A concrete quality data visualization management method and system

Abstract

The application relates to the technical field of facility maintenance, and particularly discloses a concrete quality data visualization management method and system, wherein the method comprises the following steps: acquiring spatial layer information of different concrete structure members based on a GIS database; establishing a deterioration model of different concrete structure members about key quality indexes, and correlating the deterioration model with position coordinates; predicting quality data according to the deterioration model, and screening and acquiring concrete structure members that need to be maintained according to the quality data, and generating predicted deterioration state information correlated with corresponding position coordinates; generating a maintenance task list; and displaying the maintenance task list on a GIS map based on the position coordinates of the concrete structure members that need to be maintained. The method forms a closed-loop management process from data collection, state evaluation, future prediction, resource matching to task visualization, and realizes predictive maintenance and intelligent scheduling of concrete structure quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of facility maintenance, in particular to a concrete quality data visualization management method and system. BACKGROUND

[0002] In urban infrastructure, concrete structures such as bridges, tunnels, underground pipe galleries, etc. play a key role. However, these structures are long-term in complex environments, affected by environmental erosion, traffic load, and material aging, etc. multiple factors, prone to cracks, spalling, steel corrosion and other diseases, posing a serious threat to structural safety and service life.

[0003] The traditional maintenance mode mainly relies on manual inspection, which has many drawbacks. The data collection efficiency is low, the records are scattered and lack of spatio-temporal correlation, making the maintenance decision often lagging behind, lacking scientific basis, and difficult to meet the needs of real-time monitoring of structural dynamic deterioration.

[0004] To solve these problems, a concrete structure quality data visualization management method combined with GIS has emerged. The key is to combine quality data with spatial location information to build a health condition monitoring and maintenance decision support platform. This requires systematic inspection and multi-source data collection, including manual visual inspection, non-destructive testing, SHM sensor data, and unmanned aerial vehicle image analysis, etc. Then, integrate different formats of data into the GIS platform, assign a unique spatial identifier to the structure or component and store the quality data, so as to intuitively display the spatialized data and improve cognitive efficiency.

[0005] However, infrastructure deterioration is a dynamic process, and existing systems have bottlenecks in maintenance task transformation, which cannot comprehensively consider the deterioration severity, resource scheduling cost, and task dispatch timeliness, etc. constraints, making it difficult to achieve timely global arrangement of maintenance plans.

[0006] At present, there is no effective technical solution to the above problems. SUMMARY

[0007] The purpose of the present application is to provide a concrete quality data visualization management method and system to comprehensively consider the deterioration severity, resource scheduling cost, and task dispatch timeliness, etc. constraints to achieve timely global arrangement of maintenance plans and visual display.

[0008] In a first aspect, the present application provides a concrete quality data visualization management method, which comprises the following steps:

[0009] S1, acquiring spatial layer information of different concrete structure components based on a GIS database, the spatial layer information including component type, location coordinates, detection time and detection results of previous times;

[0010] S2. Based on the component type, the time of each test, and the results of each test, establish a deterioration model for key quality indicators for different concrete structural components, and associate the deterioration model with the location coordinates;

[0011] S3. Based on the deterioration model, predict the quality data of key quality indicators of each concrete structural component within a preset time window, and select the concrete structural components that need to be maintained based on the quality data, and generate the predicted deterioration status information associated with the corresponding location coordinates.

[0012] S4. Obtain comprehensive resource information of the maintenance team, and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained.

[0013] S5. Display the maintenance task list on a GIS map based on the location coordinates of the concrete structural components that need maintenance.

[0014] The method in this application utilizes the spatial management capabilities of GIS, the predictive capabilities of degradation models, the scheduling capabilities of resource matching algorithms, and the visualization capabilities of GIS to jointly address the problems of data dispersion, decision lag, and task scheduling difficulties in traditional methods. It enables dynamic monitoring of concrete structure quality, predictive maintenance planning, and optimal resource allocation, and has the advantages of improving the efficiency and scientific nature of concrete structure maintenance management, and realizing prediction-based maintenance planning and optimal resource allocation.

[0015] The concrete quality data visualization management method, wherein the step of generating a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that is to be maintained includes:

[0016] S41. Based on the comprehensive resource information, determine the available man-hours and available equipment types for the maintenance team;

[0017] S42. Based on the predicted deterioration status information, determine the required maintenance time and equipment type for each concrete structural component that needs maintenance.

[0018] S43. Based on the available man-hours and available equipment types of the maintenance team, as well as the required man-hours and required equipment types for each concrete structural component to be maintained, a constrained optimization algorithm is applied to generate a maintenance task list with the objective of minimizing the total maintenance time and total transportation distance.

[0019] The aforementioned technical solution obtains information on available resources for maintenance teams and the maintenance requirements of components to be maintained. Using this information as constraints, a constrained optimization algorithm is applied to generate a maintenance task list with the objective of minimizing total maintenance time and total transportation distance. This enables intelligent scheduling and optimized arrangement of maintenance tasks, improves the utilization efficiency of maintenance resources, reduces transportation costs incurred by teams traveling to and from different component locations, ensures the timeliness and global optimization of maintenance plans, and solves the problems of inefficiency, unreasonable resource allocation, or excessively high maintenance costs that may result from the lack of detailed basis for maintenance task allocation in existing technologies.

[0020] The concrete quality data visualization management method further includes the step of generating a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural member that needs maintenance:

[0021] S44. Based on the maintenance task list, calculate the task load balancing deviation of each maintenance team. If the task load balancing deviation exceeds a preset threshold, then based on the skill attribute information of the maintenance team, the tasks that exceed the skill range of the maintenance team are redistributed to maintenance teams with the corresponding skills, and the maintenance task list is regenerated until the task load balancing deviation is lower than the preset threshold.

[0022] The concrete quality data visualization management method, wherein the step of calculating the task load balancing deviation of each maintenance team based on the maintenance task list includes:

[0023] S441. Calculate the number of tasks assigned to each maintenance team and the total maintenance man-hours required for each task in the maintenance task list, and record the component type corresponding to each task;

[0024] S442. Calculate the task load rate of each maintenance team based on the number of tasks, total maintenance hours and component type. The task load rate is equal to the total maintenance hours divided by the available hours and multiplied by a weighting coefficient related to the component type. The weighting coefficient is determined based on the average maintenance difficulty of different component types in historical data.

