Drainage system node anomaly monitoring method and device based on spatial prediction

Abstract

The invention relates to a drainage system node anomaly monitoring method and device based on spatial prediction. The method comprises the following steps: acquiring spatial position coordinates corresponding to a target drainage system; a plurality of sampling monitoring nodes are selected from the target drainage system, and precipitation indexes corresponding to the sampling monitoring nodes are obtained through calculation according to a preset precipitation index calculation mode; the position coordinates of the sampling monitoring nodes and the precipitation indexes are correlated to serve as key parameters of the sampling monitoring nodes, and a sampling monitoring node set based on the key parameters is obtained; target monitoring drainage system nodes selected from the target drainage system are obtained, position coordinates of the target monitoring drainage system nodes are read, and the target monitoring drainage system nodes are new sampling monitoring nodes not in the sampling monitoring node set; and according to the sampling monitoring node set based on the key parameters and the position coordinates of the target monitoring drainage system node, predicting whether the target monitoring drainage system node has a drainage abnormal risk.

Description

Technical Field

[0001] The present invention relates to the technical field related to drainage system monitoring, and in particular to a method and device for monitoring drainage system node anomalies based on spatial prediction. Background Art

[0002] Drainage system node anomaly monitoring is an important area of ​​urban infrastructure management, which involves the effective operation and maintenance of urban drainage systems. The drainage system is an important part of the city, responsible for collecting, transporting and treating rainwater, domestic sewage and industrial wastewater to ensure the cleanliness of the urban environment and the safety of residents' lives. The normal operation of the drainage system is of great significance to preventing urban waterlogging, protecting the water environment, and ensuring public health.

[0003] At present, abnormal monitoring of drainage system nodes is often completed through regular inspections and evaluations by experienced engineers. On the one hand, this method is very dependent on the level of engineers, and on the other hand, the workload is also very large. Summary of the invention

[0004] The purpose of the present invention is to solve at least one of the deficiencies of the prior art and to provide a method and device for monitoring drainage system node anomalies based on spatial prediction.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: Specifically, a drainage system node anomaly monitoring method based on spatial prediction is proposed, including the following: Obtain the spatial position coordinates corresponding to the target drainage system; Selecting a number of sampling monitoring nodes in the target drainage system, and calculating the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; The location coordinates of the sampling monitoring nodes are associated with their precipitation indicators as key parameters of the sampling monitoring nodes, and a sampling monitoring node set based on the key parameters is obtained; Acquire a target monitoring drainage system node selected in the target drainage system, and read its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; Based on a set of sampling monitoring nodes based on key parameters and the position coordinates of the target monitoring drainage system nodes, it is predicted whether the target monitoring drainage system nodes have a risk of abnormal drainage.

[0006] Further, specifically, the spatial position coordinates corresponding to the target drainage system are obtained by conversion based on the WGS84 World Geodetic System 1984 coordinate system.

[0007] Further, specifically, the precipitation index corresponding to each sampling monitoring node is calculated according to the preset precipitation index calculation method, including: By conducting experiments in advance, with rainfall distribution, intensity and duration as parameters, a calculation model for the impact of rainfall on the urban drainage system based on the parameters is established, and the value calculated by the calculation model is the precipitation index.

[0008] Furthermore, specifically, the computing model is obtained by training and computing based on a CNN neural network model or a random forest model.

