CN116071900A - Bridge monitoring and early warning method and device, readable storage medium and electronic equipment - Google Patents
Bridge monitoring and early warning method and device, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a bridge monitoring and early warning method, a device, a readable storage medium and electronic equipment, wherein a plurality of monitoring areas are distributed on a bridge, each monitoring area is provided with a plurality of types of monitoring points, each monitoring point is used for monitoring detection data of a plurality of detection projects, and the bridge monitoring and early warning method comprises the following steps: acquiring detection data of each type of monitoring point in the monitoring area, and carrying out anomaly analysis on the acquired detection data of each monitoring point; when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal, calculating the correlation among the monitoring points of the abnormal types; and when the calculated correlation coefficients are all larger than a threshold value, determining that the monitoring area is abnormal, and carrying out early warning. By analyzing the data of the plurality of detection items and the association relation among the plurality of detection items, misjudgment caused by the influence of factors such as environment or sensor equipment abnormality can be avoided, and the accuracy of an early warning mechanism is improved.
Description
Technical Field
The invention relates to the field of bridge monitoring, in particular to a bridge monitoring and early warning method and device, a readable storage medium and electronic equipment.
Background
The bridge is a building which is visible everywhere in life, and in order to ensure the use safety and durability of the bridge structure, the health condition of the bridge needs to be known in real time so as to discover hidden danger endangering the safety of the bridge as soon as possible.
Current bridges are subject to health assessment by human detection, or by health assessment systems. However, the manual measurement is low in efficiency and inaccurate, and most health evaluation systems are simple and low in accuracy, so that the monitoring effect on the bridge state is poor.
Disclosure of Invention
In view of the above, it is necessary to provide a bridge monitoring and early warning method, device, readable storage medium and electronic equipment for the problem of inaccurate bridge monitoring.
The invention discloses a bridge monitoring and early warning method, wherein a plurality of monitoring areas are distributed on a bridge, each monitoring area is provided with a plurality of types of monitoring points, each monitoring point is used for monitoring detection data of a plurality of detection projects, and the bridge monitoring and early warning method comprises the following steps:
acquiring detection data of each type of monitoring point in the monitoring area, and carrying out anomaly analysis on the acquired detection data of each monitoring point;
when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal, calculating the correlation among the monitoring points of the abnormal types;
and when the calculated correlation coefficients are all larger than a threshold value, determining that the monitoring area is abnormal, and carrying out early warning.
Further, in the bridge monitoring and early warning method, three types of monitoring points are arranged on each monitoring area, and are respectively settlement monitoring points, inclination monitoring points and stress monitoring points.
Further, in the bridge monitoring and early warning method, the step of performing anomaly analysis on the acquired detection data of each monitoring point includes:
performing exception analysis on the detection data of the settlement monitoring points;
when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively;
when the detection data of more than a preset number of types of monitoring points in the current monitoring area are abnormal, the step of calculating the correlation between the abnormal types of monitoring points comprises the following steps:
when detection data of settlement monitoring points, inclination monitoring points and stress monitoring points in the current monitoring area are abnormal, calculating correlation between the settlement monitoring points and the inclination monitoring points and calculating correlation between the stress monitoring points and the inclination monitoring points.
Further, in the bridge monitoring and early warning method, before the step of performing anomaly analysis on the obtained detection data of each monitoring point, the method further includes:
and carrying out median taking, amplitude limiting and sliding tie processing on the acquired detection data of each monitoring point.
Further, in the bridge monitoring and early warning method, the step of performing anomaly analysis on the acquired detection data of each monitoring point includes:
and comparing the acquired detection data with a threshold value corresponding to the detection item, and determining that the acquired detection data is abnormal when the threshold value is exceeded.
The invention also discloses a bridge monitoring and early warning device, wherein a plurality of monitoring areas are distributed on the bridge, each monitoring area is provided with a plurality of types of monitoring points, each monitoring point is used for monitoring detection data of a plurality of detection projects, and the bridge monitoring and early warning device comprises:
the data acquisition module is used for acquiring detection data of each type of monitoring point in the monitoring area;
the analysis module is used for carrying out abnormal analysis on the acquired detection data of each monitoring point;
the correlation calculation module is used for calculating the correlation among the monitoring points of the abnormal type when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal;
and the early warning module is used for determining that the monitoring area is abnormal and carrying out early warning when the calculated correlation coefficients are all larger than a threshold value.
