CN116071900A - Bridge monitoring and early warning method, device, readable storage medium and electronic equipment - Google Patents

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

Bridge monitoring and early warning method, device, readable storage medium and electronic equipment

technical field

The invention relates to the field of bridge monitoring, in particular to a bridge monitoring and early warning method, device, readable storage medium and electronic equipment.

Background technique

Bridges are buildings that can be seen everywhere in our lives. In order to ensure the safety and durability of bridge structures, it is necessary to understand the health status of bridges in real time, so as to detect hidden dangers that endanger bridge safety early.

Current bridges are inspected by humans, or are assessed for health through a health assessment system. However, manual measurement is inefficient and inaccurate, and most health assessment systems are relatively simple, with low accuracy and poor monitoring effect on bridge status.

Contents of the invention

In view of the above situation, 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. The bridge is distributed with a plurality of monitoring areas, and each detection area is provided with various types of monitoring points, and each monitoring point is used to monitor the detection data of various detection items. , the bridge monitoring and early warning method includes:

Obtaining detection data of various types of monitoring points in the monitoring area, and performing abnormal analysis on the detection data of each monitoring point obtained;

When the detection data of more than a predetermined number of types of monitoring points in the current monitoring area are abnormal, calculate the correlation between the abnormal type monitoring points;

When the calculated correlation coefficients are all greater than the threshold, it is determined that the monitoring area is abnormal, and an early warning is given.

Further, in the above-mentioned bridge monitoring and early warning method, three types of monitoring points are set in each monitoring area, which are respectively settlement monitoring points, inclination monitoring points and stress monitoring points.

Further, the above-mentioned bridge monitoring and early warning method, wherein, the step of abnormally analyzing the acquired detection data of each monitoring point includes:

Abnormal analysis of the detection data of settlement monitoring points;

When the detection data of the settlement monitoring point is abnormal, abnormal analysis is performed on the detection data of the inclination monitoring point and the stress monitoring point;

When the detection data of more than a predetermined number of types of monitoring points in the current monitoring area are abnormal, the step of calculating the correlation between the abnormal type monitoring points includes:

When the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are all abnormal, calculate the correlation between the settlement monitoring point and the inclination monitoring point, and calculate the correlation between the stress monitoring point and the stress monitoring point The correlation between the tilt monitoring points.

Further, the above-mentioned bridge monitoring and early warning method, wherein, before the step of analyzing the abnormality of the acquired detection data of each monitoring point, it also includes:

The acquired detection data of each monitoring point is processed by median value, limit and sliding balance.

Further, the above-mentioned bridge monitoring and early warning method, wherein, the step of abnormally analyzing the acquired detection data of each monitoring point includes:

The acquired detection data is compared with the threshold corresponding to the detection item, and when the threshold is exceeded, it is determined that the acquired detection data is abnormal.

The present invention also discloses a bridge monitoring and early warning device. There are multiple monitoring areas distributed on the bridge, and each detection area is provided with various types of monitoring points, and each monitoring point is used to monitor the detection of various detection items. data, the bridge monitoring and early warning device includes:

A data acquisition module, configured to acquire detection data of various types of monitoring points in the monitoring area;

The analysis module is used to analyze the abnormality of the acquired detection data of each monitoring point;

The correlation calculation module is used to calculate the correlation between the abnormal type monitoring points when the detection data of the monitoring points exceeding a predetermined number of types in the current monitoring area are all abnormal;

The early warning module is used to determine that the monitoring area is abnormal and give an early warning when the calculated correlation coefficients are greater than the threshold.

Further, in the above-mentioned bridge monitoring and early warning device, three types of monitoring points are set on each monitoring area, which are respectively settlement monitoring points, inclination monitoring points and stress monitoring points.

Further, the above-mentioned bridge monitoring and early warning device, wherein the analysis module is specifically used for:

Abnormal analysis of the detection data of settlement monitoring points;

When the detection data of the settlement monitoring point is abnormal, abnormal analysis is performed on the detection data of the inclination monitoring point and the stress monitoring point;

The correlation calculation module is specifically used for:

When the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are all abnormal, calculate the correlation between the settlement monitoring point and the inclination monitoring point, and calculate the correlation between the stress monitoring point and the stress monitoring point The correlation between the tilt monitoring points.