[0025] S443. Calculate the standard deviation of the task load rate of all maintenance teams as the task load balancing deviation.

[0026] The concrete quality data visualization management method, wherein the step of establishing a deterioration model for key quality indicators of different concrete structural components based on the component type, the time of each inspection, and the results of each inspection includes:

[0027] S21. For the component type, select a degradation model framework that matches the component type from a preset degradation model library. The degradation model framework includes parameter types for determining the parameters required for the degradation model.

[0028] S22. Based on the previous detection times and results, and according to the parameter type, the parameters in the deterioration model framework are fitted using the least squares method or the maximum likelihood estimation method to obtain a deterioration model corresponding to the concrete structural member. The deterioration model can characterize the functional relationship between the key quality indicators and time.

[0029] S23. For the deteriorated model obtained by fitting, calculate its goodness of fit. If the goodness of fit is lower than the preset threshold, then based on the previous detection results, identify the abnormal detection data that causes the low goodness of fit, and remove the abnormal detection data. Based on the previous detection time and previous detection results after removing the abnormal detection data, refit the deteriorated model until the goodness of fit reaches the preset threshold.

[0030] The concrete quality data visualization management method is described above. In step S2, one or more deterioration models for key quality indicators are established for each concrete structural component.

[0031] The concrete quality data visualization management method, wherein the steps of predicting the quality data of key quality indicators of each concrete structural component within a preset time window based on the deterioration model, filtering and obtaining the concrete structural components that need to be maintained based on the quality data, and generating predicted deterioration state information associated with the corresponding location coordinates include:

[0032] S31. For each concrete structural component, based on the aforementioned degradation model and combined with Monte Carlo simulation, generate multiple predicted degradation curves for key quality indicators within a preset time window.

[0033] S32. Determine whether the quality data of each predicted deterioration curve exceeds the maintenance threshold within the preset time window, and record the number of predicted deterioration curves that exceed the maintenance threshold for each concrete structural component.

[0034] S33. If the number of predicted deterioration curves exceeding the maintenance threshold is greater than the preset ratio, the corresponding concrete structural member is defined as the concrete structural member that needs maintenance, and the predicted deterioration status information associated with the corresponding location coordinates is obtained based on the predicted deterioration curves exceeding the maintenance threshold.

[0035] The concrete quality data visualization management method, wherein step S3, the predicted deterioration state information includes the predicted occurrence time, problem type, and severity, and the step of obtaining the predicted deterioration state information associated with the corresponding location coordinates based on the predicted deterioration curve exceeding the maintenance threshold includes:

[0036] S331. Extract the time points when each predicted degradation curve exceeds the maintenance threshold, and calculate the average of the time points as the predicted occurrence time.

[0037] S332. Determine the problem type based on the type of the key quality indicators;

[0038] S333. Extract the quality data corresponding to the predicted occurrence time from the predicted degradation curve that exceeds the maintenance threshold, calculate the mean of the quality data, and determine the severity according to the difference between the mean of the data and the maintenance threshold, based on the preset severity level classification standard.

[0039] S334. Integrate the predicted occurrence time, the problem type, and the severity to generate predicted deterioration status information associated with the position coordinates of the corresponding concrete structural member.

[0040] The concrete quality data visualization management method described above, wherein step S5 includes:

[0041] S51. Extract the task identifier, component location coordinates, maintenance team identifier, and planned time window from the maintenance task list;

[0042] S52. Based on the coordinates of the component location, locate the corresponding concrete structure component on the GIS map and overlay the maintenance task layer. The maintenance task layer attributes include task identifier, problem type and severity, wherein different severity levels are coded with different colors.

[0043] S53. Based on the maintenance team identifier, locate the corresponding maintenance team's location on the GIS map and display the maintenance team's availability status and skill attribute information.

[0044] S54. Display the planned time window of the maintenance task in the form of a timeline on the GIS map, and allow maintenance personnel to adjust the planned time of the task. The adjusted time information is synchronously updated to the maintenance task list.

[0045] Secondly, this application also provides a concrete quality data visualization management system, the system comprising:

[0046] The acquisition module is used to acquire spatial layer information of different concrete structural components based on a GIS database. The spatial layer information includes component type, location coordinates, detection time, and detection results.

[0047] The modeling module is used to establish a deterioration model for key quality indicators of different concrete structural components based on the component type, the time of each inspection, and the results of each inspection, and to associate the deterioration model with the location coordinates.

[0048] The prediction module is used to predict the quality data of key quality indicators of each concrete structural component within a preset time window based on the deterioration model, and to filter and obtain the concrete structural components that need to be maintained based on the quality data, and generate predicted deterioration status information associated with the corresponding location coordinates.

[0049] The generation module is used to obtain the comprehensive resource information of the maintenance team and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained.

[0050] The display module is used to display the maintenance task list on a GIS map based on the location coordinates of the concrete structural components that need to be maintained.

[0051] The system in this application utilizes the spatial management capabilities of GIS, the predictive capabilities of degradation models, the scheduling capabilities of resource matching algorithms, and the visualization capabilities of GIS to jointly address the problems of data dispersion, decision lag, and task scheduling difficulties in traditional systems. It achieves dynamic monitoring of concrete structure quality, predictive maintenance planning, and optimal resource allocation, thus improving the efficiency and scientific nature of concrete structure maintenance management and realizing prediction-based maintenance planning and optimal resource allocation.

[0052] As can be seen from the above, this application provides a concrete quality data visualization management method and system. The concrete quality data visualization management method of this application forms a closed-loop management process from data acquisition, status assessment, future prediction, resource matching to task visualization, realizing predictive maintenance and intelligent scheduling of concrete structure quality. By establishing a deterioration model and making predictions, it can identify the maintenance needs of components in advance, achieving proactive maintenance based on prediction, thus improving the scientific and forward-looking nature of maintenance decisions. By comprehensively considering the predicted deterioration state of components and the comprehensive resource information of maintenance teams, it can generate a realistic and resource-efficient maintenance task list, improving the efficiency and rationality of maintenance task scheduling. By visually displaying the maintenance task list on a GIS map, it improves the manager's understanding of the overall maintenance situation, facilitating task scheduling and on-site management. Attached Figure Description

[0053] Figure 1 A flowchart of a concrete quality data visualization management method provided in an embodiment of this application.

[0054] Figure 2 A schematic diagram of the structure of the concrete quality data visualization management method system provided in the embodiments of this application.

