A bridge quality safety monitoring device and method

Abstract

The present invention proposes a bridge quality and safety monitoring device and method, which relate to the technical field of safety monitoring. A three-dimensional bridge model is established according to the collected bridge data, a safety monitoring index is generated, and index data is collected to obtain the index collection data; an index abnormality judgment is performed according to the index collection data to obtain the index abnormality judgment result, and an index risk coefficient is calculated according to the index abnormality judgment result combined with the index weight to perform an index early warning; the safety monitoring index is classified to obtain a plurality of safety monitoring indicator categories, and a category risk coefficient of each safety monitoring indicator category is calculated according to the indicator risk coefficient combined with the category weight, and a category early warning is performed according to the category risk coefficient; the abnormal range of the three-dimensional bridge model is determined according to the abnormal category and the monitoring position of the index, and a range early warning is performed. The present invention realizes real-time, comprehensive and accurate monitoring and early warning of the bridge, and improves the accuracy and timeliness of the bridge early warning.

Description

Technical Field

[0001] The present invention proposes a bridge quality safety monitoring device and method, which relate to the technical field of safety monitoring, and in particular to the technical field of bridge quality safety monitoring. Background Art

[0002] With the rapid development of urban infrastructure construction, bridges, as key nodes in the transportation network, are receiving more and more attention for their safety and stability. Traditional bridge monitoring methods mainly rely on manual inspections and regular testing, which is not only inefficient but also difficult to detect and deal with potential safety hazards in a timely manner. It is difficult to quantify abnormalities and provide early warnings for each category and each category of indicators that need to be tested for bridge safety. Summary of the invention

[0003] The present invention provides a bridge quality and safety monitoring device and method, which is used to solve the problems that the traditional bridge monitoring method mainly relies on manual inspection and regular inspection, which is not only inefficient, but also difficult to timely discover and deal with potential safety hazards, and difficult to perform abnormal quantitative judgment and early warning for each category and each category of indicators that need to be detected for bridge safety.

[0004] The present invention provides a bridge quality and safety monitoring device and method, the device comprising:

[0005] The acquisition model building module is used to establish a three-dimensional bridge model based on the acquired bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data;

[0006] The indicator abnormality analysis module is used to determine the indicator abnormality based on the indicator collection data, obtain the indicator abnormality determination result, calculate the indicator risk coefficient based on the indicator abnormality determination result combined with the indicator weight, and perform indicator early warning;

[0007] The category anomaly analysis module is used to classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and issue a category warning according to the category risk coefficient;

[0008] The range anomaly analysis module is used to determine the abnormal range of the bridge three-dimensional model according to the abnormal category and the monitoring position of the indicator, and to issue a range warning.

[0009] Furthermore, the acquisition model building module includes:

[0010] A data acquisition and processing module is used to collect bridge construction and maintenance data and obtain bridge acquisition data;

[0011] Preprocessing the bridge collected data to obtain preprocessed bridge collected data;

[0012] A model building module is used to train the BIM model based on the pre-processed bridge acquisition data to obtain a three-dimensional model of the bridge;

[0013] An indicator generation module is used to obtain different types of bridge data of the three-dimensional bridge model, set each bridge data type as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators;

[0014] A data characterization processing module is used to continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data;

[0015] The continuously collected data is compared with the existing data of the indicator, characteristic data is extracted, and the characteristic data is added to the corresponding indicator to obtain the indicator collection data.

[0016] Furthermore, the indicator abnormality analysis module includes:

[0017] The indicator comparison module is used to compare the indicator collection data volume with the preset data volume threshold to obtain the data volume comparison result;

[0018] An indicator determination module is used to determine the abnormality of the corresponding security monitoring indicator according to the data volume comparison result, and obtain the indicator abnormality determination result;

[0019] An indicator risk calculation module is used to calculate the indicator risk coefficient based on the indicator abnormality judgment result combined with the corresponding indicator weight information;

[0020] The indicator early warning module is used to compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result;

[0021] According to the indicator risk comparison results, the corresponding indicators are warned, and the warning level is the indicator weight level of the corresponding indicators.

[0022] Furthermore, the category anomaly analysis module includes:

[0023] A category risk calculation module is used to calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight;

[0024] A category comparison module, used to compare the category risk coefficient with a preset category risk threshold to obtain a category comparison result;

[0025] A category warning module is used to make an abnormality determination on the category of the security monitoring indicator according to the category comparison result, and obtain the category abnormality determination result;

[0026] According to the category abnormality determination results, an early warning is issued for the security monitoring indicator category, and the early warning level is the average indicator weight level of the security monitoring indicator category.

[0027] Furthermore, the range anomaly analysis module includes:

[0028] A range determination module, used to obtain the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional bridge model according to the monitoring position;

[0029] The range warning module is used to issue a warning for the abnormal range and obtain range warning data.

[0030] Further, the method comprises:

[0031] S1. Establish a three-dimensional bridge model based on the collected bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data;

[0032] S2. Perform indicator abnormality determination based on indicator collection data, obtain indicator abnormality determination results, calculate indicator risk coefficient based on indicator abnormality determination results combined with indicator weights, and perform indicator early warning;

[0033] S3, classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and perform category warning according to the category risk coefficient;

[0034] S4. Determine the abnormal range of the three-dimensional bridge model according to the abnormal category and the monitoring position of the indicator, and issue a range warning.

[0035] Furthermore, the S1 includes:

[0036] Collect bridge construction and maintenance data to obtain bridge collection data;

[0037] Preprocessing the bridge collected data to obtain preprocessed bridge collected data;

[0038] The BIM model is trained according to the preprocessed bridge acquisition data to obtain a three-dimensional model of the bridge;

[0039] Obtain different types of bridge data of the three-dimensional bridge model, set each type of bridge data as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators;

[0040] Continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data;

[0041] The continuously collected data is compared with the existing data of the indicator, characteristic data is extracted, and the characteristic data is added to the corresponding indicator to obtain the indicator collection data.

