A prestressed concrete continuous beam bridge construction monitoring system

Abstract

The application discloses a prestressed concrete continuous beam bridge construction monitoring system, comprising a data acquisition module, a preprocessing module, a database module, a data processing module and a data output module; the preprocessing module is used for denoising and enhancing real-time image data; the data processing module is used for monitoring abnormal data in the beam bridge construction process, and an alarm report is generated when abnormal data exists; the application firstly analyzes the layout of steel bar bundling, sets a minimum monitoring area in combination with the size of the steel bar, the minimum area can guarantee the change of the number of steel bars within the existing error, so that the effectiveness of monitoring and the representativeness during data acquisition are ensured; then each monitoring area is monitored individually, the reasons for data abnormality during steel bar bundling are inferred according to the monitoring result, and feedback is carried out in a targeted manner, so that the monitoring personnel can quickly lock the hidden danger in the steel bar bundling process and the detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligence, and in particular to a prestressed concrete continuous beam bridge construction monitoring system. Background Art

[0002] Steel bars play a key role in bearing loads in beam bridge structures. When steel bars are laid unevenly, the beam bridge will produce uneven deformation during the stress process, which will affect its overall bearing capacity. Specifically, areas with sparse steel bars may fail prematurely due to insufficient bearing capacity when subjected to stress, while areas with dense steel bars may produce stress concentration due to excessive bearing, which is also not conducive to the stability of the structure. In the process of manufacturing prestressed concrete beam bridge structural members, a large amount of steel bars are required as a skeleton. After the steel bars are laid, it is difficult to visually observe whether the layout of the steel bars is reasonable. During acceptance, evaluation and acceptance are often carried out through fixed-point measurements at arbitrary positions. However, the measurement data has great limitations, resulting in large errors between the evaluation results and the actual acceptance standards. In order to improve the construction quality of beam bridges, a prestressed concrete continuous beam bridge construction monitoring system is proposed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is: how to solve the problem that the existing safety protection system has a single protection type, resulting in poor protection effect and bringing certain impacts on the use of the safety protection system, and provides a prestressed concrete continuous beam bridge construction monitoring system.

[0004] The present invention solves the above technical problems through the following technical solutions, which include a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module;

[0005] The data acquisition module is used to collect real-time data information of the beam bridge construction process, and the real-time data information includes real-time image data;

[0006] The preprocessing module is used to receive real-time image data and perform denoising and enhancement on the real-time image data;

[0007] The database module is used to store historical data information during the construction of the beam bridge;

[0008] The data processing module is used to receive real-time data information and historical data information, analyze the real-time data information in combination with the historical data information, monitor abnormal data during the beam bridge construction process, and generate a warning report when abnormal data exists;

[0009] The data output module is used to receive and output warning reports.

[0010] Preferably, the specific processing process of the pre-processing module is:

[0011] Import real-time image data;

[0012] Divide the image evenly into multiple block units;

[0013] Use the multithreaded programming library to create threads;

[0014] Distribute the divided block units evenly to each thread;

[0015] In each thread, the mean filtering algorithm and histogram equalization algorithm are applied to the assigned block units for processing;

[0016] After all threads have completed processing, the processed block units are merged into a complete image and output.

[0017] Preferably, the real-time data information also includes real-time size data, and the generation process of the warning report is specifically as follows:

[0018] Collect real-time image data of steel bar bundling before pouring beam bridges;

[0019] Randomly establish multiple first virtual straight lines in the target direction;

[0020] Collect the dimensions of a preset number M1 of steel bars on each first virtual straight line and obtain the average dimension Le;

[0021] Use the formula Lr=Le / M1 to calculate the average gap between two adjacent steel bars;

[0022] Collect the steel bar size Lv arranged perpendicular to the target direction;

[0023] Use the formula Lu=Lr / Lv to calculate the minimum monitoring size in the target direction;

[0024] When the target direction is the length direction of the beam bridge, the first minimum monitoring size Lu1 is obtained;

[0025] When the target direction is the width direction of the beam bridge, the second minimum monitoring size Lu2 is obtained;