The present invention also discloses an electronic device, including a memory and a processor, the memory stores a program, and when the program is executed by the processor, any one of the above-mentioned methods is realized.

The present invention also discloses a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, any one of the above-mentioned methods is realized.

The present invention combines the correlation theory knowledge of data mining and signal system, establishes the correlation between multiple monitoring points, and determines whether the monitoring area is abnormal according to the correlation of abnormal monitoring points between different detection items in the same monitoring area, and if so, proceeds early warning. Through the analysis of the data of multiple detection items and the analysis of the correlation between multiple detection items, it is possible to avoid misjudgments caused by factors such as abnormal environment or sensor equipment, and improve the accuracy of the early warning mechanism.

Description of drawings

Fig. 1 is the flow chart of bridge monitoring early warning method in the first embodiment of the present invention;

Fig. 2 is the flow chart of the bridge monitoring early warning method in the second embodiment of the present invention;

Fig. 3 is the trend diagram of settlement measuring points;

Figure 4 is a schematic diagram of the arrangement of monitoring points on the north abutment;

Fig. 5 is the X-direction trend diagram of the abutment inclination monitoring point;

Fig. 6 is the Y-direction trend diagram of the abutment inclination monitoring point;

Figure 7 is a comparison chart of stress monitoring points on the north abutment;

Fig. 8 is a correlation trend diagram of settlement monitoring points CJ-01, CJ-02 and inclination monitoring point CX-02;

Fig. 9 is the correlation scatter diagram of settlement monitoring point CJ-01, CJ-02 and inclination monitoring point CX-02;

Fig. 10 is the associated trend diagram of tilt monitoring point CX-02 and stress monitoring point YB-13;

Fig. 11 is the correlation scatter diagram of tilt monitoring point CX-02 and stress monitoring point YB-13;

Fig. 12 is a structural block diagram of the bridge monitoring and early warning device in the third embodiment;

FIG. 13 is a schematic structural diagram of an electronic device in an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

These and other aspects of embodiments of the invention will become apparent with reference to the following description and drawings. In these descriptions and drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited by this limit. On the contrary, the embodiments of the present invention include all changes, modifications and equivalents falling within the spirit and scope of the appended claims.

The bridge monitoring and early warning method in the present invention is mainly used for monitoring the health state of the bridge and performing early warning. There are multiple monitoring areas distributed on the bridge, and each monitoring area is provided with various types of monitoring points, and each monitoring point is used to monitor the detection data of various detection items. For example, each monitoring area is set with three types of monitoring points, settlement monitoring points, inclination monitoring points and stress monitoring points, which are used to monitor the settlement, inclination and stress of the bridge respectively. During specific implementation, the settlement monitoring point, the inclination monitoring point and the stress monitoring point can respectively measure data through displacement sensors, inclinometers and strain gauges.

It can be understood that the types of monitoring points set in different monitoring areas are increased or decreased according to the actual situation of the bridge, the types of monitoring points in different monitoring areas can be different, and in the same monitoring area, multiple monitoring points of the same type can be set.

Please refer to FIG. 1 , which shows the bridge monitoring and early warning method in the first embodiment of the present invention, including steps S11-S13.

Step S11 , acquiring detection data of various types of monitoring points in the monitoring area, and performing anomaly analysis on the acquired detection data of each monitoring point.

In this embodiment, detection data of various types of monitoring points in each monitoring area are obtained in real time, and abnormality analysis is performed to determine whether the monitoring points are abnormal. When performing anomaly analysis, the detection data can be compared with the threshold of the corresponding detection item. When the detection data exceeds the corresponding threshold, it is determined that the data of the monitoring point is abnormal, and the current monitoring point is determined to be an abnormal monitoring point.

Step S12, when the detection data of monitoring points exceeding a predetermined number of types in the current monitoring area are all abnormal, the correlation between monitoring points of abnormal type is calculated.

Step S13, when the calculated correlation coefficients are all greater than the threshold, it is determined that the monitoring area is abnormal, and an early warning is given.

