Intelligent maintenance method and equipment for concrete construction

Abstract

The present application provides an intelligent maintenance method and equipment for concrete construction, which belongs to the field of concrete construction technology. The method can determine the concrete construction data set corresponding to each monitoring equipment group; determine the quality risk score based on the concrete construction data set and the preset quality risk assessment algorithm; determine the maintenance resource allocation level of each preset monitoring grid area based on the concrete construction data set, each quality risk score and the GBDT model, so as to control the maintenance equipment to execute the maintenance strategy corresponding to the maintenance resource allocation level; determine the maintenance resource allocation deviation information corresponding to each preset monitoring grid area according to each maintenance resource allocation level and the corresponding preset resource allocation level curve; perform cluster analysis on each maintenance resource allocation deviation information based on the preset deviation difference analysis model, determine the maintenance grid area to be intervened in the concrete construction monitoring area, generate corresponding intervention maintenance prompt information and send it to the maintenance management terminal.

Description

Technical Field

[0001] The present application relates to the technical field of concrete construction, and in particular to an intelligent maintenance method and equipment for concrete construction. Background Art

[0002] Concrete construction plays a central role in construction projects, and its quality is directly related to the safety and durability of building structures. Curing, a key step in concrete construction, has a decisive impact on the development of concrete performance. Traditional curing strategies rely primarily on the experience of staff and lack accurate data support. This leads to unstable curing results, especially when dealing with complex situations, such as hydration heat issues in large-volume concrete construction. Accurately determining curing measures based on experience alone is difficult, and can lead to excessive temperature stresses, causing cracks and seriously compromising concrete performance.

[0003] Furthermore, the irrational allocation of maintenance resources is a major drawback of traditional maintenance methods. In large-scale construction projects, concrete in different areas varies in structure, size, and environmental conditions. However, traditional methods often employ a uniform allocation of maintenance resources, resulting in uneven resource allocation. Some areas experience excess resources, leading to waste, while others experience insufficient resources, impacting concrete quality. Traditional maintenance methods are difficult to adapt to the increasingly complex building structures and the high demands placed on concrete construction quality.

[0004] Based on this, there is an urgent need for a technical solution for efficient, accurate and intelligent maintenance of concrete during the concrete construction process. Summary of the Invention

[0005] To solve the above problems, the embodiments of the present application provide an intelligent maintenance method and equipment for concrete construction.

[0006] In one aspect, an embodiment of the present application provides an intelligent maintenance method for concrete construction, the method comprising:

[0007] Determining a concrete construction data set corresponding to each monitoring equipment group; wherein one of the monitoring equipment groups is used to collect maintenance monitoring parameters within a preset monitoring grid area of ​​the concrete construction monitoring area;

[0008] Determining a quality risk score for the corresponding preset monitoring grid area based on the concrete construction data set and a preset quality risk assessment algorithm;

[0009] Based on the concrete construction dataset, each of the quality risk scores, and a pre-trained gradient boosting decision tree (GBDT) model, determining the maintenance resource allocation level of each of the preset monitoring grid areas, so as to control the maintenance equipment to execute the maintenance strategy corresponding to the maintenance resource allocation level;

[0010] Determining maintenance resource allocation deviation information corresponding to each of the preset monitoring grid areas according to each of the maintenance resource allocation levels and the corresponding preset resource allocation level curve;

[0011] Based on a preset deviation difference analysis model, cluster analysis is performed on the maintenance resource allocation deviation information to determine the grid area to be intervened for maintenance in the concrete construction monitoring area, so as to generate corresponding intervention maintenance prompt information and send it to the maintenance management terminal.

[0012] In one implementation of the present application, determining the concrete construction data set corresponding to each monitoring equipment group specifically includes:

[0013] Obtaining an initial monitoring data set collected by each monitoring device group for the corresponding preset monitoring network area; wherein the preset monitoring grid area is divided into grid cells having a shape similar to that of the concrete construction monitoring area; the initial monitoring data set includes at least the following maintenance monitoring parameters: stress value, strain value, hydration heat value, ambient temperature value, and ambient humidity value;

[0014] Inputting each of the initial monitoring data groups into a pre-trained Gaussian Markov random field (GMRF) model to determine a maintenance difference feature matrix and fused monitoring data between each of the preset monitoring grid areas based on the model output results; wherein the maintenance difference feature matrix includes a spatial correlation covariance matrix between the initial monitoring data groups of each of the preset monitoring grid areas; and the fused monitoring data includes a monitoring data group adjusted according to the spatial correlation between each of the initial monitoring data groups;

[0015] The maintenance difference feature matrix, the fused monitoring data and the initial monitoring data group are merged to construct the concrete construction data set.

[0016] In one implementation of the present application, determining the quality risk score of the corresponding preset monitoring grid area based on the concrete construction data set and a preset quality risk assessment algorithm specifically includes:

[0017] Calculating, based on the concrete construction data set corresponding to the preset curing monitoring period, the mean value and standard deviation of each parameter corresponding to a first monitoring parameter group; wherein the first monitoring parameter group consists of stress value, strain value, and hydration heat value;

[0018] Inputting the corresponding parameters of the initial monitoring data set of the concrete construction data set at the current monitoring time, the mean value of each parameter, the standard deviation of each parameter, and the preset environmental factor impact index into the preset quality risk assessment algorithm to calculate the quality risk score of the corresponding preset monitoring grid area;

[0019] The formula of the preset quality risk assessment algorithm is as follows:

[0020]

[0021] Among them, R i represents the quality risk score of the i-th preset monitoring grid area; σ i represents the stress value of the i-th preset monitoring grid area at the current monitoring moment; μ σ represents the mean stress value of the preset monitoring grid area i during the preset maintenance monitoring period; σ σ represents the standard deviation of the stress value of the i-th preset monitoring grid area during the preset maintenance monitoring period; ε i represents the strain value of the i-th preset monitoring grid area at the current monitoring moment; μ ε represents the mean strain value of the u-th preset monitoring grid area during the preset maintenance monitoring period; σ ε represents the standard deviation of the strain value of the i-th preset monitoring grid area during the preset maintenance monitoring period; Q i represents the hydration heat value of the i-th preset monitoring grid area at the current monitoring moment; μ Q represents the average hydration heat value of the i-th preset monitoring grid area during the preset maintenance monitoring period; σ Q represents the standard deviation of the calorific value of hydration of the preset monitoring grid area in the preset maintenance monitoring period; e is a natural constant; λ is the influence coefficient of the preset environmental factors; E i represents the preset environmental factor impact index of the i-th preset monitoring grid area at the current monitoring moment.

