Method for detecting water quality and quantity coupled resilience state of water network node

Abstract

The present application relates to the technical field of multi-parameter coupling detection, and discloses a method for detecting water quality and quantity coupling resilience state of nodes in a water network, which comprises the following steps: performing space-time resolution matching on original water quality time series and original water quantity time series of each monitoring node in a target water network to obtain synchronized water quality series and synchronized water quantity series of each monitoring node; constructing water quality-quantity coupling feature vectors of the monitoring nodes according to the synchronized water quality series and the synchronized water quantity series, and performing node resilience coupling to obtain a node resilience coupling coefficient of each monitoring node; and generating a water quality-quantity coupling resilience state distribution map of the target water network based on the node resilience coupling coefficients of each monitoring node and the topological connection relationship of the target water network to complete the detection of the water quality-quantity coupling resilience state of the nodes in the water network. The present application can improve the detection efficiency of the water quality-quantity coupling resilience state of the nodes in the water network.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter coupling detection technology, and in particular to a method for detecting the coupled resilience state of water quality and quantity at water network nodes. Background Technology

[0002] The existing water network operation monitoring system only collects and analyzes water quality and quantity data independently, without establishing a spatiotemporal resolution matching mechanism between the two. It cannot achieve time-series alignment of water quality and quantity time series, nor can it perform spatial lag correction based on the water flow propagation characteristics of the water network. This results in time-series deviations and spatial misalignments in the monitoring data, making it impossible to truly restore the synchronous correlation between water quality and quantity during physical transmission. The obtained node status data lacks consistency and accuracy, making it difficult to accurately reflect the true operational characteristics of water network nodes.

[0003] Existing methods for assessing the resilience of water networks do not construct a water quality-quantity coupling characteristic system, cannot complete the calculation of node resilience coupling and the quantification of coupling coefficients, can only achieve the state discrimination of a single indicator, and cannot characterize the coordinated resilience level of water quality and quantity. Such methods do not combine the topological connection relationship of the water network to carry out a global state analysis, cannot generate a visual distribution map of coupled resilience state, cannot intuitively present the resilience differences of each node in the entire water network, and cannot quickly identify and locate vulnerable nodes. The overall detection process is cumbersome and inefficient, and cannot provide reliable data support for water network resilience optimization and safety control. Summary of the Invention

[0004] This invention provides a method for detecting the coupled resilience of water quality and quantity at water network nodes, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for detecting the coupled resilience state of water quality and quantity at water network nodes, comprising:

[0006] R1: Perform spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronous water quality series and synchronous water quantity series of each monitoring node.

[0007] R2: Based on the synchronized water quality sequence and the synchronized water quantity sequence, construct the water quality-water quantity coupling feature vector of the monitoring node, and perform node resilience coupling to obtain the node resilience coupling coefficient of each monitoring node.

[0008] R3: Based on the node resilience coupling coefficient of each monitoring node and combined with the topological connection relationship of the target water network, generate a distribution map of the water quality and quantity coupling resilience of the target water network, and complete the detection of the water quality and quantity coupling resilience of the water network nodes.

[0009] In a preferred embodiment, the original water quality time series and original water quantity time series of each monitoring node in the target water network include:

[0010] Obtain the original water quality time series and original water quantity time series of each monitoring node in the target water network;

[0011] The original water quality time series includes chemical oxygen demand concentration, ammonia nitrogen concentration, and dissolved oxygen concentration;

[0012] The original water volume time series includes time series data of two types of water volume parameters: flow rate and water level.

[0013] In a preferred embodiment, the step of performing spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronized water quality sequence and synchronized water quantity sequence of each monitoring node includes:

[0014] Using the sampling time points of the original water volume time series as the reference time axis, the original water quality time series is interpolated using a piecewise linear interpolation method to obtain a preliminary aligned water quality series.

[0015] Based on the water flow propagation time between adjacent monitoring nodes in the target water network, the preliminary aligned water quality sequence is spatially lag corrected to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process.

[0016] The water volume data corresponding to the missing water quality data time points in the original water volume time series are removed, and the remaining water volume data after removal are rearranged in chronological order to obtain a synchronized water volume series.

[0017] In a preferred embodiment, the step of performing spatial lag correction on the initially aligned water quality sequence based on the water flow propagation time between adjacent monitoring nodes in the target water network to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process includes:

[0018] Obtain the water flow propagation time between the upstream monitoring node and the current monitoring node;

[0019] The water propagation time is obtained by dividing the length of the hydraulic transport path between the upstream monitoring node and the current monitoring node by the average flow velocity of the hydraulic transport path.

[0020] Using the water flow propagation time as the lag time, the water quality parameter values ​​corresponding to the upstream monitoring nodes in the preliminary aligned water quality sequence are shifted backward according to the lag time, so that the shifted water quality parameter values ​​of the upstream monitoring nodes are aligned in time to the sampling time of the water volume data of the current monitoring node.

[0021] The water quality parameter values ​​of the upstream monitoring node after translation are fused with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence.

[0022] In a preferred embodiment, the step of fusing the water quality parameter values ​​of the translated upstream monitoring node with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence includes:

[0023] The water quality parameter values ​​of the upstream monitoring node after translation are merged with the preliminary aligned water quality sequence of the current monitoring node itself;

[0024] If the current monitoring node has its own water quality interpolation result and the translational water quality data from the upstream node at a certain sampling time, then the weighted average of its own water quality interpolation result and the translational water quality data from the upstream node is taken to obtain the synchronous water quality value.

[0025] If only one type of data is available, the current data will be used directly as the synchronous water quality value at the sampling time.

[0026] All synchronized water quality values ​​are integrated into the synchronized water quality sequence.