When the detection data of more than a preset number (M) of monitoring points in a certain monitoring area are abnormal, it indicates that there is a greater possibility of bridge deformation in this area. In order to further judge the condition of the bridge, it is necessary to conduct correlation analysis, that is, to calculate the correlation between various types of abnormal monitoring points and obtain the corresponding correlation coefficient. If the correlation coefficient is greater than the threshold, it means that the reasons for the abnormality of the two data are the same, and it can be confirmed that there is abnormality such as deformation in the monitoring area.

It should be noted that the preset number can be set according to actual conditions, for example, it can be equal to the number of monitoring point types, or be half (or more) of the number of monitoring point types.

This embodiment combines the theoretical knowledge of data mining and signal systems to establish the relationship between multiple monitoring points, and according to the relationship between abnormal monitoring points between different detection items in the same monitoring area, determine whether the monitoring area is abnormal, and if so, then Forewarning. Through the analysis of the data of multiple detection items and the analysis of the correlation between multiple detection items, it is possible to avoid misjudgments caused by factors such as abnormal environment or sensor equipment, and improve the accuracy of the early warning mechanism.

Referring to Fig. 2, it is the bridge monitoring and early warning method in the second embodiment of the present invention, the bridge is distributed with a plurality of monitoring areas, and each of the monitoring areas is provided with three kinds of monitoring points, i.e. settlement monitoring points, inclination monitoring Points and stress monitoring points are used to detect the settlement, inclination and stress of the bridge respectively, which can be measured by sensors such as displacement sensors, inclinometers and strain gauges during specific implementation. The bridge monitoring and early warning method includes steps S21-S25.

Step S21 , acquiring detection data of various types of monitoring points in the monitoring area, and performing median value, clipping and sliding draw processing on the acquired detection data of each monitoring point.

Due to the influence of environmental temperature or equipment stability and other factors, the detection data of the collected monitoring points may have sudden changes. For data changes caused by some factors, it can be overcome by taking median, clipping and sliding draw.

Taking the median value can have a good effect on slowly changing measured values and can overcome changes caused by accidental factors. Sort the data in the window from small to large, if the total number of data is odd, take the average of the middle two data, otherwise take the middle value.

Limiting has a good effect on slowly changing measured values and can overcome changes caused by accidental factors. During specific implementation, the difference between the current data and the previous data is used to determine the magnitude of the absolute value x and the amplitude y of the difference. If x>y, the current data is replaced with the previous data, otherwise the data is not processed.

The moving average has a good inhibitory effect on periodic interference, and the average value of the data in the window is taken during the specific implementation.

Furthermore, most of the sensors are installed in the bridge body at the same time when the bridge is built. Since the service life of the sensors is mostly less than the normal operating life of the bridge, for bridges with a long operating time, the probability of sensor failure is relatively high, so the collected data The probability of abnormal data is high. For single-point distortion, it is necessary to complete the missing value. For single-point abnormal values, identify and correct the abnormal values according to the measurement threshold of the sensor and the normal measurement data sequence. For continuous abnormal values, according to the relatively large correlation Other location sensors or other attribute sensor data sequences to update.

Step S22, abnormality analysis is performed on the detection data of the settlement monitoring point.

Step S23, when the detection data of the settlement monitoring point is abnormal, analyze the abnormality of the detection data of the inclination monitoring point and the stress monitoring point respectively.

For bridges, the main cause of deformation is the settlement of the bridge deck, so the settlement monitoring points can be analyzed first. When the detection data of the settlement monitoring points is abnormal, the abnormality analysis of the tilt monitoring points and stress monitoring points can be performed.

Step S24, when the detection data of the settlement monitoring point, inclination monitoring point and stress monitoring point in the current monitoring area are all abnormal, calculating the correlation between the settlement monitoring point and the inclination monitoring point, and calculating the stress The correlation between the monitoring point and the tilt monitoring point.

Step S25, when the calculated correlation coefficients are all greater than the threshold, it is determined that the monitoring area is abnormal, and an early warning is given.

