River intelligent governance monitoring method and system based on complex network and application thereof

Abstract

The application discloses a river intelligent management monitoring method and system based on a complex network and application thereof, and ingeniously realizes monitoring linkage through ground-air-space, utilizes a ground sensor group as a normal monitoring part, triggers air / space observation when abnormal monitoring feedback occurs, constructs a time-varying relation graph through a desourced sensor sequence data in the aspect of data collection, and effectively discovers overall network abnormality based on transition detection of a correlation, partial correlation and mutual information matrix and a corresponding baseline; in combination with calculation of a node abnormality degree, an abnormal monitoring node in a river network can be quickly located; the application realizes a self-adaptive monitoring closed loop of "letting the ground command the air / space", plays the high-precision and high-frequency advantages of the ground sensor, takes into account the planar coverage and rapid response of the remote sensing technology, and significantly improves the real-time performance, accuracy and resource utilization efficiency of river pollution monitoring.

Description

Technical Field

[0001] This invention relates to the fields of river monitoring technology and computational model application technology, and in particular to a method, system and application of intelligent river governance monitoring based on complex networks. Background Technology

[0002] Traditional river pollution monitoring mainly relies on ground-based in-situ sensors or periodic manual sampling. While this method offers high quantitative accuracy, its spatial coverage is limited, making it difficult to detect sudden pollution from tributaries or upstream discharge outlets in a timely manner. Furthermore, its minute-to-hour temporal resolution cannot adequately capture short-term peaks in pollution pulse emissions. In recent years, drones and satellite remote sensing technologies have been applied in water environment monitoring due to their advantages of acquiring large-scale, rapid areal information. However, they are easily limited by conditions such as cloud cover, rain, nighttime, shaded areas, or extremely narrow river sections. Moreover, remote sensing products are mostly "optically active" indicators such as turbidity and chlorophyll, making it difficult to directly retrieve accurate values ​​of "optically inactive" pollutant concentrations such as COD and ammonia nitrogen.

[0003] However, due to the high cost of satellite remote sensing and drone aerial photography, current technologies lack a real-time adaptive monitoring scheme that can organically couple high-precision continuous ground monitoring with large-scale air-space remote sensing coverage. This would allow for the timely capture of pollution pulses using high-frequency data from sensor arrays, and precise guidance for aerial / satellite photography when necessary, quickly supplementing area information with minimal patrol missions, thereby constructing an integrated "point-area-network" monitoring closed loop. The lack of such a mechanism often leads to over-reliance on ground-based monitoring points while neglecting the risks across the entire watershed, or to the blind initiation of aerial and satellite missions, wasting resources and missing the best opportunity for evidence collection. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a river intelligent governance monitoring method, system and its application based on complex networks that is reliable in implementation, flexible in application, and capable of initiating drone aerial photography and satellite remote sensing monitoring in a timely manner according to the monitoring situation, so as to comprehensively optimize the scheduling of resources.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0006] A smart river management and monitoring method based on complex networks, wherein sensor arrays for monitoring water quality, environmental conditions and / or pollutants are deployed on the main stream and tributaries of a pre-defined river basin, comprising:

[0007] S01. Acquire multi-source monitoring data within the river basin through sensor arrays, collect and preprocess the data to generate a monitoring data sequence;

[0008] S02, Construct a weighted directed graph of the river network. GThe intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data.

[0009] S03. Calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix;

[0010] S04. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, and then the generation of the space / sky observation task is triggered.

[0011] S05. Based on the air / space observation mission, schedule drones and / or satellites to collect data on the corresponding watershed area of ​​the river.

[0012] As one possible implementation, further, in this solution S01, the multi-source monitoring data includes one or more of the following: water quality data, hydrological data, environmental data, and operating condition data.

[0013] The water quality data includes one or more of the following parameters for the area corresponding to the sensor group: COD, ammonia nitrogen, dissolved oxygen (DO), pH, conductivity, turbidity, and water temperature.

[0014] The hydrological data includes one or more of the following: water level, water flow velocity, and flow rate in the area corresponding to the sensor group.

[0015] The environmental data includes one or more of the following: rainfall, wind speed, and wind direction in the area corresponding to the sensor group.

[0016] The operating data includes the pumping station flow rate, water gate operation, and / or water intake in the area corresponding to the sensor group.

[0017] To facilitate tracking, each sensor group deployed in the river described in this scheme has a unique ID, and the corresponding generated data sequence is also associated with the sensor group ID. In addition, the ID of each sensor group is also associated with its corresponding deployment location information.

