Monitoring and early warning method and system for road traffic protection facilities

Abstract

The invention relates to the technical field of protection facility monitoring, in particular to a monitoring and early warning method and system for a road traffic protection facility, and the method comprises the steps: collecting the operation state data of different node positions of the road traffic protection facility in real time, carrying out the data preprocessing of the operation state data, and obtaining to-be-analyzed data; analyzing the to-be-analyzed data to obtain damage prediction values corresponding to different node positions of the road traffic protection facility, performing safety assessment on the road traffic protection facility according to the node positions and the damage prediction values, and judging whether to perform early warning on the node positions of the road traffic protection facility according to a safety assessment result. And if the node position of the road traffic protection facility needs to be pre-warned, generating a pre-warning signal according to the node position and the safety evaluation result, and sending the pre-warning signal to the target terminal. According to the invention, the accuracy of monitoring and early warning of the road traffic protection facility is improved.

Description

Technical Field

[0001] The present application relates to the technical field of protective facility monitoring, and in particular to a monitoring and early warning method and system for highway traffic protective facilities. Background Art

[0002] Highway transportation, as a vital infrastructure in modern society, carries a vast amount of passenger and freight traffic. Its safety and stability are directly linked to the safety of life and property. However, due to the complexity and variability of the highway traffic environment, various natural disasters and human factors can damage highway traffic protection facilities, thereby affecting normal highway traffic and driving safety. Therefore, it is crucial to develop an efficient and accurate monitoring and early warning method to monitor the status of highway traffic protection facilities in real time and provide early warning of potential safety hazards.

[0003] Currently, monitoring methods for highway traffic protection facilities primarily rely on manual inspections and periodic testing. These methods are not only inefficient but also struggle to achieve real-time monitoring and early warning of highway traffic protection facilities. Furthermore, due to the complexity and variability of the highway traffic environment, current monitoring methods often struggle to accurately capture subtle changes in facility status, hindering timely warning of potential safety hazards. Summary of the Invention

[0004] In order to solve at least one of the above technical problems, the present application provides a monitoring and early warning method and system for highway traffic protection facilities.

[0005] In a first aspect, the present application provides a monitoring and early warning method for highway traffic protection facilities, which adopts the following technical solutions: Real-time collection of operating status data at different node locations of highway traffic protection facilities; Performing data preprocessing on the operating status data to obtain data to be analyzed; Performing data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities; Conducting a safety assessment of the highway traffic protection facilities based on the node locations and damage prediction values, and determining whether to issue an early warning for the node locations of the highway traffic protection facilities based on the safety assessment results; If it is necessary to issue an early warning for the node location of the highway traffic protection facility, an early warning signal is generated according to the node location and the safety assessment result, and the early warning signal is sent to the target terminal.

[0006] By implementing the above technical solution, real-time operational status data for different node locations of highway traffic protection facilities is collected, ensuring data timeliness and accuracy. This provides a solid foundation for subsequent data preprocessing, enabling more comprehensive and detailed status monitoring of highway traffic protection facilities. The data obtained after data preprocessing removes noise and redundancy, improving the efficiency and accuracy of data analysis. In-depth data analysis of the data to be analyzed accurately calculates damage prediction values ​​for different node locations of highway traffic protection facilities. This effectively utilizes the inherent patterns of the data and provides a scientific basis for safety assessment. Safety assessments based on node locations and damage prediction values ​​accurately reflect the safety status of the facilities, providing strong support for early warning decisions. Based on the safety assessment, the assessment results determine whether to issue an early warning for the node location of the highway traffic protection facilities. This avoids unnecessary resource waste while ensuring timely and targeted early warnings. If an early warning is required, the warning signal generated based on the node location and safety assessment results can be rapidly transmitted to the target terminal, providing timely warning information to relevant personnel, effectively preventing potential safety hazards and improving the efficiency of monitoring and early warning of highway traffic protection facilities.

[0007] In one possible implementation, preprocessing the operating status data to obtain data to be analyzed includes: Decomposing the operating status data into data modal components with different frequencies based on a preset modal number; The data modal components are subjected to noise elimination processing, and the eliminated data modal components are superimposed to obtain the data to be analyzed.

[0008] By adopting the above technical solution, when preprocessing the operating status data, the data is first decomposed into data modal components with different frequencies based on a preset modal number. This effectively divides the complex operating status data according to its frequency characteristics, making subsequent processing more targeted. Next, the decomposed data modal components are subjected to noise removal, which can significantly reduce the interference information in the data and improve the purity of the data. Finally, the noise-removed data modal components are superimposed to obtain the data to be analyzed. This process makes the data more concentrated and reflects the essential characteristics of the operating status, providing a more accurate data foundation for subsequent analysis.

[0009] In one possible implementation, performing data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facility includes: Performing time series transformation analysis on the data to be analyzed to obtain time series analysis data; Collecting environmental data around the highway traffic protection facility, and constructing a three-dimensional environmental characteristic map of the highway traffic protection facility in different time periods based on the environmental data; Determining first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map; Performing cluster analysis on the data to be analyzed to obtain irregular items, regular items, and regular development item data of the data to be analyzed; Performing prediction and deduction processing on the irregular item, the regular item, and the regular development item data to obtain a second damage prediction value; The first damage prediction value and the second damage prediction value are superimposed and calculated to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facility.

[0010] By employing the above technical solution, time series transformation analysis of the data under analysis effectively captures temporal trends in the operational status of highway traffic protection facilities. This step not only reveals temporal correlations in the data but also provides a temporal basis for subsequent analysis. A three-dimensional environmental feature map constructed using collected surrounding environmental data intuitively reflects the actual operating environment of highway traffic protection facilities over different time periods, thereby enhancing the environmental adaptability of damage prediction. This combination of factors makes the first damage prediction value more realistic, improving the accuracy and reliability of the prediction. Cluster analysis of the data under analysis can further uncover inherent patterns and regularities within the data. Cluster analysis categorizes the data under analysis into irregular items, regular items, and regular development items. This classification not only simplifies the data structure but also provides a clear data framework for subsequent prediction and deduction. The second damage prediction value derived from these three data types fully accounts for the diversity and complexity of the data, further enhancing the accuracy and comprehensiveness of the prediction. Finally, the first damage prediction value and the second damage prediction value are superimposed and calculated, combining the advantages of both. The resulting damage prediction value is more accurate and comprehensive, providing strong data support for the maintenance and management of highway traffic protection facilities.

