Bridge state diagnosis method based on data visualization

Abstract

The invention discloses a bridge state diagnosis method based on data visualization, and relates to the technical field of state diagnosis, and the method comprises the following steps: obtaining the rigidity value of a main beam monitored at each monitoring point, judging the fuzzy state of the monitoring point, screening to-be-analyzed monitoring points, setting a first mark, calculating the diffusivity according to the first mark, and setting a second mark. And calculating a growth rate according to the second mark, judging a damage level, and constructing a state judgment model. According to the invention, a safety early warning function is realized, abnormal conditions of a bridge structure can be monitored in real time, bridge operation safety can be guaranteed, the service life of the bridge can be prolonged, and scientific basis and guidance are provided for bridge managers and engineers based on bridge bearing capacity evaluation and damage identification results of big data and artificial intelligence. The method helps the bridge to formulate an effective maintenance and repair strategy, analyzes the sensor data of the bridge through a deep learning algorithm, recognizes potential damage and structural defects, and improves the accuracy of damage recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of state diagnosis, and in particular to a bridge state diagnosis method based on data visualization. Background Art

[0002] In recent years, bridge inspection technology is developing in the direction of intelligence, using drones, robots and other carriers equipped with high-precision video equipment to inspect the appearance of bridge structures in order to improve data accuracy and reduce the influence of subjective factors. By using various sensors and communication equipment to continuously measure and record structural responses, timely danger warnings and safety assessments can be achieved, thereby extending the service life of bridges and guiding bridge management and maintenance.

[0003] At present, a Chinese invention patent with publication number CN112989456B discloses a bridge performance degradation diagnosis method and system. The method constructs a first temperature-displacement similarity factor, sets the vehicle weight distribution mode of the bridge, the beam bottom strain reference value, the daily operation status of the bridge, the expansion joint displacement, the structural temperature, the number of vehicles with a vehicle weight not less than the vehicle weight distribution mode, and the beam bottom dynamic strain data, constructs a second temperature-displacement similarity factor, obtains the number of strain peaks, constructs a state degradation factor, and determines the degree of degradation of the bridge. However, the related art does not optimize the distribution of monitoring points according to the specific driving trajectory of the vehicle and the specific number of times the bridge deck is traveled, resulting in a large amount of calculation, which is not conducive to the rapid responsiveness of the diagnosis. The state of the bridge is not judged based on the key parameters of the main beam of the vehicle traveling over the bridge deck, which is not conducive to simplifying the judgment process and the flexibility of application, and has certain limitations. Summary of the invention

[0004] The technical problem solved by the present invention is that the related technology does not optimize the distribution of monitoring points according to the specific driving trajectory of the vehicle and the specific number of times the bridge deck is traveled, resulting in a large amount of calculation, which is not conducive to the rapid responsiveness of the diagnosis. The state of the bridge is not judged based on the key parameters of the main beam of the bridge deck when the vehicle travels across it, which is not conducive to simplifying the judgment process and the flexibility of application, and has certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a bridge status diagnosis method based on data visualization, comprising the following steps: Step S100, setting the monitoring point location, simulation parameters and simulation cycle and performing testing; Step S200, obtaining the main beam stiffness value monitored at each monitoring point, obtaining the standard main beam stiffness range according to the bridge type, and obtaining the service time and the standard main beam stiffness range according to the bridge code; Step S300, pre-processing the main beam stiffness value of the monitoring point to determine the fuzzy state of the monitoring point; Step S400, selecting the monitoring points to be analyzed according to the fuzzy state, first marking the time point at which the initial fuzzy state changes and calculating the diffusion rate, second marking the time point at which the initial fuzzy state of the monitoring point to be analyzed changes and calculating the growth rate, judging the damage level according to the growth rate and the diffusion rate, and constructing a state judgment model through hierarchical analysis; Step S500, determining the current damage level based on the current data and the state judgment model.

