Concrete strength remote monitoring method and system suitable for complex environment

Abstract

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of civil engineering structure health monitoring, and more particularly to a concrete strength remote monitoring method and system suitable for complex environments. BACKGROUND

[0002] As the most commonly used structural material in construction engineering, the strength development process of concrete is directly related to the safety and durability of the structure. In various large-scale infrastructure construction, bridge construction, high-rise buildings and underground engineering, real-time monitoring of concrete strength not only helps to ensure construction quality, but also optimizes construction progress and maintenance strategies, and improves engineering management efficiency. Traditional concrete strength detection mainly relies on specimen preparation and destructive testing methods, such as standard cubic compressive strength test. This method has the defects of long cycle, high manual intervention, data lag, and inability to provide real-time feedback on the development of concrete strength in actual structures.

[0003] In recent years, with the development of intelligent sensing technology, wireless communication technology and Internet of Things, researchers have attempted to combine embedded sensors with wireless transmission methods to achieve real-time monitoring of internal temperature, strain and other physical parameters of concrete, and to infer the strength evolution process through empirical formulas or data models. However, existing systems generally have poor sensor stability, unreliable wireless communication, low data fusion accuracy, high power consumption, and poor structural compatibility in complex environmental conditions (such as extreme weather, remote mountainous areas, underground tunnels or strong interference areas), making it difficult to operate continuously for a long period of time, which limits their application in practical engineering.

[0004] In addition, most existing technologies focus on local monitoring or experimental environment verification, lack systematic design and methodological support for complex field environments, and cannot meet the needs of high-strength, long-period, large-scale, and multi-point distributed monitoring. At the same time, how to effectively fuse the collected multi-source data and build a reliable strength prediction model based on the characteristics of concrete materials still faces great challenges.

[0005] In summary, how to construct a concrete strength remote monitoring method and system suitable for complex environments to achieve real-time, stable, low-power, high-precision monitoring of the evolution process of concrete strength has become a technical problem that needs to be solved. SUMMARY

[0006] In order to overcome the series of defects existing in the prior art, the purpose of the present application is to provide a concrete strength remote monitoring method suitable for complex environments, which includes the following steps.

[0007] Step 1: Obtain performance data of the concrete structure and related environmental factor data, and transmit the multi-source heterogeneous data in real time to the data processing center through a pre-set wireless communication protocol.

[0008] Step 2: Construct a five-dimensional tensor data structure and achieve accurate mathematical expression of the complex coupling relationship between environmental factors and material properties through tensor decomposition.

[0009] Step 3: Introduce a recursive update mechanism in the time dimension to capture the nonlinear time-varying characteristics of concrete strength evolution.

[0010] Step 4: Quantify the age effect through the strength development rate.

[0011] Step 5: Based on the trend of strength development rate change, adaptively adjust the data sampling frequency and monitor environmental condition fluctuations.

[0012] Step 6: Real-time assessment of the safety state of the concrete structure to ensure the safe and reliable operation of the concrete structure under complex environments.

[0013] Further, Step 1 includes the following steps.

[0014] Determine the distribution of key monitoring points based on finite element analysis, optimize the monitoring layout scheme in combination with load transmission path and stress concentration area, and construct a multi-scale monitoring network topology structure.

[0015] Embed micro-level ion concentration probe arrays inside the concrete, use ion-selective electrode technology to detect real-time changes in chloride ions, sulfate ions and alkalinity in the pore solution, and deploy pH sensors to monitor the evolution of the concrete alkaline environment.

[0016] Deploy mesoscopic acoustic sensor networks at key cross-sections of the structure, analyze acoustic propagation time and waveform characteristics to assess the development of concrete internal density, porosity and micro-cracks.

[0017] Combine fiber Bragg grating technology and resistance strain measurement to construct a three-dimensional strain monitoring network, realize real-time monitoring of concrete deformation, cracking and displacement changes, and establish a dynamic map of strain field distribution.

[0018] Deploy environmental parameter acquisition units outside the structure, including high-precision temperature and humidity sensors, barometers, rain gauges and ultraviolet intensity detectors, to construct a microclimate monitoring network; at the same time, collect groundwater level and soil parameters to establish a complete environmental influence factor database.

[0019] Use low-power wide-area Internet of Things technology to build a data transmission network, combine edge computing technology for preprocessing and compression of raw data, and through multi-hop self-organizing network protocol, efficiently transmit data to the data processing center.

[0020] Further, the micro-level ion concentration probe array comprises: a chloride ion sensor with a detection range of 0.01%-5% and an accuracy of ±0.005%; a sulfate ion sensor with a detection range of 0.02%-8% and an accuracy of ±0.01%; and a pH sensor with a detection range of 5-14 and an accuracy of ±0.2.

[0021] The acoustic wave sensor network is composed of 16-64 ultrasonic sensors with a working frequency of 20-100 kHz and a measurement accuracy of ±1 μs, can detect micro-cracks with a width greater than 0.1 mm, and has a sensor spacing of 1-3 m, forming a multi-path acoustic wave propagation network.

[0022] The environmental parameter acquisition unit specifically comprises: a temperature sensor with a measurement range of -30°C to +80°C and an accuracy of ±0.5°C; a humidity sensor with a measurement range of 5-95% RH and an accuracy of ±3% RH; a barometer with a measurement range of 300-1100 hPa and an accuracy of ±1 hPa; a rain gauge with a measurement range of 0-200 mm / h and an accuracy of ±0.5 mm; an ultraviolet intensity detector with a measurement range of 0-1200 μW / cm² and an accuracy of ±10 μW / cm²; a wind speed sensor with a measurement range of 0-50 m / s and an accuracy of ±0.5 m / s; and a sulfur dioxide concentration sensor with a measurement range of 0-10 ppm and an accuracy of ±0.05 ppm.

[0023] Further, step 2 comprises the following steps.

[0024] The principal component analysis is used to reduce the dimension of the environmental data, extract the main variables, and perform time-frequency analysis by wavelet transform to construct the feature vector of the environmental state.

[0025] Combined with the concrete mix proportion parameters, curing conditions and measured strength grade, the microstructure characteristics of concrete are quantitatively described based on the multi-scale model of the material, including pore distribution, hydration product ratio and interface transition zone characteristics, and a comprehensive material state descriptor is constructed to realize the accurate digital expression of the concrete material characteristics.

[0026] The exponential weighted decay factor is introduced to process the historical data, and a recursive time series structure is constructed to capture the evolution law of concrete strength; at the same time, the strength development curves at different stages are simulated to realize the accurate mathematical expression of the time-varying characteristics of concrete strength.

[0027] The mapping relationship between the micro-physical and chemical processes and the macro-mechanical properties is established through the scale conversion function to eliminate the differences in monitoring data at different structural scales, and reliable inference from local monitoring points to overall structural performance is realized.

[0028] The performance degradation rate and limit state function are introduced to construct a comprehensive evaluation system based on performance indicators, and the reliability theory is used to set the weights of each indicator to form a multi-objective performance evaluation criterion.

[0029] A five-dimensional tensor structure T(E, M, τ, S, P) is constructed, where each dimension corresponds to environmental factors, material properties, time evolution, structural scale, and performance indicators.

[0030] T(E, M, τ, S, P) is decomposed into the product of a core tensor and five factor matrices, the main characteristic patterns of each dimension are extracted, and the coupling strength coefficients between dimensions are calculated to quantitatively represent the influence of environmental factors and material properties on concrete performance at different times and scales.

[0031] Further, step 3 includes the following steps.

[0032] A segmented nonlinear model of concrete strength development is established, which divides the entire age period into a rapid growth period, a transition period, and a stable period, and connects each stage through a segmented continuous function to achieve accurate mathematical expression of the strength evolution throughout the age period.

[0033] A recursive prediction model based on long short-term memory network is constructed, which takes historical monitoring data and five-dimensional tensor feature representation as input, learns the internal law of concrete strength development, and realizes intelligent prediction and real-time correction of strength development trajectory.

[0034] By integrating environmental impact factors, the actual calendar age is converted into an equivalent age that accurately reflects the true hydration degree, thereby realizing unified expression of the strength development curve under different environmental conditions.

[0035] The exponential weighted moving average method is used to enhance the importance of recent data, and the forgetting factor is set to gradually reduce the influence of long-term data; at the same time, model parameters are forced to calibrate at key age nodes to ensure that the prediction results are highly consistent with the actual strength development.

[0036] By calculating the deviation of the measured strength and the theoretical curve, a multi-level warning threshold is set, when the deviation exceeds the allowed range, the abnormal analysis process is triggered, and the Bayesian inference method is introduced to identify the influencing factors, to distinguish between normal strength fluctuations and potential quality problems.