[0055] Attached reference numerals: 201, Acquisition module; 202, Modeling module; 203, Prediction module; 204, Generation module; 205, Display module. Detailed Implementation

[0056] 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 a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0057] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0058] Firstly, please refer to Figure 1 This application provides a concrete quality data visualization management method in some embodiments, the method including the following steps:

[0059] S1. Obtain spatial layer information of different concrete structural components based on the GIS database. The spatial layer information includes component type, location coordinates, time of each inspection, and results of each inspection.

[0060] S2. Based on the component type, the time of each test, and the results of each test, establish a deterioration model for key quality indicators for different concrete structural components, and associate the deterioration model with the location coordinates.

[0061] S3. Based on the deterioration model, predict the quality data of key quality indicators of each concrete structural component within a preset time window, and select the concrete structural components that need to be maintained based on the quality data, and generate the predicted deterioration status information associated with the corresponding location coordinates.

[0062] S4. Obtain comprehensive resource information of the maintenance team, and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained.

[0063] S5. Display the maintenance task list on the GIS map based on the location coordinates of the concrete structural components that need to be maintained.

[0064] Specifically, obtaining spatial layer information from a GIS database refers to extracting geographical location and related attribute data of concrete structural components from a geographic information system database. This can be achieved using database queries and spatial data interfaces. This process associates the physical location of the components with inspection data, providing data for spatial analysis and visualization. The spatial layer information includes component type, location coordinates, inspection times, and inspection results—data content obtained from the GIS database. This can be achieved using structured data storage and management. This data provides component attributes, geographical location, and inspection history over time, which is necessary for establishing deterioration models and conducting spatial management.

[0065] More specifically, step S2 can employ regression analysis, machine learning, or physical model fitting to construct a degradation model, quantifying the degradation process of the component and providing a tool for predicting its future state. Associating the degradation model with location coordinates means binding the established degradation model to the geographical location information of the corresponding concrete component. This can be achieved using association fields in a database or spatial indexes. This process ensures that the prediction result for each component can be accurately mapped to its spatial location.

[0066] More specifically, step S3 aims to use a degradation model based on associated location coordinates to calculate the predicted quality indicators of components within a preset time period, and identify components requiring maintenance according to preset standards. This process can be implemented using model calculation and threshold judgment. This step achieves the prediction of future maintenance needs. Specifically, generating predicted degradation status information associated with corresponding location coordinates involves integrating the predicted future degradation of components, including problem type, severity, and predicted occurrence time, into data and associating it with the component's location coordinates. This can be achieved using data structure definition and information encapsulation. This information provides a requirement description for maintenance task planning.

[0067] More specifically, step S4, obtaining comprehensive resource information for the maintenance team, refers to collecting available resource data such as the number of equipment and personnel, and their skill levels. This can be achieved through a resource management system interface or manual data entry. This data provides the basis for maintenance task allocation and scheduling.

[0068] More specifically, step S5 is used to comprehensively consider the available resources of the maintenance team and the predicted deterioration state of the components to be maintained, formulate a maintenance work plan, and overlay the generated maintenance task information onto a geographic information system map for visualization. The formulation process can be implemented using optimization algorithms or rule matching, which combines maintenance needs with resource capabilities to generate an executable maintenance plan; the presentation process can be implemented using a GIS software development kit and map services, which spatially displays the distribution and details of the maintenance tasks.

[0069] More specifically, the method of this application utilizes the spatial management capabilities of GIS, the predictive capabilities of degradation models, the scheduling capabilities of resource matching algorithms, and the visualization capabilities of GIS to jointly address the problems of data dispersion, decision lag, and task scheduling difficulties in traditional methods. It achieves dynamic monitoring of concrete structure quality, predictive maintenance planning, and optimal resource allocation, which has the advantages of improving the efficiency and scientific nature of concrete structure maintenance management and realizing prediction-based maintenance planning and optimal resource allocation.

[0070] The concrete quality data visualization management method of this application forms a closed-loop management process from data collection, status assessment, future prediction, resource matching to task visualization. It realizes predictive maintenance and intelligent scheduling of concrete structure quality. By establishing a deterioration model and making predictions, it can identify the maintenance needs of components in advance, realize proactive maintenance based on prediction, and improve the scientificity and foresight of maintenance decisions. By comprehensively considering the predicted deterioration status of components and the comprehensive resource information of maintenance teams, it can generate a maintenance task list that is realistic and makes reasonable use of resources, improving the efficiency and rationality of maintenance task scheduling. By visually displaying the maintenance task list on a GIS map, it improves the manager's awareness of the overall maintenance situation and facilitates task scheduling and on-site management.

[0071] In some preferred embodiments, the step of generating a maintenance task list based on comprehensive resource information and predicted deterioration status information of each concrete structural member that is to be maintained includes:

[0072] S41. Based on comprehensive resource information, determine the available man-hours and available equipment types for the maintenance team;

[0073] S42. Based on the predicted deterioration status information, determine the required maintenance man-hours and equipment types for each concrete structural component that needs maintenance.

[0074] S43. Based on the available man-hours and equipment types of the maintenance team, as well as the required man-hours and equipment types for each concrete structural component to be maintained, apply a constrained optimization algorithm to generate a maintenance task list with the objective of minimizing the total maintenance time and total transportation distance.

[0075] Specifically, step S41, based on the acquired comprehensive resource information, clarifies the total amount of working hours each maintenance team can contribute within a specific time period, as well as the types and quantities of equipment they possess. The comprehensive resource information may include equipment resource information, human resource information, and capability attribute information. This allows for the determination of the total available working hours for each maintenance team within a preset time window. Simultaneously, based on the equipment resource information, the types and quantities of equipment possessed by the team can be determined. This provides a clear resource ceiling and capability range for subsequent task allocation, serving as the foundational information for rational scheduling.

[0076] More specifically, step S42 assesses the man-hours and specific types of equipment required to complete the repair task for each concrete structural member based on the predicted deterioration status information. For example, for a beam member predicted to have severe cracks, based on its problem type and severity, it can be estimated that repairing the crack will require approximately 16 man-hours and one grouting machine. This quantifies the requirements for each maintenance task and is a key input for resource matching and task allocation.

