A monitoring system and method for the structural condition of bridges

Abstract

This invention discloses a monitoring system and method for the structural condition of bridges, belonging to the field of wireless communication technology. The monitoring system utilizes an RF transceiver to broadcast ambient RF signals to power a data acquisition terminal, enabling it to acquire a target transmission signal suitable for passive communication. The target transmission signal is then loaded onto the ambient RF signal and scattered. The RF transceiver demodulates the received superimposed signal to obtain the target transmission signal, which is then transmitted to a data processing terminal for structural condition identification of the bridge under monitoring. This invention achieves data transmission without relying on an external power source, utilizing ambient RF signals for energy harvesting and data transmission, allowing the data acquisition terminal to operate for extended periods in a low-energy environment. The reflection efficiency for the next monitoring cycle is adaptively adjusted based on the actual noise level and signal strength of the superimposed signal in the current cycle, ensuring stable and reliable data transmission under low-energy and high-noise conditions.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and more specifically, relates to a system and method for monitoring the structural condition of bridges. Background Technology

[0002] With the rapid development of modern society, bridges, as important infrastructure, directly affect the normal operation of transportation and the safety of people's lives and property. Traditional bridge monitoring methods mainly rely on regular manual inspections. This method is not only time-consuming and labor-intensive, but also difficult to monitor and respond quickly to subtle changes in the bridge structure in real time, and cannot predict and prevent structural damage in a timely manner.

[0003] Currently, bridge monitoring systems based on image acquisition technology are gradually being applied in practical engineering projects. These systems acquire dynamic and static information about the bridge in real time through various image acquisition units installed on the bridge structure, such as acceleration image acquisition units and displacement image acquisition units. Although these methods improve the accuracy and efficiency of monitoring, they often require external power supply due to the energy consumption of image acquisition units and data transmission equipment.

[0004] Therefore, developing a system capable of low-power bridge structural health monitoring has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a bridge structural condition monitoring system and method, the purpose of which is to solve the technical problem of high system energy consumption caused by image acquisition unit and data transmission equipment in the existing bridge monitoring system.

[0006] To achieve the above objectives, according to one aspect of the present invention, a bridge structural condition monitoring system is provided, comprising:

[0007] The data acquisition terminal is deployed at multiple preset locations on the bridge to be monitored and powered by received environmental radio frequency signals. It is used to acquire simulated image data corresponding to the structural state of the bridge to be monitored at each preset location during the current monitoring cycle, and convert the simulated image data into a target transmission signal that meets the requirements of passive communication. Then, the target transmission signal is loaded onto the environmental radio frequency signal to obtain a superimposed signal and scattered out.

[0008] The radio frequency signal transceiver is communicatively connected to the data acquisition terminal. It is used to broadcast the environmental radio frequency signal with the transmission parameters of the current monitoring period, and optimize the transmission parameters of the next monitoring period according to the actual signal strength and actual noise level corresponding to the superimposed signal. It is also used to receive the superimposed signal and demodulate it with the environmental radio frequency signal to obtain the target transmission signal, and then transmit the target transmission signal.

[0009] The data processing end is communicatively connected to the radio frequency signal transceiver end, and is used to receive the target transmission signal sent by the radio frequency signal transceiver end, and to detect the target transmission signal using a trained deep neural network to obtain the identification result corresponding to the structural state of the bridge to be monitored.

[0010] In one embodiment, the data acquisition terminal includes the following components connected in sequence:

[0011] The image acquisition module is used to acquire surface images of the bridge to be monitored to generate simulated image data;

[0012] An analog-to-digital conversion module is used to convert the analog image data into digital image data through differential encoding.

[0013] The data processing module is used to convert the digital image data into the target transmission signal in the form of a binary data stream;

[0014] The passive communication module is used to collect energy from the ambient radio frequency signal broadcast by the radio frequency signal transceiver to provide power, and to load the target transmission signal onto the ambient radio frequency signal to obtain the superimposed signal, and to group and scatter it.