[0037] The real-time monitoring data is aggregated and analyzed according to different time scales, and the wavelet transform is used to extract characteristic patterns at different time scales, and a cross-scale time correlation network is established to realize full-spectrum monitoring and prediction from short-term strength fluctuations to long-term performance evolution.

[0038] Further, step 4 includes the following steps.

[0039] The sensitivity of different types of concrete to environmental factors is quantified by calculating an environmental-material sensitivity matrix.

[0040] A segmented temperature compensation function is established to divide the temperature range into low, normal, and high temperature zones, and different mathematical models are used in each zone to describe the influence of temperature on the hydration reaction rate, achieving accurate prediction of concrete strength development in the full temperature range.

[0041] The size effect of concrete members is combined to build a humidity gradient-strength distribution mapping relationship, enabling reliable inference from surface humidity monitoring to internal strength distribution.

[0042] The environmental comprehensive action index is used to quantitatively characterize the environmental conditions and as a standardized input parameter.

[0043] The dynamic relationship between the strength development rate index and the environmental comprehensive action index is described by a differential equation, and an SDR-ECI response surface is constructed to quantify the acceleration or deceleration effect of strength development under different environmental conditions. At the same time, a material sensitivity factor is introduced to adjust the response surface shape to adapt to the characteristics of different concrete mixtures, achieving accurate prediction of the strength development law in changing environments.

[0044] The correlation between equivalent age and actual strength development is established, the strength contribution rate in different time periods is calculated, and a strength history evolution archive is established to record the long-term impact of key environmental events on concrete strength development.

[0045] Further, step 5 includes the following steps.

[0046] The first and second derivatives of the strength development rate are calculated in real time, the first derivative reflects the direction of rate change, and the second derivative represents the acceleration size. When a turning point or fluctuation in the strength development rate is detected, the sampling density is intelligently adjusted to achieve optimal allocation of monitoring resources.

[0047] The temperature change rate, humidity gradient, carbon dioxide concentration fluctuation, and chloride ion permeation rate parameters are integrated, and a multivariate weight function is used to calculate the comprehensive sensitivity score, achieving accurate quantification and hierarchical monitoring of environmental condition changes.

[0048] The normal fluctuation range of environmental parameters is established, and seasonal variation characteristics and geographical location factors are considered to dynamically update threshold settings while identifying environmental change patterns, thereby distinguishing between normal seasonal fluctuations and abnormal climate events.

[0049] A multi-level triggering mechanism is established to divide environmental parameter fluctuations into three levels: attention, warning, and emergency, and to consider multi-parameter combination triggering conditions to achieve intelligent response to complex environmental changes.

[0050] Lightweight computing modules are deployed at the field monitoring nodes to perform real-time data preprocessing and preliminary analysis, and immediately adjust the local sampling strategy when abnormal environmental conditions are detected, while starting the data caching mechanism to ensure the preservation of critical data in the case of communication interruption, and establishing a clear task division and data synchronization mechanism with the central data processing center.

[0051] When a specific environmental event is detected, automatically enter the high-frequency sampling mode, activate additional environmental monitoring units, record comprehensive environmental event process data, and perform concrete strength special evaluation after the event ends, and build an environmental event-strength change causal relationship database.

[0052] Further, step 6 includes the following steps.

[0053] A multi-source heterogeneous data fusion framework is constructed to extract abnormal feature patterns from various sensor data and establish a concrete strength evolution feature library.

[0054] Realize the rolling prediction of the strength values in the next 7 days, 30 days and 90 days.

[0055] According to the importance level of the structure and the characteristics of the service environment, a strength safety threshold system is established for different regions and levels, and combined with the seasonal environmental change characteristics, the warning threshold parameters are automatically calibrated, and the safety margin is adjusted in time according to the structure aging rate.

[0056] Trigger different levels of warning response according to the degree of strength decline: when the strength is less than 90% of the design value, trigger a first-level prompt warning, remind potential risks; when the strength is less than 85%, trigger a second-level intervention warning, suggest taking preventive measures; when the strength is less than 80% or the strength decline rate exceeds 0.5MPa / day, trigger a third-level emergency warning, require immediate intervention measures to prevent further deterioration.

[0057] Based on the finite element mesh division principle, a three-dimensional distribution cloud map of structural strength is generated, and a structure health assessment report containing strength numerical analysis, environmental impact assessment, safety risk classification and maintenance suggestions is automatically generated.

[0058] Combined with the strength development trend and environmental sensitivity analysis, maintenance decision suggestions are automatically generated, including detection frequency adjustment, protection measures strengthening and necessary reinforcement scheme, to ensure the long-term stability and safety of concrete structures in complex environments.

[0059] Further, the method for generating the three-dimensional distribution cloud map is.

[0060] Based on tetrahedral mesh partitioning technology, 5000-20000 unit cells are adaptively generated according to the complexity of the structure.

[0061] Each grid cell is associated with data from 2-3 nearest sensor nodes.

[0062] Linear interpolation is used to calculate the intensity value within the grid cell.

[0063] Laplace smoothing algorithm is used to consider spatial correlation and smooth the transition between adjacent cells.

[0064] A three-color coloring scheme is used, with red indicating a dangerous area where the intensity is less than 80% of the design value, yellow indicating a warning area where the intensity is between 80% and 90% of the design value, and green indicating a safe area where the intensity is greater than 90% of the design value.

[0065] The visualization effect is achieved through a simplified three-dimensional rendering algorithm to ensure smooth display on ordinary mobile terminals.

[0066] The purpose of the present application is also to provide a concrete strength remote monitoring system suitable for complex environments, comprising the following modules.

[0067] The multi-level intelligent sensing monitoring module realizes comprehensive monitoring of the internal chemical environment, acoustic characteristics and strain distribution of concrete through a three-level sensor network of micro, meso and macro.

[0068] The environmental parameter acquisition and communication module is responsible for acquiring environmental parameters around the concrete structure and transmitting data to the processing center through low-power Internet of Things technology.

[0069] The data processing module is used to realize the unified mathematical expression of the coupling relationship between environmental factors, material properties, time evolution, structure size and performance indicators.

[0070] The time series strength evolution prediction module realizes the full spectrum prediction of concrete strength from short-term fluctuations to long-term evolution under different environmental conditions, accurately capturing nonlinear time-varying characteristics.

[0071] The strength development rate evaluation module is used to quantify the influence of different environmental conditions on the development rate of concrete strength and accurately evaluate the age effect.

[0072] The adaptive monitoring control module intelligently adjusts the sampling frequency and tracks environmental events based on the derivative analysis of the strength development rate and the environmental sensitivity score, realizing the optimal allocation of monitoring resources and accurate capture of key environmental changes.

[0073] The structure safety state evaluation module fuses multi-source heterogeneous data for strength rolling prediction, establishes a hierarchical safety threshold system and a multi-level early warning response mechanism, and generates a three-dimensional strength distribution cloud map and a health assessment report.

[0074] Compared with the prior art, the present application has the following beneficial effects.

[0075] This application realizes high-precision, real-time, full-life cycle remote monitoring of concrete strength in complex environments. Through multi-source heterogeneous data fusion, tensor modeling and intelligent prediction mechanism, it improves the scientificity and reliability of structural safety assessment and effectively optimizes monitoring resource allocation and response strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flow chart of a method for remotely monitoring concrete strength in complex environments disclosed in an embodiment of the present application.

[0077] Figure 2 This is a structural diagram of a remote monitoring system for concrete strength suitable for complex environments disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0078] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0079] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0080] 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.

[0081] like Figure 1 As shown, a remote monitoring method for concrete strength suitable for complex environments includes the following steps.

[0082] Step 1: Acquire performance data of the concrete structure and environmental factor data related thereto, and transmit the multi-source heterogeneous data to a data processing center in real time via a preset wireless communication protocol.

[0083] Step 2: Construct a five-dimensional tensor data structure and realize the precise mathematical expression of the complex coupling relationship between environmental factors and material properties through tensor decomposition.

[0084] Step 3: Introduce a recursive updating mechanism in the time dimension to capture the nonlinear time-varying characteristics of concrete strength evolution.

[0085] Step 4: quantify the age effect through the strength development rate index.

[0086] Step 5: Based on the trend of intensity development rate, adaptively adjust the data sampling frequency and monitor the fluctuation of environmental conditions.

[0087] Step 6: Real-time assessment of the safety state of the concrete structure, ensuring the safe and reliable operation of the concrete structure under complex environment.