[0011] In one possible implementation, determining the first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map includes: Acquire facility damage information within a historical time period and damage environment information corresponding to the facility damage information, and determine the damage location and damage time series data of each facility with the same damage type based on the facility damage information. The damage time series data is the damage development data of the facility damage location within a preset time period before and after the damage occurs; Determining damage environment data corresponding to the damage location of the facility according to the damage environment information; Performing matrix association between the damage environment data and the damage development data to obtain a damage environment development matrix; Determining node environmental data corresponding to different node positions of the highway traffic protection facility according to the three-dimensional environmental characteristic map; Determining state change data of different node positions of the highway traffic protection facility within a certain time period based on the time series analysis data; The damage environment development matrix is ​​screened according to the state change data and the node environment data to determine whether there is a matching data segment that matches the state change data and the node environment data. If so, the first damage prediction value corresponding to the different node positions of the highway traffic protection facility is determined according to the matching data segment.

[0012] By employing the above technical solution, facility damage information and its corresponding damage environment information over a historical period are first acquired, providing a rich historical data foundation for subsequent damage prediction. By determining the damage location and time series data for each facility with the same damage type, the trajectory of facility damage development can be clearly depicted. Furthermore, damage environment data is determined by combining the damage environment information. The damage environment and damage development are then matrix-correlated. The resulting damage environment development matrix reveals the complex relationship between damage and the environment. This process not only enhances data correlation but also provides a more refined data model for subsequent prediction. Node environmental data for different node locations of highway traffic protection facilities is determined based on the three-dimensional environmental characteristic map, allowing the prediction process to fully consider the specific environment in which the facility resides, improving the environmental adaptability of the prediction. Furthermore, the damage environment development matrix is ​​filtered by combining state change data determined by time series analysis. This process not only simplifies data processing but also enables rapid identification of historical data segments that match the current facility status. If there are matching data segments, the first damage prediction value determined based on these data segments will be closer to reality, and the prediction result will be more accurate and reliable, providing strong data support for the maintenance and management of highway traffic protection facilities.

[0013] In one possible implementation, the performing of prediction and deduction processing on the irregular item, the regular item, and the regular development item data to obtain the second damage prediction value includes: Determining the highest parameter value and the lowest parameter value corresponding to the irregular item in the data to be analyzed; Performing time-series sorting on the data of the regular item and the regular development item to obtain a regular item data matrix; Inputting the regular item data matrix into a trained vector extraction model to perform vector feature extraction to obtain regular item dimensions, and combining the obtained regular item dimensions, the highest parameter value, the lowest parameter value, and the regular data matrix to generate a comprehensive data matrix; performing data processing on the data contained in the comprehensive data matrix to obtain comprehensive development data; Inputting the obtained comprehensive development data into a preset algorithm model for data extrapolation to obtain future development data; The future development data is input into the trained damage model for training to obtain a second damage prediction value.

[0014] By employing the above technical solution, the highest and lowest parameter values ​​of the irregular items in the analyzed data are determined. This provides critical boundary conditions for subsequent data processing and helps to more accurately grasp the data's fluctuation range. Next, the data for regular items and regular development items are sorted without time series to form a regular item data matrix, laying the foundation for subsequent vector feature extraction. This series of steps effectively integrates the effective information in the data and provides data support for subsequent predictions. Inputting the regular item data matrix into a trained vector extraction model efficiently extracts the data's vector features and forms the regular item dimensions. This step reduces the high-dimensional data to facilitate subsequent processing. Simultaneously, the extracted regular item dimensions are combined with the highest and lowest parameter values ​​and the regular data matrix to generate a comprehensive data matrix, further enriching the data's dimensionality and depth and improving the accuracy of data predictions. Data in the comprehensive data matrix is ​​processed to generate comprehensive development data, uncovering potential patterns in the data and providing a reliable basis for future data predictions. Subsequently, the comprehensive development data is input into a pre-set algorithm model for data extrapolation to generate future development data, enabling a reasonable prediction of future trends based on historical data. Finally, future development data is input into the trained damage model for training to obtain the second damage prediction value. This process achieves accurate prediction of damage conditions and provides strong support for subsequent repairs and maintenance.

[0015] In one possible implementation, generating a warning signal according to the node location and the security assessment result, and sending the warning signal to the target terminal, generating a warning signal according to the node location and the security assessment result, and sending the warning signal to the target terminal, further includes: Performing a hazard score on the damage prediction value to obtain a facility hazard score; Determine whether the facility hazard score is greater than a preset hazard score. If so, generate a maintenance cycle frequency and maintenance items based on the damage prediction value, and send the facility hazard score, the maintenance cycle frequency and the maintenance items to the target device.

[0016] By implementing this technical solution, a warning signal is first accurately generated based on node location and safety assessment results, ensuring timely and targeted warnings. Subsequently, the warning signal is rapidly transmitted to the target terminal, enabling relevant personnel to receive the warning information immediately, saving valuable time for subsequent action. This series of operations not only improves the efficiency and practicality of warnings, but also enhances their practicality, effectively ensuring the safe operation of the facility. After the warning signal is transmitted, the damage prediction value is further evaluated for hazard severity, resulting in a facility hazard score. This quantitative assessment of the potential hazard level of damage provides a scientific basis for subsequent maintenance decisions. Furthermore, by determining whether the facility hazard score exceeds a preset hazard score, facilities requiring priority attention are intelligently identified, avoiding waste of resources and misjudgments. This process not only improves the targeted nature of maintenance but also optimizes the allocation of maintenance resources. If the facility hazard score exceeds the preset hazard score, the maintenance cycle frequency and maintenance items are automatically generated based on the damage prediction value. This enables intelligent maintenance planning, avoids human interference, and improves the accuracy and efficiency of maintenance. At the same time, the facility hazard score, maintenance cycle frequency, and maintenance items are sent to the target equipment, allowing relevant personnel to fully understand the current status of the facility and maintenance needs, providing comprehensive guidance for subsequent maintenance work. This not only improves the scientific and standardized nature of maintenance work, but also lays a solid foundation for the long-term safe operation of the facility.