[0077] More specifically, step S43 integrates the available resource information of the maintenance team obtained in step S41 and the component maintenance requirement information obtained in step S42, and inputs this information as constraints and parameters into a constrained optimization algorithm. Constraints include, but are not limited to, the available working hours of the maintenance team, the available equipment types, and the working hours and equipment types required by the components. The optimization objective is set to minimize the total maintenance time and total transportation distance. The total maintenance time can be defined as the sum of the time required to complete all tasks, or the time point at which the last task is completed. The total transportation distance can be defined as the total distance the maintenance team moves between different component locations. The constrained optimization algorithm, such as integer linear programming, genetic algorithms, or simulated annealing algorithms, calculates the optimal task allocation scheme to form a maintenance task list, under the premise of satisfying resource constraints and task requirements. The maintenance task list may include data such as task identifiers, component location coordinates, maintenance team identifiers, planned start times, and planned end times.

[0078] More specifically, by applying a constrained optimization algorithm, the concrete quality data visualization management method of this application embodiment can systematically balance factors such as the priority of each task, the capability of the maintenance team, and geographical location, avoiding resource idleness or overload that may result from simple allocation, effectively improving overall maintenance efficiency, and reducing transportation costs caused by the team traveling to and from different component locations.

[0079] More specifically, the above technical solution obtains information on available resources for maintenance teams and maintenance requirements of components to be maintained. Using this information as constraints, a constrained optimization algorithm is applied to generate a maintenance task list with the objective of minimizing total maintenance time and total transportation distance. This achieves intelligent scheduling and optimized arrangement of maintenance tasks, improves the utilization efficiency of maintenance resources, reduces transportation costs incurred by teams traveling to and from different component locations, ensures the timeliness and global optimization of maintenance plans, and solves the problems of inefficiency, unreasonable resource allocation, or excessively high maintenance costs that may result from the lack of detailed basis for maintenance task allocation in existing technologies.

[0080] In some preferred embodiments, the step of generating a maintenance task list based on comprehensive resource information and predicted deterioration status information of each concrete structural member that is to be maintained further includes the step of:

[0081] S44. Based on the maintenance task list, calculate the task load balancing deviation of each maintenance team. If the task load balancing deviation exceeds the preset threshold, then based on the skill attribute information of the maintenance team, the tasks that are outside the skill range of the maintenance team are redistributed to maintenance teams with the corresponding skills, and the maintenance task list is regenerated until the task load balancing deviation is lower than the preset threshold.

[0082] Specifically, the maintenance task list generated in step S43 may have issues with uneven task distribution among different maintenance teams, or tasks beyond the skill level of some maintenance teams may be assigned to teams lacking the corresponding skills. This could lead to excessive workload, low efficiency, or even inability to complete tasks for some teams, affecting the effective execution of the overall maintenance plan. Therefore, the method of this application introduces step S44 to optimize and adjust the maintenance task list initially generated in step S43.

[0083] More specifically, step S44 calculates the task load balancing deviation for each maintenance team based on the current maintenance task list. The task load balancing deviation is a quantitative indicator used to assess the evenness of task distribution among different maintenance teams, reflecting the differences in workload between teams. If the calculated task load balancing deviation is lower than a preset threshold, it indicates that the current task allocation is unbalanced or unreasonable and needs adjustment.

[0084] More specifically, when the task load balancing deviation exceeds a preset threshold, step S44 identifies, based on the skill attribute information of the maintenance teams, whether any tasks in the current task allocation have been assigned to maintenance teams lacking the corresponding skills. These tasks outside the skill range will be reassigned. During reassignment, the system will search for maintenance teams with the required skills and relatively low current load, and transfer the tasks to these teams. Therefore, after completing skill matching adjustments and task reassignment, the maintenance task list needs to be regenerated, updating the correspondence between tasks and teams, as well as possible planned time windows. Subsequently, the task load balancing deviation corresponding to the new task list is recalculated. This process is iterative, that is, the load balancing deviation is repeatedly calculated, tasks are reassigned based on skills, and the list is regenerated, until the calculated task load balancing deviation is lower than the preset threshold.

[0085] More specifically, through the above iterative optimization process, the concrete quality data visualization management method of this application embodiment, based on the initial consideration of total maintenance time and total transportation distance, further ensures the balance of task allocation among different maintenance teams and solves the problem of mismatch between tasks and team skills. The final maintenance task list generated is more in line with the actual capabilities and resource conditions of the maintenance teams, improving the efficiency and success rate of task execution, thereby enhancing the effectiveness of the overall maintenance plan.

[0086] In some preferred embodiments, the step of calculating the task load balancing deviation of each maintenance team based on the maintenance task list includes:

[0087] S441. In the maintenance task list, count the number of tasks assigned to each maintenance team and the total maintenance man-hours required for each task, and record the component type corresponding to each task.

[0088] S442. Calculate the task load rate for each maintenance team based on the number of tasks, total maintenance hours, and component type. The task load rate is equal to the total maintenance hours divided by the available hours, and multiplied by a weighting coefficient related to the component type. The weighting coefficient is determined based on the average maintenance difficulty of different component types in historical data.

[0089] S443. Calculate the standard deviation of the task load rate of all maintenance teams as the task load balancing deviation.

[0090] Specifically, step S441 extracts the specific task items assigned to each maintenance team from the maintenance task list. For each maintenance team, the number of all assigned tasks needs to be summarized. Simultaneously, the required maintenance man-hours for each task need to be added together to obtain the team's total maintenance man-hours. Furthermore, the types of concrete structural components involved in each task need to be identified and recorded; these statistics can be stored in a temporary data structure.

[0091] More specifically, step S442 calculates the task load rate for each maintenance team based on the statistical results of step S441. The formula for calculating the task load rate can be: Task Load Rate = (Total Maintenance Hours / Available Hours) * Weighting Coefficient. The weighting coefficient reflects the relative difficulty of maintenance tasks for different component types and can be determined based on the average maintenance difficulty of different component types in historical maintenance data. By introducing the weighting coefficient, even if two teams have the same ratio of total maintenance hours to available hours, if a team's tasks involve more difficult component types, its calculated task load rate will be higher, more accurately reflecting its workload.

[0092] More specifically, step S443 calculates the standard deviation of the task load rate for all maintenance teams. Standard deviation is a statistic that measures the dispersion of a dataset, and thus can be used as a measure of task load balancing deviation. If the calculated standard deviation (task load balancing deviation) is higher than a preset threshold, it indicates an imbalance in task allocation, requiring task reallocation (such as the skill-based reallocation described in S44) to attempt to reduce the standard deviation until it falls below the preset threshold, thereby achieving a more balanced task allocation. This iterative optimization process based on a quantitative indicator (standard deviation) provides a clear basis and objective for assessing and improving the balance of task allocation.

