CN116429055A - Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points - Google Patents
Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points Download PDFInfo
- Publication number
- CN116429055A CN116429055A CN202211707697.7A CN202211707697A CN116429055A CN 116429055 A CN116429055 A CN 116429055A CN 202211707697 A CN202211707697 A CN 202211707697A CN 116429055 A CN116429055 A CN 116429055A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- measuring point
- abnormal measuring
- points
- dam
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 168
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000002159 abnormal effect Effects 0.000 claims abstract description 314
- 230000005856 abnormality Effects 0.000 claims abstract description 48
- 238000009826 distribution Methods 0.000 claims abstract description 16
- 230000002776 aggregation Effects 0.000 claims abstract description 10
- 238000004220 aggregation Methods 0.000 claims abstract description 10
- 230000015654 memory Effects 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 5
- 230000001351 cycling effect Effects 0.000 claims description 3
- 230000006378 damage Effects 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 3
- 238000013178 mathematical model Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000004643 material aging Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention provides a dam deformation monitoring method, device, equipment and medium based on a knowledge graph and multiple monitoring points. When abnormal measuring points appear on the dam, the distribution condition and aggregation degree of the abnormal measuring points on the dam are rapidly obtained by means of strong multi-hop searching capability of the graph database; further, the local operation abnormality degree of each abnormal measuring point group position and the integral operation abnormality degree score of the dam are obtained by establishing a mathematical model of the influence area of the abnormal measuring point group. The dam safety monitoring method solves the problems that the damage of the locally aggregated abnormal measuring points is large 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
The invention belongs to the technical field of dam safety monitoring, and particularly relates to a dam deformation monitoring method, device, equipment and medium based on a knowledge graph and multiple monitoring points.
Background
The Chinese operators have wide places and numerous rivers, the topography is greatly different, the river drop is great, and the water energy resources are abundant, so that the method is a famous and practical large water conservancy country. At present, 9.8 ten thousand or more reservoirs with 9300 hundred million cubic meters total reservoir capacity and 3.9 hundred million kilowatts water installation capacity have been built in China, and water conservancy facilities play an increasingly important role in guaranteeing social economy stability and people living standard. Most of the reservoir dams are built in the fifty to seventies of the last century, are restricted by the conditions of technology, equipment, economy and the like at the time, have generally lower design requirements and construction standards, and have poor overall quality. Along with the growth of dam age, the dam structure is gradually aged, and the factors such as defects of the dam body, environmental transition, improper operation, monitoring inadequacy and the like cause that a considerable part of dams have hidden dangers such as material aging and deterioration, structural property attenuation, foundation leakage damage and the like, so that the exertion of engineering benefits is seriously influenced, and the risk of dam break possibly exists.
The consequences of dam break are catastrophic, leading to immeasurable losses in downstream town construction, people's lives and properties. Dam break accidents occur in the water storage process of the barrage in the United states, so that 11 people die, more than ten thousands of people flow away from the place, and a large number of farmlands and traffic facilities are destroyed; the French Ma Erba plug dam suddenly breaks in the storm, so that 423 people die, the downstream city of Lei Jiasi is changed into ruins, and nearby buildings, roads and power supply facilities are almost completely destroyed; the Italian Van dam has landslide and overtopping accidents, which lead to death of more than two thousand people and cause huge economic property loss. The domestic dam break accident is not few, and data of China's economic periodicals report that the dam break accident occurs in 3515 reservoirs nationwide from 1954 to 2011, wherein the small and medium reservoirs account for 98.8%. In 1975, two large reservoirs in Henan province, namely a bridge and a stone flood beach, successively flood and break a dam, so that 2.6 thousands of people die, 1100 thousands of people are subjected to disaster and nearly billions of economic losses, and the dam break event is the most tragic in China; in 1993, the reservoir located behind the ditch in Qinghai province suddenly breaks a dam, causing 320 deaths and a large number of villages and towns to suffer from disasters.
