Concrete curing device for constructional engineering

Abstract

The invention discloses a concrete curing device for constructional engineering, and belongs to the technical field of constructional engineering. The device comprises the following modules: a data acquisition and sensing module for acquiring real-time parameters of the interior and surface of concrete through a temperature and humidity sensing network and a plurality of detection devices so as to support subsequent data analysis and decision; the internal temperature control execution module is used for controlling the internal temperature of the concrete through a snake-shaped cooling pipeline and precise flow regulation and maintaining the concrete within a proper curing temperature range; the surface curing execution module is used for continuously curing the concrete surface, and all-directional spraying of concrete surface curing moisture and cyclic utilization of water resources are achieved; the central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, and intelligent control and remote monitoring of the maintenance process are achieved; and the performance evaluation module monitors the concrete strength development in real time, evaluates the maintenance effect, provides trend analysis and optimization suggestions, and provides a basis for continuous improvement of a maintenance decision.

Description

Technical Field

[0001] The present application relates to the technical field of construction engineering, and more specifically, to a concrete curing device for construction engineering. Background Art

[0002] In the construction process, concrete pouring and maintenance are important links to ensure the quality and durability of the structure. During the hardening process, concrete requires certain humidity and temperature conditions to achieve ideal strength and performance. If improperly maintained, quality problems such as cracks, surface sanding, and substandard strength are likely to occur, which not only affects the service life of the building, but also brings subsequent maintenance and safety hazards. Therefore, how to scientifically and effectively maintain concrete has always been a key research direction in construction projects.

[0003] Traditional concrete curing methods mainly include watering, covering and spraying. Although these methods can keep the concrete surface moist to a certain extent, their effects are affected by many factors such as weather, construction conditions and personnel operations, and it is difficult to achieve precise control. For example, in high or low temperature weather, the evaporation rate of water on the concrete surface is accelerated, and the frequency of watering is difficult to control, resulting in uneven curing effects; in some remote construction sites or complex construction environments, manual curing is time-consuming and labor-intensive, and the stability of curing quality cannot be guaranteed. In addition, manual operation methods have problems such as low efficiency and waste of resources, which do not meet the needs of efficient and green development of modern construction projects.

[0004] In recent years, with the development of intelligent technology, some maintenance devices have tried to add automatic control and monitoring functions to improve the maintenance effect. For example, the surface conditions of concrete are monitored through humidity and temperature sensors, and water is sprayed on demand using an automatic sprinkler system to achieve humidity control. However, most of the current equipment has the following shortcomings: First, it lacks environmental adaptability and cannot adaptively adjust the maintenance strategy according to external changes such as temperature and humidity; second, the system function is single, and it is difficult to monitor and handle abnormal conditions that occur during the maintenance process in real time; third, the device is large in size, inconvenient to move, and has low adaptability, and cannot be flexibly applied to complex construction environments. Especially in the middle and late stages of the concrete maintenance process, traditional devices are often unable to accurately control the maintenance parameters, which can easily lead to problems such as uneven strength growth and surface cracking, affecting the final quality.

[0005] In summary, how to design an efficient, intelligent and adaptable concrete curing device for construction projects has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to overcome a series of defects in the prior art, the purpose of the present application is to provide a concrete curing device for construction projects, including the following modules, in view of the above problems.

[0007] The data acquisition and perception module collects real-time parameters inside and on the surface of concrete through a temperature and humidity sensor network and a variety of detection devices to support subsequent data analysis and decision-making.

[0008] The internal temperature control execution module controls the internal temperature of the concrete through serpentine cooling pipes and precise flow regulation, maintaining the concrete in an appropriate curing temperature range.

[0009] The surface maintenance execution module performs continuous maintenance on the concrete surface, achieving all-round spraying of moisture for concrete surface maintenance and recycling of water resources.

[0010] The central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, realizing intelligent control and remote monitoring of the maintenance process.

[0011] The performance evaluation module monitors the concrete strength development in real time and evaluates the curing effect, providing trend analysis and optimization suggestions, and providing a basis for the continuous improvement of maintenance decisions.

[0012] Furthermore, the data acquisition and perception module includes the following components.

[0013] The temperature sensor array is arranged inside and on the surface of the concrete to collect temperature field distribution data in real time for evaluating the thermal evolution process of concrete hydration.

[0014] The humidity sensor array is responsible for collecting the humidity parameters of the concrete surface and the environment to realize dynamic monitoring of the humidity status.

[0015] Stress and strain sensors continuously collect data on the internal mechanical state of concrete, providing a basis for evaluating the structural integrity and strength development of construction projects.

[0016] The pH detection unit monitors the changes in alkalinity on the concrete surface in real time to effectively warn of potential deterioration risks.

[0017] The data acquisition control unit uniformly manages the collection and preprocessing of sensor data to ensure data format standardization and transmission accuracy.

[0018] Furthermore, the internal temperature control execution module includes the following components.

[0019] The coolant circulation unit realizes the circulation of cooling medium through the pre-buried serpentine pipe network to ensure uniform distribution of temperature field inside the concrete.

[0020] A coolant storage unit is used to store coolant to ensure a continuous supply of the required amount of fluid in the serpentine pipe network.

[0021] The cooling medium pump maintains the flow and pressure parameters required by the serpentine pipeline network and drives the coolant to circulate stably in the pipeline.

[0022] The flow control valve can achieve precise control of the coolant flow to meet the temperature regulation requirements in different maintenance stages.

[0023] The temperature control execution control unit receives instructions from the central control module, dynamically adjusts the flow valve opening and the cooling medium pump operating parameters, and realizes closed-loop automatic control of the temperature field.

[0024] Furthermore, the surface maintenance execution module includes the following components.

[0025] The all-round spraying unit adopts a multi-directional nozzle array design to achieve all-round and uniform spraying on the concrete surface, ensuring that the concrete surface is evenly moistened in every corner.

[0026] The water supply unit provides a stable water source for the all-round spray unit to ensure sufficient water during the maintenance process.

[0027] The water quality monitoring unit collects and analyzes the water quality parameters of maintenance water in real time to ensure that it meets the technical specifications.

[0028] The water circulation collection unit collects the water lost during the spraying process, and recycles it after filtering and purification, thereby improving the efficiency of water resource utilization.

[0029] The maintenance execution control unit receives instructions from the central control module and dynamically adjusts the spraying parameters to achieve precise control of the maintenance process.

[0030] Furthermore, the central control module includes the following components.

[0031] The real-time data analysis unit is responsible for the centralized processing, storage and real-time analysis of all sensor data, providing data support for decision-making.

[0032] The intelligent decision-making unit generates short-term maintenance control strategies based on real-time data analysis results and preset maintenance standards, and converts them into specific execution instructions.

[0033] The communication management unit uniformly manages the data transmission and command issuance between modules to ensure the reliability and real-time performance of communication.

[0034] The remote monitoring interface provides a human-computer interaction platform and supports networked remote operation and real-time data visualization.

[0035] The system coordination unit coordinates the coordinated operation of the internal temperature control execution module and the surface maintenance execution module to ensure the overall effect of the maintenance measures.

[0036] Furthermore, it is responsible for the centralized processing, storage and real-time analysis of all sensor data to provide data support for decision making, including the following steps.

[0037] The sensor data collected in real time is verified in real time to identify abnormal situations, and the integrity and validity of the data are ensured through data range checking and sampling timestamp verification to form the initial data stream.

[0038] The verified raw data is standardized, and the sliding average algorithm is used to remove data noise and outliers.

[0039] Establish a data tiered storage architecture to store real-time data, short-term historical data, and long-term archive data in different storage media.

[0040] Based on the stored time series data, statistical analysis methods are used to calculate the statistical characteristics of various parameters, including mean, standard deviation and coefficient of variation. At the same time, multi-dimensional cross-analysis is performed to explore the correlation and influencing factors between parameters.

[0041] Historical data is archived regularly to achieve hierarchical storage and automatic cleanup of data. At the same time, a multi-level data backup mechanism is established, including real-time backup, incremental backup and full backup, to ensure data security and recoverability.

[0042] Furthermore, based on the real-time data analysis results and preset maintenance standards, a short-term maintenance control strategy is generated and converted into specific execution instructions, including the following steps.

[0043] Based on the real-time data analysis results, the internal temperature field distribution, surface moisture distribution and strength development curve indicators of the concrete are calculated, and compared with the preset maintenance standards in real time to comprehensively evaluate the degree of deviation between the current maintenance status and the target requirements.

[0044] According to the status assessment results, combined with the requirements of concrete curing specifications and actual project needs, the target parameters of the next curing cycle are dynamically optimized and determined, including the temperature control target value and allowable fluctuation range, surface humidity maintenance level and curing medium dosage, and the target parameters are sorted according to priority.

[0045] Based on the optimized target parameters, the fuzzy control algorithm is used to generate specific maintenance control strategies, including cooling network flow regulation scheme, water recycling scheme, and surface spray intensity and frequency arrangement, to ensure that various maintenance measures can cooperate and transition smoothly.

[0046] The maintenance control strategy is decomposed into specific control instructions for different execution modules, and signal conversion and protocol adaptation are performed to ensure that each execution module can accurately understand and execute control commands and realize the precise implementation of the control strategy.

[0047] By real-time monitoring of the response of each execution module and instant feedback on the maintenance effect, the execution effect of the control instructions is evaluated. If execution deviation or abnormal situation is found, the optimization and adjustment mechanism is initiated in time, and the evaluation results are fed back to the status evaluation link to form a closed-loop control and ensure the continuous optimization of the maintenance process.

[0048] Furthermore, the deviation degree between the current curing state and the target requirement is comprehensively evaluated by the following formula: D(t)=α·△T+β·△H+γ·△S, where D(t) is the comprehensive deviation degree between the current curing state and the target requirement; α, β, γ are weight coefficients, which respectively represent the importance of temperature, humidity and strength to the overall evaluation; △T represents the difference between the internal temperature of concrete and the target curing temperature, which is expressed by the formula: , where N is the number of sampling points for the internal temperature of concrete; T(x i ,y i ,z i ,t) is the position (x i ,y i ,z i ) and the internal temperature of the concrete at time t; T target (t) is the preset curing temperature standard; △H represents the difference between the concrete surface humidity and the target curing humidity, which is expressed by the formula: , where M is the number of sampling points for humidity on the concrete surface; H(x j ,y j ,t) is the position (x j ,y j ) and concrete surface humidity at time t; H target (t) is the preset curing humidity standard; △S represents the difference between the concrete strength and the target curing strength, which can be expressed by the formula: △S=∣S(t)-S target (t)|, where S(t) is the concrete strength at time t; S target (t) is the preset strength development standard.

[0049] Furthermore, the performance evaluation module includes the following components.

[0050] Comprehensive evaluation unit to assess the impact of maintenance measures on concrete performance and identify key influencing factors.