[0088] As can be seen from the above, the concrete strength remote monitoring method fully integrates multi-source data acquisition, tensor data structure modeling, time dimension recursive update, intensity development rate quantification, adaptive sampling adjustment, and real-time safety assessment, etc. It constitutes an efficient monitoring system. Each step complements each other, not only ensures the real-time and integrity of data transmission, but also realizes the accurate description and prediction of the performance evolution of concrete under complex environment. The application of the whole method can not only capture and feedback the internal and external influences on the concrete structure in the service process in time, but also can quickly respond when abnormal trends are found, ensuring the safe and stable operation of the structure. With such integrated and intelligent technical means, key indicators can be extracted from massive data, and targeted maintenance and reinforcement measures can be developed, thereby greatly reducing the safety risk and accident rate.

[0089] Further, step 1 includes the following steps.

[0090] Based on finite element analysis to determine the distribution of key monitoring points of the structure, combined with load transmission path and stress concentration area, the monitoring arrangement scheme is optimized, and a multi-scale covering monitoring network topology structure is constructed.

[0091] Microscopic ion concentration probe arrays are pre-embedded in the concrete, and ion-selective electrode technology is used to detect the changes of chloride ions, sulfate ions and alkalinity in the pore solution in real time. At the same time, pH sensors are arranged to monitor the evolution of the alkaline environment of concrete.

[0092] Mesoscopic acoustic sensor networks are deployed at key cross sections of the structure, and by analyzing the acoustic propagation time and waveform characteristics, the development of the density, porosity and microcracks in the concrete is evaluated.

[0093] Combined with fiber Bragg grating technology and resistance strain measurement, a three-dimensional strain monitoring network is constructed to realize real-time monitoring of concrete deformation, cracking and displacement changes, and a dynamic map of strain field distribution is established.

[0094] Environmental parameter acquisition units are deployed outside the structure, including high-precision temperature and humidity sensors, barometers, rain gauges and ultraviolet intensity detectors, to construct a microclimate monitoring network; at the same time, underground water level and soil parameters are collected to establish a complete database of environmental impact factors.

[0095] A low-power wide-area Internet of Things technology is adopted to construct a data transmission network, and an edge computing technology is combined to pre-process and compress the original data, and meanwhile, a multi-hop self-organizing network protocol is used to efficiently transmit the data to a data processing center.

[0096] The distribution of the key monitoring points of the structure is determined based on finite element analysis by the following steps: a three-dimensional entity model of the concrete structure is established by using ANSYS Mechanical APDL or ABAQUS / Standard finite element analysis software, and the model is meshed by using C3D8R eight-node hexahedral elements, and the element size is controlled within 1 / 10-1 / 20 of the characteristic size of the structure; the stress distribution of the structure under the design load is calculated by solving the linear static equation; the stress concentration area is identified by using the Von Mises equivalent stress criterion, and the key monitoring section is determined by using the maximum principal stress theory; and the optimal arrangement scheme of the monitoring points is determined by using a multi-objective optimization algorithm by considering the load transmission path and the fatigue vulnerable area.

[0097] The monitoring network topology structure based on graph theory is constructed by the following steps: each monitoring node is regarded as a vertex of a graph, and the communication link between the monitoring nodes is regarded as an edge, the minimum spanning tree algorithm is used to ensure the network connectivity while minimizing the communication cost; a hierarchical star-mesh hybrid topology structure is used, in which the micro-level sensors form a star-shaped sub-network, and the aggregation nodes of each sub-network are interconnected through a mesh structure to realize multi-hop transmission and redundant backup of data; the network topology satisfies the reliability requirement of connectivity degree >=2, and the shortest path is calculated by using the Dijkstra algorithm to optimize the data transmission efficiency.

[0098] The three-dimensional strain monitoring network is constructed by combining the fiber grating technology and the resistance strain measurement, and the specific implementation steps are as follows: a distributed fiber Bragg grating sensor is used, the working wavelength range is 1525-1565 nm, the strain measurement range is ±5000με, the temperature measurement range is-40℃ to +80℃, and the spatial resolution can reach 1 mm; the fiber sensor is arranged according to the orthogonal three-axis direction, is fixed on the surface of the steel bar through a prefabricated spiral channel or a special clamp, and a strain measurement matrix under a three-dimensional coordinate system is formed; meanwhile, 120Ω precision foil type resistance strain gauges are arranged as a supplementary measurement means, a 1 / 4 bridge, a half bridge and a full bridge measurement circuit are used, the strain is converted into a voltage signal through the Wheatstone bridge principle; a calibration equation of the FBG wavelength drift and the strain relationship is established, the coupling effect of the strain and the temperature is separated through a temperature compensation algorithm; a wavelength division multiplexing technology is used to integrate 16-32 FBG sensors on a single optical fiber, an optical time domain reflectometer (OTDR) and an optical frequency domain reflectometer (OFDR) are used for signal demodulation, distributed long-distance strain monitoring is realized; a three-dimensional strain tensor matrix is constructed, the strain field distribution between the monitoring points is calculated through a finite element interpolation algorithm, the crack development direction is identified in combination with the principal strain theory, and a dynamic map of the strain field distribution is established.

[0099] The data transmission network is configured as follows: an STM32L4 series ultra-low power microcontroller is used as a main control chip, a 16-bit ADC converter is integrated, a multi-channel analog switch CD4051 is configured to realize time division multiplexing collection of multi-sensor signals, the working current is only 35 mu A, and up to 64 channels of sensor signals can be collected in parallel; a 32 GB industrial grade eMMC flash memory chip (such as Samsung KLM8G1GETF-B041) is configured as a local data cache, supports power-off protection and data integrity verification, and an integrated real-time clock chip PCF8563 provides accurate timestamps, and the storage capacity can support 30 days of continuous data recording in an offline state; an ARM Cortex-A53 quad-core processor (such as Raspberry Pi Compute Module 4) is used, with a main frequency of 1.5 GHz, a 4GB LPDDR4 memory is configured, an embedded Linux system is run, a TensorFlow Lite inference engine is integrated, data preprocessing, anomaly detection and compression algorithms can be executed locally, and the processing capacity can reach 100,000 floating point operations per second; the main communication mode uses a Semtech SX1276 LoRa chip, the working frequency band is 470-510 MHz, the transmission power is 20 dBm, the transmission distance can reach 10-15 km, the data rate is 0.3-50 kbps, and a high-gain directional antenna (gain 12dBi) is configured; the standby communication uses a Yida BC95-GNB-IoT module, supports Cat-NB1 / Cat-NB2 standards, and the power consumption is only 3 mu A standby, ensuring the reliability of data transmission when the LoRaWAN signal is unstable; a high-efficiency DC-DC converter (such as TI TPS63070) is used, the conversion efficiency reaches 95%, a 18650 lithium battery pack (capacity 20 Ah) and a 30W solar panel are configured, MPPT maximum power point tracking is supported, and the system can work continuously for more than 6 months without external power supply; automatic routing discovery and maintenance between devices are realized through the multi-hop self-organizing network protocol AODV (Ad-hoc On-Demand Distance Vector), and a TCP / IP protocol stack is integrated to support reliable connection with a cloud server; the original data volume is reduced by 60-80% by using the LZ77 lossless compression algorithm, digital filters (such as Kalman filters) are integrated to eliminate sensor noise, data anomaly detection and feature extraction are performed through edge AI algorithms, only key information and abnormal data are uploaded to the data processing center, and the network bandwidth demand is greatly reduced.

[0100] The above series of steps realizes accurate monitoring and analysis of the concrete structure in complex environment by combining multi-dimensional sensor network with advanced transmission and calculation technology. Through finite element analysis to optimize the arrangement of monitoring points, and deploying micro-level ion concentration probe array and mesoscopic level acoustic sensor network inside the structure, it can detect key parameters such as chloride ion, sulfate ion, alkalinity change, porosity, density and micro-crack expansion in real time. Combined with fiber grating technology and strain measurement, the three-dimensional strain monitoring network realizes accurate tracking of concrete deformation, cracking and displacement. Real-time collection of external environmental parameters such as temperature, humidity, air pressure, rainfall and ultraviolet intensity enables the correlation analysis of concrete structure performance changes and external environmental influencing factors. The combination of low-power wide-area Internet of Things technology and edge computing not only improves data transmission efficiency and reduces energy consumption, but also ensures efficient data transmission and timely feedback through multi-hop self-organizing network protocol.

[0101] Further, the micro-level ion concentration probe array comprises.

[0102] Chloride ion sensor, detection range is 0.01%-5%, accuracy is ±0.005%.

[0103] Sulfate ion sensor, detection range is 0.02%-8%, accuracy is ±0.01%.

[0104] pH sensor, detection range is 5-14, accuracy is ±0.2.