[0027] In a preferred embodiment, constructing a water quality-quantity coupled feature vector for the monitoring node based on the synchronized water quality sequence and the synchronized water quantity sequence includes:

[0028] Extract the water quality parameter values ​​of the current monitoring node at the detection time from the synchronized water quality sequence, and use them as the absolute state components of the water quality;

[0029] Extract the water volume parameter values ​​of the current monitoring node at the same detection time from the synchronous water volume sequence, and use them as the absolute state components of the water volume;

[0030] Based on the current detection time, the synchronous water proton sequence and synchronous water quantum sequence within a set time window are extracted. The coefficient of variation of each water quality parameter in the synchronous water proton sequence within the set time window is calculated, and the rate of change of each water quantity parameter in the synchronous water quantum sequence within the set time window is calculated.

[0031] The ratio of the coefficient of variation of each water quality parameter to the rate of change of the corresponding water quantity parameter is taken as the relative water quantity change component of the water quality parameter, and all the relative water quantity change components are combined into a relative water quantity change component.

[0032] The absolute state component of water quality, the absolute state component of water quantity, and the relative change component of water quality and water quantity are spliced ​​together in dimensional order to obtain a water quality-water quantity coupled feature vector.

[0033] In a preferred embodiment, the step of performing node toughness coupling to obtain the node toughness coupling coefficient for each monitoring node includes:

[0034] Obtain the preset water quality absolute state reference vector, water quantity absolute state reference vector, and water quality relative water quantity change reference value;

[0035] Each element in the absolute water quality state reference vector corresponds to a standard reference concentration of a water quality parameter.

[0036] Each element in the absolute state reference vector of water volume corresponds to a standard reference value of a water volume parameter.

[0037] The water quality absolute state component of the monitoring node is divided by the corresponding element of the water quality absolute state reference vector to obtain the normalized water quality absolute state vector.

[0038] The normalized absolute water state vector is obtained by dividing the corresponding element of the absolute water state component of the monitoring node by the absolute water state reference vector.

[0039] The normalized absolute water quality state vector and the normalized absolute water quantity state vector are subjected to a power-weighted multiplication and a water quantity change correction to obtain the nodal resilience coupling coefficient.

[0040] In a preferred embodiment, the formula for calculating the node tough coupling coefficient is:

[0041]

[0042] in, Let be the node's toughness coupling coefficient. For consecutive multiplication, The first element in the normalized absolute state vector of water quality One element, Let be the dimension of the absolute state components of the water quality. The first in the normalized absolute state vector of water quantity One element, For the preset first The resilience contribution index of each water quality parameter Let be the dimension of the absolute state component of the water volume. For the preset first The resilience contribution index of each water quantity parameter, The preset coupling adjustment index, This refers to the component representing the relative change in water quality and quantity. This is the baseline value for the relative change in water quality and water quantity.

[0043] In a preferred embodiment, the step of generating a water quality and quantity coupling resilience distribution map of the target water network based on the node resilience coupling coefficients of each monitoring node and the topological connectivity of the target water network, thereby completing the detection of the water quality and quantity coupling resilience status of the water network nodes, includes:

[0044] A water network topology graph is constructed using each monitoring node of the target water network as a graph node and the actual hydraulic transport paths between each monitoring node as graph edges.

[0045] The node resilience coupling coefficient of each monitoring node is assigned as the attribute value of the corresponding graph node.

[0046] According to the preset toughness level classification rules, the numerical range of the node toughness coupling coefficient is divided into continuous toughness level intervals, and the toughness level interval to which each graph node belongs is determined.

[0047] On the water network topology map, different visual markers are assigned to graph nodes in different resilience level ranges, wherein the visual markers include color markers, shape markers, or size markers;

[0048] The graph nodes carrying the visual markers of resilience level are drawn according to the graph edge connection relationship of the water network topology graph to generate the water quality and water quantity coupled resilience state distribution map, and the water quality and water quantity coupled resilience state distribution map is output as the detection result of the water quality and water quantity coupled resilience state of the water network nodes.

[0049] In a preferred embodiment, the node toughness coupling coefficient of the monitoring node further includes:

[0050] When the node resilience coupling coefficient of the monitoring node in the target water network is detected to be lower than the preset node resilience threshold, the current node is marked as a vulnerable node.

[0051] The location information of the vulnerable nodes and the synchronous water quality and water quantity sequences of adjacent upstream and downstream nodes are output as reference data for resilience enhancement intervention in the target water network.

[0052] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0053] 1. This invention performs complete spatiotemporal resolution matching processing on the original water quality time series and the original water quantity time series of water network monitoring nodes. It completes the interpolation of the water quality series based on the sampling time of the water quantity series, and performs precise spatial lag correction by combining the water flow propagation time between adjacent monitoring nodes. This generates synchronized water quality and synchronized water quantity series that are completely synchronized with the physical transmission process of the water network, and fully restores the real coupling relationship between water quality and water quantity in the pipeline network. This provides highly accurate and consistent basic data for subsequent coupling feature construction and resilience coefficient calculation, ensuring the accuracy and reliability of the detection results of the coupling resilience state of water network nodes from the data source.