[0042] Further, the S2 includes:

[0043] Compare the amount of data collected by the indicator with the preset data amount threshold to obtain a data amount comparison result;

[0044] According to the data volume comparison result, the corresponding security monitoring indicator is judged to be abnormal, and the indicator abnormality judgment result is obtained;

[0045] Calculate the indicator risk coefficient based on the indicator abnormality judgment results combined with the corresponding indicator weight information;

[0046] Compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result;

[0047] According to the indicator risk comparison results, the corresponding indicators are warned, and the warning level is the indicator weight level of the corresponding indicators.

[0048] Further, the S3 includes:

[0049] Obtaining a security monitoring category, and classifying the types of security monitoring indicators according to the security monitoring category to obtain multiple security monitoring indicator categories;

[0050] Calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight;

[0051] Comparing the category risk coefficient with a preset category risk threshold to obtain a category comparison result;

[0052] According to the category comparison result, the category of the security monitoring indicator is judged to be abnormal, and the category abnormality judgment result is obtained;

[0053] According to the category abnormality determination results, an early warning is issued for the security monitoring indicator category, and the early warning level is the average indicator weight level of the security monitoring indicator category.

[0054] Further, the S4 includes:

[0055] Acquire the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional model of the bridge according to the monitoring position;

[0056] An early warning is issued for the abnormal range to obtain range early warning data.

[0057] Beneficial effects of the present invention: the device can collect and analyze various data of the bridge in real time, timely discover and warn potential safety hazards, and improve the efficiency and accuracy of bridge management. By displaying the structure and status of the bridge through a three-dimensional model, the safety status of the bridge can be more intuitively understood, which is convenient for problem location and analysis. The device can accurately warn according to the risk coefficient of the indicator and category, and can quantify the abnormal situation for each category and each category of indicators, so as to realize the quantitative warning generated according to the risk coefficient and weight on the bridge details, and can also realize the determination of the abnormal range, avoiding the possible false alarm and omission problems in the traditional method, and improving the accuracy and reliability of the warning. Through automated and intelligent monitoring means, the workload of manual inspection and detection is reduced, and the efficiency and level of bridge management are improved. The device can discover potential safety problems in advance, provide timely maintenance suggestions for managers, help to achieve preventive maintenance of bridges, and extend the service life of bridges. As an important part of transportation infrastructure, the index analysis of bridges can be directly carried out on the structure, material, operation status and other aspects of bridges, so it has strong pertinence. The index analysis of bridges usually adopts quantitative indicators, such as deformation indicators, structural strength indicators, etc. These indicators can objectively and accurately reflect the actual status of bridges. Through real-time monitoring and data analysis of bridges, it is possible to predict the problems and risks that may occur in the future, providing a scientific basis for preventive maintenance. The index analysis of bridges usually involves multiple aspects, such as structural safety, durability, and traffic capacity, which can comprehensively evaluate the overall performance of bridges. The results of bridge index analysis can provide specific guidance and suggestions for bridge maintenance, repair, and reinforcement. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a bridge quality and safety monitoring method;

[0059] Figure 2 The figure is a schematic diagram of a bridge quality and safety monitoring device. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] In one embodiment of the present invention, a bridge quality and safety monitoring device and method are provided, wherein the device comprises:

[0062] The acquisition model building module is used to establish a three-dimensional bridge model based on the acquired bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data;

[0063] The indicator abnormality analysis module is used to determine the indicator abnormality based on the indicator collection data, obtain the indicator abnormality determination result, calculate the indicator risk coefficient based on the indicator abnormality determination result combined with the indicator weight, and perform indicator early warning;

[0064] The category anomaly analysis module is used to classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and issue a category warning according to the category risk coefficient;

[0065] The range anomaly analysis module is used to determine the abnormal range of the bridge three-dimensional model according to the abnormal category and the monitoring position of the indicator, and to issue a range warning.

[0066] The working principle of the above technical solution is as follows: the device will collect various data of the bridge, such as structural data, material data, environmental data, etc. Then, the three-dimensional model of the bridge is established using these data, which helps to intuitively display the structure and status of the bridge. Based on the three-dimensional model, the device will generate a series of safety monitoring indicators, which are used to measure the safety status of the bridge in different aspects. According to the indicator type, the device will collect indicator data to obtain real-time or regular indicator data. The indicator abnormality analysis module will process and analyze the collected indicator data, and judge whether the indicator is abnormal by comparing it with the preset safety threshold or other reference data. If the indicator is found to be abnormal, the module will determine the degree of abnormality and calculate the indicator risk coefficient based on the indicator weight. According to the level of the risk coefficient, the device will trigger the corresponding indicator early warning mechanism and notify the relevant personnel to handle it. Based on the indicator abnormality analysis, the category abnormality analysis module classifies the safety monitoring indicators into categories, such as structure, material, environment, etc. The category is the upper level of the indicator; for each category, the module will calculate the category risk coefficient based on the indicator risk coefficient and category weight of the category. According to the level of risk factor of the category, the device will trigger the corresponding category warning mechanism to provide managers with more targeted safety information. When an indicator or category is abnormal, the module will determine the abnormal range on the bridge 3D model according to the type of abnormality and monitoring location. This helps managers quickly locate the problem and take targeted repair measures. According to the size and severity of the abnormal range, the device will trigger the corresponding range warning mechanism to remind relevant personnel to pay attention and deal with it in time.

[0067] The technical effects of the above technical scheme are as follows: the device can collect and analyze various data of the bridge in real time, timely discover and warn potential safety hazards, and improve the efficiency and accuracy of bridge management. By displaying the structure and status of the bridge through a three-dimensional model, the safety status of the bridge can be more intuitively understood, which is convenient for problem location and analysis. The device can accurately warn according to the risk coefficient of indicators and categories, and can quantitatively warn of abnormal situations for each category and each category of indicators, so as to realize the quantitative warning generated according to the risk coefficient and weight on the details of the bridge, and at the same time, it can also realize the determination of the abnormal range, avoiding the possible false alarm and omission problems in traditional methods, and improving the accuracy and reliability of the warning. Through automated and intelligent monitoring methods, the workload of manual inspections and detection is reduced, and the efficiency and level of bridge management are improved. The device can detect potential safety problems in advance and provide timely maintenance suggestions for managers, which is conducive to the preventive maintenance of bridges and prolonging the service life of bridges.