[0105] Further, the acoustic sensor network is composed of 16-64 ultrasonic sensors, the working frequency is 20-100 kHz, the measurement accuracy is ±1 μs, the micro-crack with a width greater than 0.1 mm can be detected, the sensor spacing is 1-3 m, and a multi-path acoustic propagation network is formed.

[0106] Further, the environmental parameter acquisition unit specifically comprises.

[0107] Temperature sensor, measurement range is -30℃ to +80℃, accuracy is ±0.5℃.

[0108] Humidity sensor, measurement range is 5-95%RH, accuracy is ±3%RH.

[0109] Barometer, measurement range is 300-1100 hPa, accuracy is ±1 hPa.

[0110] Rain gauge, measurement range is 0-200 mm / h, accuracy is ±0.5 mm.

[0111] Ultraviolet intensity detector, measurement range is 0-1200 μW / cm², accuracy is ±10 μW / cm².

[0112] Wind speed sensor, measurement range 0-50 m / s, accuracy ±0.5 m / s.

[0113] Sulfur dioxide concentration sensor, measurement range 0-10 ppm, accuracy ±0.05 ppm.

[0114] Further, step 2 includes the following steps.

[0115] The principal component analysis is used for dimension reduction processing of multi-source environmental data, specifically including the following steps: the multi-dimensional environmental parameters including temperature, humidity, chloride ion concentration, pH value, strain, air pressure and wind speed are standardized pretreated to eliminate the influence of dimension difference and numerical magnitude; the environmental data covariance matrix is constructed, the first 5-8 principal components are extracted through eigenvalue decomposition, the cumulative contribution rate is more than 85%, the data dimension reduction is realized and the key information is retained; the principal component loading matrix is established, the dominant environmental factor combination such as temperature-humidity coupling and ion concentration-pH correlation is identified; the continuous wavelet transform is used for multi-scale decomposition of environmental time series data, the frequency domain features of different time scales such as 1-24 hours, 1-7 days and 1-30 days are extracted; the discrete wavelet transform is used for signal denoising and feature extraction, the time-frequency localization features of environmental parameters are obtained through wavelet coefficient reconstruction; the principal component weight coefficient, wavelet transform coefficient and time domain statistical feature are fused to construct 128-dimensional environmental state feature vector containing mean value, variance, skewness, kurtosis, energy density and frequency distribution.

[0116] Combined with the concrete mix proportion parameters, curing conditions and measured strength grade, the microstructure characteristics of concrete are quantitatively described based on the multi-scale model of materials, including pore distribution, hydration product ratio and interface transition zone characteristics, and through the construction of comprehensive material state descriptors, the precise digital expression of concrete material characteristics is realized.

[0117] The exponential weighted decay factor is introduced to process historical data, and a recursive time series structure is constructed to capture the evolution law of concrete strength; at the same time, the strength development curves at different stages are simulated to realize the accurate mathematical expression of the time-varying characteristics of concrete strength.

[0118] The mapping relationship between micro-physical and chemical processes and macro-mechanical properties is established through the scale conversion function to eliminate the differences of monitoring data at different structural scales, and the reliable inference from local monitoring points to overall structural performance is realized.

[0119] The performance degradation rate and limit state function are introduced to construct a comprehensive evaluation system based on performance indicators, and the reliability theory is used to set the weights of each indicator to form a multi-objective performance evaluation criterion.

[0120] A five-dimensional tensor structure T(E, M, τ, S, P) is constructed, where each dimension corresponds to environmental factors, material properties, time evolution, structural scale, and performance indicators, respectively.

[0121] T(E, M, τ, S, P) is decomposed into the product of a core tensor and five factor matrices, the main characteristic patterns of each dimension are extracted, and the coupling strength coefficients between dimensions are calculated to quantitatively represent the influence of environmental factors and material properties on concrete performance at different times, scales, and performance indicators.

[0122] Among them, the precise digital expression of concrete material properties is realized by constructing a comprehensive material state descriptor, which includes the following steps: based on the results of X-ray diffraction and scanning electron microscopy analysis, the relative content and distribution characteristics of cement hydration products C-S-H gel, calcium hydroxide, and ettringite are quantified, the pore structure parameters are measured by mercury intrusion method and nitrogen adsorption method, including porosity, average pore size, pore size distribution curve, and specific surface area, and the digital mapping relationship between pore structure and permeability is established; the mechanical property gradient distribution of the interfacial transition zone is characterized by nanoindentation technology and microhardness test, and the aggregate-paste interface bonding strength and micro elastic modulus are quantified; the atomic level interaction of molecular dynamics simulation, the representative volume element of micromechanics, and the macroscopic continuum mechanics are combined; a material degradation index system is constructed, and the material performance degradation degree is quantified by key parameters such as carbonation depth, chloride ion diffusion coefficient, and sulfate erosion rate; the microstructure parameters, mechanical performance indicators, and durability evaluation results are integrated to form a material state description matrix containing 96 key parameters, and the comprehensive digital characterization of concrete material properties is realized.

[0123] Among them, the mapping relationship between micro-physical and chemical processes and macro-mechanical properties is established through a scale conversion function, which includes the following sub-steps: a scale bridging function is established from nanoscale C-S-H gel structure to micrometer-scale cement paste performance, quantifying the contribution of intermolecular forces to the macroscopic elastic modulus of the material; an effective medium theory is established for the aggregate-paste-interface three-phase composite system, realizing the mathematical conversion from micro-component performance to macro-composite material performance; the micro-crack initiation and propagation process is associated with the macro-stiffness degradation and strength attenuation, and a cross-scale transfer function of damage evolution is established; a scale conversion neural network is trained, with microstructure parameters and physical and chemical indicators as input and macro-mechanical properties as output, to realize intelligent modeling of nonlinear mapping relationship; considering the influence of time-varying processes such as hydration reaction, ion migration, and stress redistribution on the cross-scale mapping relationship, dynamic scale conversion is realized; the accuracy of the scale conversion function is verified by multi-scale finite element analysis, a quantitative prediction model of microstructure parameter changes to macroscopic performance response is established, and reliable inference from local monitoring data to overall structural performance is realized.

[0124] The performance degradation rate and limit state function are introduced to construct a comprehensive performance evaluation system based on performance indicators, and the reliability theory is used to set the weight of each indicator to form a multi-objective performance evaluation criterion, which includes the following steps: the time-varying monitoring data of key indicators such as strength, stiffness and durability are fitted by exponential function and regressed by power function to quantify the degradation rate constant and acceleration factor of each performance parameter; a multi-level limit state function system is constructed, including bearing capacity limit state, normal use limit state and durability limit state, and the structure failure criterion and early warning mechanism are established by setting the critical threshold and safety factor; the initial weight distribution of strength index, deformation index, durability index and environmental adaptability index is determined; the reliability index and failure probability of each performance indicator are calculated, and the weight coefficient is dynamically adjusted according to the reliability level to realize risk-based weight optimization; the TOPSIS method and fuzzy comprehensive evaluation method are used to combine quantitative indicators with qualitative evaluation to form a comprehensive performance evaluation index including safety, applicability and durability; a dynamic early warning threshold system is constructed, and according to the performance degradation trend and reliability change, the green safety, yellow warning, orange warning and red danger four-level warning grades are set to realize real-time evaluation and risk pre-control of the structure state.

[0125] The above series of steps construct a comprehensive concrete performance evaluation framework through multi-level and multi-scale analysis methods. First, the principal component analysis and wavelet transform are used to reduce the dimension and time-frequency analysis of environmental data, effectively extracting key environmental features. Combined with concrete mix proportion, curing conditions and measured strength grade, the material multi-scale model is used to accurately describe the microstructure characteristics of concrete, including pore distribution and interface transition zone characteristics, thereby realizing the digital expression of concrete material characteristics. At the same time, by introducing the exponential weighted decay factor and recursive time sequence structure, the time-varying characteristics of concrete strength are successfully captured, and the strength development curves at different stages are simulated. Further, through the scale conversion function, the differences between monitoring data at different structural scales are eliminated, ensuring reliable inference from local to overall structural performance. Considering the performance degradation rate and limit state function, a multi-objective performance evaluation system is constructed, and the reliability theory is used to give appropriate weights to each indicator. Finally, the five-dimensional tensor structure T(E, M, τ, S, P) is used to extract the characteristics of each dimension and quantify the coupling strength coefficient, thereby accurately representing the influence of environmental factors and material characteristics on concrete performance.

[0126] Further, the accurate mathematical expression of the time-varying characteristics of concrete strength is realized, including the following steps.