[0015] In one embodiment, the passive communication module has an adaptive adjustment function that automatically adjusts the scattering parameters according to the channel level and actual noise level of the ambient radio frequency signal.

[0016] In one embodiment, the analog-to-digital conversion module is used to perform high-precision analog-to-digital conversion on the analog image data by differential coding using a multi-channel multiplexing method to obtain the digital image data.

[0017] In one embodiment, the analog-to-digital conversion unit is used to perform differential processing on the analog image data, calculate the difference between each pixel and its neighboring pixels, then map the pixel difference to a preset range and add an offset to make it non-negative; and encode the non-negative difference to obtain the digital image data.

[0018] In one embodiment, the radio frequency signal transceiver includes:

[0019] The transmitting module is used to generate and transmit the ambient radio frequency signal;

[0020] The receiving module is used to receive the superimposed signal scattered back by the passive communication module;

[0021] An adaptive adjustment module, connected to the transmitting module and the receiving module, is used to compare the actual signal strength and actual noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle, the transmission parameters including transmission power, transmission frequency and rate; it is also used to feed back the transmission parameters for the next monitoring cycle to the transmitting module so that it synchronizes the transmission parameters for the next monitoring cycle with the receiving module.

[0022] A radio frequency signal processing module, connected to the receiving module, is used to demodulate the superimposed signal using the ambient radio frequency signal to obtain the target transmission signal.

[0023] In one embodiment, the adaptive adjustment module compares the actual signal strength and actual noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle, including:

[0024] When the actual signal strength of the superimposed signal is lower than the signal strength threshold, the transmission power of the current monitoring period is increased as the transmission power of the next monitoring period. The increased power value is the difference between the actual signal strength and the signal strength threshold multiplied by the first adjustment coefficient.

[0025] When the actual noise intensity in the actual noise level exceeds the noise tolerance or the signal-to-noise ratio is insufficient, the transmission rate of the current monitoring cycle is reduced as the transmission power of the next monitoring cycle. The reduction rate is the difference between the actual noise intensity and the noise tolerance multiplied by the second adjustment coefficient.

[0026] In one embodiment, the data processing terminal includes:

[0027] The image processing module is used to obtain edge features by calculating the gradient and direction of pixels in the target transmitted signal, and to analyze the texture features of pixels in the target transmitted signal using gray-level co-occurrence matrix technology.

[0028] The risk assessment module, connected to the image processing module, is used to input the edge features and texture features into a trained deep learning model to obtain the identification result corresponding to the structural state of the bridge to be monitored.

[0029] In one embodiment, the data processing terminal includes an alarm notification module connected to the risk assessment module, used to generate an alarm signal when the identification result corresponding to the structural state of the bridge to be monitored indicates the presence of structural defects.

[0030] According to another aspect of the present invention, a method for monitoring the structural condition of a bridge is provided, characterized in that the monitoring system applied to the structural condition of the bridge includes:

[0031] The environmental radio frequency signal is broadcast using the transmission parameters of the current monitoring period through the radio frequency signal transceiver terminal;

[0032] The data acquisition terminal uses the received environmental radio frequency signal for power supply, and collects simulated image data corresponding to the structural state of the bridge to be monitored at each preset position during the current monitoring cycle. The simulated image data is then converted into a target transmission signal that meets the requirements of passive communication. The target transmission signal is then loaded onto the environmental radio frequency signal to obtain a superimposed signal and scattered out.

[0033] The superimposed signal is received using the radio frequency signal transceiver terminal, and the transmission parameters for the next monitoring cycle are optimized based on the actual signal strength and actual noise level corresponding to the superimposed signal; the superimposed signal is received and demodulated using the environmental radio frequency signal to obtain the target transmission signal, and then the target transmission signal is transmitted.

[0034] The data processing terminal receives the target transmission signal sent by the radio frequency signal transceiver terminal, and the trained deep neural network detects the target transmission signal to obtain the identification result corresponding to the structural state of the bridge to be monitored.