[0022] In one implementation of the present application, the preset environmental factor impact index is obtained based on the ambient temperature value, the ambient humidity value, and a preset environmental factor impact index calculation formula; wherein the preset environmental factor impact index calculation formula is specifically as follows:

[0023]

[0024] Among them, T i represents the ambient temperature value of the i-th preset monitoring grid area at the current monitoring moment; T opt represents the preset optimal ambient temperature value at the current monitoring moment; T ad Indicates the preset adjustable ambient temperature range value at the current monitoring moment; H i represents the ambient humidity value of the i-th preset monitoring grid area at the current monitoring moment; H optrepresents the preset optimal ambient humidity value at the current monitoring moment; H ad represents the preset adjustable ambient humidity range value at the current monitoring moment; α is the preset ambient temperature influence weight; β is the preset ambient humidity influence weight.

[0025] In one implementation of the present application, based on the concrete construction dataset, each of the quality risk scores, and a pre-trained gradient boosting decision tree (GBDT) model, the maintenance resource allocation level of each of the preset monitoring grid areas is determined, specifically including:

[0026] Constructing a model input feature vector based on the concrete construction dataset and each of the quality risk scores;

[0027] Inputting the model into the GBDT model of the feature vector to determine the maintenance resource demand prediction value according to the model output result; wherein the GBDT model is trained based on a number of historical maintenance data samples and their corresponding quality risk scores;

[0028] After determining that the maintenance resource demand forecast value is greater than a first risk threshold, determining the maintenance resource allocation level to be a first allocation level, so that the maintenance equipment performs a first maintenance strategy with a first heating power and a curing agent spraying cycle of a first spraying cycle;

[0029] After determining that the maintenance resource demand forecast value is less than or equal to the first risk threshold and greater than or equal to the second risk threshold, determining the maintenance resource allocation level to be a second allocation level, so that the maintenance equipment executes a second maintenance strategy of a second heating power and a curing agent spraying cycle of a second spraying cycle; wherein the second heating power is less than the first heating power, and the second spraying cycle is greater than the first spraying cycle;

[0030] After determining that the maintenance resource demand forecast value is less than the second risk threshold, the maintenance resource allocation level is determined to be the third allocation level, so that the maintenance equipment performs heating maintenance with a third heating time and the maintenance agent spraying cycle is a third maintenance strategy of a third spraying cycle; wherein, the third heating time is less than the preset normal heating time, the third spraying cycle is greater than the preset normal spraying cycle, and the preset normal spraying cycle is greater than the second spraying cycle.

[0031] In one implementation of the present application, determining maintenance resource allocation deviation information corresponding to each of the preset monitoring grid areas according to each of the maintenance resource allocation levels and the corresponding preset resource allocation level curves specifically includes:

[0032] Matching each maintenance resource allocation level with the corresponding preset resource allocation level curve according to the time correspondence to determine whether there is an allocation level offset in each preset monitoring grid area; wherein the preset resource allocation level curve includes the correspondence between the maintenance resource allocation level and the maintenance monitoring time within a preset maintenance monitoring period;

[0033] If so, the allocation level offset between each of the preset monitoring grid areas is determined, and an allocation level offset vector corresponding to the preset monitoring grid area is generated, and the allocation level offset vector is used as the maintenance resource allocation deviation information.

[0034] In one implementation of the present application, before determining the maintenance resource allocation deviation information corresponding to each of the preset monitoring grid areas based on each of the maintenance resource allocation levels and the corresponding preset resource allocation level curve, the method further includes:

[0035] Obtaining historical maintenance resource allocation data corresponding to the concrete construction monitoring area through a preset concrete construction sample database;

[0036] Performing curve fitting on the historical maintenance resource allocation data through a nonlinear regression model to generate an initial resource allocation grade curve corresponding to each of the preset monitoring grid areas;

[0037] Determining a continuous maintenance resource allocation level sequence corresponding to each of the preset monitoring grid areas within a preset sampling time to generate a sampling resource allocation level curve;

[0038] Calculate the similarity of level changes between the sampled resource allocation level curve and the initial resource allocation level curve, and determine that the initial resource allocation level curve is the corresponding preset resource allocation level curve when the level change similarity is greater than a preset threshold; otherwise, add the historical maintenance resource allocation data to the continuous maintenance resource allocation level sequence and refit the initial resource allocation level curve.

[0039] In one implementation of the present application, cluster analysis is performed on each of the maintenance resource allocation deviation information based on a preset deviation difference analysis model to determine the maintenance grid area to be intervened in the concrete construction monitoring area, specifically including:

[0040] Inputting each of the maintenance resource allocation deviation information into the pre-trained preset deviation difference analysis model, so that the preset deviation difference analysis model traverses each of the maintenance resource allocation deviation information according to a preset area radius and a minimum number of samples to generate a corresponding plurality of clusters; wherein the preset deviation difference analysis model is a density-based clustering algorithm DBSCAN model; and one cluster includes at least one of the preset monitoring grid areas;

[0041] According to the deviation mean and preset deviation threshold corresponding to each of the clusters, the cluster that meets the preset intervention maintenance condition is determined, and the preset monitoring grid area corresponding to the cluster is used as the grid area to be intervened for maintenance; the preset intervention maintenance condition is that the deviation mean is greater than the preset deviation threshold; the preset deviation threshold is obtained based on the sum of the average value of each of the deviation means and the standard deviation of each of the deviation means of a preset multiple.