[0006] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, the method for setting the monitoring points includes: Obtain a three-dimensional model of the bridge deck, set the size of the segmentation frame, segment the three-dimensional model into sub-bridge decks, obtain the historical rolling track of the sub-bridge deck, set the length, width and height of the framed rectangular parallelepiped, so that one and only one corner of the framed rectangular parallelepiped is located on the sub-bridge deck, and the sub-bridge deck is completely distributed in the framed rectangular parallelepiped, take the corner as the origin, take the length direction, width direction and height direction as the X-axis, Y-axis and Z-axis, construct a three-dimensional coordinate system, and obtain the three-dimensional coordinates of the rolling track curve; Mapping the historical rolling track into a three-dimensional coordinate system to obtain a rolling track equation, and counting the number of times the same three-dimensional coordinates appear in the rolling track equation, which is recorded as the first number; The first value is set as the number threshold, when the first number is less than or equal to the first value, the corresponding sub-bridge deck is deleted, and when the first number is greater than the first value, a stiffness sensor is placed at the three-dimensional coordinate.

[0007] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, the average vehicle density, average vehicle speed and average vehicle weight are respectively expressed as the average vehicle density, average vehicle speed and average vehicle weight of the entire bridge deck when the bridge is in operation, the vehicle weight includes the weight of the vehicle itself, and the weight of people and objects in the vehicle, and the average vehicle density is expressed as the ratio of the number of vehicles on the bridge deck per second to the area of ​​the bridge deck.

[0008] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, step S200 includes the following sub-steps: Step S201, obtaining a bridge code; Step S202, calling up a bridge database, inputting the bridge code into the bridge database, and matching the bridge type, bridge service time, and standard main beam stiffness range of the bridge corresponding to the bridge code; The bridge types include reinforced concrete slab bridges, reinforced concrete beam bridges and reinforced concrete soil bridges.

[0009] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, step S300 includes the following sub-steps: Step S301, preprocessing the main beam stiffness value of the monitoring point, wherein the preprocessing includes removing abnormal values ​​and filling missing values; Step S303, judging the fuzzy state of the monitoring point according to the main beam stiffness range, the fuzzy state includes abnormal response and normal response, and the judging method of the fuzzy state includes: The main beam stiffness value is compared with the standard main beam stiffness range. When the main beam stiffness value is distributed within the standard main beam stiffness range, the fuzzy state is set to a normal response. When the main beam stiffness value is not distributed within the standard main beam stiffness range, the fuzzy state is set to an abnormal response. When the main beam stiffness value at any monitoring point at any time is not distributed within the standard main beam stiffness range, the fuzzy state is judged as an abnormal state.

[0010] As a preferred solution of the bridge state diagnosis method based on data visualization described in the present invention, the screening method of screening the monitoring points to be analyzed according to the fuzzy state includes: The fuzzy state of the monitoring point is obtained. When the fuzzy state of the monitoring point is an abnormal state, the monitoring point is set as a monitoring point to be analyzed. Otherwise, the main beam stiffness value corresponding to the monitoring point is deleted.

[0011] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, the method for setting the first mark includes: Obtain the initial monitoring time points of each fuzzy state change, sort the initial monitoring time points in chronological order, calculate the time intervals of adjacent initial monitoring time points after sorting, set the second value as the time interval threshold, calculate the monitoring point corresponding to the initial monitoring time point at the front of the time sequence and set it as the first mark of the first color, calculate the sum of the initial monitoring time point at the front of the time sequence and the second value, record it as the second sum value, and set the monitoring point corresponding to the initial monitoring time point before the second sum value as the first mark of the second color; The second sum value and the second value are weighted to obtain a third sum value, and the monitoring point corresponding to the initial monitoring time point before the third sum value is set as a first mark of a third color; The weighted step is repeated to obtain the Nth sum value, and the monitoring point corresponding to the initial monitoring time point before the Nth sum value is set as the first mark of the Nth color.