[0068] In one embodiment of the present invention, the acquisition model building module includes:

[0069] A data acquisition and processing module is used to collect bridge construction and maintenance data and obtain bridge acquisition data;

[0070] Preprocessing the bridge collected data to obtain preprocessed bridge collected data;

[0071] A model building module is used to train the BIM model based on the pre-processed bridge acquisition data to obtain a three-dimensional model of the bridge;

[0072] An indicator generation module is used to obtain different types of bridge data of the three-dimensional bridge model, set each bridge data type as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators;

[0073] A data characterization processing module is used to continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data;

[0074] The continuously collected data is compared with the existing data of the indicator, characteristic data is extracted, and the characteristic data is added to the corresponding indicator to obtain the indicator collection data. The characteristic data can reflect the state change of each type of indicator.

[0075] The working principle of the above technical solution is as follows: The acquisition model building module uses professional sensors and measurement equipment to collect data on the bridge, including structural data, environmental data, material data, etc. These data reflect the state of the bridge at different times and under different conditions. The collected raw data is cleaned to remove errors and invalid data. The data is formatted for subsequent analysis and modeling. It may also be necessary to normalize or standardize the data to eliminate dimensional differences between different data.

[0076] Use the preprocessed data to train the BIM model. The BIM model can simulate the structure and behavior of the bridge and generate a three-dimensional model of the bridge. This model contains not only the geometric information of the bridge, but also various data related to the performance of the bridge. Extract different types of bridge data from the BIM model, such as stress, displacement, vibration frequency, etc. Set each data type as a safety monitoring indicator, which is used to measure the safety status of the bridge. Setting multiple types of safety monitoring indicators can comprehensively monitor the status of the bridge. Continuously collect data for each type of safety monitoring indicator. This means using sensors or other devices to monitor various parameters of the bridge in real time to obtain the latest data. Compare the continuously collected data with the existing indicator data. By comparing, the trend and anomalies of the data can be found. Extract characteristic data that can reflect the change of the indicator status, such as the rate of change, fluctuation range, etc. Add these characteristic data to the corresponding indicators to enrich the connotation of the indicator collection data.

[0077] The technical effect of the above technical solution is: the acquisition model construction module can comprehensively monitor all aspects of the bridge by setting multiple types of safety monitoring indicators to ensure that the safety status of the bridge is comprehensively evaluated. Through continuous data collection and comparison, abnormal changes in the bridge status can be discovered in time, and the early warning mechanism can be triggered to provide timely intervention and repair suggestions for managers. The extracted feature data can be used for further data analysis and mining to provide scientific decision-making support for the maintenance, reinforcement and renovation of the bridge.

[0078] Automated and intelligent data collection and analysis processes can greatly reduce the workload of manual inspections and improve the efficiency and accuracy of bridge management. By timely discovering and warning of potential safety hazards, preventive maintenance of bridges can be achieved, extending the service life of bridges and reducing maintenance costs.

[0079] In one embodiment of the present invention, the indicator abnormality analysis module includes:

[0080] The indicator comparison module is used to compare the indicator collection data volume with the preset data volume threshold to obtain the data volume comparison result;

[0081] When the amount of data collected by an indicator is greater than the preset data volume threshold, the corresponding security monitoring indicator is determined to be an abnormal indicator;

[0082] When the amount of data collected by the indicator is less than or equal to the preset data amount threshold, the corresponding security monitoring indicator is determined to be a normal indicator;

[0083] An indicator determination module is used to determine the abnormality of the corresponding security monitoring indicator according to the data volume comparison result, and obtain the indicator abnormality determination result;

[0084] The indicator risk calculation module is used to calculate the indicator risk coefficient according to the indicator abnormality judgment result combined with the corresponding indicator weight information; when the corresponding safety monitoring indicator is an abnormal indicator, the indicator risk coefficient is calculated;

[0085] The calculation formula of the indicator risk coefficient is:

[0086]

[0087] Among them, Z is the indicator risk coefficient, ΔC j is the latest amount of indicator data collection, j is the total number of indicator data collection times, ΔC i is the amount of data collected for the i-th indicator, and B is the corresponding indicator weight;

[0088] The indicator early warning module is used to compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result;

[0089] When the indicator risk coefficient is greater than the preset indicator threshold, it is determined that the corresponding security monitoring indicator has a risk;

[0090] When the indicator risk coefficient is less than or equal to the preset indicator threshold, it is determined that there is no risk in the corresponding security monitoring indicator;

[0091] When there is a risk in the corresponding security monitoring indicator, an early warning will be issued for the corresponding indicator;

[0092] According to the indicator risk comparison results, the corresponding indicators are warned, and the warning level is the indicator weight level of the corresponding indicators.

[0093] For example, if the indicator weight is 0.7, the indicator weight level is 0.7, and the corresponding indicator warning level is 0.7.

[0094] The working principle of the above technical solution is as follows: the indicator abnormality analysis module compares the amount of data collected by the indicator with the preset data volume threshold. This step is to preliminarily determine whether the amount of data of the security monitoring indicator is normal. If the amount of data collected by the indicator is greater than the preset data volume threshold, the security monitoring indicator is determined to be an abnormal indicator. If the amount of data collected by the indicator is less than or equal to the preset data volume threshold, the security monitoring indicator is determined to be a normal indicator. Only the security monitoring indicators determined to be abnormal are calculated. Combined with the weight information of the corresponding indicator, the indicator risk coefficient is calculated. The weight information reflects the importance of different indicators in security monitoring. The calculated indicator risk coefficient is compared with the preset indicator threshold. This step is to determine the risk level of the abnormal indicator. If the indicator risk coefficient is greater than the preset indicator threshold, the security monitoring indicator is determined to be risky. The warning level is determined according to the indicator weight level, and the warning level corresponds to the indicator weight level. The high or low warning level reflects the severity of the risk. Warnings are issued for security monitoring indicators with risks, and the warning level is the indicator weight level of the corresponding indicator. Warnings can be triggered in a variety of ways, such as sound and light alarms, SMS notifications, etc.