[0042] In one implementation of the present application, the method further includes:

[0043] Determine the maintenance period subinterval corresponding to the current monitoring moment according to the preset maintenance monitoring period; wherein different maintenance period subintervals correspond to different data collection time intervals;

[0044] Determining a corresponding data collection time interval according to the maintenance period subinterval;

[0045] The next data collection time is determined based on the data collection time adjacent to the current monitoring time and the data collection time interval, so as to control the monitoring equipment group to obtain the maintenance monitoring parameters; the monitoring equipment group includes at least: a strain gauge, a temperature sensor, a humidity sensor and a hydration heat sensor arranged in a three-dimensional grid.

[0046] On the other hand, an embodiment of the present application further provides an intelligent maintenance device for concrete construction, the device comprising:

[0047] At least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent maintenance method for concrete construction as described above.

[0048] Compared with the prior art, this application has the following significant effects:

[0049] Through the above-mentioned scheme, this application integrates multi-parameter monitoring technology to capture key data of the concrete interior and environment in real time, providing a scientific basis for maintenance decision-making, significantly improving the accuracy and intelligence level of maintenance operations, reducing reliance on manual experience, and making the maintenance process more scientific and efficient. This application conducts a quantitative assessment of concrete quality risks and intelligently adjusts the allocation of maintenance resources based on the assessment results, achieving optimal resource allocation, avoiding excessive or insufficient resource investment, effectively reducing maintenance costs, and improving resource utilization efficiency. In addition, this application can also promptly detect deviations in the maintenance process, use a preset deviation difference analysis model for precise positioning, generate targeted intervention maintenance prompts, and ensure the timeliness and effectiveness of maintenance measures. Furthermore, this application improves the intelligence and accuracy of concrete maintenance, reduces reliance on manual experience, and achieves the rational allocation and efficient utilization of maintenance resources, providing a strong guarantee for improving the quality of concrete construction and the safety and stability of construction projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0051] Figure 1 A schematic flow chart of an intelligent maintenance method for concrete construction according to an embodiment of the present application;

[0052] Figure 2 This is a structural diagram of an intelligent maintenance equipment for concrete construction in an embodiment of the present application. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The embodiments of the present application provide an intelligent maintenance method and equipment for concrete construction, which are used to solve technical problems existing in traditional concrete maintenance, such as extensiveness, excessive reliance on manual experience, difficulty in efficient, accurate and intelligent maintenance, and unreasonable allocation of maintenance resources.

[0055] The following describes in detail various embodiments of the present application with reference to the accompanying drawings.

[0056] The embodiment of the present application provides an intelligent maintenance method for concrete construction, such as Figure 1 As shown, the method may include steps S101-S105:

[0057] S101: The server determines the concrete construction data set corresponding to each monitoring equipment group.

[0058] Among them, a monitoring equipment group is used to collect maintenance monitoring parameters within a preset monitoring grid area of ​​the concrete construction monitoring area.

[0059] It should be noted that the server, as the executor of the intelligent curing method for concrete construction, is merely an example. This application does not specifically limit the executor to servers. The executor may also be an electronic device connected to the curing equipment, either wired or wirelessly; curing equipment includes, but is not limited to, heating equipment, humidification equipment, and film curing equipment.

[0060] In the embodiment of the present application, the above-mentioned determination of the concrete construction data set corresponding to each monitoring equipment group specifically includes:

[0061] Obtain the initial monitoring data group collected by each monitoring equipment group for the corresponding preset monitoring network area. The preset monitoring grid area is divided by grid units with a shape similar to that of the concrete construction monitoring area. The initial monitoring data group includes at least the following maintenance monitoring parameters: stress value, strain value, hydration heat value, ambient temperature value and ambient humidity value. Input each initial monitoring data group into a pre-trained Gaussian Markov random field GMRF model to determine the maintenance difference feature matrix and fused monitoring data between each preset monitoring grid area based on the model output result. The maintenance difference feature matrix includes the spatial correlation covariance matrix between the initial monitoring data groups of each preset monitoring grid area. The fused monitoring data includes the monitoring data group adjusted according to the spatial correlation between each initial monitoring data group. The maintenance difference feature matrix, the fused monitoring data and the initial monitoring data group are merged to construct a concrete construction data set.

[0062] That is to say, the present application deploys monitoring equipment groups for different preset monitoring grid areas. The monitoring equipment groups can be arranged in a three-dimensional grid, that is, each device in the same monitoring equipment group is placed on a three-dimensional grid node in the same preset monitoring grid area. The three-dimensional grid node can be a key area in the preset monitoring grid area, such as a beam-column node, a large-span slab, a wall corner, etc. The specific deployment can be made by the user according to the actual usage scenario, and the present application does not make specific restrictions on this. The server can obtain the maintenance monitoring parameters collected by the monitoring equipment group within the preset maintenance monitoring period to form an initial monitoring data group. The preset monitoring grid area is obtained by dividing the grid units with a shape similar to the concrete construction monitoring area. For example, if the concrete construction monitoring area is a rectangular parallelepiped, the grid unit is a small rectangular parallelepiped grid area with a similar shape. The concrete construction monitoring area is divided by an integer multiple of the grid units to obtain an integer multiple of the preset monitoring grid area. For example, if the concrete construction monitoring area is divided by 10 grid units, the preset monitoring grid area is 10.

[0063] After the server obtains the initial monitoring data set for each preset monitoring grid area, the initial monitoring data set can be input into a pre-trained Gaussian Markov Random Field (GMRF) model for processing. The GMRF model predefines the neighborhood structure and constructs the covariance matrix. It is then trained using a number of historical monitoring data set samples that have undergone data preprocessing (such as cleaning outliers and filling missing values). The trained GMRF model can output fused monitoring data (including maintenance monitoring parameters for different preset monitoring grid areas) that fuse stress values, strain values, hydration heat values, ambient temperature values, and ambient humidity values. These data also need to be organized into a format acceptable to the GBDT model, such as a two-dimensional array or data frame, where each row represents a sample and each column represents a feature. A spatial correlation covariance matrix (i.e., a maintenance difference feature matrix) is generated. This spatial correlation covariance matrix is ​​used to characterize the spatial correlation between the data in each initial monitoring data set. Before being input into the Gradient Boosting Decision Tree (GBDT) model, this matrix requires feature engineering processing, such as calculating the stress value difference and average value between adjacent preset monitoring grid areas. The server adds the obtained maintenance difference feature matrix, the fused monitoring data, and each of the initial monitoring data sets to the concrete construction dataset, which can be stored in a database connected to the server.