Further, the bridge monitoring and early warning device is characterized in that three types of monitoring points are arranged on each monitoring area, and the monitoring points are respectively settlement monitoring points, inclination monitoring points and stress monitoring points.
Further, the bridge monitoring and early warning device, wherein the analysis module is specifically configured to:
performing exception analysis on the detection data of the settlement monitoring points;
when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively;
the correlation calculation module is specifically configured to:
when detection data of settlement monitoring points, inclination monitoring points and stress monitoring points in the current monitoring area are abnormal, calculating correlation between the settlement monitoring points and the inclination monitoring points and calculating correlation between the stress monitoring points and the inclination monitoring points.
The invention also discloses an electronic device, which comprises a memory and a processor, wherein the memory stores a program, and the program realizes any one of the methods when being executed by the processor.
The invention also discloses a computer readable storage medium having stored thereon a program which when executed by a processor implements any of the methods described above.
According to the method, association relation among multiple monitoring points is established by combining data mining and association theoretical knowledge of a signal system, whether the monitoring area is abnormal or not is determined according to association relation among abnormal monitoring points among different detection projects of the same monitoring area, and early warning is carried out if the monitoring area is abnormal. By analyzing the data of the plurality of detection items and the association relation among the plurality of detection items, misjudgment caused by the influence of factors such as environment or sensor equipment abnormality can be avoided, and the accuracy of an early warning mechanism is improved.
Drawings
FIG. 1 is a flow chart of a bridge monitoring and early warning method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a bridge monitoring and early warning method according to a second embodiment of the present invention;
FIG. 3 is a plot of sedimentation measurement point trends;
fig. 4 is a schematic diagram of the arrangement of each monitoring point of the north bridge abutment;
FIG. 5 is a graph of abutment inclination monitoring point X-direction trend;
FIG. 6 is a Y-direction trend graph of abutment inclination monitoring points;
FIG. 7 is a graph comparing north bridge abutment stress monitoring point trends;
FIG. 8 is a graph of trends associated with settlement monitoring points CJ-01, CJ-02 and inclination monitoring point CX-02;
FIG. 9 is a plot of correlations of settlement monitoring points CJ-01, CJ-02 and inclination monitoring point CX-02;
FIG. 10 is a graph of the trend of the relationship between the inclined monitoring point CX-02 and the stress monitoring point YB-13;
FIG. 11 is a plot of the correlation scatter of the tilt monitoring point CX-02 with the stress monitoring point YB-13;
fig. 12 is a block diagram of a bridge monitoring and early warning device in a third embodiment;
fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
These and other aspects of embodiments of the invention will be apparent from and elucidated with reference to the description and drawings described hereinafter. In the description and drawings, particular implementations of embodiments of the invention are disclosed in detail as being indicative of some of the ways in which the principles of embodiments of the invention may be employed, but it is understood that the scope of the embodiments of the invention is not limited correspondingly. On the contrary, the embodiments of the invention include all alternatives, modifications and equivalents as may be included within the spirit and scope of the appended claims.
The bridge monitoring and early warning method is mainly used for monitoring the health state of the bridge and carrying out early warning. A plurality of monitoring areas are distributed on the bridge, and each monitoring area is provided with a plurality of types of monitoring points which are used for monitoring detection data of a plurality of detection projects. For example, each monitoring area is provided with three types of monitoring points, a settlement monitoring point, an inclination monitoring point and a stress monitoring point, which are respectively used for monitoring settlement, inclination and stress of the bridge. In specific implementation, the settlement monitoring point, the inclination monitoring point and the stress monitoring point can respectively measure data through a displacement sensor, an inclinometer and a strain gauge.
It can be understood that the types of the monitoring points set in different monitoring areas are increased or decreased according to the actual conditions of the bridge, the types of the monitoring points in different monitoring areas can be different, and in the same monitoring area, a plurality of monitoring points of the same type can be set.