[0095] The technical effect of the above technical solution is that the indicator abnormality analysis module can accurately evaluate the risk level of the security monitoring indicator by comparing the amount of indicator collection data with the preset threshold and calculating the indicator risk coefficient. j Or when B increases relatively, Increases, the indicator risk factor increases. The abnormal indicators are determined by the risk coefficient, and the indicators are quantified and warned. The warning level corresponds to the indicator weight level, making the warning more flexible and accurate. The warning level is quantified by the indicator weight, and different warning methods or degrees of indicators with different weights are quantified. Indicators with different weights will receive different degrees of attention during the warning. Once an abnormal indicator is detected, the system can immediately trigger an early warning and provide timely intervention and repair suggestions for managers. The automated risk assessment and early warning mechanism can reduce the workload of manual inspections and improve the efficiency of bridge management. By timely discovering and warning potential safety hazards, preventive maintenance of bridges can be achieved, the service life of bridges can be extended, and maintenance costs can be reduced. The data-based risk assessment and early warning mechanism can provide a scientific basis for managers' decision-making and improve the rationality and accuracy of decision-making.

[0096] In one embodiment of the present invention, the category anomaly analysis module includes:

[0097] A category classification module is used to obtain a security monitoring category, classify the types of security monitoring indicators according to the security monitoring category, and obtain multiple security monitoring indicator categories;

[0098] A category risk calculation module is used to calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight;

[0099] The calculation formula of the category risk coefficient is:

[0100]

[0101] Among them, L is the category risk coefficient, A is the total number of safety monitoring indicators in the safety monitoring indicator category, and Z n is the indicator risk coefficient of the nth safety monitoring indicator, Y is the number of safety monitoring indicators with risks in the total number of safety monitoring indicators, and Q is the category weight;

[0102] A category comparison module, used to compare the category risk coefficient with a preset category risk threshold to obtain a category comparison result;

[0103] A category warning module is used to make an abnormality determination on the category of the security monitoring indicator according to the category comparison result, and obtain the category abnormality determination result;

[0104] When the category risk coefficient is greater than the preset category risk threshold, the safety monitoring indicator category is determined to be an abnormal category; when the category risk coefficient is less than or equal to the preset category risk threshold, the safety monitoring indicator category is determined to be a normal category;

[0105] When the security monitoring indicator category is an abnormal category, an early warning is issued for the corresponding security monitoring indicator category.

[0106] According to the category abnormality determination results, an early warning is issued for the security monitoring indicator category, and the early warning level is the average indicator weight level of the security monitoring indicator category.

[0107] For example, if the average indicator weight of the security monitoring indicator category is 0.7, then the average indicator weight level is 0.7, and the corresponding indicator warning level is 0.7.

[0108] The working principle of the above technical solution is as follows: the category anomaly analysis module obtains the category information of the security monitoring indicators, and divides the types of security monitoring indicators according to the category information to form multiple security monitoring indicator categories. For each security monitoring indicator category, the category risk coefficient is calculated according to the indicator risk coefficient and category weight of all security monitoring indicators under the category. The calculation formula of the category risk coefficient takes into account the risk coefficient of each indicator and the weight of the category, and can comprehensively reflect the risk status of the entire category. The calculated category risk coefficient is compared with the preset category risk threshold. This step is to determine whether the risk level of the security monitoring indicator category exceeds the preset threshold. According to the comparison result, the security monitoring indicator category is judged as abnormal. If the category risk coefficient is greater than the preset category risk threshold, the category is judged as an abnormal category; otherwise, it is judged as a normal category. For the security monitoring indicator category judged as abnormal, the early warning mechanism is triggered. The early warning level is determined according to the average indicator weight level of the category. This can provide managers with appropriate early warning information based on the relative importance of different categories.

[0109] The technical effect of the above technical solution is as follows: the category anomaly analysis module can comprehensively evaluate the risk status of the entire category by dividing the safety monitoring indicator categories and calculating the category risk coefficient, and provide managers with more comprehensive and accurate risk information. The abnormal judgment and early warning mechanism based on the category risk coefficient can trigger different levels of early warnings according to the risk status of different categories, thereby improving the pertinence and effectiveness of the early warning. The early warning level is determined based on the average indicator weight level of the safety monitoring indicator category, which not only takes into account the importance of the indicator, but also avoids excessive early warning levels caused by abnormal individual indicators, making the early warning more reasonable and accurate. The automated risk assessment and early warning mechanism can reduce the workload of manual inspections and improve the efficiency of bridge management. At the same time, timely early warning information helps managers take timely measures to reduce the possibility of risks. By timely discovering and warning of potential safety hazards, preventive maintenance of bridges can be achieved, the service life of bridges can be extended, and maintenance costs can be reduced.

[0110] In one embodiment of the present invention, the range anomaly analysis module includes:

[0111] A range determination module, used to obtain the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional bridge model according to the monitoring position;

[0112] The range warning module is used to issue a warning for the abnormal range and obtain range warning data.