[0017] In one possible implementation, the method further includes: Determine the facility material, facility construction parameters, facility environment, and facility deformation state of different node positions of the highway traffic protection facility based on the operating status data; Through digital twin technology, data fusion processing is performed on the facility materials, facility construction parameters, facility environment and the deformation state of the facility to construct a facility digital model corresponding to the highway traffic protection facility, and the facility digital model is sent to the target terminal.

[0018] By implementing the above technical solution, the operational status data comprehensively and accurately determines the facility materials, construction parameters, environment, and deformation status at different node locations of highway traffic protection facilities. This step provides detailed and reliable foundational data for subsequent data fusion processing, ensuring the accuracy of the digital model. By comprehensively considering these key factors, a more comprehensive reflection of the actual operational status of the facility can be achieved, providing strong data support for facility maintenance and management. Using digital twin technology, data on facility materials, construction parameters, environment, and deformation status are fused to construct a digital model of the highway traffic protection facility. Through highly integrated data processing technology, real-time simulation and accurate prediction of the facility's operational status are achieved. The digital model not only intuitively displays the facility's structure and status but also possesses powerful data analysis and prediction capabilities, providing a scientific basis for maintenance decisions. The constructed digital model of the facility is then transmitted to the target terminal. This step enables rapid information transmission and sharing, allowing relevant personnel to obtain the latest facility status information anytime, anywhere. Furthermore, the target terminal can perform further analysis and processing based on the data provided by the digital model, providing more comprehensive support for facility maintenance and management. The implementation of this process not only improves the utilization of information, but also enhances the maintenance efficiency and management level of facilities.

[0019] In a second aspect, the present application provides a monitoring and early warning system for highway traffic protection facilities, which adopts the following technical solutions: A monitoring and early warning system for highway traffic protection facilities, comprising: Data acquisition module, used to collect real-time operating status data of different node locations of highway traffic protection facilities; A data processing module, configured to perform data preprocessing on the operating status data to obtain data to be analyzed; A data analysis module is used to perform data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities; a safety assessment module, configured to perform a safety assessment on the highway traffic protection facilities based on the node locations and damage prediction values, and determine whether to issue an early warning for the node locations of the highway traffic protection facilities based on the safety assessment results; The facility warning module is used to generate a warning signal according to the node location and safety assessment results when a warning is needed for the node location of the highway traffic protection facility, and send the warning signal to the target terminal.

[0020] In a possible implementation, when the data processing module preprocesses the operating status data to obtain the data to be analyzed, it is specifically configured to: Decomposing the operating status data into data modal components with different frequencies based on a preset modal number; The data modal components are subjected to noise elimination processing, and the eliminated data modal components are superimposed to obtain the data to be analyzed.

[0021] In another possible implementation, when the data analysis module performs data analysis on the data to be analyzed and obtains damage prediction values ​​corresponding to different node positions of the highway traffic protection facility, it is specifically configured to: Performing time series transformation analysis on the data to be analyzed to obtain time series analysis data; Collecting environmental data around the highway traffic protection facility, and constructing a three-dimensional environmental characteristic map of the highway traffic protection facility in different time periods based on the environmental data; Determining first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map; Performing cluster analysis on the data to be analyzed to obtain irregular items, regular items, and regular development item data of the data to be analyzed; Performing prediction and deduction processing on the irregular item, the regular item, and the regular development item data to obtain a second damage prediction value; The first damage prediction value and the second damage prediction value are superimposed and calculated to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facility.

[0022] In another possible implementation, when the data analysis module determines the first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map, it is specifically configured to: Acquire facility damage information within a historical time period and damage environment information corresponding to the facility damage information, and determine the damage location and damage time series data of each facility with the same damage type based on the facility damage information. The damage time series data is the damage development data of the facility damage location within a preset time period before and after the damage occurs; Determining damage environment data corresponding to the damage location of the facility according to the damage environment information; Performing matrix association between the damage environment data and the damage development data to obtain a damage environment development matrix; Determining node environmental data corresponding to different node positions of the highway traffic protection facility according to the three-dimensional environmental characteristic map; Determining state change data of different node positions of the highway traffic protection facility within a certain time period based on the time series analysis data; The damage environment development matrix is ​​screened according to the state change data and the node environment data to determine whether there is a matching data segment that matches the state change data and the node environment data. If so, the first damage prediction value corresponding to the different node positions of the highway traffic protection facility is determined according to the matching data segment.

[0023] In another possible implementation, when the data analysis module performs prediction and deduction processing on the irregular item, the regular item, and the regular development item data to obtain the second damage prediction value, it is specifically configured to: Determining the highest parameter value and the lowest parameter value corresponding to the irregular item in the data to be analyzed; Performing time-series sorting on the data of the regular item and the regular development item to obtain a regular item data matrix; Inputting the regular item data matrix into a trained vector extraction model to perform vector feature extraction to obtain regular item dimensions, and combining the obtained regular item dimensions, the highest parameter value, the lowest parameter value, and the regular data matrix to generate a comprehensive data matrix; performing data processing on the data contained in the comprehensive data matrix to obtain comprehensive development data; Inputting the obtained comprehensive development data into a preset algorithm model for data extrapolation to obtain future development data; The future development data is input into the trained damage model for training to obtain a second damage prediction value.