[0064] Through the above scheme, the GMRF model can be used to fuse data, capture the spatial dependencies between data, and construct a dataset containing spatial correlation relationships for subsequent reference.

[0065] In one embodiment of the present application, since the preset maintenance monitoring period may be relatively long, such as 7 days or 14 days, in order to effectively acquire monitoring data and reduce ineffective monitoring, the method further includes:

[0066] Based on the preset maintenance monitoring period, the maintenance period subinterval corresponding to the current monitoring moment is determined. Different maintenance period subintervals correspond to different data collection time intervals. The corresponding data collection time interval is determined based on the maintenance period subinterval. Based on the data collection moments and data collection time intervals adjacent to the current monitoring moment, the next data collection moment is determined to control the monitoring equipment group to obtain maintenance monitoring parameters. The monitoring equipment group includes at least the following: a strain gauge, a temperature sensor, a humidity sensor, and a hydration heat sensor arranged in a three-dimensional grid.

[0067] That is to say, this application can pre-divide the maintenance period into sub-intervals according to the preset maintenance monitoring period, for example, the first day is the first maintenance period sub-interval, and the second to third days are the second maintenance period sub-interval... At the same time, the data collection time monitoring is pre-specified for different first maintenance period sub-intervals, and the monitoring equipment group performs data collection according to the data collection time interval or the server performs steps S101-S105 of this application according to the data collection time interval. The data collection time interval can be set by the user according to the actual usage scenario, and this application does not make specific restrictions on this. This application can determine the data collection moment closest to the current monitoring moment through the current monitoring moment, and calculate the time difference between the current monitoring period and the most recent data collection period. The next data collection moment is calculated through the time difference and the data collection time interval. For example, if the time difference is 30 minutes and the data collection time interval is 40 minutes, then the next data collection moment is ten minutes later. The maintenance monitoring parameters collected by each sensor of the monitoring equipment group are obtained at the data collection moment determined by the server.

[0068] It should be noted that the present application can execute S101-S105 in real time, or it can be executed according to the data collection time interval through the above embodiment. The specific setting can be made by the user according to the actual usage scenario and needs, and is not specifically limited here.

[0069] S102: The server determines the quality risk score of the corresponding preset monitoring grid area based on the concrete construction data set and the preset quality risk assessment algorithm.

[0070] In the embodiment of the present application, the quality risk score of the corresponding preset monitoring grid area is determined based on the concrete construction data set and the preset quality risk assessment algorithm, specifically including:

[0071] Based on the concrete construction dataset corresponding to the preset maintenance monitoring period, the mean and standard deviation of each parameter corresponding to the first monitoring parameter group are calculated. The first monitoring parameter group consists of stress values, strain values, and hydration heat values. The corresponding parameters, their mean and standard deviations, and the preset environmental factor impact index in the initial monitoring data group of the concrete construction dataset at the current monitoring time are input into the preset quality risk assessment algorithm to calculate the quality risk score for the corresponding preset monitoring grid area.

[0072] In other words, the present application can calculate the parameter mean and parameter standard deviation corresponding to the stress value, strain value and hydration heat value through the preset quality risk assessment algorithm and concrete construction data set, and then calculate the quality risk score of the preset monitoring grid area.

[0073] Among them, the formula of the preset quality risk assessment algorithm is as follows:

[0074]

[0075] Among them, R i represents the quality risk score of the i-th preset monitoring grid area; σ i represents the stress value of the i-th preset monitoring grid area at the current monitoring moment; μ σ represents the mean stress value of the i-th preset monitoring grid area during the preset maintenance monitoring period; σ σ represents the standard deviation of the stress value of the i-th preset monitoring grid area during the preset maintenance monitoring period; ε i represents the strain value of the i-th preset monitoring grid area at the current monitoring moment; μ ε represents the mean strain value of the i-th preset monitoring grid area during the preset maintenance monitoring period; σ ε represents the standard deviation of the strain value of the i-th preset monitoring grid area during the preset maintenance monitoring period; Q i represents the hydration heat value of the i-th preset monitoring grid area at the current monitoring moment; μ Q represents the mean hydration heat value of the i-th preset monitoring grid area during the preset maintenance monitoring period; σ Q represents the standard deviation of the calorific value of hydration in the preset monitoring grid area during the preset maintenance monitoring period; e is a natural constant; λ is the influence coefficient of the preset environmental factors; E i represents the impact index of the preset environmental factor in the i-th preset monitoring grid area at the current monitoring moment. The above stress value can be derived from the strain value obtained by the strain gauge.

[0076] The above-mentioned preset environmental factor impact index is obtained based on the ambient temperature value, the ambient humidity value, and the preset environmental factor impact index calculation formula; the preset environmental factor impact index calculation formula is as follows:

[0077]

[0078] Among them, T i represents the ambient temperature value of the i-th preset monitoring grid area at the current monitoring moment; T opt Indicates the preset optimal ambient temperature value at the current monitoring moment; T ad Indicates the preset adjustable ambient temperature range value at the current monitoring moment; H i Represents the ambient humidity value of the i-th preset monitoring grid area at the current monitoring moment; H opt Indicates the preset optimal ambient humidity value at the current monitoring moment; H ad It represents the preset adjustable ambient humidity range value at the current monitoring moment; α is the preset ambient temperature influence weight; β is the preset ambient humidity influence weight.

[0079] In addition, this application can preset a risk score threshold. When the quality risk score is greater than the preset risk score threshold, the early warning mechanism can be triggered to send an alarm message to the maintenance management terminal so that relevant personnel can take timely measures to intervene and handle the corresponding preset monitoring grid area to avoid the occurrence of quality accidents.