[0113] The working principle of the above technical solution is as follows: the range anomaly analysis module extracts indicators that are judged to be abnormal from the safety monitoring system. These abnormal indicators are usually associated with specific locations on the bridge, so it is necessary to obtain the actual monitoring locations of these indicators on the bridge. Using a three-dimensional model of the bridge (such as a BIM model), the monitoring locations of the abnormal indicators are mapped to the model. Since the abnormal indicators may be distributed in different areas, it is necessary to integrate multiple monitoring locations to determine one or more abnormal ranges. The abnormal range can be a specific area of ​​the bridge, such as a pier, bridge deck or bridge span. Once the abnormal range is determined, the system can automatically trigger an early warning mechanism. The early warning may include highlighting the abnormal range on the three-dimensional model, sending an alarm message to the management personnel, or notifying relevant personnel by other means. The early warning information may include the specific location of the abnormal range, the type and value of the abnormal indicator, and the recommended countermeasures.

[0114] The technical effect of the above technical solution is as follows: the range anomaly analysis module directly displays the abnormal range on the three-dimensional model, so that managers can intuitively understand the location and range of the anomaly, which is convenient for quickly locating the problem and formulating countermeasures. Based on the precise monitoring position and three-dimensional model, the abnormal range can be accurately determined, avoiding the misjudgment and omission that may occur in traditional methods. Once an abnormal indicator is detected, the system can immediately trigger an early warning to ensure that managers can obtain relevant information in a timely manner and take corresponding measures to reduce the possibility of risks. The automated early warning mechanism can greatly reduce the workload of manual inspections and improve the efficiency of bridge management. At the same time, accurate early warning information also helps managers to formulate maintenance plans more accurately and reduce maintenance costs. Through timely early warning and positioning of the abnormal range, preventive maintenance of bridges can be achieved, potential safety hazards can be discovered and repaired in a timely manner, and the service life of bridges can be extended.

[0115] In one embodiment of the present invention, the method comprises:

[0116] S1. Establish a three-dimensional bridge model based on the collected bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data;

[0117] S2. Perform indicator abnormality determination based on indicator collection data, obtain indicator abnormality determination results, calculate indicator risk coefficient based on indicator abnormality determination results combined with indicator weights, and perform indicator early warning;

[0118] S3, classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and perform category warning according to the category risk coefficient;

[0119] S4. Determine the abnormal range of the three-dimensional bridge model according to the abnormal category and the monitoring position of the indicator, and issue a range warning.

[0120] The working principle of the above technical solution is as follows: various data of the bridge will be collected, such as structural data, material data, environmental data, etc. Then, a three-dimensional model of the bridge is established using these data, which helps to intuitively display the structure and status of the bridge. Based on the three-dimensional model, a series of safety monitoring indicators are generated, which are used to measure the safety status of the bridge in different aspects. According to the indicator type, indicator data is collected to obtain real-time or regular indicator data. The collected indicator data is processed and analyzed, and by comparing with the preset safety threshold or other reference data, it is determined whether the indicator is abnormal. If the indicator is found to be abnormal, the degree of abnormality is determined, and the indicator risk coefficient is calculated in combination with the indicator weight. According to the level of the risk coefficient, the corresponding indicator early warning mechanism is triggered, and the relevant personnel are notified to handle it. Based on the abnormal analysis of the indicator, the safety monitoring indicators are classified into categories, such as structural category, material category, environmental category, etc. The category is the upper level of the indicator; for each category, the category risk coefficient is calculated based on the indicator risk coefficient and category weight of the category. According to the level of the category risk coefficient, the corresponding category early warning mechanism is triggered to provide more targeted safety information for managers. When an indicator or category is abnormal, the abnormal range will be determined on the 3D bridge model according to the type of abnormality and the monitoring location. This helps managers quickly locate the problem and take targeted repair measures. According to the size and severity of the abnormal range, the corresponding range warning mechanism is triggered to remind relevant personnel to pay attention and handle it in time.

[0121] The technical effects of the above technical scheme are as follows: this method can collect and analyze various data of the bridge in real time, timely discover and warn potential safety hazards, and improve the efficiency and accuracy of bridge management. By displaying the structure and status of the bridge through a three-dimensional model, the safety status of the bridge can be more intuitively understood, which is convenient for problem location and analysis. It can accurately warn according to the risk coefficient of indicators and categories, and can quantitatively warn of abnormal situations for each category and each category of indicators, so as to realize the quantitative warning generated according to the risk coefficient and weight on the bridge details. At the same time, it can also realize the determination of the abnormal range, avoiding the possible false alarm and omission problems in traditional methods, and improving the accuracy and reliability of the warning. Through automated and intelligent monitoring methods, the workload of manual inspection and detection is reduced, and the efficiency and level of bridge management are improved. It can detect potential safety problems in advance and provide timely maintenance suggestions for managers, which is conducive to the preventive maintenance of bridges and prolonging the service life of bridges.

[0122] In one embodiment of the present invention, the S1 includes:

[0123] Collect bridge construction and maintenance data to obtain bridge collection data;

[0124] Preprocessing the bridge collected data to obtain preprocessed bridge collected data;

[0125] The BIM model is trained according to the preprocessed bridge acquisition data to obtain a three-dimensional model of the bridge;

[0126] Obtain different types of bridge data of the three-dimensional bridge model, set each type of bridge data as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators;

[0127] Continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data;

[0128] The continuously collected data is compared with the existing data of the indicator, characteristic data is extracted, and the characteristic data is added to the corresponding indicator to obtain the indicator collection data. The characteristic data can reflect the state change of each type of indicator.

[0129] The working principle of the above technical solution is: use professional sensors and measuring equipment to collect data on the bridge, including structural data, environmental data, material data, etc. These data reflect the state of the bridge at different times and under different conditions. Clean the collected raw data to remove errors and invalid data. Format the data for subsequent analysis and modeling. It may also be necessary to normalize or standardize the data to eliminate dimensional differences between different data.