[0051] The trend prediction unit predicts the development trend and potential risks of concrete strength based on the fusion analysis of historical data and real-time data.

[0052] The long-term optimization unit proposes long-term optimization suggestions for maintenance strategies and resource allocation based on the performance of the entire maintenance process.

[0053] The report generation unit regularly outputs standardized performance evaluation reports, integrating monitoring data, analysis results and optimization suggestions.

[0054] The quality management unit establishes a continuous improvement mechanism for maintenance quality, tracks the implementation effect of optimization suggestions, and realizes closed-loop management.

[0055] Furthermore, the effects of maintenance measures on concrete properties are evaluated and key influencing factors are identified, including the following steps.

[0056] According to engineering specifications and actual needs, the key performance indicators of concrete are quantitatively expressed, and a complete performance evaluation index system is established by combining non-destructive testing methods with standard specimen testing.

[0057] The multivariate statistical analysis method is used to calculate the correlation coefficient matrix between various control parameters in the curing process and the concrete performance indicators, and a correlation heat map is drawn to intuitively display the degree of correlation between various factors.

[0058] Based on the correlation analysis results, the most influential control parameter combination in the maintenance process is determined to provide directional guidance for optimizing the maintenance strategy.

[0059] Through the control variable method, the value ranges of different curing parameters are adjusted respectively, the changing responses of concrete performance indicators are observed and recorded, the parameter-performance response curve is established, and the sensitivity coefficient of each influencing factor is quantified, providing a basis for the precise control of curing parameters.

[0060] Based on the above analysis results, a comprehensive evaluation model for maintenance effects is constructed, and various performance indicators are weighted and integrated according to their weights to form a quantitative scoring mechanism for maintenance quality. At the same time, the importance ranking and control suggestions of key influencing factors are output to provide decision-making support for the continuous optimization of maintenance plans.

[0061] Furthermore, the most influential control parameter combination in the maintenance process is determined through the following steps.

[0062] The multi-dimensional maintenance parameters are standardized to eliminate the influence of different dimensions.

[0063] Calculate the covariance matrix C of the standardized maintenance parameter data: C = (1 / (n-1))·Z T Z, Z is the standardized maintenance parameter data matrix, n is the number of samples; Z T Represents the transpose of the standardized maintenance parameter data matrix Z.

[0064] Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors: Cv k =λ k v k , where λk is the eigenvalue of the kth principal component, indicating the contribution of the principal component to the variance; v k is the eigenvector of the kth principal component, indicating the weight of each maintenance parameter.

[0065] Calculate the contribution rate of each principal component to determine the degree of influence of each principal component on the concrete performance index. The specific formula is: CR k =λ k / Λ, where CR k represents the contribution rate of the kth principal component to the total variance; Λ represents the sum of the eigenvalues ​​of all principal components, which can be expressed as: , where m is the total number of principal components; λ r represents the eigenvalue of the rth principal component.

[0066] By calculating the cumulative contribution rate, the main control parameter combinations with significant influence are screened out: , among which, CCR p is the cumulative contribution rate of the first p principal components. When CCR p When the preset threshold is reached, the main influencing factors can be determined.

[0067] Furthermore, based on the fusion analysis of historical data and real-time data, the development trend and potential risks of concrete strength are predicted, including the following steps.

[0068] The concrete mix proportions, curing conditions and strength development characteristics were extracted from the historical maintenance project database, classified and standardized to establish a benchmark data set, and the typical patterns and key influencing factors affecting the strength development of concrete were identified.

[0069] The real-time monitoring data of the current project is dynamically compared and similarity analyzed with historical data. Multi-source information is integrated through data fusion algorithms to establish a comprehensive indicator system that reflects the current performance status of concrete, thus achieving an organic combination of historical experience and real-time status.

[0070] Based on the fused data, multiple prediction models are used simultaneously to calculate the development trend of concrete strength.

[0071] The output results of multiple prediction models are weighted and fused to generate the final concrete strength prediction value, and the confidence interval and uncertainty range of the prediction results are calculated.

[0072] Based on the comparative analysis between the prediction results and the design requirements, a multi-level risk warning mechanism is established. When the predicted intensity is lower than expected or the development trend is abnormal, a warning is issued in time. At the same time, combined with the changes in environmental conditions and the construction process arrangement, possible maintenance risks are predicted to provide warning information for timely adjustment of the maintenance plan.

[0073] Furthermore, the calculation formula for the predicted value of concrete strength is: , where P final (g) is the predicted value of concrete strength obtained by the final fusion, reflecting the current performance status of concrete; w e is the weight of model e, reflecting the relative importance of the model in prediction; P e (g) is the predicted value of concrete strength by model e at time g; L represents the number of models used for prediction.

[0074] The confidence interval CI(g) of the prediction result is expressed as: CI(g)=(P final (g)-z·σ final (g),P final (g)+z·σ final (g)), where z is the critical value under the standard normal distribution; σ final (g) is the standard error of the final strength prediction value, which is used to evaluate the reliability of the prediction.

[0075] The uncertainty range of the prediction result U(g) is expressed by the formula: U(g)=k 1 ·σ final (g), where k 1 To adjust the factor, set it according to the actual application requirements.

[0076] Compared with the prior art, the present application has the following beneficial effects.

[0077] This application achieves real-time monitoring and dynamic adjustment of temperature and humidity inside and on the surface of concrete through multi-module integration to ensure precise control of the concrete curing environment, and provides real-time performance evaluation and optimization suggestions, significantly improving the quality and efficiency of concrete curing. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a schematic structural diagram of a concrete curing device for a construction project disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0080] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0081] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0082] like Figure 1 As shown, a concrete curing device for construction engineering includes the following modules.

[0083] The data acquisition and perception module collects real-time parameters inside and on the surface of concrete through a temperature and humidity sensor network and a variety of detection devices to support subsequent data analysis and decision-making.

[0084] The internal temperature control execution module controls the internal temperature of the concrete through serpentine cooling pipes and precise flow regulation, maintaining the concrete in an appropriate curing temperature range.

[0085] The surface maintenance execution module performs continuous maintenance on the concrete surface, achieving all-round spraying of moisture for concrete surface maintenance and recycling of water resources.

[0086] The central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, realizing intelligent control and remote monitoring of the maintenance process.

[0087] The performance evaluation module monitors the concrete strength development in real time and evaluates the curing effect, providing trend analysis and optimization suggestions, and providing a basis for the continuous improvement of maintenance decisions.

[0088] In this embodiment, the data acquisition and perception module plays a fundamental role in the concrete curing device. It obtains the environmental parameters inside and on the surface of the concrete in real time through a temperature and humidity sensor network and a variety of detection devices. These sensors can accurately measure the temperature, humidity, air pressure, and moisture changes in the concrete structure, providing reliable data support for subsequent maintenance decisions and temperature control. Through high-frequency data acquisition and precise monitoring, this module can adjust the acquisition frequency according to actual conditions, thereby reducing power consumption and improving efficiency. At the same time, it also realizes the preliminary processing and early warning functions of the data, such as automatically triggering an alarm when the concrete temperature or humidity exceeds the preset range, reminding the manager to perform timely temperature control or water replenishment operations. The data acquisition and perception module provides real-time data support for the entire curing device, ensuring the accuracy and controllability of the curing process, and providing a reliable basis for subsequent analysis and decision-making.

[0089] In this embodiment, the main task of the internal temperature control execution module is to achieve temperature control inside the concrete through serpentine cooling pipes and precise flow regulation. The serpentine cooling pipes can be evenly distributed in the internal structure of the concrete to ensure that the temperature of each area is evenly regulated. The module combines the concrete temperature data monitored in real time by the temperature sensor to automatically adjust the flow of the coolant, thereby effectively controlling the temperature and keeping the concrete within a suitable curing temperature range. This control method can prevent excessive temperatures from causing the concrete to harden and shrink too quickly in the early stage, and reduce the generation of temperature stress and cracks. In addition, precise control of the flow rate also makes the use of coolant more economical, improves the overall energy efficiency of cooling, and provides a guarantee for the long-term sustainability of the curing process.

[0090] In this embodiment, the surface maintenance execution module is designed to continuously maintain the moisture of the concrete surface to ensure that the surface layer obtains sufficient moisture support during the initial hardening stage. The module uses a carefully designed water spraying mechanism to evenly spray water in all directions to keep the concrete surface moist and avoid shrinkage and cracking caused by water loss. At the same time, the module also introduces a water resource recycling device to recycle excess water, reduce the waste of water resources, and achieve energy-saving and environmental protection effects. Through the action of the surface maintenance execution module, the humidity of the concrete surface can be maintained within an appropriate range, ensuring the synchronous hardening of the surface and internal structure, reducing the risk of cracking and other structural defects, and thus improving the overall quality of the construction project.

[0091] In this embodiment, the central control module is the core brain of the entire concrete curing device, responsible for comprehensive data analysis, optimization of maintenance decisions and distribution of various execution instructions. It receives data from the data acquisition and perception module, performs real-time data processing and algorithm analysis, and thus formulates the optimal temperature control and humidity control strategy. The central control module is also equipped with a remote monitoring function, allowing managers to view the operating status of the curing device in real time through the network and adjust the control parameters remotely. Through this intelligent control method, the central control module can dynamically adjust the curing strategy according to the real-time situation of the concrete to ensure the efficiency and accuracy of the curing process. At the same time, the module also has data storage and analysis functions, providing rich data accumulation and reference basis for subsequent construction and maintenance.

[0092] In this embodiment, the performance evaluation module is mainly responsible for real-time monitoring and analysis of the effect of concrete curing, and evaluates the strength development of concrete through data comparison and trend analysis. This module uses non-destructive detection technology to monitor the internal structural changes of concrete, and combines the actual temperature and humidity conditions and moisture distribution to form a complete curing effect evaluation system. In addition, the performance evaluation module can also make optimization suggestions based on the hardening development trend of concrete and guide the central control module to adjust parameters. The existence of this module gives the entire curing process a feedback loop mechanism, which provides data support and improvement suggestions for future projects while ensuring the quality of curing, and has important practical application value.

[0093] In summary, the concrete curing device of this construction project is characterized by modular design, covering key links such as data acquisition, temperature and humidity control, surface curing, intelligent control and performance evaluation, forming a complete closed-loop management system. Through the real-time data support of the data acquisition and perception module, the synchronous control of the internal temperature control execution and surface curing execution modules, the intelligent decision-making and remote monitoring of the central control module, and the effect feedback and optimization suggestions of the performance evaluation module, the modules cooperate with each other to ensure the efficiency, accuracy and intelligence of the concrete curing process. The application of this device not only improves the curing quality and strength development of concrete, but also provides advanced technical support for the standardization and quality control of construction engineering, laying a solid foundation for achieving the long life and high quality of construction projects.

[0094] Furthermore, the data acquisition and perception module includes the following components.

