Bridge monitoring method and system
By integrating multiple sensors and data acquisition devices, combining risk assessment models of neural networks and decision tree algorithms, the operating status of bridges is adjusted in real time, and the problems of single monitoring and insufficient data analysis capabilities of traditional bridge monitoring are solved, and the intelligence of bridge monitoring is improved and accuracy enhances the safety and operational efficiency of the bridge.
Patent Information
- Application Number
- CN202411431247.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2024-12-27
AI Technical Summary
Traditional bridge monitoring methods have single monitoring methods and limited data analysis capabilities, and lack in-depth monitoring of the internal state of the bridge structure, which affects the accuracy and safety of monitoring.
A variety of sensors and data acquisition equipment are used to obtain bridge structure, operating environment and traffic conditions data, and based on the risk assessment model of neural network and decision tree algorithm, adjust the operating status of the bridge in real time, including bridge deck lighting brightness and traffic flow, and improve monitoring. Intelligence and accuracy.
In-depth monitoring and real-time perception of the internal state of the bridge structure are achieved, the accuracy and security of monitoring are improved, and through big data analysis and artificial intelligence technology, bridge management is provided and operational efficiency is improved.
Smart Images
Figure CN119204688A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a bridge monitoring method and system. Background Art
[0002] With the rapid development of urbanization, bridges, as an important part of urban transportation, are increasingly valued for their safety and operational efficiency. However, traditional bridge monitoring methods have many shortcomings, such as single monitoring means, limited data analysis capabilities, and low management efficiency.
[0003] Traditional bridge monitoring methods usually rely on a small number of fixed surveillance cameras and manual inspections to monitor the basic status of bridges. They mainly focus on the visible parts of the bridge, such as road damage and traffic flow, and lack in-depth monitoring of the internal status of the bridge structure. Some bridge monitoring methods use a single sensor (such as a displacement sensor or a stress sensor) to monitor specific parameters of the bridge, which can obtain certain key data in real time. However, most bridge monitoring methods remain at the data collection level, and data analysis and intelligent control bridge functions are not yet perfect, which affects the accuracy of bridge monitoring. Summary of the invention
[0004] In response to at least one problem in the prior art, the present application proposes a bridge monitoring method and system, which can improve the intelligence and accuracy of bridge monitoring, and thus improve the safety of the bridge.
[0005] In order to solve the above technical problems, this application provides the following technical solutions:
[0006] In a first aspect, the present application provides a bridge monitoring method, comprising:
[0007] Acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data, and traffic condition data;
[0008] Determine the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels;
[0009] According to the risk level of the target bridge, the operation status of the target bridge is adjusted.
[0010] In one embodiment, the obtaining of monitoring data of the target bridge includes:
[0011] The monitoring data collected by the data collection equipment deployed at the target bridge is obtained, and the data collection equipment includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor and a traffic flow monitoring device.
[0012] In one embodiment, before determining the risk level of the target bridge according to the preset risk assessment model and the monitoring data of the target bridge, the method further includes:
[0013] Collect batch historical monitoring data and their corresponding actual risk levels;
[0014] The decision tree algorithm is trained using batch historical monitoring data and their respective corresponding actual risk levels to obtain the risk assessment model.
[0015] In one embodiment, adjusting the operating state of the target bridge according to the risk level of the target bridge includes:
[0016] According to the risk level of the target bridge, the operating status information corresponding to the target bridge is obtained from a preset risk level and operating status relationship table, and the target bridge is adjusted to the operating status corresponding to the operating status information;
[0017] The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
[0018] In a second aspect, the present application provides a bridge monitoring system, comprising:
[0019] An acquisition module, used to acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data and traffic condition data;
[0020] An evaluation module determines the risk level of the target bridge according to a preset risk assessment model and monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels;
[0021] The adjustment module is used to adjust the operating status of the target bridge according to the risk level of the target bridge.
[0022] In one embodiment, the acquisition module includes:
[0023] An acquisition unit is used to acquire the monitoring data collected by a data acquisition device deployed at the target bridge, wherein the data acquisition device includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor and a traffic flow monitoring device.
[0024] In one embodiment, the bridge monitoring system further includes:
[0025] The collection module is used to collect batch historical monitoring data and their corresponding actual risk levels;
[0026] The training module is used to train the decision tree algorithm using batch historical monitoring data and their corresponding actual risk levels to obtain the risk assessment model.