The damage and break of the dam are a process from the variable quantity to the variable quality and gradually developing, the dam is monitored in all directions in real time, potential problems of the dam are found in advance, and the dam is treated and repaired, so that the method is the most effective means for preventing the dam break accident. Therefore, in the construction process of the dam, a large number of sensors are installed in and on the dam to form a monitoring network, and indexes such as temperature, deformation, seepage and the like of each part of the dam are measured in real time. However, the stress characteristics and the damage mechanism of the dam are very complex, and the influence degree of abnormal monitoring point distribution on the dam structure is not clear. The existing monitoring means generally uses the percentage of abnormal monitoring points as an index to evaluate the abnormal operation degree of the dam, but neglects the possible serious influence of local aggregation of the abnormal monitoring points on the dam structure. Therefore, a large number of monitoring points on the dam are required to be connected, and when abnormal monitoring points appear, the number and aggregation degree of the abnormal monitoring points are firstly judged, so that the operation abnormal degree of the dam is qualitatively evaluated.
The knowledge graph is a new technology for describing the association relationship between knowledge and modeling everything by using a graph model, and is good at constructing and processing complex relationships. As an efficient knowledge expression form, the knowledge graph represents the connection between targets in a way that nodes are connected to form a directed graph or an undirected graph. The nodes in the knowledge graph can be entities such as a dam, a monitoring point and the like, or abstract concepts such as artificial intelligence, knowledge graph and the like; edges in the knowledge graph can be attributes of the entities, such as serial numbers of monitoring points, monitoring values and the like, or relations between the entities, such as adjacency, distance and the like, which relate the entities to form a complete knowledge network. Therefore, the basic composition units of the knowledge graph are triples formed by head entities, relations and tail entities, each triplet corresponds to one knowledge in the real world, and a large number of triples can describe complex relations among things, so that real everything interconnection is realized.
Disclosure of Invention
The first aim of the invention is to provide a method for safely monitoring the integral structure of the dam by combining a knowledge graph technology and a mathematical model, aiming at the problems that the number of monitoring points on the dam is large, the relation is complex, the multi-jump relation among the monitoring points is difficult to process by adopting a traditional database, the gathering relation of abnormal monitoring points is relatively difficult to obtain, the damage of the local gathering abnormal monitoring points of the dam is large and the abnormal monitoring points are difficult to identify.
For this purpose, the above object of the present invention is achieved by the following technical solutions:
a dam deformation monitoring method based on a knowledge graph and multiple monitoring points comprises the following steps:
s1, constructing a dam deformation monitoring data knowledge graph based on a graph database by taking each monitoring point as an entity, the adjacent relation of each monitoring point as an edge, the current monitoring data, the historical monitoring data, the belonged part and the three-dimensional coordinates of each monitoring point as attributes of the entity according to the distribution condition of the dam deformation monitoring points;
s2, judging whether current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judgment rule:
if no abnormal measuring point exists, the dam is in a safe state;
if the abnormal measuring points exist, marking all the abnormal measuring points;
s3, selecting one of the abnormal measuring points, namely a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of a searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not;
if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group;
if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group;
s4, continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group according to the method in the step S3; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group;
s5, repeating the step S3 and the step S4 until all the abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
s6, scoring and evaluating the local operation abnormality degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
s7, adding the local operation abnormal degree of each abnormal measuring point group to obtain a dam overall operation abnormal degree score W, wherein the higher the score is, the higher the operation abnormal degree of the dam is:
W＝∑P i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i The local operation abnormality degree of each abnormality point group.
The invention can also adopt or combine the following technical proposal when adopting the technical proposal:
as a preferred technical scheme of the 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 on the dam deformation monitoring data knowledge graph as adjacent, and all adjacent monitoring points on the dam are established according to the rule.
As a preferred technical scheme of the invention: in step S2, the single-measuring-point abnormality determination rule is that if the measured value of the single monitoring point exceeds the allowed normal value range, the single-measuring-point abnormality determination rule is determined as an abnormal measuring point, and the normal value range is selected according to specifications according to different dam scales.
As a preferred technical scheme of the invention: in step S6, the parameter r affects the difference of the dam operation anomaly degree scores of the anomaly measurement points with different aggregation degrees, and the specific numerical value is determined by industry experts, but the higher the aggregation degree of the measurement points is, the higher the dam operation anomaly degree score is.