[0093] More specifically, the above processing method provides a clear and objective indicator to measure the dispersion of task allocation, i.e., the load balancing deviation, by calculating the standard deviation of the task load rate of all work groups. The smaller the standard deviation, the closer the load of each work group is, and the more balanced the allocation. This provides a clear implementation basis and quantitative standard for subsequent task reallocation based on load balancing deviation, which helps to optimize the allocation of maintenance tasks.

[0094] In some preferred embodiments, the step of establishing a deterioration model for key quality indicators of different concrete structural components based on component type, testing time, and testing results includes:

[0095] S21. For the component type, select a degradation model framework that matches the component type from the preset degradation model library. The degradation model framework includes parameter types that determine the parameters required for the degradation model.

[0096] S22. Based on the time and results of each test, and according to the parameter type, the parameters in the deterioration model framework are fitted using the least squares method or the maximum likelihood estimation method to obtain the deterioration model corresponding to the concrete structural member. The deterioration model can characterize the functional relationship of key quality indicators changing over time.

[0097] S23. For the deteriorated model obtained by fitting, calculate its goodness of fit. If the goodness of fit is lower than the preset threshold, identify the abnormal detection data that causes the low goodness of fit based on the results of each detection. After removing the abnormal detection data, refit the deteriorated model based on the detection time and results of each detection after removing the abnormal detection data, until the goodness of fit reaches the preset threshold.

[0098] Specifically, in step S21, the degradation model library can contain various model types, such as linear models, exponential models, power-law models, or models based on physical mechanisms. The selected model framework determines the basic mathematical form of the degradation model and the types of parameters that need to be determined. For example, for a linear model, parameter types may include intercept and slope; for an exponential model, parameter types may include initial values ​​and decay rates. Choosing a suitable model framework is fundamental to establishing an effective degradation model, because the degradation patterns of different component types vary under different environments.

[0099] More specifically, in step S22, commonly used fitting methods include least squares or maximum likelihood estimation. Least squares determines the parameters by minimizing the sum of squared residuals between the model's predicted values ​​and the actual detection results; maximum likelihood estimation estimates the parameters by maximizing the probability of observed data occurrences. Through these fitting processes, a deterioration model is obtained that specifically describes the functional relationship between the key quality indicators of the concrete structural member and time.

[0100] More specifically, in step S23, the evaluation metric is the model's goodness of fit, such as the coefficient of determination (R-squared) or root mean square error (RMSE). Goodness of fit reflects the model's ability to interpret historical detection data. If the calculated goodness of fit is lower than a preset threshold, it indicates that the current model has failed to adequately fit the data, which may be caused by outlier data points in previous detection results. In this case, based on previous detection results, outlier detection data that negatively impact the goodness of fit is identified. Outlier identification methods can employ statistical methods, such as those based on residual analysis, Z-score, IQR (interquartile range), or RANSAC (random sample consensus) algorithms. Identified outlier data is then removed. Subsequently, based on the previous detection times and results after removing outlier data, the parameters of the degraded model are refitted. This process of evaluation, identification, removal, and refitting is iterative until the goodness of fit of the fitted degraded model reaches the preset threshold requirement. Therefore, by introducing goodness-of-fit evaluation and outlier removal mechanisms, the interference of outliers on model fitting can be effectively reduced, improving the fitting accuracy and robustness of the degraded model. The resulting degraded model has higher fitting accuracy, which provides a more reliable data foundation for subsequent model-based prediction and maintenance task selection, thereby improving the effectiveness of the entire quality data visualization management method.

[0101] More specifically, the method of this application establishes a degradation model by selecting an appropriate model framework for different component types and using statistical methods to fit historical test data. During the fitting process, a goodness-of-fit evaluation mechanism is introduced. When the model fit is poor, abnormal test data is automatically identified and removed. Then, the model is refitted based on the cleaned-up data until the goodness-of-fit meets the requirements. This iterative process ensures that the final degradation model can more accurately reflect the actual degradation trend of the component, avoids the negative impact of abnormal data on the model's predictive ability, and solves the problem of abnormal or noisy data in previous test results affecting the accuracy of the degradation model fitting.

[0102] In some preferred embodiments, in step S2, one or more degradation models for key quality indicators are established for each concrete structural member.

[0103] Specifically, the above limitations aim to address the problem that a single degradation model cannot comprehensively reflect the multifaceted degradation state of concrete structural members. In step S2, each concrete structural member can have one or more degradation models corresponding to key quality indicators. This scheme allows for the establishment of corresponding degradation models for different key quality indicators of the member (such as strength, durability, cracks, etc.), or different models can be used for analysis of the same indicator. This approach can more precisely and comprehensively depict the degradation process of the member and capture the impact of different degradation mechanisms on different indicators. Establishing multiple degradation models enables the system to assess the health status of the member from multiple dimensions. Specifically, in step S2, based on the member type, previous inspection times, and previous inspection results obtained from the GIS database, the relevant key quality indicators are identified for each concrete structural member. Ultimately, the concrete structural member will be associated with a set (one or more) of such degradation models, each model corresponding to a key quality indicator, which can more comprehensively reflect the degradation state and development trend of the member in different aspects such as strength, durability, and cracks.

[0104] In some preferred embodiments, the steps of predicting the quality data of key quality indicators of each concrete structural member within a preset time window based on the deterioration model, selecting concrete structural members that need maintenance based on the quality data, and generating predicted deterioration state information associated with the corresponding location coordinates include:

[0105] S31. For each concrete structural component, based on the deterioration model and combined with Monte Carlo simulation, generate multiple predicted deterioration curves for key quality indicators within a preset time window.

[0106] S32. Determine whether the quality data of each predicted deterioration curve exceeds the maintenance threshold within the preset time window, and record the number of predicted deterioration curves that exceed the maintenance threshold for each concrete structural component.

[0107] S33. If the number of predicted deterioration curves exceeding the maintenance threshold is greater than the preset ratio, the corresponding concrete structural member is defined as the concrete structural member that needs maintenance, and the predicted deterioration status information associated with the corresponding location coordinates is obtained based on the predicted deterioration curves exceeding the maintenance threshold.