[0009] Further, specifically, predicting whether there is a drainage abnormality risk at the target monitoring drainage system node is performed based on the sampling monitoring node set based on the key parameters and the position coordinates of the target monitoring drainage system node, including: The target monitoring drainage system node set is recorded as set Aset, the number of target monitoring drainage system nodes in set Aset is qn, the sequence number of the target monitoring drainage system node in set Aset is qi, qi∈[1,qn], the target monitoring drainage system node with sequence number qi in set Aset is recorded as Aset(qi), and the position coordinates corresponding to Aset(qi) are x(qi), y(qi) and z(qi); Among them, in the position coordinates, x(qi) represents the coordinate of the sampling monitoring node qi on the X-axis, y(qi) represents the coordinate of the target monitoring drainage system node qi on the Y-axis, and z(qi) represents the coordinate of the sampling monitoring node qi on the Z-axis, that is, the height of the target monitoring drainage system node qi from the horizontal plane. Therefore, z(qi) also represents the plane composed of the target monitoring drainage system nodes at the same height from the horizontal plane. The process of predicting the precipitation index of the target monitoring drainage system node includes the following: For any target monitoring drainage system node Aset(qi); Get the two sampling monitoring nodes with the smallest Euclidean distance from Aset(qi) in spatial position coordinates, and denote one of the sampling monitoring nodes as Aset(qi1) and the other sampling monitoring node as Aset(qi2); the position coordinates corresponding to the sampling monitoring node Aset(qi1) are x(qi1), y(qi1) and z(qi1), and the position coordinates corresponding to the sampling monitoring node Aset(qi2) are x(qi2), y(qi2) and z(qi2); Get the precipitation index corresponding to the sampling monitoring node Aset(qi1) and label it as p(qi1), and get the precipitation index corresponding to the sampling monitoring node Aset(qi2) and label it as p(qi2); Get the corresponding plane of z(qi1) as plane(qi1), and get the corresponding plane of z(qi2) as plane(qi2); Obtain a set of position coordinate points in the spatial position coordinates that fall on plane (qi1) as Set (qi1), the number of position coordinate points in Set (qi1) is n1, the sequence number of the position coordinate points in Set (qi1) is i1, i1∈[1, n1], the position coordinate point with sequence number i1 in Set (qi1) is Set (qi1, i1), and the position coordinates corresponding to Set (qi1, i1) are x (qi1, i1), y (qi1, i1) and z (qi1, i1); Get the set of position coordinate points in the spatial position coordinates that fall on plane (qi2) as Set (qi2), the number of position coordinate points in Set (qi2) is n2, the sequence number of the position coordinate points in Set (qi2) is i2, i2∈[1, n2], the position coordinate point with sequence number i1 in Set (qi2) is Set (qi2, i2), and the position coordinates corresponding to Set (qi2, i2) are x (qi2, i2), y (qi2, i2) and z (qi2, i2); The spatial similarity trend(qi) of Aset(qi) is calculated as follows: ; Among them, function E( ) is a function for obtaining the Euclidean distance between two points; Get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi1) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi1)); get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi2) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi2)); Calculate the drainage anomaly risk index p(qi) of the target monitoring drainage system node Aset(qi). The method for calculating p(qi) is p(qi)= [p(plane(qi1))+ p(plane(qi2))]* trend(qi)*α / 2, where α is the adjustment coefficient, which is a manually set value; Determine whether the drainage anomaly risk index p(qi) of the target monitored drainage system node Aset(qi) is greater than the risk index threshold. If so, it is determined that there is a drainage anomaly risk at this point.

[0010] Furthermore, the monitoring method further comprises: Locations with drainage anomaly risks are marked in spatial coordinates and displayed visually.

[0011] The present invention also proposes a drainage system node abnormality monitoring device based on spatial prediction, comprising: A spatial position coordinate construction module is used to obtain the spatial position coordinates corresponding to the target drainage system; A sampling monitoring module is used to select a number of sampling monitoring nodes in the target drainage system, and calculate the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; A sampling monitoring node set making module is used to associate the location coordinates of the sampling monitoring nodes with their precipitation indicators as key parameters of the sampling monitoring nodes, and obtain a sampling monitoring node set based on the key parameters; A data acquisition module, used to acquire a target monitoring drainage system node selected in the target drainage system and read its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; The drainage anomaly risk prediction module is used to predict whether there is a drainage anomaly risk at the target monitoring drainage system node based on a sampling monitoring node set based on key parameters and the position coordinates of the target monitoring drainage system node.