[0012] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, the calculation method of the diffusion rate includes: The areas of the sub-bridge decks corresponding to the first marks of each color are counted, the difference in the areas of the sub-bridge decks corresponding to the first marks of adjacent colors is calculated, recorded as the first difference, the ratio of adjacent first differences is calculated, and the ratio of the adjacent first differences is set as the diffusion rate. The order of the adjacent colors is from the first color to the Nth color.

[0013] As a preferred solution of the bridge state diagnosis method based on data visualization described in the present invention, the method for setting the second mark of the monitoring time point at which the initial fuzzy state of the same monitoring point to be analyzed changes includes: Obtaining the main beam stiffness value of any monitoring point to be analyzed, and marking the monitoring time point at which the initial fuzzy state of the monitoring point to be analyzed changes, wherein the second mark represents the time point at which the fuzzy state of the monitoring point changes from a normal state to an abnormal state for the first time; The growth rate calculation method includes: Obtain the main beam stiffness value at and after the monitoring time point when the initial fuzzy state changes, calculate the ratio of adjacent main beam stiffness values, and calculate the average value of each ratio of the monitoring point, calculate the average value of the average values ​​of each ratio of each monitoring point, record it as a first average value, and set the first average value as the growth rate; Calculate the sum of the first average value and the second average value, record it as the first sum value, and set the third value and the fourth value as the sum value threshold; The damage level is determined based on the sum value threshold. The damage level determination methods include: When the first sum is less than the third value, the damage level is set to the first level; when the first sum is greater than or equal to the third value and less than the fourth value, the damage level is set to the second level; when the first sum is greater than or equal to the fourth value, the damage level is set to the third level; The first level, second level, and third level represent the degree of bridge damage in increasing order.

[0014] As a preferred solution of the bridge status diagnosis method based on data visualization described in the present invention, wherein: taking simulation parameters and bridge service time as the criterion layer, taking damage level as the target layer, taking rules for judging damage level as the solution layer, hierarchical analysis is performed to obtain the weights of various simulation parameters, constructing a status judgment model according to the weights, obtaining current data, the current data including current simulation parameters and current bridge service time, and judging the current damage level according to the status judgment model; The calculation expressions of the status judgment level include: ; in, is the damage level, is the parameter of the i-th criterion layer, is the coefficient corresponding to the parameter of the i-th criterion layer, is the number of parameters of the criterion layer.

[0015] The beneficial effects of the present invention are as follows: it realizes the safety early warning function, can monitor the abnormal conditions of the bridge structure in real time, provides a scientific and reliable basis for the decision-making of bridge disease prevention and control and maintenance and reinforcement, helps to ensure the safety of bridge operation and extend its service life, and provides scientific basis and guidance for bridge managers and engineers based on the bridge bearing capacity assessment and damage identification results of big data and artificial intelligence to help them formulate effective maintenance and repair strategies, and uses deep learning algorithms to analyze the sensor data of the bridge to identify potential damage and structural defects, thereby improving the accuracy of damage identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A basic flow chart of a bridge status diagnosis method based on data visualization is provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0018] Example, see Figure 1 , as an embodiment of the present invention, provides a bridge status diagnosis method based on data visualization, a bridge status diagnosis method based on data visualization, comprising the following steps: Step S100, setting the monitoring point location, simulation parameters and simulation cycle and performing testing; Step S200, obtaining the main beam stiffness value monitored at each monitoring point, obtaining the standard main beam stiffness range according to the bridge type, and obtaining the service time and the standard main beam stiffness range according to the bridge code; Step S300, pre-processing the main beam stiffness value of the monitoring point to determine the fuzzy state of the monitoring point; Step S400, selecting the monitoring points to be analyzed according to the fuzzy state, first marking the time point at which the initial fuzzy state changes and calculating the diffusion rate, second marking the time point at which the initial fuzzy state of the monitoring point to be analyzed changes and calculating the growth rate, judging the damage level according to the growth rate and the diffusion rate, and constructing a state judgment model through hierarchical analysis; Step S500, determining the current damage level based on the current data and the state judgment model.