[0018] To facilitate the tracing of past situations, in this solution S01, when collecting data, the historical monitoring data of the area corresponding to the sensor group is also associated with it.

[0019] In S01, the preprocessing includes time alignment, outlier removal, and missing value imputation of the multi-source monitoring data collected by the sensor group, as well as de-exogenization processing of some data according to preset conditions; the de-exogenization processing is achieved by establishing a feature matrix of external variables. Then, based on the external variables, the data collected by each sensor group are processed to eliminate the influence of the external variables, as defined below:

[0020]

[0021] in, This refers to multi-source monitoring data after time alignment, outlier removal, and missing value imputation. This refers to the multi-source monitoring data after removing interference from external variables, i.e., the preprocessed monitoring data sequence. is the ridge regression coefficient.

[0022] As a preferred implementation method, preferably, in this scheme S02, the river network weighted directed graph... G Represented as ,in, It is a set of nodes, which contains nodes that represent the areas corresponding to the sensor groups in the watershed, i.e., monitoring points; It is a set of directed edges, which contain directed edges that represent the hydraulic flow direction between nodes, that is, the possible paths for upstream nodes to migrate to downstream nodes. is a weight function that corresponds to the directed edges in the directed edge set. It is used to quantify the connectivity strength or propagation probability between upstream nodes and downstream nodes.

[0023] As a preferred implementation method, preferably, in scheme S02, the weighted directed graph of the river network is constructed. G Subsequently, monitoring data sequences For the input item, where, Number the nodes. , Weighted directed graph of river network G The set of nodes, i.e., the total number of monitoring points; , This represents the sampling time of the sensor group.

[0024] The sliding window length is defined as follows: Step size is Then the first The data time period corresponding to the monitoring data sequence for each window is: ; data segments corresponding to the sliding time window The definition is as follows:

[0025] ;

[0026] Node set Correlation matrix of sensor group corresponding to middle node The definition is as follows:

[0027]

[0028] in, , The first Nodes within a window , Data fragments, It is a rank transformation function. The Pearson correlation function; For the first Nodes within a window , The correlation matrix values ​​between data fragments.

[0029] This scheme uses data segments within a sliding time window for calculating the partial correlation matrix. Estimate covariance Its definition is as follows:

[0030]

[0031] in, For the first Data fragments of a window, For the first Monitoring data sequence of each window The data matrix after column averaging The length of the sliding window. For the first The covariance corresponding to the data fragments in each window.

[0032] This scheme also inverses the covariance matrix to obtain the accuracy matrix. Its definition is: Then calculate the partial correlation matrix. Its definition is as follows:

[0033]

[0034] in, , which represents a node ,node The net linear dependence strength, i.e., the partial correlation value, For the accuracy matrix in the th Okay, number The correlation matrix values ​​of the columns, i.e., the nodes ,node In the The inverse matrix of the linear covariance of each window ; , The precision matrix is ​​located on the diagonal. , No. Each element represents a node. ,node The reciprocal of its own conditional contrast after removing the influence of other nodes; The value range is [-1, 1].

[0035] In this scheme, the mutual information matrix To analyze data segments within a sliding time window The kNN neighbor algorithm is used for estimation, and its definition is as follows:

[0036]

[0037] in, In the first Within each window, nodes With nodes Mutual information estimates between them This is the derivative of the gamma function, i.e., the Digamma function. The number of neighbors in the kNN nearest neighbor algorithm. The sliding window length is the total number of nodes. The neighbor numbers in the kNN nearest neighbor algorithm. , These represent the nodes respectively. ,node Counting neighbors within a one-dimensional projected space.

[0038] This solution involves selecting a length. The time window is used as the baseline window, and its length is greater than that of the sliding window. Within the time period of the baseline window, the arithmetic mean of the relation matrices for each sliding window is calculated to obtain the baseline matrices corresponding to the correlation matrix, partial correlation matrix, and mutual information matrix. , , .

[0039] As a preferred implementation method, solution S03 preferably includes:

[0040] The correlation matrix at time t Partial correlation matrix and / or mutual information matrix Baseline matrix obtained from long-period data estimation , , The calculation is performed to obtain the corresponding global transition degree, i.e., the graph structure transition degree, and the calculation formula is as follows:

[0041]

[0042]

[0043]

[0044] in, It is the Frobenius norm. , , These are related to the correlation matrix. Partial correlation matrix and / or mutual information matrix The global transition degree is used to measure the overall deviation of the linear synchronization of all node pairs in a weighted directed graph of a river network. G When several key edges in the middle suddenly rise, , , The corresponding increase serves as a notification of an anomaly in the overall network.