[0012] Furthermore, the monitoring device also includes a visual display module. The visualization display module is used to mark the positions of target monitoring drainage system nodes with drainage abnormality risks in spatial position coordinates and to perform visualization display.

[0013] The beneficial effects of the present invention are: The present invention proposes a drainage system node abnormality monitoring method and device based on spatial prediction, establishes spatial position coordinates for the target drainage system, and performs spatial prediction on the target monitoring drainage system nodes according to the sampled monitoring nodes in the target drainage system, and then issues an alarm to the nodes with drainage abnormality risks in the spatial prediction results, which are then processed by the staff. This can greatly improve the monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features of the present disclosure will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present disclosure represent the same or similar sampling monitoring nodes. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings: Figure 1Shown is a flow chart of the drainage system node anomaly monitoring method based on spatial prediction of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0016] Example 1, reference Figure 1 The present invention proposes a drainage system node abnormality monitoring method based on spatial prediction, which includes the following: Step 110, obtaining the spatial position coordinates corresponding to the target drainage system; Step 120: selecting a number of sampling monitoring nodes in the target drainage system, and calculating the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; Step 130: Associating the location coordinates of the sampling monitoring nodes with their precipitation indexes as key parameters of the sampling monitoring nodes, and obtaining a sampling monitoring node set based on the key parameters; Step 140, obtaining a target monitoring drainage system node selected in the target drainage system, and reading its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; Step 150: predict whether there is a risk of abnormal drainage at the target monitoring drainage system node based on the sampling monitoring node set based on the key parameters and the position coordinates of the target monitoring drainage system node.

[0017] In this embodiment 1, the spatial position coordinates are established for the target drainage system, and the target monitoring drainage system nodes are spatially predicted based on the sampled monitoring nodes in the target drainage system, and then the nodes with drainage abnormality risks in the spatial prediction results are alarmed for subsequent processing by the staff, which can greatly improve the monitoring efficiency.

[0018] As a preferred embodiment of the present invention, specifically, the spatial position coordinates corresponding to the target drainage system are obtained by conversion based on the WGS84 World Geodetic System 1984 coordinate system.

[0019] In this preferred embodiment, the spatial position coordinates corresponding to the target drainage system can be accurately converted through the WGS84 World Geodetic System 1984 coordinate system, thereby determining the position information.

[0020] As a preferred embodiment of the present invention, specifically, the precipitation index corresponding to each sampling monitoring node is calculated according to a preset precipitation index calculation method, including: By conducting experiments in advance, with rainfall distribution, intensity and duration as parameters, a calculation model for the impact of rainfall on the urban drainage system based on the parameters is established, and the value calculated by the calculation model is the precipitation index.

[0021] In this preferred embodiment, considering that the impact of rainfall on the urban drainage system can be understood by analyzing the distribution, intensity and duration of rainfall, a calculation model of the impact of rainfall on the urban drainage system based on the parameters of rainfall distribution, intensity and duration is established. The specific model establishment can be constructed using mature existing technologies.

[0022] As a preferred implementation manner of the present invention, specifically, the calculation model is obtained by training and calculation based on a CNN neural network model or a random forest model.

[0023] In this preferred embodiment, the above-mentioned calculation model can be accurately obtained by using a CNN neural network model or a random forest model.