[0026] Establish multiple monitoring areas with Lu1 as the length dimension and Lu2 as the width dimension, wherein the length direction of the monitoring area is parallel to the length direction of the beam bridge, and the width direction of the monitoring area is parallel to the width direction of the beam bridge;

[0027] Count the number of steel bars M2 in the length direction and the number of steel bars M3 in the width direction in each monitoring area respectively;

[0028] Calculate the steel density P in each monitoring area. The specific calculation process is:

[0029] P = M2·M3;

[0030] Get the average density of steel bars in the multiple monitoring areas ;

[0031] When the average density <Preset threshold Q1 or average density >When the preset threshold Q2 is exceeded, it indicates that there is an abnormality in the arrangement of the steel bars, and a first type of warning report is generated.

[0032] Preferably, when the average density preset threshold Q1 ≤ average density ≤preset threshold Q2, the process of generating the warning report further includes:

[0033] Read monitoring area data;

[0034] Import historical data and obtain steel bar dimension data in length and width directions during the production process of multiple beam bridges;

[0035] Calculate the average length spacing dimension La in the length direction when multiple steel bars are bundled and the average width spacing dimension Lb in the width direction when multiple steel bars are bundled;

[0036] Randomly acquire multiple steel bars in the target direction within the monitoring area;

[0037] Randomly collect X steel bar spacings in the target direction, namely Lc1, Lc2, Lc3, ..., LcX;

[0038] Calculate the difference E1 of the distance in the target direction. The specific calculation process is:

[0039] ;

[0040] When the difference E1 is greater than the preset threshold Q3, it is considered abnormal data and marked;

[0041] When the target direction is the length direction, Lx is La, and the first mark data is obtained;

[0042] When the target direction is the width direction, Lx is Lb, and the second mark data is obtained;

[0043] Counting the total number D1 of the first labeled data and the second labeled data;

[0044] Use the formula E2=D1 / X to calculate the first anomaly ratio;

[0045] When the first abnormality proportion E2 is greater than the preset threshold Q4, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

[0046] Preferably, when the first abnormality proportion E2 ≤ the preset threshold Q4, the process of generating the warning report further includes:

[0047] Read abnormal data and obtain the abnormal data location;

[0048] A first standard circle is made with the geometric center of the abnormal data position as the center and a preset size as the radius;

[0049] Get any steel bar node within the first standard circle and establish a coordinate system with the length and width directions as coordinate directions;

[0050] In the coordinate system, a virtual rectangular network is made with La as the length unit and Lb as the width unit;

[0051] Get any Y steel bar detection nodes;

[0052] Collect the real-time size data Lz of the steel bar detection nodes and the nodes of the virtual rectangular network;

[0053] When the real-time size data Lz> the preset threshold Q5, the steel bar detection node data is marked;

[0054] Count the number of marked nodes M4;

[0055] Use the formula E3=M4 / Y to calculate the second abnormality ratio of the steel bar detection node data;

[0056] When E3>preset threshold Q6, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

[0057] Preferably, the real-time data information further includes real-time vibration frequency data, and the generation process of the warning report further includes:

[0058] Read monitoring area data;

[0059] Collect real-time vibration frequency data of steel bars in preset states within the monitoring area; they are Fe1, Fe2, Fe3, ..., Fen;

[0060] Calculate the dispersion E4 of multiple vibration data. The specific calculation process is as follows:

[0061] ;

[0062] Among them, Feβ is the βth real-time vibration frequency data, is the average vibration frequency data;

[0063] When the dispersion E4 is greater than the preset threshold Q7, it indicates that there is a problem with the tightness of the steel bar bundling, and a third type of warning report is generated.

[0064] Preferably, the data processing module is further configured to analyze the real-time data information to infer whether there is any abnormality in the installation status of the template, and generate a feedback report when an abnormality exists; the real-time data information includes real-time image data and real-time size information, and the generation process of the feedback report is as follows:

[0065] Collect real-time image data during the beam bridge production process after the template is installed;

[0066] Randomly obtain three first reference points on the template, and make virtual lines perpendicular to the template through the three first reference points respectively;

[0067] Obtain a second reference point on each of the three virtual lines, and the distances between the three second reference points and the template are equal, all Lm;

[0068] Create a virtual plane J through the three second reference points;

[0069] Randomly obtain a first detection point on the template;

[0070] Collect the dimension data Lj of the first detection point from the virtual plane J;

[0071] Calculate the deviation degree E5 of the first detection point data. The specific calculation process is as follows:

[0072] ;

[0073] When the deviation degree E5 is greater than the preset threshold Q8, it indicates that the data is abnormal, and a second standard circle is made with the first detection point as the center and the preset size as the radius;

[0074] Acquire N second verification points continuously and equidistantly on the second standard circle, and collect the distances of the second verification points from the virtual plane J, which are Lg1, Lg2, Lg3, ..., LgN respectively;

[0075] Sort the distances of the plurality of second verification points from the virtual plane J in descending order;

[0076] Get the maximum size LgA and the minimum size LgB;

[0077] Calculate the deformation degree E6 of the template. The specific calculation process is:

[0078] ;

[0079] When the deformation degree E6 is greater than the preset threshold value Q9, it indicates that the template is locally deformed due to excessive force, and a first type of feedback report is generated.

[0080] Preferably, the process of generating the feedback report further includes:

[0081] Obtain the first detection point and the geometric center point of the template;

[0082] Acquire a third detection point on the template, wherein the geometric center point of the template is located at the center point of the connecting line between the first detection point and the second detection point;

[0083] A third standard circle is made with the third detection point as the center and the preset size as the radius. The third standard circle is symmetrical with the second standard circle about the geometric center point of the template.

[0084] Calculate the deformation degree E6' of the template at the third standard circle;

[0085] Calculate the difference in deformation of the template symmetrical position E7. The specific calculation process is as follows:

[0086] ;

[0087] When the difference degree E7 is less than the preset threshold value Q10, it indicates that the template is deformed due to compression, and a second type of feedback report is generated.

[0088] Preferably, the system further comprises a control module and an alarm module, wherein the alarm module is used to receive an alarm report and perform an alarm action, and the control module is used to control the opening or closing of the alarm module.

[0089] Compared with the existing technology, the present invention has the following advantages: the system first analyzes the layout of the steel bar bundling, and sets a minimum monitoring area based on the size of the steel bar. The minimum area can ensure that there is a change in the number of steel bars within the existing error, so as to ensure the effectiveness of monitoring and the representativeness of data collection; then, each monitoring area is monitored separately, and the cause of the abnormal data during steel bar bundling is inferred based on the monitoring results, and targeted feedback is provided to assist monitoring personnel to quickly identify hidden dangers in the steel bar bundling process and improve detection efficiency; and further improve the construction quality of beam bridges to implement scientific and effective monitoring guidance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is the overall module diagram of the present invention. DETAILED DESCRIPTION

[0091] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0092] like Figure 1As shown, this embodiment provides a technical solution: a prestressed concrete continuous beam bridge construction monitoring system, including a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module;

[0093] The data acquisition module is used to collect real-time data information of the beam bridge construction process, and the real-time data information includes real-time image data;

[0094] The preprocessing module is used to receive real-time image data and perform denoising and enhancement on the real-time image data;

[0095] The database module is used to store historical data information during the construction of the beam bridge;

[0096] The data processing module is used to receive real-time data information and historical data information, analyze the real-time data information in combination with the historical data information, monitor abnormal data during the beam bridge construction process, and generate a warning report when abnormal data exists;

[0097] The data output module is used to receive and output warning reports.

[0098] The system first analyzes the layout of the steel bar bundling, and sets a minimum monitoring area based on the size of the steel bars. This minimum area can ensure that there are changes in the number of steel bars within the existing error, so as to ensure the effectiveness of monitoring and representativeness of data collection; then, each monitoring area is monitored separately, and the reasons for the abnormal data during steel bar bundling are inferred based on the monitoring results, and targeted feedback is provided to assist monitoring personnel in quickly identifying hidden dangers in the steel bar bundling process and improving detection efficiency; and further improve the construction quality of beam bridges to implement scientific and effective monitoring guidance strategies.

[0099] Among them, the specific processing process of the preprocessing module is:

[0100] Import real-time image data;

[0101] Divide the image evenly into multiple block units;

[0102] Use the multithreaded programming library to create threads;

[0103] It should be noted that the multi-threaded programming library can be C++'s std::thread module, Python's threading module, etc., which is the existing technology and will not be described in detail.

[0104] Distribute the divided block units evenly to each thread;

[0105] In each thread, the mean filtering algorithm and histogram equalization algorithm are applied to the assigned block units for processing;

[0106] After all threads have completed processing, the processed block units are merged into a complete image and output.

[0107] The real-time data information also includes real-time dimension data. The specific process of generating the warning report is as follows:

[0108] Collect real-time image data of steel bar bundling before pouring beam bridges;

[0109] Randomly establish multiple first virtual straight lines in the target direction;

[0110] Collect the dimensions of a preset number M1 of steel bars on each first virtual straight line and obtain the average dimension Le;

[0111] Use the formula Lr=Le / M1 to calculate the average gap between two adjacent steel bars;

[0112] Collect the steel bar size Lv arranged perpendicular to the target direction;

[0113] Use the formula Lu=Lr / Lv to calculate the minimum monitoring size in the target direction;

[0114] When the target direction is the length direction of the beam bridge, the first minimum monitoring size Lu1 is obtained;

[0115] When the target direction is the width direction of the beam bridge, the second minimum monitoring size Lu2 is obtained;

[0116] Establish multiple monitoring areas with Lu1 as the length dimension and Lu2 as the width dimension. The length direction of the monitoring area is parallel to the length direction of the beam bridge, and the width direction of the monitoring area is parallel to the width direction of the beam bridge.

[0117] Count the number of steel bars M2 in the length direction and the number of steel bars M3 in the width direction in each monitoring area respectively;

[0118] Calculate the steel density P in each monitoring area. The specific calculation process is:

[0119] P = M2·M3;

[0120] Get the average density of steel bars in multiple monitoring areas ;

[0121] When the average density <Preset threshold Q1 or average density >When the preset threshold Q2 is exceeded, it indicates that there is an abnormality in the arrangement of the steel bars, and a first type of warning report is generated.

[0122] When implementing this embodiment, the layout of the steel bars during bundling is first analyzed, and a minimum monitoring area is set in combination with the size of the steel bars. This minimum area can ensure that there are changes in the number of steel bars within the existing error, so as to ensure the effectiveness of monitoring and representativeness during data collection; the density of the steel bars in the monitoring area is calculated; when the density of the steel bars is too large or too small, it indicates that there is an abnormality in the arrangement of the steel bars, which is specifically manifested as uniform arrangement of the steel bars but abnormal spacing. Targeted warnings are issued to assist monitors in carefully searching for abnormal problems, thereby improving monitoring efficiency and further improving the quality of prestressed beam bridges.

[0123] There may be local anomalies in the arrangement of steel bars. This may manifest as normal overall steel bar density, but local steel bars may be bundled too tightly or too sparsely. This has a significant impact on the bearing capacity of prestressed beam bridges. To improve the system monitoring capabilities, the following further technical solutions are proposed:

[0124] Furthermore, when the average density preset threshold Q1 ≤ average density When the value is less than or equal to the preset threshold Q2, the warning report generation process also includes:

[0125] Read monitoring area data;

[0126] Import historical data and obtain steel bar dimension data in length and width directions during the production process of multiple beam bridges;

[0127] Calculate the average length spacing dimension La in the length direction when multiple steel bars are bundled and the average width spacing dimension Lb in the width direction when multiple steel bars are bundled;

[0128] Randomly acquire multiple steel bars in the target direction within the monitoring area;

[0129] Randomly collect X steel bar spacings in the target direction, namely Lc1, Lc2, Lc3, ..., LcX;

[0130] Calculate the difference E1 of the distance in the target direction. The specific calculation process is:

[0131] ;

[0132] When the difference E1 is greater than the preset threshold Q3, it is considered abnormal data and marked;

[0133] When the target direction is the length direction, Lx is La, and the first mark data is obtained;

[0134] When the target direction is the width direction, Lx is Lb, and the second mark data is obtained;

[0135] Counting the total number D1 of the first labeled data and the second labeled data;

[0136] Use the formula E2=D1 / X to calculate the first anomaly ratio;

[0137] When the first abnormality proportion E2 is greater than the preset threshold Q4, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

[0138] During the specific implementation of this embodiment, detection positions are randomly selected at different locations within the monitoring area. At different detection positions, the spacing in the length direction and the spacing in the width direction of the steel bars are measured respectively, and then the number of measurement data that do not meet the construction requirements, that is, the number of abnormal data, is counted. When the number of abnormalities is large, it indicates that the local arrangement of the steel bars is uneven, and targeted warnings are issued to improve the accuracy of the system's judgment.

[0139] When the above real-time example is executed, the detection locations are randomly selected for detection. Since the amount of steel bars arranged during the construction of beam bridge components is large, when the abnormal locations of the local arrangement of steel bars are relatively concentrated, randomly selecting data may cause the proportion of the first abnormality to be too large or too small, affecting the judgment accuracy. To solve this problem, the following further solution is proposed:

[0140] Furthermore, when the first abnormality proportion E2 ≤ the preset threshold Q4, the process of generating the warning report further includes:

[0141] Read abnormal data and obtain the abnormal data location;

[0142] A first standard circle is made with the geometric center of the abnormal data position as the center and a preset size as the radius;

[0143] Get any steel bar node within the first standard circle and establish a coordinate system with the length and width directions as coordinate directions;

[0144] In the coordinate system, a virtual rectangular network is made with La as the length unit and Lb as the width unit;

[0145] Get any Y steel bar detection nodes;

[0146] Collect the real-time size data Lz of the steel bar detection nodes and the nodes of the virtual rectangular network;

[0147] When the real-time size data Lz> the preset threshold Q5, the steel bar detection node data is marked;

[0148] Count the number of marked nodes M4;

[0149] Use the formula E3=M4 / Y to calculate the second abnormality ratio of the steel bar detection node data;

[0150] When E3>preset threshold Q6, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

[0151] This embodiment establishes a virtual grid based on the standard arrangement size of steel bars and detects the steel bar nodes within the detection area. When the nodes of the virtual grid deviate significantly from the steel bar nodes, it indicates that there must be some abnormal arrangement of steel bars in the detection area. By further judging the situation of abnormal local spacing of steel bars in this way, the judgment accuracy of the system can be improved, and the detection quality of the steel bar bundling process can be improved.

[0152] Furthermore, the real-time data information also includes real-time vibration frequency data, and the generation process of the warning report also includes:

[0153] Read monitoring area data;

[0154] Collect real-time vibration frequency data of steel bars in preset states within the monitoring area; they are Fe1, Fe2, Fe3, ..., Fen;

[0155] It should be noted that the preset state is to strike the steel bars at a preset position within the monitoring area with a preset striking force and a preset frequency.

[0156] Calculate the dispersion E4 of multiple vibration data. The specific calculation process is as follows:

[0157] ;

[0158] Among them, Feβ is the βth real-time vibration frequency data, is the average vibration frequency data;

[0159] When the dispersion E4 is greater than the preset threshold Q7, it indicates that there is a problem with the tightness of the steel bar bundling, and a third type of warning report is generated.

[0160] This embodiment further analyzes the cause of abnormal local arrangement of steel bars by detecting the vibration frequency of the steel mesh under knocking; after the steel bars are bundled, the vibration frequency of different positions of the steel mesh is detected; when the difference between the vibration frequencies at different positions is large, it indicates that the steel bars are vibrating abnormally; the cause of the abnormal vibration of the steel bars is that the steel bars are not tightly bundled and the force is uneven; during the vibration of the steel bars, the bundling nodes become loose, causing relative movement of the two steel bars at the two bundling locations, which in turn causes abnormal vibration of the steel bars; by further judging the abnormal state of the steel bars and issuing targeted reports in this way, the detection efficiency of the monitoring personnel can be further improved.

[0161] The data processing module is also used to analyze real-time data information to infer whether there are any abnormalities in the template installation status. If there are any abnormalities, a feedback report is generated. The real-time data information includes real-time image data and real-time size information. The feedback report generation process is as follows:

[0162] Collect real-time image data during the beam bridge production process after the template is installed;

[0163] Randomly obtain three first reference points on the template, and make virtual lines perpendicular to the template through the three first reference points respectively;

[0164] Obtain a second reference point on each of the three virtual lines, and the distances between the three second reference points and the template are equal, all Lm;

[0165] Create a virtual plane J through the three second reference points;

[0166] Randomly obtain a first detection point on the template;

[0167] Collect the dimension data Lj of the first detection point from the virtual plane J;

[0168] Calculate the deviation degree E5 of the first detection point data. The specific calculation process is as follows:

[0169] ;

[0170] When the deviation degree E5 is greater than the preset threshold Q8, it indicates that the data is abnormal, and a second standard circle is made with the first detection point as the center and the preset size as the radius;

[0171] Acquire N second verification points continuously and equidistantly on the second standard circle, and collect the distances of the second verification points from the virtual plane J, which are Lg1, Lg2, Lg3, ..., LgN respectively;

[0172] Sort the distances of the plurality of second verification points from the virtual plane J from large to small;

[0173] Get the maximum size LgA and the minimum size LgB;

[0174] Calculate the deformation degree E6 of the template. The specific calculation process is:

[0175] ;

[0176] When the deformation degree E6 is greater than the preset threshold value Q9, it indicates that the template is locally deformed due to excessive force, and a first type of feedback report is generated.

[0177] During the specific implementation of this embodiment, a virtual plane is set at a position parallel to the template, and dimensional data between different positions of the template and the virtual plane are collected. When there is an abnormality in the dimensional data, it indicates that the template is deformed, and the abnormal position is locked; the abnormal position is further detected to determine whether the cause of the abnormality is a defect in the template itself or a bending deformation; a circular area for detection is established at the abnormal position, and points are taken on the circular area to obtain dimensional data from different points to the virtual plane; when the maximum value of the dimension in the circular area deviates greatly from the minimum value of the dimension, it indicates that the cause of the abnormality is template deformation, and template deformation will cause abnormal grooves or protrusions in the concrete on the surface of the beam bridge, affecting the surface quality of the beam bridge; a feedback report is made to remind staff to make timely adjustments to avoid affecting the beam bridge and improve the quality of the beam bridge.

[0178] Furthermore, the feedback report generation process also includes:

[0179] Obtain the first detection point and the geometric center point of the template;

[0180] Obtaining a third detection point on the template, where the geometric center of the template is located at the center of a line connecting the first detection point and the second detection point;

[0181] A third standard circle is made with the third detection point as the center and the preset size as the radius. The third standard circle is symmetrical with the second standard circle about the geometric center point of the template.

[0182] Calculate the deformation degree E6' of the template at the third standard circle;

[0183] Calculate the difference in deformation of the template symmetrical position E7. The specific calculation process is as follows:

[0184] ;

[0185] When the difference degree E7 is less than the preset threshold value Q10, it indicates that the template is deformed due to compression, and a second type of feedback report is generated.

[0186] This solution detects the symmetrical positions of the abnormal positions of the template; when the data at the symmetrical positions of the template are close to the data at the abnormal positions, it means that the template itself is bent due to squeezing on both sides during installation, and the size of the template needs to be adjusted; targeted feedback is issued to improve the monitoring efficiency and execution efficiency of the monitoring personnel, thereby improving the practicality of the system.

[0187] The system also includes a control module and an alarm module. The alarm module is used to receive an alarm report and take an alarm action. The control module is used to control the opening or closing of the alarm module.

[0188] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0189] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0190] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A prestressed concrete continuous beam bridge construction monitoring system, characterized in that: Including data acquisition module, pre-processing module, database module, data processing module and data output module; The data acquisition module is used to collect real-time data information of the beam bridge construction process, and the real-time data information includes real-time image data; The preprocessing module is used to receive real-time image data and perform denoising and enhancement on the real-time image data; The database module is used to store historical data information during the construction of the beam bridge; The data processing module is used to receive real-time data information and historical data information, analyze the real-time data information in combination with the historical data information, monitor abnormal data during the beam bridge construction process, and generate a warning report when abnormal data exists; The data output module is used to receive and output warning reports; The real-time data information also includes real-time size data. The generation process of the warning report is specifically as follows: Collect real-time image data of steel bar bundling before pouring beam bridges; Randomly establish multiple first virtual straight lines in the target direction; Collect the dimensions of a preset number M1 of steel bars on each first virtual straight line and obtain the average dimension Le; Use the formula Lr=Le / M1 to calculate the average gap between two adjacent steel bars; Collect the steel bar size Lv arranged perpendicular to the target direction; Use the formula Lu=Lr / Lv to calculate the minimum monitoring size in the target direction; When the target direction is the length direction of the beam bridge, the first minimum monitoring size Lu1 is obtained; When the target direction is the width direction of the beam bridge, the second minimum monitoring size Lu2 is obtained; Establish multiple monitoring areas with Lu1 as the length dimension and Lu2 as the width dimension, wherein the length direction of the monitoring area is parallel to the length direction of the beam bridge, and the width direction of the monitoring area is parallel to the width direction of the beam bridge; Count the number of steel bars M2 in the length direction and the number of steel bars M3 in the width direction in each monitoring area respectively; Calculate the steel density P in each monitoring area. The specific calculation process is: P = M2·M3; Get the average density of steel bars in the multiple monitoring areas ; When the average density <Preset threshold Q1 or average density >When the preset threshold Q2 is exceeded, it indicates that there is an abnormality in the arrangement of the steel bars, and a first type of warning report is generated.

2. A prestressed concrete continuous beam bridge construction monitoring system according to claim 1, characterized in that: The specific processing process of the pre-processing module is as follows: Import real-time image data; Divide the image evenly into multiple block units; Use the multithreaded programming library to create threads; Distribute the divided block units evenly to each thread; In each thread, the mean filtering algorithm and histogram equalization algorithm are applied to the assigned block units for processing; After all threads have completed processing, the processed block units are merged into a complete image and output.

3. The prestressed concrete continuous beam bridge construction monitoring system according to claim 1, characterized in that: When the average density preset threshold Q1 ≤ average density ≤preset threshold Q2, the process of generating the warning report further includes: Read monitoring area data; Import historical data and obtain steel bar dimension data in length and width directions during the production process of multiple beam bridges; Calculate the average length spacing dimension La in the length direction when multiple steel bars are bundled and the average width spacing dimension Lb in the width direction when multiple steel bars are bundled; Randomly acquire multiple steel bars in the target direction within the monitoring area; Randomly collect X steel bar spacings in the target direction, namely Lc1, Lc2, Lc3, ..., LcX; Calculate the difference E1 of the distance in the target direction. The specific calculation process is: ； When the difference E1 is greater than the preset threshold Q3, it is considered abnormal data and marked; When the target direction is the length direction, Lx is La, and the first mark data is obtained; When the target direction is the width direction, Lx is Lb, and the second mark data is obtained; Counting the total number D1 of the first labeled data and the second labeled data; Use the formula E2=D1 / X to calculate the first anomaly ratio; When the first abnormality proportion E2 is greater than the preset threshold Q4, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

4. A prestressed concrete continuous beam bridge construction monitoring system according to claim 3, characterized in that: When the first abnormality proportion E2 is less than or equal to the preset threshold Q4, the process of generating the warning report further includes: Read abnormal data and obtain the abnormal data location; A first standard circle is made with the geometric center of the abnormal data position as the center and a preset size as the radius; Get any steel bar node within the first standard circle and establish a coordinate system with the length and width directions as coordinate directions; In the coordinate system, a virtual rectangular network is made with La as the length unit and Lb as the width unit; Get any Y steel bar detection nodes; Collect the real-time size data Lz of the steel bar detection nodes and the nodes of the virtual rectangular network; When the real-time size data Lz> the preset threshold Q5, the steel bar detection node data is marked; Count the number of marked nodes M4; Use the formula E3=M4 / Y to calculate the second abnormality ratio of the steel bar detection node data; When E3>preset threshold Q6, it indicates that the local spacing of the steel bar bundles is abnormal, and a second type of warning report is generated.

5. A prestressed concrete continuous beam bridge construction monitoring system according to claim 3 or 4, characterized in that: The real-time data information also includes real-time vibration frequency data, and the generation process of the warning report also includes: Read monitoring area data; Collect real-time vibration frequency data of steel bars in preset states within the monitoring area; they are Fe1, Fe2, Fe3, ..., Fen; Calculate the dispersion E4 of multiple vibration data. The specific calculation process is as follows: ； Among them, Feβ is the βth real-time vibration frequency data, is the average vibration frequency data; When the dispersion E4 is greater than the preset threshold Q7, it indicates that there is a problem with the tightness of the steel bar bundling, and a third type of warning report is generated.

6. The prestressed concrete continuous beam bridge construction monitoring system according to claim 1, characterized in that: The data processing module is further configured to analyze the real-time data information to infer whether there is any abnormality in the installation status of the template, and generate a feedback report if an abnormality exists. The real-time data information includes real-time image data and real-time size information. The generation process of the feedback report is as follows: Collect real-time image data during the beam bridge production process after the template is installed; Randomly obtain three first reference points on the template, and make virtual lines perpendicular to the template through the three first reference points respectively; Obtain a second reference point on each of the three virtual lines, and the distances between the three second reference points and the template are equal, all Lm; Create a virtual plane J through the three second reference points; Randomly obtain a first detection point on the template; Collect the dimension data Lj of the first detection point from the virtual plane J; Calculate the deviation degree E5 of the first detection point data. The specific calculation process is as follows: ； When the deviation degree E5 is greater than the preset threshold Q8, it indicates that the data is abnormal, and a second standard circle is made with the first detection point as the center and the preset size as the radius; Acquire N second verification points continuously and equidistantly on the second standard circle, and collect the distances of the second verification points from the virtual plane J, which are Lg1, Lg2, Lg3, ..., LgN respectively; Sort the distances of the plurality of second verification points from the virtual plane J in descending order; Get the maximum size LgA and the minimum size LgB; Calculate the deformation degree E6 of the template. The specific calculation process is: ； When the deformation degree E6 is greater than the preset threshold value Q9, it indicates that the template is locally deformed due to excessive force, and a first type of feedback report is generated.

7. The prestressed concrete continuous beam bridge construction monitoring system according to claim 6, characterized in that: The process of generating the feedback report further includes: Obtain the first detection point and the geometric center point of the template; Acquire a third detection point on the template, wherein the geometric center point of the template is located at the center point of the connecting line between the first detection point and the second detection point; A third standard circle is made with the third detection point as the center and the preset size as the radius. The third standard circle is symmetrical with the second standard circle about the geometric center point of the template. Calculate the deformation degree E6' of the template at the third standard circle; Calculate the difference in deformation of the symmetrical position of the template E7. The specific calculation process is as follows: ； When the difference degree E7 is less than the preset threshold value Q10, it indicates that the template is deformed due to compression, and a second type of feedback report is generated.

8. The prestressed concrete continuous beam bridge construction monitoring system according to claim 1, characterized in that: It also includes a control module and an alarm module. The alarm module is used to receive an alarm report and take an alarm action, and the control module is used to control the opening or closing of the alarm module.

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