[0080] The above formula, which considers structural performance parameters such as stress, strain, and calorific value of hydration, as well as the impact of environmental factors on structural quality risk, enables a quantitative assessment of concrete quality risk. Furthermore, the calculated quality risk score can promptly identify potential risk points in the concrete structure. Using this formula for quality risk assessment enables automated data processing and real-time monitoring. This not only reduces manual intervention and errors but also improves management efficiency, enabling relevant personnel to more promptly and accurately understand the quality risk status of the structure and implement appropriate management measures.

[0081] S103: The server determines the maintenance resource allocation level of each preset monitoring grid area based on the concrete construction dataset, each quality risk score, and the pre-trained gradient boosting decision tree (GBDT) model, so as to control the maintenance equipment to execute the maintenance strategy corresponding to the maintenance resource allocation level.

[0082] In the embodiment of the present application, based on the concrete construction dataset, each quality risk score and the pre-trained gradient boosting decision tree (GBDT) model, the maintenance resource allocation level of each preset monitoring grid area is determined, specifically including:

[0083] Based on the concrete construction dataset and the quality risk scores, a model input feature vector is constructed. This feature vector is then fed into the GBDT model, which then uses the model output to determine the predicted maintenance resource demand. The GBDT model is trained using several historical maintenance data samples and their corresponding quality risk scores.

[0084] That is to say, the present application can pre-train the initialized GBDT model (including setting model parameters, such as learning rate, number of iterations, number and depth of trees, etc.) through several historical maintenance data samples and quality risk scores, and the corresponding pre-labeled maintenance resource demand prediction values. The historical maintenance data samples and quality risk scores can be data that have undergone data cleaning, missing value processing, and feature selection. Before the GBDT model is trained, the historical maintenance data samples and quality risk scores are encoded into vectors, which contain sample elements encoded from the maintenance difference feature matrix, fused monitoring data, and initial monitoring data groups. Through the training data, the GBDT model iteratively trains a series of decisions, and in each round of iteration, adjusts the new decision tree according to the prediction error of the current model until the output accuracy of the GBDT model is greater than the preset accuracy threshold. Subsequently, the server encodes the concrete construction data set and each quality risk score separately, and constructs a model input feature vector corresponding to the preset monitoring grid area. The trained GBDT model is used to predict the model input feature vector to obtain the maintenance resource demand prediction value of the preset monitoring grid area.

[0085] Furthermore, when the predicted maintenance resource demand value is determined to be greater than the first risk threshold, the maintenance resource allocation level is determined to be the first allocation level, so that the maintenance equipment implements the first maintenance strategy with the first heating power and the first curing agent spraying cycle. For example, the heating equipment power is increased to maximum and the curing agent spraying cycle is shortened to 50% of the preset normal spraying cycle.

[0086] After determining that the predicted maintenance resource demand value is less than or equal to the first risk threshold and greater than or equal to the second risk threshold, the maintenance resource allocation level is set to the second allocation level, causing the maintenance equipment to implement a second maintenance strategy with a second heating power and a curing agent spraying cycle of a second spraying cycle. The second heating power is less than the first heating power, and the second spraying cycle is greater than the first spraying cycle. For example, the heating equipment power is maintained at a medium level, and the curing agent spraying cycle is shortened to 75% of the preset normal spraying cycle.

[0087] After determining that the predicted maintenance resource demand value is less than the second risk threshold, the maintenance resource allocation level is determined to be the third allocation level, so that the maintenance equipment performs heating maintenance for a third heating duration and the curing agent spraying cycle is a third spraying cycle. The third heating duration is less than the preset normal heating duration, the third spraying cycle is greater than the preset normal spraying cycle, and the preset normal spraying cycle is greater than the second spraying cycle. For example, the heating equipment usage time is reduced to 70% of the preset normal heating duration, and the curing agent spraying cycle is extended to 120% of the preset normal spraying cycle.

[0088] Through the above scheme, a reasonable strategy for the allocation of maintenance resources can be set. Combined with the spatial correlation between the preset monitoring grid areas analyzed by GMRF, the mutual influence of maintenance between different areas can be obtained. The GBDT model can be used to further reasonably obtain the predicted value of maintenance resource demand, thereby obtaining a more comprehensive and reasonable differentiated maintenance strategy for concrete maintenance.

[0089] S104: The server determines maintenance resource allocation deviation information corresponding to each preset monitoring grid area according to each maintenance resource allocation level and the corresponding preset resource allocation level curve.

[0090] In the embodiment of the present application, the maintenance resource allocation deviation information corresponding to each preset monitoring grid area is determined based on each maintenance resource allocation level and the corresponding preset resource allocation level curve, specifically including:

[0091] Based on the time correspondence, each maintenance resource allocation level is matched with the corresponding preset resource allocation level curve to determine whether there is an allocation level offset in each preset monitoring grid area. The preset resource allocation level curve includes the correspondence between the maintenance resource allocation level and the maintenance monitoring time within the preset maintenance monitoring period. If a preset monitoring grid area is determined to have an allocation level offset, the allocation level offset between the preset monitoring grid areas is determined, and an allocation level offset vector corresponding to the preset monitoring grid area is generated. The allocation level offset vector is used as maintenance resource allocation deviation information.

[0092] In other words, the server can match the maintenance resource allocation level preset by the curve from the preset resource allocation level curve according to the collection time (monitoring time) of the maintenance detection parameters corresponding to the maintenance resource allocation level, which is hereinafter referred to as the predetermined level. The server calculates whether the difference between the maintenance resource allocation level and the predetermined level is greater than the preset deviation. If it is 1, it means that there is an allocation level offset. At this time, the server calculates the allocation level offset between each preset monitoring grid area. Among them, the calculation formula of the allocation level offset is as follows:

[0093]

[0094] Among them, O ij represents the allocation level offset between the ith preset monitoring grid area and the jth preset monitoring grid area; a is the first weight corresponding to the ith preset monitoring grid area, and f is the allocation level offset change weight relative to its own preset level; i The difference between the maintenance resource allocation level and the predetermined level for the i-th preset monitoring grid area; is the predetermined level of the i-th preset monitoring grid area; x1 is the first normalized weight for normalizing the difference between the maintenance resource allocation level and the predetermined level; b is the second weight corresponding to the j-th preset monitoring grid area, s+b=1; f j is the difference between the maintenance resource allocation level of the jth preset monitoring grid area and its corresponding predetermined level (obtained according to the preset resource allocation level curve corresponding to the jth preset monitoring grid area); x2 is the second normalization weight used to normalize the difference between the maintenance resource allocation level of the jth preset monitoring grid area and its corresponding predetermined level. The specific values ​​of a, b, x1, x2, and the preset deviation can be adjusted by the user based on actual scenarios and are not specifically limited here.