[0024] In another possible implementation, the system further includes: a hazard assessment module and a hazard judgment module, wherein: The hazard assessment module is used to score the hazard degree of the damage prediction value to obtain a facility hazard score; The hazard judgment module is used to determine whether the facility hazard score is greater than a preset hazard score. If it is, a maintenance cycle frequency and maintenance items are generated according to the damage prediction value, and the facility hazard score, the maintenance cycle frequency and the maintenance items are sent to the target device.

[0025] In another possible implementation, the system further includes: a determination module and a model building module, wherein: The determination module is used to determine the facility material, facility construction parameters, facility environment and facility deformation state of different node positions of the highway traffic protection facility based on the operating status data; The model construction module is used to perform data fusion processing on the facility materials, facility construction parameters, facility environment and facility deformation state through digital twin technology, construct a facility digital model corresponding to the highway traffic protection facility, and send the facility digital model to the target terminal.

[0026] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a monitoring and early warning method for highway traffic protection facilities as described in any one of the first aspects.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program thereon. When the computer program is executed in a computer, the computer is caused to execute a monitoring and early warning method for highway traffic protection facilities as described in any one of the first aspects.

[0028] In summary, this application includes at least one of the following beneficial technical effects: By implementing the above technical solution, real-time operational status data for different node locations of highway traffic protection facilities is collected, ensuring data timeliness and accuracy. This provides a solid foundation for subsequent data preprocessing, enabling more comprehensive and detailed status monitoring of highway traffic protection facilities. The data obtained after data preprocessing removes noise and redundancy, improving the efficiency and accuracy of data analysis. In-depth data analysis of the data to be analyzed accurately calculates damage prediction values ​​for different node locations of highway traffic protection facilities. This effectively utilizes the inherent patterns of the data and provides a scientific basis for safety assessment. Safety assessments based on node locations and damage prediction values ​​accurately reflect the safety status of the facilities, providing strong support for early warning decisions. Based on the safety assessment, the assessment results determine whether to issue an early warning for the node location of the highway traffic protection facilities. This avoids unnecessary resource waste while ensuring timely and targeted early warnings. If an early warning is required, the warning signal generated based on the node location and safety assessment results can be rapidly transmitted to the target terminal, providing timely warning information to relevant personnel, effectively preventing potential safety hazards and improving the efficiency of monitoring and early warning of highway traffic protection facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a monitoring and early warning method for highway traffic protection facilities provided in an embodiment of the present application.

[0030] Figure 2 A schematic structural diagram of a monitoring and early warning system for highway traffic protection facilities provided in an embodiment of the present application.

[0031] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following is combined with Figure 1-3 This application is described in further detail.

[0033] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0034] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, 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 part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0036] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0037] The embodiment of the present application provides a method for monitoring and early warning of highway traffic protection facilities, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: collecting the operating status data of different node positions of highway traffic protection facilities in real time.

[0038] Specifically, real-time collection means that data is collected immediately when it is generated, without delay or lag, to ensure the timeliness and accuracy of the data. Highway traffic protection facilities refer to various facilities installed to ensure highway traffic safety, including but not limited to guardrails, crash pads, signs, reflectors, isolation fences, etc., which together constitute the highway safety protection system. Different node positions are used to represent specific locations on or around highway traffic protection facilities. These locations vary depending on the type, function or safety requirements of the facility. Operational status data refers to data that reflects the current working status or performance of highway traffic protection facilities, including information such as the integrity of the facilities, the degree of damage, the reflective effect, the anti-collision capability, and environmental parameters.

[0039] In the embodiments of the present application, sensor technology and drone inspection technology are used. First, sensors, such as vibration sensors, pressure sensors, optical sensors, etc., are installed at key node locations to monitor the stress conditions and reflective effects of the facilities. Second, the data collected by the sensors is transmitted to the data center via wired or wireless means. The data center processes and analyzes the data and generates a report on the operating status of the facilities. The drone's flight route is then planned to ensure coverage of all key node locations. The drone is equipped with high-definition cameras and infrared thermal imagers and other equipment to take photos and videos of the facilities. Finally, the collected images and videos are processed using image recognition technology and data analysis software to identify damage and abnormal conditions of the facilities.

[0040] Step S11: pre-process the running status data to obtain data to be analyzed.

[0041] Specifically, the operating status data is decomposed into data modal components with different frequencies based on a preset modal number, noise is removed from the data modal components, and the removed data modal components are superimposed to obtain the data to be analyzed.

[0042] Specifically, the preset modal number refers to a value preset according to actual needs or experience before data decomposition, which determines how many data modal components of different frequencies the data is decomposed into. The setting of this value is usually related to the characteristics of the data, the purpose of analysis, and the needs of subsequent processing. The data modal component refers to the various components obtained after decomposing the operating status data according to the frequency characteristics. Each component represents a specific frequency range and reflects the characteristics of the data within this frequency range. Noise removal processing refers to removing unnecessary noise components that interfere with the analysis from the data modal components to improve the accuracy and reliability of the data. Noise comes from errors in the data acquisition process, environmental interference, etc. For example, if the operating status data contains a high-frequency noise component, then in the noise removal processing, this high-frequency noise component needs to be removed from the data to avoid it interfering with subsequent analysis.

[0043] In the embodiments of the present application, a wavelet transform method is employed. Based on a preset modal number, an appropriate wavelet basis function and number of decomposition levels are selected. The operating status data is then subjected to a wavelet transform to obtain data modal components of different frequencies. Each data modal component is then subjected to noise removal, including but not limited to methods such as thresholding, soft thresholding, or hard thresholding. Finally, the noise-removed data modal components are reconstructed or superimposed to obtain the data to be analyzed.

[0044] Step S12: performing data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of highway traffic protection facilities.