The second object of the invention is to provide a dam deformation monitoring device based on a knowledge graph and multiple monitoring points.
For this purpose, the above object of the present invention is achieved by the following technical solutions:
dam deformation monitoring device based on knowledge graph-many monitoring points includes:
the dam deformation monitoring data knowledge graph construction unit is used for constructing a dam deformation monitoring data knowledge graph based on a graph database by taking all monitoring points as entities, the adjacent relation of all monitoring points as edges, the current monitoring data, the historical monitoring data, the affiliated parts and the three-dimensional coordinates of all monitoring points as attributes of the entities according to the distribution condition of the dam deformation monitoring points;
the monitoring data judging unit judges whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judging rule: if no abnormal measuring point exists, the dam is in a safe state; if the abnormal measuring points exist, marking all the abnormal measuring points;
the abnormal measuring point group classifying unit is used for selecting one abnormal measuring point, marking the abnormal measuring point as a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of the searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not; if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group; if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group; continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group; cycling until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
the local operation abnormal degree scoring unit is used for scoring and evaluating the local operation abnormal degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
the dam overall operation abnormal degree scoring unit is used for adding the local operation abnormal degree of each abnormal measuring point group to obtain the dam overall operation abnormal degree score:
W＝ΣP i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i The local operation abnormality degree of each abnormality point group.
A third object of the present invention is to provide an electronic device, the electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus, the electronic device is characterized in that:
a memory for storing a computer program;
and the processor is used for executing a computer program stored on the memory to realize the dam deformation monitoring method based on the knowledge graph and the multiple monitoring points.
A fourth object of the present invention is to provide a computer readable storage medium, in which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the method for monitoring deformation of a dam based on a knowledge graph and multiple monitoring points.
The invention provides a dam deformation monitoring method, device, equipment and medium based on a knowledge graph and 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 measurement data of all the monitoring points are stored in a knowledge graph database, so that storage and display of all the deformation monitoring information of the dam and expression of spatial relations among all the monitoring points are realized. When abnormal measuring points appear on the dam, the distribution condition and aggregation degree of the abnormal measuring points on the dam are rapidly obtained by means of the strong multi-hop searching capability of the graph database. Further, the local operation abnormality degree of each abnormal measuring point group position and the integral operation abnormality degree score of the dam are obtained by establishing a mathematical model of the influence area of the abnormal measuring point group. The method and the device consider the influence of the number and the distribution of the abnormal measuring points on the dam structure, solve the problems of large harm and difficult identification of the locally gathered abnormal measuring points, improve the accuracy and the reliability of the safety monitoring of the dam, and realize a more reasonable safety monitoring effect of the dam.
Description of the drawings:
FIG. 1 is an effect diagram of a dam deformation monitoring method based on a knowledge graph and multiple monitoring points;
FIG. 2 is a schematic illustration of an anomaly measure point set impact region;
in the figure: 1-entity; 2-relation; 3-monitoring points; 4, an abnormal measuring point group influence area.
Specific embodiments:
the invention will be described in further detail with reference to the drawings and specific embodiments.
A dam deformation monitoring method based on a knowledge graph and multiple monitoring points comprises the following steps:
s1, constructing a dam deformation monitoring data knowledge graph based on a graph database by taking each monitoring point as an entity, the adjacent relation of each monitoring point as an edge, the current monitoring data, the historical monitoring data, the belonged part and the three-dimensional coordinates of each monitoring point as attributes of the entity according to the distribution condition of the dam deformation monitoring points;
s2, judging whether current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judgment rule:
if no abnormal measuring point exists, the dam is in a safe state;
if the abnormal measuring points exist, marking all the abnormal measuring points;
s3, selecting one of the abnormal measuring points, namely a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of a searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not;
if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group;
if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group;
s4, continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group according to the method in the step S3; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group;
s5, repeating the step S3 and the step S4 until all the abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
s6, scoring and evaluating the local operation abnormality degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
s7, adding the local operation abnormal degree of each abnormal measuring point group to obtain a dam overall operation abnormal degree score W, wherein the higher the score is, the higher the operation abnormal degree of the dam is:
W＝∑P i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i The local operation abnormality degree of each abnormality point group.