[0095] After obtaining the allocation level offset of a preset monitoring grid area relative to other preset monitoring grid areas, the allocation level offset vector corresponding to the preset monitoring grid area can be constructed according to the regional distance relationship, thereby obtaining maintenance resource allocation deviation information.

[0096] Furthermore, in another embodiment of the present application, before determining the maintenance resource allocation deviation information corresponding to each preset monitoring grid area based on each maintenance resource allocation level and the corresponding preset resource allocation level curve, the method further includes:

[0097] The historical maintenance resource allocation data corresponding to the concrete construction monitoring area is obtained through the preset concrete construction sample database. The historical maintenance resource allocation data is curve-fitted through the nonlinear regression model to generate the initial resource allocation grade curve corresponding to each preset monitoring grid area. The continuous maintenance resource allocation grade sequence corresponding to each preset monitoring grid area within the preset sampling time is determined to generate the sampling resource allocation grade curve. The grade change similarity of the sampling resource allocation grade curve and the initial resource allocation grade curve is calculated, and when the grade change similarity is greater than the preset threshold, the initial resource allocation grade curve is determined to be the corresponding preset resource allocation grade curve. When the grade change similarity is not greater than the preset threshold, the historical maintenance resource allocation data is added to the continuous maintenance resource allocation grade sequence, and the initial resource allocation grade curve is refitted.

[0098] Specifically, this application pre-sets historical maintenance resource allocation data stored in a database of concrete construction samples and performs nonlinear regression to obtain an initial resource allocation grade curve. During a preset sampling period preceding a preset maintenance monitoring period, the server records the continuous maintenance resource allocation grades during that sampling period to generate a sampled resource allocation grade curve. The server then calculates the curve similarity between the sampled resource allocation grade curve and the initial resource allocation grade curve, and uses this curve similarity as the grade change similarity. The greater the curve similarity, the more similar the curves are. The server may calculate this using algorithms such as the inverse of the Euclidean distance or the Pearson correlation coefficient, though this application does not specify this. If the grade change similarity exceeds a preset threshold, the initial resource allocation grade curve is used as the preset resource allocation grade curve for the corresponding preset monitoring grid area. Otherwise, the initial resource allocation grade curve is refitted, and the refitted initial resource allocation grade curve is used as the preset resource allocation grade curve. This preset threshold is set by the user based on actual usage and is not specified in this application.

[0099] Through the above solution, reliable resource allocation level reference data can be provided for the intelligent maintenance of the concrete construction monitoring area in the future, and a preset resource allocation level curve that best matches the actual construction scenario can be provided.

[0100] S105, the server performs cluster analysis on the maintenance resource allocation deviation information based on a preset deviation difference analysis model, determines the grid area to be intervened for maintenance in the concrete construction monitoring area, generates corresponding intervention maintenance prompt information, and sends it to the maintenance management terminal.

[0101] The maintenance management terminal can be a mobile phone, computer or other device of maintenance-related personnel, and this application does not make specific restrictions on this.

[0102] In the embodiment of the present application, cluster analysis is performed on the maintenance resource allocation deviation information based on a preset deviation difference analysis model to determine the maintenance grid area to be intervened in the concrete construction monitoring area, specifically including:

[0103] The deviation information of each maintenance resource allocation is input into a pre-trained preset deviation difference analysis model, so that the preset deviation difference analysis model traverses the deviation information of each maintenance resource allocation according to the preset field radius and the minimum number of samples to generate corresponding multiple clusters. Among them, the preset deviation difference analysis model is a density-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) model. A cluster includes at least one preset monitoring grid area. According to the deviation mean and the preset deviation threshold corresponding to each cluster, the cluster that meets the preset intervention maintenance condition is determined, and the preset monitoring grid area corresponding to the cluster is used as the maintenance grid area to be intervened. The preset intervention maintenance condition is that the deviation mean is greater than the preset deviation threshold. The preset deviation threshold is obtained based on the sum of the average value of each deviation mean and the standard deviation of each deviation mean of a preset multiple.

[0104] That is to say, this application sets up a DBSCAN model with pre-trained model parameters (including neighborhood radius and minimum number of samples). The server inputs the pre-processed maintenance resource allocation deviation information into the DBSCAN model. The algorithm then traverses each point in the data set corresponding to the input data and determines the category of the point based on the neighborhood radius and the minimum number of samples. If the number of samples of a point in its neighborhood is greater than or equal to the minimum number of samples, then the point is a core point, and the density-connected points are divided into a cluster with the core point as the center; if a point is not a core point and is in the neighborhood of a core point, then the point is a boundary point and belongs to the cluster where the corresponding core point is located; if a point is neither a core point nor in the neighborhood of any core point, then the point is a noise point.

[0105] Subsequently, the server will also calculate the deviations between each cluster and obtain the mean deviation between the cluster and other clusters. If the mean deviation is greater than the preset deviation threshold preset by the user, it means that the cluster needs to be paid special attention, and the preset monitoring grid area corresponding to the cluster will be used as the grid area to be intervened for maintenance. The preset deviation threshold can be the sum of the average value of the mean deviations corresponding to all clusters and the standard deviation of the mean deviations corresponding to all clusters at a preset multiple (such as twice). The preset deviation threshold can also be set in other ways, which are specifically specified by the user and are not specifically limited in this application.