Referring to fig. 1, a bridge monitoring and early warning method according to a first embodiment of the present invention includes steps S11 to S13.
And S11, acquiring detection data of each type of monitoring point in the monitoring area, and performing anomaly analysis on the acquired detection data of each monitoring point.
In this embodiment, detection data of each type of monitoring point in each monitoring area is obtained in real time, and abnormality analysis is performed to determine whether the monitoring point is abnormal. When the abnormality analysis is carried out, the detection data can be compared with the threshold value of the corresponding detection item, when the detection data exceeds the corresponding threshold value, the abnormality of the data of the monitoring point is judged, and the current monitoring point is determined to be the abnormal monitoring point.
And step S12, when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal, calculating the correlation among the monitoring points of the abnormal types.
And S13, determining that the monitoring area is abnormal when the calculated correlation coefficients are all larger than a threshold value, and carrying out early warning.
When the detection data of monitoring points exceeding a preset number (M) of types in a certain monitoring area are abnormal, the possibility that the bridge deformation exists in the area is high. In order to further judge the condition of the bridge, correlation analysis is needed, namely, correlation among monitoring points of different types of abnormality is calculated, and corresponding correlation coefficients are obtained. If the correlation coefficient is larger than the threshold value, the reason for the abnormality of the two data is identical, and the abnormal conditions such as deformation of the monitoring area can be confirmed.
It should be noted that the preset number may be set according to actual situations, for example, may be equal to the number of types of the monitoring points, or be half (or more) of the number of types of the monitoring points.
In the embodiment, the association relation between multiple monitoring points is established by combining the data mining and the association theoretical knowledge of the signal system, whether the monitoring area is abnormal is determined according to the association relation of abnormal monitoring points among different detection projects of the same monitoring area, and if yes, early warning is carried out. By analyzing the data of the plurality of detection items and the association relation among the plurality of detection items, misjudgment caused by the influence of factors such as environment or sensor equipment abnormality can be avoided, and the accuracy of an early warning mechanism is improved.
Referring to fig. 2, in the bridge monitoring and early warning method according to the second embodiment of the present invention, a plurality of monitoring areas are distributed on the bridge, and three monitoring points, namely, a settlement monitoring point, an inclination monitoring point and a stress monitoring point, are disposed on each monitoring area, and are respectively used for detecting settlement, inclination and stress of the bridge, and can be respectively measured by sensors such as a displacement sensor, an inclinometer and a strain gauge during specific implementation. The bridge monitoring and early warning method comprises the steps S21 to S25.
And S21, acquiring detection data of each type of monitoring point in the monitoring area, and performing median taking, amplitude limiting and sliding tie processing on the acquired detection data of each monitoring point.
The collected detection data of the monitoring points may have abrupt change due to the influence of factors such as environmental temperature or equipment stability. For some factor-induced data changes, this can be overcome by taking the median, clipping, and sliding tie processing.
The median value can have good effect on the measurement value which changes slowly, and can overcome the change caused by accidental factors. And sorting the data in the window from small to large, taking the average value of the middle two data if the total number of the data is odd, otherwise taking the middle value.
The amplitude limiting has good effect on the measured value which changes slowly, and can overcome the change caused by accidental factors. In the specific implementation, the current data and the previous data are used as differences, and the absolute value x and the amplitude y of the differences are judged. If x > y, then the current data is replaced with the previous data, otherwise the data is not processed.
The moving average has good inhibition effect on periodic interference, and the average value of data in a window is taken when the method is implemented.
Furthermore, most of the sensors are installed in the bridge body at the same time when the bridge is built, and as the service life of the sensors is mostly smaller than the normal service life of the bridge, the probability of the sensors failing is higher for the bridge with longer service time, so that the probability of the collected data being abnormal data is higher. The missing value is complemented for the distortion of a single point, the abnormal value is identified and corrected according to the measurement threshold value of the sensor and the normal measurement data sequence for the abnormal value of the single point, and the abnormal value is updated according to the sensor or other attribute sensor data sequences at other positions with larger correlation for the continuous abnormal value.
And S22, carrying out anomaly analysis on the detection data of the sedimentation monitoring points.
And S23, when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively.