[0054] 2. This invention constructs a multidimensional water quality-quantity coupling feature vector based on synchronized water quality and quantity sequences, completes the quantitative calculation of node resilience coupling, generates standardized node resilience coupling coefficients, constructs a water network topology map based on the target water network topology connection relationship, completes the resilience level classification, and generates a visualized water quality-quantity coupling resilience state distribution map, which intuitively presents the resilience distribution characteristics of nodes throughout the entire water network. At the same time, it automatically identifies vulnerable nodes with insufficient resilience and outputs corresponding upstream and downstream node synchronization data, which greatly improves the detection efficiency and intuitiveness of the water network node coupling resilience state, and provides comprehensive and effective data support for water network resilience improvement, safe operation and precise control. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below refer to only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0056] Figure 1 This is a flowchart illustrating a method for detecting the coupled resilience of water quality and quantity at water network nodes according to an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0058] Reference Figure 1The diagram shown is a flowchart illustrating a method for detecting the coupled resilience of water quality and quantity at a water network node according to an embodiment of the present invention. In this embodiment, the method for detecting the coupled resilience of water quality and quantity at a water network node includes:

[0059] R1: Perform spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronous water quality series and synchronous water quantity series of each monitoring node.

[0060] In this embodiment of the invention, the original water quality time series and the original water quantity time series of each monitoring node in the target water network include:

[0061] Obtain the original water quality time series and original water quantity time series of each monitoring node in the target water network.

[0062] The original water quality time series includes chemical oxygen demand (COD) concentration, ammonia nitrogen concentration, and dissolved oxygen concentration.

[0063] The original water volume time series includes time series data of two types of water volume parameters: flow rate and water level.

[0064] The process of performing spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronized water quality sequence and synchronized water quantity sequence of each monitoring node includes:

[0065] Using the sampling time points of the original water volume time series as the reference time axis, the original water quality time series is interpolated using a piecewise linear interpolation method to obtain a preliminary aligned water quality series.

[0066] Based on the water flow propagation time between adjacent monitoring nodes in the target water network, the preliminary aligned water quality sequence is spatially lag-corrected to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process.

[0067] The water volume data corresponding to the missing water quality data time points in the original water volume time series are removed, and the remaining water volume data after removal are rearranged in chronological order to obtain a synchronized water volume series.

[0068] The step of performing spatial lag correction on the initially aligned water quality sequence based on the water flow propagation time between adjacent monitoring nodes in the target water network to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process includes:

[0069] Obtain the water flow propagation time between the upstream monitoring node and the current monitoring node.

[0070] The water propagation time is obtained by dividing the length of the hydraulic transport path between the upstream monitoring node and the current monitoring node by the average flow velocity of the hydraulic transport path.

[0071] Using the water flow propagation time as the lag time, the water quality parameter values ​​corresponding to the upstream monitoring nodes in the preliminary aligned water quality sequence are shifted backward according to the lag time, so that the shifted water quality parameter values ​​of the upstream monitoring nodes are aligned in time to the sampling time of the water volume data of the current monitoring node.

[0072] The water quality parameter values ​​of the upstream monitoring node after translation are fused with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence.

[0073] The process of fusing the water quality parameter values ​​of the translated upstream monitoring node with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence includes:

[0074] The water quality parameter values ​​of the upstream monitoring node after translation are merged with the preliminary aligned water quality sequence of the current monitoring node.

[0075] If a current monitoring node has both its own water quality interpolation result and the translational water quality data from the upstream node at a certain sampling time, then the node will perform a weighted average of its own water quality interpolation result and the translational water quality data from the upstream node to obtain the synchronous water quality value.

[0076] If only one type of data is available, the current data will be used directly as the synchronous water quality value at the sampling time.

[0077] All synchronized water quality values ​​are integrated into the synchronized water quality sequence.

[0078] According to the fixed locations of the monitoring nodes that have been planned and deployed in advance in the target water network, full-coverage data collection is carried out for each monitoring node. The data collection cycle is based on a preset continuous hourly monitoring period. Water quality monitoring data of each monitoring node is continuously acquired for all monitoring times within that period. All collected water quality monitoring data are arranged one by one in strict chronological order from morning to night. After removing redundant data collected repeatedly, the original water quality time series of each monitoring node in the target water network is formed.

[0079] Throughout the entire process of generating the original water quality time series, only real-time monitoring values ​​of three fixed water quality parameters—chemical oxygen demand (COD), ammonia nitrogen (NH3), and dissolved oxygen (DO)—are collected to ensure that the accurate values ​​of these three water quality parameters are fully recorded at each monitoring moment in the original water quality time series, with no parameter omissions or missing values.

[0080] According to the preset continuous hourly monitoring period that is exactly the same as the water quality monitoring data, the water volume data collection work of each monitoring node in the target water network is started simultaneously. The water volume monitoring data of each monitoring node at the corresponding monitoring time is continuously acquired. All the collected water volume monitoring data are arranged one by one in strict order from morning to night according to the monitoring time. After removing the redundant values ​​collected repeatedly, the original water volume time series of each monitoring node in the target water network is formed.

[0081] Throughout the entire process of generating the original water volume time series, only the real-time monitoring values ​​of two fixed water volume parameters, namely flow rate and water level, are collected to ensure that the accurate values ​​of these two water volume parameters are completely recorded at each monitoring moment in the original water volume time series, with no parameter omissions or missing values.

[0082] All fixed sampling time points are extracted from the original water volume time series. These time points are combined to form a unified reference time axis for the entire water network. The monitoring times of the original water quality time series are compared one by one to accurately identify all missing times that do not coincide with the reference time axis. Between two adjacent valid monitoring values ​​in the original water quality time series, the water quality values ​​for the missing times are determined based on the trend of water quality value changes between the previous and next times, combined with the time interval ratio of the monitoring times. All the supplemented water quality values ​​are arranged in the time order of the reference time axis to form a preliminary aligned water quality sequence with preliminary time dimension alignment.