[0127] An exponential decay factor α(t) = exp(-λt) is introduced to weight the historical data, where α(t) is the exponential decay factor, indicating the degree of decay of the influence of historical data on the current strength over time; λ is the decay constant, controlling the decay rate of the influence of historical data on the current concrete strength.

[0128] The recursive formula S recursive (t) = S(t-1) + ∑ i=0 t-1 is used to process the time series data, and the strength increment ΔS(i) at each time step is accumulated according to the exponential weighting factor to capture the gradual evolution of concrete strength, where S recursive (t) represents the concrete strength based on recursive calculation, i.e., the strength at the current time is accumulated from the strength at the previous time and the weighted increment in the historical period, reflecting the contribution of each time to the overall strength change; S(t-1) represents the strength value of concrete at time t-1; α(t-i) represents the exponential decay factor at time step t-i, indicating the influence of historical data on the current strength, which gradually decreases over time.

[0129] The S evolution (t) = S ∞ ·(1-exp(-kt)) is used to describe the evolution process of strength from initial rapid growth to stable in later period, where S evolution (t) represents the ideal evolution curve of concrete strength, which describes the trend of strength change over time through an exponential growth model, emphasizing the overall development process from the initial state to the final steady state; S ∞ represents the stable value of concrete strength after a long time; k represents the strength growth rate constant, controlling the strength growth rate of concrete at different stages.

[0130] The recursive time series structure is combined with the stage development curve to construct a comprehensive expression S weighted (t) = S0+ ∑ i=1 t exp(-λ(t-i))·ΔS(i) + S ∞ ·(1-exp(-kt)), which realizes the accurate mathematical expression of the time-varying characteristics of concrete strength, where S weighted (t) represents the comprehensive strength expression combining the weighted effect of historical data and the overall evolution trend; S0 is the initial strength, representing the initial strength value of concrete at the beginning.

[0131] In summary, by introducing the exponential weighting decay factor and the recursive time structure, the accurate mathematical expression of the time-varying characteristics of concrete strength is realized. First, the historical data is weighted using the exponential weighting decay factor α(t) to ensure that the influence of historical data gradually decays over time, thus reflecting the gradual law of concrete strength evolution over time. Through the recursive formula, the strength increment of the historical period is accumulated according to the weighting factor to capture the contribution of each time to the overall strength change. Then, an exponential growth model is used to describe the evolution process of concrete strength from initial rapid growth to stable later stage, emphasizing the overall development trend of concrete strength. Finally, by combining the recursive time structure with the stage evolution curve, a comprehensive expression is constructed, which considers both the weighted effect of historical data and the overall evolution trend of concrete strength, providing an accurate mathematical model of concrete strength time-varying characteristics, which helps to more accurately predict and analyze the strength change of concrete at different stages.

[0132] Further, the five-dimensional tensor decomposition employs a penalty term Tucker decomposition algorithm, wherein.

[0133] The decomposition order is respectively 2-4 order for environmental factor dimension, 3-6 order for material property dimension, 2-3 order for time evolution dimension, 2-4 order for structure scale dimension, and 3-5 order for performance index dimension.

[0134] An L1 regularization term is introduced to control the sparsity of the factor matrix, and the regularization coefficient is in the range of 0.02-0.15.

[0135] The alternating least squares method is used to solve the decomposition problem, and the convergence threshold is set to 10 -4 and the maximum number of iterations is 200.

[0136] By calculating the element distribution characteristics in the Tucker core tensor, the coupling degree between dimensions is quantified, and the dominant interaction mode is extracted.

[0137] Further, step 3 includes the following steps.

[0138] A segmented nonlinear model of concrete strength development is established, which divides the entire age period into a rapid growth period, a transition period and a stable period, and connects each stage through a segmented continuous function to realize accurate mathematical expression of the strength evolution in the whole age period.

[0139] A recursive prediction model based on long short-term memory network is constructed, which takes historical monitoring data and five-dimensional tensor feature expression as input, learns the internal law of concrete strength development, and realizes intelligent prediction and real-time correction of strength development trajectory.

[0140] By integrating the environmental influence factor, the actual calendar age is converted into an equivalent age that accurately reflects the true hydration degree, thereby realizing the unified expression of the strength development curve under different environmental conditions.

[0141] The exponential moving average method is used to enhance the importance of recent data, and a forgetting factor is set to gradually reduce the influence of long-term data. The exponential moving average method is used to enhance the importance of recent data, and a forgetting factor ω (0 < ω < 1) is set to gradually reduce the influence of long-term data. The historical monitoring data is weighted by the recursive formula S(t) = ω × X(t) + (1-ω) × S(t-1), where the weight of recent data increases exponentially, and the weight of long-term data decreases exponentially, realizing adaptive smoothing of time series data. At the same time, the model parameters are calibrated at the key age nodes to ensure that the prediction results are highly consistent with the actual strength development.

[0142] The percentage deviation formula Δ = |measured value-theoretical value| / theoretical value × 100% is used to quantify the strength deviation, and the threshold values of slight deviation (5-10%), moderate deviation (10-20%) and severe deviation (>20%) are set. According to the importance level of the structure and the severity of the service environment, the threshold boundaries are dynamically adjusted to realize graded early warning management. When the deviation exceeds the allowed range, the abnormal analysis process is triggered, and the Bayesian inference method is introduced to identify the influencing factors to distinguish between normal strength fluctuations and potential quality problems.

[0143] The real-time monitoring data is aggregated and analyzed according to different time scales, and wavelet transform is used to extract characteristic patterns of different time scales, and a cross-scale time correlation network is established to realize full-spectrum monitoring and prediction from short-term strength fluctuations to long-term performance evolution.

[0144] Among them, the real-time monitoring data is aggregated and analyzed according to different time scales, and wavelet transform is used to extract characteristic patterns of different time scales, and a cross-scale time correlation network is established, including the following steps: According to the time characteristics of concrete strength evolution, the real-time monitoring data is divided into day, week and month three time scales, which are used to capture the performance change trend at different stages; In each time scale, the original data is statistically aggregated, such as mean, variance and peak value extraction, to reduce high-frequency noise interference and improve feature stability; Discrete wavelet transform is applied to the aggregated multi-scale data to separate the signal characteristics of different frequency bands, revealing the implicit periodicity and mutation characteristics in the concrete performance evolution process; From the wavelet decomposition results, the main change patterns at each time scale are extracted to construct a multi-dimensional feature vector including mutation points, periodic changes and trend changes; Through graph structure or time recursion mechanism, a dynamic correlation network between different scale features is established to reflect the influence path of short-term fluctuations on long-term performance evolution.

[0145] In summary, by constructing a piecewise nonlinear model and a recursive prediction model based on a long short-term memory (LSTM) network, accurate prediction and real-time correction of concrete strength development were achieved. First, the concrete age was divided into a rapid growth period, a transition period, and a stable period. A piecewise continuous function was used to accurately represent the strength evolution process in different stages. Next, an LSTM network was used to combine historical monitoring data with five-dimensional tensor features to learn the inherent laws of strength development and intelligently predict the strength trajectory. By integrating and accumulating environmental impact factors, the actual calendar age was converted into an equivalent age, providing a unified representation of the strength curve under different environmental conditions. Furthermore, an exponentially weighted moving average method was used to enhance the importance of recent data, while a forgetting factor was set to gradually reduce the influence of long-term data to ensure prediction accuracy. At critical age points, model parameters were forced to calibrate to ensure high consistency between predicted results and actual strength. By calculating the deviation between the measured strength and the theoretical curve, warning thresholds were set. Bayesian inference methods were then used to identify potential quality issues and distinguish between normal fluctuations and abnormal conditions. In addition, wavelet transform is used to extract characteristic patterns at different time scales and establish a cross-scale time correlation network to provide full spectrum monitoring and prediction from short-term strength fluctuations to long-term performance evolution, further enhancing the intelligent prediction capability of the time-varying characteristics of concrete strength.

[0146] Furthermore, the long short-term memory network has the following structure and parameters.

[0147] The input layer dimension is 20-40, corresponding to the environmental parameters and material property feature vectors.

[0148] 2-3 LSTM hidden layers, each containing 32-128 neurons.

[0149] 1 fully connected output layer that outputs intensity forecasts for the next 7 days, 30 days, and 90 days.

[0150] The Adam optimizer is used for training, with an initial learning rate of 0.001-0.003 and a learning rate decay coefficient of 0.85-0.95 every 50 rounds.

[0151] The root mean square error is used as the loss function, the ratio of training data to validation data is 8:2, and the early stopping mechanism is triggered when there is no improvement in the validation set loss for 10 consecutive rounds.