[0130] Use the preprocessed data to train the BIM model. The BIM model can simulate the structure and behavior of the bridge and generate a three-dimensional model of the bridge. This model contains not only the geometric information of the bridge, but also various data related to the performance of the bridge. Extract different types of bridge data from the BIM model, such as stress, displacement, vibration frequency, etc. Set each data type as a safety monitoring indicator, which is used to measure the safety status of the bridge. Setting multiple types of safety monitoring indicators can comprehensively monitor the status of the bridge. Continuously collect data for each type of safety monitoring indicator. This means using sensors or other devices to monitor various parameters of the bridge in real time to obtain the latest data. Compare the continuously collected data with the existing indicator data. By comparing, the trend and anomalies of the data can be found. Extract characteristic data that can reflect the change of the indicator status, such as the rate of change, fluctuation range, etc. Add these characteristic data to the corresponding indicators to enrich the connotation of the indicator collection data.

[0131] The technical effect of the above technical solution is: by setting multiple types of safety monitoring indicators, all aspects of the bridge can be fully monitored to ensure that the safety status of the bridge is fully evaluated. Through continuous data collection and comparison, abnormal changes in the bridge status can be discovered in time, and the early warning mechanism can be triggered to provide timely intervention and repair suggestions for managers. The extracted feature data can be used for further data analysis and mining to provide scientific decision-making support for bridge maintenance, reinforcement and renovation.

[0132] Automated and intelligent data collection and analysis processes can greatly reduce the workload of manual inspections and improve the efficiency and accuracy of bridge management. By timely discovering and warning of potential safety hazards, preventive maintenance of bridges can be achieved, extending the service life of bridges and reducing maintenance costs.

[0133] In one embodiment of the present invention, S2 includes:

[0134] Compare the amount of data collected by the indicator with the preset data amount threshold to obtain a data amount comparison result;

[0135] When the amount of data collected by an indicator is greater than the preset data volume threshold, the corresponding security monitoring indicator is determined to be an abnormal indicator;

[0136] When the amount of data collected by the indicator is less than or equal to the preset data amount threshold, the corresponding security monitoring indicator is determined to be a normal indicator;

[0137] According to the data volume comparison result, the corresponding security monitoring indicator is judged to be abnormal, and the indicator abnormality judgment result is obtained;

[0138] Calculate the indicator risk coefficient based on the indicator abnormality judgment result combined with the corresponding indicator weight information; when the corresponding safety monitoring indicator is an abnormal indicator, calculate the indicator risk coefficient;

[0139] The calculation formula of the indicator risk coefficient is:

[0140]

[0141] Among them, Z is the indicator risk coefficient, ΔC j is the latest amount of indicator data collection, j is the total number of indicator data collection times, ΔC i is the amount of data collected for the i-th indicator, and B is the corresponding indicator weight;

[0142] Compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result;

[0143] When the indicator risk coefficient is greater than the preset indicator threshold, it is determined that the corresponding security monitoring indicator has risks;

[0144] When the indicator risk coefficient is less than or equal to the preset indicator threshold, it is determined that there is no risk in the corresponding security monitoring indicator;

[0145] When there is a risk in the corresponding security monitoring indicator, an early warning will be issued for the corresponding indicator;

[0146] According to the indicator risk comparison results, the corresponding indicators are warned, and the warning level is the indicator weight level of the corresponding indicators.

[0147] For example, if the indicator weight is 0.7, the indicator weight level is 0.7, and the corresponding indicator warning level is 0.7.

[0148] The working principle of the above technical solution is: compare the amount of data collected by the indicator with the preset data volume threshold. This step is to preliminarily determine whether the amount of data of the security monitoring indicator is normal. If the amount of data collected by the indicator is greater than the preset data volume threshold, the security monitoring indicator is determined to be an abnormal indicator. If the amount of data collected by the indicator is less than or equal to the preset data volume threshold, the security monitoring indicator is determined to be a normal indicator. Only the security monitoring indicators determined to be abnormal are calculated. Combined with the weight information of the corresponding indicator, the indicator risk coefficient is calculated. The weight information reflects the importance of different indicators in security monitoring. Compare the calculated indicator risk coefficient with the preset indicator threshold. This step is to determine the risk level of the abnormal indicator. If the indicator risk coefficient is greater than the preset indicator threshold, the security monitoring indicator is determined to be risky. The warning level is determined according to the indicator weight level, and the warning level corresponds to the indicator weight level. The high or low warning level reflects the severity of the risk. Warnings are issued for security monitoring indicators with risks, and the warning level is the indicator weight level of the corresponding indicator. Warnings can be triggered in a variety of ways, such as sound and light alarms, SMS notifications, etc.

[0149] The technical effect of the above technical solution is: by comparing the amount of data collected by the indicator with the preset threshold and calculating the risk coefficient of the indicator, the risk level of the safety monitoring indicator can be accurately evaluated. The abnormal indicator is determined by the risk coefficient, and the indicator is quantified and warned. The warning level corresponds to the indicator weight level, making the warning more flexible and accurate. The warning level is quantified by the indicator weight, and different warning methods or degrees of indicators with different weights are quantified. Indicators with different weights will receive different degrees of attention during the warning. Once an abnormal indicator is detected, the system can immediately trigger an early warning and provide timely intervention and repair suggestions for managers. The automated risk assessment and early warning mechanism can reduce the workload of manual inspections and improve the efficiency of bridge management. By timely discovering and warning potential safety hazards, preventive maintenance of bridges can be achieved, the service life of bridges can be extended, and maintenance costs can be reduced. The data-based risk assessment and early warning mechanism can provide a scientific basis for managers' decision-making and improve the rationality and accuracy of decision-making.

[0150] In one embodiment of the present invention, S3 includes:

[0151] Obtaining a security monitoring category, and classifying the types of security monitoring indicators according to the security monitoring category to obtain multiple security monitoring indicator categories;

[0152] Calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight;

[0153] The calculation formula of the category risk coefficient is:

[0154]

[0155] Among them, L is the category risk coefficient, A is the total number of safety monitoring indicators in the safety monitoring indicator category, and Z n is the indicator risk coefficient of the nth safety monitoring indicator, Y is the number of safety monitoring indicators with risks in the total number of safety monitoring indicators, and Q is the category weight;

[0156] Comparing the category risk coefficient with a preset category risk threshold to obtain a category comparison result;

[0157] According to the category comparison result, the category of the security monitoring indicator is judged to be abnormal, and the category abnormality judgment result is obtained;

[0158] When the category risk coefficient is greater than the preset category risk threshold, the safety monitoring indicator category is determined to be an abnormal category; when the category risk coefficient is less than or equal to the preset category risk threshold, the safety monitoring indicator category is determined to be a normal category;

[0159] When the security monitoring indicator category is an abnormal category, an early warning is issued for the corresponding security monitoring indicator category.