[0083] The actual laying length of the hydraulic transport path between each group of adjacent monitoring nodes in the target water network is determined in advance. At the same time, the average flow velocity of the water body in the hydraulic transport path is measured. The actual length of the hydraulic transport path is divided by the average flow velocity to calculate the water flow propagation time between adjacent monitoring nodes. This water flow propagation time is set as a fixed lag time. All water quality parameter values ​​corresponding to the upstream monitoring nodes in the preliminary aligned water quality sequence are extracted. The corresponding monitoring time points of these water quality parameters are adjusted backward according to the fixed lag time so that the adjusted water quality parameter time points of the upstream monitoring nodes completely coincide with the sampling time points of the water volume data of the current monitoring nodes. The time-aligned water quality values ​​of the upstream monitoring nodes are merged and sorted with the preliminary aligned water quality values ​​of the current monitoring nodes to obtain a synchronized water quality sequence that is completely synchronized with the water volume data of the current nodes in the physical transmission process.

[0084] By comparing the effective sampling time points of the synchronized water quality sequence with the sampling time points of the original water volume time series, the time points in the original water volume time series where there are no corresponding data in the synchronized water quality sequence are accurately located. All water volume values ​​corresponding to these time points are removed. After the removal is completed, the remaining effective water volume values ​​are reorganized and sorted according to the chronological order of the reference time axis to form a synchronized water volume sequence that perfectly matches the time and water quality.

[0085] The water flow propagation time between the upstream monitoring node and the current monitoring node, which has been measured and verified in the field, can be directly retrieved from the pre-established and regularly calibrated water network hydraulic operation parameter archive, thus completing the accurate acquisition of the water flow propagation time between the two nodes.

[0086] The actual length of the hydraulic transport path between the upstream monitoring node and the current monitoring node is measured on-site. At the same time, the stable average flow velocity of the water body in the hydraulic transport path is determined by taking the average value of multiple on-site monitoring. The measured actual length of the hydraulic transport path is divided by the stable average flow velocity, and the result is directly used as the water flow propagation time between the upstream monitoring node and the current monitoring node.

[0087] The water flow propagation time, which has been acquired and calculated, is set as a fixed lag time. All water quality parameter values ​​corresponding to the upstream monitoring nodes in the preliminary aligned water quality sequence are extracted completely. The monitoring time points corresponding to these water quality parameter values ​​are uniformly adjusted backward according to the fixed lag time. After the adjustment is completed, it is ensured that the time points of the water quality parameter values ​​of the upstream monitoring nodes after the shift are completely and accurately corresponding to the sampling time points of the water volume data of the current monitoring nodes.

[0088] The water quality parameter values ​​of the upstream monitoring nodes that have completed time shifting and are aligned with the water volume data of the current node are fully integrated with the preliminary aligned water quality sequence values ​​of the current monitoring node itself. After integration, all valid water quality parameter values ​​are arranged in a fixed order according to the reference time axis, and finally a synchronized water quality sequence that is completely synchronized with the water volume data of the current node in the physical propagation process is generated.

[0089] The water quality parameter values ​​of the upstream monitoring nodes after time shift adjustment are matched with the preliminary aligned water quality sequence values ​​of the current monitoring nodes calculated by piecewise linear interpolation. The data are then collected one-to-one according to each sampling moment of the reference time axis to complete the fusion processing of water quality data from two different sources.

[0090] Each sampling moment on the reference time axis is iterated one by one. When the current sampling moment has both the piecewise linear interpolation water quality result of the current monitoring node and the water quality data after the upstream node is shifted, the corresponding shares of the two types of data are allocated according to the preset fixed proportion of local monitoring data and fixed proportion of upstream transmission data, and then the data are merged and calculated to obtain the accurate synchronous water quality value of the sampling moment.

[0091] Each sampling moment on the reference time axis is iterated one by one. When only the piecewise linear interpolation water quality result of the current monitoring node exists at the sampling moment, or only the water quality data after the upstream node is shifted, the only valid data value that exists is directly determined as the synchronous water quality value of the current sampling moment.

[0092] The synchronous water quality values ​​corresponding to all sampling times on the reference time axis are arranged continuously and systematically in chronological order from early to late sampling times to form a complete synchronous water quality sequence without interruption or omission.

[0093] R2: Based on the synchronized water quality sequence and the synchronized water quantity sequence, construct the water quality-water quantity coupling feature vector of the monitoring node, and perform node resilience coupling to obtain the node resilience coupling coefficient of each monitoring node.

[0094] In this embodiment of the invention, the step of constructing a water quality-quantity coupled feature vector for the monitoring node based on the synchronized water quality sequence and the synchronized water quantity sequence includes:

[0095] The water quality parameter values ​​of the current monitoring node at the detection time are extracted from the synchronized water quality sequence and used as the absolute state components of the water quality.

[0096] Extract the water volume parameter values ​​of the current monitoring node at the same detection time from the synchronous water volume sequence, and use them as the absolute state components of the water volume.

[0097] Based on the current detection time, the synchronous water proton sequence and synchronous water quantum sequence within a set time window are extracted. The coefficient of variation of each water quality parameter in the synchronous water proton sequence within the set time window is calculated, and the rate of change of each water quantity parameter in the synchronous water quantum sequence within the set time window is calculated.

[0098] The ratio of the coefficient of variation of each water quality parameter to the rate of change of the corresponding water quantity parameter is taken as the relative water quantity change component of the water quality parameter, and all the relative water quantity change components are combined into a relative water quantity change component.

[0099] The absolute state component of water quality, the absolute state component of water quantity, and the relative change component of water quality and water quantity are spliced ​​together in dimensional order to obtain a water quality-water quantity coupled feature vector.

[0100] The node resilience coupling process, which involves obtaining the node resilience coupling coefficient for each monitoring node, includes:

[0101] Obtain the preset water quality absolute state reference vector, water quantity absolute state reference vector, and water quality relative water quantity change reference value.

[0102] Each element in the absolute water quality reference vector corresponds to a standard reference concentration of a water quality parameter.