[0035] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0036] (1) This invention provides a bridge structural condition monitoring system, comprising a data acquisition terminal, a radio frequency (RF) signal transceiver terminal, and a data processing terminal connected in sequence. The RF signal transceiver terminal broadcasts the ambient RF signal to the data acquisition terminal using the transmission parameters of the current monitoring cycle, enabling the data acquisition terminal to acquire a target transmission signal that meets the requirements of passive communication, and loads it onto the ambient RF signal and scatters it out. The RF signal transceiver terminal demodulates the received superimposed signal to obtain the target transmission signal, which is then transmitted to the data processing terminal for structural condition identification of the bridge to be monitored. This invention achieves data transmission without relying on an external power source, utilizing the ambient RF signal for energy harvesting and data transmission, allowing the data acquisition terminal to operate for extended periods in a low-energy environment. The RF signal transceiver terminal adaptively adjusts the reflection efficiency of the next monitoring cycle based on the actual noise level and actual signal strength of the superimposed signal in the current cycle, ensuring stable and reliable data transmission under low-energy and high-noise conditions.

[0037] (2) The data acquisition terminal described in this solution includes, in sequence, an image acquisition module, an analog-to-digital conversion module, a data processing module and a passive communication module. It is powered by environmental radio frequency signals, has a simple structure and low energy consumption, and is suitable for application scenarios that monitor the structural condition of bridges for a long time.

[0038] (3) The passive communication module described in this solution has an adaptive adjustment function, which automatically adjusts the scattering parameters according to the channel level and actual noise level of the environmental radio frequency signal, thereby improving the communication stability and sustainability of the bridge structural condition monitoring system.

[0039] (4) This scheme uses a multi-channel multiplexing method to group the simulated image data for high-precision analog-to-digital conversion, which can improve the analog-to-digital conversion rate and further improve the operating efficiency of the entire bridge structural condition monitoring system.

[0040] (5) The analog-to-digital conversion unit described in this scheme is used to perform differential encoding on the analog image data to achieve analog-to-digital conversion, which has high accuracy and low computational complexity.

[0041] (6) In this scheme, the adaptive adjustment module in the radio frequency signal transceiver compares the actual signal strength and actual noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle, and feeds back the transmission parameters for the next monitoring cycle to the transmitting module so that it is synchronized with the receiving module for the transmission parameters of the next monitoring cycle; it can adaptively adjust the reflection efficiency of the next monitoring cycle and synchronize with the transmitting module and receiving module to ensure that the bridge structural condition monitoring system is in a stable and healthy communication environment.

[0042] (7) This scheme provides a complete strategy for adjusting the transmission parameters of the next monitoring cycle based on the actual signal strength and noise level of the superimposed signal in the current detection cycle. It has high computational complexity and adjustment efficiency.

[0043] (8) The edge and texture features of the data processing terminal described in this solution can effectively reflect the structural defects of the bridge under monitoring. By integrating advanced image analysis algorithms and deep learning technology, the system can efficiently process and analyze large-scale image data to identify cracks and structural defects on the bridge surface. Through historical data analysis models and machine learning technology, the system can predict structural change trends, provide accurate and reliable monitoring results, and enhance the intelligence level of bridge safety assessment.

[0044] (9) The data processing terminal described in this solution includes: an alarm prompting module, which is used to generate an alarm signal when the identification result indicates that there is a structural defect in the bridge to be monitored, so as to avoid safety accidents and improve safety. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the overall structure of the bridge structural condition monitoring system provided in Embodiment 1 of the present invention;

[0046] Figure 2This is a schematic diagram of the data acquisition terminal in the bridge structural condition monitoring system provided in Embodiment 1 of the present invention;

[0047] Figure 3 This is a schematic diagram of the communication process between the data acquisition terminal and the radio frequency signal transceiver terminal provided in Embodiment 1 of the present invention;

[0048] Figure 4 This is a schematic diagram of the deployment of the bridge structural condition monitoring system provided in Embodiment 1 of the present invention;