[0019] The present invention realizes the safety early warning function and can monitor the abnormal conditions of the bridge structure in real time, providing a scientific and reliable basis for bridge disease prevention and control and maintenance and reinforcement decision-making, which helps to ensure the safety of bridge operation and extend its service life. The bridge bearing capacity assessment and damage identification results based on big data and artificial intelligence provide scientific basis and guidance for bridge managers and engineers, helping them to formulate effective maintenance and repair strategies, and use deep learning algorithms to analyze the sensor data of the bridge to identify potential damage and structural defects, thereby improving the accuracy of damage identification.

[0020] The method for setting the monitoring points includes: Obtain a three-dimensional model of the bridge deck, set the size of the segmentation frame, segment the three-dimensional model into sub-bridge decks, obtain the historical rolling track of the sub-bridge deck, set the length, width and height of the framed rectangular parallelepiped, so that one and only one corner of the framed rectangular parallelepiped is located on the sub-bridge deck, and the sub-bridge deck is completely distributed in the framed rectangular parallelepiped, take the corner as the origin, take the length direction, width direction and height direction as the X-axis, Y-axis and Z-axis, construct a three-dimensional coordinate system, and obtain the three-dimensional coordinates of the rolling track curve; Mapping the historical rolling track into a three-dimensional coordinate system to obtain a rolling track equation, and counting the number of times the same three-dimensional coordinates appear in the rolling track equation, which is recorded as the first number; The first value is set as the number threshold, when the first number is less than or equal to the first value, the corresponding sub-bridge deck is deleted, and when the first number is greater than the first value, a stiffness sensor is placed at the three-dimensional coordinate.

[0021] In the specific implementation, the three-dimensional model of the bridge deck is obtained and the segmentation frame size is set to divide the three-dimensional model into sub-bridge decks. This method can more accurately locate the monitoring points, improve the accuracy of the monitoring data and the efficiency of the monitoring work, and use the historical rolling trajectory and the setting of the three-dimensional coordinate system to more scientifically arrange the monitoring points to ensure that the monitoring points can cover key areas and improve the representativeness and practicality of the monitoring data. By counting the number of times the same three-dimensional coordinates appear in the rolling trajectory equation and setting a number threshold, those sub-bridge decks with fewer appearances can be effectively deleted, reducing redundant data and improving data processing efficiency.

[0022] The three-dimensional model of the bridge deck is obtained by using high-precision three-dimensional scanning technology or by building the bridge deck based on design drawings.

[0023] The average vehicle density, average vehicle speed and average vehicle weight are respectively expressed as the average vehicle density, average vehicle speed and average vehicle weight of the entire bridge deck when the bridge is in operation. The vehicle weight includes the weight of the vehicle itself, as well as the weight of people and objects in the vehicle. The average vehicle density is expressed as the ratio of the number of vehicles on the bridge deck per second to the area of ​​the bridge deck.

[0024] The step S200 includes the following sub-steps: Step S201, obtaining a bridge code; Step S202, calling up a bridge database, inputting the bridge code into the bridge database, and matching the bridge type, bridge service time, and standard main beam stiffness range of the bridge corresponding to the bridge code; The bridge types include reinforced concrete slab bridges, reinforced concrete beam bridges and reinforced concrete soil bridges.

[0025] In the specific implementation, by accurately obtaining the bridge code and matching the corresponding bridge information, the accuracy of the acquired data such as bridge type, service time and standard main beam stiffness range is ensured, thereby improving the accuracy of bridge status diagnosis, quickly matching bridge codes with database information, reducing the time of manual query and data processing, and improving the efficiency of bridge status diagnosis. For example, the bridge code is "XJZ-2024-001", where "XJZ" indicates the bridge type, "2024" indicates the year of bridge construction, and "001" is the serial number of that year. According to the code "XJZ-2024-001", the bridge type is matched in the database as "reinforced concrete beam bridge", and the service life of the bridge is 15 years. For "reinforced concrete beam bridge", the standard main beam stiffness range is 1000kN / m~1500 kN / m.

