Bridge disease early warning method, device, equipment, storage medium and computer product

Abstract

The present invention provides a bridge disease early warning method, device, equipment, storage medium and computer product, which relates to the field of electrical digital data processing technology. The method includes: obtaining the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera; based on the strain signal in the target time period, the vibration signal in the target time period and the target image in the target time period, calculating the current bridge health index of the target bridge; if the bridge health index is lower than the first threshold, it is determined that the target bridge is in a high-risk state; if the bridge health index is greater than or equal to the first threshold and less than the second threshold, it is determined that the target bridge is in a structural function degradation state; wherein the second threshold is greater than the first threshold; if the bridge health index is greater than the second threshold, it is determined that the target bridge is in a risk-free state. The present invention can timely and accurately determine whether the bridge is currently in a normal state to avoid accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a bridge disease early warning method, device, equipment, storage medium and computer product. Background Art

[0002] As the core nodes of the transportation network, bridges and tunnels are affected by environmental erosion, vehicle loads, material aging and other factors for a long time, and are prone to cracks, settlement, leakage and other defects. Traditional detection methods mainly rely on manual inspections or single sensor monitoring, which have the following problems: poor timeliness: the manual inspection cycle is long and it is difficult to capture sudden defects in time; high false alarm rate: single sensors are easily affected by the environment and the data reliability is insufficient; weak comprehensive judgment ability: the fusion analysis of multi-dimensional data (such as structural stress, temperature and humidity, images, etc.) has not been realized. Summary of the invention

[0003] The embodiments of the present invention provide a bridge disease early warning method, device, equipment, storage medium and computer product to solve the problems of poor timeliness, high false alarm rate and weak comprehensive judgment ability in existing bridge disease detection.

[0004] In a first aspect, an embodiment of the present invention provides a bridge disease early warning method, comprising: The strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera are obtained; wherein the target image is an image of the bridge including the strain sensor and the three-dimensional accelerometer.

[0005] The current bridge health index of the target bridge is calculated based on the strain signal within the target time period, the vibration signal within the target time period and the target image within the target time period.

[0006] If the bridge health index is lower than the first threshold, the target bridge is deemed to be in a high-risk state.

[0007] If the bridge health index is greater than or equal to a first threshold and less than a second threshold, the target bridge is deemed to be in a state of structural function degradation; wherein the second threshold is greater than the first threshold.

[0008] If the bridge health index is greater than the second threshold, the target bridge is deemed to be in a risk-free state.

[0009] In a possible implementation, the current bridge health index of the target bridge is calculated based on the strain signal within the target time period, the vibration signal within the target time period, and the target image within the target time period, including: For each moment in the target time period, a breakpoint in the target image at that moment is identified.

[0010] Based on the fracture point in the target image at each moment, the strain signal collected by the strain sensor within a preset range around the fracture point and the vibration signal collected by the three-dimensional accelerometer within a preset range around the fracture point are determined and recorded as the target strain signal and the target vibration signal.

[0011] According to the target strain signal and the target vibration signal, it is determined whether the fracture point is an abnormal point.

[0012] If the breakpoints in the target images at multiple consecutive moments within the target time period are the same breakpoint and are all identified as abnormal points, the breakpoint is defined as the target point.

[0013] Based on the number of target points within the target time period, the bridge health index is obtained.

[0014] In a possible implementation, based on the number of target points in a target time period, a bridge health index is obtained, including: Using the first formula, the bridge health index is obtained.

[0015] The first formula is:

[0016] in, Indicates the number of target points within the target time period. represents the bridge health index, Indicates preset parameters.

[0017] In a possible implementation, determining whether the fracture point is an abnormal point according to the target strain signal and the target vibration signal includes: Get the strain signal threshold and vibration signal threshold.

[0018] If the target strain signal is greater than the strain signal threshold or the target vibration signal is greater than the vibration signal threshold, the fracture point is determined to be an abnormal point.

[0019] In a possible implementation, the method further includes: Get the temperature and humidity signals from the temperature and humidity sensor.

[0020] Based on the temperature and humidity signals and in combination with the second formula, the strain signal threshold and the vibration signal threshold are adjusted.

[0021] The second formula is:

[0022] in, Indicates the strain signal threshold before adjustment or the vibration signal threshold before adjustment. Indicates temperature and humidity signal, Indicates the standard value of temperature and humidity signal. Indicates the adjusted strain signal threshold or the adjusted vibration signal threshold.