[0160] According to the category abnormality determination results, an early warning is issued for the security monitoring indicator category, and the early warning level is the average indicator weight level of the security monitoring indicator category.

[0161] For example, if the average indicator weight of the security monitoring indicator category is 0.7, then the average indicator weight level is 0.7, and the corresponding indicator warning level is 0.7.

[0162] The working principle of the above technical solution is: obtain the category information of the security monitoring indicators, and divide the types of security monitoring indicators according to the category information to form multiple security monitoring indicator categories. For each security monitoring indicator category, the category risk coefficient is calculated according to the indicator risk coefficient and category weight of all security monitoring indicators under the category. The calculation formula of the category risk coefficient takes into account the risk coefficient and category weight of each indicator, and can comprehensively reflect the risk status of the entire category. The calculated category risk coefficient is compared with the preset category risk threshold. This step is to determine whether the risk level of the security monitoring indicator category exceeds the preset threshold. According to the comparison result, the security monitoring indicator category is judged as abnormal. If the category risk coefficient is greater than the preset category risk threshold, the category is judged as an abnormal category; otherwise, it is judged as a normal category. For the security monitoring indicator category judged as abnormal, the early warning mechanism is triggered. The early warning level is determined according to the average indicator weight level of the category. This can provide managers with appropriate early warning information based on the relative importance of different categories.

[0163] The technical effect of the above technical solution is: through the formula The proportion of risky indicators in the category to the total indicators can be calculated, Y, Z n Or the larger Q is, the larger the category risk coefficient is, relatively speaking. By dividing the categories of safety monitoring indicators and calculating the category risk coefficients, the risk status of the entire category can be comprehensively evaluated, providing managers with more comprehensive and accurate risk information. The abnormal judgment and early warning mechanism based on the category risk coefficient can trigger different levels of early warnings according to the risk status of different categories, thereby improving the pertinence and effectiveness of the early warning. The early warning level is determined based on the average indicator weight level of the safety monitoring indicator category, which not only takes into account the importance of the indicator, but also avoids the excessively high early warning level caused by the abnormality of individual indicators, making the early warning more reasonable and accurate. The automated risk assessment and early warning mechanism can reduce the workload of manual inspections and improve the efficiency of bridge management. At the same time, timely early warning information helps managers take timely measures to reduce the possibility of risk. By timely discovering and warning of potential safety hazards, preventive maintenance of bridges can be achieved, the service life of bridges can be extended, and maintenance costs can be reduced.

[0164] In one embodiment of the present invention, the S4 includes:

[0165] Acquire the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional model of the bridge according to the monitoring position;

[0166] An early warning is issued for the abnormal range to obtain range early warning data.

[0167] The working principle of the above technical solution is: extract indicators that are judged to be abnormal from the security monitoring system. These abnormal indicators are usually associated with specific locations on the bridge, so it is necessary to obtain the actual monitoring locations of these indicators on the bridge. Using a three-dimensional model of the bridge (such as a BIM model), the monitoring locations of the abnormal indicators are mapped to the model. Since the abnormal indicators may be distributed in different areas, it is necessary to integrate multiple monitoring locations to determine one or more abnormal ranges. The abnormal range can be a specific area of ​​the bridge, such as a pier, bridge deck or bridge span. Once the abnormal range is determined, the system can automatically trigger an early warning mechanism. The early warning may include highlighting the abnormal range on the three-dimensional model, sending an alarm message to the management personnel, or notifying relevant personnel by other means. The early warning information may include the specific location of the abnormal range, the type and value of the abnormal indicator, and recommended countermeasures.

[0168] The technical effect of the above technical solution is: by directly displaying the abnormal range on the three-dimensional model, managers can intuitively understand the location and range of the abnormality, which is convenient for quickly locating the problem and formulating countermeasures. Based on the precise monitoring position and three-dimensional model, the abnormal range can be accurately determined, avoiding the misjudgment and omission that may occur in traditional methods. Once an abnormal indicator is monitored, the system can immediately trigger an early warning to ensure that managers can obtain relevant information in a timely manner and take corresponding measures to reduce the possibility of risks. The automated early warning mechanism can greatly reduce the workload of manual inspections and improve the efficiency of bridge management. At the same time, accurate early warning information can also help managers to formulate maintenance plans more accurately and reduce maintenance costs. Through timely early warning and positioning of the abnormal range, preventive maintenance of bridges can be achieved, potential safety hazards can be discovered and repaired in a timely manner, and the service life of bridges can be extended.