[0045] Specifically, a time series transformation analysis is performed on the data to be analyzed to obtain time series analysis data, environmental data around the highway traffic protection facilities are collected, and three-dimensional environmental characteristic maps of the highway traffic protection facilities in different time periods are constructed based on the environmental data. The first damage prediction value corresponding to different node positions of the highway traffic protection facilities is determined according to the time series analysis data and the three-dimensional environmental characteristic map. A cluster analysis is performed on the data to be analyzed to obtain irregular items, regular items and regular development item data of the data to be analyzed. The irregular items, regular items and regular development item data are predicted and deduced to obtain a second damage prediction value. The first damage prediction value and the second damage prediction value are superimposed and calculated to obtain the damage prediction value corresponding to different node positions of the highway traffic protection facilities.

[0046] In the embodiments of this application, time series analysis data refers to converted feature data, such as a frequency spectrum of the vibration frequency of a bridge guardrail. Environmental data represents dynamic information such as weather conditions (temperature, humidity, wind speed) and traffic flow (vehicle density, vehicle type distribution) surrounding the facility. A three-dimensional environmental feature map is a dynamic three-dimensional model constructed based on multi-source environmental data (such as lidar point clouds and meteorological sensor data). It can visually display temporal and spatial changes in the facility's surroundings, such as a three-dimensional heat map of water depth around a guardrail on a road section during heavy rain. The first damage prediction value is a node-level prediction result obtained by correlating time series analysis with environmental characteristics, such as the fatigue damage prediction value of a guardrail post caused by long-term vibration. Cluster analysis groups data based on similarity, for example, categorizing traffic flow data into peak, flat, and trough categories. Irregular terms represent abnormal data that cannot be fitted by conventional models, such as instantaneous impact force data caused by sudden traffic accidents. Regular terms and regular development terms refer to stable periodic data (such as diurnal temperature cycles) and trend data (such as the cumulative effects of material aging), respectively. The second damage prediction value is a weighted prediction result of anomalies, cycles, and trends based on clustering results, such as a comprehensive damage value obtained by combining emergencies and long-term aging.

[0047] Specifically, the execution process is as follows: Time series analysis stage: After noise reduction preprocessing of the original monitoring data (such as vibration and strain), the time-frequency features are extracted through short-time Fourier transform to generate a spectrum containing the main frequency components as the time series analysis data.

[0048] Environmental modeling phase: Synchronously collect environmental parameters such as temperature, humidity, wind speed, and traffic volume, and use the BIM model to fuse with real-time sensor data to construct a three-dimensional environmental characteristic map with spatiotemporal labels (such as a thermal-mechanical coupling model updated hourly).

[0049] First prediction calculation: The time series characteristics and the mechanical parameters (such as wind load) of the corresponding period in the environmental model are input into the regression model to calculate the initial damage index of each node (such as the guardrail connection).

[0050] Data clustering stage: The DBSCAN algorithm is used to perform density clustering on the pre-processed data to be analyzed, separating three types of data subsets: sudden shocks (irregular items), periodic fluctuations (regular items), and linear degradation (regular development items).

[0051] Second prediction calculation: The three subsets are predicted using the LSTM network (to handle time series dependencies), the ARIMA model (to predict periodicity), and the linear regression (to fit degradation trends). The second damage prediction value is obtained after weighting.

[0052] Result fusion: The physical model-driven first prediction value is fused with the data-driven prediction value through Bayesian estimation to generate the final node-level damage probability distribution map.

[0053] Step S13: Conduct a safety assessment on the highway traffic protection facilities based on the node positions and damage prediction values, and determine whether to issue an early warning for the node positions of the highway traffic protection facilities based on the safety assessment results.

[0054] Specifically, node locations represent critical structural connection points or stress-sensitive areas of traffic protection facilities, such as the welds between bridge guardrail columns and beams or the anchor points of slope protection nets. The predicted damage value is a quantified value of the damage probability, calculated using the time series analysis and data clustering methods described above. It typically ranges from 0 (no risk) to 1 (complete failure). For example, a predicted value of 0.75 for a guardrail node indicates a 75% probability of failure. Safety assessment involves a comprehensive assessment of the facility's status based on the predicted value, design specifications, and safety thresholds, such as comparing the predicted value with international bridge maintenance standards. Alerts represent the actions that trigger risk warnings and are categorized by level (e.g., yellow, orange, or red) and form (audio / visual alarms, SMS notifications, etc.). For example, a red alert is triggered when three consecutive node predicted values ​​exceed 0.8. Decisions involve the logical decision-making process of whether to initiate an alert based on the assessment results. These decisions must take into account contextual factors such as facility importance and traffic volume. For example, stricter thresholds may be used for guardrails at highway interchanges.

[0055] For the embodiment of the present application, in the data association stage: the damage prediction value is bound to the facility node coordinates in the GIS system to generate a risk heat map with geographical labels, for example, the predicted values ​​of 10 guardrail nodes at a tunnel entrance are marked at the corresponding positions on the three-dimensional map.

[0056] Threshold setting stage: Set three-level warning thresholds based on industry standards (such as the "Highway Bridge Technical Condition Assessment Standards"): Green (predicted value < 0.3): normal state, no intervention required Yellow (0.3≤predicted value<0.7): low risk, included in the regular inspection checklist Red (predicted value ≥ 0.7): High risk, triggering immediate warning Multi-factor evaluation stage: Introduce environmental weight coefficients (such as reducing the anchor warning threshold by 20% in heavy rain weather) and traffic impact coefficients (such as increasing the guardrail warning priority by 50% during peak hours) to dynamically adjust the judgment criteria.

[0057] Decision execution stage: Activate emergency plans for red alert nodes: call on the drone inspection system to obtain real-time images and dispatch maintenance teams for on-site verification.

[0058] Generate maintenance work orders for yellow warning nodes: include them in the next quarter's maintenance plan, and give priority to non-destructive testing.

[0059] Maintain regular monitoring of green nodes: extend the data sampling interval to 2 hours / time.

[0060] Step S14: If an early warning is required for the node location of the highway traffic protection facility, an early warning signal is generated according to the node location and the safety assessment result, and the early warning signal is sent to the target terminal.