[0023] In a possible implementation, obtaining a strain signal of a strain sensor in a target bridge, a vibration signal of a three-dimensional accelerometer, and a target image captured by a camera includes: The original data of the strain signal, the original data of the vibration signal and the original data of the target image are obtained.

[0024] The original data is subjected to denoising and normalization processing to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera.

[0025] In a second aspect, an embodiment of the present invention provides a bridge disease early warning device, comprising: The first processing module is used to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera; wherein the target image is an image of the bridge including the strain sensor and the three-dimensional accelerometer.

[0026] The second processing module is used to calculate the current bridge health index of the target bridge based on the strain signal within the target time period, the vibration signal within the target time period and the target image within the target time period.

[0027] The third processing module is used to determine that the target bridge is in a high-risk state if the bridge health index is lower than the first threshold; if the bridge health index is greater than or equal to the first threshold and less than the second threshold, the target bridge is determined to be in a structural function degradation state; wherein the second threshold is greater than the first threshold; if the bridge health index is greater than the second threshold, the target bridge is determined to be in a risk-free state.

[0028] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method in the first aspect or any possible implementation of the first aspect is implemented.

[0029] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method in the first aspect or any possible implementation of the first aspect.

[0030] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the method in the first aspect or any possible implementation manner of the first aspect.

[0031] In the embodiment of the present invention, the bridge health index of the target bridge can be calculated in time through the strain signal of the strain sensor, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera. In addition to combining the strain signal and vibration signal of the sensor, it is also combined with the physical reality image, which can be comprehensively analyzed from multiple angles to analyze the current state of the bridge. In addition, the bridge health index is calculated based on the data in the target time period, which can fully eliminate interference signals and improve the accuracy of the final warning. Because the data of the sensor and the camera are real-time monitoring data, the real-time performance of this solution is greatly improved, and the bridge can be monitored in real time, and a timely warning can be issued when the bridge is currently damaged. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of an implementation of a bridge disease early warning method provided by an embodiment of the present invention; Figure 2 is a structural schematic diagram of a bridge disease early warning device provided by an embodiment of the present invention; Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0034] See also Figure 1 , which shows a flow chart of the implementation of the bridge disease early warning method provided by an embodiment of the present invention, and is described in detail as follows: Step 101, obtaining a strain signal of a strain sensor in a target bridge, a vibration signal of a three-dimensional accelerometer, and a target image captured by a camera; wherein the target image is an image of a bridge including a strain sensor and a three-dimensional accelerometer.

[0035] In some specific embodiments, step 101 may include: The original data of the strain signal, the original data of the vibration signal and the original data of the target image are obtained.

[0036] The original data is subjected to noise reduction and normalization processing to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera.

[0037] Specifically, the above process may include: 1. Data Collection Strain sensor data Hardware setup: Install strain sensors at key locations on the bridge (such as the mid-span of the main beam, the bottom of the pier, etc.) to ensure that the sensors can accurately capture the stress changes of the structure.

[0038] Data logging: Connect the sensor through a data acquisition system (such as NI DAQ device) to record the raw strain signal at a predetermined sampling frequency (such as 100 times per second). This data is usually stored in the form of a text file or database.

[0039] Vibration signal (3D accelerometer) Hardware setup: Place the three-dimensional accelerometer at the key vibration detection points of the bridge (e.g., select a maximum deflection test section in each of the main span and side span of the main bridge).

[0040] Data recording: The data acquisition system is also used to record the raw vibration signal output by the accelerometer.

[0041] Target image data Camera setup: Configure surveillance cameras to ensure that they cover the target area and clearly capture the parts that need to be monitored (such as bridge surfaces or key structural parts and contain strain sensors and 3D accelerometers).

[0042] Data recording: Capture images or video streams on demand through camera software or SDK and save them as image files (such as JPEG, PNG). These images will be used for subsequent visual analysis.

[0043] 2. Data Preprocessing Noise reduction Strain and vibration signals: Use filtering techniques to remove noise. Common methods include median filtering, low-pass filtering, or wavelet transform. It is important to choose appropriate filter parameters (such as window size, cutoff frequency) to avoid over-smoothing and loss of useful information. For vibration signals, frequency domain analysis methods (such as fast Fourier transform FFT) can also be applied to identify and remove unnecessary high-frequency components.

[0044] Image data: Image denoising can be done using a variety of methods such as Gaussian blurring, mean filtering or the more advanced non-local means filtering (NLM). These methods help reduce random noise in an image while trying to preserve edges and other important features.