[0024] As a preferred embodiment of the present invention, specifically, predicting whether there is a drainage abnormality risk at the target monitoring drainage system node based on a sampling monitoring node set based on key parameters and the position coordinates of the target monitoring drainage system node includes: The target monitoring drainage system node set is recorded as set Aset, the number of target monitoring drainage system nodes in set Aset is qn, the sequence number of the target monitoring drainage system node in set Aset is qi, qi∈[1,qn], the target monitoring drainage system node with sequence number qi in set Aset is recorded as Aset(qi), and the position coordinates corresponding to Aset(qi) are x(qi), y(qi) and z(qi); Among them, in the position coordinates, x(qi) represents the coordinate of the sampling monitoring node qi on the X-axis, y(qi) represents the coordinate of the target monitoring drainage system node qi on the Y-axis, and z(qi) represents the coordinate of the sampling monitoring node qi on the Z-axis, that is, the height of the target monitoring drainage system node qi from the horizontal plane. Therefore, z(qi) also represents the plane composed of the target monitoring drainage system nodes at the same height from the horizontal plane. The process of predicting the precipitation index of the target monitoring drainage system node includes the following: For any target monitoring drainage system node Aset(qi); Get the two sampling monitoring nodes with the smallest Euclidean distance from Aset(qi) in spatial position coordinates, and denote one of the sampling monitoring nodes as Aset(qi1) and the other sampling monitoring node as Aset(qi2); the position coordinates corresponding to the sampling monitoring node Aset(qi1) are x(qi1), y(qi1) and z(qi1), and the position coordinates corresponding to the sampling monitoring node Aset(qi2) are x(qi2), y(qi2) and z(qi2); Get the precipitation index corresponding to the sampling monitoring node Aset(qi1) and label it as p(qi1), and get the precipitation index corresponding to the sampling monitoring node Aset(qi2) and label it as p(qi2); Get the corresponding plane of z(qi1) as plane(qi1), and get the corresponding plane of z(qi2) as plane(qi2); Obtain a set of position coordinate points in the spatial position coordinates that fall on plane (qi1) as Set (qi1), the number of position coordinate points in Set (qi1) is n1, the sequence number of the position coordinate points in Set (qi1) is i1, i1∈[1, n1], the position coordinate point with sequence number i1 in Set (qi1) is Set (qi1, i1), and the position coordinates corresponding to Set (qi1, i1) are x (qi1, i1), y (qi1, i1) and z (qi1, i1); Get the set of position coordinate points in the spatial position coordinates that fall on plane (qi2) as Set (qi2), the number of position coordinate points in Set (qi2) is n2, the sequence number of the position coordinate points in Set (qi2) is i2, i2∈[1, n2], the position coordinate point with sequence number i1 in Set (qi2) is Set (qi2, i2), and the position coordinates corresponding to Set (qi2, i2) are x (qi2, i2), y (qi2, i2) and z (qi2, i2); The spatial similarity trend(qi) of Aset(qi) is calculated as follows: ; Among them, function E( ) is a function for obtaining the Euclidean distance between two points; Get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi1) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi1)); get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi2) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi2)); Calculate the drainage anomaly risk index p(qi) of the target monitoring drainage system node Aset(qi). The method for calculating p(qi) is p(qi)= [p(plane(qi1))+ p(plane(qi2))]* trend(qi)*α / 2, where α is the adjustment coefficient, which is a manually set value; Determine whether the drainage anomaly risk index p(qi) of the target monitored drainage system node Aset(qi) is greater than the risk index threshold. If so, it is determined that there is a drainage anomaly risk at this point.

[0025] In this preferred embodiment, through the above method, the drainage abnormality risk index of the target monitoring drainage system node is estimated by sampling monitoring nodes combined with Euclidean distance, and then the drainage abnormality risk is judged, which can accurately determine whether the target monitoring drainage system node has drainage abnormality risk.

[0026] As a preferred embodiment of the present invention, the monitoring method further comprises: Locations with drainage anomaly risks are marked in spatial coordinates and displayed visually.

[0027] The present invention also proposes a drainage system node abnormality monitoring device based on spatial prediction, comprising: A spatial position coordinate construction module is used to obtain the spatial position coordinates corresponding to the target drainage system; A sampling monitoring module is used to select a number of sampling monitoring nodes in the target drainage system, and calculate the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; A sampling monitoring node set making module is used to associate the location coordinates of the sampling monitoring nodes with their precipitation indicators as key parameters of the sampling monitoring nodes, and obtain a sampling monitoring node set based on the key parameters; A data acquisition module, used to acquire a target monitoring drainage system node selected in the target drainage system and read its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; The drainage anomaly risk prediction module is used to predict whether there is a drainage anomaly risk at the target monitoring drainage system node based on a sampling monitoring node set based on key parameters and the position coordinates of the target monitoring drainage system node.

[0028] As a preferred embodiment of the present invention, the monitoring device further includes a visual display module. The visualization display module is used to mark the positions of target monitoring drainage system nodes with drainage abnormality risks in spatial position coordinates and to perform visualization display.

[0029] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0030] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RqndomAccess Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0031] Although the description of the present invention has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims in view of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the above description of the present invention is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that have not yet been foreseen may still represent equivalent changes to the present invention.

[0032] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, it should belong to the protection scope of the present invention. Within the protection scope of the present invention, its technical scheme and / or implementation method can have various modifications and changes.

Claims

1. A drainage system node abnormality monitoring method based on spatial prediction, characterized in that: Includes the following: Obtain the spatial position coordinates corresponding to the target drainage system; Selecting a number of sampling monitoring nodes in the target drainage system, and calculating the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; The location coordinates of the sampling monitoring nodes are associated with their precipitation indicators as key parameters of the sampling monitoring nodes, and a sampling monitoring node set based on the key parameters is obtained; Acquire a target monitoring drainage system node selected in the target drainage system, and read its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; Based on a set of sampling monitoring nodes based on key parameters and the position coordinates of the target monitoring drainage system nodes, it is predicted whether the target monitoring drainage system nodes have a risk of abnormal drainage.

2. The drainage system node abnormality monitoring method based on spatial prediction according to claim 1 is characterized in that: Specifically, the spatial position coordinates corresponding to the target drainage system are obtained by conversion based on the WGS84 World Geodetic System 1984 coordinate system.

3. The drainage system node abnormality monitoring method based on spatial prediction according to claim 1 is characterized in that: Specifically, the precipitation index corresponding to each sampling monitoring node is calculated according to the preset precipitation index calculation method, including: By conducting experiments in advance, with rainfall distribution, intensity and duration as parameters, a calculation model for the impact of rainfall on the urban drainage system based on the parameters is established, and the value calculated by the calculation model is the precipitation index.

4. The drainage system node abnormality monitoring method based on spatial prediction according to claim 3 is characterized in that: Specifically, the computing model is obtained by training and computing based on a CNN neural network model or a random forest model.