[0045] In this scheme, the calculation of node anomaly includes calculating the row norm and maximum deviation for each node, and the formula is as follows:

[0046]

[0047] in, , Number the nodes. For about nodes row norm, For about nodes The maximum deviation, For node at time t , The correlation matrix values, For nodes , The baseline matrix values, This represents the total number of nodes, i.e., the total number of monitoring points.

[0048] As a preferred implementation method, solution S04 preferably includes:

[0049] The graph structure transition degree and node anomaly degree of the nodes corresponding to the sensor group are compared and judged according to the preset threshold. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, the sensor group ID of the node corresponding to the abnormal event is recorded, and at the same time, the generation of the space / sky observation task is triggered.

[0050] As an example, the correlation matrix Partial correlation matrix Mutual information matrix Corresponding global transition degree , , The system compares the data with the corresponding preset thresholds. If one of the thresholds exceeds the preset threshold, an abnormal event is identified. The sensor group ID of the node corresponding to the abnormal event is recorded, and an air / sky observation task is triggered.

[0051] As a preferred implementation method, solution S05 preferably includes:

[0052] The system counts the number of nodes that record abnormal events. When the number of nodes exceeds a preset value, it coordinates drones and satellites to jointly collect data on the corresponding watershed area of ​​the river. When the number of nodes is less than or equal to the preset value, it coordinates drones to collect data on the corresponding watershed area of ​​the river.

[0053] As a preferred implementation method, in S05 of this solution, when selecting a UAV for data collection, the deployment location is obtained according to the sensor group ID, and then the flight trajectory of the UAV is planned to generate flight route information, which is then used by the UAV to collect data.

[0054] As a preferred implementation method, this solution plans the UAV's flight path with the goal of minimizing the flight path, which includes the following:

[0055] The node information of recorded abnormal events is collected, and the two-dimensional position information of the corresponding sensor group in the river is obtained based on the node information. Then, the Euclidean distance between different nodes is calculated, which is defined as follows:

[0056]

[0057] in, For nodes To node distance, , For nodes Coordinate values ​​in two-dimensional space, , For nodes Coordinates in two-dimensional space.

[0058] As a preferred implementation method, the objective function of this scheme, which aims to minimize the navigation path, is defined as follows:

[0059]

[0060] in, The number of nodes that recorded abnormal events. For path length, , Number the nodes. For nodes To node distance, It is a binary degree constraint variable, used to ensure node... The distance to all other nodes has been calculated once.

[0061] Based on the above, this solution also proposes a river intelligent governance monitoring system based on complex networks, which includes:

[0062] The monitoring terminal management unit is connected to multiple sensor groups, which are deployed on the main stream and tributaries of a river in a predetermined basin and are used to monitor water quality, environmental conditions and / or pollutants. The monitoring terminal management unit acquires multi-source monitoring data in the river basin through the sensor groups, collects and preprocesses the data, and generates a monitoring data sequence.

[0063] Complex network units are used to construct weighted directed graphs of river networks based on monitoring data sequences. G The intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data.

[0064] The data judgment unit is used to calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix;

[0065] The task generation unit is used to determine the existence of an abnormal event when one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, and then trigger the generation of air / sky observation tasks.

[0066] The task scheduling module is used to schedule UAVs and / or satellites to collect data on the corresponding watershed area of ​​the river according to the air / space observation task.

[0067] Based on the above, this solution also proposes a remote monitoring and management method for rivers, which applies the aforementioned intelligent river governance and monitoring method based on complex networks; it includes:

[0068] A01. Receive monitoring instructions regarding river pollution and execute S01-S05;

[0069] A02: Obtain the data collected in S05 and generate a monitoring and management report.

[0070] Compared with existing technologies, the present invention, employing the above technical solution, has the following advantages: This solution ingeniously provides monitoring support for monitoring nodes within a river basin through ground-air-space monitoring linkage. It utilizes ground-based sensor arrays as a routine monitoring component, triggering air / space observations when anomalies occur in the monitoring feedback, thus providing a ground-based monitoring foundation for intelligent river monitoring. Regarding data acquisition, this solution constructs a time-varying relationship graph from the de-exogenized sensor sequence data and effectively detects overall network anomalies based on correlation, partial correlation, mutual information matrices, and baseline transition detection. Furthermore, combined with the calculation of node anomaly degrees, this enables the rapid location of abnormal monitoring nodes within the river network. By statistically analyzing the number of nodes recording abnormal events, this solution aims to optimize monitoring costs, improve targeting, and minimize flight paths. It plans UAV aerial photography routes and timely dispatches satellite remote sensing for monitoring data collection. Compared to existing technologies, this solution achieves a balance between timeliness and accuracy in intelligent river governance monitoring. Aerial / satellite photography is triggered only when necessary, ensuring spatiotemporal alignment between area information and point data, significantly improving resource utilization efficiency. Furthermore, based on global transition degree and node-level anomaly degree positioning, this solution can significantly narrow down the region of interest, avoiding ineffective patrols. The use of the Euclidean shortest path model for route planning reduces flight mileage and the number of missions. This solution achieves an adaptive monitoring closed loop of "ground-based command of air / space," leveraging the high precision and high frequency advantages of ground sensors while also considering the area coverage and rapid response of remote sensing technology, significantly improving the real-time performance, accuracy, and resource utilization efficiency of river pollution monitoring. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a simplified implementation flowchart of the monitoring method in this scheme;