In step S1, when the positions of two monitoring points on the dam are adjacent, the relationship between the two monitoring points is established on the dam deformation monitoring data knowledge graph as adjacent, and all adjacent monitoring points on the dam are established according to the rule.
In step S2, the single-measuring-point abnormality determination rule is that if the measured value of the single monitoring point exceeds the allowed normal value range, the single-measuring-point abnormality determination rule is determined as an abnormal measuring point, and the normal value range is selected according to specifications according to different dam scales.
In step S6, the parameter r affects the difference of the dam operation anomaly degree scores of the anomaly measurement points with different aggregation degrees, and the specific numerical value is determined by industry experts, but the higher the aggregation degree of the measurement points is, the higher the dam operation anomaly degree score is.
The second object of the present invention is to provide a dam deformation monitoring device based on knowledge graph-multiple monitoring points, comprising:
the dam deformation monitoring data knowledge graph construction unit is used for constructing a dam deformation monitoring data knowledge graph based on a graph database by taking all monitoring points as entities, the adjacent relation of all monitoring points as edges, the current monitoring data, the historical monitoring data, the affiliated parts and the three-dimensional coordinates of all monitoring points as attributes of the entities according to the distribution condition of the dam deformation monitoring points;
the monitoring data judging unit judges whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judging rule: if no abnormal measuring point exists, the dam is in a safe state; if the abnormal measuring points exist, marking all the abnormal measuring points;
the abnormal measuring point group classifying unit is used for selecting one abnormal measuring point, marking the abnormal measuring point as a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of the searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not; if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group; if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group; continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group; cycling until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
the local operation abnormal degree scoring unit is used for scoring and evaluating the local operation abnormal degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
the dam overall operation abnormal degree scoring unit is used for adding the local operation abnormal degree of each abnormal measuring point group to obtain the dam overall operation abnormal degree score:
W＝ΣP i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i For the local operation abnormality degree of each abnormal measuring point group。
A third object of the present invention is to provide an electronic device, the electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus, the electronic device is characterized in that:
a memory for storing a computer program;
and the processor is used for executing a computer program stored on the memory to realize the dam deformation monitoring method based on the knowledge graph and the multiple monitoring points.
A fourth object of the present invention is to provide a computer readable storage medium, in which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the method for monitoring deformation of a dam based on a knowledge graph and multiple monitoring points.
The computer readable storage medium may be any available medium or data storage device that can be accessed by a processor in an electronic device, including but not limited to magnetic memories such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc., optical memories such as CD, DVD, BD, HVD, etc., and semiconductor memories such as ROM, EPROM, EEPROM, nonvolatile memories (NAND FLASH), solid State Disks (SSD), etc.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. A dam deformation monitoring method based on a knowledge graph and multiple monitoring points is characterized in that: the dam deformation monitoring method comprises the following steps:
s1, constructing a dam deformation monitoring data knowledge graph based on a graph database by taking each monitoring point as an entity, the adjacent relation of each monitoring point as an edge, the current monitoring data, the historical monitoring data, the belonged part and the three-dimensional coordinates of each monitoring point as attributes of the entity according to the distribution condition of the dam deformation monitoring points;
s2, judging whether current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judgment rule:
if no abnormal measuring point exists, the dam is in a safe state;
if the abnormal measuring points exist, marking all the abnormal measuring points;
s3, selecting one of the abnormal measuring points, namely a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of a searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not;
if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group;
if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group;
s4, continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group according to the method in the step S3; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group;
s5, repeating the step S3 and the step S4 until all the abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
s6, scoring and evaluating the local operation abnormality degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
s7, adding the local operation abnormal degree of each abnormal measuring point group to obtain a dam overall operation abnormal degree score W, wherein the higher the score is, the higher the operation abnormal degree of the dam is:
W＝∑P i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i The local operation abnormality degree of each abnormality point group.
2. The knowledge-graph-multi-monitoring-point-based dam deformation monitoring method as claimed in claim 1, wherein the method comprises the following steps: in step S1, when the positions of two monitoring points on the dam are adjacent, the relationship between the two monitoring points is established on the dam deformation monitoring data knowledge graph as adjacent, and all adjacent monitoring points on the dam are established according to the rule.