When the settlement monitoring point, inclination monitoring point and stress monitoring point are all abnormal, analyze the correlation between the detection data of the settlement monitoring point and the inclination monitoring point, and analyze the correlation between the detection data of the inclination monitoring point and the stress monitoring point Correlation, get the corresponding correlation coefficient. When the obtained correlation coefficients are greater than the threshold value, it means that there is a strong correlation between the data, and the monitoring area is abnormal, and an early warning will be given.

The implementation of the present invention will be described below with a specific application example.

The length of a bridge is 67.5m, the bridge is designed as double width, and the width of each bridge is 6.5m. The superstructure of the bridge is designed according to the standard span of 15m. In order to ensure the safety of the bridge, a height-limiting viaduct is set at the upper and lower positions of the end of the bridge pier. The upper steel truss structure, the pier body of the lower structure adopts double rows of steel pipe columns, and the foundation of the pier adopts concrete pouring pile foundation. The steel pipe columns adopt D813×12mm and D508×10mm. The steel pipe column and the foundation are connected by a flange.

Obtain the detection data of the settlement monitoring point. The data is shown in Figure 3. According to Figure 3, from the data obtained by the sensor, at 12 noon on December 19, the settlement measuring point CJ-02 and the settlement measuring point CJ-03 The amount of settlement gradually increased. By 19:00 on the evening of the 23rd, the settlement values of settlement measuring point CJ-02 and settlement measuring point CJ-03 reached -45.10mm and -87.55mm respectively.

As shown in Figure 4, according to the CAD drawings of the measuring points, near the abnormal settlement monitoring point, the measuring points near the north abutment include settlement measuring point CJ-02, settlement measuring point CJ-03 and tilt measuring point CX-02, as shown in Figure 4 The marked abnormal monitoring points are marked in the middle box, and the following will focus on analyzing the abnormal points.

Obtain the detection data of the inclination monitoring point, and understand the inclination degree of the abutment foundation through monitoring the inclination of the abutment. 2 measuring lines are arranged on the bridge, and 1 measuring point is arranged on each measuring line; a total of 2 abutment inclination monitoring points are arranged.

a. According to the site layout and installation, the inclined measuring point CX-01 is installed on the concrete surface of the south abutment, and the inclined side point CX-02 is installed on the concrete surface of the north abutment. From the angle trend diagram in the X direction (Fig. 5), it can be seen that the changes of the inclinometer points are different, the angle change of the inclination measuring point CX-01 is stable, and the angle change of the inclination measuring point CX-02 is in a stepwise decline, with constant values. Increase, from the 19th to the 23rd, the angle change value is 0.41°, the direction is the X-direction, towards the south side, that is, the direction inside the bridge. According to the angle trend diagram in the Y direction (Figure 6), the measuring points CX-01 and CX-02 have not changed significantly, and the data is relatively stable, and there is no change in the inclination of the east-west transverse bridge.

b. According to the above data, it can be seen that the south abutment has no obvious inclination, and the north abutment may be due to settlement, which may cause the abutment to change in the X-direction.

Obtain the detection data of the stress monitoring points. In the stress monitoring project, focus on comparing the comparison trend chart between the north abutment and other stress measurement points (Figure 7). Among them, the stress measurement point YB-01 is located at the south pier, far away from the abnormal point It can be seen that the trend of measuring point YB-01 and the north abutment stress measuring point (YB-13) is obviously different after the bridge deck is opened to traffic. The data was opened to traffic on the 19th to the morning of the 21st, and the extreme value of the stress change reached about 45Mpa, which was consistent with the abnormal conditions of the settlement and inclined measuring points.

Correlation analysis was carried out between the settlement monitoring points and the inclination monitoring points. The correlation trend diagram is shown in Figure 8. It can be seen from the correlation trend diagram that after the bridge deck was opened to traffic, the settlement point deflected and the inclination data decreased accordingly. According to the data The situation draws the correlation coefficient scatter diagram of the two monitoring points as shown in 9.

It can be seen from Figure 6 that the correlation coefficient of the measuring points of the two monitoring items is 0.989, indicating that the trend of the monitored measuring points is highly correlated, confirming that the data anomalies are synchronized in time and trend. For the actual situation of the structure, the inspectors can be notified for further inspection and confirmation.