[0073] Figure 2 This is a simplified diagram of the monitoring method in this scheme, in which sensor groups are deployed on the main stream and tributaries of the river to form monitoring nodes (indicated by dots in the figure);

[0074] Figure 3 This is a diagram illustrating the flight path of the drone collecting aerial data above the river in the monitoring method of this scheme (shown as a dashed line in the diagram);

[0075] Figure 4 This is a simplified connection diagram of the unit modules of the monitoring system in this solution. Detailed Implementation

[0076] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] like Figure 1 As shown in the figure, this embodiment presents a river intelligent governance and monitoring method based on complex networks. Sensor arrays for monitoring water quality, environmental conditions, and / or pollutants are deployed on the main stream and tributaries of a pre-defined river basin (see reference). Figure 2 As shown), it includes:

[0078] S01. Acquire multi-source monitoring data within the river basin through sensor arrays, collect and preprocess the data to generate a monitoring data sequence;

[0079] S02, Construct a weighted directed graph of the river network. G The intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data.

[0080] S03. Calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix;

[0081] S04. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, and then the generation of the space / sky observation task is triggered.

[0082] S05. Based on the air / space observation mission, schedule drones and / or satellites to collect data on the corresponding watershed area of ​​the river.

[0083] This solution acquires multi-source monitoring data within the river basin through sensor arrays, using it as daily monitoring information to provide back-end management personnel with on-site data support for obtaining water quality information at various monitoring locations in the river. The constructed weighted directed graph of the river network helps to associate the nodes where the sensor arrays are deployed in the river network, providing correlation judgment for subsequent overall analysis.

[0084] When an anomalies occur in a river, the monitoring nodes in the corresponding area will experience abnormal monitoring data. By using the correlation matrix and partial correlation matrix formed by the monitoring data sequence obtained by the node and its corresponding baseline matrix, the graph structure transition degree and node anomaly degree can be calculated, enabling quantitative analysis of the data. If one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, an abnormal event is determined to exist, which in turn triggers the generation of air / space observation tasks, providing linkage measures to ensure more refined and global river condition monitoring in the next step.

[0085] Regarding the data acquisition type, as a possible implementation method, in this solution S01, the multi-source monitoring data includes one or more of the following: water quality data, hydrological data, environmental data, and operating condition data.

[0086] The water quality data includes one or more of the following parameters for the area corresponding to the sensor group: COD, ammonia nitrogen, dissolved oxygen (DO), pH, conductivity, turbidity, and water temperature.

[0087] The hydrological data includes one or more of the following: water level, water flow velocity, and flow rate in the area corresponding to the sensor group.

[0088] The environmental data includes one or more of the following: rainfall, wind speed, and wind direction in the area corresponding to the sensor group.

[0089] The operating data includes the pumping station flow rate, water gate operation, and / or water intake in the area corresponding to the sensor group.

[0090] To facilitate tracking, each sensor group deployed in the river described in this scheme has a unique ID, and the corresponding generated data sequence is also associated with the sensor group ID. In addition, the ID of each sensor group is also associated with its corresponding deployment location information.

[0091] To facilitate the tracing of past situations, in this solution S01, when collecting data, the historical monitoring data of the area corresponding to the sensor group is also associated with it.

[0092] In S01, the preprocessing includes time alignment, outlier removal, and missing value imputation of the multi-source monitoring data collected by the sensor group, as well as de-exogenization processing of some data according to preset conditions; the de-exogenization processing is achieved by establishing a feature matrix of external variables. Then, based on the external variables, the data collected by each sensor group are processed to eliminate the influence of the external variables, as defined below:

[0093]

[0094] in, This refers to multi-source monitoring data after time alignment, outlier removal, and missing value imputation. This refers to the multi-source monitoring data after removing interference from external variables, i.e., the preprocessed monitoring data sequence. is the ridge regression coefficient.