[0045] Normalization Strain signal and vibration signal: Common methods include Min-Max Normalization, which scales the data to the range of [0, 1]; or Z-Score Standardization, which converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0046] Image data: For images, normalization usually refers to converting pixel values ​​from integers in the range 0-255 to floating point numbers in the range 0-1.

[0047] For example, the camera monitors the risk area 24 hours a day, and using image recognition algorithms, the camera can identify specific target objects or phenomena. The three-dimensional accelerometer can measure the inclination angle and acceleration information of an object. It is based on the principle of accelerometer, and calculates the inclination angle by detecting the acceleration component of the object in the gravity field. It can also sense the linear acceleration of the object. It can work normally under various harsh environmental conditions, such as high temperature, low temperature, high humidity, strong vibration, etc., and can still maintain stable performance and accurate data output, reducing equipment failures and data errors caused by environmental factors.

[0048] For example, in order to better calculate the bridge health index of the bridge, the distribution position of the sensors (strain sensors and three-dimensional accelerometers) is generally combined with the specific structural form and stress characteristics of the bridge, and is set at a position that is prone to damage, so as to improve the sensitivity of monitoring and handle the fault in time before the fault occurs. Specifically, the reference positions are generally: the maximum positive bending moment section in the span of the main beam, the maximum negative bending moment section of the main beam, the maximum compressive stress section of the pier, the stress concentration section at the consolidation position of the tower beam, a maximum deflection test section for each side span of the main span of the main bridge, a maximum deflection test section for each approach bridge on both sides, and a horizontal displacement measurement point on the top of each main bridge tower. Strain sensors and three-dimensional accelerometers will be placed at these points. In addition, a camera for photographing the bridge will be set up. The pictures taken by this camera will capture the placed strain sensors and three-dimensional accelerometers. The pictures taken by the camera are small-range pictures, not images of the entire bridge. Small-range pictures are convenient for subsequent abnormal point judgment.

[0049] Step 102: Calculate the current bridge health index of the target bridge based on the strain signal within the target time period, the vibration signal within the target time period, and the target image within the target time period.

[0050] In some specific embodiments, step 102 may include: For each moment in the target time period, a breakpoint in the target image at that moment is identified.

[0051] Based on the fracture point in the target image at each moment, the strain signal collected by the strain sensor within a preset range around the fracture point and the vibration signal collected by the three-dimensional accelerometer within a preset range around the fracture point are determined and recorded as the target strain signal and the target vibration signal.

[0052] According to the target strain signal and the target vibration signal, it is determined whether the fracture point is an abnormal point.

[0053] If the breakpoints in the target images at multiple consecutive moments within the target time period are the same breakpoint and are all identified as abnormal points, the breakpoint is defined as the target point.

[0054] Based on the number of target points within the target time period, the bridge health index is obtained.

[0055] Exemplarily, an image recognition algorithm may be used to identify the breakpoints in the target image, which can speed up the identification of the breakpoints and improve the accuracy of the identification of the breakpoints.

[0056] Specifically, identifying the breakpoints in the target image based on the image recognition algorithm may include: Preprocessing: First, the image needs to be preprocessed, which can include grayscale, denoising (such as using Gaussian blur), binarization, etc. These steps help reduce noise interference and highlight possible breakpoints.

[0057] Edge detection: Use edge detection algorithms (Canny operator, Sobel operator, etc.) to find the position of the object contour in the image, thereby helping to locate the breakpoint.

[0058] Feature extraction and description: Key points and their descriptors can be extracted using techniques such as HOG (Histogram of Oriented Gradients), SIFT (Scale-Invariant Feature Transform), or SURF (Speeded-Up Robust Features).

[0059] Breakpoint detection: Directly analyze the results after edge detection and look for discontinuities as potential breakpoints.

[0060] Post-processing: Further analysis and filtering of the detected breakpoints, such as removing false positives based on geometric shapes or application domain knowledge. The final filtered result is used as the final identified breakpoint.

[0061] In some specific embodiments, obtaining the bridge health index based on the number of target points within the target time period may include: Using the first formula, the bridge health index is obtained.

[0062] The first formula can be:

[0063] in, Indicates the number of target points within the target time period. represents the bridge health index, Indicates preset parameters.

[0064] In some specific embodiments, determining whether the breakpoint is an abnormal point according to the target strain signal and the target vibration signal may include: Get the strain signal threshold and vibration signal threshold.

[0065] If the target strain signal is greater than the strain signal threshold or the target vibration signal is greater than the vibration signal threshold, the fracture point is determined to be an abnormal point.