[0027] In one embodiment, the adjustment module includes:
[0028] an adjusting unit, configured to obtain, according to the risk level of the target bridge, operating state information corresponding to the target bridge from a preset risk level and operating state relationship table, and adjust the target bridge to an operating state corresponding to the operating state information;
[0029] The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
[0030] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the bridge monitoring method is implemented when the processor executes the program.
[0031] In a fourth aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, wherein the instructions implement the bridge monitoring method when executed by a processor.
[0032] As can be seen from the above technical solution, the present application provides a bridge monitoring method and system. The method includes: obtaining monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data and traffic condition data; determining the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels; completing the adjustment of the operating state of the target bridge according to the risk level of the target bridge, which can improve the intelligence and accuracy of bridge monitoring, and thus improve the safety of the bridge; being able to conduct in-depth monitoring of the internal state of the bridge structure, and improve data analysis and intelligent control of bridge functions; by integrating a variety of sensors and data acquisition equipment, achieving comprehensive perception and real-time monitoring of the bridge, and improving the accuracy and real-time nature of bridge management; by adopting big data analysis and artificial intelligence technology, conducting in-depth mining and analysis of monitoring data, providing a scientific basis for the management and control of the bridge, and improving the scientificity and intelligence level of management; by using intelligent control technology, automatically adjusting the operating state of the bridge according to the early warning prediction results, and improving the safety and operational efficiency of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0034] Figure 1 is a first flow chart of the bridge monitoring method in an embodiment of the present application;
[0035] Figure 2 is a second flow chart of the bridge monitoring method in an embodiment of the present application;
[0036] Figure 3 is a first structural schematic diagram of a bridge monitoring system in an embodiment of the present application;
[0037] Figure 4 is a second structural schematic diagram of the bridge monitoring system in an embodiment of the present application;
[0038] Figure 5 It is a schematic block diagram of the system structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0039] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0040] In order to solve the problems existing in the above-mentioned prior art, the present application provides a bridge monitoring method and system, which can realize comprehensive perception and real-time monitoring of information such as bridge structure, operating environment, and traffic conditions by integrating multiple sensors, data acquisition equipment, data processing and analysis algorithms, etc. At the same time, it has powerful data processing and analysis capabilities, can conduct in-depth mining and analysis of monitoring data, and provide a basis for the management and control of bridges.
[0041] The details are described through the following embodiments.
[0042] In order to improve the intelligence and accuracy of bridge monitoring, and thus improve the safety of bridges, this embodiment provides a bridge monitoring method in which the execution subject is a bridge monitoring system, and the bridge monitoring system includes but is not limited to a server, such as Figure 1 As shown, the method specifically includes the following contents:
[0043] Step 100: Acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data, and traffic condition data.
[0044] Specifically, the bridge structure data may include displacement and stress, etc.; the operating environment data may include temperature and wind speed, etc.; the traffic condition data may include traffic flow, vehicle type and vehicle speed, etc.
[0045] Step 200: Determine the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels.
[0046] Specifically, the monitoring data may be bridge structure data, operating environment data, and traffic condition data collected multiple times within a preset time period. The preset time period may be set according to actual conditions, and the present application does not impose any restrictions thereon. The monitoring data may be preprocessed, and the preprocessing may include: data cleaning and formatting; extracting key features from the preprocessed monitoring data, such as displacement, stress, temperature, wind speed, and traffic flow; calculating the statistics of key features, such as at least one of the average value, maximum value, minimum value, and standard deviation; and applying the average value of displacement, the average value of stress, the average value of temperature, the average value of wind speed, the average value of traffic flow collected multiple times within the preset time period and a preset risk assessment model to determine the risk level of the target bridge. Big data analysis and artificial intelligence technology may be used to deeply mine and analyze monitoring data and extract valuable information.
[0047] Step 300: According to the risk level of the target bridge, the operating state of the target bridge is adjusted.
[0048] Specifically, the operating status of the bridge can be automatically adjusted according to the risk level of the target bridge, such as adjusting the brightness of the bridge deck lighting, limiting traffic flow, etc.