[0095] The temperature sensor array is arranged inside and on the surface of the concrete to collect temperature field distribution data in real time for evaluating the thermal evolution process of concrete hydration.

[0096] The humidity sensor array is responsible for collecting the humidity parameters of the concrete surface and the environment to realize dynamic monitoring of the humidity status.

[0097] Stress and strain sensors continuously collect data on the internal mechanical state of concrete, providing a basis for evaluating the structural integrity and strength development of construction projects.

[0098] The pH detection unit monitors the changes in alkalinity on the concrete surface in real time to effectively warn of potential deterioration risks.

[0099] The data acquisition control unit uniformly manages the collection and preprocessing of sensor data to ensure data format standardization and transmission accuracy.

[0100] In this embodiment, the temperature sensor array plays a key role in the data acquisition and perception module. It is carefully arranged inside and on the surface of the concrete to capture the heat changes generated by the concrete during the hydration reaction. The temperature sensor can collect temperature field distribution data in real time and record the dynamic changes in the temperature of each layer of concrete. This multi-point temperature monitoring provides detailed temperature data support for the hydration heat evolution process of concrete, ensuring that engineering managers can adjust the cooling strategy in time according to temperature changes to prevent local areas from generating temperature stress due to excessive temperature, causing concrete cracking. The array reflects the temperature field distribution of concrete at different depths and times through multi-dimensional data recording, helping to form an accurate temperature analysis model, thereby laying the foundation for the scientific maintenance of concrete. The precise monitoring of the temperature sensor array not only ensures the effectiveness of temperature control, but also provides important environmental parameter support for the development of concrete strength.

[0101] In this embodiment, the humidity sensor array focuses on collecting humidity data of the concrete surface and the surrounding environment, so as to realize dynamic monitoring of the humidity status. The humidity of concrete is particularly important in the early maintenance stage, because too low humidity will cause the concrete surface to dry and crack, while too high humidity may cause delayed surface hardening. The humidity sensor array can reflect the wetness of the concrete surface and the humidity in the air in real time, providing a basis for the surface moisture replenishment decision of the intelligent maintenance device. The distributed collection method of the humidity sensor ensures the comprehensiveness and accuracy of the data, which can help identify the problem of uneven humidity and adjust the moisture spraying in real time. In addition, the humidity data can also reflect the drying speed and evaporation of concrete, providing a scientific basis for subsequent water resource management and recycling. The dynamic monitoring of the humidity sensor array can not only ensure the maintenance effect, but also reduce the risk of cracks and deterioration, greatly improving the stability and durability of the concrete structure.

[0102] In this embodiment, the stress and strain sensors in this module are responsible for collecting the internal mechanical state data of the concrete. Through the stress and strain sensors installed in the concrete, these devices can monitor the internal stress and deformation of the concrete during the hardening process in real time. Concrete will shrink and expand during the hydration reaction. These stress data can intuitively reflect the mechanical changes of concrete in different parts and possible structural stress concentration areas, helping engineers to detect potential crack risks early. This mechanical monitoring not only provides direct reference data for the development of concrete strength, but also predicts possible stress peaks in the future through trend analysis, thereby optimizing the internal structure of the concrete in advance. The stress and strain sensor data provides an objective basis for structural integrity assessment, greatly reduces the risk of cracks during maintenance, and helps to improve the safety and durability of construction projects.

[0103] In this embodiment, the pH detection unit monitors the alkalinity changes on the concrete surface in real time, providing an early warning function for potential deterioration risks during the maintenance process. When exposed to the environment, the pH value of concrete may decrease due to carbonation, and this alkalinity change will affect its durability. The pH detection unit monitors the alkalinity in real time through a high-sensitivity sensor to identify possible acid-base imbalances. pH data can help predict the corrosion process and carbonization degree of concrete, especially in concrete structures containing steel bars, where reduced alkalinity may accelerate the corrosion of steel bars. By timely monitoring and analyzing changes in pH, the unit can assist the central control module in making appropriate maintenance adjustments, such as spraying a curing solution to adjust the alkalinity. The deployment of the pH detection unit greatly enhances the protective capabilities of the maintenance process, thereby extending the service life of the concrete structure and ensuring the quality of the project.

[0104] In this embodiment, the data acquisition control unit, as the core of the data acquisition and perception module, is responsible for the unified management of the data information flow of each sensor. It collects data on temperature, humidity, stress strain, pH value, etc., formats and performs preliminary analysis, and ensures the real-time and accuracy of transmission. The data acquisition control unit has data fusion and preprocessing functions, so that data from different sources can be stored and transmitted according to unified standards, which is convenient for subsequent comprehensive analysis and decision-making. At the same time, the existence of this unit greatly optimizes the efficiency of data collection and processing, and avoids interference and waste of resources between sensors. Through precise timing control and data synchronization, the data acquisition control unit also ensures the integrity and continuity of the data. The data acquisition control unit provides strong data support and information integration capabilities for the entire maintenance device, providing a solid foundation for realizing intelligent maintenance decisions.

[0105] In summary, the data acquisition and perception module realizes all-round monitoring of the internal and external environment of concrete through the coordinated work of the temperature sensor array, humidity sensor array, stress strain sensor, pH detection unit and data acquisition control unit, providing accurate and comprehensive real-time data support for intelligent maintenance. Each sensor component provides indispensable data in its respective monitoring field, and these data are uniformly managed and processed by the control unit, laying the foundation for temperature control, humidity management, structural stress detection and alkalinity protection of concrete. Through this modular design, the concrete maintenance process becomes more intelligent and efficient, greatly improving the structural quality and long-term durability of concrete.

[0106] Furthermore, the internal temperature control execution module includes the following components.

[0107] The coolant circulation unit realizes the circulation of cooling medium through the pre-buried serpentine pipe network to ensure uniform distribution of temperature field inside the concrete.

[0108] A coolant storage unit is used to store coolant to ensure a continuous supply of the required amount of fluid in the serpentine pipe network.

[0109] The cooling medium pump maintains the flow and pressure parameters required by the serpentine pipeline network and drives the coolant to circulate stably in the pipeline.

[0110] The flow control valve can achieve precise control of the coolant flow to meet the temperature regulation requirements in different maintenance stages.

[0111] The temperature control execution control unit receives instructions from the central control module, dynamically adjusts the flow valve opening and the cooling medium pump operating parameters, and realizes closed-loop automatic control of the temperature field.

[0112] In this embodiment, the coolant circulation unit is the core component of the internal temperature control execution module, and its main function is to realize the circulation of the cooling medium through the pre-buried serpentine pipe network. This design not only ensures the uniform distribution of the temperature field inside the concrete, but also effectively reduces the heat generated during the hydration process of the concrete, thereby avoiding cracks and strength loss caused by uneven temperature. Through the rationally arranged serpentine pipes, the coolant can form a flow loop inside the concrete, promote the rapid removal of heat, and enable the concrete to be kept within a suitable curing temperature range. The unit is designed to withstand high temperature and high pressure while preventing coolant leakage, thereby improving safety and reliability. In addition, the efficiency of the coolant circulation unit also lies in its ability to work in conjunction with other temperature control components, and adjust the flow path and speed of the coolant by real-time monitoring of the temperature changes of the concrete to achieve precise temperature control. This uniform temperature distribution and efficient cooling effect significantly improve the strength development and crack resistance of the concrete, and play a vital role in the overall construction quality.

[0113] In this embodiment, the design purpose of the coolant storage unit is to ensure that the required amount of fluid is continuously supplied in the coolant circulation unit to meet the temperature control requirements during the concrete curing process. The unit can replenish the coolant in time when the coolant is consumed or the demand fluctuates through appropriate storage capacity, thereby ensuring stability and continuity. The coolant storage unit is usually equipped with a liquid level monitoring device to monitor the storage status in real time, and is linked with the central control module to automatically adjust the opening and closing of the valve of the replenishing liquid to avoid insufficient or excessive storage. The design of the storage unit also needs to take into account the properties of the coolant to ensure that its chemical stability and physical properties at different temperatures are not affected. In addition, a reasonable storage design can reduce the temperature loss of the coolant, so that the circulating coolant maintains a lower temperature and improves the overall cooling efficiency. Through the effective operation of this unit, the temperature management during the concrete curing process can be highly automated, thereby ensuring the quality and safety of the construction.

[0114] In this embodiment, the cooling medium pump is an indispensable component of the internal temperature control execution module, and its main function is to maintain the flow and pressure parameters required by the serpentine pipe network to ensure that the coolant can circulate stably in the pipeline. The selection and configuration of the cooling medium pump directly affects the operating efficiency and reliability of the entire temperature control mechanism. The high-efficiency pump body design can provide sufficient flow to meet the cooling needs, and at the same time, the variable frequency drive technology is used to realize the intelligent adjustment of flow and pressure, so that it can maintain the best working state in different maintenance stages. In addition, the cooling medium pump usually has anti-interference ability and can operate stably in harsh environments to ensure that the cooling effect of concrete is not affected by external factors. The real-time monitoring and fault diagnosis functions of the pump can promptly detect and solve potential operating problems, thereby avoiding the decline in maintenance quality caused by temperature control failure. Through the efficient operation of the cooling medium pump, the response speed and adjustment ability of the entire temperature control mechanism have been significantly improved, providing a strong guarantee for the maintenance of concrete.

[0115] In this embodiment, the flow control valve is an important device for realizing precise control of the flow of the coolant, and its design is intended to meet the temperature control requirements of different curing stages. Through the intelligent control of the flow control valve, the flow rate of the coolant can be adjusted in real time to ensure that the temperature inside the concrete is uniform and stable. This unit usually integrates electronic control technology, and with the data input of the temperature sensor, it can achieve fast response and high-precision flow control. When the concrete temperature rises, the flow control valve can quickly increase the flow of the coolant to enhance the cooling effect; conversely, when the temperature drops, the flow will be reduced accordingly to avoid quality problems caused by excessive cooling. The flow control valve also has a fault self-detection function. Once an abnormal situation is detected, it can automatically switch to a safe mode to ensure that the curing process is not disturbed. Through the efficient operation of the flow control valve, the temperature control accuracy of the concrete is greatly improved, providing an ideal environment for the hydration reaction of the concrete.

[0116] In this embodiment, the temperature control execution control unit is the "brain" of the internal temperature control execution module, which is responsible for receiving instructions from the central control module and dynamically adjusting the opening of the flow valve and the operating parameters of the cooling medium pump according to the real-time monitoring data. The unit integrates advanced control algorithms to achieve closed-loop automatic control of the concrete temperature field, ensuring the timeliness and accuracy of temperature regulation. The temperature control execution control unit can not only quickly respond to changes in various environments and internal parameters, but also perform data analysis and decision-making during operation, optimize control strategies, and further improve temperature control efficiency. When dealing with complex temperature regulation tasks, the unit can coordinate the working status of multiple components to achieve precise temperature management. In addition, the real-time monitoring function of the temperature control execution control unit can promptly detect abnormal conditions in operation and issue alarms, thereby ensuring the safety and reliability of the concrete curing process. Through the efficient control of this unit, the overall effect of concrete curing is maximized, ensuring that the concrete completes the hydration reaction under the best conditions.