[0026] The step S300 includes the following sub-steps: Step S301, preprocessing the main beam stiffness value of the monitoring point, wherein the preprocessing includes removing abnormal values ​​and filling missing values; Step S303, judging the fuzzy state of the monitoring point according to the main beam stiffness range, the fuzzy state includes abnormal response and normal response, and the judging method of the fuzzy state includes: The main beam stiffness value is compared with the standard main beam stiffness range. When the main beam stiffness value is distributed within the standard main beam stiffness range, the fuzzy state is set to a normal response. When the main beam stiffness value is not distributed within the standard main beam stiffness range, the fuzzy state is set to an abnormal response. When the main beam stiffness value at any monitoring point at any time is not distributed within the standard main beam stiffness range, the fuzzy state is judged as an abnormal state.

[0027] In the specific implementation, the preprocessing step improves the quality of monitoring data by eliminating outliers and filling missing values, providing a more reliable data basis for subsequent analysis. Accurate outlier processing and missing value filling ensure the authenticity and integrity of the main beam stiffness value, thereby enhancing the accuracy of the monitoring results. For example, monitoring point A is selected, and the preprocessed main beam stiffness value is compared with the standard main beam stiffness range. 1200 kN / m and 1500 kN / m belong to normal responses, and 1100 kN / m does not belong to the standard main beam stiffness range, so it is judged as an abnormal response. For monitoring point A, due to the existence of abnormal response (1100 kN / m), the overall fuzzy state is judged to be an abnormal state, which requires further inspection and analysis.

[0028] The screening methods for screening monitoring points to be analyzed according to the fuzzy state include: The fuzzy state of the monitoring point is obtained. When the fuzzy state of the monitoring point is an abnormal state, the monitoring point is set as a monitoring point to be analyzed. Otherwise, the main beam stiffness value corresponding to the monitoring point is deleted.

[0029] The method for setting the first mark includes: Obtain the initial monitoring time points of each fuzzy state change, sort the initial monitoring time points in chronological order, calculate the time intervals of adjacent initial monitoring time points after sorting, set the second value as the time interval threshold, calculate the monitoring point corresponding to the initial monitoring time point at the front of the time sequence and set it as the first mark of the first color, calculate the sum of the initial monitoring time point at the front of the time sequence and the second value, record it as the second sum value, and set the monitoring point corresponding to the initial monitoring time point before the second sum value as the first mark of the second color; The second sum value and the second value are weighted to obtain a third sum value, and the monitoring point corresponding to the initial monitoring time point before the third sum value is set as a first mark of a third color; The weighted step is repeated to obtain the Nth sum value, and the monitoring point corresponding to the initial monitoring time point before the Nth sum value is set as the first mark of the Nth color.

[0030] In the specific implementation, by setting the first mark of different colors, the time and location of bridge damage can be more finely identified and distinguished, the accuracy of damage detection can be improved, the initial monitoring time points can be sorted and the time interval can be calculated, the time and speed of damage development can be analyzed, and data support can be provided for the time series analysis of bridge damage. Each time point, as well as the time point after the weighting of the time point and the time interval, are distributed within the monitoring period. The initial monitoring time points are sorted and the time interval is calculated, the time and speed of damage development can be analyzed, and data support can be provided for the time series analysis of bridge damage. By calculating the time interval and the weighted sum value, the damage development trend can be predicted, and forward-looking guidance can be provided for the maintenance and repair of bridges. The calculation method of diffusion rate includes: The areas of the sub-bridge decks corresponding to the first marks of each color are counted, the difference in the areas of the sub-bridge decks corresponding to the first marks of adjacent colors is calculated, recorded as the first difference, the ratio of adjacent first differences is calculated, and the ratio of the adjacent first differences is set as the diffusion rate. The order of the adjacent colors is from the first color to the Nth color.