Correlation analysis is carried out between the settlement monitoring points and the stress monitoring points. The correlation trend diagram is shown in Figure 10, and the correlation coefficient scatter diagram is shown in Figure 11. From the analysis of the correlation scatter diagram, it can be known that the inclination measurement point and the stress are at the same position , the fluctuations are strongly correlated, and the correlation coefficient is 0.880. According to the distribution map, the inclination and stress measurement points are located at the north abutment, and it is speculated that there is actual deformation of the structure.

Please refer to Fig. 12, which is a bridge monitoring and early warning device in the third embodiment of the present invention. There are a plurality of monitoring areas distributed on the bridge, and various types of monitoring points are arranged on each detection area, and each monitoring point is used for To monitor the detection data of various detection items, the bridge monitoring and early warning device includes:

A data acquisition module 31, configured to acquire detection data of various types of monitoring points in the monitoring area;

An analysis module 32, configured to perform abnormal analysis on the acquired detection data of each monitoring point;

The correlation calculation module 33 is used to calculate the correlation between the abnormal type monitoring points when the detection data of the monitoring points exceeding a predetermined number of types in the current monitoring area are all abnormal;

The early warning module 34 is configured to determine that the monitoring area is abnormal and give an early warning when the calculated correlation coefficients are all greater than a threshold.

Further, in the above-mentioned bridge monitoring and early warning device, three types of monitoring points are set on each monitoring area, which are respectively settlement monitoring points, inclination monitoring points and stress monitoring points.

Further, in the above-mentioned bridge monitoring and early warning device, the analysis module is specifically used for:

Abnormal analysis of the detection data of settlement monitoring points;

When the detection data of the settlement monitoring point is abnormal, abnormal analysis is performed on the detection data of the inclination monitoring point and the stress monitoring point;

The correlation calculation module is specifically used for:

When the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are all abnormal, calculate the correlation between the settlement monitoring point and the inclination monitoring point, and calculate the correlation between the stress monitoring point and the stress monitoring point The correlation between the tilt monitoring points.

The bridge monitoring and early warning device provided by the embodiment of the present invention has the same realization principle and technical effect as the aforementioned method embodiment. For a brief description, for the parts not mentioned in the device embodiment part, you can refer to the corresponding content in the aforementioned method embodiment. .

Another aspect of the present invention also proposes an electronic device, please refer to FIG. 13 , which shows an electronic device in an embodiment of the present invention, including a processor 10, a memory 20, and a computer stored in the memory and capable of running on the processor. The program 30, when the processor 10 executes the computer program 30, implements the bridge monitoring and early warning method as described above.

Wherein, the electronic device may be, but not limited to, computer devices such as personal computers and mobile phones. In some embodiments, the processor 10 can be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chips for running program codes or processing stored in the memory 20. data etc.

Wherein, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (eg, SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 may 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 equipped on the electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, Flash card (FlashCard), etc. Further, the memory 20 may also include both an internal storage unit of the electronic device and an external storage device. The memory 20 can be used not only to store application software and various data installed in the electronic device, but also to temporarily store data that has been output or will be output.

Optionally, the electronic device may also include a user interface, a network interface, a communication bus, etc., the user interface may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface may also include a standard wired interface, wireless interface. Optionally, 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, organic light-emitting diode) touch device, and the like. Wherein, the display may also be properly referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device and for displaying a visualized user interface. The network interface may optionally include standard wired interfaces and wireless interfaces (such as WI-FI interfaces), which are usually used to establish communication connections between the device and other electronic devices. A communication bus is used to implement connection communication between these components.

It should be noted that the structure shown in FIG. 13 does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the illustration, or combine certain components, or be different layout of the components.

The present invention also proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned bridge monitoring and early warning method is realized.

Those skilled in the art will understand that the logic and/or steps shown in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, which can be specifically implemented in In any computer-readable medium for use in an instruction execution system, device (such as a computer-based system, a system including a processor, or other system that can obtain instructions from an instruction execution system or device and execute instructions), or in combination with these instructions It is used to execute the system and device. As far as this specification is concerned, "computer-readable medium" can be any device that can contain, store, communicate, spread or transmit programs for use in instruction execution systems and devices or in combination with these instruction execution systems and devices.