[0049] Figure 5 This is a flowchart of a bridge structural condition monitoring method provided in Embodiment 2 of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Example 1

[0052] This embodiment provides a bridge structural state monitoring system, including: a data acquisition terminal, a radio frequency (RF) signal transceiver terminal, and a data processing terminal. The data acquisition terminal, deployed at multiple preset locations on the bridge to be monitored, is powered by received environmental RF signals. It is used to acquire simulated image data corresponding to the structural state of the bridge at each preset location during the current monitoring cycle, convert the simulated image data into a target transmission signal suitable for passive communication, and then load the target transmission signal onto the environmental RF signal to obtain a superimposed signal, which is then scattered. The RF signal transceiver terminal, communicatively connected to the data acquisition terminal, is used to broadcast the environmental RF signal with the transmission parameters of the current monitoring cycle, and optimize the transmission parameters for the next monitoring cycle based on the actual signal strength and noise level corresponding to the superimposed signal. It is also used to receive the superimposed signal, demodulate it using the environmental RF signal to obtain the target transmission signal, and then transmit the target transmission signal. The data processing terminal, communicatively connected to the RF signal transceiver terminal, is used to receive the target transmission signal transmitted by the RF signal transceiver terminal, detect the target transmission signal using a trained deep neural network, and obtain the identification result corresponding to the structural state of the bridge to be monitored.

[0053] The data acquisition module at the data acquisition end captures the physical characteristics of the bridge surface and receives pre-observed images of the bridge surface. It then generates simulated image data based on these predicted images and converts the simulated image data into target transmission data suitable for passive communication. For example, the image acquisition module could be an AHT10 temperature and humidity image acquisition module or an OV2640 camera module, acquiring images of bridge cracks and environmental data, and converting the acquired simulated image data into digital image data.

[0054] Furthermore, the radio frequency signal transceiver includes: a transmitting module, a receiving module, an adaptive adjustment module, and a radio frequency signal processing module. The transmitting module generates and transmits ambient radio frequency signals; the receiving module receives the superimposed signal scattered back by the passive communication module; the adaptive adjustment module is connected to the transmitting module and compares the actual signal strength and noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle, including transmission power, transmission frequency, and rate; it also feeds back the transmission parameters for the next monitoring cycle to the transmitting module to synchronize them with the receiving module; the radio frequency signal processing module, connected to the receiving module, demodulates the superimposed signal using the ambient radio frequency signal to obtain the target transmission signal.

[0055] Furthermore, the adaptive adjustment module compares the actual signal strength and actual noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle. This includes: when the actual signal strength of the superimposed signal is lower than the signal strength threshold, increasing the transmission power of the current monitoring cycle as the transmission power of the next monitoring cycle, with the increased power value being the difference between the actual signal strength and the signal strength threshold multiplied by a first adjustment coefficient; and when the actual noise strength in the actual noise level exceeds the noise tolerance or the signal-to-noise ratio is insufficient, decreasing the transmission rate of the current monitoring cycle as the transmission power of the next monitoring cycle, with the decreased rate value being the difference between the actual noise strength and the noise tolerance multiplied by a second adjustment coefficient.

[0056] The radio frequency (RF) transceiver has an adaptive adjustment function. By receiving and collecting environmental data, it dynamically adjusts communication parameters, such as frequency, power, and transmission rate, to optimize energy harvesting and data transmission. It generates environmental RF signals of specific frequency and power to drive the data acquisition end to operate normally. In this embodiment, the RF switches ADG902 and ADG904 can be used.

[0057] Among them, environmental radio frequency signals can be defined as f0 is the signal center frequency. Let h1 be the channel between the signal source and the data acquisition terminal, Γ be the reflection coefficient of the data acquisition terminal, and h2 be the channel between the data acquisition terminal and the receiving module. Then the received signal is: The passive communication module at the data acquisition end employs backscattering technology to reflect the processed superimposed signal back to the receiving module at the RF transceiver end. This backscattering technology utilizes the broadcast environmental RF signal from the transmitting module at the RF transceiver end as the ambient RF signal. By selecting whether to match the antenna impedance, it scatters or absorbs the incident signal. The passive communication module scatters the superimposed signal to enable reception at the RF transceiver end, thus controlling the transmission power. for: Where ε is the backscattering efficiency of the transmitting antenna, satisfying 0<ε≤1.