[0031] The method for setting a second mark for the monitoring time point at which the initial fuzzy state of the same monitoring point to be analyzed changes includes: Obtaining the main beam stiffness value of any monitoring point to be analyzed, and marking the monitoring time point at which the initial fuzzy state of the monitoring point to be analyzed changes, wherein the second mark represents the time point at which the fuzzy state of the monitoring point changes from a normal state to an abnormal state for the first time; The growth rate calculation method includes: Obtain the main beam stiffness value at and after the monitoring time point when the initial fuzzy state changes, calculate the ratio of adjacent main beam stiffness values, and calculate the average value of each ratio of the monitoring point, calculate the average value of the average values ​​of each ratio of each monitoring point, record it as a first average value, and set the first average value as the growth rate; Calculate the sum of the first average value and the second average value, record it as the first sum value, and set the third value and the fourth value as the sum value threshold; The damage level is determined based on the sum value threshold. The damage level determination methods include: When the first sum is less than the third value, the damage level is set to the first level; when the first sum is greater than or equal to the third value and less than the fourth value, the damage level is set to the second level; when the first sum is greater than or equal to the fourth value, the damage level is set to the third level; The first level, second level, and third level represent the degree of bridge damage in increasing order.

[0032] In the specific implementation, the area of ​​the sub-bridge deck corresponding to the first mark of each color is counted. This step involves quantifying the damage status of different parts of the bridge, providing basic data for subsequent analysis, calculating the difference in the area of ​​the sub-bridge deck corresponding to the first marks of adjacent colors, recorded as the first difference, which helps to identify the scale change of damage diffusion. The order of adjacent colors is from the first color to the Nth color, ensuring the continuity and comprehensiveness of the diffusion rate calculation. The second mark accurately identifies the time point when the fuzzy state of the monitoring point changes from normal to abnormal, which helps to timely discover the starting moment of bridge damage and provide key time nodes for subsequent damage analysis. The division of damage levels provides a scientific method for assessing the degree of damage, which helps to formulate targeted maintenance strategies and improve the service life and safety of bridges.

[0033] For example, the monitoring time point when the initial fuzzy state of monitoring point A changes is TA=2024−01−01, which is marked as the second mark, indicating the time point when the fuzzy state of monitoring point A changes from a normal state to an abnormal state for the first time. The stiffness value of the main beam after TA is 1000, and the stiffness value after TA+30 is 950. The ratio of the stiffness values ​​of adjacent main beams is 0.95. The average value of each ratio of monitoring point A is 0.950.95, and the average value of each ratio of all monitoring points is also 0.950.95, that is, the growth rate. The first sum value is 1.90, the third value is 1.51, and the fourth value is 2.02. Since the first sum value 1.901.90 is greater than or equal to the third value 1.51.5 and less than the fourth value 2.02, the damage level is set to the second level, indicating that the bridge damage is moderate.

[0034] Taking simulation parameters and bridge service time as the criterion layer, damage level as the target layer, and rules for judging damage level as the solution layer, hierarchical analysis is performed to obtain the weights of various simulation parameters, and a state judgment model is constructed according to the weights to obtain current data, which includes current simulation parameters and current bridge service time, and the current damage level is judged according to the state judgment model; The calculation expressions of the status judgment level include: ; in, is the damage level, is the parameter of the i-th criterion layer, is the coefficient corresponding to the parameter of the i-th criterion layer, is the number of parameters of the criterion layer.

[0035] In the specific implementation, the hierarchical analysis constructs a pairwise comparison matrix of the criterion layer to the target layer. The expression of the pairwise comparison matrix is: ; in, is a pairwise comparison matrix, which is a 5×5 matrix.