[0108] Specifically, the Monte Carlo simulation introduced in step S31 can generate a large number of prediction samples based on the uncertainty of the degradation process reflected by the degradation model, forming multiple possible degradation paths, thereby more comprehensively depicting the probability distribution of the future degradation state of the component and overcoming the limitations of traditional deterministic prediction methods.

[0109] More specifically, step S32 is a preliminary analysis of the Monte Carlo simulation results. By comparing each predicted curve with a preset maintenance threshold, the probability of a component deteriorating beyond the limit within a preset time window can be quantified, providing a data basis for subsequent risk assessment and screening.

[0110] More specifically, in step S33, the screening method based on the number of predicted curves exceeding the threshold (i.e., the probability of exceeding the threshold) makes the screening process more scientific and robust. It can identify components that have a high risk of deterioration exceeding the standard in the future, avoiding misjudgments or omissions that may occur when judging based on a single deterministic prediction result. At the same time, based on the predicted deterioration curves that exceed the maintenance threshold, the predicted deterioration status information associated with the corresponding location coordinates is obtained, and the predicted deterioration status information is clearly defined, including the predicted occurrence time, problem type, and severity. This provides more detailed and instructive prediction results.

[0111] More specifically, through the above steps, the prediction, filtering, and information generation processes take into account the uncertainty of the degradation process, perform filtering based on probability, and generate detailed information including time, type, and severity, thereby solving the problem of insufficient precision and robustness in prediction, filtering, and information generation.

[0112] More specifically, through the above technical solution, the method of this application solves the problems of existing prediction methods failing to fully consider the uncertainty of the degradation process and the inaccuracy of prediction results. It provides a probability-based screening mechanism, improves the scientificity and robustness of identifying components that need future maintenance, and generates predicted degradation status information containing detailed information such as the predicted occurrence time, problem type and severity, providing sufficient basis for the planning of subsequent maintenance tasks.

[0113] In some preferred embodiments, step S3, the predicted degradation status information includes the predicted occurrence time, problem type, and severity. The step of obtaining the predicted degradation status information associated with the corresponding location coordinates based on the predicted degradation curve exceeding the maintenance threshold includes:

[0114] S331. Extract the time points when each predicted degradation curve exceeds the maintenance threshold, and calculate the average of the time points as the predicted occurrence time.

[0115] S332. Determine the problem type based on the type of key quality indicators;

[0116] S333. Extract the quality data corresponding to the predicted occurrence time from the predicted degradation curve that exceeds the maintenance threshold, calculate the mean of the quality data, and determine the severity according to the preset severity level classification standard based on the difference between the mean data and the maintenance threshold.

[0117] S334. Integrate the predicted occurrence time, problem type and severity to generate predicted deterioration status information associated with the location coordinates of the corresponding concrete structural components.

[0118] Specifically, step S331 extracts the time point at which each predicted degradation curve exceeding the maintenance threshold first exceeds the threshold, and calculates the mean of these time points to obtain a statistically significant and representative predicted occurrence time. This step addresses the differences in the time dimension among multiple curves, providing a single time point for subsequent analysis.

[0119] More specifically, step S332 directly determines the problem type based on the type of key quality indicators. Different key quality indicators, such as crack width, rebar corrosion rate, and concrete strength, typically correspond to different types of defects or deterioration patterns. Linking the indicator type with the problem type gives the predicted problem type a clear physical meaning. For example, if the key quality indicator is crack width, the problem type is determined to be crack; if the key quality indicator is rebar corrosion rate, the problem type is determined to be rebar corrosion. This step connects quantitative quality indicators with qualitative problem descriptions.

[0120] More specifically, in step S333, at the predicted occurrence time point determined in step S331, quality data of all predicted degradation curves exceeding the maintenance threshold are extracted at that time point. Then, by comparing the difference between the mean and the maintenance threshold, and based on a preset severity level classification standard, the severity of the problem is quantified. For example, the preset severity level classification standard can be defined as follows: a difference between the mean quality data and the maintenance threshold of less than 10% is considered minor, 10% to 30% is considered moderate, and greater than 30% is considered severe. This method provides a way to quantitatively assess severity based on predicted data, providing information for subsequent maintenance prioritization and resource scheduling. This step utilizes the quality data of all relevant curves at the predicted occurrence time point to provide a comprehensive assessment of the problem's severity.

[0121] More specifically, step S334 integrates the predicted occurrence time, problem type, and severity obtained above, and associates them with the location coordinates of the corresponding concrete structural components to ultimately generate complete predicted degradation status information. This information can be stored in a data structure, for example, containing fields such as component location coordinates, predicted occurrence time, problem type, and severity. This step ensures that the predicted information is closely bound to the specific components and their spatial locations, providing foundational data for subsequent visualization on GIS maps and the generation of maintenance tasks. Through these steps, multiple probabilistic prediction curves are transformed into a structured, deterministic predicted degradation status information. This information contains the key elements required for maintenance decisions and is associated with the component location, allowing it to be directly used for subsequent maintenance task planning and visualization, thus solving the problem of transforming probabilistic predictions into deterministic maintenance decision-making basis.

[0122] In some preferred embodiments, step S31 includes:

[0123] S311. For each concrete structural member, based on the deterioration model and combined with the results of previous tests, determine the initial values ​​of key quality indicators and the probability distribution of deterioration rates.

[0124] S312. Based on the probability distribution of initial values ​​and degradation rates, the Monte Carlo simulation method is used to randomly select multiple sets of sample values ​​of initial values ​​and degradation rates, and generate multiple predicted degradation curves of quality data for key quality indicators within a preset time window.

[0125] Specifically, the above processing aims to provide a more accurate and representative probabilistic input for Monte Carlo simulation by combining the degradation model and historical detection data, thereby generating a prediction curve that can reflect the actual degradation uncertainty.

[0126] More specifically, step S311, for each concrete structural member, determines the initial values ​​of key quality indicators and the probability distribution of the deterioration rate based on the established deterioration model and the member's historical inspection results. This step utilizes historical data to quantify the uncertainties in the deterioration process. By combining the theoretical model (deterioration model) and actual observation data (historical inspection results), the possible range and probability distribution of the member's state at the prediction start time (initial value) and its rate of change over time (deterioration rate) can be estimated. If the deterioration model includes one or more rate parameters, the estimated values ​​of these parameters and their confidence intervals or covariance matrices can be used to construct the joint probability distribution of the rate parameters, providing a probabilistic basis based on actual data for subsequent Monte Carlo simulations.