[0049] In order to improve the reliability of bridge monitoring, it is also possible to apply a multivariate linear regression model, and perform correlation analysis on temperature and stress within a preset time period to predict the stress change of the bridge under different temperature conditions; perform time domain analysis on the monitoring data to predict the change of the monitoring data over time to obtain the predicted monitoring data, which includes: predicted stress, predicted displacement, predicted traffic flow, etc.; perform Fourier transform on the monitoring data to convert the monitoring data from time domain signals to frequency domain signals, which include: stress frequency and displacement frequency, etc. When at least one of the following conditions is met: the changed stress is greater than the stress threshold, the risk level is greater than the risk level threshold, the predicted monitoring data exceeds the corresponding prediction range, and the frequency domain signal exceeds the corresponding frequency domain range, an early warning prompt is output.
[0050] In order to achieve in-depth monitoring of the internal state of the bridge structure and improve the reliability and comprehensiveness of monitoring data collection, in one embodiment, step 100 includes:
[0051] Step 101: Acquire the monitoring data collected by the data acquisition equipment deployed at the target bridge, wherein the data acquisition equipment includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor, and a traffic flow monitoring device.
[0052] Specifically, wireless communication technology can be used to obtain the monitoring data collected by the data acquisition equipment deployed at the target bridge to ensure real-time and reliable transmission of the monitoring data; high-precision and high-reliability sensors can be used to achieve real-time monitoring of information such as bridge structure and operating environment. Various sensors and data acquisition equipment are deployed on the bridge, such as displacement sensors, stress sensors, temperature sensors, wind speed sensors, and traffic flow monitoring equipment. These devices will monitor the structural status, operating environment, traffic conditions and other information of the bridge in real time, and transmit the data to the bridge monitoring system for processing and analysis. The bridge monitoring system can process and analyze the received data, including: data cleaning, feature extraction, pattern recognition, etc., and then automatically adjust the operating status of the bridge according to the data analysis results, such as adjusting the bridge deck lighting brightness, limiting traffic flow, etc. At the same time, corresponding reports and early warning information can also be generated for reference and decision-making by managers.
[0053] In order to improve the reliability of training risk assessment models and thus improve the accuracy of risk assessment using risk assessment models, such as Figure 2 As shown, in one embodiment, before step 200, the method further includes:
[0054] Step 001: Collect batch historical monitoring data and their corresponding actual risk levels.
[0055] It can be understood that the historical monitoring data is the monitoring data collected before the monitoring data of the target bridge is obtained.
[0056] Step 002: Apply batch historical monitoring data and their corresponding actual risk levels to train the decision tree algorithm to obtain the risk assessment model.
[0057] In order to achieve control and adjustment of the operating state of the bridge, in one embodiment, step 300 includes:
[0058] Step 301: According to the risk level of the target bridge, the operating status information corresponding to the target bridge is obtained from a preset risk level and operating status relationship table, and the target bridge is adjusted to the operating status corresponding to the operating status information.
[0059] Specifically, the preset risk level and operating status relationship table can be set according to actual conditions, and this application does not limit this. For example, the preset risk level and operating status relationship table can be shown in Table 1:
[0060] Table 1
[0061]
[0062] Step 302: The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
[0063] To further illustrate the present solution, the present application provides an application example of a bridge monitoring method. In this application example, the method includes:
[0064] Step 1: Data reception: The transmission layer receives monitoring data such as bridge structure, operating environment, and traffic conditions from the perception layer.
[0065] Step 2: Data preprocessing. Data cleaning: remove duplicate data, process missing values, correct erroneous data, etc. to improve data quality. Data formatting: convert data from different sources and formats into a unified format for subsequent processing.
[0066] Step 3: Feature extraction. Extract key features from the cleaned data, such as displacement, stress, temperature, wind speed, and traffic flow. Calculate the statistics of these parameters, such as the mean, maximum, minimum, and standard deviation.
[0067] Step 4: Data analysis. Time domain analysis: Analyze the changing trend of data on the time axis, such as periodicity and mutation points. Frequency domain analysis: Analyze the characteristics of data in the frequency domain through methods such as Fourier transform. Correlation analysis: Analyze the correlation between different parameters, such as the relationship between temperature and stress. Health status assessment: Evaluate the health status of the bridge based on historical data and empirical models.
[0068] Step 5: Early warning prediction. Anomaly detection: Identify outliers in the data and determine whether there are potential problems. Trend prediction: Predict the possible future changes in the bridge based on historical data and current status. Risk assessment: Assess the severity and likelihood of potential problems and generate early warning information.
[0069] Step 6: Intelligent decision-making. Based on the results of data analysis and early warning prediction, formulate intelligent control strategies to adjust the bridge deck lighting brightness, limit traffic flow, and send maintenance notifications.