[0036] The present invention realizes the safety early warning function and can monitor the abnormal conditions of the bridge structure in real time, providing a scientific and reliable basis for bridge disease prevention and control and maintenance and reinforcement decision-making, which helps to ensure the safety of bridge operation and extend its service life. The bridge bearing capacity assessment and damage identification results based on big data and artificial intelligence provide scientific basis and guidance for bridge managers and engineers, helping them to formulate effective maintenance and repair strategies, and use deep learning algorithms to analyze the sensor data of the bridge to identify potential damage and structural defects, thereby improving the accuracy of damage identification.

[0037] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Wherein, the storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0038] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A bridge status diagnosis method based on data visualization, characterized in that: The following steps are involved: Step S100, setting the monitoring point location, simulation parameters and simulation cycle and performing testing; Step S200, obtaining the main beam stiffness value monitored at each monitoring point, obtaining the standard main beam stiffness range according to the bridge type, and obtaining the service time and the standard main beam stiffness range according to the bridge code; Step S300, pre-processing the main beam stiffness value of the monitoring point to determine the fuzzy state of the monitoring point; Step S400, selecting the monitoring points to be analyzed according to the fuzzy state, first marking the time point at which the initial fuzzy state changes and calculating the diffusion rate, second marking the time point at which the initial fuzzy state of the monitoring point to be analyzed changes and calculating the growth rate, judging the damage level according to the growth rate and the diffusion rate, and constructing a state judgment model through hierarchical analysis; Step S500, determining the current damage level based on the current data and the state judgment model; The step S300 includes the following sub-steps: Step S301, preprocessing the main beam stiffness value of the monitoring point, wherein the preprocessing includes removing abnormal values ​​and filling missing values; Step S302, judging the fuzzy state of the monitoring point according to the main beam stiffness range, the fuzzy state includes abnormal response and normal response, and the judging method of the fuzzy state includes: Compare the main beam stiffness value with the standard main beam stiffness range. When the main beam stiffness value is distributed within the standard main beam stiffness range, set the fuzzy state to a normal response. When the main beam stiffness value is not distributed within the standard main beam stiffness range, set the fuzzy state to an abnormal response. When the main beam stiffness value at any monitoring point at any time is not distributed within the standard main beam stiffness range, the fuzzy state is judged as an abnormal state. The change of the initial fuzzy state is represented by the change of the detection point to be analyzed from a normal response to an abnormal response.

2. A bridge status diagnosis method based on data visualization as claimed in claim 1, characterized in that: The method for setting the monitoring points includes: Obtain a three-dimensional model of the bridge deck, set the size of the segmentation frame, segment the three-dimensional model into sub-bridge decks, obtain the historical rolling track of the sub-bridge deck, set the length, width and height of the framed rectangular parallelepiped, so that one and only one corner of the framed rectangular parallelepiped is located on the sub-bridge deck, and the sub-bridge deck is completely distributed in the framed rectangular parallelepiped, take the corner as the origin, take the length direction, width direction and height direction as the X-axis, Y-axis and Z-axis, construct a three-dimensional coordinate system, and obtain the three-dimensional coordinates of the rolling track curve; Mapping the historical rolling track into a three-dimensional coordinate system to obtain a rolling track equation, and counting the number of times the same three-dimensional coordinates appear in the rolling track equation, which is recorded as the first number; The first value is set as the number threshold, when the first number is less than or equal to the first value, the corresponding sub-bridge deck is deleted, and when the first number is greater than the first value, a stiffness sensor is placed at the three-dimensional coordinate.

3. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The average vehicle density, average vehicle speed and average vehicle weight are respectively expressed as the average vehicle density, average vehicle speed and average vehicle weight of the entire bridge deck when the bridge is in operation. The vehicle weight includes the weight of the vehicle itself, as well as the weight of people and objects in the vehicle. The average vehicle density is expressed as the ratio of the number of vehicles on the bridge deck per second to the area of ​​the bridge deck.

4. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The step S200 includes the following sub-steps: Step S201, obtaining a bridge code; Step S202, calling up a bridge database, inputting the bridge code into the bridge database, and matching the bridge type, bridge service time, and standard main beam stiffness range of the bridge corresponding to the bridge code; The bridge types include reinforced concrete slab bridges, reinforced concrete beam bridges and reinforced concrete soil bridges.

5. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The screening methods for screening monitoring points to be analyzed according to the fuzzy state include: The fuzzy state of the monitoring point is obtained. When the fuzzy state of the monitoring point is an abnormal state, the monitoring point is set as a monitoring point to be analyzed. Otherwise, the main beam stiffness value corresponding to the monitoring point is deleted.

6. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The method for setting the first mark includes: Obtain the initial monitoring time points of each fuzzy state change, sort the initial monitoring time points in chronological order, calculate the time intervals of adjacent initial monitoring time points after sorting, set the second value as the time interval threshold, calculate the monitoring point corresponding to the initial monitoring time point at the front of the time sequence and set it as the first mark of the first color, calculate the sum of the initial monitoring time point at the front of the time sequence and the second value, record it as the second sum value, and set the monitoring point corresponding to the initial monitoring time point before the second sum value as the first mark of the second color; The second sum value and the second value are weighted to obtain a third sum value, and the monitoring point corresponding to the initial monitoring time point before the third sum value is set as a first mark of a third color; The weighted step is repeated to obtain the Nth sum value, and the monitoring point corresponding to the initial monitoring time point before the Nth sum value is set as the first mark of the Nth color.

7. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The calculation method of diffusion rate includes: The areas of the sub-bridge decks corresponding to the first marks of each color are counted, the difference in the areas of the sub-bridge decks corresponding to the first marks of adjacent colors is calculated, recorded as the first difference, the ratio of adjacent first differences is calculated, and the ratio of the adjacent first differences is set as the diffusion rate. The order of the adjacent colors is from the first color to the Nth color.

8. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: The method for setting a second mark for the monitoring time point at which the initial fuzzy state of the same monitoring point to be analyzed changes includes: Obtaining the main beam stiffness value of any monitoring point to be analyzed, and marking the monitoring time point at which the initial fuzzy state of the monitoring point to be analyzed changes, wherein the second mark represents the time point at which the fuzzy state of the monitoring point changes from a normal state to an abnormal state for the first time; The growth rate calculation method includes: Obtain the main beam stiffness value at and after the monitoring time point when the initial fuzzy state changes, calculate the ratio of adjacent main beam stiffness values, and calculate the average value of each ratio of the monitoring point, calculate the average value of the average values ​​of each ratio of each monitoring point, record it as a first average value, and set the first average value as the growth rate; Calculate the sum of the first average value and the second average value, record it as the first sum value, and set the third value and the fourth value as the sum value threshold; The damage level is determined based on the sum value threshold. The damage level determination methods include: When the first sum is less than the third value, the damage level is set to the first level; when the first sum is greater than or equal to the third value and less than the fourth value, the damage level is set to the second level; when the first sum is greater than or equal to the fourth value, the damage level is set to the third level; The first level, second level, and third level represent the degree of bridge damage in increasing order.

9. The bridge status diagnosis method based on data visualization according to claim 1, characterized in that: Taking simulation parameters and bridge service time as the criterion layer, damage level as the target layer, and rules for judging damage level as the solution layer, hierarchical analysis is performed to obtain the weights of various simulation parameters, and a state judgment model is constructed according to the weights to obtain current data, which includes current simulation parameters and current bridge service time, and the current damage level is judged according to the state judgment model; The calculation expressions of the status judgment level include: ； in, is the damage level, is the parameter of the i-th criterion layer, is the coefficient corresponding to the parameter of the i-th criterion layer, is the number of parameters of the criterion layer.

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