[0127] More specifically, step S312, based on the probability distribution of initial values ​​and degradation rates determined in step S311, uses Monte Carlo simulation to randomly sample multiple sets of initial values ​​and degradation rates. Monte Carlo simulation simulates various possible outcomes of an uncertain process through extensive random sampling. Here, multiple sets of parameter samples are randomly selected from the probability distribution obtained in S311, each set representing a possible combination of initial state and degradation rate for the component. Then, using these randomly selected parameter samples, combined with the degradation model, multiple predicted degradation curves for key quality indicators are calculated and generated within a preset time window. Each curve represents the predicted degradation path of the component under a specific parameter combination. By generating a sufficient number of curves, a distribution of the predicted results can be obtained.

[0128] More specifically, the predicted curve generated in step S312 can more accurately reflect the uncertainties in the actual deterioration process of the component. This method of combining historical data with parameter probabilistics and then performing Monte Carlo simulation makes the distribution of the predicted results closer to the actual situation, thereby improving the reliability of the predicted deterioration curve. This provides a basis for subsequent steps S32 to determine whether the maintenance threshold has been exceeded and for S33 to determine the components that need maintenance and the predicted deterioration status information, thus improving the accuracy of the entire method in screening components that need maintenance and generating a maintenance task list.

[0129] In some preferred embodiments, step S5 includes:

[0130] S51. Extract the task identifier, component location coordinates, maintenance team identifier, and planned time window from the maintenance task list;

[0131] S52. Based on the component location coordinates, locate the corresponding concrete structure component on the GIS map and overlay the maintenance task layer. The maintenance task layer attributes include task identifier, problem type and severity. Different severity levels are coded with different colors.

[0132] S53. Based on the maintenance team identification, locate the corresponding maintenance team's location on the GIS map and display the maintenance team's availability status and skill attribute information.

[0133] S54. Display the planned time window of maintenance tasks on the GIS map in the form of a timeline, and allow maintenance personnel to adjust the planned time of the tasks. The adjusted time information is synchronously updated to the maintenance task list.

[0134] Specifically, the above processing method refines the steps for displaying the maintenance task list on the GIS map, aiming to provide a richer and more interactive visual management interface.

[0135] More specifically, step S51 extracts task identifiers, component location coordinates, maintenance team identifiers, and planned time windows from the maintenance task list. This data forms the basis for subsequent multi-dimensional visualization on the GIS map.

[0136] More specifically, based on the component's location coordinates, step S52 precisely locates the concrete structural components requiring maintenance on the GIS map. By overlaying a maintenance task layer, information such as task identification, problem type, and severity is directly linked to the component's spatial location. For example, components can be represented on the map as marked points or areas, and clicking a mark displays an information box containing the task identification, problem type (e.g., cracks, spalling), and severity (e.g., minor, moderate, severe). Different severity levels are color-coded; for example, minor is represented by green, moderate by yellow, and severe by red, allowing maintenance personnel to quickly distinguish the urgency of the task.

[0137] More specifically, step S53 locates the corresponding maintenance team's location on the GIS map based on the maintenance team identifier. For example, the team's location can be displayed as another type of marker on the map. This step also displays the maintenance team's availability status and skill attribute information. This allows maintenance personnel to understand the location and resource status of the team responsible for the task while viewing the task location.

[0138] More specifically, step S54 displays the planned time window for maintenance tasks on the GIS map in the form of a timeline. For example, a timeline control can be displayed on the side or bottom of the map interface, and the planned start and end times of each task can be represented by bars or markers on the timeline. This step also allows maintenance personnel to adjust the planned times of tasks directly on the GIS map interface. For example, maintenance personnel can modify the planned times by dragging the task bars on the timeline, and the adjusted time information can be synchronously updated in the maintenance task list. This interactive method makes plan adjustments more convenient.

[0139] More specifically, steps S51-54 integrate task information, component location, resource status, and time plan into a visual interface, enabling maintenance personnel to obtain multifaceted information related to task execution from a unified view and make plan adjustments. This creates a visual platform that supports viewing maintenance tasks, understanding resources, and adjusting plans, thereby improving the efficiency of maintenance task execution and management.

[0140] Secondly, please refer to Figure 2 Some embodiments of this application also provide a concrete quality data visualization management system, the system comprising:

[0141] The acquisition module 201 is used to acquire spatial layer information of different concrete structural components based on the GIS database. The spatial layer information includes component type, location coordinates, detection time and detection results.

[0142] Modeling module 202 is used to establish a deterioration model for key quality indicators of different concrete structural components based on component type, inspection time and inspection results, and associate the deterioration model with location coordinates.

[0143] The prediction module 203 is used to predict the quality data of key quality indicators of each concrete structural component within a preset time window based on the deterioration model, and to filter and obtain the concrete structural components that need to be maintained based on the quality data, and generate the predicted deterioration status information associated with the corresponding location coordinates.

[0144] The generation module 204 is used to obtain the comprehensive resource information of the maintenance team and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained.

[0145] Display module 205 is used to display a list of maintenance tasks on a GIS map based on the location coordinates of the concrete structural members that need to be maintained.

[0146] The system in this application utilizes the spatial management capabilities of GIS, the predictive capabilities of degradation models, the scheduling capabilities of resource matching algorithms, and the visualization capabilities of GIS to jointly address the problems of data dispersion, decision lag, and task scheduling difficulties in traditional systems. It achieves dynamic monitoring of concrete structure quality, predictive maintenance planning, and optimal resource allocation, thus improving the efficiency and scientific nature of concrete structure maintenance management and realizing prediction-based maintenance planning and optimal resource allocation.

[0147] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0149] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0150] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for visual management of concrete quality data, characterized in that, The method includes the following steps: S1. Obtain spatial layer information of different concrete structural components based on the GIS database. The spatial layer information includes component type, location coordinates, detection time and detection results. S2. Based on the component type, the time of each test, and the results of each test, establish a deterioration model for key quality indicators for different concrete structural components, and associate the deterioration model with the location coordinates; S3. Based on the deterioration model, predict the quality data of key quality indicators of each concrete structural component within a preset time window, and select the concrete structural components that need to be maintained based on the quality data, and generate the predicted deterioration status information associated with the corresponding location coordinates. S4. Obtain comprehensive resource information of the maintenance team, and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained. S5. Display the maintenance task list on a GIS map based on the location coordinates of the concrete structural components that need maintenance.