3. The knowledge-graph-multi-monitoring-point-based dam deformation monitoring method as claimed in claim 1, wherein the method comprises the following steps: in step S2, the single-measuring-point abnormality determination rule is that if the measured value of the single monitoring point exceeds the allowed normal value range, the single-measuring-point abnormality determination rule is determined as an abnormal measuring point, and the normal value range is selected according to specifications according to different dam scales.
4. The knowledge-graph-multi-monitoring-point-based dam deformation monitoring method as claimed in claim 1, wherein the method comprises the following steps: in step S6, the parameter r affects the difference of the dam operation anomaly degree scores of the anomaly measurement points with different aggregation degrees, and the specific numerical value is determined by industry experts, which is based on the principle that the higher the aggregation degree of the measurement points is, the higher the dam operation anomaly degree score is.
5. Dam deformation monitoring device based on knowledge graph-many monitoring points, its characterized in that: the dam deformation monitoring device based on the knowledge graph and multiple monitoring points comprises:
the dam deformation monitoring data knowledge graph construction unit is used for constructing a dam deformation monitoring data knowledge graph based on a graph database by taking all monitoring points as entities, the adjacent relation of all monitoring points as edges, the current monitoring data, the historical monitoring data, the affiliated parts and the three-dimensional coordinates of all monitoring points as attributes of the entities according to the distribution condition of the dam deformation monitoring points;
the monitoring data judging unit judges whether the current monitoring data of all monitoring points on the knowledge graph are abnormal according to a single-measuring-point abnormality judging rule: if no abnormal measuring point exists, the dam is in a safe state; if the abnormal measuring points exist, marking all the abnormal measuring points;
the abnormal measuring point group classifying unit is used for selecting one abnormal measuring point, marking the abnormal measuring point as a No. 1 abnormal measuring point, searching all monitoring points adjacent to the No. 1 abnormal measuring point by means of the searching function of a knowledge graph, and judging whether the adjacent monitoring points are abnormal or not; if no abnormal measuring point exists in the adjacent measuring points, the number 1 abnormal measuring point is singly classified into an abnormal measuring point group; if the abnormal measuring points exist in the adjacent measuring points, classifying the abnormal measuring points and the No. 1 abnormal measuring points into the same abnormal measuring point group; continuously searching and judging whether an abnormal measuring point adjacent to any monitoring point in the abnormal measuring point group exists outside the abnormal measuring point group; if so, the new abnormal measuring points are integrated into the abnormal measuring point group, and the circulation is carried out until no abnormal measuring points adjacent to the abnormal measuring point exist outside the abnormal measuring point group; cycling until all abnormal measuring points in the dam deformation monitoring data knowledge graph are classified into different abnormal measuring point groups;
the local operation abnormal degree scoring unit is used for scoring and evaluating the local operation abnormal degree of the dam by each abnormal measuring point group according to the number and distribution conditions of the abnormal measuring points in the abnormal measuring point group:
if only one abnormal measuring point exists in the abnormal measuring point group, the influence area of the abnormal measuring point group is a circular area with the measuring point group as the center and the radius r, and the local operation abnormal degree P is as follows:
P＝πr 2
if two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is an elliptical area with the L+2r as a long axis and the 2r as a short axis, wherein: l is the distance between two abnormal measuring points, and the local operation abnormality degree P is:
if more than two abnormal measuring points exist in the abnormal measuring point group, the influence area of the abnormal measuring point group is L 1 +2r is the long axis, taken as L 2 +2r is the elliptical region of the minor axis, wherein: l (L) 1 L is the distance between two most distant abnormal measuring points in the abnormal measuring point group 2 The local operation abnormality degree P is twice the distance from the long axis in the abnormality measuring point, and is:
the dam overall operation abnormal degree scoring unit is used for adding the local operation abnormal degree of each abnormal measuring point group to obtain the dam overall operation abnormal degree score:
W＝∑P i
wherein: w is the score of the abnormal degree of the whole operation of the dam, P i The local operation abnormality degree of each abnormality point group.