More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and 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 can be printed, as it may be possible, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or other suitable processing if necessary. The program is processed electronically and stored in computer memory.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic 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 embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (10)

1. A bridge monitoring and early warning method is characterized in that a plurality of monitoring areas are distributed on the bridge, and multiple types of monitoring points are arranged on each detection area, and each monitoring point is used to monitor multiple detection items. Detection data, the bridge monitoring and early warning method includes: Obtaining detection data of various types of monitoring points in the monitoring area, and performing abnormal analysis on the detection data of each monitoring point obtained; When the detection data of more than a predetermined number of types of monitoring points in the current monitoring area are abnormal, calculate the correlation between the abnormal type monitoring points; When the calculated correlation coefficients are all greater than the threshold, it is determined that the monitoring area is abnormal, and an early warning is given. 2. The bridge monitoring and early warning method according to claim 1, wherein three types of monitoring points are arranged on each of the monitoring areas, which are respectively settlement monitoring points, inclination monitoring points and stress monitoring points. 3. bridge monitoring and early warning method as claimed in claim 2, is characterized in that, the described step of carrying out abnormality analysis to the detection data of each monitoring point that obtains comprises: Abnormal analysis of the detection data of settlement monitoring points; When the detection data of the settlement monitoring point is abnormal, abnormal analysis is performed on the detection data of the inclination monitoring point and the stress monitoring point; When the detection data of more than a predetermined number of types of monitoring points in the current monitoring area are abnormal, the step of calculating the correlation between the abnormal type monitoring points includes: When the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are all abnormal, calculate the correlation between the settlement monitoring point and the inclination monitoring point, and calculate the correlation between the stress monitoring point and the stress monitoring point The correlation between the tilt monitoring points. 4. bridge monitoring and early warning method as claimed in claim 1, is characterized in that, before the step of carrying out abnormality analysis to the detection data of each monitoring point that obtains, also comprises: The acquired detection data of each monitoring point is processed by median value, limit and sliding balance. 5. bridge monitoring and early warning method as claimed in claim 1, is characterized in that, the described step of carrying out abnormality analysis to the detection data of each monitoring point that obtains comprises: The acquired detection data is compared with the threshold corresponding to the detection item, and when the threshold is exceeded, it is determined that the acquired detection data is abnormal. 6. A bridge monitoring and early warning device, characterized in that the bridge is distributed with a plurality of monitoring areas, each detection area is provided with multiple types of monitoring points, each monitoring point is used to monitor multiple detection items Detection data, the bridge monitoring and early warning device includes: A data acquisition module, configured to acquire detection data of various types of monitoring points in the monitoring area; The analysis module is used to analyze the abnormality of the acquired detection data of each monitoring point; The correlation calculation module is used to calculate the correlation between the abnormal type monitoring points when the detection data of the monitoring points exceeding a predetermined number of types in the current monitoring area are all abnormal; When the calculated correlation coefficients are all greater than the threshold, it is determined that the monitoring area is abnormal, and an early warning is given. 7 . The bridge monitoring and early warning device according to claim 6 , wherein three types of monitoring points are set on each of the monitoring areas, which are respectively settlement monitoring points, inclination monitoring points and stress monitoring points. 8. bridge monitoring and early warning device as claimed in claim 7, is characterized in that, described analysis module is specifically used for: Abnormal analysis of the detection data of settlement monitoring points; When the detection data of the settlement monitoring point is abnormal, abnormal analysis is performed on the detection data of the inclination monitoring point and the stress monitoring point; The correlation calculation module is specifically used for: When the detection data of the settlement monitoring point, the inclination monitoring point and the stress monitoring point in the current monitoring area are all abnormal, calculate the correlation between the settlement monitoring point and the inclination monitoring point, and calculate the correlation between the stress monitoring point and the stress monitoring point The correlation between the tilt monitoring points. 9. An electronic device, comprising a memory and a processor, the memory stores a program, and when the program is executed by the processor, the method according to any one of claims 1-5 is implemented. 10. A computer-readable storage medium, on which a program is stored, wherein, when the program is executed by a processor, the method according to any one of claims 1-5 is realized.

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