[0058] It should be noted that data acquisition terminals can be deployed at various key locations on the bridge to be monitored, while radio frequency (RF) signal transceivers can be deployed on the automatic inspection device. The automatic inspection device can inspect the entire path of the bridge under monitoring, and the location of structural defects on the bridge can be determined by the tags of various sensors in the data acquisition terminals and the positioning information of the automatic inspection device. Specifically, the automatic inspection device uses an unmanned vehicle (UAV) to deploy the RF signal transceiver for automated inspection, monitoring blind spots on the bridge. It also facilitates scene changes; based on the detection results and environmental changes, the system can automatically adjust the detection parameters and the UAV's inspection path to adapt to different detection needs and environmental conditions, making it convenient and fast.

[0059] Furthermore, the data acquisition terminal includes, in sequence, an image acquisition module, an analog-to-digital conversion module, a data processing module, and a passive communication module; the image acquisition module is used to acquire surface images of the bridge to be monitored to generate analog image data; the analog-to-digital conversion module is used to perform analog-to-digital conversion on the analog image data to obtain digital image data; the data processing module is used to convert the digital image data into a target transmission signal in the form of a binary data stream; the passive communication module is used to acquire the energy on the environmental radio frequency signal broadcast by the radio frequency signal transceiver to power the data acquisition terminal, and to superimpose the target transmission signal onto the environmental radio frequency signal to obtain a superimposed signal, and to group and scatter it.

[0060] Furthermore, the passive communication module has an adaptive adjustment function, which automatically adjusts the scattering parameters for the next monitoring cycle based on the channel level of the ambient radio frequency signal and the actual noise level.

[0061] Furthermore, the analog-to-digital conversion module is used to perform high-precision analog-to-digital conversion on analog image data by using a multi-channel multiplexing method to obtain digital image data.

[0062] Specifically, the analog-to-digital conversion module converts the analog signals captured by the image acquisition module into high-precision digital signals to generate digital image data. Environmental noise and interference are filtered out during the signal conversion process. A multi-channel processor simultaneously processes multiple analog signal channels, improving signal conversion efficiency and system response speed; time-slicing processing of multiple channels is implemented, completing multi-channel signal conversion on a single high-speed digital-to-analog converter; and signal synchronization is achieved through a shared clock signal and hardware triggering mechanism.

[0063] Furthermore, the analog-to-digital conversion unit is used to perform differential processing on the analog image data, calculate the difference between each pixel and its neighboring pixels, map the pixel difference to a preset range and add an offset to make it non-negative, and encode the non-negative difference to obtain digital image data.

[0064] Specifically, the simulated image data is first read and stored in matrix form. Then, the simulated image data is differentially processed to calculate the difference between each pixel and its neighboring pixels. A difference value mapping is used to map the calculated difference values ​​to an appropriate range, and an offset is added to the difference values ​​to ensure all values ​​are non-negative. The processed difference values ​​are then encoded using integers. The transmitted data is then: One of the one-dimensional image data sequences is I = {I1, I2, I3, ..., I...} n The data to be sent is D = {D1, D2, D3, ..., D}. n The difference values ​​are then encoded using Huffman coding to further compress the data. The encoded difference values ​​are then converted into a binary data stream to obtain the compressed target transmission data suitable for passive communication.

[0065] Furthermore, the data processing module includes an image processing module and a risk assessment module. The image processing module is used to obtain edge features by calculating the gradient and direction of pixels in the target's transmitted signal, and to analyze the texture features of pixels in the target's transmitted signal using gray-level co-occurrence matrix technology. The risk assessment module, connected to the image processing module, is used to input the edge features and texture features into a trained deep learning model to obtain the identification results corresponding to the structural state of the bridge to be monitored.