[0103] Each element in the absolute state reference vector of water volume corresponds to a standard reference value for a water volume parameter.

[0104] The normalized absolute water quality vector is obtained by dividing the corresponding element of the absolute water quality state component of the monitoring node by the absolute water quality state reference vector.

[0105] The normalized absolute water state vector is obtained by dividing the corresponding element of the absolute water state component of the monitoring node by the absolute water state reference vector.

[0106] The normalized absolute water quality state vector and the normalized absolute water quantity state vector are subjected to a power-weighted multiplication and a water quantity change correction to obtain the nodal resilience coupling coefficient.

[0107] The formula for calculating the node toughness coupling coefficient is as follows:

[0108]

[0109] in, Let be the node's toughness coupling coefficient. For consecutive multiplication, The first element in the normalized absolute state vector of water quality One element, Let be the dimension of the absolute state components of the water quality. The first in the normalized absolute state vector of water quantity One element, For the preset first The resilience contribution index of each water quality parameter Let be the dimension of the absolute state component of the water volume. For the preset first The resilience contribution index of each water quantity parameter, The preset coupling adjustment index, This refers to the component representing the relative change in water quality and quantity. This is the baseline value for the relative change in water quality and water quantity.

[0110] The preset fixed detection time is located from the synchronous water quality sequence. All valid values ​​of the three water quality parameters, namely chemical oxygen demand concentration, ammonia nitrogen concentration and dissolved oxygen concentration, are accurately extracted at the current monitoring node at that time. The extracted values ​​of the three water quality parameters are combined in a fixed parameter order to form the absolute water quality state component of the current monitoring node to characterize the instantaneous water quality state.

[0111] Locate the time point in the synchronous water volume sequence that is completely consistent with the above fixed detection time, accurately extract all valid values ​​of the two water volume parameters, flow rate and water level, corresponding to the current monitoring node at that time, and combine the extracted two water volume parameter values ​​in a fixed parameter order to form the absolute water volume state component of the current monitoring node used to characterize the instantaneous water volume state.

[0112] Using a preset fixed detection time as the endpoint, a preset 30-minute fixed-duration time window is extracted along the historical time direction. All synchronous water quality data within this window are extracted to form a synchronous water quality subsequence, and all synchronous water quantity data within the same window are extracted to form a synchronous water quantum sequence. For each water quality parameter in the synchronous water quality subsequence, the standard deviation of all values ​​of the parameter within the 30-minute time window is first calculated as the discrete distribution feature, and then the arithmetic mean of all values ​​of the parameter within the time window is calculated. The coefficient of variation of the water quality parameter is obtained by performing a correlation operation between the discrete distribution feature and the arithmetic mean. For each water quantity parameter in the synchronous water quantum sequence, the initial value of the parameter at the start time and the last value at the end time of the 30-minute time window are first determined, and then the difference between the last value and the initial value is divided by the 30-minute duration of the time window to obtain the rate of change of the water quantity parameter.

[0113] For each water quality parameter in the synchronous water quality subsequence, the coefficient of variation of the parameter obtained within a 30-minute time window is compared with the rate of change of the corresponding water quantity parameter in the synchronous water quantum sequence within the same 30-minute time window to obtain the water quality relative quantity change component specific to that water quality parameter. The water quality relative quantity change components corresponding to the three water quality parameters of chemical oxygen demand concentration, ammonia nitrogen concentration, and dissolved oxygen concentration are combined in a fixed parameter arrangement order to form the water quality relative quantity change component used by the current monitoring node to characterize the water quality change characteristics with water quantity.

[0114] Following a fixed dimensional arrangement order with the absolute water quality component first, the absolute water quantity component in the middle, and the relative water quality-water quantity change component last, the three independent components are sequentially multidimensionally integrated and spliced ​​together. After removing redundant information between dimensions, a complete and unique water quality-water quantity coupled feature vector for the current monitoring node is formed.

[0115] From the pre-built and industry-standard calibrated water network operation standard parameter system, the pre-set absolute water quality state benchmark vector, absolute water quantity state benchmark vector, and relative water quality and quantity change benchmark value are retrieved to ensure that the three types of benchmark data are retrieved completely without any omissions, thus completing the acquisition of all benchmark data.

[0116] The absolute water quality baseline vector contains three independent elements, which correspond to the legal standard reference concentrations of three water quality parameters: chemical oxygen demand (COD), ammonia nitrogen (NH3), and dissolved oxygen (DO). Each element corresponds to only one water quality parameter, maintaining a unique correspondence between elements and water quality parameters.

[0117] The absolute state reference vector of water volume contains two independent elements, which correspond to the standard reference values ​​of two types of water volume parameters: flow rate and water level. Each element corresponds to only one water volume parameter, maintaining a unique correspondence between elements and water volume parameters.

[0118] Each water quality parameter value in the absolute water quality state component of the current monitoring node is matched one by one with the standard reference concentration value of the same type of water quality parameter in the absolute water quality state baseline vector. The same parameter value is divided, and all the results obtained by division are arranged in the original water quality parameter order to form a normalized absolute water quality state vector for unified dimensions.

[0119] Each water quantity parameter value in the absolute water quantity state component of the current monitoring node is matched one by one with the standard reference value of the same type of water quantity parameter in the absolute water quantity state baseline vector. The same parameter value is divided, and all the results obtained by division are arranged in the original water quantity parameter order to form a normalized absolute water quantity state vector for unified dimensions.

[0120] Based on the pre-set resilience contribution weights of each water quality parameter and each water quantity parameter in the water network operation standard parameter system, weighting is performed on all elements of the normalized absolute state vector of water quality and the normalized absolute state vector of water quantity. All weighted elements are then multiplied sequentially to obtain intermediate calculation results. Based on the numerical correspondence between the relative water quality and water quantity change components and the baseline value of the relative water quality and water quantity change, the intermediate calculation results are corrected and adjusted for water quantity change, and finally the node resilience coupling coefficient of the current monitoring node is obtained.