[0066] Step 103: if the bridge health index is lower than the first threshold, the target bridge is deemed to be in a high-risk state; if the bridge health index is greater than or equal to the first threshold and less than the second threshold, the target bridge is deemed to be in a structural function degradation state; wherein the second threshold is greater than the first threshold; if the bridge health index is greater than the second threshold, the target bridge is deemed to be in a risk-free state.

[0067] Exemplarily, the method further comprises: Get the temperature and humidity signals from the temperature and humidity sensor.

[0068] Based on the temperature and humidity signals and in combination with the second formula, the strain signal threshold and the vibration signal threshold are adjusted.

[0069] The second formula is:

[0070] in, Indicates the strain signal threshold before adjustment or the vibration signal threshold before adjustment. Indicates temperature and humidity signal, Indicates the standard value of temperature and humidity signal. Indicates the adjusted strain signal threshold or the adjusted vibration signal threshold.

[0071] The above-mentioned bridge disease early warning method can timely calculate the bridge health index of the target bridge through the strain signal of the strain sensor, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera. In addition to combining the strain signal and vibration signal of the sensor, it also combines the physical reality image, and can conduct a comprehensive analysis from multiple angles to analyze the current state of the bridge. In addition, the bridge health index is calculated based on the data within the target time period, which can fully eliminate interference signals and improve the accuracy of the final early warning. Because the data of the sensor and the camera are real-time monitoring data, the real-time performance of this solution is greatly improved, and the bridge can be monitored in real time, and timely warning can be issued when the bridge is currently damaged.

[0072] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0073] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0074] Figure 2 The structural diagram of the bridge disease early warning device provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: like Figure 2 As shown, the bridge disease early warning device includes: The first processing module 201 is used to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera; wherein the target image is an image of the bridge including the strain sensor and the three-dimensional accelerometer.

[0075] The second processing module 202 is used to calculate the current bridge health index of the target bridge based on the strain signal within the target time period, the vibration signal within the target time period and the target image within the target time period.

[0076] The third processing module 203 is used to determine that the target bridge is in a high-risk state if the bridge health index is lower than the first threshold; if the bridge health index is greater than or equal to the first threshold and less than the second threshold, the target bridge is determined to be in a structural function degradation state; wherein the second threshold is greater than the first threshold; if the bridge health index is greater than the second threshold, the target bridge is determined to be in a risk-free state.

[0077] In a possible implementation, the second processing module 202 may be used to: For each moment in the target time period, a breakpoint in the target image at that moment is identified.

[0078] Based on the fracture point in the target image at each moment, the strain signal collected by the strain sensor within a preset range around the fracture point and the vibration signal collected by the three-dimensional accelerometer within a preset range around the fracture point are determined and recorded as the target strain signal and the target vibration signal.

[0079] According to the target strain signal and the target vibration signal, it is determined whether the fracture point is an abnormal point.

[0080] If the breakpoints in the target images at multiple consecutive moments within the target time period are the same breakpoint and are all identified as abnormal points, the breakpoint is defined as the target point.

[0081] Based on the number of target points within the target time period, the bridge health index is obtained.

[0082] In a possible implementation, the second processing module 202 may be used to: Using the first formula, the bridge health index is obtained.

[0083] The first formula can be:

[0084] in, Indicates the number of target points within the target time period. represents the bridge health index, Indicates preset parameters.

[0085] In a possible implementation, the second processing module 202 may be used to: Get the strain signal threshold and vibration signal threshold.

[0086] If the target strain signal is greater than the strain signal threshold or the target vibration signal is greater than the vibration signal threshold, the fracture point is determined to be an abnormal point.

[0087] In a possible implementation, the third processing module 203 may be used to: Get the temperature and humidity signals from the temperature and humidity sensor.

[0088] Based on the temperature and humidity signals and in combination with the second formula, the strain signal threshold and the vibration signal threshold are adjusted.

[0089] The second formula can be:

[0090] in, Indicates the strain signal threshold before adjustment or the vibration signal threshold before adjustment. Indicates temperature and humidity signal, Indicates the standard value of temperature and humidity signal. Indicates the adjusted strain signal threshold or the adjusted vibration signal threshold.

[0091] In a possible implementation, the first processing module 201 may be used to: The original data of the strain signal, the original data of the vibration signal and the original data of the target image are obtained.

[0092] The original data is subjected to noise reduction and normalization processing to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera.

[0093] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, the electronic device 5 of this embodiment includes: a processor 50 and a memory 51. The memory 51 stores a computer program 52. When the processor 50 executes the computer program 52, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 50 executes the computer program 52, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0094] Exemplarily, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 52 in the electronic device 5.