[0169] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A bridge quality and safety monitoring device, characterized in that: The device comprises: The acquisition model building module is used to establish a three-dimensional bridge model based on the acquired bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data; Wherein, the acquisition model building module includes: A data acquisition and processing module is used to collect bridge construction and maintenance data and obtain bridge acquisition data; Preprocessing the bridge collected data to obtain preprocessed bridge collected data; A model building module is used to train the BIM model based on the pre-processed bridge acquisition data to obtain a three-dimensional model of the bridge; An indicator generation module is used to obtain different types of bridge data of the three-dimensional bridge model, set each bridge data type as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators; A data characterization processing module is used to continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data; Comparing the continuously collected data with the existing data of the indicator, extracting feature data, adding the feature data to the corresponding indicator, and obtaining the indicator collection data; The indicator abnormality analysis module is used to determine the indicator abnormality based on the indicator collection data, obtain the indicator abnormality determination result, calculate the indicator risk coefficient based on the indicator abnormality determination result combined with the indicator weight, and perform indicator early warning; The indicator abnormality analysis module includes: The indicator comparison module is used to compare the indicator collection data volume with the preset data volume threshold to obtain the data volume comparison result; An indicator determination module is used to determine the abnormality of the corresponding security monitoring indicator according to the data volume comparison result, and obtain the indicator abnormality determination result; An indicator risk calculation module is used to calculate the indicator risk coefficient based on the indicator abnormality judgment result combined with the corresponding indicator weight information; When the corresponding safety monitoring indicator is an abnormal indicator, calculate the indicator risk coefficient; The calculation formula of the indicator risk coefficient is: Among them, Z is the indicator risk coefficient, ΔC j is the latest amount of indicator data collection, j is the total number of indicator data collection times, ΔC i is the amount of data collected for the i-th indicator, and B is the corresponding indicator weight; The indicator early warning module is used to compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result; According to the indicator risk comparison results, the corresponding indicator is warned, and the warning level is the indicator weight level of the corresponding indicator; The category anomaly analysis module is used to classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and issue a category warning according to the category risk coefficient; Wherein, the category anomaly analysis module includes: A category classification module is used to obtain a security monitoring category, classify the types of security monitoring indicators according to the security monitoring category, and obtain multiple security monitoring indicator categories; A category risk calculation module is used to calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight; The calculation formula of the category risk coefficient is: Among them, L is the category risk coefficient, A is the total number of safety monitoring indicators in the safety monitoring indicator category, and Z n is the indicator risk coefficient of the nth safety monitoring indicator, Y is the number of safety monitoring indicators with risks in the total number of safety monitoring indicators, and Q is the category weight; A category comparison module, used to compare the category risk coefficient with a preset category risk threshold to obtain a category comparison result; A category warning module is used to make an abnormality determination on the category of the security monitoring indicator according to the category comparison result, and obtain the category abnormality determination result; According to the category abnormality determination results, the security monitoring indicator category is warned, and the warning level is the average indicator weight level of the security monitoring indicator category; The range anomaly analysis module is used to determine the abnormal range of the bridge 3D model according to the abnormal category and the monitoring position of the indicator, and to issue a range warning; Wherein, the range anomaly analysis module includes: A range determination module, used to obtain the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional bridge model according to the monitoring position; The range warning module is used to issue a warning for the abnormal range and obtain range warning data.

2. A bridge quality and safety monitoring method, characterized in that: The method comprises: S1. Establish a three-dimensional bridge model based on the collected bridge data, generate safety monitoring indicators, collect indicator data according to the types of safety monitoring indicators, and obtain indicator collection data; Wherein, the S1 includes: Collect bridge construction and maintenance data to obtain bridge collection data; Preprocessing the bridge collected data to obtain preprocessed bridge collected data; The BIM model is trained according to the preprocessed bridge acquisition data to obtain a three-dimensional model of the bridge; Obtain different types of bridge data of the three-dimensional bridge model, set each type of bridge data as a safety monitoring indicator, and then obtain multiple types of safety monitoring indicators; Continuously collect indicator data for each type of security monitoring indicator to obtain continuously collected data; Comparing the continuously collected data with the existing data of the indicator, extracting feature data, adding the feature data to the corresponding indicator, and obtaining the indicator collection data; S2. Perform indicator abnormality determination based on indicator collection data, obtain indicator abnormality determination results, calculate indicator risk coefficient based on indicator abnormality determination results combined with indicator weights, and perform indicator early warning; Wherein, the S2 includes: Compare the amount of data collected by the indicator with the preset data amount threshold to obtain a data amount comparison result; According to the data volume comparison result, the corresponding security monitoring indicator is judged abnormally to obtain the indicator abnormality judgment result; Calculate the indicator risk coefficient based on the indicator abnormality judgment results combined with the corresponding indicator weight information; When the corresponding safety monitoring indicator is an abnormal indicator, calculate the indicator risk coefficient; The calculation formula of the indicator risk coefficient is: Among them, Z is the indicator risk coefficient, ΔC j is the latest amount of indicator data collection, j is the total number of indicator data collection times, ΔC i is the amount of data collected for the i-th indicator, and B is the corresponding indicator weight; Compare the indicator risk coefficient with the preset indicator threshold to obtain the indicator risk comparison result; According to the indicator risk comparison results, the corresponding indicator is warned, and the warning level is the indicator weight level of the corresponding indicator; S3, classify the security monitoring indicators into categories, obtain multiple security monitoring indicator categories, calculate the category risk coefficient of each security monitoring indicator category according to the indicator risk coefficient combined with the category weight, and perform category warning according to the category risk coefficient; Wherein, the S3 includes: Obtaining a security monitoring category, and classifying the types of security monitoring indicators according to the security monitoring category to obtain multiple security monitoring indicator categories; Calculate the category risk coefficient of each safety monitoring indicator category based on the indicator risk coefficient combined with the category weight; The calculation formula of the category risk coefficient is: Among them, L is the category risk coefficient, A is the total number of safety monitoring indicators in the safety monitoring indicator category, and Z n is the indicator risk coefficient of the nth safety monitoring indicator, Y is the number of safety monitoring indicators with risks in the total number of safety monitoring indicators, and Q is the category weight; Comparing the category risk coefficient with a preset category risk threshold to obtain a category comparison result; According to the category comparison result, the category of the security monitoring indicator is judged to be abnormal, and the category abnormality judgment result is obtained; According to the category abnormality determination results, the security monitoring indicator category is warned, and the warning level is the average indicator weight level of the security monitoring indicator category; S4. Determine the abnormal range of the three-dimensional bridge model according to the abnormal category and the monitoring position of the indicator, and issue a range warning; Wherein, the S4 includes: Acquire the monitoring position of the abnormal indicator of each abnormal category, and determine the abnormal range of the three-dimensional model of the bridge according to the monitoring position; An early warning is issued for the abnormal range to obtain range early warning data.

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