[0070] Step 7: Result output: Generate and output analysis reports, including: bridge health status, early warning information, and intelligent control suggestions, etc.
[0071] Step 8: Data is stored in backup. Back up data regularly to ensure data security.
[0072] From the software level, in order to improve the intelligence and accuracy of bridge monitoring, and thus improve the safety of bridges, the present application provides an embodiment of a bridge monitoring system for implementing all or part of the contents of the bridge monitoring method, see Figure 3 , the bridge monitoring system specifically includes the following contents:
[0073] Acquisition module 01, used to acquire monitoring data of a target bridge, the monitoring data including: bridge structure data, operating environment data and traffic condition data;
[0074] Evaluation module 02, determining the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels;
[0075] The adjustment module 03 is used to adjust the operating status of the target bridge according to the risk level of the target bridge.
[0076] In one embodiment, the acquisition module includes:
[0077] An acquisition unit is used to acquire the monitoring data collected by a data acquisition device deployed at the target bridge, wherein the data acquisition device includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor and a traffic flow monitoring device.
[0078] like Figure 4 As shown, in one embodiment, the bridge monitoring system further includes:
[0079] Collection module 04, used to collect batch historical monitoring data and their corresponding actual risk levels;
[0080] The training module 05 is used to train the decision tree algorithm using batch historical monitoring data and their corresponding actual risk levels to obtain the risk assessment model.
[0081] In one embodiment, the adjustment module includes:
[0082] an adjusting unit, configured to obtain, according to the risk level of the target bridge, operating state information corresponding to the target bridge from a preset risk level and operating state relationship table, and adjust the target bridge to an operating state corresponding to the operating state information;
[0083] The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
[0084] The embodiment of the bridge monitoring system provided in this specification can be specifically used to execute the processing flow of the embodiment of the above-mentioned bridge monitoring method. Its functions are not described in detail here, and reference can be made to the detailed description of the embodiment of the above-mentioned bridge monitoring method.
[0085] To further illustrate the present solution, the present application provides an application example of a bridge monitoring system. In this application example, the system includes:
[0086] The real-time monitoring module is used to display the bridge's structural status, operating environment, traffic conditions and other information in real time.
[0087] The data analysis module is used to conduct in-depth mining and analysis of monitoring data and extract valuable information, such as bridge health status assessment and traffic flow analysis.
[0088] The early warning prediction module is used to predict possible problems of bridges and changing trends of traffic conditions based on data analysis results, and take corresponding measures in advance.
[0089] The intelligent control module is used to automatically adjust the operating status of the bridge according to the early warning prediction results, such as adjusting the brightness of the bridge deck lighting, limiting traffic flow, etc., to improve the safety and operational efficiency of the bridge.
[0090] In this application example, the system architecture of the bridge monitoring system may include three layers: a perception layer, a transmission layer, and an application layer.
[0091] The perception layer realizes comprehensive perception and data collection of information such as bridge structure, operating environment, and traffic conditions through various sensors and data acquisition equipment deployed on the bridge.
[0092] The transport layer uses wireless communication technology to ensure real-time and reliable data transmission. The transport layer is responsible for transmitting the data collected by the perception layer to the application layer.
[0093] The application layer has functions such as real-time monitoring, data analysis, early warning prediction and intelligent control, and can automatically adjust the operation status of the bridge according to the data analysis results. The application layer is responsible for data processing, analysis and application, including real-time monitoring, data analysis, early warning prediction, intelligent control and other functions.
[0094] From the hardware level, in order to improve the intelligence and accuracy of bridge monitoring, and thus improve the safety of bridges, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the bridge monitoring method. The electronic device specifically includes the following contents:
[0095] A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface communicate with each other through the bus; the communications interface is used to realize information transmission between the bridge monitoring system and related devices such as user terminals; the electronic device can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the electronic device can be implemented with reference to the embodiments for realizing the bridge monitoring method and the embodiments for realizing the bridge monitoring system, and the contents thereof are incorporated herein, and the repeated parts are not repeated.
[0096] Figure 5 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 5 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Figure 5 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.
[0097] In one or more embodiments of the present application, the bridge monitoring function may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
[0098] Step 100: Acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data, and traffic condition data.
[0099] Step 200: Determine the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels.
[0100] Step 300: According to the risk level of the target bridge, the operating state of the target bridge is adjusted.