[0121] The normalized water quality absolute state vector elements used in the calculation of the node resilience coupling coefficient are obtained by dividing the values ​​of the same type of water quality parameters of the absolute state components of the monitoring node one by one with the standard reference concentration values ​​of the absolute state of the water quality baseline vector, thus ensuring the uniformity of the dimensions of the element values.

[0122] The normalized absolute water state vector elements used in the calculation of the node resilience coupling coefficient are obtained by dividing the values ​​of the same type of water quantity parameters of the absolute water state components of the monitoring node one by one with the standard reference values ​​of the absolute water state baseline vector, thus ensuring the uniformity of the unit of measurement of the element values.

[0123] The resilience contribution index of water quality parameters used in the calculation of the node resilience coupling coefficient is directly retrieved from the preset resilience contribution weight values ​​of each water quality parameter in the water network operation standard parameter system, which have been verified by water network operation. The weight values ​​are close to the actual influence of water quality on resilience in the water network.

[0124] The resilience contribution index of the water quantity parameter used in the calculation of the node resilience coupling coefficient is directly retrieved from the preset resilience contribution weight values ​​of each water quantity parameter in the water network operation standard parameter system, which have been verified by water network operation. The weight values ​​are close to the influence of actual water network water quantity on resilience.

[0125] The coupling adjustment index used in the calculation of the node resilience coupling coefficient is directly retrieved from the preset adjustment weight values ​​in the water network operation standard parameter system, which are used to balance the correlation between water quality and water quantity, to ensure the rationality of the coupling calculation.

[0126] The water quality relative to water quantity change component used in the calculation of the node toughness coupling coefficient is obtained by calculating the ratio of the coefficient of variation of each water quality parameter within a fixed 30-minute time window to the rate of change of the corresponding water quantity parameter within the same time window, reflecting the correlation characteristics of water quality with water quantity fluctuations.

[0127] The benchmark value of relative water quality change used in the calculation of the node resilience coupling coefficient is directly retrieved from the preset standard reference value of relative water quality change that meets the requirements of stable operation of the water network in the standard parameter system of water network operation, and is used as the reference standard for water quantity change correction.

[0128] The node resilience coupling coefficient can integrate three core types of information: normalized absolute water quality state, normalized absolute water quantity state, and relative water quality and quantity change. Through quantitative calculation, it can intuitively characterize the coupling resilience level of the monitoring node under the synergistic effect of water quality and water quantity.

[0129] The node resilience coupling coefficient is calculated by combining weighted multiplication with water volume change correction, so as to achieve standardized, unified and quantitative judgment of the resilience status of water network monitoring nodes, eliminate the dimensional differences of different nodes and parameters, and ensure the objectivity and stability of the judgment results.

[0130] The final calculation result of the node resilience coupling coefficient can be directly assigned to the attribute value of the corresponding node in the water network topology diagram, providing core quantitative support for subsequent node resilience level classification and resilience interval determination, and is the basic data for generating the water quality and water quantity coupled resilience state distribution map.

[0131] The node resilience coupling coefficient can accurately reflect the real-time resilience status of a single monitoring node. By comparing it with the preset node resilience threshold, it can quickly identify vulnerable nodes with insufficient resilience, providing a direct and accurate quantitative basis for outputting upstream and downstream correlation data of vulnerable nodes.

[0132] R3: Based on the node resilience coupling coefficient of each monitoring node and combined with the topological connection relationship of the target water network, generate a distribution map of the water quality and quantity coupling resilience of the target water network, and complete the detection of the water quality and quantity coupling resilience of the water network nodes.

[0133] In this embodiment of the invention, the step of generating a water quality and quantity coupling resilience state distribution map of the target water network based on the node resilience coupling coefficients of each monitoring node and the topological connection relationship of the target water network, thereby completing the detection of the water quality and quantity coupling resilience state of the water network nodes, includes:

[0134] A water network topology graph is constructed using each monitoring node of the target water network as a graph node and the actual hydraulic transport paths between each monitoring node as graph edges.

[0135] The node resilience coupling coefficient of each monitoring node is assigned as the attribute value of the corresponding graph node.

[0136] According to the preset toughness level classification rules, the numerical range of the node toughness coupling coefficient is divided into continuous toughness level intervals, and the toughness level interval to which each graph node belongs is determined.

[0137] On the water network topology map, different visual markers are assigned to graph nodes in different resilience level ranges, wherein the visual markers include color markers, shape markers, or size markers.

[0138] The graph nodes carrying the visual markers of resilience level are drawn according to the graph edge connection relationship of the water network topology graph to generate the water quality and water quantity coupled resilience state distribution map, and the water quality and water quantity coupled resilience state distribution map is output as the detection result of the water quality and water quantity coupled resilience state of the water network nodes.

[0139] The node resilience coupling coefficient of the monitoring node also includes:

[0140] When the node resilience coupling coefficient of a monitoring node in the target water network is detected to be lower than the preset node resilience threshold, the current node is marked as a vulnerable node.

[0141] The location information of the vulnerable nodes and the synchronous water quality and water quantity sequences of adjacent upstream and downstream nodes are output as reference data for resilience enhancement intervention in the target water network.