6. An electronic device, the electronic device includes a processor, a communication interface, a memory and a communication bus, where the processor, the communication interface and the memory complete communication with each other through the communication bus, and the electronic device is characterized in that:
a memory for storing a computer program;
a processor for executing a computer program stored on a memory to implement the knowledge graph-multi-monitoring point based dam deformation monitoring method steps of any of claims 1-4.
7. A computer-readable storage medium, characterized by: the computer readable storage medium stores a computer program which, when executed by a processor, implements the steps of the knowledge graph-multi-monitoring point based dam deformation monitoring method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211707697.7A CN116429055A (en) | 2022-12-29 | 2022-12-29 | Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211707697.7A CN116429055A (en) | 2022-12-29 | 2022-12-29 | Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116429055A true CN116429055A (en) | 2023-07-14 |
Family
ID=87084318
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211707697.7A Pending CN116429055A (en) | 2022-12-29 | 2022-12-29 | Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116429055A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117453923A (en) * | 2023-08-30 | 2024-01-26 | 广东电白建设集团有限公司 | Method for optimizing relation between construction site construction equipment and building facilities |
-
2022
- 2022-12-29 CN CN202211707697.7A patent/CN116429055A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117453923A (en) * | 2023-08-30 | 2024-01-26 | 广东电白建设集团有限公司 | Method for optimizing relation between construction site construction equipment and building facilities |
| CN117453923B (en) * | 2023-08-30 | 2024-03-19 | 广东电白建设集团有限公司 | Method for optimizing relation between construction site construction equipment and building facilities |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Vulnerability analysis of transportation network under scenarios of sea level rise | |
| CN101718634B (en) | Equipment state comprehensive dynamic alarming method based on multivariate probability model | |
| CN104239970A (en) | Power transmission line gallop risk early-warning method based on Adaboost | |
| CN110705795A (en) | Tunnel gush risk grade fuzzy prediction method based on comprehensive advanced geological prediction technology | |
| CN116429055A (en) | Dam deformation monitoring method, device, equipment and medium based on knowledge graph and multiple monitoring points | |
| CN101706571A (en) | Method for processing non-meteorological noise in radar echoes | |
| CN113935609B (en) | Combined risk assessment method for flood inundation with multiple breach in long river section | |
| CN108133578A (en) | Mountain flood dangerous situation dynamic early-warning method, the classified Monitoring that becomes more meticulous method for early warning | |
| Zhou et al. | Study of the comprehensive risk analysis of dam-break flooding based on the numerical simulation of flood routing. Part II: Model application and results | |
| CN102289734A (en) | Dynamic risk evaluation and management method for foundation pit engineering | |
| CN105045091A (en) | Dredging process intelligent decision analysis method based on fuzzy neural control system | |
| CN115270527A (en) | Real-time assessment method, equipment and storage medium for road collapse risk | |
| CN113869354B (en) | Comprehensive evaluation method for risk of embankment in flood season | |
| CN108536980B (en) | Gas detector discrete site selection optimization method considering reliability factor | |
| CN107944219A (en) | A kind of method and apparatus for characterizing different periods drought and waterlogging and causing calamity feature | |
| CN107564276A (en) | A kind of traffic incidents detection method based on traffic behavior mutation | |
| CN112434887B (en) | Water supply network risk prediction method combining network kernel density estimation and SVM | |
| CN111350498A (en) | Method for describing weak flooding distribution characteristics of medium-high permeability oil reservoir in ultra-high water content development period | |
| CN113742814B (en) | Dam safety early warning method, dam safety early warning device, computer equipment and storage medium | |
| Anuar et al. | Early prediction system using neural network in Kelantan River, Malaysia | |
| CN113722904B (en) | Analysis and identification method for key river and lake and sluice in plain water network area | |
| CN115564264A (en) | Safety risk assessment method for dam break destructiveness of tailing pond | |
| CN115358504A (en) | National logistics city pivotal sorting model under complex network view angle | |
| CN107704850B (en) | A kind of Creek system landform identifying processing method | |
| CN114023049B (en) | Mountain torrent disaster early warning index inspection rechecking method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |