Bridge data collection management method and device based on dial indicator, equipment and medium

Abstract

The application relates to the field of data analysis, and in particular to a bridge data acquisition management method and device based on a dial gauge, equipment and a medium. The method comprises the following steps: acquiring dial gauge data, obtaining a plurality of data acquisition points and acquisition data corresponding to each data acquisition point according to the dial gauge data, then judging whether the acquisition data exceeds preset data, if yes, performing first-level labeling on the data acquisition point corresponding to the acquisition data to obtain labeled acquisition points, then determining the acquisition region between the labeled acquisition points and adjacent data acquisition points in the plurality of data acquisition points, then performing bridge state detection on each acquisition region respectively to obtain bridge state data, then judging whether the bridge state data meets preset state standards, if not, performing second-level labeling on the adjacent data acquisition points of the acquisition region corresponding to the bridge state data, and controlling the display of the plurality of data acquisition points. The application has the effect of improving the detection efficiency of the bridge.

Description

Technical Field

[0001] This application relates to the field of data analysis, and in particular to methods, apparatus, equipment and media for bridge data acquisition and management based on percentage tables. Background Technology

[0002] In the construction of high-speed railways in China, bridges account for an increasingly high proportion, with precast beams making up about 85% and cast-in-place beams about 5% to 10%. The quality of bridges is directly related to the safety of railway operations and is closely related to national property and the safety of people's lives.

[0003] Currently, static load testing is the only means of evaluating bridge performance. To ensure that bridges meet the 100-year lifespan requirement, static load testing must guarantee accurate loading, correct judgment, reliable test data, and complete recording. If excessive errors or distortions occur in the test data, or if there are errors or omissions in the recording, the test results will not be accurately reflected, leading to misjudgments or omissions. This can cause incalculable economic losses to the country and enterprises, and also have adverse social impacts. During static load testing, surveyors typically use dial indicators to measure the displacement of the entire bridge at multiple points. The measured data is then read and recorded by workers. Surveyors then use the dial indicator data to conduct an overall condition check of the bridge to determine if any anomalies exist. If anomalies are found, the area of ​​the bridge surrounding the anomaly point needs to be checked to determine the area of ​​the anomaly.

[0004] Regarding the aforementioned technologies, the inventors believe that during bridge static load tests, since the points measured by the dial gauge at multiple displacement points are marked and confirmed by the surveyors, when abnormal points appear, the surveyors need to spend a long time searching for the abnormal points and abnormal areas of the bridge, resulting in low efficiency in bridge inspection. Summary of the Invention

[0005] To improve the efficiency of bridge inspection, this application provides a bridge data acquisition and management method, device, equipment, and medium based on a dial gauge.

[0006] Firstly, this application provides a bridge data acquisition and management method based on a percentage table, employing the following technical solution:

[0007] A bridge data acquisition and management method based on a percentage table includes:

[0008] Acquire percentage table data and mark the bridge points based on the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point;

[0009] Determine whether the collected data exceeds the preset data. If it does, perform first-level labeling on the data collection points corresponding to the collected data to obtain labeled collection points.

[0010] Based on the multiple data collection points and the labeled collection points, the bridge is divided into collection areas, and the collection areas between the labeled collection points and adjacent data collection points among the multiple data collection points are determined.

[0011] Bridge status detection is performed on each of the acquisition areas to obtain bridge status data;

[0012] Determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, then perform secondary labeling on the adjacent data collection points in the collection area corresponding to the bridge status data, and control the display of the multiple data collection points after secondary labeling.

[0013] By adopting the above technical solution, during static load tests on bridges, dial gauge data is acquired to mark bridge points, resulting in multiple data collection points and corresponding data for each point. It is then determined whether each data point exceeds a preset threshold. If so, the corresponding data is labeled at the first level, creating labeled collection points. Based on these multiple data points and labeled collection points, the bridge's collection area is divided, identifying the collection areas between the labeled collection points and adjacent data points. Bridge status is then detected in each collection area, yielding bridge status data. It is determined whether the bridge status data meets preset status standards. If not, adjacent data points in the corresponding collection area are labeled at the second level, and the multiple labeled data points are displayed. Surveyors can then use these labeled data points to identify abnormal collection points and abnormal bridge areas, thus improving bridge inspection efficiency.

[0014] In another possible implementation, determining whether the bridge state data meets a preset state criterion includes:

[0015] If the bridge status data meets the preset status criteria, the bridge type is determined based on the percentage table data, and the bridge type and the bridge status data are input into the prediction model for prediction to obtain bridge prediction data.

[0016] Through the above technical solution, when the bridge status data meets the preset status standard, in order to determine the future risk level of the bridge, the bridge type and bridge status data are input into the prediction model for prediction, and the bridge prediction data is obtained. In this way, the surveyors can monitor the future risk level of the bridge based on the bridge prediction data, thereby reducing the occurrence of bridge hazards.

[0017] In another possible implementation, the bridge type and bridge status data are input into a prediction model for prediction to obtain bridge prediction data, which further includes:

[0018] Obtain bridge hazard information within a preset time period in the past, wherein the bridge hazard information is bridge hazard information corresponding to different bridge status data for different bridge types;

[0019] The bridge hazard information is analyzed to determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination. Based on the time series length and the number of type combinations, the bridge hazard information is processed into unsupervised time series data to obtain the first bridge matrix data.

[0020] A prediction model is created, and the first bridge matrix data is input into the prediction model for training to obtain a trained prediction model.

[0021] The above technical solution trains the prediction model by acquiring bridge hazard information for different bridge types and their corresponding state data within a preset time period. Then, the bridge hazard information is analyzed to determine the number of different bridge type combinations and the time series length for each combination. Based on the time series length and the number of type combinations, the bridge hazard information is processed into unsupervised time series data to obtain the first bridge matrix data. A prediction model is then created, and the first bridge matrix data is used as training data to train the model, resulting in a trained prediction model that can then be used to predict the future risk level of bridges.

[0022] In another possible implementation, the bridge hazard information is analyzed to determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination, including:

[0023] At least one set of bridge treatment data is determined based on the bridge hazard information.

[0024] Tag acquisition is performed on the at least one set of bridge processing data to obtain the bridge data and processing time data in each set of bridge processing data.

[0025] Based on the processing time data, it is determined whether the bridge processing data has been processed. If it has not been processed, the bridge processing data is disassembled. If it has been processed, the bridge data is bound to the processing time data to obtain bridge bound data.

[0026] The bridge binding data is filtered by type combination to obtain the number of type combinations of different bridge types in the bridge hazard information and the time series length corresponding to each type combination.

[0027] Through the above technical solution, when analyzing bridge hazard information, at least one set of bridge processing data is obtained from the bridge hazard information. Then, each set of bridge processing data is tagged to obtain the bridge data and processing time data in each set. It is then determined whether the processing time data has been completed, that is, whether the current processing status of the bridge processing data is still in progress. If it has not been completed, the bridge processing data is disassembled and not included in the bridge hazard information. If it has been completed, the bridge data is bound to the processing time data to obtain the bridge bound data. Subsequently, the bridge bound data is filtered by type combination to obtain the number of type combinations and the time series length. By disassembling the bridge processing data that has not been completed, the accuracy of bridge hazard information is improved.

[0028] In another possible implementation, the method further includes:

[0029] The first bridge matrix data is input into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions. The obtained number of bridge feature dimensions is then combined with the first bridge matrix data to generate the second bridge matrix data.

[0030] The data contained in the second bridge matrix data is processed to obtain bridge hazard data. The obtained bridge hazard data is then input into a preset algorithm model for data extrapolation to generate bridge prediction data for each type combination in the number of type combinations within a preset time period in the future.

[0031] Through the above technical solution, when predicting bridges of different types and combinations, the first bridge matrix data is input into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions. The obtained number of bridge feature dimensions is then combined with the first bridge matrix data to generate the second bridge matrix data. The data contained in the second bridge matrix data is then processed to obtain bridge hazard data. The obtained bridge hazard data is then input into a preset algorithm model for data extrapolation to generate bridge prediction data for each type combination within a preset time period in the future, thereby improving the accuracy of bridge prediction.

[0032] In another possible implementation, the first bridge matrix data is input into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions, including:

[0033] Based on the first bridge matrix data, determine the event name, event time, and event type of each hazard event in the bridge hazard information;

[0034] The event name, event time, and event type are respectively input into the bridge prediction model for vector extraction to obtain a text feature vector corresponding to the event name, a time feature vector corresponding to the event time, and a type feature vector corresponding to the event type.

[0035] The number of bridge feature dimensions is obtained by counting the text feature vector, the time feature vector, and the type feature vector.

[0036] Through the above technical solution, when acquiring the number of bridge feature dimensions, the event name, event time, and event type of each hazard event in the bridge hazard information are determined based on the first bridge matrix data. Then, the event name, event time, and event type are input into the bridge prediction model for vector extraction to obtain the text feature vector corresponding to the event name, the time feature vector corresponding to the event time, and the type feature vector corresponding to the event type. Then, by counting the number of text feature vectors, time feature vectors, and type feature vectors, the number of bridge feature dimensions is obtained. By counting the feature dimensions of each hazard event in the bridge hazard information, the accuracy of the number of bridge feature dimensions is improved.

[0037] In another possible implementation, the data processing of the data contained in the second bridge matrix data to obtain bridge hazard data includes:

[0038] Calculate the normal distribution mean and normal distribution variance of the data contained in the second bridge matrix data, and determine the 3δ range of the second bridge matrix data based on the normal distribution mean and normal distribution variance;

[0039] Determine whether the data is outside the 3δ range. If the data is outside the 3δ range, determine the first matrix sequence of the second bridge matrix data in which the data is located. Calculate the sequence average value based on the first matrix sequence. Replace the data with the sequence average value to obtain the replaced second matrix sequence. Then, process the missing values ​​in the second matrix sequence.

[0040] The second matrix sequence in the second bridge matrix data is normalized to obtain bridge hazard data.

[0041] Using the above technical solution, when acquiring bridge hazard data, the 3δ range of the second bridge matrix data is determined by calculating the normal distribution mean and normal distribution variance of the data contained in the second bridge matrix data. It is then determined whether the current data is outside the 3δ range. If so, the data is removed, and the sequence mean is added to the position of the data in the first matrix sequence to obtain the second matrix sequence. Then, missing values ​​are processed in the second matrix sequence to ensure the integrity of the matrix sequence. Finally, the second matrix sequence in the second bridge matrix data is normalized to obtain the bridge hazard data for subsequent data processing.

[0042] Secondly, this application provides a bridge data acquisition and management device based on a dial indicator, which adopts the following technical solution:

[0043] A bridge data acquisition and management device based on a percentage scale, comprising:

[0044] The data acquisition module is used to acquire percentage table data and mark the bridge points according to the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point.

[0045] The first judgment module is used to determine whether the collected data exceeds the preset data. If it does, the data collection points corresponding to the collected data are marked at the first level to obtain the marked collection points.

[0046] The region determination module is used to divide the bridge into collection areas based on the multiple data collection points and the labeled collection points, and to determine the collection area between the labeled collection points and adjacent data collection points among the multiple data collection points.

[0047] The status detection module is used to perform bridge status detection on each of the acquisition areas to obtain bridge status data.

[0048] The second judgment module is used to determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, the adjacent data collection points in the collection area corresponding to the bridge status data are marked with secondary labels, and the multiple data collection points after secondary labeling are displayed.

[0049] By adopting the above technical solution, during static load tests on bridges, dial gauge data is acquired to mark bridge points, resulting in multiple data collection points and corresponding data for each point. It is then determined whether each data point exceeds a preset threshold. If so, the corresponding data is labeled at the first level, creating labeled collection points. Based on these multiple data points and labeled collection points, the bridge's collection area is divided, identifying the collection areas between the labeled collection points and adjacent data points. Bridge status is then detected in each collection area, yielding bridge status data. It is determined whether the bridge status data meets preset status standards. If not, adjacent data points in the corresponding collection area are labeled at the second level, and the multiple labeled data points are displayed. Surveyors can then use these labeled data points to identify abnormal collection points and abnormal bridge areas, thus improving bridge inspection efficiency.

[0050] In one possible implementation, when determining whether the bridge status data meets a preset status standard, the second judgment module is specifically used for:

[0051] If the bridge status data meets the preset status criteria, the bridge type is determined based on the percentage table data, and the bridge type and the bridge status data are input into the prediction model for prediction to obtain bridge prediction data.

[0052] In another possible implementation, the device further includes: an information acquisition module, an analysis and processing module, and a model creation module, wherein,

[0053] The information acquisition module is used to acquire bridge hazard information within a preset time period in the past. The bridge hazard information is bridge hazard information corresponding to different bridge status data for different bridge types.

[0054] The analysis and processing module is used to analyze the bridge hazard information, determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination, and perform unsupervised time series data processing on the bridge hazard information based on the time series length and the number of type combinations to obtain the first bridge matrix data.

[0055] The model creation module is used to create a prediction model and input the first bridge matrix data into the prediction model for training to obtain a trained prediction model.

[0056] In another possible implementation, when the analysis and processing module analyzes the bridge hazard information and determines the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination, it is specifically used for:

[0057] At least one set of bridge treatment data is determined based on the bridge hazard information.

[0058] Tag acquisition is performed on the at least one set of bridge processing data to obtain the bridge data and processing time data in each set of bridge processing data.

[0059] Based on the processing time data, it is determined whether the bridge processing data has been processed. If it has not been processed, the bridge processing data is disassembled. If it has been processed, the bridge data is bound to the processing time data to obtain bridge bound data.

[0060] The bridge binding data is filtered by type combination to obtain the number of type combinations of different bridge types in the bridge hazard information and the time series length corresponding to each type combination.

[0061] In another possible implementation, the apparatus further includes a feature extraction module and a data prediction module, wherein,

[0062] The feature extraction module is used to input the first bridge matrix data into the prediction model to extract vector features, obtain the number of bridge feature dimensions, and combine the obtained number of bridge feature dimensions with the first bridge matrix data to generate the second bridge matrix data.

[0063] The data prediction module is used to process the data contained in the second bridge matrix data to obtain bridge hazard data, and input the obtained bridge hazard data into a preset algorithm model for data extrapolation to generate bridge prediction data for each type combination in the number of type combinations in the future preset time period.

[0064] In another possible implementation, when the feature extraction module inputs the first bridge matrix data into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions, it is specifically used for:

[0065] Based on the first bridge matrix data, determine the event name, event time, and event type of each hazard event in the bridge hazard information;

[0066] The event name, event time, and event type are respectively input into the bridge prediction model for vector extraction to obtain a text feature vector corresponding to the event name, a time feature vector corresponding to the event time, and a type feature vector corresponding to the event type.

[0067] The number of bridge feature dimensions is obtained by counting the text feature vector, the time feature vector, and the type feature vector.

[0068] In another possible implementation, when the data prediction module processes the data contained in the second bridge matrix data to obtain bridge hazard data, it is specifically used for:

[0069] Calculate the normal distribution mean and normal distribution variance of the data contained in the second bridge matrix data, and determine the 3δ range of the second bridge matrix data based on the normal distribution mean and normal distribution variance;

[0070] Determine whether the data is outside the 3δ range. If the data is outside the 3δ range, determine the first matrix sequence of the second bridge matrix data in which the data is located. Calculate the sequence average value based on the first matrix sequence. Replace the data with the sequence average value to obtain the replaced second matrix sequence. Then, process the missing values ​​in the second matrix sequence.

[0071] The second matrix sequence in the second bridge matrix data is normalized to obtain bridge hazard data.

[0072] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0073] An electronic device comprising:

[0074] At least one processor;

[0075] Memory;

[0076] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described percentage-based bridge data acquisition and management method.

[0077] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0078] A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described bridge data acquisition and management method based on a percentage table.

[0079] In summary, this application includes the following beneficial technical effects:

[0080] 1. During static load testing of a bridge, dial gauge data is acquired to mark bridge points, resulting in multiple data collection points and corresponding data for each point. Each data point is then checked to see if it exceeds a preset threshold. If it does, the corresponding data is labeled at the primary level, creating labeled collection points. Based on these data points and labels, the bridge is divided into collection areas, identifying the areas between the labeled collection points and adjacent data points. Bridge status is then checked in each collection area to obtain bridge status data. If the data does not meet preset standards, adjacent data points in the corresponding collection area are labeled at the secondary level, and the multiple labeled data points are displayed. Surveyors can then use these labeled data points to identify abnormal collection points and abnormal bridge areas, thus improving bridge inspection efficiency.

[0081] 2. When analyzing bridge hazard information, at least one set of bridge processing data is obtained from the bridge hazard information. Then, each set of bridge processing data is tagged to obtain the bridge data and processing time data in each set. It is determined whether the processing time data is completed, that is, whether the current processing status of the bridge processing data is still in progress. If it is not completed, the bridge processing data is disassembled and not included in the bridge hazard information. If it is completed, the bridge data is bound to the corresponding processing time data to obtain the bridge bound data. Subsequently, the bridge bound data is filtered by type combination to obtain the number of type combinations and the time series length. By disassembling the bridge processing data that is not completed, the accuracy of bridge hazard information is improved. Attached Figure Description

[0082] Figure 1 This is a flowchart illustrating a bridge data acquisition and management method based on a percentage table according to an embodiment of this application.

[0083] Figure 2 This is a block diagram illustrating a bridge data acquisition and management method based on a percentage table according to an embodiment of this application;

[0084] Figure 3 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0085] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0086] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0087] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0088] Furthermore, the term "and / or" in this article merely describes the relationship between related objects, indicating that three relationships can exist. For example, "bridge data acquisition and management method, device, equipment and medium based on percentage table and / or B" can represent: the existence of "bridge data acquisition and management method, device, equipment and medium based on percentage table" alone; the existence of "bridge data acquisition and management method, device, equipment and medium based on percentage table" and B simultaneously; and the existence of B alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the related objects before and after it are in an "or" relationship.

[0089] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0090] This application provides a bridge data acquisition and management method based on a percentage table, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Figure 1 As shown, the method includes:

[0091] Step S10: Obtain percentage table data and mark the bridge points according to the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point.

[0092] In this embodiment of the application, the method of obtaining percentage table data includes: scanning the QR code on the percentage table with a designated scanning device and transmitting the scanned percentage table data to an electronic device; the percentage table transmitting the percentage table data to the electronic device via wireless / Bluetooth.

[0093] Specifically, the percentage table data includes bridge data collection and location data. The bridge is marked with points based on the location data in the percentage table data, resulting in multiple data collection points and the corresponding data collection data.

[0094] Step S11: Determine whether the collected data exceeds the preset data. If it does, perform first-level annotation on the data collection points corresponding to the collected data to obtain the annotated collection points.

[0095] Specifically, the collected data includes deflection value data, with the preset data being the ratio of L to 600, where L is the total length of the bridge. When the deflection value data is greater than the preset data, it indicates that there is an anomaly at the data collection point corresponding to the current collected data, and therefore the data collection point is marked at the first level.

[0096] Specifically, the location of the data collection points is arranged according to the location data in the data collection points to obtain a bridge data collection map. In this embodiment of the application, the data collection points are marked with a first-level label, that is, the location data corresponding to the data collection points is marked with a color change. For example, the initial location data is black, and after the labeling, the color changes from black to red.

[0097] Step S12: Divide the bridge into collection areas based on multiple data collection points and labeled collection points, and determine the collection area between the labeled collection points and adjacent data collection points among the multiple data collection points.

[0098] Specifically, based on the distribution of data collection points and labeled collection points, the data collection points adjacent to the labeled collection points are determined. Then, based on the area formed between the adjacent data collection points and the labeled collection points, the collection area is determined. For example, if the labeled collection points are A and B, and the data collection points are A, B, C, and D, then the data collection areas adjacent to A include C and B, and the data collection areas adjacent to B include D and A.

[0099] Step S13: Perform bridge status detection on each collection area to obtain bridge status data.

[0100] In this embodiment of the application, image information of the acquisition area is obtained by acquiring images of the acquisition area, and then the bridge status is detected based on the image information, the labeled acquisition points, and the data corresponding to the data acquisition points to obtain bridge status data.

[0101] Step S14: Determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, then perform secondary labeling on the adjacent data collection points in the collection area corresponding to the bridge status data, and control the display of multiple data collection points after secondary labeling.

[0102] In the embodiments of this application, the status standards include: Class I, Class II, Class III, Class IV and Class V, wherein Class I and Class II are preset status standards.

[0103] One category refers to the bridge being in good condition, including that the important components are in good function and material condition, the minor components are in good function condition, and the materials have a small amount (less than 3%) of minor defects or contamination, and the load-bearing capacity and bridge deck traffic conditions meet the design specifications.

[0104] Category II refers to a relatively good condition, including important components functioning well, materials with localized (within 3%) minor defects or contamination, crack widths less than the limit, minor components with more (within 10%) moderate defects or contamination, and load-bearing capacity and bridge deck traffic conditions meeting design specifications.

[0105] Category III refers to a poor condition, including: significant (within 10%) moderate defects in important components, cracks exceeding the limit, or mild functional defects that develop slowly and can still maintain normal use; significant (10%-20%) severe defects in minor components, reduced functionality, and further deterioration will be detrimental to important components and affect normal traffic; and a load-bearing capacity reduced by less than 10% compared to the design, resulting in uncomfortable driving on the bridge surface.

[0106] Category IV refers to a poor condition, including significant (10%-20%) severe defects in important components, cracks exceeding the limit, severe weathering, spalling, exposed reinforcement, and corrosion, or the appearance of minor functional defects that are developing rapidly. Structural deformation is less than or equal to the specification value, with a significant reduction in function; more than 20% of secondary components are severely missing, losing their intended function; seriously affecting normal traffic; and load-bearing capacity is 10%-25% lower than the design value.

[0107] Category 5 refers to dangerous conditions, including severe functional defects in important components with continued expansion, and the strength of some materials in critical parts reaching their limit, resulting in partial steel reinforcement fractures and concrete damage.

[0108] The damage phenomenon is crushing or unstable deformation of the members, the deformation is greater than the standard value, the strength, stiffness, stability and dynamic effect of the structure cannot meet the requirements of normal traffic safety, and the load-bearing capacity is reduced by more than 25% compared with the design.

[0109] This application provides a bridge data acquisition and management method based on a dial gauge. During static load testing of a bridge, dial gauge data is acquired to mark bridge points, resulting in multiple data acquisition points and corresponding data for each point. It is then determined whether each data point exceeds a preset threshold. If so, the corresponding data is labeled at the first level, creating labeled acquisition points. Based on these data points and labels, the bridge is divided into acquisition areas, identifying the acquisition regions between the labeled acquisition points and adjacent data points. Bridge status is then detected in each acquisition region to obtain bridge status data. It is determined whether the bridge status data meets preset status standards. If not, adjacent data points in the acquisition region corresponding to the bridge status data are labeled at the second level, and the multiple labeled data points are displayed. Surveyors can then use these labeled data points to identify abnormal acquisition points and abnormal bridge areas, thereby improving bridge inspection efficiency.

[0110] One possible implementation of this application embodiment includes step S141 (not shown in the figure), wherein...

[0111] Step S141: If the bridge status data meets the preset status standard, the bridge type is determined based on the percentage table data, and the bridge type and bridge status data are input into the prediction model for prediction to obtain the bridge prediction data.

[0112] In one possible implementation of this application embodiment, steps S131 (not shown in the figure), S132 (not shown in the figure), and S133 (not shown in the figure) are included before step S141, wherein...

[0113] Step S131: Obtain bridge hazard information within a preset time period in the past.

[0114] Among them, bridge hazard information refers to bridge hazard information corresponding to different bridge status data for different bridge types.

[0115] In the embodiments of this application, the preset time period was previously used for input by staff through designated terminal devices, including tablets, mobile phones, and computers.

[0116] Specifically, staff input a preset time period (e.g., January 1, 2020 – October 3, 2021) into a designated terminal device. The terminal device then sends the preset time period to an electronic device for processing. After receiving the preset time period, the electronic device obtains bridge hazard information based on the different bridge status data corresponding to different bridge types within the preset time period.

[0117] Specifically, one possible method for obtaining bridge hazard information is through big data acquisition. This involves acquiring all bridge hazard information using big data technology, and then filtering all bridge hazard information based on a preset past time period to obtain bridge hazard information within that preset past time period.

[0118] Step S132: Analyze the bridge hazard information, determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination, and perform unsupervised time series data processing on the bridge hazard information based on the time series length and the number of type combinations to obtain the first bridge matrix data.

[0119] Specifically, bridge hazard information refers to hazard information occurring on different bridge types within a preset time period. This information includes the specific location and start time of the hazard. For example, a bridge hazard occurred on [Date] at [Location] on a bridge of type [Type]. The start time of this hazard was from July 2021 to March 2022, and the incident has been resolved. After acquiring the bridge hazard information, the electronic device extracts the specific location from the information content to determine the type combinations of different bridge hazard information (one of the type combinations is [Province] [City] [District] [Bridge Type]). The number of type combinations is then statistically analyzed to obtain the total number of type combinations.

[0120] Specifically, a time series is a set of random variables ordered by time. It is typically the result of observing a potential process at equal intervals according to a given sampling rate. Time series data essentially reflects the trend of one or more random variables changing over time, and the core of time series forecasting methods is to extract this pattern from the data and use it to estimate future data.

[0121] In this embodiment, the time series length represents the length of time over which bridge hazard information changes continuously.

[0122] Based on the time series length and the number of type combinations, unsupervised time series data processing of bridge hazard information was performed to obtain the following first bridge matrix data:

[0123]

[0124] Where m is the number of type combinations and n is the length of the time series.

[0125] Step S133: Create a prediction model and input the first bridge matrix data into the prediction model for training to obtain a trained prediction model.

[0126] Specifically, the predictive model is a pre-trained neural network model. Neural networks (NNs) are complex network systems formed by the extensive interconnection of a large number of simple processing units (called neurons). They reflect many fundamental characteristics of human brain function and are highly complex nonlinear dynamic learning systems. Neural networks possess massive parallelism, distributed storage and processing, self-organization, self-adaptation, and self-learning capabilities, making them particularly suitable for handling imprecise and fuzzy information processing problems that require simultaneous consideration of many factors and conditions. The development of neural networks is related to neuroscience, mathematical science, cognitive science, computer science, artificial intelligence, information science, cybernetics, robotics, microelectronics, psychology, optical computing, and molecular biology.

[0127] One possible implementation of this application embodiment includes step S132, which specifically includes steps S1321 (not shown in the figure), S1322 (not shown in the figure), S1323 (not shown in the figure), and S1324 (not shown in the figure).

[0128] Step S1321: Determine at least one set of bridge treatment data based on bridge hazard information.

[0129] Specifically, the bridge handling data included in the bridge hazard information is compiled by technical personnel such as surveyors and maintenance technicians based on the bridge hazard information.

[0130] Step S1322: Acquire labels for at least one set of bridge processing data to obtain bridge data and processing time data in each set of bridge processing data.

[0131] Specifically, tags are obtained based on data tags in at least one set of bridge processing data, and the specified tag content is obtained, namely, by obtaining the location of the bridge hazard, details of the bridge hazard, degree of bridge hazard, time of occurrence of the bridge hazard, and time of resolution of the bridge hazard.

[0132] Step S1323: Determine whether the bridge processing data has been completed based on the processing time data. If it has not been completed, the bridge processing data is disassembled. If it has been completed, the bridge data is bound to the processing time data to obtain the bridge bound data.

[0133] Specifically, by obtaining the information corresponding to the bridge hazard resolution time tag, it is determined whether the current bridge processing data has been completed. If the bridge hazard resolution time tag does not have corresponding time information, it means that the bridge processing data is still being processed. Therefore, the bridge processing data is decomposed. If the bridge hazard resolution time tag has corresponding time information, the bridge data is bound to the bridge processing time data.

[0134] Step S1324: Filter the bridge binding data by type combination to obtain the number of different bridge types in the bridge hazard information and the time series length corresponding to each type combination.

[0135] In one possible implementation of this application embodiment, step S14 is followed by steps S15 (not shown in the figure) and S16 (not shown in the figure), wherein...

[0136] Step S15: Input the first bridge matrix data into the prediction model to extract vector features, obtain the number of bridge feature dimensions, and combine the obtained number of bridge feature dimensions with the first bridge matrix data to generate the second bridge matrix data.

[0137] Step S16: Process the data contained in the second bridge matrix data to obtain bridge hazard data, and input the obtained bridge hazard data into the preset algorithm model for data extrapolation to generate bridge prediction data for each type combination in the future preset time period.

[0138] For the embodiments of this application, a bidirectional LSTM model is used as a preset algorithm model for illustration, including but not limited to the bidirectional LSTM model.

[0139] Specifically, the pre-defined algorithm model is constructed, and the main body of the model adopts a bidirectional LSTM as a trend prediction model. The LSTM mainly consists of a forget gate, an input gate, and an output gate.

[0140] Forgotten Gate: ;

[0141] Input Gate: ;

[0142] After information filtering through the forget gate and the input gate, the historical memories and the current stage's memories are merged, generating the following value:

[0143] ;

[0144] Output gate: ;

[0145] Following the LSTM described above, another LSTM network layer is fed in reverse to obtain the BI-LSTM layer. Since it is trained together by several groups of buildings, a joint learning layer of building spatial features is added. The size of the association vector matrix is ​​initialized to M*V*K. The output vector of the last layer of the LSTM is taken, transposed, and multiplied by the association vector parameter matrix. Finally, the regression loss function is connected to complete the construction of the preset algorithm model.

[0146] One possible implementation of this application embodiment includes step S15, which specifically comprises: step S151 (not shown in the figure), step S152 (not shown in the figure), and step S153 (not shown in the figure), wherein,

[0147] Step S151: Based on the first bridge matrix data, determine the event name, event time, and event type of each hazard event in the bridge hazard information.

[0148] Step S152: Input the event name, event time, and event type into the bridge prediction model for vector extraction to obtain the text feature vector corresponding to the event name, the time feature vector corresponding to the event time, and the type feature vector corresponding to the event type.

[0149] Step S153: Count the number of text feature vectors, time feature vectors, and type feature vectors to obtain the number of bridge feature dimensions.

[0150] Specifically, the total number of eigenvectors of a matrix is ​​calculated as follows: number = n - rank of the eigenmatrix, number = nr(λE-A), where n is the order. Not every matrix can be diagonalized. If a matrix has distinct eigenvalues, then it is always diagonalizable. The projections (coordinates) of eigenvectors onto the basis vectors are used to represent the eigenvectors. Here, we assume the vector space is h-dimensional. Therefore, it can be directly represented as coordinate vectors. Using basis vectors, linear transformations can also be represented by a simple matrix multiplication.

[0151] One possible implementation of this application embodiment includes step S16, which specifically comprises: step S161 (not shown in the figure), step S162 (not shown in the figure), and step S163 (not shown in the figure), wherein...

[0152] Step S161: Calculate the normal distribution mean and normal distribution variance of the data contained in the second bridge matrix data, and determine the 3δ range of the second bridge matrix data based on the normal distribution mean and normal distribution variance.

[0153] Step S162: Determine whether the data is outside the 3δ range. If the data is outside the 3δ range, determine the first matrix sequence of the second bridge matrix data where the data is located. Calculate the sequence average based on the first matrix sequence, replace the data with the sequence average, obtain the replaced second matrix sequence, and process the missing values ​​in the second matrix sequence.

[0154] Specifically, the 3δ range is based on repeated measurements with equal precision following a normal distribution, where interference or noise from outlier data makes it difficult to maintain a normal distribution. If the absolute value of the residual error νi of a measurement in a set of data is greater than 3δ, then that measurement is an outlier and should be discarded. Errors equal to ±3δ are usually considered the limiting error. For a normally distributed random error, the probability of falling outside ±3δ is only 0.27%, which is very small in a finite number of measurements; hence, the 3δ criterion exists. The 3δ criterion is the most commonly used and simplest gross error criterion. It is generally applied when the number of measurements is sufficiently large (n≥30) or when a rough judgment is made when n>10.

[0155] Specifically, missing values ​​refer to data clustering, grouping, censoring, or truncation caused by missing information in the second matrix sequence. Handling missing values ​​generally involves two methods: deleting cases with missing values ​​and imputation. This application uses the deletion of cases with missing values ​​to process the second matrix sequence. Deletion of cases with missing values ​​mainly employs simple deletion and weighted methods. Simple deletion is the most basic method for handling missing values. It removes cases with missing values. If the data missing problem can be solved by simply deleting a small portion of the samples, this method is the most effective. When the missing values ​​are not completely random, bias can be reduced by weighting the complete data. After labeling incomplete cases, complete data cases are assigned different weights, which can be obtained using logistic or probit regression.

[0156] Step S163: Normalize the second matrix sequence in the second bridge matrix data to obtain bridge hazard data.

[0157] Specifically, there are two forms of normalization methods: one is to transform numbers into decimals between (0, 1), and the other is to transform dimensional expressions into dimensionless expressions. These methods are primarily proposed for ease of data processing, mapping data to the range of 0 to 1 for more convenient and faster processing.

[0158] The specific normalization method is as follows: .

[0159] The above embodiments describe a bridge data acquisition and management method based on a dial gauge from the perspective of the process flow. The following embodiments describe a bridge data acquisition and management device based on a dial gauge from the perspective of a virtual module or virtual unit. For details, please refer to the following embodiments.

[0160] This application provides a bridge data acquisition and management device based on a percentage gauge, such as... Figure 2As shown, the bridge data acquisition and management device 20 based on a percentage scale may specifically include: a data acquisition module 21, a first judgment module 22, a region determination module 23, a status detection module 24, and a second judgment module 25, wherein,

[0161] The data acquisition module 21 is used to acquire percentage table data and mark the bridge points according to the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point.

[0162] The first judgment module 22 is used to judge whether the collected data conforms to the preset data range. If it does not conform, the data collection points corresponding to the collected data are marked at the first level to obtain the marked collection points.

[0163] The region determination module 23 is used to divide the bridge into collection areas based on multiple data collection points and labeled collection points, and to determine the collection area between the labeled collection points and adjacent data collection points among the multiple data collection points.

[0164] The status detection module 24 is used to perform bridge status detection on each collection area to obtain bridge status data;

[0165] The second judgment module 25 is used to determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, the adjacent data collection points in the collection area corresponding to the bridge status data will be marked with secondary labels, and the multiple data collection points after secondary labeling will be displayed.

[0166] In one possible implementation of this application embodiment, the second judgment module 25, when judging whether the bridge state data meets the preset state standard, is specifically used for:

[0167] If the bridge status data meets the preset status criteria, the bridge type is determined based on the percentage table data, and the bridge type and bridge status data are input into the prediction model for prediction to obtain the bridge prediction data.

[0168] In another possible implementation of this application embodiment, the apparatus 20 further includes: an information acquisition module, an analysis and processing module, and a model creation module, wherein...

[0169] The information acquisition module is used to acquire bridge hazard information within a preset time period in the past. The bridge hazard information is bridge hazard information corresponding to different bridge status data for different bridge types.

[0170] The analysis and processing module is used to analyze bridge hazard information, determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination, and perform unsupervised time series data processing on the bridge hazard information based on the time series length and the number of type combinations to obtain the first bridge matrix data.

[0171] The model creation module is used to create a prediction model and input the first bridge matrix data into the prediction model for training to obtain a trained prediction model.

[0172] In another possible implementation of this application embodiment, when the analysis and processing module analyzes bridge hazard information and determines the number of type combinations of different bridge types in the bridge hazard information and the time series length corresponding to each type combination, it is specifically used for:

[0173] At least one set of bridge treatment data is determined based on bridge hazard information;

[0174] Labels are obtained for at least one set of bridge processing data to obtain bridge data and processing time data in each set of bridge processing data.

[0175] Based on the processing time data, determine whether the bridge processing data has been completed. If it has not been completed, the bridge processing data will be disassembled. If it has been completed, the bridge data will be bound to the processing time data to obtain the bridge bound data.

[0176] By filtering the bridge binding data by type combination, we can obtain the number of type combinations of different bridge types in the bridge hazard information and the time series length corresponding to each type combination.

[0177] In another possible implementation of this application embodiment, the apparatus further includes: a feature extraction module and a data prediction module, wherein...

[0178] The feature extraction module is used to input the first bridge matrix data into the prediction model to extract vector features, obtain the number of bridge feature dimensions, and combine the obtained number of bridge feature dimensions with the first bridge matrix data to generate the second bridge matrix data.

[0179] The data prediction module is used to process the data contained in the second bridge matrix data to obtain bridge hazard data, and input the obtained bridge hazard data into the preset algorithm model for data extrapolation to generate bridge prediction data for each type combination in the future preset time period.

[0180] In another possible implementation of this application embodiment, when the feature extraction module inputs the first bridge matrix data into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions, it is specifically used for:

[0181] Based on the first bridge matrix data, determine the event name, event time, and event type of each hazard event in the bridge hazard information;

[0182] The event name, event time, and event type are input into the bridge prediction model for vector extraction, resulting in a text feature vector corresponding to the event name, a time feature vector corresponding to the event time, and a type feature vector corresponding to the event type.

[0183] The number of bridge feature dimensions is obtained by statistically analyzing the text feature vector, time feature vector, and type feature vector.

[0184] In another possible implementation of this application embodiment, when the data prediction module processes the data contained in the second bridge matrix data to obtain bridge hazard data, it is specifically used for:

[0185] Calculate the mean and variance of the normal distribution of the data contained in the second bridge matrix data, and determine the 3δ range of the second bridge matrix data based on the mean and variance of the normal distribution;

[0186] Determine whether the data is outside the 3δ range. If the data is outside the 3δ range, determine the first matrix sequence of the second bridge matrix data where the data is located. Calculate the sequence average based on the first matrix sequence. Replace the data with the sequence average to obtain the replaced second matrix sequence. Then, handle the missing values ​​in the second matrix sequence.

[0187] The second matrix sequence in the second bridge matrix data is normalized to obtain bridge hazard data.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0189] This application also describes an electronic device from the perspective of a physical device, such as... Figure 3 As shown, Figure 3The illustrated electronic device 300, in addition to conventional configuration devices, includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0190] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0191] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0192] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0193] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0194] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0195] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0196] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A bridge data acquisition and management method based on a percentage table, characterized in that, include: Acquire percentage table data and mark the bridge points based on the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point; Determine whether the collected data exceeds the preset data. If it does, perform first-level labeling on the data collection points corresponding to the collected data to obtain labeled collection points. Based on the multiple data collection points and the labeled collection points, the bridge is divided into collection areas, and the collection areas between the labeled collection points and adjacent data collection points among the multiple data collection points are determined. Bridge status detection is performed on each of the acquisition areas to obtain bridge status data; Determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, then perform secondary labeling on the adjacent data collection points in the collection area corresponding to the bridge status data, and control the display of the multiple data collection points after secondary labeling.

2. The method according to claim 1, characterized in that, Determining whether the bridge status data meets preset status criteria includes: If the bridge status data meets the preset status criteria, the bridge type is determined based on the percentage table data, and the bridge type and the bridge status data are input into the prediction model for prediction to obtain bridge prediction data.

3. The method according to claim 2, characterized in that, The bridge type and bridge status data are input into the prediction model for prediction, resulting in bridge prediction data. This process also includes: Obtain bridge hazard information within a preset time period in the past, wherein the bridge hazard information is bridge hazard information corresponding to different bridge status data for different bridge types; The bridge hazard information is analyzed to determine the number of different bridge type combinations in the bridge hazard information and the time series length corresponding to each type combination. Based on the time series length and the number of type combinations, the bridge hazard information is processed into unsupervised time series data to obtain the first bridge matrix data. A prediction model is created, and the first bridge matrix data is input into the prediction model for training to obtain a trained prediction model.

4. The method according to claim 3, characterized in that, The bridge hazard information is analyzed to determine the number of different bridge type combinations and the time series length corresponding to each type combination, including: At least one set of bridge treatment data is determined based on the bridge hazard information. Tag acquisition is performed on the at least one set of bridge processing data to obtain the bridge data and processing time data in each set of bridge processing data. Based on the processing time data, it is determined whether the bridge processing data has been processed. If it has not been processed, the bridge processing data is disassembled. If it has been processed, the bridge data is bound to the processing time data to obtain bridge bound data. The bridge binding data is filtered by type combination to obtain the number of type combinations of different bridge types in the bridge hazard information and the time series length corresponding to each type combination.

5. The method according to claim 3, characterized in that, The method further includes: The first bridge matrix data is input into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions. The obtained number of bridge feature dimensions is then combined with the first bridge matrix data to generate the second bridge matrix data. The data contained in the second bridge matrix data is processed to obtain bridge hazard data. The obtained bridge hazard data is then input into a preset algorithm model for data extrapolation to generate bridge prediction data for each type combination in the number of type combinations within a preset time period in the future.

6. The method according to claim 5, characterized in that, The first bridge matrix data is input into the prediction model for vector feature extraction to obtain the number of bridge feature dimensions, including: Based on the first bridge matrix data, determine the event name, event time, and event type of each hazard event in the bridge hazard information; The event name, event time, and event type are respectively input into the bridge prediction model for vector extraction to obtain a text feature vector corresponding to the event name, a time feature vector corresponding to the event time, and a type feature vector corresponding to the event type. The number of bridge feature dimensions is obtained by counting the text feature vector, the time feature vector, and the type feature vector.

7. The method according to claim 5, characterized in that, The data processing of the data contained in the second bridge matrix data to obtain bridge hazard data includes: Calculate the normal distribution mean and normal distribution variance of the data contained in the second bridge matrix data, and determine the 3δ range of the second bridge matrix data based on the normal distribution mean and normal distribution variance; Determine whether the data is outside the 3δ range. If the data is outside the 3δ range, determine the first matrix sequence of the second bridge matrix data in which the data is located. Calculate the sequence average value based on the first matrix sequence. Replace the data with the sequence average value to obtain the replaced second matrix sequence. Then, process the missing values ​​in the second matrix sequence. The second matrix sequence in the second bridge matrix data is normalized to obtain bridge hazard data.

8. A bridge data acquisition and management device based on a percentage gauge, characterized in that, include: The data acquisition module is used to acquire percentage table data and mark the bridge points according to the percentage table data to obtain multiple data collection points and the collection data corresponding to each data collection point. The first judgment module is used to determine whether the collected data exceeds the preset data. If it does, the data collection points corresponding to the collected data are marked at the first level to obtain the marked collection points. The region determination module is used to divide the bridge into collection areas based on the multiple data collection points and the labeled collection points, and to determine the collection area between the labeled collection points and adjacent data collection points among the multiple data collection points. The status detection module is used to perform bridge status detection on each of the acquisition areas to obtain bridge status data. The second judgment module is used to determine whether the bridge status data meets the preset status standard. If the bridge status data does not meet the preset status standard, the adjacent data collection points in the collection area corresponding to the bridge status data are marked with secondary labels, and the multiple data collection points after secondary labeling are displayed.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the bridge data acquisition and management method based on a percentage table as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, the computer is instructed to perform the bridge data acquisition and management method based on a percentage table as described in any one of claims 1 to 7.

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