[0066] Furthermore, the data processing end includes an alarm notification module, which is connected to the risk assessment module and is used to generate an alarm signal when the identification result corresponding to the structural state of the bridge to be monitored indicates the existence of structural defects.

[0067] Example 2

[0068] This embodiment discloses a method for monitoring the structural state of a bridge, applied to the aforementioned bridge structural state monitoring system. The method includes: broadcasting an environmental radio frequency (RF) signal using the transmission parameters of the current monitoring period via an RF signal transceiver; using a data acquisition terminal to power the received environmental RF signal and acquire simulated image data corresponding to the structural state of the bridge under monitoring at various preset locations during the current monitoring period, converting the simulated image data into a target transmission signal adapted to passive communication requirements, then loading the target transmission signal onto the environmental RF signal to obtain a superimposed signal and scattering it; receiving the superimposed signal using the RF signal transceiver and optimizing the transmission parameters for the next monitoring period based on the actual signal strength and noise level of the superimposed signal; receiving the superimposed signal and demodulating it using the environmental RF signal to obtain the target transmission signal, then transmitting the target transmission signal; receiving the target transmission signal transmitted by the RF signal transceiver via a data processing terminal, detecting the target transmission signal using a trained deep neural network, and obtaining the identification result corresponding to the structural state of the bridge under monitoring.

[0069] The first step involves the transmitter module of the radio frequency signal transmitter generating and transmitting environmental radio frequency signals based on preset initial parameters, including transmit power and frequency. These signals carry basic communication information and propagate within the monitoring area, ensuring coverage of all predetermined monitoring points.

[0070] The second step involves the data acquisition terminal receiving the environmental radio frequency signal from the transmitting module. It then uses the built-in image acquisition module to encode the structural health information of the bridge into the environmental radio frequency signal, and the signal carrying this information is reflected back to the adaptive adjustment module.

[0071] Thirdly, the adaptive adjustment module automatically adjusts the reflection parameters based on channel conditions and noise levels. The receiving module is equipped with environmental awareness, using a low-power image acquisition module to monitor signal strength and noise levels in real time to obtain the current environmental RF conditions. The receiving module analyzes the environmental data and dynamically adjusts communication parameters according to preset analysis standards, including signal strength thresholds, noise tolerance, and signal-to-noise ratio. These parameters include, but are not limited to, transmit power and transmission rate. Let the transmit power be P. transmit The transmission rate is R transmit When the actual signal strength S actual Below the signal strength threshold S threshold At that time, increase the transmission power:

[0072] P transmit,new =P transmit,old +k1(S threshold -S actual );

[0073] Where k1 is an adjustment coefficient used to control the increase in transmit power. When the actual noise intensity N... actualExceeding noise tolerance N tolerance If the signal-to-noise ratio is insufficient, reduce the transmission rate:

[0074]

[0075] Where k2 is an adjustment coefficient used to control the amount of reduction in transmission rate.

[0076] Fourth, the adaptive adjustment module transmits the adjusted communication parameters to the transmitting module at the radio frequency signal transmitter. The transmitting module makes corresponding adjustments based on the received parameters to further optimize signal transmission in the next monitoring cycle. The transmitting module also synchronizes the transmission parameters to the receiving module.

[0077] The fifth step involves the radio frequency (RF) signal transmitter transmitting the demodulated target signal to the data processing unit. The data processing unit analyzes the target signal according to preset analysis standards, such as signal strength threshold, noise tolerance, and signal-to-noise ratio, and dynamically adjusts communication parameters such as frequency, power, and transmission rate. Specifically, the data processing unit uses edge detection, texture analysis, and deep learning technologies to identify cracks and potential defects on the bridge surface. It then combines time series analysis and big data technology to predict structural changes, optimize the crack detection process, and improve the accuracy and efficiency of the monitoring system.