For the bridge, the main deformation cause is that the bridge deck is settled, so that settlement monitoring points can be analyzed preferentially, and when detection data of the settlement monitoring points are abnormal, abnormal analysis is performed on the inclined monitoring points and the stress monitoring points.
And S24, calculating the correlation between the settlement monitoring point and the inclination monitoring point and the correlation between the stress monitoring point and the inclination monitoring point when the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are abnormal.
And S25, determining that the monitoring area is abnormal when the calculated correlation coefficients are all larger than a threshold value, and carrying out early warning.
When the settlement monitoring point, the inclination monitoring point and the stress monitoring point are abnormal, the correlation between the detection data of the settlement monitoring point and the inclination monitoring point and the correlation between the detection data of the inclination monitoring point and the detection data of the stress monitoring point are analyzed, and corresponding correlation coefficients are obtained. When the obtained correlation coefficients are all larger than the threshold value, the strong correlation among the data is indicated, the monitoring area is abnormal, and early warning is carried out.
The implementation of the present invention is described below with a specific application example.
The length of a certain bridge is 67.5m, the bridge is designed to be double-width, and the width of each bridge is 6.5m. The bridge superstructure is designed according to 15m standard span, in order to ensure bridge safety, sets up the limit for height frame in the pier tip in the bridge, down going position. The pier body of the pier of the upper portion steel truss structure and the pier body of the lower portion structure adopt double rows of steel pipe columns, the pier foundation adopts a concrete filling pile foundation, the steel pipe columns adopt two types of D813×12mm and D508×10mm, the longitudinal and transverse directions are connected by adopting steel pipes with the diameter of D273×8mm, and the steel pipe columns are connected with the foundation by adopting flange plates.
The detection data of the settlement monitoring points are obtained, the data are shown in figure 3, from the data obtained by the sensor, the settlement amount of the settlement measuring points CJ-02 and CJ-03 gradually rises from 12 noon in 12 months and 19 days, and the settlement values of the settlement measuring points CJ-02 and CJ-03 respectively reach-45.10 mm and-87.55 mm when the settlement amount reaches 19 in the evening of 23 days.
As shown in fig. 4, in the vicinity of the abnormal settlement monitoring point, there are a settlement measuring point CJ-02, a settlement measuring point CJ-03 and an inclination measuring point CX-02 at the vicinity of the north bridge abutment according to the CAD drawing of the measuring point, and the marked abnormal monitoring point is shown in the box of fig. 4, and the abnormal point will be analyzed in the following.
And acquiring detection data of the inclination monitoring points, and knowing the inclination degree of the abutment base through monitoring the abutment inclination. 2 measuring lines are arranged on the bridge, and each measuring line is provided with 1 measuring point; and 2 abutment inclination monitoring points are arranged in total.
a. According to the site distribution installation, the inclined measuring point CX-01 is installed on the surface of the south bridge abutment concrete, and the inclined side point CX-02 is installed on the surface of the north bridge abutment concrete. From the X-direction angle trend graph (fig. 5), it can be seen that the inclination measuring point changes differently, the angle change of the inclination measuring point CX-01 is stable, the angle change of the inclination measuring point CX-02 decreases stepwise, the value is continuously increased, the angle change value is 0.41 degrees in 19-23 days, and the direction is X-direction, and the direction is toward the south side, namely the direction in the bridge. The Y-direction angle trend chart (figure 6) shows that the measuring points CX-01 and CX-02 are not obviously changed, the data are stable, and the transverse bridge inclination change of east-west trend is not generated.
b. From the above data, it is clear that the south bridge is not significantly inclined, and the north bridge may be inclined in the X-direction due to sedimentation.
The method comprises the steps of obtaining detection data of stress monitoring points, and in a stress monitoring project, mainly comparing a comparison trend graph (figure 7) of a north bridge abutment with other stress measuring points, wherein the stress measuring point YB-01 is located at a south bridge pier and is far away from an abnormal point group, and the trend of the stress measuring point YB-01 is obviously different from that of the north bridge abutment stress measuring point (YB-13) after bridge deck traffic, wherein the data of the former is basically stable and unchanged, the data of the latter YB-13 point is time-division from 19 days traffic to 21 days am, and the stress variation extremum reaches about 45Mpa and is consistent with abnormal conditions of settlement and inclined measuring points.