[0101] It can be seen from the above description that the electronic device provided by the embodiments of the present application can improve the intelligence and accuracy of bridge monitoring, thereby improving the safety of the bridge.
[0102] In another embodiment, the bridge monitoring system may be configured separately from the central processor 9100. For example, the bridge monitoring system may be configured as a chip connected to the central processor 9100, and the bridge monitoring function may be implemented under the control of the central processor.
[0103] like Figure 5 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 5 In addition, the electronic device 9600 may also include Figure 5 For components not shown, reference may be made to the prior art.
[0104] like Figure 5 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.
[0105] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.
[0106] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.
[0107] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.
[0108] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).
[0109] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.
[0110] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.
[0111] It can be seen from the above description that the electronic device provided by the embodiments of the present application can improve the intelligence and accuracy of bridge monitoring, thereby improving the safety of the bridge.
[0112] The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the bridge monitoring method in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the bridge monitoring method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:
[0113] Step 100: Acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data, and traffic condition data.
[0114] Step 200: Determine the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels.
[0115] Step 300: According to the risk level of the target bridge, the operating state of the target bridge is adjusted.
[0116] It can be seen from the above description that the computer-readable storage medium provided in the embodiments of the present application can improve the intelligence and accuracy of bridge monitoring, thereby improving the safety of the bridge.
[0117] In this application, each embodiment of the above method is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For relevant parts, refer to the partial description of the method embodiment.
[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0122] Specific embodiments are used in this application to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core idea of this application. At the same time, for those skilled in the art, according to the idea of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.
Claims
1. A bridge monitoring method, characterized in that: include: Acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data, and traffic condition data; Determine the risk level of the target bridge according to a preset risk assessment model and the monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels; According to the risk level of the target bridge, the operation status of the target bridge is adjusted.
2. The bridge monitoring method according to claim 1, characterized in that: The obtaining of monitoring data of the target bridge includes: The monitoring data collected by the data collection equipment deployed at the target bridge is obtained, and the data collection equipment includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor and a traffic flow monitoring device.
3. The bridge monitoring method according to claim 1, characterized in that: Before determining the risk level of the target bridge according to the preset risk assessment model and the monitoring data of the target bridge, the method further includes: Collect batch historical monitoring data and their corresponding actual risk levels; The decision tree algorithm is trained using batch historical monitoring data and their respective corresponding actual risk levels to obtain the risk assessment model.
4. The bridge monitoring method according to claim 1, characterized in that: The step of adjusting the operating state of the target bridge according to the risk level of the target bridge includes: According to the risk level of the target bridge, the operating status information corresponding to the target bridge is obtained from a preset risk level and operating status relationship table, and the target bridge is adjusted to the operating status corresponding to the operating status information; The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
5. A bridge monitoring system, characterized in that: include: An acquisition module, used to acquire monitoring data of a target bridge, wherein the monitoring data includes: bridge structure data, operating environment data and traffic condition data; An evaluation module determines the risk level of the target bridge according to a preset risk assessment model and monitoring data of the target bridge, wherein the preset risk assessment model is obtained by pre-training a neural network model based on batch historical monitoring data and their respective corresponding actual risk levels; The adjustment module is used to adjust the operating status of the target bridge according to the risk level of the target bridge.
6. The bridge monitoring system according to claim 5, characterized in that: The acquisition module comprises: An acquisition unit is used to acquire the monitoring data collected by a data acquisition device deployed at the target bridge, wherein the data acquisition device includes: a displacement sensor, a stress sensor, a temperature sensor, a wind speed sensor and a traffic flow monitoring device.
7. The bridge monitoring system according to claim 5, characterized in that: Also includes: The collection module is used to collect batch historical monitoring data and their corresponding actual risk levels; The training module is used to train the decision tree algorithm using batch historical monitoring data and their corresponding actual risk levels to obtain the risk assessment model.
8. The bridge monitoring system according to claim 5, characterized in that: The adjustment module comprises: an adjusting unit, configured to obtain, according to the risk level of the target bridge, operating state information corresponding to the target bridge from a preset risk level and operating state relationship table, and adjust the target bridge to an operating state corresponding to the operating state information; The preset risk level and operation status relationship table includes: risk level, operation status information of the bridge and the corresponding relationship between the two; the operation status information includes: bridge deck lighting brightness and restricted traffic flow.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the bridge monitoring method according to any one of claims 1 to 4 is implemented.
10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the bridge monitoring method according to any one of claims 1 to 4 is implemented.