2. The concrete quality data visualization management method according to claim 1, characterized in that, The step of generating a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural member that is to be maintained includes: S41. Based on the comprehensive resource information, determine the available man-hours and available equipment types for the maintenance team; S42. Based on the predicted deterioration status information, determine the required maintenance time and equipment type for each concrete structural component that needs maintenance. S43. Based on the available man-hours and available equipment types of the maintenance team, as well as the required man-hours and required equipment types for each concrete structural component to be maintained, a constrained optimization algorithm is applied to generate a maintenance task list with the objective of minimizing the total maintenance time and total transportation distance.

3. The concrete quality data visualization management method according to claim 2, characterized in that, The step of generating a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural member that is to be maintained further includes the following steps: S44. Based on the maintenance task list, calculate the task load balancing deviation of each maintenance team. If the task load balancing deviation exceeds a preset threshold, then based on the skill attribute information of the maintenance team, the tasks that exceed the skill range of the maintenance team are redistributed to maintenance teams with the corresponding skills, and the maintenance task list is regenerated until the task load balancing deviation is lower than the preset threshold.

4. The concrete quality data visualization management method according to claim 3, characterized in that, The step of calculating the task load balancing deviation of each maintenance team based on the maintenance task list includes: S441. Calculate the number of tasks assigned to each maintenance team and the total maintenance man-hours required for each task in the maintenance task list, and record the component type corresponding to each task; S442. Calculate the task load rate of each maintenance team based on the number of tasks, total maintenance hours and component type. The task load rate is equal to the total maintenance hours divided by the available hours and multiplied by a weighting coefficient related to the component type. The weighting coefficient is determined based on the average maintenance difficulty of different component types in historical data. S443. Calculate the standard deviation of the task load rate of all maintenance teams as the task load balancing deviation.

5. The concrete quality data visualization management method according to claim 1, characterized in that, The steps for establishing a deterioration model for key quality indicators of different concrete structural components based on the component type, the time of each test, and the results of each test include: S21. For the component type, select a degradation model framework that matches the component type from a preset degradation model library. The degradation model framework includes parameter types for determining the parameters required for the degradation model. S22. Based on the previous detection times and results, and according to the parameter type, the parameters in the deterioration model framework are fitted using the least squares method or the maximum likelihood estimation method to obtain a deterioration model corresponding to the concrete structural member. The deterioration model can characterize the functional relationship between the key quality indicators and time. S23. For the deteriorated model obtained by fitting, calculate its goodness of fit. If the goodness of fit is lower than the preset threshold, then based on the previous detection results, identify the abnormal detection data that causes the low goodness of fit, and remove the abnormal detection data. Based on the previous detection time and previous detection results after removing the abnormal detection data, refit the deteriorated model until the goodness of fit reaches the preset threshold.

6. A method for visual management of concrete quality data according to claim 1 or 5, characterized in that, In step S2, one or more deterioration models for key quality indicators are established for each concrete structural member.

7. The concrete quality data visualization management method according to claim 1, characterized in that, The steps of predicting the quality data of key quality indicators of each concrete structural member within a preset time window based on the deterioration model, filtering and obtaining the concrete structural members that need maintenance based on the quality data, and generating predicted deterioration state information associated with the corresponding location coordinates include: S31. For each concrete structural component, based on the aforementioned degradation model and combined with Monte Carlo simulation, generate multiple predicted degradation curves for key quality indicators within a preset time window. S32. Determine whether the quality data of each predicted deterioration curve exceeds the maintenance threshold within the preset time window, and record the number of predicted deterioration curves that exceed the maintenance threshold for each concrete structural component. S33. If the number of predicted deterioration curves exceeding the maintenance threshold is greater than the preset ratio, the corresponding concrete structural member is defined as the concrete structural member that needs maintenance, and the predicted deterioration status information associated with the corresponding location coordinates is obtained based on the predicted deterioration curves exceeding the maintenance threshold.

8. The concrete quality data visualization management method according to claim 7, characterized in that, In step S3, the predicted degradation status information includes the predicted occurrence time, problem type, and severity. The step of obtaining the predicted degradation status information associated with the corresponding location coordinates based on the predicted degradation curve exceeding the maintenance threshold includes: S331. Extract the time points when each predicted degradation curve exceeds the maintenance threshold, and calculate the average of the time points as the predicted occurrence time. S332. Determine the problem type based on the type of the key quality indicators; S333. Extract the quality data corresponding to the predicted occurrence time from the predicted degradation curve that exceeds the maintenance threshold, calculate the mean of the quality data, and determine the severity according to the difference between the mean of the data and the maintenance threshold, based on the preset severity level classification standard. S334. Integrate the predicted occurrence time, the problem type, and the severity to generate predicted deterioration status information associated with the position coordinates of the corresponding concrete structural member.

9. The concrete quality data visualization management method according to claim 1, characterized in that, Step S5 includes: S51. Extract the task identifier, component location coordinates, maintenance team identifier, and planned time window from the maintenance task list; S52. Based on the coordinates of the component location, locate the corresponding concrete structure component on the GIS map and overlay the maintenance task layer. The maintenance task layer attributes include task identifier, problem type and severity, wherein different severity levels are coded with different colors. S53. Based on the maintenance team identifier, locate the corresponding maintenance team's location on the GIS map and display the maintenance team's availability status and skill attribute information. S54. Display the planned time window of the maintenance task in the form of a timeline on the GIS map, and allow maintenance personnel to adjust the planned time of the task. The adjusted time information is synchronously updated to the maintenance task list.

10. A concrete quality data visualization management system, characterized in that, The system includes: The acquisition module is used to acquire spatial layer information of different concrete structural components based on a GIS database. The spatial layer information includes component type, location coordinates, detection time, and detection results. The modeling module is used to establish a deterioration model for key quality indicators of different concrete structural components based on the component type, the time of each inspection, and the results of each inspection, and to associate the deterioration model with the location coordinates. The prediction module is used to predict the quality data of key quality indicators of each concrete structural component within a preset time window based on the deterioration model, and to filter and obtain the concrete structural components that need to be maintained based on the quality data, and generate predicted deterioration status information associated with the corresponding location coordinates. The generation module is used to obtain the comprehensive resource information of the maintenance team and generate a maintenance task list based on the comprehensive resource information and the predicted deterioration status information of each concrete structural component that needs to be maintained. The display module is used to display the maintenance task list on a GIS map based on the location coordinates of the concrete structural components that need to be maintained.

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