[0061] In the embodiment of the present application, the target terminal is a terminal device used by maintenance personnel, including but not limited to mobile phones, tablets and other terminal devices.

[0062] The present embodiment provides a monitoring and early warning method for highway traffic protection facilities. This method collects operational status data at different node locations of highway traffic protection facilities in real time, ensuring the timeliness and accuracy of the data. This method provides a solid foundation for subsequent data preprocessing, enabling more comprehensive and detailed status monitoring of highway traffic protection facilities. The data obtained after data preprocessing removes noise and redundancy, improving the efficiency and accuracy of data analysis. In-depth data analysis of the data to be analyzed accurately calculates damage prediction values ​​for different node locations of highway traffic protection facilities. This method effectively utilizes the inherent laws of the data and provides a scientific basis for safety assessment. Safety assessments based on node locations and damage prediction values ​​accurately reflect the safety status of the facilities, providing strong support for early warning decisions. Based on the safety assessment, the assessment results determine whether to issue an early warning for the node location of the highway traffic protection facility. This method avoids unnecessary resource waste while ensuring timely and targeted early warnings. If an early warning is required, a warning signal generated based on the node location and safety assessment results can be rapidly transmitted to the target terminal, providing timely warning information to relevant personnel, effectively preventing potential safety hazards and improving the efficiency of monitoring and early warning for highway traffic protection facilities.

[0063] Furthermore, determining first damage prediction values ​​corresponding to different node locations of highway traffic protection facilities based on the time series analysis data and the three-dimensional environmental characteristic map includes: obtaining facility damage information and damage environment information corresponding to the facility damage information within a historical time period; determining the damage location and damage time series data of each facility of the same damage type based on the facility damage information; the damage time series data being damage development data at the facility damage location within a preset time period before and after the damage occurs; determining damage environment data corresponding to the facility damage location based on the damage environment information; performing matrix correlation between the damage environment data and the damage development data to obtain a damage environment development matrix; determining node environment data corresponding to different node locations of highway traffic protection facilities based on the three-dimensional environmental characteristic map; and determining state change data exhibited by different node locations of highway traffic protection facilities within a certain time period based on the time series analysis data. The damage environment development matrix is ​​screened based on the state change data and the node environment data to determine whether there are matching data segments that match the state change data and the node environment data. If so, determining first damage prediction values ​​corresponding to different node locations of highway traffic protection facilities based on the matching data segments.

[0064] Furthermore, the data of irregular items, regular items, and regular development items are predicted and deduced to obtain a second damage prediction value, including: determining the highest parameter value and the lowest parameter value corresponding to the irregular items in the data to be analyzed, and performing time-series-free sorting based on the regular items and regular development item data to obtain a regular item data matrix. The regular item data matrix is ​​input into a trained vector extraction model for vector feature extraction to obtain the regular item dimension, and the obtained regular item dimension, the highest parameter value, the lowest parameter value, and the regular data matrix are combined for data processing to generate a comprehensive data matrix. Data processing is performed on the data contained in the comprehensive data matrix to obtain comprehensive development data. The obtained comprehensive development data is input into a preset algorithm model for data deduction to obtain future development data. The future development data is input into the trained damage model for training to obtain a second damage prediction value.

[0065] In this embodiment of the present application, the highest / lowest parameter values ​​represent the extreme values ​​of irregular items along a specific dimension, for example, a sensor's maximum displacement during an abnormal period is 15 mm, and its minimum temperature is -20°C. Regular items refer to data subsets that exhibit periodic or trending characteristics, such as the bimodal distribution of daily traffic flow. Regular development item data represents derived indicators of regular items that evolve over time, such as the correlation between weekly average traffic volume and quarterly GDP growth rates. Time-series-free cleanup refers to data reconstruction operations that eliminate timestamp dependencies. The regular item data matrix represents the cleaned multidimensional data structure, with rows representing feature dimensions (such as temperature, humidity, and traffic volume) and columns representing observation samples, such as a matrix of shape [5×1000]. The vector extraction model is a neural network module used for automatic feature learning, such as a Transformer-based encoder. The regular item dimension represents the low-dimensional representation vector of the model output, for example, compressing 50-dimensional original features into 10-dimensional semantic vectors. The integrated data matrix is ​​a mathematical representation of the fusion of multidimensional information, such as the enhanced tensor formed by concatenating extreme value parameters with the model output. Data processing includes operations such as normalization, noise reduction, and missing value filling, such as applying Min-Max standardization to temperature data. A preset algorithm model refers to a predefined mathematical operation framework, such as an ARIMA time series forecasting model or an LSTM neural network. Future development data represents the model's output of future time period forecasts, such as a traffic flow forecast curve for the next 12 months. A damage model is a machine learning model used to assess structural damage, such as a random forest classifier. The second damage prediction value represents the updated prediction result after combining data from multiple sources, and typically ranges from 0 (no damage) to 1 (complete failure).

[0066] Furthermore, a warning signal is generated based on the node location and safety assessment results, and the warning signal is sent to the target terminal. The system then includes: assigning a hazard score to the damage prediction value to obtain a facility hazard score. Determining whether the facility hazard score is greater than a preset hazard score; if so, generating a maintenance cycle frequency and maintenance items based on the damage prediction value, and sending the facility hazard score, maintenance cycle frequency, and maintenance items to the target device.

[0067] Furthermore, the operational status data is used to determine the facility materials, construction parameters, environment, and deformation status of each node of the highway traffic protection facility. Using digital twin technology, these data are integrated to construct a digital model of the highway traffic protection facility, which is then transmitted to the target terminal.

