A dam deformation monitoring method, device, equipment and medium based on knowledge graph-multiple monitoring points

Abstract

The present invention provides a dam deformation monitoring method, device, equipment and medium based on a knowledge graph and multiple monitoring points. By constructing a knowledge graph of dam deformation monitoring data, a large number of monitoring points on the dam are connected, and the measurement data of each monitoring point is stored in a knowledge graph database, thereby realizing the storage and display of all deformation monitoring information of the dam and the expression of the spatial relationship between each monitoring point. When abnormal measuring points appear on the dam, the distribution and degree of aggregation of the abnormal measuring points on the dam are quickly obtained with the help of the powerful multi-hop search capability of the graph database; further, by establishing a mathematical model of the impact area of ​​the abnormal measuring point group, the local operation abnormality degree of each abnormal measuring point group position and the overall operation abnormality degree score of the dam are obtained. The present invention solves the problem that locally clustered abnormal measuring points are highly harmful and difficult to identify, improves the accuracy and reliability of dam safety monitoring, and achieves a more reasonable dam safety monitoring effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of dam safety monitoring, and in particular relates to a dam deformation monitoring method, device, equipment and medium based on a knowledge graph and multiple monitoring points. Background Art

[0002] Dam damage and failure are a gradual process, evolving from quantitative change to qualitative change. Comprehensive, real-time monitoring of dams, identifying potential problems early and implementing remediation measures, is the most effective way to prevent dam failures. Therefore, during dam construction, a large number of sensors were installed inside and on the dam's surface, forming a monitoring network that provides real-time measurements of temperature, deformation, seepage, and other indicators at various locations. However, the stress characteristics and failure mechanisms of dams are complex, and the extent to which the distribution of abnormal monitoring points affects the dam structure remains unclear. Existing monitoring methods generally use the percentage of abnormal monitoring points as an indicator to assess the degree of dam operational anomaly, but they overlook the fact that localized clustering of abnormal monitoring points can have even more serious impacts on the dam structure. Therefore, it is necessary to connect a large number of monitoring points on the dam. When an abnormal monitoring point appears, the number and clustering of these points should be determined to provide a qualitative assessment of the degree of dam operational anomaly.

[0003] Knowledge graphs are a new technology that uses graphical models to describe knowledge and model the relationships between all things. They excel at constructing and processing complex relationships. As an efficient form of knowledge representation, knowledge graphs represent the connections between various targets by connecting nodes to form directed or undirected graphs. Nodes in a knowledge graph can be entities, such as a dam or a monitoring point, or abstract concepts, such as artificial intelligence or knowledge graphs. Edges in a knowledge graph can be attributes of an entity, such as a monitoring point number or monitoring value, or relationships between entities, such as adjacency and distance. These relationships connect entities to form a complete knowledge network. Therefore, the basic building block of a knowledge graph is a triple consisting of a head entity, a relationship, and a tail entity. Each triple corresponds to a piece of knowledge in the real world. A large number of triples can describe the complex relationships between things, realizing a true Internet of Everything. Summary of the Invention

[0004] The first purpose of the present invention is to address the problems that there are a large number of monitoring points on the dam and the relationships are complex. It is difficult to use traditional databases to handle the multi-hop relationships between the monitoring points, and it is relatively difficult to obtain the clustering relationship of abnormal monitoring points. In addition, the local clustering of abnormal monitoring points on the dam is very harmful and difficult to identify. Therefore, a method combining knowledge graph technology with mathematical models is proposed to monitor the overall structure of the dam for safety.