[0152] Furthermore, by integrating and accumulating environmental impact factors, the actual calendar age is converted into an equivalent age that can accurately reflect the actual hydration degree, thereby achieving a unified expression of the strength development curve under different environmental conditions, including the following steps.

[0153] The environmental impact factor λ(θ) at each moment θ is calculated based on the environmental parameters and is expressed as follows: where T(θ) is the ambient temperature at time θ; R is the gas constant; E a is the activation energy, which describes the sensitivity of hydration reaction to temperature; RH(θ) is the ambient relative humidity at time θ; RH ref is the reference humidity value, which is used for normalizing humidity influence; μ is the humidity sensitivity index, which describes the influence of humidity on hydration rate; γ(θ) is other environmental correction factor.

[0154] By integrating and accumulating the environmental influence factor, the actual calendar age is converted into the equivalent age t e , the formula is: t e =∫0 t λ(θ)dθ, where dθ is a small time increment for integral calculation of equivalent age.

[0155] According to the calculated equivalent age t e , the original strength development curve is corrected.

[0156] Under different environmental conditions, by adjusting the parameters of the environmental influence factor λ(θ), it ensures that the strength development curve can adapt to the changes of temperature, humidity and other environmental factors, so as to realize unified and accurate strength prediction.

[0157] In summary, by introducing the integral accumulation method of environmental influence factor, the actual calendar age is converted into the equivalent age reflecting the true hydration degree, so as to accurately unify the strength development curve under different environmental conditions. Specifically, first, the environmental influence factor at each time is calculated according to the environmental parameters (such as temperature, relative humidity, etc.), and it is quantified by formula, considering the influence of temperature, humidity and other correction factors on hydration rate. Then, by integrating and accumulating the environmental influence factor, the equivalent age is calculated, so as to correct the original strength development curve, ensuring that the strength curve accurately reflects the hydration process under different environments. By adjusting the parameters of the environmental influence factor, this method can flexibly adapt to the changes of temperature, humidity and other environmental factors, ensuring the unity and accuracy of the strength development curve, and realizing accurate strength prediction under variable environmental conditions.

[0158] Further, step 4 includes the following steps.

[0159] By calculating the environmental-material sensitivity matrix, the sensitivity of different types of concrete to environmental factors is quantified.

[0160] A segmented temperature compensation function is established, which divides the temperature range into low temperature zone, normal temperature zone and high temperature zone, and uses different mathematical models in each zone to describe the influence of temperature on hydration reaction rate, realizing accurate prediction of concrete strength development in the whole temperature range.

[0161] Combined with the size effect of concrete members, the mapping relationship between humidity gradient and strength distribution is established, so as to realize the reliable inference from surface humidity monitoring to internal strength distribution.

[0162] The environmental comprehensive action index is used to quantitatively characterize the environmental conditions and as a standardized input parameter.

[0163] The differential equation is used to describe the dynamic relationship between the strength development rate index and the environmental comprehensive action index, and the SDR-ECI response surface is established to quantify the acceleration or deceleration effect of strength development under different environmental conditions. At the same time, the material sensitivity factor is introduced to adjust the response surface form to adapt to the characteristics of different concrete mix proportions, so as to realize the accurate prediction of the strength development law in changing environment.

[0164] The correlation between equivalent age and actual strength development is established, the strength contribution rate in different time periods is calculated, and the strength history evolution file is established to record the long-term influence of key environmental events on concrete strength development.

[0165] The environmental-material sensitivity matrix is calculated to quantify the sensitivity of different types of concrete to environmental factors, including the following steps: representative environmental factors such as temperature, humidity, chloride ion concentration, sulfate content and carbon dioxide concentration are selected and normalized to ensure comparability of variables on a unified scale; samples are classified according to the mix proportion, strength grade, admixture type and curing method of concrete, and a basic database of multiple types of concrete materials is established; the response indicators of concrete under various environmental conditions are selected, such as strength decay rate, crack development speed and carbonation depth, and quantitative response results are extracted combined with long-term monitoring data; the sensitivity coefficients between environmental factors and concrete response are calculated to form a numerical sensitivity matrix; the sensitivity coefficient matrix is visualized by heat map to identify the key coupling relationship between high sensitivity environmental factors and corresponding concrete types, and cluster analysis is performed on different material types; according to the sensitivity strength, the concrete materials are divided into high, medium and low sensitivity levels.

[0166] For the normal temperature and high temperature zone of 10℃ to 60℃, the Arrhenius model is used; for the low temperature zone of <10℃, the Hyperbolic Tangent model is used; for the high temperature zone of >60℃, the Gaussian model is used.

[0167] The construction process of the environmental comprehensive action index includes: according to the sensitivity analysis results of the evolution of concrete strength, the environmental parameters with significant influence on performance are screened to form a unified input variable set; the selected parameters are dimensionless processed to make the numerical value in the interval [0, 1]; for the response relationship between different parameters and concrete performance, a nonlinear weight mechanism is designed to enhance the sensitivity to key parameter changes and suppress the fluctuation influence of secondary factors; based on historical monitoring data, the weight coefficients corresponding to each parameter are set to combine the standardized parameters to form the environmental comprehensive action index.

[0168] In summary, by establishing the environmental-material sensitivity matrix, the segmented temperature compensation function and the humidity gradient-strength distribution mapping relationship, the strength development of concrete under different environmental conditions is accurately predicted. First, by quantifying the sensitivity of concrete to environmental factors, the environmental adaptability analysis of different types of concrete is realized. Then, the segmented temperature compensation function is constructed, and different mathematical models are used to describe the influence of temperature on the hydration reaction rate in different intervals, ensuring accurate prediction of strength in the whole temperature range. At the same time, combined with the size effect of the component, the relationship between humidity gradient and strength distribution is established to realize the inference from surface humidity monitoring to internal strength. By designing the environmental comprehensive action index and using the nonlinear weighting function to integrate key environmental parameters, a unified index is formed to quantify environmental impact. This scheme describes the dynamic relationship between strength development rate and environmental impact through differential equations and SDR-ECI response surface, and introduces material sensitivity factors to adapt to the characteristics of different concrete mixtures, realizing accurate prediction of the strength development law. In addition, the correlation between equivalent age and actual strength development is established to record the long-term impact of key environmental events on strength evolution and provide a comprehensive historical evolution file.

[0169] Further, combined with the size effect of the concrete component, the humidity gradient-strength distribution mapping relationship is constructed to realize reliable inference from surface humidity monitoring to internal strength distribution, including the following steps.

[0170] The humidity gradient formula G H (x)=(H(x)-H0) / x is used to calculate the humidity change rate at different depths, thereby representing the diffusion rate of water in the component interior, where G H (x) represents the humidity change rate at depth x, i.e. the change of humidity with depth, which reflects the diffusion rate of water in the concrete component; H(x) represents the humidity value at depth x in the concrete interior; H0 represents the humidity value at the surface of the concrete component.

[0171] Combined with the component size D, the size effect function f(D)=(D / D0) -δThe influence of humidity on strength is modified to reflect the strength variation law caused by the reduction of humidity gradient in large-scale components, wherein f(D) is a size effect function for describing the correction effect of the size of the concrete component on the strength; D0 is a reference size for normalizing the component size D; and δ is a correction factor of size effect, indicating the influence degree of the component size on the strength.

[0172] Based on the strength formula σ(x) = σ0(1 + ηG H (x)) β f(D) is used to couple the humidity gradient and the size effect, and a mapping relationship between the humidity gradient and the strength distribution is established, wherein σ(x) represents the strength value of the concrete at a depth x; σ0 represents the strength value of the concrete surface; η represents a sensitivity coefficient of the humidity gradient on the strength, reflecting the influence degree of the humidity change on the concrete strength; and β represents a nonlinear influence coefficient of the humidity gradient on the strength, determining the amplitude of the strength response when the humidity gradient changes.

[0173] The measured surface humidity and the calculated humidity gradient are used to calculate the strength distribution at different depths in combination with the mapping relationship, so as to realize the non-destructive inference of the internal strength of the component.

[0174] Further, by combining the size effect of the concrete component, the mapping relationship between the humidity gradient and the strength distribution is established, so as to realize the reliable inference from the surface humidity monitoring to the internal strength distribution. First, the humidity change rate at different depths is calculated through the humidity gradient formula, which represents the diffusion rate of water in the component and reflects the influence of water on the strength development. Then, considering the influence of the component size on the strength, the size effect function is used to modify the influence of humidity on the strength, especially in large-scale components, the reduction of the humidity gradient may lead to the change of the strength. Then, the mapping relationship between the humidity gradient and the strength distribution is established based on the strength formula in combination with the humidity gradient and the size effect. Finally, the internal strength distribution of the component is inferred by applying the mapping relationship through the measured surface humidity and the calculated humidity gradient, so as to realize the non-destructive strength evaluation.