[0106] Through the above-mentioned scheme, this application integrates multi-parameter monitoring technology to capture key data of the concrete interior and environment in real time, providing a scientific basis for maintenance decision-making, significantly improving the accuracy and intelligence level of maintenance operations, reducing reliance on manual experience, and making the maintenance process more scientific and efficient. This application conducts a quantitative assessment of concrete quality risks and intelligently adjusts the allocation of maintenance resources based on the assessment results, achieving optimal resource allocation, avoiding excessive or insufficient resource investment, effectively reducing maintenance costs, and improving resource utilization efficiency. In addition, this application can also promptly detect deviations in the maintenance process, use a preset deviation difference analysis model for precise positioning, generate targeted intervention maintenance prompts, and ensure the timeliness and effectiveness of maintenance measures. Furthermore, this application improves the intelligence and accuracy of concrete maintenance, reduces reliance on manual experience, and achieves the rational allocation and efficient utilization of maintenance resources, providing a strong guarantee for improving the quality of concrete construction and the safety and stability of construction projects.

[0107] Figure 2 A schematic diagram of the structure of an intelligent maintenance device for concrete construction provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the device includes:

[0108] At least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0109] Determine the concrete construction data set corresponding to each monitoring equipment group. Among them, one monitoring equipment group is used to collect maintenance monitoring parameters in a preset monitoring grid area of ​​the concrete construction monitoring area. According to the concrete construction data set and the preset quality risk assessment algorithm, determine the quality risk score of the corresponding preset monitoring grid area. Based on the concrete construction data set, each quality risk score and the pre-trained gradient boosting decision tree GBDT model, determine the maintenance resource allocation level of each preset monitoring grid area to control the maintenance equipment to execute the maintenance strategy corresponding to the maintenance resource allocation level. According to each maintenance resource allocation level and the corresponding preset resource allocation level curve, determine the maintenance resource allocation deviation information corresponding to each preset monitoring grid area. Based on the preset deviation difference analysis model, perform cluster analysis on each maintenance resource allocation deviation information, determine the maintenance grid area to be intervened in the concrete construction monitoring area, generate corresponding intervention maintenance prompt information and send it to the maintenance management terminal.

[0110] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0111] The device and method provided in the embodiments of the present application correspond one to one, and therefore, the device also has similar beneficial technical effects as its corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device will not be repeated here.

[0112] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0113] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An intelligent maintenance method for concrete construction, characterized in that: The method comprises: Determining a concrete construction data set corresponding to each monitoring equipment group; wherein one of the monitoring equipment groups is used to collect maintenance monitoring parameters within a preset monitoring grid area of ​​the concrete construction monitoring area; Determining a quality risk score for the corresponding preset monitoring grid area based on the concrete construction data set and a preset quality risk assessment algorithm; Based on the concrete construction dataset, each of the quality risk scores, and a pre-trained gradient boosting decision tree (GBDT) model, determining the maintenance resource allocation level of each of the preset monitoring grid areas, so as to control the maintenance equipment to execute the maintenance strategy corresponding to the maintenance resource allocation level; Determining maintenance resource allocation deviation information corresponding to each of the preset monitoring grid areas according to each of the maintenance resource allocation levels and the corresponding preset resource allocation level curve; Performing cluster analysis on the maintenance resource allocation deviation information based on a preset deviation difference analysis model to determine the grid area to be intervened for maintenance in the concrete construction monitoring area, so as to generate corresponding intervention maintenance prompt information and send it to the maintenance management terminal; Wherein, according to the concrete construction data set and the preset quality risk assessment algorithm, the quality risk score of the corresponding preset monitoring grid area is determined, specifically including: Calculating, based on the concrete construction data set corresponding to the preset curing monitoring period, the mean value and standard deviation of each parameter corresponding to a first monitoring parameter group; wherein the first monitoring parameter group consists of stress value, strain value, and hydration heat value; Inputting the corresponding parameters of the initial monitoring data set of the concrete construction data set at the current monitoring time, the mean value of each parameter, the standard deviation of each parameter, and the preset environmental factor impact index into the preset quality risk assessment algorithm to calculate the quality risk score of the corresponding preset monitoring grid area; The formula of the preset quality risk assessment algorithm is as follows: in, Indicates the The quality risk score of each of the preset monitoring grid areas; Indicates the the stress value of each of the preset monitoring grid areas at the current monitoring moment; Indicates the The average stress value of each of the preset monitoring grid areas during the preset maintenance monitoring period; Indicates the The standard deviation of the stress values ​​of the preset monitoring grid area during the preset maintenance monitoring period; Indicates the the strain value of each of the preset monitoring grid areas at the current monitoring moment; Indicates the The average strain value of each of the preset monitoring grid areas during the preset maintenance monitoring period; Indicates the The standard deviation of the strain values ​​of the preset monitoring grid area in the preset maintenance monitoring period; Indicates the the calorific value of hydration of each of the preset monitoring grid areas at the current monitoring moment; Indicates the The average calorific value of hydration of each of the preset monitoring grid areas during the preset maintenance monitoring period; Indicates the The standard deviation of the calorific value of hydration of each of the preset monitoring grid areas during the preset maintenance monitoring period; is a natural constant; is the influence coefficient of preset environmental factors; To indicate the The preset environmental factor impact index of each of the preset monitoring grid areas at the current monitoring moment; The preset environmental factor impact index is obtained based on the ambient temperature value, the ambient humidity value, and the preset environmental factor impact index calculation formula; wherein the preset environmental factor impact index calculation formula is specifically as follows: in, Indicates the The ambient temperature value of each of the preset monitoring grid areas at the current monitoring moment; represents the preset optimal ambient temperature value at the current monitoring moment; Indicates the preset adjustable ambient temperature range value at the current monitoring moment; Indicates the The ambient humidity value of each of the preset monitoring grid areas at the current monitoring moment; represents the preset optimal ambient humidity value at the current monitoring moment; Indicates the preset adjustable ambient humidity range value at the current monitoring moment; The preset ambient temperature influence weight; The preset environmental humidity impact weight.