[0078] Specifically, the data processing unit uses the CPU to process pixel information from image data acquired from the image acquisition module or camera, capturing changes on the bridge surface, including cracks and potential defects. Image analysis algorithms are used to determine whether potential structural damage or cracks exist in the acquired images based on preset crack identification parameters. This includes image preprocessing, such as image enhancement, which preprocesses the image before analysis and uses histogram equalization to improve image contrast, making features like cracks more prominent. The purpose of histogram equalization is to expand the dynamic range of the image. Further feature processing is performed, using the Canny edge detection algorithm to identify edges in the image by calculating the gradient and direction of pixels. These features typically manifest as distinct edges in the image. Texture analysis is then used, employing gray-level co-occurrence matrix (GLCM) technology to analyze image texture. Gray-level co-occurrence matrix (GLCM) is used to measure the gray-level spatial relationship between pixels. Its calculation formula can be expressed as matrix P(i,j|d,θ), representing the transition probability of gray-level value i to j in the directions of distance d and angle θ. Cracks often alter the consistency of local textures, and texture analysis helps identify the texture features of crack regions. Crack detection and classification utilize deep learning models, employing convolutional neural networks for deeper image feature learning, and automatically learning the hierarchical features of the image through multi-layer filters. The operation of the convolutional neural network model can be expressed by the following formula:

[0079] a l+1 =σ(W l *al +b l );

[0080] Where a l It represents the activation of layer l, * indicates the convolution operation, W l and b l These are the convolution kernel and bias, respectively, with σ being the ReLU activation function. The convolutional neural network model matches and classifies the extracted features to identify cracks and other structural defects in the image. The model classifies and labels cracks based on their shape, size, and location. Finally, the processed crack images and bridge temperature and humidity information can be displayed on a PC visualization platform or a mobile app.

[0081] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A monitoring system for the structural condition of a bridge, characterized in that, include: The data acquisition terminal is deployed at multiple preset locations on the bridge to be monitored and powered by received environmental radio frequency signals. It is used to acquire simulated image data corresponding to the structural state of the bridge to be monitored at each preset location during the current monitoring cycle, and convert the simulated image data into a target transmission signal that meets the requirements of passive communication. Then, the target transmission signal is loaded onto the environmental radio frequency signal to obtain a superimposed signal and scattered out. The radio frequency signal transceiver is communicatively connected to the data acquisition terminal. It is used to broadcast the environmental radio frequency signal with the transmission parameters of the current monitoring period, and optimize the transmission parameters of the next monitoring period according to the actual signal strength and actual noise level corresponding to the superimposed signal. It is also used to receive the superimposed signal and demodulate it with the environmental radio frequency signal to obtain the target transmission signal, and then transmit the target transmission signal. The data processing end is communicatively connected to the radio frequency signal transceiver end, and is used to receive the target transmission signal sent by the radio frequency signal transceiver end, and to detect the target transmission signal using a trained deep neural network to obtain the identification result corresponding to the structural state of the bridge to be monitored. The data acquisition terminal uses a passive communication module to collect energy from the environmental radio frequency signal broadcast by the radio frequency signal transceiver terminal for power supply, and loads the target transmission signal onto the environmental radio frequency signal to obtain the superimposed signal, and then groups and scatters it; the passive communication module has an adaptive adjustment function, which automatically adjusts the scattering parameters of the next monitoring cycle according to the channel level and actual noise level of the environmental radio frequency signal; The radio frequency signal transceiver uses an adaptive adjustment module to compare the actual signal strength and actual noise level of the superimposed signal with a preset threshold to determine the transmission parameters for the next monitoring cycle. Specifically, this includes: when the actual signal strength of the superimposed signal is lower than the signal strength threshold, increasing the transmission power of the current monitoring cycle as the transmission power for the next monitoring cycle, with the increased power value being the difference between the actual signal strength and the signal strength threshold multiplied by a first adjustment coefficient; when the actual noise level exceeds the noise tolerance or the signal-to-noise ratio is insufficient, decreasing the transmission rate of the current monitoring cycle as the transmission power for the next monitoring cycle, with the decreased rate value being the difference between the actual noise level and the noise tolerance multiplied by a second adjustment coefficient.