And carrying out association analysis on the settlement monitoring points and the inclination monitoring points, wherein an association trend graph is shown in fig. 8, and can be seen from the association trend graph, after the bridge deck is in traffic, the settlement points are subjected to downwarping, inclination data are descended along with the downwarping, and a correlation coefficient scatter graph of the two monitoring points is drawn according to the data condition and is shown in fig. 9.
As can be seen from fig. 6, the correlation coefficient of the two monitoring points is 0.989, which indicates that the trend of the monitored points is highly correlated, and it is confirmed that the data anomalies are synchronous in time and trend, and it is presumed that the actual deformation occurs at the north bridge abutment on site, and the inspection personnel can be notified of further inspection confirmation for the actual condition of the structure.
The settlement monitoring points and the stress monitoring points are subjected to association analysis, an association trend diagram is shown in fig. 10, a correlation coefficient scatter diagram is shown in fig. 11, the analysis of the correlation scatter diagram shows that the inclined measuring points and the stress are at the same position, fluctuation change is strongly correlated, the correlation coefficient is 0.880, the inclined measuring points and the stress measuring points are all positioned at the north bridge abutment according to the scatter diagram, and the actual deformation of the structure is estimated.
Referring to fig. 12, in a bridge monitoring and early warning device according to a third embodiment of the present invention, a plurality of monitoring areas are distributed on the bridge, each monitoring area is provided with a plurality of types of monitoring points, each monitoring point is used for monitoring detection data of a plurality of detection items, and the bridge monitoring and early warning device includes:
the data acquisition module 31 is configured to acquire detection data of each type of monitoring point in the monitoring area;
the analysis module 32 is used for carrying out abnormal analysis on the acquired detection data of each monitoring point;
a correlation calculation module 33, configured to calculate correlation between monitoring points of abnormal type when the detection data of monitoring points exceeding a predetermined number of types in the current monitoring area are all abnormal;
and the early warning module 34 is used for determining that the monitoring area is abnormal and carrying out early warning when the calculated correlation coefficients are all larger than a threshold value.
Further, in the bridge monitoring and early warning device, three types of monitoring points are arranged on each monitoring area, namely a settlement monitoring point, an inclination monitoring point and a stress monitoring point.
Further, in the bridge monitoring and early warning device, the analysis module is specifically configured to:
performing exception analysis on the detection data of the settlement monitoring points;
when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively;
the correlation calculation module is specifically configured to:
when detection data of settlement monitoring points, inclination monitoring points and stress monitoring points in the current monitoring area are abnormal, calculating correlation between the settlement monitoring points and the inclination monitoring points and calculating correlation between the stress monitoring points and the inclination monitoring points.
The bridge monitoring and early warning device provided by the embodiment of the invention has the same implementation principle and technical effects as those of the embodiment of the method, and for the sake of brief description, the corresponding contents in the embodiment of the method can be referred to for the parts of the embodiment of the device which are not mentioned.
In another aspect, referring to fig. 13, an electronic device according to an embodiment of the present invention includes a processor 10, a memory 20, and a computer program 30 stored in the memory and capable of running on the processor, where the bridge monitoring and early warning method is implemented by the processor 10 when the processor executes the computer program 30.
The electronic device may be, but is not limited to, a personal computer, a mobile phone, or other computer devices. The processor 10 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for executing program code or processing data stored in the memory 20, etc.
The memory 20 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 20 may in some embodiments be an internal storage unit of the electronic device, such as a hard disk of the electronic device. The memory 20 may also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like. Further, the memory 20 may also include both internal storage units and external storage devices of the electronic device. The memory 20 may be used not only for storing application software installed in an electronic device, various types of data, and the like, but also for temporarily storing data that has been output or is to be output.
Optionally, the electronic device may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), a network interface, a communication bus, etc., and an optional user interface may further comprise a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and is typically used to establish a communication connection between the device and other electronic devices. The communication bus is used to enable connected communication between these components.
It should be noted that the structure shown in fig. 13 does not constitute a limitation of the electronic device, and in other embodiments the electronic device may comprise fewer or more components than shown, or may combine certain components, or may have a different arrangement of components.
The invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the bridge monitoring and early warning method as described above.
Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus (e.g., a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus). For the purposes of this description, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The bridge monitoring and early warning method is characterized in that a plurality of monitoring areas are distributed on the bridge, each monitoring area is provided with a plurality of types of monitoring points, each monitoring point is used for monitoring detection data of a plurality of detection projects, and the bridge monitoring and early warning method comprises the following steps:
acquiring detection data of each type of monitoring point in the monitoring area, and carrying out anomaly analysis on the acquired detection data of each monitoring point;
when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal, calculating the correlation among the monitoring points of the abnormal types;
and when the calculated correlation coefficients are all larger than a threshold value, determining that the monitoring area is abnormal, and carrying out early warning.
2. The bridge monitoring and early warning method according to claim 1, wherein three types of monitoring points are respectively arranged on each monitoring area, namely a settlement monitoring point, an inclination monitoring point and a stress monitoring point.
3. The bridge monitoring and early warning method according to claim 2, wherein the step of performing anomaly analysis on the acquired detection data of each monitoring point comprises:
performing exception analysis on the detection data of the settlement monitoring points;
when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively;
when the detection data of more than a preset number of types of monitoring points in the current monitoring area are abnormal, the step of calculating the correlation between the abnormal types of monitoring points comprises the following steps:
when detection data of settlement monitoring points, inclination monitoring points and stress monitoring points in the current monitoring area are abnormal, calculating correlation between the settlement monitoring points and the inclination monitoring points and calculating correlation between the stress monitoring points and the inclination monitoring points.
4. The bridge monitoring and early warning method according to claim 1, wherein the step of performing anomaly analysis on the acquired detection data of each monitoring point further comprises:
and carrying out median taking, amplitude limiting and sliding tie processing on the acquired detection data of each monitoring point.
5. The bridge monitoring and early warning method according to claim 1, wherein the step of performing anomaly analysis on the acquired detection data of each monitoring point comprises:
and comparing the acquired detection data with a threshold value corresponding to the detection item, and determining that the acquired detection data is abnormal when the threshold value is exceeded.
6. Bridge monitoring early warning device, its characterized in that, it has a plurality of monitoring areas to distribute on the bridge, is provided with the monitoring point of multiple type on every detection area, and each monitoring point is used for monitoring the detection data of multiple detection project, bridge monitoring early warning device includes:
the data acquisition module is used for acquiring detection data of each type of monitoring point in the monitoring area;
the analysis module is used for carrying out abnormal analysis on the acquired detection data of each monitoring point;
the correlation calculation module is used for calculating the correlation among the monitoring points of the abnormal type when the detection data of the monitoring points exceeding the preset number of types in the current monitoring area are abnormal;
and when the calculated correlation coefficients are all larger than a threshold value, determining that the monitoring area is abnormal, and carrying out early warning.
7. The bridge monitoring and early warning device according to claim 6, wherein three types of monitoring points are respectively arranged on each monitoring area, namely a settlement monitoring point, an inclination monitoring point and a stress monitoring point.
8. The bridge monitoring and early warning device according to claim 7, wherein the analysis module is specifically configured to:
performing exception analysis on the detection data of the settlement monitoring points;
when the detection data of the settlement monitoring points are abnormal, carrying out abnormal analysis on the detection data of the inclination monitoring points and the stress monitoring points respectively;
the correlation calculation module is specifically configured to:
when detection data of settlement monitoring points, inclination monitoring points and stress monitoring points in the current monitoring area are abnormal, calculating correlation between the settlement monitoring points and the inclination monitoring points and calculating correlation between the stress monitoring points and the inclination monitoring points.
9. An electronic device comprising a memory and a processor, the memory storing a program that when executed by the processor implements the method of any of claims 1-5.
10. A computer readable storage medium, on which a program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117870608A (en) * | 2024-01-22 | 2024-04-12 | 中煤地质集团有限公司 | Stratum deformation early warning method and stratum deformation early warning system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117870608A (en) * | 2024-01-22 | 2024-04-12 | 中煤地质集团有限公司 | Stratum deformation early warning method and stratum deformation early warning system |
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