[0005] To this end, the above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0006] A dam deformation monitoring method based on knowledge graph and multiple monitoring points includes the following steps:

[0007] S1. Based on the distribution of dam deformation monitoring points, a knowledge graph of dam deformation monitoring data is constructed based on a graph database, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as the attributes of the entity;

[0008] S2. According to the single-point anomaly judgment rule, determine whether the current monitoring data of all monitoring points on the knowledge graph are abnormal:

[0009] If there are no abnormal measuring points, the dam is in a safe state;

[0010] If there are abnormal measurement points, all abnormal measurement points will be marked;

[0011] S3. Select one of the abnormal measurement points, record it as abnormal measurement point 1, and use the search function of the knowledge graph to search for all the monitoring points adjacent to abnormal measurement point 1, and determine whether these adjacent monitoring points are abnormal;

[0012] If there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 will be classified as an abnormal measuring point group alone;

[0013] If there are abnormal measuring points among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 will be classified into the same abnormal measuring point group;

[0014] S4. Continue searching and determining whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group according to the method in step S3; if so, merge the new abnormal measuring point into the abnormal measuring point group, and repeat the process until there is no abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group;

[0015] S5, repeating steps S3 and S4 until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;

[0016] S6. Based on the number and distribution of abnormal measuring points in the abnormal measuring point group, score and evaluate the degree of local operation abnormality of the dam for each abnormal measuring point group:

[0017] If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is:

[0018] P=πr 2

[0019] If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is:

[0020]

[0021] If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is:

[0022]

[0023] S7. Add up the local operation abnormality levels of each abnormal measurement point group to obtain the overall operation abnormality level score W of the dam. The higher the score, the higher the degree of dam operation abnormality:

[0024] W=∑P i

[0025] Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

[0026] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:

[0027] As a preferred technical solution of the present invention: in step S1, when the positions of two monitoring points on the dam are adjacent, the relationship between the two monitoring points is established as adjacent on the dam deformation monitoring data knowledge graph, and all adjacent monitoring points on the dam are established with this rule.

[0028] As a preferred technical solution of the present invention: in step S2, the single measuring point abnormality judgment rule is that if the measurement value of a single monitoring point exceeds the allowed normal value range, it will be judged as an abnormal measuring point. According to different dam scales, the normal value range is selected according to the specifications.

[0029] As a preferred technical solution of the present invention: in step S6, the parameter r will affect the difference in the dam operation abnormality degree scores of abnormal measurement points with different concentration levels. The specific value must be determined by industry experts, but the principle is that the higher the concentration level of the measurement points, the higher the dam operation abnormality degree score.

[0030] The second object of the present invention is to provide a dam deformation monitoring device based on knowledge graph-multiple monitoring points.

[0031] To this end, the above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0032] The dam deformation monitoring device based on knowledge graph and multiple monitoring points includes:

[0033] A dam deformation monitoring data knowledge graph construction unit, which constructs a dam deformation monitoring data knowledge graph based on a graph database according to the distribution of dam deformation monitoring points, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as attributes of the entity;

[0034] A monitoring data determination unit, which determines whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to the single-point abnormality determination rule: if there are no abnormal measuring points, the dam is in a safe state; if there are abnormal measuring points, all abnormal measuring points are marked;

[0035] An abnormal measuring point group classification unit is used to select any abnormal measuring point, denoted as abnormal measuring point No. 1, and search all monitoring points adjacent to abnormal measuring point No. 1 with the help of the search function of the knowledge graph, and determine whether these adjacent monitoring points are abnormal; if there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 is separately classified into an abnormal measuring point group; if there is an abnormal measuring point among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 are classified into the same abnormal measuring point group; and continue to search and determine whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group; if so, the new abnormal measuring point is merged into the abnormal measuring point group, and the cycle is repeated until there is no abnormal measuring point adjacent to it outside the abnormal measuring point group; the cycle is repeated until all abnormal measuring points in the knowledge graph of dam deformation monitoring data are classified into different abnormal measuring point groups;

[0036] The local operation abnormality degree scoring unit is used to score and evaluate the local operation abnormality degree of the dam for each abnormal measurement point group based on the number and distribution of abnormal measurement points in the abnormal measurement point group:

[0037] If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is:

[0038] P=πr 2

[0039] If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is:

[0040]

[0041] If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is:

[0042]

[0043] The dam overall operation abnormality degree scoring unit is used to add the local operation abnormality degree of each abnormal measurement point group to obtain the dam overall operation abnormality degree score:

[0044] W=∑P i

[0045] Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

[0046] A third object of the present invention is to provide an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and wherein:

[0047] a memory for storing a computer program;

[0048] A processor, the processor being used to execute a computer program stored in a memory to implement the steps of the dam deformation monitoring method based on knowledge graph and multiple monitoring points as described above.

[0049] The fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the dam deformation monitoring method based on knowledge graph and multiple monitoring points as described above.

[0050] The present invention provides a dam deformation monitoring method, device, equipment and medium based on a knowledge graph-multiple monitoring points, which has the following beneficial effects: by constructing a knowledge graph of dam deformation monitoring data, a large number of monitoring points on the dam are connected, and the measurement data of each monitoring point is stored in a knowledge graph database, thereby realizing the storage and display of all dam deformation monitoring information and the expression of the spatial relationship between each monitoring point. When abnormal measuring points appear on the dam, the distribution and aggregation degree of the abnormal measuring points on the dam are quickly obtained with the help of the powerful multi-hop search capability of the graph database. Furthermore, by establishing a mathematical model of the impact area of ​​the abnormal measuring point group, the local operation abnormality degree of each abnormal measuring point group position and the overall operation abnormality degree score of the dam are obtained. The present invention simultaneously takes into account the impact of the number and distribution of abnormal measuring points on the dam structure, solves the problem that locally clustered abnormal measuring points are highly harmful and difficult to identify, improves the accuracy and reliability of dam safety monitoring, and achieves a more reasonable dam safety monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a diagram showing the effect of the dam deformation monitoring method based on knowledge graph and multiple monitoring points provided by the present invention;

[0052] Figure 2 This is a schematic diagram of the area affected by the abnormal measurement point group;

[0053] In the figure: 1-entity; 2-relationship; 3-monitoring point; 4-affected area of ​​the abnormal monitoring point group. DETAILED DESCRIPTION

[0054] The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

[0055] A dam deformation monitoring method based on knowledge graph and multiple monitoring points includes the following steps:

[0056] S1. Based on the distribution of dam deformation monitoring points, a knowledge graph of dam deformation monitoring data is constructed based on a graph database, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as the attributes of the entity;

[0057] S2. According to the single-point anomaly judgment rule, determine whether the current monitoring data of all monitoring points on the knowledge graph are abnormal:

[0058] If there are no abnormal measuring points, the dam is in a safe state;

[0059] If there are abnormal measurement points, all abnormal measurement points will be marked;

[0060] S3. Select one of the abnormal measurement points, record it as abnormal measurement point 1, and use the search function of the knowledge graph to search for all the monitoring points adjacent to abnormal measurement point 1, and determine whether these adjacent monitoring points are abnormal;

[0061] If there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 will be classified as an abnormal measuring point group alone;

[0062] If there are abnormal measuring points among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 will be classified into the same abnormal measuring point group;

[0063] S4. Continue searching and determining whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group according to the method in step S3; if so, merge the new abnormal measuring point into the abnormal measuring point group, and repeat the process until there is no abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group;

[0064] S5, repeating steps S3 and S4 until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;

[0065] S6. Based on the number and distribution of abnormal measuring points in the abnormal measuring point group, score and evaluate the degree of local operation abnormality of the dam for each abnormal measuring point group:

[0066] If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is:

[0067] P=πr 2

[0068] If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is:

[0069]

[0070] If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is:

[0071]

[0072] S7. Add up the local operation abnormality levels of each abnormal measurement point group to obtain the overall operation abnormality level score W of the dam. The higher the score, the higher the degree of dam operation abnormality:

[0073] W=∑P i

[0074] Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

[0075] In step S1, when the positions of two monitoring points on the dam are adjacent, the relationship between the two monitoring points is established as adjacent on the dam deformation monitoring data knowledge graph, and all adjacent monitoring points on the dam are established in a relationship based on this rule.

[0076] In step S2, the single measuring point abnormality determination rule is that if the measurement value of a single monitoring point exceeds the allowed normal value range, it will be determined as an abnormal measuring point. According to different dam scales, the normal value range is selected according to the specifications.

[0077] In step S6, the parameter r will affect the difference in the dam operation abnormality degree scores at abnormal measurement points with different concentration levels. The specific value must be determined by industry experts, but the principle is that the higher the concentration level of the measurement points, the higher the dam operation abnormality degree score.

[0078] The second object of the present invention is to provide a dam deformation monitoring device based on a knowledge graph and multiple monitoring points, comprising:

[0079] A dam deformation monitoring data knowledge graph construction unit, which constructs a dam deformation monitoring data knowledge graph based on a graph database according to the distribution of dam deformation monitoring points, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as attributes of the entity;

[0080] A monitoring data determination unit, which determines whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to the single-point abnormality determination rule: if there are no abnormal measuring points, the dam is in a safe state; if there are abnormal measuring points, all abnormal measuring points are marked;

[0081] An abnormal measuring point group classification unit is used to select any abnormal measuring point, denoted as abnormal measuring point No. 1, and search all monitoring points adjacent to abnormal measuring point No. 1 with the help of the search function of the knowledge graph, and determine whether these adjacent monitoring points are abnormal; if there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 is separately classified into an abnormal measuring point group; if there is an abnormal measuring point among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 are classified into the same abnormal measuring point group; and continue to search and determine whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group; if so, the new abnormal measuring point is merged into the abnormal measuring point group, and the cycle is repeated until there is no abnormal measuring point adjacent to it outside the abnormal measuring point group; the cycle is repeated until all abnormal measuring points in the knowledge graph of dam deformation monitoring data are classified into different abnormal measuring point groups;

[0082] The local operation abnormality degree scoring unit is used to score and evaluate the local operation abnormality degree of the dam for each abnormal measurement point group based on the number and distribution of abnormal measurement points in the abnormal measurement point group:

[0083] If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is:

[0084] P=πr 2

[0085] If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is:

[0086]

[0087] If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is:

[0088]

[0089] The dam overall operation abnormality degree scoring unit is used to add the local operation abnormality degree of each abnormal measurement point group to obtain the dam overall operation abnormality degree score:

[0090] W=∑P i

[0091] Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

[0092] A third object of the present invention is to provide an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and wherein:

[0093] a memory for storing a computer program;

[0094] A processor, the processor being used to execute a computer program stored in a memory to implement the steps of the dam deformation monitoring method based on knowledge graph and multiple monitoring points as described above.

[0095] The fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the dam deformation monitoring method based on knowledge graph and multiple monitoring points as described above.

[0096] The above-mentioned computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor in the electronic device, including but not limited to magnetic storage such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc., optical storage such as CDs, DVDs, BDs, HVDs, etc., and semiconductor storage such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs), etc.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0100] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A dam deformation monitoring method based on knowledge graph and multiple monitoring points, characterized by: The dam deformation monitoring method comprises the following steps: S1. Based on the distribution of dam deformation monitoring points, a knowledge graph of dam deformation monitoring data is constructed based on a graph database, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as the attributes of the entity; S2. According to the single-point anomaly judgment rule, determine whether the current monitoring data of all monitoring points on the knowledge graph are abnormal: If there are no abnormal measuring points, the dam is in a safe state; If there are abnormal measurement points, all abnormal measurement points will be marked; S3. Select one of the abnormal measurement points, record it as abnormal measurement point 1, and use the search function of the knowledge graph to search for all the monitoring points adjacent to abnormal measurement point 1, and determine whether these adjacent monitoring points are abnormal; If there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 will be classified as an abnormal measuring point group alone; If there are abnormal measuring points among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 will be classified into the same abnormal measuring point group; S4. Continue searching and determining whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group according to the method in step S3; if so, merge the new abnormal measuring point into the abnormal measuring point group, and repeat the process until there is no abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group; S5, repeating steps S3 and S4 until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups; S6. Based on the number and distribution of abnormal measuring points in the abnormal measuring point group, score and evaluate the degree of local operation abnormality of the dam for each abnormal measuring point group: If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is: P＝πr 2 If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is: If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is: S7. Add up the local operation abnormality levels of each abnormal measurement point group to obtain the overall operation abnormality level score W of the dam. The higher the score, the higher the degree of dam operation abnormality: W=∑P i Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

2. The dam deformation monitoring method based on knowledge graph and multiple monitoring points according to claim 1 is characterized by: In step S1, when the positions of two monitoring points on the dam are adjacent, the relationship between the two monitoring points is established as adjacent on the dam deformation monitoring data knowledge graph, and all adjacent monitoring points on the dam are established with this rule.

3. The dam deformation monitoring method based on knowledge graph and multiple monitoring points according to claim 1 is characterized by: In step S2, the single measuring point abnormality determination rule is that if the measurement value of a single monitoring point exceeds the allowed normal value range, it will be determined as an abnormal measuring point. According to different dam scales, the normal value range is selected according to the specifications.

4. The dam deformation monitoring method based on knowledge graph and multiple monitoring points according to claim 1 is characterized in that: In step S6, the parameter r will affect the difference in the dam operation abnormality degree scores of abnormal measurement points with different concentration levels. The specific value must be determined by industry experts. The principle is that the higher the concentration level of the measurement points, the higher the dam operation abnormality degree score.

5. The dam deformation monitoring device based on knowledge graph and multiple monitoring points is characterized by: The dam deformation monitoring device based on knowledge graph-multiple monitoring points includes: A dam deformation monitoring data knowledge graph construction unit, which constructs a dam deformation monitoring data knowledge graph based on a graph database according to the distribution of dam deformation monitoring points, with each monitoring point as an entity, the adjacent relationship between each monitoring point as an edge, and the current monitoring data, historical monitoring data, location, and three-dimensional coordinates of each monitoring point as attributes of the entity; A monitoring data determination unit, which determines whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to the single-point abnormality determination rule: if there are no abnormal measuring points, the dam is in a safe state; if there are abnormal measuring points, all abnormal measuring points are marked; An abnormal measuring point group classification unit is used to select any abnormal measuring point, denoted as abnormal measuring point No. 1, and search all monitoring points adjacent to abnormal measuring point No. 1 with the help of the search function of the knowledge graph, and determine whether these adjacent monitoring points are abnormal; if there is no abnormal measuring point among the adjacent measuring points, abnormal measuring point No. 1 is separately classified into an abnormal measuring point group; if there is an abnormal measuring point among the adjacent measuring points, these abnormal measuring points and abnormal measuring point No. 1 are classified into the same abnormal measuring point group; and continue to search and determine whether there is an abnormal measuring point outside the abnormal measuring point group that is adjacent to any monitoring point in the abnormal measuring point group; if so, the new abnormal measuring point is merged into the abnormal measuring point group, and the cycle is repeated until there is no abnormal measuring point adjacent to it outside the abnormal measuring point group; the cycle is repeated until all abnormal measuring points in the knowledge graph of dam deformation monitoring data are classified into different abnormal measuring point groups; The local operation abnormality degree scoring unit is used to score and evaluate the local operation abnormality degree of the dam for each abnormal measurement point group based on the number and distribution of abnormal measurement points in the abnormal measurement point group: If there is only one abnormal measuring point in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is a circular area with the measuring point group as the center and a radius of r. The local operation abnormality degree P is: P＝πr 2 If there are two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L+2r as the major axis and 2r as the minor axis, where L is the distance between the two abnormal measuring points and the local operation abnormality degree P is: If there are more than two abnormal measuring points in the abnormal measuring point group, the affected area of ​​the abnormal measuring point group is an elliptical area with L1+2r as the major axis and L2+2r as the minor axis, where L1 is the distance between the two abnormal measuring points farthest apart in the abnormal measuring point group, and L2 is twice the distance from the farthest abnormal measuring point to the major axis. The local operation abnormality level P is: The dam overall operation abnormality degree scoring unit is used to add the local operation abnormality degree of each abnormal measurement point group to obtain the dam overall operation abnormality degree score: W=∑P i Where: W is the overall abnormality score of the dam operation, P i It is the degree of local operation abnormality of each abnormal measurement point group.

6. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that: a memory for storing a computer program; A processor, the processor being configured to execute a computer program stored in a memory to implement the steps of the dam deformation monitoring method based on a knowledge graph and multiple monitoring points as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the dam deformation monitoring method based on knowledge graph and multiple monitoring points according to any one of claims 1 to 4 are implemented.

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