2. The bridge structural condition monitoring system as described in claim 1, characterized in that, The data acquisition terminal includes the following components connected in sequence: The image acquisition module is used to acquire surface images of the bridge to be monitored to generate simulated image data; An analog-to-digital conversion module is used to convert the analog image data into digital image data. The data processing module is used to convert the digital image data into the target transmission signal in the form of a binary data stream; The passive communication module is used to collect the energy from the ambient radio frequency signal broadcast by the radio frequency signal transceiver to power the data acquisition terminal, and to load the target transmission signal onto the ambient radio frequency signal to obtain the superimposed signal, and to group and scatter it.

3. The bridge structural condition monitoring system as described in claim 2, characterized in that, The analog-to-digital conversion module is used to perform high-precision analog-to-digital conversion on the analog image data in groups using a multi-channel multiplexing method to obtain the digital image data.

4. The bridge structural condition monitoring system as described in claim 2, characterized in that, The analog-to-digital conversion module is used to perform differential processing on the analog image data and calculate the difference between each pixel and its neighboring pixels; Then map the pixel difference to a preset range and add an offset to make it non-negative; The non-negative differences are encoded to obtain the digital image data.

5. The bridge structural condition monitoring system as described in claim 1, characterized in that, The radio frequency signal transceiver includes: The transmitting module is used to generate and transmit the ambient radio frequency signal; The receiving module is used to receive the superimposed signal scattered back by the passive communication module; An adaptive adjustment module, connected to the transmitting module and the receiving module, is used to compare the actual signal strength and actual noise level of the superimposed signal received by the receiving module with a preset threshold to determine the transmission parameters for the next monitoring cycle, the transmission parameters including transmission power, transmission frequency and rate; it is also used to feed back the transmission parameters for the next monitoring cycle to the transmitting module so that it synchronizes the transmission parameters for the next monitoring cycle with the receiving module. A radio frequency signal processing module, connected to the receiving module, is used to demodulate the superimposed signal using the ambient radio frequency signal to obtain the target transmission signal.

6. The bridge structural condition monitoring system as described in claim 1, characterized in that, The data processing terminal includes: The image processing module is used to obtain edge features by calculating the gradient and direction of pixels in the target transmitted signal, and to analyze the texture features of pixels in the target transmitted signal using gray-level co-occurrence matrix technology. The risk assessment module, connected to the image processing module, is used to input the edge features and texture features into a trained deep learning model to obtain the identification result corresponding to the structural state of the bridge to be monitored.

7. The bridge structural condition monitoring system as described in claim 6, characterized in that, The data processing terminal includes an alarm notification module connected to the risk assessment module, used to generate an alarm signal when the identification result corresponding to the structural state of the bridge to be monitored indicates the existence of structural defects.

8. A method for monitoring the structural condition of a bridge, characterized in that, A bridge structural condition monitoring system applied to any one of claims 1-7, comprising: The environmental radio frequency signal is broadcast using the transmission parameters of the current monitoring period through the radio frequency signal transceiver terminal; The data acquisition terminal uses the received environmental radio frequency signal for power supply, and collects simulated image data corresponding to the structural state of the bridge to be monitored at each preset position during the current monitoring cycle. The simulated image data is then converted into a target transmission signal that meets the requirements of passive communication. The target transmission signal is then loaded onto the environmental radio frequency signal to obtain a superimposed signal and scattered out. The superimposed signal is received using the radio frequency signal transceiver terminal, and the transmission parameters for the next monitoring cycle are optimized based on the actual signal strength and actual noise level corresponding to the superimposed signal; the superimposed signal is received and demodulated using the environmental radio frequency signal to obtain the target transmission signal, and then the target transmission signal is transmitted. The data processing terminal receives the target transmission signal sent by the radio frequency signal transceiver terminal, and the trained deep neural network detects the target transmission signal to obtain the identification result corresponding to the structural state of the bridge to be monitored.

Images