[0095] In the construction of complex networks, as a preferred implementation method, the river network weighted directed graph in this scheme S02 is preferred. G Represented as ,in, It is a set of nodes, which contains nodes that represent the areas corresponding to the sensor groups in the watershed, i.e., monitoring points; It is a set of directed edges, which contain directed edges that represent the hydraulic flow direction between nodes, that is, the possible paths for upstream nodes to migrate to downstream nodes. is a weight function that corresponds to the directed edges in the directed edge set. It is used to quantify the connectivity strength or propagation probability between upstream nodes and downstream nodes.

[0096] Based on the above, as a preferred implementation method, preferably, in scheme S02, when constructing the river network weighted directed graph... G Subsequently, monitoring data sequences For the input item, where, Number the nodes. , Weighted directed graph of river network G The set of nodes, i.e., the total number of monitoring points; , This represents the sampling time of the sensor group.

[0097] By establishing a weighted directed graph of the river network, it is helpful to associate the monitoring nodes corresponding to the sensor groups scattered in the river network, thus providing a guarantee for subsequent data analysis.

[0098] The sliding window length is defined as follows: Step size is Then the first The data time period corresponding to the monitoring data sequence for each window is: ; data segments corresponding to the sliding time window The definition is as follows:

[0099] ;

[0100] Node set Correlation matrix of sensor group corresponding to middle node The definition is as follows:

[0101]

[0102] in, , The first Nodes within a window , Data fragments, It is a rank transformation function. The Pearson correlation function; For the first Nodes within a window , The correlation matrix values ​​between data fragments.

[0103] This scheme uses data segments within a sliding time window for calculating the partial correlation matrix. Estimate covariance Its definition is as follows:

[0104]

[0105] in, For the first Data fragments of a window, For the first Monitoring data sequence of each window The data matrix after column averaging The length of the sliding window. For the first The covariance corresponding to the data fragments in each window.

[0106] Based on the above, this scheme also inverses the covariance matrix to obtain the accuracy matrix. Its definition is: Then calculate the partial correlation matrix. Its definition is as follows:

[0107]

[0108] in, , which represents a node ,node The net linear dependence strength, i.e., the partial correlation value, For the accuracy matrix in the th Okay, number The correlation matrix values ​​of the columns, i.e., the nodes ,node In the The inverse matrix of the linear covariance of each window ; , The precision matrix is ​​located on the diagonal. , No. Each element represents a node. ,node The reciprocal of its own conditional contrast after removing the influence of other nodes; The value range is [-1, 1].

[0109] In this scheme, the mutual information matrix To analyze data segments within a sliding time window The kNN neighbor algorithm is used for estimation, and its definition is as follows:

[0110]

[0111] in, In the first Within each window, nodes With nodes Mutual information estimates between them This is the derivative of the gamma function, i.e., the Digamma function. The number of neighbors in the kNN nearest neighbor algorithm. The sliding window length is the total number of nodes. The neighbor numbers in the kNN nearest neighbor algorithm. , These represent the nodes respectively. ,node Counting neighbors within a one-dimensional projected space.

[0112] This solution involves selecting a length. The time window is used as the baseline window, and its length is greater than that of the sliding window. Within the time period of the baseline window, the arithmetic mean of the relation matrices for each sliding window is calculated to obtain the baseline matrices corresponding to the correlation matrix, partial correlation matrix, and mutual information matrix. , , .

[0113] In this scheme, the graph structure transition degree is used to express the overall anomaly in the river network, which helps the back-end management personnel to understand the global situation in the river network. As a preferred implementation method, S03 of this scheme preferably includes:

[0114] The correlation matrix at time t Partial correlation matrix and / or mutual information matrix Baseline matrix obtained from long-period data estimation , , The calculation is performed to obtain the corresponding global transition degree, i.e., the graph structure transition degree, and the calculation formula is as follows:

[0115]

[0116]

[0117]

[0118] in, It is the Frobenius norm. , , These are related to the correlation matrix. Partial correlation matrix and / or mutual information matrix The global transition degree is used to measure the overall deviation of the linear synchronization of all node pairs in a weighted directed graph of a river network. G When several key edges in the middle suddenly rise, , , The corresponding increase serves as a notification of an anomaly in the overall network.