2. The intelligent maintenance method for concrete construction according to claim 1, characterized in that: Determine the concrete construction data set corresponding to each monitoring equipment group, including: Obtaining an initial monitoring data set collected by each monitoring device group for the corresponding preset monitoring network area; wherein the preset monitoring grid area is divided into grid cells having a shape similar to that of the concrete construction monitoring area; the initial monitoring data set includes at least the following maintenance monitoring parameters: stress value, strain value, hydration heat value, ambient temperature value, and ambient humidity value; Inputting each of the initial monitoring data groups into a pre-trained Gaussian Markov random field (GMRF) model to determine a maintenance difference feature matrix and fused monitoring data between each of the preset monitoring grid areas based on the model output results; wherein the maintenance difference feature matrix includes a spatial correlation covariance matrix between the initial monitoring data groups of each of the preset monitoring grid areas; and the fused monitoring data includes a monitoring data group adjusted according to the spatial correlation between each of the initial monitoring data groups; The maintenance difference feature matrix, the fused monitoring data and the initial monitoring data group are merged to construct the concrete construction data set.

3. The intelligent maintenance method for concrete construction according to claim 1, characterized in that: Based on the concrete construction dataset, each quality risk score, and a pre-trained gradient boosting decision tree (GBDT) model, the maintenance resource allocation level of each preset monitoring grid area is determined, specifically including: Constructing a model input feature vector based on the concrete construction dataset and each of the quality risk scores; Inputting the model into the GBDT model of the feature vector to determine the maintenance resource demand prediction value according to the model output result; wherein the GBDT model is trained based on a number of historical maintenance data samples and their corresponding quality risk scores; After determining that the maintenance resource demand forecast value is greater than a first risk threshold, determining the maintenance resource allocation level to be a first allocation level, so that the maintenance equipment performs a first maintenance strategy with a first heating power and a curing agent spraying cycle of a first spraying cycle; After determining that the maintenance resource demand forecast value is less than or equal to the first risk threshold and greater than or equal to the second risk threshold, determining the maintenance resource allocation level to be a second allocation level, so that the maintenance equipment executes a second maintenance strategy of a second heating power and a curing agent spraying cycle of a second spraying cycle; wherein the second heating power is less than the first heating power, and the second spraying cycle is greater than the first spraying cycle; After determining that the maintenance resource demand forecast value is less than the second risk threshold, the maintenance resource allocation level is determined to be the third allocation level, so that the maintenance equipment performs heating maintenance with a third heating time and the maintenance agent spraying cycle is a third maintenance strategy of a third spraying cycle; wherein, the third heating time is less than the preset normal heating time, the third spraying cycle is greater than the preset normal spraying cycle, and the preset normal spraying cycle is greater than the second spraying cycle.

4. The intelligent maintenance method for concrete construction according to claim 1, characterized in that: Determining maintenance resource allocation deviation information corresponding to each preset monitoring grid area according to each maintenance resource allocation level and the corresponding preset resource allocation level curve, specifically including: Matching each maintenance resource allocation level with the corresponding preset resource allocation level curve according to the time correspondence to determine whether there is an allocation level offset in each preset monitoring grid area; wherein the preset resource allocation level curve includes the correspondence between the maintenance resource allocation level and the maintenance monitoring time within a preset maintenance monitoring period; If so, the allocation level offset between each of the preset monitoring grid areas is determined, and an allocation level offset vector corresponding to the preset monitoring grid area is generated, and the allocation level offset vector is used as the maintenance resource allocation deviation information.

5. The intelligent maintenance method for concrete construction according to claim 4, characterized in that: Before determining the maintenance resource allocation deviation information corresponding to each of the preset monitoring grid areas according to each of the maintenance resource allocation levels and the corresponding preset resource allocation level curves, the method further includes: Obtaining historical maintenance resource allocation data corresponding to the concrete construction monitoring area through a preset concrete construction sample database; Performing curve fitting on the historical maintenance resource allocation data through a nonlinear regression model to generate an initial resource allocation grade curve corresponding to each of the preset monitoring grid areas; Determining a continuous maintenance resource allocation level sequence corresponding to each of the preset monitoring grid areas within a preset sampling time to generate a sampling resource allocation level curve; Calculate the similarity of level changes between the sampled resource allocation level curve and the initial resource allocation level curve, and determine that the initial resource allocation level curve is the corresponding preset resource allocation level curve when the level change similarity is greater than a preset threshold; otherwise, add the historical maintenance resource allocation data to the continuous maintenance resource allocation level sequence and refit the initial resource allocation level curve.

6. The intelligent maintenance method for concrete construction according to claim 1, characterized in that: Performing cluster analysis on the maintenance resource allocation deviation information based on a preset deviation difference analysis model to determine the maintenance grid area to be intervened in the concrete construction monitoring area, specifically including: Inputting each of the maintenance resource allocation deviation information into the pre-trained preset deviation difference analysis model, so that the preset deviation difference analysis model traverses each of the maintenance resource allocation deviation information according to a preset area radius and a minimum number of samples to generate a corresponding plurality of clusters; wherein the preset deviation difference analysis model is a density-based clustering algorithm DBSCAN model; and one cluster includes at least one of the preset monitoring grid areas; According to the deviation mean and preset deviation threshold corresponding to each of the clusters, the cluster that meets the preset intervention maintenance condition is determined, and the preset monitoring grid area corresponding to the cluster is used as the grid area to be intervened for maintenance; the preset intervention maintenance condition is that the deviation mean is greater than the preset deviation threshold; the preset deviation threshold is obtained based on the sum of the average value of each of the deviation means and the standard deviation of each of the deviation means of a preset multiple.

7. The intelligent maintenance method for concrete construction according to claim 1, characterized in that: The method further comprises: Determine the maintenance period subinterval corresponding to the current monitoring moment according to the preset maintenance monitoring period; wherein different maintenance period subintervals correspond to different data collection time intervals; Determining a corresponding data collection time interval according to the maintenance period subinterval; The next data collection time is determined based on the data collection time adjacent to the current monitoring time and the data collection time interval, so as to control the monitoring equipment group to obtain the maintenance monitoring parameters; the monitoring equipment group includes at least: a strain gauge, a temperature sensor, a humidity sensor and a hydration heat sensor arranged in a three-dimensional grid.

8. An intelligent maintenance device for concrete construction, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent maintenance method for concrete construction as described in any one of claims 1 to 7 above.

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