: Based on the locations where water quality does not meet standards, the locations where water quality is sensitive, and the areas where water quality problems need to be investigated, the investigation type of the management grid is determined; Based on the investigation type of the management grid, the corresponding investigation direction and / or the corresponding control level are indicated.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the water quality control method for water supply networks based on grid management as described in any one of claims 1-9.

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