[0119] In this scheme, node anomaly is used to express the monitoring status of the node area where the sensor group is located. It enables back-end management personnel to quickly locate the source of the anomaly and its development trajectory when global anomalies occur. The calculation of node anomaly includes calculating the row norm and maximum deviation for each node, and the formula is as follows:

[0120]

[0121] in, , Number the nodes. For about nodes row norm, For about nodes The maximum deviation, For node at time t , The correlation matrix values, For nodes , The baseline matrix values, This represents the total number of nodes, i.e., the total number of monitoring points.

[0122] In terms of air-space cooperation, as a preferred implementation method, this solution S04 preferably includes:

[0123] The graph structure transition degree and node anomaly degree of the nodes corresponding to the sensor group are compared and judged according to the preset threshold. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, the sensor group ID of the node corresponding to the abnormal event is recorded, and at the same time, the generation of the space / sky observation task is triggered.

[0124] As a preferred implementation method, solution S05 preferably includes:

[0125] The system counts the number of nodes that record abnormal events. When the number of nodes exceeds a preset value, it coordinates drones and satellites to jointly collect data on the corresponding watershed area of ​​the river. When the number of nodes is less than or equal to the preset value, it coordinates drones to collect data on the corresponding watershed area of ​​the river.

[0126] For example, if there are 100 nodes in total and 21 nodes record abnormal events, exceeding the preset first threshold of 20, then a drone and a satellite will be dispatched to jointly collect data on the corresponding watershed area of ​​the river. If 19 nodes record abnormal events, then a drone will be dispatched to collect data on the corresponding watershed area of ​​the river.

[0127] This solution flexibly selects drones to collect data from specific river basins by statistically analyzing the number of nodes recording abnormal events, thereby improving the reliability of aerial river monitoring. In the event of large-scale anomalies, data collection using satellite remote sensing provides timely, reliable, and diverse data for back-end monitoring personnel, offering a data-driven basis for developing intervention measures and improving the efficiency and relevance of monitoring responses.

[0128] In this scheme, since river networks generally have a large span, in order to improve the efficiency and flexibility of drone aerial photography and increase the amount of effective data collected in a single patrol, as a preferred implementation method, in S05 of this scheme, when selecting a drone for data collection, the deployment location is also obtained according to the sensor group ID, and then the drone's flight trajectory is planned to generate flight route information, and the drone collects data according to the flight route information.

[0129] For path planning, this solution aims to minimize the flight path when planning the UAV's flight trajectory, which includes the following:

[0130] The node information of recorded abnormal events is collected, and the two-dimensional position information of the corresponding sensor group in the river is obtained based on the node information. Then, the Euclidean distance between different nodes is calculated, which is defined as follows:

[0131]

[0132] in, For nodes To node distance, , For nodes Coordinate values ​​in two-dimensional space, , For nodes Coordinates in two-dimensional space.

[0133] As a preferred implementation method, the objective function of this scheme, which aims to minimize the navigation path, is defined as follows:

[0134]

[0135] in, The number of nodes that recorded abnormal events. For path length, , Number the nodes. For nodes To node distance, It is a binary degree constraint variable, used to ensure node... The distance to all other nodes has been calculated once.

[0136] in, Figure 3 An example of path planning for aerial photography using a drone is shown.

[0137] Based on the above, this solution also proposes a remote monitoring and management method for rivers, which applies the aforementioned intelligent river governance and monitoring method based on complex networks; it includes:

[0138] A01. Receive monitoring instructions regarding river pollution and execute S01-S05;

[0139] A02: Obtain the data collected in S05 and generate a monitoring and management report.

[0140] Combination Figure 4 As shown above, this solution also proposes a river intelligent governance and monitoring system based on complex networks, which includes:

[0141] The monitoring terminal management unit is connected to multiple sensor groups, which are deployed on the main stream and tributaries of a river in a predetermined basin and are used to monitor water quality, environmental conditions and / or pollutants. The monitoring terminal management unit acquires multi-source monitoring data in the river basin through the sensor groups, collects and preprocesses the data, and generates a monitoring data sequence.

[0142] Complex network units are used to construct weighted directed graphs of river networks based on monitoring data sequences. G The intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data.

[0143] The data judgment unit is used to calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix;

[0144] The task generation unit is used to determine the existence of an abnormal event when one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, and then trigger the generation of air / sky observation tasks.

[0145] The task scheduling module is used to schedule UAVs and / or satellites to collect data on the corresponding watershed area of ​​the river according to the air / space observation task.