5. The drainage system node abnormality monitoring method based on spatial prediction according to claim 1 is characterized in that: Specifically, according to the sampling monitoring node set based on the key parameters and the position coordinates of the target monitoring drainage system node, it is predicted whether the target monitoring drainage system node has the risk of abnormal drainage. include, The target monitoring drainage system node set is recorded as set Aset, the number of target monitoring drainage system nodes in set Aset is qn, the sequence number of the target monitoring drainage system node in set Aset is qi, qi∈[1,qn], the target monitoring drainage system node with sequence number qi in set Aset is recorded as Aset(qi), and the position coordinates corresponding to Aset(qi) are x(qi), y(qi) and z(qi); Among them, in the position coordinates, x(qi) represents the coordinate of the sampling monitoring node qi on the X-axis, y(qi) represents the coordinate of the target monitoring drainage system node qi on the Y-axis, and z(qi) represents the coordinate of the sampling monitoring node qi on the Z-axis, that is, the height of the target monitoring drainage system node qi from the horizontal plane. Therefore, z(qi) also represents the plane composed of the target monitoring drainage system nodes at the same height from the horizontal plane. The process of predicting the precipitation index of the target monitoring drainage system node includes the following: For any target monitoring drainage system node Aset(qi); Get the two sampling monitoring nodes with the smallest Euclidean distance from Aset(qi) in spatial position coordinates, and denote one of the sampling monitoring nodes as Aset(qi1) and the other sampling monitoring node as Aset(qi2); the position coordinates corresponding to the sampling monitoring node Aset(qi1) are x(qi1), y(qi1) and z(qi1), and the position coordinates corresponding to the sampling monitoring node Aset(qi2) are x(qi2), y(qi2) and z(qi2); Get the precipitation index corresponding to the sampling monitoring node Aset(qi1) and label it as p(qi1), and get the precipitation index corresponding to the sampling monitoring node Aset(qi2) and label it as p(qi2); Get the corresponding plane of z(qi1) as plane(qi1), and get the corresponding plane of z(qi2) as plane(qi2); Obtain a set of position coordinate points in the spatial position coordinates that fall on plane (qi1) as Set (qi1), the number of position coordinate points in Set (qi1) is n1, the sequence number of the position coordinate points in Set (qi1) is i1, i1∈[1, n1], the position coordinate point with sequence number i1 in Set (qi1) is Set (qi1, i1), and the position coordinates corresponding to Set (qi1, i1) are x (qi1, i1), y (qi1, i1) and z (qi1, i1); Get the set of position coordinate points in the spatial position coordinates that fall on plane (qi2) as Set (qi2), the number of position coordinate points in Set (qi2) is n2, the sequence number of the position coordinate points in Set (qi2) is i2, i2∈[1, n2], the position coordinate point with sequence number i1 in Set (qi2) is Set (qi2, i2), and the position coordinates corresponding to Set (qi2, i2) are x (qi2, i2), y (qi2, i2) and z (qi2, i2); The spatial similarity trend(qi) of Aset(qi) is calculated as follows: ； Among them, function E( ) is a function for obtaining the Euclidean distance between two points; Get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi1) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi1)); get the sampling monitoring nodes whose target monitoring drainage system nodes fall on plane(qi2) and calculate the mean of the precipitation index corresponding to each sampling monitoring node as p(plane(qi2)); Calculate the drainage anomaly risk index p(qi) of the target monitoring drainage system node Aset(qi). The method for calculating p(qi) is p(qi)= [p(plane(qi1))+ p(plane(qi2))]* trend(qi)*α / 2, where α is the adjustment coefficient, which is a manually set value; Determine whether the drainage anomaly risk index p(qi) of the target monitored drainage system node Aset(qi) is greater than the risk index threshold. If so, it is determined that the target monitored drainage system node has a drainage anomaly risk.

6. The drainage system node abnormality monitoring method based on spatial prediction according to claim 5 is characterized in that: The monitoring method further comprises: Locations with drainage anomaly risks are marked in spatial coordinates and displayed visually.

7. A drainage system node abnormality monitoring device based on spatial prediction, characterized in that: include: A spatial position coordinate construction module is used to obtain the spatial position coordinates corresponding to the target drainage system; A sampling monitoring module is used to select a number of sampling monitoring nodes in the target drainage system, and calculate the precipitation index corresponding to each sampling monitoring node according to a preset precipitation index calculation method; A sampling monitoring node set making module is used to associate the location coordinates of the sampling monitoring nodes with their precipitation indicators as key parameters of the sampling monitoring nodes, and obtain a sampling monitoring node set based on the key parameters; A data acquisition module, used to acquire a target monitoring drainage system node selected in the target drainage system and read its position coordinates, wherein the target monitoring drainage system node is a new sampling monitoring node that is not in the sampling monitoring node set; The drainage anomaly risk prediction module is used to predict whether there is a drainage anomaly risk at the target monitoring drainage system node based on a sampling monitoring node set based on key parameters and the position coordinates of the target monitoring drainage system node.

8. The drainage system node abnormality monitoring device based on spatial prediction according to claim 7 is characterized in that: The monitoring device also includes a visual display module. The visualization display module is used to mark the positions of target monitoring drainage system nodes with drainage abnormality risks in spatial position coordinates and to perform visualization display.

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