[0068] Specifically, facility materials represent the properties of the raw materials that comprise protective facilities, such as the yield strength of Q235 steel (≥235 MPa) and the density of HDPE plastic (0.95 g / cm³). Facility architectural parameters refer to geometric design specifications during construction, such as a corrugated guardrail with a plate width of 310 mm, column spacing of 4 m, and a burial depth of 1.2 m. The facility environment encompasses meteorological data such as temperature, humidity, rainfall, wind speed, and sunlight intensity, as well as surrounding topographical features. Facility deformation states represent the degree of structural deformation, such as the lateral displacement of a corrugated guardrail, column inclination angle, and degree of loosening of connecting bolts. Digital twin technology is an information technology framework that maps virtual models to physical entities, encompassing functional modules such as data-driven modeling, real-time synchronization, and simulation prediction. Data fusion processing refers to the integrated analysis of multi-source heterogeneous data, such as using a Kalman filter to fuse accelerometer and GPS data. A facility digital model is a dynamically updated 3D visualization that displays engineering parameters such as stress distribution and fatigue life. For example, a BIM model can be used to visualize the collision response of a corrugated guardrail.

[0069] The following is an introduction to a monitoring and early warning system for highway traffic protection facilities provided by an embodiment of the present application. The monitoring and early warning system for highway traffic protection facilities described below and the monitoring and early warning method for highway traffic protection facilities described above can be referred to each other. Please refer to Figure 2 , Figure 2 : is a schematic structural diagram of a monitoring and early warning system 20 for highway traffic protection facilities provided in an embodiment of the present application, comprising: The data collection module 21 is used to collect the operation status data of different nodes of the highway traffic protection facilities in real time; The data processing module 22 is used to pre-process the running status data to obtain data to be analyzed; The data analysis module 23 is used to analyze the data to be analyzed and obtain damage prediction values ​​corresponding to different node positions of highway traffic protection facilities; A safety assessment module 24 is used to perform a safety assessment on the highway traffic protection facilities based on the node locations and damage prediction values, and determine whether to issue an early warning for the node locations of the highway traffic protection facilities based on the safety assessment results; The facility warning module 25 is used to generate a warning signal according to the node location and the safety assessment result when a warning is needed for the node location of the highway traffic protection facility, and send the warning signal to the target terminal.

[0070] In one possible implementation of the embodiment of the present application, when the data processing module 22 pre-processes the operating status data to obtain the data to be analyzed, it is specifically configured to: Decomposing the operating status data into data modal components with different frequencies based on a preset modal number; The data modal components are subjected to noise elimination, and the eliminated data modal components are superimposed to obtain the data to be analyzed.

[0071] In another possible implementation of the embodiment of the present application, the data analysis module 23 performs data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities, specifically for: Perform time series transformation analysis on the data to be analyzed to obtain time series analysis data; Collect environmental data around highway traffic protection facilities and construct three-dimensional environmental characteristic maps of highway traffic protection facilities in different time periods based on the environmental data; Determine the first damage prediction value corresponding to different node positions of highway traffic protection facilities based on time series analysis data and three-dimensional environmental characteristic maps; Perform cluster analysis on the data to be analyzed to obtain the irregular items, regular items and regular development items of the data to be analyzed; Performing prediction and deduction processing on the irregular item, regular item and regular development item data to obtain a second damage prediction value; The first damage prediction value and the second damage prediction value are superimposed and calculated to obtain the damage prediction values ​​corresponding to different node positions of the highway traffic protection facility.

[0072] In another possible implementation of the embodiment of the present application, when the data analysis module 23 determines the first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map, it is specifically configured to: Obtain facility damage information within a historical time period and damage environment information corresponding to the facility damage information, and determine the damage location and damage time series data of each facility with the same damage type based on the facility damage information. The damage time series data is the damage development data of the facility damage location within a preset time period before and after the damage occurs; Determine the damaged environment data corresponding to the damaged location of the facility based on the damaged environment information; Perform matrix correlation between damaged environment data and damaged development data to obtain a damaged environment development matrix; Determine the node environmental data corresponding to different node positions of highway traffic protection facilities based on the three-dimensional environmental characteristic map; Determine the state change data of different node positions of highway traffic protection facilities within a certain time period based on time series analysis data; The damage environment development matrix is ​​screened according to the state change data and the node environment data to determine whether there is a matching data segment that matches the state change data and the node environment data. If so, the first damage prediction value corresponding to the different node positions of the highway traffic protection facilities is determined according to the matching data segment.

[0073] In another possible implementation of the embodiment of the present application, when the data analysis module 23 performs prediction and deduction processing on the irregular item, regular item, and regular development item data to obtain the second damage prediction value, it is specifically configured to: Determine the highest parameter value and the lowest parameter value corresponding to the irregular item in the data to be analyzed; According to the data of regular items and regular development items, the data matrix of regular items is obtained by sorting them without time series; The regular item data matrix is ​​input into the trained vector extraction model to extract vector features and obtain the regular item dimension. The obtained regular item dimension, highest parameter value, lowest parameter value and regular data matrix are combined for data processing to generate a comprehensive data matrix. Perform data processing on the data contained in the comprehensive data matrix to obtain comprehensive development data; Input the obtained comprehensive development data into the preset algorithm model for data extrapolation to obtain future development data; The future development data is input into the trained damage model for training to obtain a second damage prediction value.

[0074] In another possible implementation of the embodiment of the present application, the system 20 further includes: a hazard assessment module and a hazard judgment module, wherein: The hazard assessment module is used to score the hazard degree of the damage prediction value and obtain the facility hazard score; The hazard judgment module is used to determine whether the facility hazard score is greater than the preset hazard score. If it is, the maintenance cycle frequency and maintenance items are generated according to the damage prediction value, and the facility hazard score, maintenance cycle frequency and maintenance items are sent to the target equipment.

[0075] In another possible implementation of the embodiment of the present application, the system 20 further includes: a determination module and a model building module, wherein: A determination module is used to determine the facility materials, facility construction parameters, facility environment, and facility deformation status of different node locations of the highway traffic protection facility based on the operation status data; The model building module is used to perform data fusion processing on facility materials, facility construction parameters, facility environment and facility deformation status through digital twin technology, build a digital model of the facility corresponding to the highway traffic protection facility, and send the digital model of the facility to the target terminal.