[0146] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0147] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A smart river management and monitoring method based on complex networks, wherein sensor arrays for monitoring water quality and / or environmental conditions are deployed on the main stream and tributaries of a pre-defined river basin, wherein... Environmental conditions include environmental data, which includes one or more of the following: rainfall, wind speed, and wind direction in the area corresponding to the sensor group; the monitoring method includes: S01. Acquire multi-source monitoring data within the river basin through sensor arrays, collect and preprocess the data to generate a monitoring data sequence; S02, Construct a weighted directed graph of the river network. G The intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data. S03. Calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix; S04. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, and then the aerospace observation mission is generated. S05. Based on the aerospace observation mission, dispatch drones and / or satellites to collect data on the corresponding watershed area of ​​the river.

2. The intelligent river management and monitoring method based on complex networks as described in claim 1, characterized in that, In S01, the multi-source monitoring data includes one or more of the following: water quality data, hydrological data, environmental data, and operating condition data. The water quality data includes one or more of the following: COD, ammonia nitrogen, dissolved oxygen (DO), pH, conductivity, turbidity, and water temperature in the area corresponding to the sensor group. The hydrological data includes one or more of the following: water level, water flow velocity, and flow rate in the area corresponding to the sensor group. The operating data includes the pumping station flow rate, water gate operation, and / or water intake in the area corresponding to the sensor group; Each sensor group deployed in the river has a unique ID, and the corresponding data sequence it generates is also associated with the sensor group's ID. In S01, when collecting data, the historical monitoring data of the area corresponding to the sensor group is also associated with it; In S01, the preprocessing includes time alignment, outlier removal, missing value filling, and de-exogenization of some data according to preset conditions for the multi-source monitoring data collected by the sensor group. The de-exogenization process involves establishing a feature matrix of exogenous variables. Then, based on the external variables, the data collected by each sensor group are processed to eliminate the influence of the external variables, as defined below: in, This refers to multi-source monitoring data after time alignment, outlier removal, and missing value imputation. This refers to the multi-source monitoring data after removing interference from external variables, i.e., the preprocessed monitoring data sequence. is the ridge regression coefficient.

3. The intelligent river management and monitoring method based on complex networks as described in claim 1 or 2, characterized in that, In S02, the weighted directed graph of the river network G Represented as ,in, It is a set of nodes, which contains nodes that represent the areas corresponding to the sensor groups in the watershed, i.e., monitoring points; It is a set of directed edges, which contain directed edges that represent the hydraulic flow direction between nodes, that is, the possible paths for upstream nodes to migrate to downstream nodes. is the weight function, which corresponds to the directed edges of the directed edge set, and is used to quantify the connectivity strength or propagation probability between upstream nodes and downstream nodes. In S02, the weighted directed graph of the river network is constructed. G Subsequently, monitoring data sequences For the input item, where, Number the nodes. , Weighted directed graph of river network G The set of nodes, i.e., the total number of monitoring points; , The sampling time of the sensor group; Define the sliding window length as Step size is Then the first The data time period corresponding to the monitoring data sequence for each window is: ; data segments corresponding to the sliding time window The definition is as follows: ； Node set Correlation matrix of sensor group corresponding to middle node The definition is as follows: in, , The first Nodes within a window , Data fragments, It is a rank transformation function. The Pearson correlation function; For the first Nodes within a window , The correlation matrix values ​​between data fragments; When calculating the partial correlation matrix, data segments under a sliding time window are used. Estimate covariance Its definition is as follows: in, For the first Data fragments of a window, For the first Monitoring data sequence of each window The data matrix after column averaging The length of the sliding window. For the first The covariance of each window's data segment; Inverse the covariance matrix to obtain the precision matrix. Its definition is: Then calculate the partial correlation matrix. Its definition is as follows: in, , which represents a node ,node The net linear dependence strength, i.e., the partial correlation value, For the accuracy matrix in the th Okay, number The correlation matrix values ​​of the columns, i.e., the nodes ,node In the The inverse matrix of the linear covariance of each window ; , The precision matrix is ​​located on the diagonal. , No. Each element represents a node. ,node The reciprocal of its own conditional contrast after removing the influence of other nodes; The range of values ​​for is [-1, 1]; The mutual information matrix To analyze data segments within a sliding time window The kNN neighbor algorithm is used for estimation, and its definition is as follows: in, In the first Within each window, nodes With nodes Mutual information estimates between them This is the derivative of the gamma function, i.e., the Digamma function. The number of neighbors in the kNN nearest neighbor algorithm. The sliding window length is the total number of nodes. The neighbor numbers in the kNN nearest neighbor algorithm. , These represent the nodes respectively. ,node Counting neighbors in a one-dimensional projected space; By selecting length The time window is used as the baseline window, and its length is greater than that of the sliding window. Within the time period of the baseline window, the arithmetic mean of the relation matrices for each sliding window is calculated to obtain the baseline matrices corresponding to the correlation matrix, partial correlation matrix, and mutual information matrix. , , .