[0142] Based on the coordinates of the monitoring nodes and the actual distribution points of the pipeline network obtained from the on-site survey of the target water network, each physical monitoring node is accurately mapped to a graph node in the topology diagram and its fixed position is locked. Then, according to the actual hydraulic transmission pipeline routes and upstream and downstream hydraulic connection relationships between the monitoring nodes in the pipeline network, graph nodes with direct hydraulic transmission associations are connected one by one using graph edges, thus completely restoring the real hydraulic transmission topology structure of the water network and building a complete water network topology diagram with no omissions and no mismatches in the connections.

[0143] First, a unique and fixed number is assigned to each monitoring node. The node resilience coupling coefficients of each monitoring node calculated in the previous stage are bound one-to-one with the graph nodes whose numbers are completely consistent with those in the water network topology diagram. The node resilience coupling coefficients are entered and bound as the core attribute values ​​of the graph nodes. The attribute assignment operation of all graph nodes is completed to ensure that there are no errors or omissions in the correspondence between coefficients and nodes.

[0144] The preset water network node resilience level classification standard is invoked. First, the minimum lower limit and maximum upper limit of the node resilience coupling coefficient are determined. Then, the entire coefficient value range is divided into multiple continuous and non-overlapping resilience level intervals according to fixed numerical intervals. The node resilience coupling coefficient of each graph node is extracted one by one and accurately compared and matched with the numerical range of each resilience level interval to determine the unique resilience level interval to which each graph node belongs.

[0145] Following the preset rules for visualizing toughness levels, each toughness level range is assigned a unique color mark, shape mark, or size mark. The visual marks for different level ranges are clearly distinguishable to avoid visual confusion. The matched visual marks are then assigned to each graph node to complete the visual mark assignment for all graph nodes.

[0146] Based on the original topological structure of the water network topology map, and strictly following the established hydraulic connection relationships of the map edges, the map nodes carrying the visualization markers of resilience level are precisely positioned and connected by hydraulic paths. This fully presents the resilience distribution characteristics of all monitoring nodes in the entire water network, generates a water quality and quantity coupled resilience state distribution map specific to the target water network, and outputs this distribution map as the final detection result, thus comprehensively completing the detection work of the water quality and quantity coupled resilience state of the water network nodes.

[0147] The node resilience coupling coefficients of all monitoring nodes within the target water network are traversed one by one. Each coefficient value is precisely compared with the preset node resilience judgment threshold. All monitoring nodes with coefficient values ​​strictly less than the preset node resilience threshold are selected, thus completing the precise preliminary screening of vulnerable nodes.

[0148] In the official node list document of the water network, the selected monitoring nodes are marked with a unified vulnerable node identifier. At the same time, a unique vulnerable node mark is added to the corresponding node position in the water network topology map to achieve synchronized marking between the node list and the topology map, ensuring that the vulnerable node identifier is clear and its location is unique and traceable.

[0149] The system retrieves complete location information from the water network project layout archives, including the actual installation latitude and longitude coordinates of vulnerable nodes, the main and branch sections of the pipeline network to which they belong, the unique node number, and the pipeline connection location. The information is then checked against the actual layout of the water network on site to ensure that the location information is complete, accurate, and perfectly matched with the site without any deviation.

[0150] The upstream adjacent monitoring nodes directly hydraulically connected to the vulnerable nodes are extracted from the data repository. After spatiotemporal resolution matching and spatial lag correction, synchronous water quality and synchronous water quantity sequences are generated. Simultaneously, the downstream adjacent monitoring nodes directly hydraulically connected to the vulnerable nodes are extracted. After the same spatiotemporal matching process, synchronous water quality and synchronous water quantity sequences are generated. This ensures that the extracted data are valid synchronous sequences.

[0151] The complete location information of vulnerable nodes, the synchronous water quality and water quantity sequences of upstream adjacent nodes, and the synchronous water quality and water quantity sequences of downstream adjacent nodes are systematically integrated in a unified format and output as a complete set, forming standardized reference data that can be directly used for water network resilience improvement and pipeline safety regulation and intervention.

[0152] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0153] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting the coupled resilience of water quality and quantity at water network nodes, characterized in that, The method includes: R1: Perform spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronous water quality series and synchronous water quantity series of each monitoring node. R2: Based on the synchronized water quality sequence and the synchronized water quantity sequence, construct the water quality-water quantity coupling feature vector of the monitoring node, and perform node resilience coupling to obtain the node resilience coupling coefficient of each monitoring node. R3: Based on the node resilience coupling coefficient of each monitoring node and combined with the topological connection relationship of the target water network, generate a distribution map of the water quality and quantity coupling resilience of the target water network, and complete the detection of the water quality and quantity coupling resilience of the water network nodes.

2. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 1, characterized in that, The original water quality time series and original water quantity time series of each monitoring node in the target water network include: Obtain the original water quality time series and original water quantity time series of each monitoring node in the target water network; The original water quality time series includes chemical oxygen demand concentration, ammonia nitrogen concentration, and dissolved oxygen concentration; The original water volume time series includes time series data of two types of water volume parameters: flow rate and water level.

3. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 1, characterized in that, The process of performing spatiotemporal resolution matching on the original water quality time series and original water quantity time series of each monitoring node in the target water network to obtain the synchronized water quality sequence and synchronized water quantity sequence of each monitoring node includes: Using the sampling time points of the original water volume time series as the reference time axis, the original water quality time series is interpolated using a piecewise linear interpolation method to obtain a preliminary aligned water quality series. Based on the water flow propagation time between adjacent monitoring nodes in the target water network, the preliminary aligned water quality sequence is spatially lag corrected to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process. The water volume data corresponding to the missing water quality data time points in the original water volume time series are removed, and the remaining water volume data after removal are rearranged in chronological order to obtain a synchronized water volume series.

4. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 3, characterized in that, The step of performing spatial lag correction on the initially aligned water quality sequence based on the water flow propagation time between adjacent monitoring nodes in the target water network to obtain a synchronized water quality sequence that is synchronized with the current node's water volume data in the physical propagation process includes: Obtain the water flow propagation time between the upstream monitoring node and the current monitoring node; The water propagation time is obtained by dividing the length of the hydraulic transport path between the upstream monitoring node and the current monitoring node by the average flow velocity of the hydraulic transport path. Using the water flow propagation time as the lag time, the water quality parameter values ​​corresponding to the upstream monitoring nodes in the preliminary aligned water quality sequence are shifted backward according to the lag time, so that the shifted water quality parameter values ​​of the upstream monitoring nodes are aligned in time to the sampling time of the water volume data of the current monitoring node. The water quality parameter values ​​of the upstream monitoring node after translation are fused with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence.

5. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 4, characterized in that, The process of fusing the water quality parameter values ​​of the translated upstream monitoring node with the preliminary aligned water quality sequence of the current monitoring node to generate the synchronized water quality sequence includes: The water quality parameter values ​​of the upstream monitoring node after translation are merged with the preliminary aligned water quality sequence of the current monitoring node itself; If the current monitoring node has its own water quality interpolation result and the translational water quality data from the upstream node at a certain sampling time, then the weighted average of its own water quality interpolation result and the translational water quality data from the upstream node is taken to obtain the synchronous water quality value. If only one type of data is available, the current data will be used directly as the synchronous water quality value at the sampling time. All synchronized water quality values ​​are integrated into the synchronized water quality sequence.

6. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 1, characterized in that, The step of constructing a water quality-quantity coupled feature vector for the monitoring node based on the synchronized water quality sequence and the synchronized water quantity sequence includes: Extract the water quality parameter values ​​of the current monitoring node at the detection time from the synchronized water quality sequence, and use them as the absolute state components of the water quality; Extract the water volume parameter values ​​of the current monitoring node at the same detection time from the synchronous water volume sequence, and use them as the absolute state components of the water volume; Based on the current detection time, the synchronous water proton sequence and synchronous water quantum sequence within a set time window are extracted. The coefficient of variation of each water quality parameter in the synchronous water proton sequence within the set time window is calculated, and the rate of change of each water quantity parameter in the synchronous water quantum sequence within the set time window is calculated. The ratio of the coefficient of variation of each water quality parameter to the rate of change of the corresponding water quantity parameter is taken as the relative water quantity change component of the water quality parameter, and all the relative water quantity change components are combined into a relative water quantity change component. The absolute state component of water quality, the absolute state component of water quantity, and the relative change component of water quality and water quantity are spliced ​​together in dimensional order to obtain a water quality-water quantity coupled feature vector.

7. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 6, characterized in that, The node resilience coupling process, which involves obtaining the node resilience coupling coefficient for each monitoring node, includes: Obtain the preset water quality absolute state reference vector, water quantity absolute state reference vector, and water quality relative water quantity change reference value; Each element in the absolute water quality state reference vector corresponds to a standard reference concentration of a water quality parameter. Each element in the absolute state reference vector of water volume corresponds to a standard reference value of a water volume parameter. The water quality absolute state component of the monitoring node is divided by the corresponding element of the water quality absolute state reference vector to obtain the normalized water quality absolute state vector. The normalized absolute water state vector is obtained by dividing the corresponding element of the absolute water state component of the monitoring node by the absolute water state reference vector. The normalized absolute water quality state vector and the normalized absolute water quantity state vector are subjected to a power-weighted multiplication and a water quantity change correction to obtain the nodal resilience coupling coefficient.

8. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 7, characterized in that, The formula for calculating the node toughness coupling coefficient is as follows: in, Let be the node's toughness coupling coefficient. For consecutive multiplication, The first element in the normalized absolute state vector of water quality One element, Let be the dimension of the absolute state components of the water quality. The first in the normalized absolute state vector of water quantity One element, For the preset first The resilience contribution index of each water quality parameter Let be the dimension of the absolute state component of the water volume. For the preset first The resilience contribution index of each water quantity parameter, The preset coupling adjustment index, This refers to the component representing the relative change in water quality and quantity. This is the baseline value for the relative change in water quality and water quantity.

9. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 1, characterized in that, Based on the node resilience coupling coefficients of each monitoring node and combined with the topological connectivity of the target water network, a water quality and quantity coupling resilience distribution map of the target water network is generated, completing the detection of the water quality and quantity coupling resilience status of the water network nodes, including: A water network topology graph is constructed using each monitoring node of the target water network as a graph node and the actual hydraulic transport paths between each monitoring node as graph edges. The node resilience coupling coefficient of each monitoring node is assigned as the attribute value of the corresponding graph node. According to the preset toughness level classification rules, the numerical range of the node toughness coupling coefficient is divided into continuous toughness level intervals, and the toughness level interval to which each graph node belongs is determined. On the water network topology map, different visual markers are assigned to graph nodes in different resilience level ranges, wherein the visual markers include color markers, shape markers, or size markers; The graph nodes carrying the visual markers of resilience level are drawn according to the graph edge connection relationship of the water network topology graph to generate the water quality and water quantity coupled resilience state distribution map, and the water quality and water quantity coupled resilience state distribution map is output as the detection result of the water quality and water quantity coupled resilience state of the water network nodes.

10. The method for detecting the coupled resilience of water quality and quantity at water network nodes as described in claim 1, characterized in that, The node resilience coupling coefficient of the monitoring node also includes: When the node resilience coupling coefficient of the monitoring node in the target water network is detected to be lower than the preset node resilience threshold, the current node is marked as a vulnerable node. The location information of the vulnerable nodes and the synchronous water quality and water quantity sequences of adjacent upstream and downstream nodes are output as reference data for resilience enhancement intervention in the target water network.

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