[0175] Further, the dynamic relationship between the strength development rate index and the environmental comprehensive action index is described by a differential equation, and a SDR-ECI response surface is constructed to quantify the acceleration or deceleration effect of the strength development under different environmental conditions, including the following steps.

[0176] The relationship between the strength development rate and time is established by a differential equation, which accurately describes the dynamic law of the change of the concrete strength with time.

[0177] According to the dynamic change of various environmental factors, a comprehensive environmental influence index is formed by using a weighted function.

[0178] The dynamic relationship between the strength development rate and the environmental comprehensive action index is described by a differential equation, and a coupling equation is established to quantify the direct impact of different environmental conditions on the strength development rate.

[0179] The relationship between the strength development rate and the environmental comprehensive action index is converted into a response surface model, and numerical optimization methods are used to fit the strength development changes under different environmental conditions to identify the sensitivity of each influencing factor.

[0180] According to the response surface analysis of the change trend of the strength development rate under different environmental conditions, the acceleration or deceleration effect of the strength development under specific environmental conditions is quantified.

[0181] Further, the material sensitivity factor is introduced to adjust the response surface shape to adapt to the characteristics of different concrete mixtures, and to realize accurate prediction of the strength development law in changing environments, including the following steps.

[0182] The influence of different concrete mixtures on the strength development rate is analyzed, and the material sensitivity factor is established to quantify the influence degree of different mixtures on the strength evolution.

[0183] According to the material characteristics of different mixtures, the material sensitivity factor is introduced to adjust the response surface shape, so that it can dynamically reflect the strength development differences of different material combinations under the same environmental conditions.

[0184] The mapping relationship between different concrete mixtures and the response surface is constructed to ensure that the surface can accurately reflect the influence of concrete mixture changes on the strength development law.

[0185] According to the real-time monitoring of environmental and material data, the value of the material sensitivity factor is adjusted to make the response surface adapt to the strength development trend under different environmental conditions.

[0186] The dynamically adjusted response surface is used to predict the strength development under different concrete mixtures, and the selection of the material sensitivity factor is optimized by comparing with the measured data.

[0187] Through continuous monitoring and data feedback, the material sensitivity factor is constantly updated to ensure that the strength development law can be accurately predicted and adjusted in real time under different environmental changes and mixture conditions.

[0188] Further, the correlation between the equivalent age and the actual strength development is established, the strength contribution rate in different time periods is calculated, and the strength history evolution file is established, including the following steps.

[0189] The equivalent age is used to describe the concrete strength evolution as f(t)=f0·(1-exp(-k1·t ewherein f(t) represents the actual strength of the concrete at time t; f0represents the maximum strength of the concrete at the theoretical limit; k1represents the rate constant of the concrete strength development; t e (t) represents the equivalent age at time t.

[0190] The contribution rate of each stage to the total strength growth is calculated by the formula C(t1, t2) = [f(t2) - f(t1)] / [f(t max ) - f(t1)], which quantifies the relative influence of each stage on the concrete strength evolution, wherein C(t1, t2) represents the contribution rate of the concrete strength development in the time period [t1, t2]; f(t2) represents the actual strength at time t2; f(t1) represents the actual strength at time t1; f(t max ) represents the final strength of the concrete at the maximum age t max .

[0191] The environmental influence factor G r (t) is introduced for key environmental events, and a profile is constructed by , which records the cumulative influence of long-term environmental events on the concrete strength change, wherein f history (t, G) represents the historical strength evolution of the concrete at time t due to the environmental influence G; a r represents the influence coefficient of the rth environmental factor on the concrete strength development.

[0192] Further, step 5 includes the following steps.

[0193] The first and second derivatives of the strength development rate are calculated in real time, the first derivative reflects the change direction of the rate, and the second derivative represents the acceleration size, when the inflection point or fluctuation of the strength development rate is detected, the optimal allocation of monitoring resources is realized through intelligent control of the sampling density.

[0194] The temperature change rate, humidity gradient, carbon dioxide concentration fluctuation and chloride ion permeation rate parameters are integrated, and a multi-element weight function is used to calculate the comprehensive sensitivity score, which realizes the accurate quantification and hierarchical monitoring of environmental condition changes.

[0195] The normal fluctuation range of environmental parameters is established, and the seasonal change characteristics and geographical location factors are considered, the threshold setting is dynamically updated, and the environmental change mode is identified, so as to distinguish between normal seasonal fluctuations and abnormal climate events.

[0196] A multi-level triggering mechanism is constructed, which divides the environmental parameter fluctuation into three levels of attention, warning and emergency, and considers the combined triggering conditions of multiple parameters, which realizes the intelligent response to complex environmental changes.

[0197] A lightweight computing module is deployed at the on-site monitoring node to perform real-time data preprocessing and preliminary analysis. When abnormal environmental conditions are detected, the local sampling strategy is immediately adjusted, and a data caching mechanism is activated to ensure the preservation of critical data in case of communication interruption. A clear task division and data synchronization mechanism is established with the central data processing center.

[0198] When specific environmental events are detected, the system automatically enters a high-frequency sampling mode, activates additional environmental monitoring units, records comprehensive data during the entire environmental event, performs a concrete strength evaluation after the event, and constructs a database of environmental event-strength change causal relationships.

[0199] In summary, by calculating the derivative of the strength development rate in real time, the dynamic trend of strength change is monitored, and the monitoring resource allocation is optimized through intelligent regulation of sampling density. A comprehensive sensitivity analysis is performed on multiple environmental parameters (such as temperature change rate, humidity gradient, carbon dioxide concentration fluctuation, and chloride ion permeation rate) to accurately quantify environmental changes and perform hierarchical monitoring. At the same time, a dynamically updated environmental parameter fluctuation threshold and multi-level triggering mechanism are established, which can effectively identify normal seasonal fluctuations and abnormal weather events and make intelligent responses. In addition, a lightweight computing module is deployed for real-time data processing, response to environmental abnormalities, and data storage, even in the case of communication interruption. When specific environmental events are triggered, the system automatically enters a high-frequency sampling mode and establishes a causal database between environmental events and strength changes, providing accurate basis for subsequent strength evaluation.

[0200] Further, the specific parameters of the multi-level triggering mechanism are as follows: Attention level: a single environmental parameter exceeds 1.5 times the normal range, or the comprehensive sensitivity score reaches 0.6, the sampling frequency is increased to 2 times / day.

[0201] Warning level: a single environmental parameter exceeds 2.0 times the normal range, or the comprehensive sensitivity score reaches 0.75, or more than 2 environmental parameters simultaneously reach the attention level threshold, the sampling frequency is increased to 4 times / day, and the strength special evaluation program is started.

[0202] Emergency level: a single environmental parameter exceeds 2.5 times the normal range, or the comprehensive sensitivity score exceeds 0.85, or more than 3 environmental parameters simultaneously reach the warning level threshold, the sampling frequency is increased to 12 times / day, the standby sensor is activated, and a warning notification is sent.

[0203] Further, the lightweight computing module has the following characteristics.

[0204] Based on ARM Cortex-M series processors, with a main frequency of 40-80 MHz and a RAM capacity of 64-128 KB.

[0205] Adopt low-power operation mode, average power consumption less than 50mW.

[0206] Perform local data preprocessing tasks of basic statistical analysis and simple feature extraction.

[0207] Equipped with 1-3MB circular buffer, can save 48-96 hours of critical data in case of communication interruption.

[0208] Adopt incremental data transmission protocol, only send changed data and abnormal features, reduce 60-80% of data transmission.

[0209] Further, step 6 includes the following steps.

[0210] Build a multi-source heterogeneous data fusion framework, extract abnormal feature patterns from various sensor data, and establish a concrete strength evolution feature library.

[0211] Realize the rolling prediction of strength values in the future 7 days, 30 days and 90 days.

[0212] According to the importance level of the structure and the characteristics of the service environment, a regional and graded strength safety threshold system is established, combined with the seasonal environmental change characteristics, the warning threshold parameters are automatically calibrated, and the safety margin is adjusted in time according to the structure aging rate.

[0213] According to the degree of strength decline, different levels of early warning response are triggered: when the strength is less than 90% of the design value, the first level of prompt warning is triggered, reminding attention to potential risks; when the strength is less than 85%, the second level of intervention warning is triggered, suggesting to take preventive measures; when the strength is less than 80% or the strength decline rate exceeds 0.5MPa / day, the third level of emergency warning is triggered, requiring immediate intervention measures to prevent further deterioration.