4. The intelligent river management and monitoring method based on complex networks as described in claim 3, characterized in that, S03 includes: The correlation matrix at time t Partial correlation matrix and / or mutual information matrix Baseline matrix obtained from long-period data estimation , , The calculation is performed to obtain the corresponding global transition degree, i.e., the graph structure transition degree, and the calculation formula is as follows: in, It is the Frobenius norm. , , These are related to the correlation matrix. Partial correlation matrix and / or mutual information matrix The global transition degree is used to measure the overall deviation of the linear synchronization of all node pairs in a weighted directed graph of a river network. G When several key edges in the middle suddenly rise, , , The corresponding increase serves as a notification of an anomaly in the overall network. The calculation of node anomaly includes calculating the row norm and maximum deviation for each node, using the following formula: in, , Number the nodes. For about nodes row norm, For about nodes The maximum deviation, For node at time t , The correlation matrix values, For nodes , The baseline matrix values, This represents the total number of nodes, i.e., the total number of monitoring points.

5. The intelligent river management and monitoring method based on complex networks as described in claim 4, characterized in that, S04 includes: The graph structure transition degree and node anomaly degree of the nodes corresponding to the sensor group are compared and judged according to the preset threshold. When one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, it is determined that there is an abnormal event, the sensor group ID of the node corresponding to the abnormal event is recorded, and the aerospace observation mission is triggered.

6. The intelligent river management and monitoring method based on complex networks as described in claim 5, characterized in that, S05 includes: The system counts the number of nodes that record abnormal events. When the number of nodes exceeds a preset value, it coordinates drones and satellites to jointly collect data on the corresponding watershed area of ​​the river. When the number of nodes is less than or equal to the preset value, it coordinates drones to collect data on the corresponding watershed area of ​​the river.

7. The intelligent river management and monitoring method based on complex networks as described in claim 6, characterized in that, In S05, when selecting a drone for data collection, the deployment location is obtained based on the sensor group ID. Then, the drone's flight path is planned to generate flight route information, which the drone then uses to collect data.

8. The intelligent river management and monitoring method based on complex networks as described in claim 7, characterized in that, When planning the flight path of a drone, the goal is to minimize the flight path, which includes the following: The node information of recorded abnormal events is collected, and the two-dimensional position information of the corresponding sensor group in the river is obtained based on the node information. Then, the Euclidean distance between different nodes is calculated, which is defined as follows: in, For nodes To node distance, , For nodes Coordinate values ​​in two-dimensional space, , For nodes Coordinate values ​​in two-dimensional space; The objective function for planning with the goal of minimizing the navigation path is defined as follows: in, The number of nodes that recorded abnormal events. For path length, , Number the nodes. For nodes To node distance, It is a binary degree constraint variable, used to ensure node... The distance to all other nodes has been calculated once.

9. A river intelligent governance and monitoring system based on complex networks, characterized in that, It includes: The monitoring terminal management unit is connected to multiple sensor groups, which are deployed on the main stream and tributaries of a river within a pre-defined basin and are used to monitor water quality and / or environmental conditions. The environmental conditions include environmental data, which includes at least one of the following: rainfall, wind speed, and wind direction in the area corresponding to each sensor group. The monitoring terminal management unit acquires multi-source monitoring data within the river basin through the sensor groups, aggregates and preprocesses the data, and generates a monitoring data sequence. Complex network units are used to construct weighted directed graphs of river networks based on monitoring data sequences. G The intra-group relationship of the monitoring data sequence is measured by a sliding time window to obtain the correlation matrix, partial correlation matrix and / or mutual information matrix, and then the corresponding baseline matrix is ​​estimated using long-period data. The data judgment unit is used to calculate the graph structure transition degree and node anomaly degree based on the correlation matrix, partial correlation matrix and their corresponding baseline matrix; The task generation unit is used to determine the existence of an abnormal event when one or more of the graph structure transition degree and node anomaly degree exceed the corresponding preset threshold, and then trigger the generation of aerospace observation tasks. The task scheduling module is used to schedule drones and / or satellites to collect data on the corresponding watershed areas of rivers based on aerospace observation tasks.

10. A method for remote monitoring and management of rivers, characterized in that, Its application includes the intelligent river management and monitoring method based on complex networks as described in any one of claims 1 to 8; It includes: A01. Receive monitoring instructions regarding river pollution and execute S01-S05; A02: Obtain the data collected in S05 and generate a monitoring and management report.

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