[0117] In summary, the internal temperature control execution module realizes precise control and management of the concrete temperature field through the close cooperation of multiple components such as the coolant circulation unit, coolant storage unit, cooling medium pump, flow control valve and temperature control execution control unit. The coolant circulation unit ensures uniform temperature distribution, the coolant storage unit ensures the continuous supply of coolant, the cooling medium pump provides stable flow and pressure, and the flow control valve realizes flexible flow control, and the temperature control execution control unit is responsible for overall intelligent management. The efficient collaboration of these components not only improves the maintenance quality of concrete, but also provides important guarantees for the safety and economy of the construction process. Through this modular design, the concrete maintenance process of construction projects has become more scientific, reliable and intelligent, helping to improve the overall quality and durability of the project.

[0118] Furthermore, the surface maintenance execution module includes the following components.

[0119] The all-round spraying unit adopts a multi-directional nozzle array design to achieve all-round and uniform spraying on the concrete surface, ensuring that the concrete surface is evenly moistened in every corner.

[0120] The water supply unit provides a stable water source for the all-round spray unit to ensure sufficient water during the maintenance process.

[0121] The water quality monitoring unit collects and analyzes the water quality parameters of maintenance water in real time to ensure that it meets the technical specifications.

[0122] The water circulation collection unit collects the water lost during the spraying process, and recycles it after filtering and purification, thereby improving the efficiency of water resource utilization.

[0123] The maintenance execution control unit receives instructions from the central control module and dynamically adjusts the spraying parameters to achieve precise control of the maintenance process.

[0124] In this embodiment, the all-round spray unit plays a core role in the surface maintenance execution module. The unit adopts a multi-directional nozzle array design, which can realize all-round uniform spraying of the concrete surface, ensuring that the maintenance moisture evenly covers every inch of the surface without leaving any dry corners. Such a design can not only prevent cracks on the concrete surface caused by dryness, but also ensure the moist environment required for the concrete hydration process and improve the early strength development rate. The all-round spray unit can adapt to different environments and concrete structure requirements through flexible multi-directional spraying control. For example, on the concrete surface of a special shape, the spray angle and intensity can be fine-tuned according to the maintenance needs to ensure uniform distribution of moisture. In addition, the multi-directional nozzle can also reduce the waste of water, accurately control the spraying flow and coverage area, and provide technical support for the efficient use of water resources. The existence of the all-round spray unit effectively ensures the moist environment of the concrete, making the surface maintenance more scientific and uniform.

[0125] In this embodiment, the water supply unit provides a stable water source for the all-round spray unit to ensure sufficient water during the entire curing process. The unit can cope with the problem of insufficient water in different construction environments through stable water supply and water storage devices, ensuring a stable water supply during long-term continuous operation. The setting of the water supply unit avoids the problem of spraying interruption caused by water shortage, allowing maintenance work to continue and avoiding the risk of concrete drying and cracking due to lack of water. Its stable water supply ensures the normal operation of the all-round spray unit, maintains the moisture of the concrete surface, and improves the curing effect. In addition, the unit can adjust the water supply according to different seasons and changes in temperature and humidity to achieve water-saving effects, thereby ensuring the quality of curing while effectively saving water resources. The water supply unit not only ensures the continuity of water, but also provides reliable support for the efficient operation of the concrete curing process.

[0126] In this embodiment, the water quality monitoring unit monitors the quality of the maintenance water in real time to ensure that the water quality of the spraying water meets the technical specifications. The unit monitors various impurities, chemical components and pH values ​​in the water through high-precision sensors, and collects and analyzes water quality parameters in real time to prevent the polluted water source from having a negative impact on the concrete surface. The water quality monitoring unit can effectively control calcium, chlorine and other components in the water that may affect the maintenance of concrete, ensuring that the water source meets the special requirements of concrete surface maintenance. At the same time, the unit can also promptly warn of abnormal water quality to avoid adverse effects on the maintenance effect of concrete. For example, if the water quality does not meet the standard, an alarm signal will be issued to remind the staff to change the water source or start the water treatment device. The application of the water quality monitoring unit not only ensures the cleanliness and uniformity of the concrete surface, but also improves the quality of the overall project, providing a guarantee for long-term durability.

[0127] In this embodiment, the water circulation collection unit is specifically responsible for collecting the water lost during the spraying process, filtering and purifying it before recycling. This design not only saves water resources, but also improves the utilization efficiency of water resources and reduces maintenance costs. The filtering device of the water circulation collection unit can effectively remove impurities such as dust and sand that may be carried in the water, ensure the cleanliness of the circulating water, and prevent impurities from wearing or polluting the concrete surface. By collecting and purifying the lost water, the unit not only greatly reduces the waste of water resources, but also significantly reduces the dependence on water resources at the construction site. The water circulation collection unit also has an automatic control function, which can automatically adjust the frequency and flow of the water circulation according to the changes in the spraying water flow to ensure that the water quality meets the spraying requirements. Its recycling function effectively reduces the consumption of water resources while improving maintenance efficiency, and is an effective practice of environmental protection and efficient construction.

[0128] In this embodiment, the maintenance execution control unit is the core control component in the surface maintenance execution module. It receives instructions from the central control module and dynamically adjusts the spraying parameters to achieve precise control of the spraying time, flow rate and coverage. This unit can intelligently adjust the spraying frequency according to the real-time humidity changes on the concrete surface to avoid the problem of excessive or insufficient moisture. The maintenance execution control unit also has data storage and analysis functions, which can record the parameters and effects of each spraying, and provide data support for subsequent maintenance optimization. By combining the spraying action with real-time monitoring data, the maintenance execution control unit can accurately adjust the spraying intensity, improve the efficiency of water resource utilization and the maintenance effect. In addition, the control unit can also perform remote control and automated operation, making the maintenance process more intelligent. The precise control of the maintenance execution control unit realizes the scientific maintenance of the concrete surface, reduces the need for manual intervention, and greatly improves the level of intelligent construction.

[0129] In summary, the surface maintenance execution module realizes intelligent and scientific maintenance of the concrete surface through the close cooperation of the all-round spray unit, water supply unit, water quality monitoring unit, water circulation collection unit and maintenance execution control unit. Each unit plays a unique role in ensuring the uniformity of moisture on the concrete surface and the safety of water quality, especially in saving water resources and improving water utilization efficiency. It reflects the advanced technical effects. The design of each component not only effectively improves the accuracy and automation level of maintenance, but also reduces resource consumption and labor costs during the construction process. Overall, the surface maintenance execution module provides a systematic solution for concrete maintenance through intelligent technical means, laying a solid foundation for the high quality and high durability of construction projects.

[0130] Furthermore, the central control module includes the following components.

[0131] The real-time data analysis unit is responsible for the centralized processing, storage and real-time analysis of all sensor data, providing data support for decision-making.

[0132] The intelligent decision-making unit generates short-term maintenance control strategies based on real-time data analysis results and preset maintenance standards, and converts them into specific execution instructions.

[0133] The communication management unit uniformly manages the data transmission and command issuance between modules to ensure the reliability and real-time performance of communication.

[0134] The remote monitoring interface provides a human-computer interaction platform and supports networked remote operation and real-time data visualization.

[0135] The system coordination unit coordinates the coordinated operation of the internal temperature control execution module and the surface maintenance execution module to ensure the overall effect of the maintenance measures.

[0136] In this embodiment, the real-time data analysis unit is a key component of the central control module, which is responsible for centralized processing of data obtained from various sensors, storage and real-time analysis, and provides scientific data support for maintenance decisions. This unit can quickly identify the changing trends of key parameters such as temperature, humidity, and stress through advanced data processing algorithms and efficient data storage systems, making it easier to control the current maintenance status of concrete. The real-time data analysis unit can not only realize the fusion and processing of multi-source data, but also ensure the integrity of historical data through data layered storage and backup mechanisms, providing a basis for subsequent effect tracing and evaluation. This unit can also perform complex trend prediction analysis, model parameters such as concrete temperature field and hydration heat change through intelligent algorithms, discover potential temperature or humidity abnormalities in advance, and thus effectively warn. In the entire central control module, the existence of the real-time data analysis unit greatly improves the data processing efficiency, enhances the response speed and real-time performance of the system, and provides a strong guarantee for the precise control of the maintenance process.

[0137] In this embodiment, the intelligent decision-making unit generates a short-term maintenance control strategy based on the analysis results provided by the real-time data analysis unit and the preset maintenance standards, and converts it into specific execution instructions, so that each execution module can automatically and accurately perform maintenance operations. The unit automatically adjusts the maintenance strategy based on real-time monitoring through a series of optimization algorithms and rule engines to ensure that the maintenance operation is highly matched with actual needs. The intelligent decision-making unit can take into account the dynamic changes of various factors in the strategy formulation process, such as ambient temperature and humidity, the release rate of concrete hydration heat, etc., and generate flexible control instructions. In addition, the unit also has self-learning and optimization functions, and can continuously adjust the maintenance strategy according to historical maintenance data, thereby improving adaptability in complex construction environments. With the support of the intelligent decision-making unit, the central control module achieves a higher level of automation and intelligence, significantly improving the response efficiency and accuracy of the maintenance device.

[0138] In this embodiment, the communication management unit plays an important bridging role in the central control module, responsible for coordinating the data transmission and instruction issuance between the modules, and ensuring the reliability and real-time performance of the communication within the system. The unit effectively avoids the delay and packet loss problems in data transmission through advanced communication protocols and data redundancy design, ensuring that the instructions and data in the maintenance process can be seamlessly transmitted. The communication management unit has automatic error detection and repair functions, and can still ensure high-quality communication effects under the complex environment of the construction site. The unit can also dynamically adjust the communication traffic to ensure the priority transmission of key data, thereby achieving precise control of the maintenance process. In addition, the communication management unit has a multi-level communication security mechanism, which effectively prevents information leakage and data tampering through data encryption and access permission control. Through the guarantee of the communication management unit, the central control module can achieve efficient collaboration between modules, providing strong support for the stable operation of the entire process.

[0139] In this embodiment, the remote monitoring interface provides a human-computer interaction platform, allowing managers to perform remote operations and visualize real-time data through the network. The interface can not only display key parameters such as temperature, humidity, and stress in real time, but also present data in intuitive forms such as charts and curves, helping managers to fully understand the maintenance status. The remote monitoring interface has parameter adjustment and manual intervention functions, allowing managers to make necessary adjustments based on on-site conditions, improving flexibility and adaptability. In addition, the interface has information security protection functions, protecting the security of remote communications through encrypted transmission and identity authentication mechanisms. The interface also supports the query and playback of historical data, which is convenient for managers to trace back and evaluate the maintenance process. Through the application of the remote monitoring interface, the maintenance device can not only realize remote management, but also improve the convenience of operation and the accuracy of control, providing managers with powerful data support and operation means.

[0140] In this embodiment, the system coordination unit is the core component that ensures the coordinated operation of the internal temperature control execution module and the surface maintenance execution module. It can comprehensively analyze the information provided by the real-time data analysis unit, reasonably allocate the tasks and working frequencies of each execution unit, and ensure the consistency and coherence of the overall maintenance process. The system coordination unit has an intelligent scheduling function, which can automatically adjust the working status of each execution module according to the temperature and humidity, environmental conditions and the release of concrete hydration heat, so as to ensure that the concrete is always in suitable maintenance conditions. The unit can also identify and handle abnormal situations, and automatically switch to the backup plan when there is a problem with temperature control or surface maintenance, so as to ensure the continuity and safety of the maintenance process. In addition, the system coordination unit also has a status monitoring and adjustment function, which can realize the dynamic management of each module and prevent resource waste and repeated operations. Through the effective control of the system coordination unit, the entire maintenance device achieves a high level of synergy, significantly improving the maintenance quality and system reliability.

[0141] In summary, each component of the central control module performs its own duties during the maintenance process, and ensures the quality of concrete maintenance and the overall efficiency of the system through efficient data processing, intelligent decision-making, reliable communication management, convenient remote monitoring and comprehensive system coordination. Real-time data analysis and intelligent decision-making units provide support for the scientific nature of maintenance strategies, the communication management unit ensures the stability of data transmission, the remote monitoring interface enables remote operation and status viewing, and the system coordination unit ensures the coordination and consistency of maintenance measures. Through the mutual cooperation of these components, the central control module realizes intelligent and automated maintenance management, which not only reduces the complexity of manual intervention, but also improves the efficiency and effectiveness of maintenance. This modular design provides advanced solutions for the automated maintenance of construction projects and lays a solid foundation for quality control and safety assurance during construction.

[0142] Furthermore, it is responsible for the centralized processing, storage and real-time analysis of all sensor data to provide data support for decision making, including the following steps.

[0143] Real-time verification of sensor data collected in real time is performed to identify abnormal situations, and the integrity and validity of the data are ensured by data range checking and sampling timestamp verification to form an initial data stream. Real-time data verification is a crucial first step in the sensor data processing process, and its main purpose is to ensure the integrity and validity of the collected data. In this link, by implementing data range checking and sampling timestamp verification, abnormal data can be effectively identified and data quality can be ensured. Data range checking can determine whether the output of each sensor is within the expected reasonable range, such as the upper and lower limits of physical quantities such as temperature, humidity and flow, which helps to detect sensor failures or environmental anomalies in a timely manner. The verification of sampling timestamps ensures the time consistency of data collection, thereby avoiding data deviations caused by delays or asynchrony. Through these measures, an initial data stream can be formed, and the data of this stream will serve as the basis for subsequent analysis and decision-making. Effective real-time data verification not only improves the reliability of the system, but also enhances the credibility of subsequent data analysis, providing accurate information support for decision-making.

[0144] The raw data that has passed the verification is standardized, and the sliding average algorithm is used to remove data noise and outliers. Standardization can eliminate data inconsistencies caused by different dimensions between different sensors, so that data from different sources can be compared and analyzed under the same standard. At the same time, the sliding average algorithm is used to remove data noise and outliers to further improve the quality of the data. The sliding average algorithm can effectively smooth data fluctuations and reduce the impact of short-term noise on the overall trend by calculating the mean of the data within a specified time window, thereby extracting more accurate signals. This process is particularly important for real-time monitoring, because environmental conditions and the noise of the sensor itself may cause large fluctuations in the collected data. Through standardization and denoising, a high-quality data set can be generated, providing a reliable basis for subsequent analysis and ensuring the scientificity and accuracy of decision-making.

[0145] Establish a data tiered storage architecture to store real-time data, short-term historical data, and long-term archived data in different storage media. By storing different types of data in different storage media, not only the efficiency of data access is improved, but also the flexibility and security of storage management are enhanced. Real-time data is usually stored in fast storage media to ensure fast read and write operations, thereby supporting real-time analysis and decision-making; short-term historical data can be stored in medium-speed media with good performance for fast access and processing; long-term archived data should be stored in high-capacity, relatively low-cost storage media for long-term preservation and subsequent query. Such a tiered storage strategy not only improves the response speed, but also effectively reduces storage costs, while facilitating the flexible retrieval and management of data. In addition, through a reasonable data storage strategy, data security and compliance can be better achieved to ensure the effective use of data at different stages.

[0146] Based on the stored time series data, statistical analysis methods are used to calculate the statistical characteristics of various parameters, including mean, standard deviation and coefficient of variation, and multi-dimensional cross analysis is performed to explore the correlation and influencing factors between parameters. By using statistical analysis methods to calculate the statistical characteristics of various parameters, the distribution and change trend of the data can be fully understood. These statistical characteristics can not only reveal the central trend and volatility of the data, but also provide a quantitative basis for subsequent anomaly detection and decision-making. At the same time, multi-dimensional cross analysis technology can explore the correlation and influencing factors between parameters, allowing decision makers to identify potential influencing factors and the inherent connections between different parameters. In this way, future behaviors can be better predicted and operation strategies can be optimized. The results of data analysis can help operation managers make more scientific decisions and improve the overall efficiency and reliability of the system.

[0147] Archive historical data regularly to achieve hierarchical storage and automatic cleanup of data. At the same time, establish a multi-level data backup mechanism, including real-time backup, incremental backup and full backup, to ensure data security and recoverability. Through hierarchical storage and automatic cleanup of data, the life cycle of data can be effectively managed, storage space can be avoided and system performance can be improved. At the same time, establish a multi-level data backup mechanism, including real-time backup, incremental backup and full backup, to ensure that data can be quickly restored in the event of data loss or system failure, reducing potential losses. Real-time backup can ensure immediate protection of data, incremental backup can reduce the time and storage space required for backup, and full backup is used to create a complete snapshot. The combination of these backup strategies can maintain high data security and reliability in the face of various risks, providing a guarantee for long-term stable operation.

[0148] In summary, the above steps constitute the core process responsible for the centralized processing, storage and real-time analysis of all sensor data. Through real-time data verification, raw data standardization and denoising, data hierarchical storage, statistical analysis and parameter mining, as well as the archiving and backup mechanism of historical data, data can be processed and utilized efficiently and reliably. This process not only improves the quality and security of data, but also provides solid data support for decision-making, ensuring effectiveness and accuracy in practical applications. In an increasingly complex engineering environment, this systematic and intelligent data processing capability will greatly promote the optimization and improvement of engineering and ensure the successful implementation of engineering projects.

[0149] Furthermore, based on the real-time data analysis results and preset maintenance standards, a short-term maintenance control strategy is generated and converted into specific execution instructions, including the following steps.

[0150] Based on the results of real-time data analysis, the internal temperature field distribution, surface moisture distribution and strength development curve indicators of the concrete are calculated, and compared with the preset maintenance standards in real time to comprehensively evaluate the degree of deviation between the current maintenance status and the target requirements. This process is crucial because it lays the foundation for the formulation of maintenance strategies. Through real-time monitoring, the temperature, humidity and strength data of each monitoring point can be obtained, and this information can be integrated using data analysis technology to form a comprehensive overview of the maintenance status. Subsequently, these calculation results will be compared with the preset maintenance standards to evaluate the degree of deviation between the current maintenance status and the target requirements. This real-time comparison can not only identify deviations in a timely manner, but also help engineers and decision makers quickly understand the gap between the actual maintenance effect of concrete and the expected target. This evaluation mechanism ensures the transparency of the maintenance process, enables maintenance measures to respond quickly to changes, and improves the overall efficiency and safety of the project.

[0151] According to the status assessment results, combined with the requirements of concrete curing specifications and the actual needs of the project, the target parameters of the next curing cycle are dynamically optimized, including the temperature control target value and the allowable fluctuation range, the surface humidity maintenance level and the amount of curing medium, and the target parameters are sorted according to priority. This step not only includes the setting of the temperature control target value, but also covers the optimization of the allowable fluctuation range, the surface humidity maintenance level and the amount of curing medium. This process requires comprehensive consideration of multiple factors such as the characteristics of concrete, environmental conditions and project progress, so as to ensure that the formulated target parameters meet both technical standards and actual construction needs. By sorting the target parameters according to priority, resources and energy can be concentrated on the most critical curing indicators to ensure that the curing effect is optimal within limited time and resources. This dynamic optimization mechanism makes concrete curing no longer static, but can adapt to environmental changes and project progress in real time, thereby improving the scientificity and effectiveness of curing.

[0152] Based on the optimized target parameters, the fuzzy control algorithm is used to generate specific maintenance control strategies, including cooling network flow regulation schemes, water recycling schemes, and surface spray intensity and frequency arrangements, to ensure that various maintenance measures can work together and transition smoothly. In the face of uncertainty and complexity, the fuzzy control algorithm can still make effective decisions. For example, based on the current temperature and humidity conditions, the flow of the cooling network, the water recycling scheme, and the intensity and frequency arrangements of the surface spray can be automatically adjusted. This flexible control strategy not only ensures that various maintenance measures can work together, but also achieves a smooth transition and reduces fluctuations in concrete maintenance quality caused by changes in control strategies. In the face of the complexity and variability of the actual environment, the fuzzy control algorithm can still maintain efficient responsiveness, providing strong support for the strength development and quality assurance of concrete.

[0153] The maintenance control strategy is decomposed into specific control instructions for different execution modules, and signal conversion and protocol adaptation are performed to ensure that each execution module can accurately understand and execute the control commands and realize the precise implementation of the control strategy. This process is crucial because different execution modules require different operating instructions to achieve collaborative work. While decomposing the control instructions, signal conversion and protocol adaptation are also required to ensure that each execution module can accurately understand and execute the control commands. In this way, the maintenance strategy can be accurately implemented, ensuring that each module operates according to the set target parameters and execution plan. The successful implementation of this link not only improves the overall coordination, but also enhances the accuracy of execution, ensuring that maintenance measures can be implemented efficiently and scientifically.

[0154] By real-time monitoring of the response of each execution module and instant feedback of the maintenance effect, the execution effect of the control instruction is evaluated. If execution deviation or abnormality is found, the optimization and adjustment mechanism is started in time, and the evaluation results are fed back to the status evaluation link to form a closed-loop control to ensure the continuous optimization of the maintenance process. This closed-loop control mechanism ensures the continuous optimization of the maintenance process. If deviations or abnormalities are found during the execution process, the optimization and adjustment mechanism will be started in time. The evaluation results are fed back to the status evaluation link, and the maintenance strategy can be dynamically adjusted according to the latest data and conditions to form a complete feedback loop. This design greatly improves the adaptability and flexibility of the maintenance process, allowing concrete maintenance to maintain the best state in complex environments. Through real-time monitoring and adjustment, the effectiveness of the maintenance process is ensured, the quality of concrete is improved, and a reliable guarantee is provided for the successful implementation of the project.

[0155] In summary, the entire process of generating short-term maintenance control strategies based on real-time data analysis results fully reflects the intelligent and automated characteristics of modern concrete maintenance technology. Through state evaluation and deviation calculation, dynamic optimization of target parameters, application of fuzzy control algorithms, decomposition and execution of control instructions, and establishment of closed-loop control and feedback mechanisms, it is not only possible to achieve precise control of concrete maintenance, but also to dynamically adapt to changes in the environment and engineering requirements. This series of steps ensures the scientificity, effectiveness and reliability of concrete maintenance, and provides solid technical support for ensuring engineering quality and safety.

[0156] Furthermore, the deviation degree between the current curing state and the target requirement is comprehensively evaluated by the following formula: D(t)=α·△T+β·△H+γ·△S, where D(t) is the comprehensive deviation degree between the current curing state and the target requirement; α, β, γ are weight coefficients, which respectively represent the importance of temperature, humidity and strength to the overall evaluation; △T represents the difference between the internal temperature of concrete and the target curing temperature, which is expressed by the formula: , where N is the number of sampling points for the internal temperature of concrete; T(xi ,y i ,z i ,t) is the position (x i ,y i ,z i ) and the internal temperature of the concrete at time t; T target (t) is the preset curing temperature standard; △H represents the difference between the concrete surface humidity and the target curing humidity, which is expressed by the formula: , where M is the number of sampling points for humidity on the concrete surface; H(x j ,y j ,t) is the position (x j ,y j ) and concrete surface humidity at time t; H target (t) is the preset curing humidity standard; △S represents the difference between the concrete strength and the target curing strength, which can be expressed as: △S=|S(t)-S target (t)|, where S(t) is the concrete strength at time t; S target (t) is the preset strength development standard.

[0157] In summary, through the comprehensive evaluation of temperature, humidity and strength, the above formula provides a scientific and systematic management basis for the concrete curing process. The weight adjustment between different factors enables the evaluation results to flexibly adapt to the needs of different curing stages, which helps to achieve precise control strategies. With the advancement of data acquisition and analysis technology, the maintenance management mechanism based on this formula will be able to monitor the curing status more accurately and in real time, providing more powerful support for the quality assurance of concrete.

[0158] Furthermore, the performance evaluation module includes the following components.

[0159] Comprehensive evaluation unit to assess the impact of maintenance measures on concrete performance and identify key influencing factors.

[0160] The trend prediction unit predicts the development trend and potential risks of concrete strength based on the fusion analysis of historical data and real-time data.

[0161] The long-term optimization unit proposes long-term optimization suggestions for maintenance strategies and resource allocation based on the performance of the entire maintenance process.

[0162] The report generation unit regularly outputs standardized performance evaluation reports, integrating monitoring data, analysis results and optimization suggestions.

[0163] The quality management unit establishes a continuous improvement mechanism for maintenance quality, tracks the implementation effect of optimization suggestions, and realizes closed-loop management.

[0164] In this embodiment, the comprehensive evaluation unit is the core component of the performance evaluation module, and its main function is to evaluate the impact of different maintenance measures on concrete performance and identify key influencing factors. By analyzing multiple indicators such as temperature, humidity, and strength, the unit can fully understand the implementation effect of maintenance measures. Using multivariate statistical analysis methods and machine learning algorithms, the comprehensive evaluation unit can identify key variables that affect concrete performance, such as curing temperature, humidity, and curing time. This analysis process not only provides an evaluation of the effectiveness of current maintenance measures, but also provides a scientific basis for subsequent maintenance decisions. In addition, the comprehensive evaluation unit can also identify abnormal situations during the maintenance process, and by comparing and analyzing historical data with real-time data, timely adjust the maintenance strategy to ensure that the performance of concrete is stable and reliable.

[0165] In this embodiment, the trend prediction unit predicts the development trend and potential risks of concrete strength based on the fusion analysis of historical data and real-time data. This unit uses advanced data analysis techniques, such as time series analysis and regression analysis, to extract valuable information from a large amount of monitoring data and predict future strength development. By analyzing the variation pattern of concrete strength under different conditions, the trend prediction unit can identify possible risk points and issue early warnings. For example, under certain temperature and humidity conditions, concrete strength may develop poorly, leading to structural safety hazards. Through this pre-judgment mechanism, timely measures can be taken to adjust the maintenance plan to ensure that the performance of concrete throughout the maintenance process meets the expected goals. This function not only improves the safety of the maintenance process, but also provides a guarantee for the long-term stable operation of the project.

[0166] In this embodiment, the long-term optimization unit focuses on the continuous evaluation and optimization of the entire maintenance process, aiming to propose targeted maintenance strategies and resource allocation suggestions. Through in-depth analysis of historical data and real-time monitoring results, the unit can identify deficiencies and optimization space in the maintenance process. For example, if it is found that the moisture retention at a certain stage is insufficient, resulting in slow strength development, the long-term optimization unit can make specific suggestions such as adjusting the amount of maintenance medium used and optimizing the spraying frequency. This data-based decision support helps to improve maintenance results, reduce resource waste, and improve cost-effectiveness. In addition, the long-term optimization unit will regularly update the optimization suggestions to adapt to changes in different construction stages and environmental conditions to achieve dynamic management.

[0167] In this embodiment, the report generation unit is responsible for regularly outputting standardized performance evaluation reports that integrate monitoring data, analysis results, and optimization suggestions. This unit improves the efficiency and accuracy of data presentation by automatically generating reports. These reports not only include various performance indicators of concrete, but also cover the implementation effects of maintenance measures and improvement suggestions. Through clear data visualization and intuitive charts, the report generation unit can help relevant managers quickly understand the maintenance status and measures that need to be taken. This process not only improves the efficiency of information transmission, but also provides important support for project transparency and decision-making. Regular reports can also provide information feedback to all stakeholders of the project and promote communication and collaboration.

[0168] In this embodiment, the quality management unit has established a continuous improvement mechanism for maintenance quality, aiming to track the implementation effect of optimization suggestions and achieve closed-loop management. Through regular evaluation and feedback, the quality management unit can ensure the effectiveness and durability of various optimization measures. The unit collects and analyzes the monitoring data after the implementation of optimization measures, evaluates its impact on concrete performance, and adjusts relevant strategies in a timely manner. In addition, the quality management unit can also put forward new improvement suggestions based on the implementation effect, forming a virtuous cycle of continuous improvement. This closed-loop management mechanism not only improves the quality of maintenance, but also provides a guarantee for the successful implementation of long-term projects.

[0169] In summary, the various components of the performance evaluation module are interrelated and complementary, forming an efficient maintenance management mechanism. Through comprehensive evaluation, trend prediction, long-term optimization, report generation and quality management, the maintenance effect can be monitored in real time, and the maintenance strategy can be adjusted in time according to the data analysis results. This not only improves the scientificity and effectiveness of concrete maintenance, but also provides strong support for the quality assurance of the project. With the continuous advancement of technology, the performance evaluation module will be able to better adapt to the changing construction environment and maintenance needs, and achieve more efficient resource utilization and maintenance management.

[0170] Furthermore, the effects of maintenance measures on concrete properties are evaluated and key influencing factors are identified, including the following steps.

[0171] According to engineering specifications and actual needs, the key performance indicators of concrete are quantitatively expressed, and a complete performance evaluation index system is established by combining non-destructive testing methods and standard specimen testing. By combining non-destructive testing methods and standard specimen testing, the physical and mechanical properties of concrete can be comprehensively and accurately evaluated. Non-destructive testing methods such as ultrasonic testing, rebound method and infrared thermal imaging can evaluate the internal defects, density and strength of concrete without damaging the concrete structure. At the same time, standard specimen testing such as compressive strength testing provides benchmark data for comparison. This dual testing method not only enhances the reliability of the data, but also provides a solid foundation for establishing a comprehensive performance evaluation index system. By setting quantitative indicators, we can better understand the specific impact of different maintenance measures on concrete performance, and provide a scientific basis for subsequent data analysis and decision-making.

[0172] The multivariate statistical analysis method is used to calculate the correlation coefficient matrix between various control parameters in the curing process and the concrete performance indicators, and a correlation heat map is drawn to intuitively display the degree of correlation between various factors. The core of this process is to reveal the relationship between various influencing factors and concrete performance. By drawing a correlation heat map, the degree of correlation between different control parameters (such as temperature, humidity, curing time, etc.) and performance indicators (such as compressive strength, impermeability, shrinkage, etc.) can be intuitively displayed. This visual method not only helps technicians quickly identify which factors have the greatest impact on concrete performance, but also identifies potential areas for improvement, thereby providing directional guidance for optimizing maintenance strategies. The effective implementation of this step makes the adjustment of maintenance measures more targeted and can maximize the maintenance effect from a data-driven perspective.

[0173] Based on the results of the correlation analysis, the most influential control parameter combinations in the curing process are determined to provide directional guidance for optimizing the curing strategy. This process involves an in-depth study of the identified control parameters to understand their synergistic effects under different conditions. By comprehensively considering multiple factors, it is possible to identify which parameter combinations can effectively improve concrete performance under specific curing environments and construction conditions. This identification process provides an important reference for the optimization of curing strategies, ensuring that these key factors can be given priority in actual construction, thereby improving the overall quality and durability of concrete.

[0174] Through the control variable method, the value range of different curing parameters is adjusted respectively, the change response of concrete performance indicators is observed and recorded, the parameter-performance response curve is established, and the sensitivity coefficient of each influencing factor is quantified to provide a basis for the precise control of curing parameters. This process not only helps to understand the reaction characteristics of concrete under different curing conditions, but also provides a quantitative basis for the subsequent precise control of curing parameters. The calculation of the sensitivity coefficient makes the design of the curing plan more scientific and reasonable, ensuring the stability and reliability of the performance of concrete during the curing process.

[0175] Based on the above analysis results, a comprehensive evaluation model for maintenance effect is constructed, and various performance indicators are weighted and integrated according to their weights to form a quantitative scoring mechanism for maintenance quality. At the same time, the importance ranking and control suggestions of key influencing factors are output to provide decision support for the continuous optimization of maintenance plans. This mechanism not only improves the scientificity and transparency of maintenance management, but also provides a basis for the adjustment of maintenance strategies for subsequent projects, so that maintenance work can more effectively respond to the challenges brought about by different construction conditions and environmental changes.

[0176] In summary, evaluating the impact of maintenance measures on concrete performance covers the entire process from the quantitative establishment of performance indicators to data analysis and model construction. Through the systematic implementation of each step, we can deeply understand the performance of concrete during the maintenance process and provide a scientific basis for optimizing maintenance strategies. This not only improves the construction quality of concrete, but also provides valuable experience and reference for similar projects in the future, and promotes the construction industry to develop in a more scientific and standardized direction. Through this series of data-driven and scientific analysis, the maintenance management of concrete will be more efficient and accurate, ensuring the long-term durability and safety of the project.

[0177] Furthermore, the most influential control parameter combination in the maintenance process is determined through the following steps.

[0178] Standardize the multidimensional maintenance parameters to eliminate the influence of different dimensions. The standardization process usually involves subtracting the mean of each parameter and dividing it by the standard deviation to convert it into a standard normal distribution with zero mean and unit variance. This process eliminates the influence of different parameter units, allowing various maintenance parameters to be compared on the same scale, thereby improving the accuracy and reliability of subsequent analysis. The standardized data can effectively reflect the relative performance of each parameter in the maintenance process, laying a good foundation for the subsequent covariance matrix calculation. The standardization process at this stage is the basis for ensuring the validity of the subsequent statistical analysis results.

[0179] Calculate the covariance matrix C of the standardized maintenance parameter data: C = (1 / (n-1))·Z T Z, Z is the standardized maintenance parameter data matrix, n is the number of samples; Z Trepresents the transpose of the standardized curing parameter data matrix Z. The construction of the covariance matrix can reveal the linear relationship between different curing parameters and their common impact on concrete performance. By analyzing the covariance matrix, technicians can understand which parameters show a high positive or negative correlation during the curing process. This analysis is essential to identify the key control parameter combinations that affect concrete performance, which helps to further focus resources and optimize curing measures, thereby effectively improving the strength and durability of concrete.

[0180] Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors: Cv k =λ k v k , where λ k is the eigenvalue of the kth principal component, indicating the contribution of the principal component to the variance; v k is the eigenvector of the kth principal component, indicating the weight of each maintenance parameter. Eigenvalue decomposition can reduce the dimensionality of complex multidimensional data to a few principal components, which not only reduces the complexity of the data, but also retains the most important information. This dimensionality reduction process allows analysts to focus more on those parameters that have a significant impact on concrete performance, ensuring the pertinence and effectiveness of maintenance measures.

[0181] Calculate the contribution rate of each principal component to determine the degree of influence of each principal component on the concrete performance index. The specific formula is: CR k =λ k / Λ, where CR k represents the contribution rate of the kth principal component to the total variance; Λ represents the sum of the eigenvalues ​​of all principal components, which can be expressed as: , where m is the total number of principal components; λ r Represents the eigenvalue of the rth principal component. The calculation of the contribution rate helps technicians identify which principal components dominate the variation in concrete performance. Through this quantitative method, we can clearly understand the relative importance of different control parameters in the overall performance impact, which is helpful for the subsequent optimization of the formulation and implementation of maintenance strategies.

[0182] By calculating the cumulative contribution rate, the main control parameter combinations with significant influence are screened out: , among which, CCR p is the cumulative contribution rate of the first p principal components. When CCR p When the preset threshold is reached, the main influencing factors can be determined. This process not only helps to determine the most critical control parameters, but also provides a clear direction for the subsequent optimization of maintenance strategies. This data-based analysis method greatly improves the scientific nature of maintenance management and makes the adjustment of maintenance measures more accurate and effective.

[0183] In summary, by standardizing the curing parameters, calculating the covariance matrix, performing eigenvalue decomposition, calculating the contribution rate of the principal components, and screening the main control parameter combinations, the key influencing factors in the concrete curing process can be systematically identified. This series of steps provides a scientific basis for improving the quality of concrete and ensures the effectiveness and pertinence of the curing measures. Through this data-driven approach, the continuous optimization of concrete curing management can be achieved, laying a solid foundation for the long-term durability and safety of the project. This not only improves the construction quality of concrete, but also provides valuable experience and reference for similar projects in the future, promoting the scientific and standardized process of the construction industry.

[0184] Furthermore, based on the fusion analysis of historical data and real-time data, the development trend and potential risks of concrete strength are predicted, including the following steps.

[0185] The concrete mix proportion, curing conditions and strength development characteristics were extracted from the historical maintenance project database, and classified and standardized to establish a benchmark data set, while identifying the typical patterns and key influencing factors that affect the strength development of concrete. This step laid the foundation for the subsequent analysis of the data set. By sorting out the historical data, the typical patterns and key influencing factors of the strength development of concrete under different curing conditions can be identified. Standardization ensures the comparability of different data sources, allowing each data point to be analyzed under the same standard, effectively eliminating the impact caused by different dimensions. In addition, these historical data provide valuable experience support for current projects, so that the design of maintenance plans can be based on previous successful cases, thereby improving the success rate and construction quality.

[0186] The real-time monitoring data of the current project is dynamically compared and similarity analyzed with historical data. Multi-source information is integrated through data fusion algorithms to establish a comprehensive indicator system that reflects the current performance status of concrete, realizing the organic combination of historical experience and real-time status. This process uses a data fusion algorithm to integrate multi-source information into a comprehensive indicator system that can reflect the performance status of concrete in real time. This step can identify the similarities and differences between the current project and historical cases through similarity analysis, thereby providing a basis for timely adjustment of maintenance measures. For example, if the maintenance conditions of the current project are similar to those of historical successful cases, but there is a deviation in the strength development trend, potential risk factors can be quickly identified. This step not only improves the efficiency of data utilization, but also provides a scientific basis for real-time decision-making and ensures the accuracy of maintenance management.

[0187] Based on the fused data, multiple prediction models are used to calculate the development trend of concrete strength. This process takes advantage of the advantages of different models and predicts concrete strength through methods such as machine learning, statistical regression and time series analysis. The application of multiple models makes the prediction results more comprehensive and reliable, and can better adapt to complex maintenance environments and conditions. In addition, the diversity of models makes it possible to analyze different influencing factors independently, thereby revealing their specific impact on the development of concrete strength. This prediction capability not only enhances the control of future strength changes, but also provides data support for the optimization of maintenance plans, making maintenance management more scientific.

[0188] The output results of multiple prediction models are weighted and fused to generate the final concrete strength prediction value, and the confidence interval and uncertainty range of the prediction results are calculated at the same time. This process not only calculates the confidence interval and uncertainty range of the prediction results, but also effectively integrates the advantages of different models, making the final prediction results more reliable and operational. Weighted fusion makes the final result no longer dependent on the prediction of a single model, but integrates information from multiple parties to reflect a more realistic strength development trend. Through the calculation of the confidence interval, managers can clearly understand the accuracy of the strength prediction, provide a reference for subsequent decision-making, and ensure that the adjustment of the maintenance plan can be based on scientific data analysis.

[0189] Based on the comparative analysis between the prediction results and the design requirements, a multi-level risk warning mechanism is established. When the predicted intensity is lower than expected or the development trend is abnormal, a warning is issued in time. At the same time, combined with the changes in environmental conditions and the construction process arrangement, the possible maintenance risks are predicted, and early warning information is provided for timely adjustment of the maintenance plan. The innovation of this mechanism is that it realizes the dynamic adjustment of maintenance management, ensuring that measures can be taken to correct the problem before it occurs. Through this proactive management method, the risks in the concrete maintenance process can be effectively reduced and the construction quality and safety can be improved. The early warning mechanism not only provides the necessary information support for project managers, but also provides flexible adjustment plans for actual operations, enhancing the intelligent level of concrete maintenance management.

[0190] In summary, the fusion analysis based on historical data and real-time data can improve the prediction accuracy and management efficiency of concrete strength development in many aspects. By standardizing historical data, dynamically comparing real-time monitoring data, using multiple prediction models, weighting fusion results and establishing a risk warning mechanism, this systematic approach not only optimizes decision support in the concrete maintenance process, but also significantly improves construction safety and quality assurance.

[0191] Furthermore, the calculation formula for the predicted value of concrete strength is: , where P final(g) is the predicted value of concrete strength obtained by the final fusion, reflecting the current performance status of concrete; w e is the weight of model e, reflecting the relative importance of the model in prediction; P e (g) is the predicted value of concrete strength by model e at time g; L represents the number of models used for prediction.

[0192] The confidence interval CI(g) of the prediction result is expressed as: CI(g)=(P final (g)-z·σ final (g),P final (g)+z·σ final (g)), where z is the critical value under the standard normal distribution; σ final (g) is the standard error of the final strength prediction value, which is used to evaluate the reliability of the prediction.

[0193] The uncertainty range of the prediction result U(g) is expressed by the formula: U(g)=k 1 ·σ final (g), where k 1 To adjust the factor, set it according to the actual application requirements.

[0194] In summary, the calculation of concrete strength prediction value, the setting of confidence interval and the evaluation of uncertainty range complement each other and jointly construct a comprehensive and scientific concrete strength prediction system. This system not only improves the scientificity and rationality of concrete maintenance, but also enhances the safety and flexibility of construction management. By introducing the integration of multiple models, the evaluation of confidence intervals and the quantification of uncertainty ranges, decision makers can make more accurate judgments and adjustments in complex construction environments. This data-driven management model is in line with the modern construction industry's pursuit of intelligence and efficiency, and provides important reference and reference for the implementation of similar projects in the future.

[0195] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A concrete curing device for construction engineering, characterized in that: Includes the following modules: The data collection and perception module collects real-time parameters inside and on the surface of concrete through a temperature and humidity sensor network and a variety of detection devices to support subsequent data analysis and decision-making; The internal temperature control execution module controls the internal temperature of the concrete through serpentine cooling pipes and precise flow regulation, maintaining the concrete in an appropriate curing temperature range; The surface maintenance execution module continuously maintains the concrete surface, realizes all-round spraying of water for concrete surface maintenance and recycling of water resources; The central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, realizing intelligent control and remote monitoring of the maintenance process; The performance evaluation module monitors the concrete strength development in real time and evaluates the curing effect, providing trend analysis and optimization suggestions, and providing a basis for the continuous improvement of maintenance decisions.

2. A concrete curing device for construction engineering according to claim 1, characterized in that: The data acquisition and perception module includes the following components: The temperature sensor array is placed inside and on the surface of the concrete to collect temperature field distribution data in real time to evaluate the hydration heat evolution process of the concrete; The humidity sensor array is responsible for collecting the humidity parameters of the concrete surface and the environment to achieve dynamic monitoring of the humidity status; Stress and strain sensors continuously collect data on the internal mechanical state of concrete, providing a basis for evaluating the structural integrity and strength development of construction projects; pH detection unit, which monitors the alkalinity changes on the concrete surface in real time to effectively warn of potential deterioration risks; The data acquisition control unit uniformly manages the collection and preprocessing of sensor data to ensure data format standardization and transmission accuracy.

3. The concrete curing device for construction engineering according to claim 1, characterized in that: The internal temperature control execution module includes the following components: The cooling liquid circulation unit realizes the circulation of cooling medium through the pre-buried serpentine pipe network to ensure the uniform distribution of temperature field inside the concrete; A coolant storage unit for storing coolant to ensure a continuous supply of the required amount of fluid in the serpentine pipe network; The cooling medium pump maintains the flow and pressure parameters required by the serpentine pipeline network and drives the coolant to circulate stably in the pipeline; Flow control valve to achieve precise control of coolant flow and meet the temperature regulation requirements at different maintenance stages; The temperature control execution control unit receives instructions from the central control module, dynamically adjusts the flow valve opening and the cooling medium pump operating parameters, and realizes closed-loop automatic control of the temperature field.

4. The concrete curing device for construction engineering according to claim 1, characterized in that: The surface care execution module includes the following components: The all-round spray unit adopts a multi-directional nozzle array design to achieve all-round and uniform spraying on the concrete surface, ensuring that the concrete surface is evenly moistened in every corner; The water supply unit provides a stable water source for the all-round spray unit to ensure sufficient water during the maintenance process; Water quality monitoring unit collects and analyzes water quality parameters of maintenance water in real time to ensure compliance with technical specifications; The water recycling collection unit collects the water lost during the spraying process, and recycles it after filtering and purification, thus improving the efficiency of water resource utilization; The maintenance execution control unit receives instructions from the central control module and dynamically adjusts the spraying parameters to achieve precise control of the maintenance process.

5. The concrete curing device for construction engineering according to claim 1, characterized in that: The central control module includes the following components: Real-time data analysis unit, responsible for the centralized processing, storage and real-time analysis of all sensor data, providing data support for decision-making; Intelligent decision-making unit generates short-term maintenance control strategies based on real-time data analysis results and preset maintenance standards, and converts them into specific execution instructions; The communication management unit manages the data transmission and command issuance between modules in a unified manner to ensure the reliability and real-time performance of communication; Remote monitoring interface, providing a human-computer interaction platform, supporting networked remote operation and real-time data visualization; The system coordination unit coordinates the coordinated operation of the internal temperature control execution module and the surface maintenance execution module to ensure the overall effect of the maintenance measures.

6. A concrete curing device for construction engineering according to claim 5, characterized in that: Responsible for the centralized processing, storage and real-time analysis of all sensor data to provide data support for decision-making, including the following steps: Real-time verification of sensor data collected in real time is performed to identify abnormal situations, and the integrity and validity of the data are ensured through data range checking and sampling timestamp verification to form the initial data stream; Standardize the verified raw data and use the sliding average algorithm to remove data noise and outliers; Establish a data tiered storage architecture to store real-time data, short-term historical data, and long-term archive data in different storage media; Based on the stored time series data, statistical analysis methods are used to calculate the statistical characteristics of various parameters, including mean, standard deviation and coefficient of variation. At the same time, multi-dimensional cross-analysis is performed to explore the correlation and influencing factors between parameters. Historical data is archived regularly to achieve hierarchical storage and automatic cleanup of data. At the same time, a multi-level data backup mechanism is established, including real-time backup, incremental backup and full backup, to ensure data security and recoverability.

7. The concrete curing device for construction engineering according to claim 5, characterized in that: Based on the real-time data analysis results and preset maintenance standards, a short-term maintenance control strategy is generated and converted into specific execution instructions, including the following steps: Based on the real-time data analysis results, calculate the internal temperature field distribution, surface moisture distribution and strength development curve index of the concrete, and compare them with the preset maintenance standards in real time to comprehensively evaluate the deviation between the current maintenance status and the target requirements; According to the status assessment results, combined with the requirements of concrete curing specifications and actual project needs, dynamically optimize and determine the target parameters of the next curing cycle, including the temperature control target value and allowable fluctuation range, surface humidity maintenance level and curing medium dosage, and sort the target parameters according to priority; Based on the optimized target parameters, the fuzzy control algorithm is used to generate specific maintenance control strategies, including cooling network flow regulation scheme, water recycling scheme, and surface spray intensity and frequency arrangement, to ensure that various maintenance measures can be coordinated and smoothly transitioned; Decompose the maintenance control strategy into specific control instructions for different execution modules, and perform signal conversion and protocol adaptation to ensure that each execution module can accurately understand and execute the control commands and achieve accurate implementation of the control strategy; By real-time monitoring of the response of each execution module and instant feedback on the maintenance effect, the execution effect of the control instructions is evaluated. If execution deviation or abnormal situation is found, the optimization and adjustment mechanism is initiated in time, and the evaluation results are fed back to the status evaluation link to form a closed-loop control and ensure the continuous optimization of the maintenance process.

8. A concrete curing device for construction engineering according to claim 7, characterized in that: The deviation degree between the current curing state and the target requirement is comprehensively evaluated by the following formula: D(t)=α·△T+β·△H+γ·△S, where D(t) is the comprehensive deviation degree between the current curing state and the target requirement; α, β, γ are weight coefficients, which respectively represent the importance of temperature, humidity and strength to the overall evaluation; △T represents the difference between the internal temperature of concrete and the target curing temperature, which is expressed by the formula: , where N is the number of sampling points for the internal temperature of concrete; T(x i ,y i ,z i ,t) is the position (x i ,y i ,z i ) and the internal temperature of the concrete at time t; T target (t) is the preset curing temperature standard; △H is the difference between the surface humidity and the target curing humidity, which can be expressed by the formula: , where M is the number of sampling points for humidity on the concrete surface; H(x j ,y j ,t) is the position (x j ,y j ) and concrete surface humidity at time t; H target (t) is the preset curing humidity standard; △S represents the difference between the concrete strength and the target curing strength, which can be expressed as: △S=|S(t)-S target (t)|, where S(t) is the concrete strength at time t; S target (t) is the preset strength development standard.

9. The concrete curing device for construction engineering according to claim 1, characterized in that: The performance evaluation module includes the following components: Comprehensive evaluation unit to evaluate the impact of maintenance measures on concrete performance and identify key influencing factors; Trend prediction unit, based on the fusion analysis of historical data and real-time data, predicts the development trend and potential risks of concrete strength; The long-term optimization unit proposes long-term optimization suggestions for maintenance strategies and resource allocation based on the performance of the entire maintenance process; Report generation unit, which regularly outputs standardized performance evaluation reports, integrating monitoring data, analysis results and optimization suggestions; The quality management unit establishes a continuous improvement mechanism for maintenance quality, tracks the implementation effect of optimization suggestions, and realizes closed-loop management.

10. A concrete curing device for construction engineering according to claim 9, characterized in that: Evaluate the effects of maintenance measures on concrete performance and identify key influencing factors, including the following steps: According to engineering specifications and actual needs, the key performance indicators of concrete are quantitatively expressed, and a complete performance evaluation index system is established by combining non-destructive testing methods with standard specimen testing; The multivariate statistical analysis method is used to calculate the correlation coefficient matrix between various control parameters in the curing process and concrete performance indicators, and a correlation heat map is drawn to intuitively display the degree of correlation between various factors; Based on the correlation analysis results, the most influential control parameter combination in the maintenance process is determined to provide directional guidance for optimizing maintenance strategies; Through the control variable method, the value ranges of different curing parameters are adjusted respectively, the change response of concrete performance indicators is observed and recorded, the parameter-performance response curve is established, the sensitivity coefficient of each influencing factor is quantified, and the basis for the precise control of curing parameters is provided; Based on the above analysis results, a comprehensive evaluation model for maintenance effect is constructed, and various performance indicators are weighted and integrated according to their weights to form a quantitative scoring mechanism for maintenance quality. At the same time, the importance ranking and control suggestions of key influencing factors are output to provide decision-making support for the continuous optimization of maintenance plans.

11. A concrete curing device for construction engineering according to claim 10, characterized in that: The most influential control parameter combination in the curing process is determined by the following steps: Standardize multi-dimensional maintenance parameters to eliminate the impact of different dimensions; Calculate the covariance matrix C of the standardized maintenance parameter data: C = (1 / (n-1))·Z T Z, Z is the standardized maintenance parameter data matrix, n is the number of samples; Z T represents the transpose of the standardized maintenance parameter data matrix Z; Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors: Cv k =λ k v k , where λ k is the eigenvalue of the kth principal component, indicating the contribution of the principal component to the variance; v k is the eigenvector of the kth principal component, indicating the weight of each maintenance parameter; Calculate the contribution rate of each principal component to determine the degree of influence of each principal component on the concrete performance index. The specific formula is: CR k =λ k / Λ, where CR k represents the contribution rate of the kth principal component to the total variance; Λ represents the sum of the eigenvalues ​​of all principal components, which can be expressed as: , where m is the total number of principal components; λ r represents the eigenvalue of the rth principal component; By calculating the cumulative contribution rate, the main control parameter combinations with significant influence are screened out: , among which, CCR p is the cumulative contribution rate of the first p principal components. When CCR p When the preset threshold is reached, the main influencing factors can be determined.

12. The concrete curing device for construction engineering according to claim 9, characterized in that: Based on the fusion analysis of historical data and real-time data, the development trend and potential risks of concrete strength are predicted, including the following steps: Extract concrete mix proportions, curing conditions and strength development characteristics from the historical maintenance project database, classify and standardize them, establish a benchmark data set, and identify typical patterns and key influencing factors that affect concrete strength development; Dynamically compare and analyze the similarity between the real-time monitoring data of the current project and historical data, integrate multi-source information through data fusion algorithms, establish a comprehensive indicator system that reflects the current concrete performance status, and achieve an organic combination of historical experience and real-time status; Based on the fused data, multiple prediction models are used to calculate the development trend of concrete strength; The output results of multiple prediction models are weighted and integrated to generate the final concrete strength prediction value, and the confidence interval and uncertainty range of the prediction results are calculated at the same time; Based on the comparative analysis between the prediction results and the design requirements, a multi-level risk warning mechanism is established. When the predicted intensity is lower than expected or the development trend is abnormal, a warning is issued in time. At the same time, combined with the changes in environmental conditions and the construction process arrangement, possible maintenance risks are predicted to provide warning information for timely adjustment of the maintenance plan.

13. A concrete curing device for construction engineering according to claim 12, characterized in that: The calculation formula for the predicted value of concrete strength is: , where P final (g) is the predicted value of concrete strength obtained by the final fusion, reflecting the current performance status of concrete; w e is the weight of model e, reflecting the relative importance of the model in prediction; P e (g) is the predicted value of concrete strength by model e at time g; L represents the number of models used for prediction; The confidence interval CI(g) of the prediction result is expressed as: CI(g)=(P final (g)-z·σ final (g),P final (g)+z·σ final (g)), where z is the critical value under the standard normal distribution; σ final (g) is the standard error of the final strength prediction value, which is used to evaluate the reliability of the prediction; The uncertainty range of the prediction result U(g) is expressed by the formula: U(g)=k1·σ final (g), where k1 is an adjustment factor, which is set according to actual application requirements.

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