[0076] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0077] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0078] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0079] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0080] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0081] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0082] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0083] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned monitoring and early warning method for highway traffic protection facilities are implemented.

[0084] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0085] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0086] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A monitoring and early warning method for highway traffic protection facilities, characterized in that: include: Real-time collection of operating status data at different node locations of highway traffic protection facilities; Performing data preprocessing on the operating status data to obtain data to be analyzed; Performing data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities; Conducting a safety assessment of the highway traffic protection facilities based on the node locations and damage prediction values, and determining whether to issue an early warning for the node locations of the highway traffic protection facilities based on the safety assessment results; If it is necessary to issue an early warning for the node location of the highway traffic protection facility, an early warning signal is generated according to the node location and the safety assessment result, and the early warning signal is sent to the target terminal.

2. A monitoring and early warning method for highway traffic protection facilities according to claim 1, characterized in that: The performing data preprocessing on the operating status data to obtain data to be analyzed includes: Decomposing the operating status data into data modal components with different frequencies based on a preset modal number; The data modal components are subjected to noise elimination processing, and the eliminated data modal components are superimposed to obtain the data to be analyzed.

3. The monitoring and early warning method for highway traffic protection facilities according to claim 1 is characterized in that: The performing of data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities includes: Performing time series transformation analysis on the data to be analyzed to obtain time series analysis data; Collecting environmental data around the highway traffic protection facility, and constructing a three-dimensional environmental characteristic map of the highway traffic protection facility in different time periods based on the environmental data; Determining first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility based on the time series analysis data and the three-dimensional environmental characteristic map; Performing cluster analysis on the data to be analyzed to obtain irregular items, regular items, and regular development item data of the data to be analyzed; Performing prediction and deduction processing on the irregular item, the regular item, and the regular development item data to obtain a second damage prediction value; The first damage prediction value and the second damage prediction value are superimposed and calculated to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facility.

4. A monitoring and early warning method for highway traffic protection facilities according to claim 3, characterized in that: The determining, based on the time series analysis data and the three-dimensional environmental characteristic map, first damage prediction values ​​corresponding to different node positions of the highway traffic protection facility includes: Acquire facility damage information within a historical time period and damage environment information corresponding to the facility damage information, and determine the damage location and damage time series data of each facility with the same damage type based on the facility damage information. The damage time series data is the damage development data of the facility damage location within a preset time period before and after the damage occurs; Determining damage environment data corresponding to the damage location of the facility according to the damage environment information; Performing matrix association between the damage environment data and the damage development data to obtain a damage environment development matrix; Determining node environmental data corresponding to different node positions of the highway traffic protection facility according to the three-dimensional environmental characteristic map; Determining state change data of different node positions of the highway traffic protection facility within a certain time period based on the time series analysis data; The damage environment development matrix is ​​screened according to the state change data and the node environment data to determine whether there is a matching data segment that matches the state change data and the node environment data. If so, the first damage prediction value corresponding to the different node positions of the highway traffic protection facility is determined according to the matching data segment.

5. The monitoring and early warning method for highway traffic protection facilities according to claim 1, characterized in that: The predictive deduction processing of the irregular item, the regular item, and the regular development item data to obtain a second damage prediction value includes: Determining the highest parameter value and the lowest parameter value corresponding to the irregular item in the data to be analyzed; Performing time-series sorting on the data of the regular item and the regular development item to obtain a regular item data matrix; Inputting the regular item data matrix into a trained vector extraction model to perform vector feature extraction to obtain regular item dimensions, and combining the obtained regular item dimensions, the highest parameter value, the lowest parameter value, and the regular data matrix to generate a comprehensive data matrix; performing data processing on the data contained in the comprehensive data matrix to obtain comprehensive development data; Inputting the obtained comprehensive development data into a preset algorithm model for data extrapolation to obtain future development data; The future development data is input into the trained damage model for training to obtain a second damage prediction value.

6. The monitoring and early warning method for highway traffic protection facilities according to claim 1 is characterized in that: The method further comprises generating a warning signal according to the node location and the safety assessment result, and sending the warning signal to the target terminal, and then further comprising: Performing a hazard score on the damage prediction value to obtain a facility hazard score; Determine whether the facility hazard score is greater than a preset hazard score. If so, generate a maintenance cycle frequency and maintenance items based on the damage prediction value, and send the facility hazard score, the maintenance cycle frequency and the maintenance items to the target device.

7. The monitoring and early warning method for highway traffic protection facilities according to claim 1 is characterized in that: The method further comprises: Determine the facility material, facility construction parameters, facility environment, and facility deformation state of different node positions of the highway traffic protection facility based on the operating status data; Through digital twin technology, data fusion processing is performed on the facility materials, facility construction parameters, facility environment and the deformation state of the facility to construct a facility digital model corresponding to the highway traffic protection facility, and the facility digital model is sent to the target terminal.

8. A monitoring and early warning system for highway traffic protection facilities, characterized in that: include: Data acquisition module, used to collect real-time operating status data of different node locations of highway traffic protection facilities; A data processing module, configured to perform data preprocessing on the operating status data to obtain data to be analyzed; A data analysis module is used to perform data analysis on the data to be analyzed to obtain damage prediction values ​​corresponding to different node positions of the highway traffic protection facilities; a safety assessment module, configured to perform a safety assessment on the highway traffic protection facilities based on the node locations and damage prediction values, and determine whether to issue an early warning for the node locations of the highway traffic protection facilities based on the safety assessment results; The facility warning module is used to generate a warning signal according to the node location and safety assessment results when a warning is needed for the node location of the highway traffic protection facility, and send the warning signal to the target terminal.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a monitoring and early warning method for highway traffic protection facilities according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that include: A computer program is stored which can be loaded by a processor and executes a monitoring and early warning method for highway traffic protection facilities according to any one of claims 1 to 7.

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