[0095] The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 5 may also include input and output devices, network access devices, buses, etc.

[0096] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0097] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the electronic device 5. The memory 51 is used to store the computer program 52 and other programs and data required by the electronic device 5. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0098] For the convenience and simplicity of description, only the division of the above functional modules / units is used as an example for illustration. In actual applications, the above functions can be assigned to different functional modules / units as needed. The above modules / units can be implemented in the form of hardware, software, or a combination of hardware and software.

[0099] The embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0100] The embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0101] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. If there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form a new embodiment according to their internal logical relationship.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A bridge disease early warning method, characterized in that: include: Acquire a strain signal of a strain sensor in a target bridge, a vibration signal of a three-dimensional accelerometer, and a target image captured by a camera; wherein the target image is an image of a bridge including a strain sensor and a three-dimensional accelerometer; Calculate a current bridge health index of a target bridge based on the strain signal within a target time period, the vibration signal within a target time period, and the target image within a target time period; If the bridge health index is lower than a first threshold, the target bridge is deemed to be in a high-risk state; If the bridge health index is greater than or equal to the first threshold and less than a second threshold, it is determined that the target bridge is in a state of structural function degradation; wherein the second threshold is greater than the first threshold; If the bridge health index is greater than the second threshold, it is determined that the target bridge is in a risk-free state.

2. The bridge disease early warning method according to claim 1 is characterized in that: The calculating the current bridge health index of the target bridge based on the strain signal within the target time period, the vibration signal within the target time period and the target image within the target time period includes: For each moment in the target time period, identifying the breakpoint in the target image at that moment; Based on the fracture point in the target image at each moment, a strain signal collected by a strain sensor within a preset range around the fracture point and a vibration signal collected by a three-dimensional accelerometer within a preset range around the fracture point are determined, and recorded as a target strain signal and a target vibration signal; determining whether the break point is an abnormal point according to the target strain signal and the target vibration signal; If the breakpoints in the target images at multiple consecutive moments within the target time period are the same breakpoint and are all identified as abnormal points, then the breakpoint is defined as the target point; Based on the number of the target points within the target time period, a bridge health index is obtained.

3. The bridge disease early warning method according to claim 2 is characterized in that: The bridge health index is obtained based on the number of the target points within the target time period, including: Using the first formula, we get the bridge health index; The first formula is: in, Indicates the number of target points within the target time period, represents the bridge health index, Indicates preset parameters.

4. The bridge disease early warning method according to claim 2 is characterized in that: The step of determining whether the breakpoint is an abnormal point according to the target strain signal and the target vibration signal includes: Obtain strain signal threshold and vibration signal threshold; If the target strain signal is greater than the strain signal threshold or the target vibration signal is greater than the vibration signal threshold, the breakpoint is determined to be an abnormal point.

5. The bridge disease early warning method according to claim 4 is characterized in that: The method further comprises: Obtain temperature and humidity signals from the temperature and humidity sensor; Based on the temperature and humidity signal, in combination with a second formula, adjusting the strain signal threshold and the vibration signal threshold; The second formula is: in, Indicates the strain signal threshold before adjustment or the vibration signal threshold before adjustment. represents the temperature and humidity signal, Indicates the standard value of temperature and humidity signal. Indicates the adjusted strain signal threshold or the adjusted vibration signal threshold.

6. The bridge disease early warning method according to claim 1 is characterized in that: The step of obtaining the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer, and the target image captured by the camera includes: Acquiring the original data of the strain signal, the original data of the vibration signal, and the original data of the target image; The original data is subjected to noise reduction and normalization processing to obtain the strain signal of the strain sensor in the target bridge, the vibration signal of the three-dimensional accelerometer and the target image taken by the camera.

7. A bridge disease early warning device, characterized in that: include: A first processing module is used to obtain a strain signal of a strain sensor in a target bridge, a vibration signal of a three-dimensional accelerometer, and a target image captured by a camera; wherein the target image is an image of a bridge including a strain sensor and a three-dimensional accelerometer; A second processing module is used to calculate a current bridge health index of a target bridge based on the strain signal within a target time period, the vibration signal within a target time period, and the target image within a target time period; The third processing module is used to determine that the target bridge is in a high-risk state if the bridge health index is lower than a first threshold; if the bridge health index is greater than or equal to the first threshold and less than a second threshold, the target bridge is determined to be in a structural function degradation state; wherein the second threshold is greater than the first threshold; if the bridge health index is greater than the second threshold, the target bridge is determined to be in a risk-free state.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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