[0214] Based on the principle of finite element mesh division, a three-dimensional distribution cloud map of structure strength is generated, and a structure health assessment report containing strength numerical analysis, environmental impact assessment, safety risk classification and maintenance suggestions is automatically generated.

[0215] Combined with the strength development trend and environmental sensitivity analysis, automatic maintenance decision suggestions are generated, including detection frequency adjustment, protection measures strengthening and necessary reinforcement scheme, to ensure the long-term stability and safety of concrete structures in complex environments.

[0216] In summary, by constructing a multi-source heterogeneous data fusion framework, extracting abnormal features from sensor data, and establishing a concrete strength evolution feature library, rolling predictions of strength values for the next 7 days, 30 days, and 90 days are realized. According to the importance of the structure and the characteristics of the service environment, a strength safety threshold system is established for different regions and levels, and the warning threshold parameters can be automatically calibrated and adjusted. By setting a multi-level warning response mechanism, different levels of warning are triggered according to the degree of strength decline, ensuring timely intervention and preventing further deterioration. In addition, using the finite element grid principle to generate a three-dimensional strength distribution cloud map, and automatically generating a structure health assessment report, combined with the strength development trend and environmental sensitivity analysis, detailed maintenance decision suggestions are provided to ensure the long-term stability and safety of concrete structures in complex environments.

[0217] Further, the multi-source heterogeneous data fusion framework comprises.

[0218] Data level fusion layer: median filtering and simplified Kalman filtering are used for raw data preprocessing to reduce measurement noise by 10-20 dB.

[0219] Feature level fusion layer: principal component analysis is used to extract key features of each sensor data, retaining more than 90% of the information content, while reducing the feature dimension by 50-70%.

[0220] Decision level fusion layer: fuzzy reasoning and support vector machines are combined to build an integrated decision mechanism, considering the reliability and confidence of each feature, with a decision accuracy of 85-90%.

[0221] Further, the rolling prediction adopts the following strategies.

[0222] The 7-day prediction result is updated every 24 hours, with a prediction accuracy of ±3.5 MPa.

[0223] The 30-day prediction result is updated every 72 hours, with a prediction accuracy of ±5 MPa.

[0224] The 90-day prediction result is updated every 7 days, with a prediction accuracy of ±7 MPa.

[0225] An adaptive confidence interval is introduced, which automatically adjusts the interval width based on historical prediction accuracy, with a confidence level of 90%.

[0226] Further, the generation method of the three-dimensional distribution cloud map is as follows.

[0227] Based on tetrahedral mesh subdivision technology, 5000-20000 grid cells are adaptively generated according to the complexity of the structure.

[0228] Each grid cell is associated with data from 2-3 nearby sensor nodes.

[0229] The linear interpolation method is used to calculate the strength value in the grid cell.

[0230] The Laplace smoothing algorithm is used to consider spatial correlation and smooth the transition between adjacent cells.

[0231] A three-color coloring scheme is used, with red representing a dangerous area with strength less than 80% of the design value, yellow representing a warning area with strength between 80% and 90% of the design value, and green representing a safe area with strength greater than 90% of the design value.

[0232] The visualization effect is achieved through a simplified three-dimensional rendering algorithm to ensure smooth display on ordinary mobile terminals.

[0233] As shown in Figure 2 The present application also aims to provide a concrete strength remote monitoring system suitable for complex environments, comprising the following modules.

[0234] The multi-level intelligent sensing monitoring module realizes comprehensive monitoring of the internal chemical environment, acoustic characteristics and strain distribution of concrete through a sensor network at three levels: microscopic, mesoscopic and macroscopic.

[0235] The environmental parameter acquisition and communication module is responsible for acquiring environmental parameters around the concrete structure and transmitting data to the processing center through low-power Internet of Things technology.

[0236] The data processing module is used to realize the unified mathematical expression of the coupling relationship between environmental factors, material properties, time evolution, structure size and performance indicators.

[0237] The time series strength evolution prediction module realizes the full spectrum prediction of concrete strength from short-term fluctuations to long-term evolution under different environmental conditions, accurately capturing nonlinear time-varying characteristics.

[0238] The strength development rate evaluation module is used to quantify the influence of different environmental conditions on the development rate of concrete strength and realize accurate evaluation of the age effect.

[0239] The adaptive monitoring control module intelligently adjusts the sampling frequency and tracks environmental events based on the derivative analysis of the strength development rate and the environmental sensitivity score, realizing the optimal allocation of monitoring resources and accurate capture of key environmental changes.

[0240] The structure safety state evaluation module fuses multi-source heterogeneous data for strength rolling prediction, establishes a hierarchical safety threshold system and a multi-level early warning response mechanism, and generates a three-dimensional strength distribution cloud map and a health assessment report.

[0241] In summary, through the multi-level intelligent sensing monitoring module, the overall monitoring of the internal chemical environment, acoustic characteristics and strain distribution of the concrete is realized, the environmental parameter acquisition and communication module is combined, and the data is transmitted through the low-power Internet of Things technology. The data processing module unifies the coupling relationship of environmental factors, material properties, time evolution, structure size and performance index. The time sequence strength evolution prediction module can accurately predict the short-term fluctuation and long-term evolution of the concrete strength, the strength development rate evaluation module quantifies the influence of different environmental conditions on the strength development rate. The adaptive monitoring control module intelligently adjusts the sampling frequency, optimizes resource allocation and captures key environmental changes. The structure safety state evaluation module combines multi-source data for strength rolling prediction, establishes a hierarchical safety threshold and early warning mechanism, generates a three-dimensional strength distribution cloud map and a health evaluation report, and comprehensively evaluates the safety of the concrete structure.

[0242] Finally, it should be pointed out that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; 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 application.

Claims

1. A remote monitoring method for concrete strength suitable for complex environments, characterized in that: The following steps are involved: Step 1: Acquire performance data of the concrete structure and its related environmental factor data, and transmit the multi-source heterogeneous data to a data processing center in real time via a preset wireless communication protocol; Step 2: Construct a five-dimensional tensor data structure and achieve an accurate mathematical expression of the complex coupling relationship between environmental factors and material properties through tensor decomposition; Step 3: introduce a recursive update mechanism in the time dimension to capture the nonlinear time-varying characteristics of concrete strength evolution; Step 4, quantify the age effect through the strength development rate index; Step 5: Based on the intensity development rate change trend, the data sampling frequency is adaptively adjusted and the environmental condition fluctuations are monitored; Step 6: Evaluate the safety status of the concrete structure in real time to ensure the safe and reliable operation of the concrete structure in complex environments.

2. A remote monitoring method for concrete strength in complex environments according to claim 1, characterized in that: Step 1 includes the following steps: Based on finite element analysis, the distribution of key monitoring points of the structure is determined. In combination with the load transfer path and stress concentration areas, the monitoring layout plan is optimized, and a multi-scale monitoring network topology is constructed. An array of microscopic ion concentration probes is embedded in the concrete, using ion-selective electrode technology to detect chloride ions, sulfate ions, and alkalinity changes in the pore fluid in real time. A pH sensor is also deployed to monitor the evolution of the concrete's alkaline environment. Deploy a mesoscopic acoustic wave sensor network at key structural sections to assess the concrete's internal density, porosity, and microcrack development by analyzing the acoustic wave propagation time and waveform characteristics. Combining fiber Bragg grating technology with electrical resistance strain measurement, a three-dimensional strain monitoring network is constructed to achieve real-time monitoring of concrete deformation, cracking, and displacement changes, and to create a dynamic map of the strain field distribution. Environmental parameter collection units, including high-precision temperature and humidity sensors, barometers, rain gauges, and UV intensity detectors, are deployed outside the structure to build a microclimate monitoring network. Groundwater level and soil parameters are also collected to establish a complete database of environmental influencing factors. Low-power wide-area IoT technology is used to build a data transmission network, and edge computing technology is combined to pre-process and compress the original data. At the same time, the data is efficiently transmitted to the data processing center through a multi-hop self-organizing network protocol.

3. A remote monitoring method for concrete strength in complex environments according to claim 2, characterized in that: The microscopic ion concentration probe array includes: a chloride ion sensor with a detection range of 0.01%-5% and an accuracy of ±0.005%; a sulfate ion sensor with a detection range of 0.02%-8% and an accuracy of ±0.01%; and a pH sensor with a detection range of 5-14 and an accuracy of ±0.2; The acoustic sensor network consists of 16-64 ultrasonic sensors with an operating frequency of 20-100kHz and a measurement accuracy of ±1μs. It can detect micro cracks with a width greater than 0.1mm. The sensor spacing is 1-3m, forming a multi-path acoustic wave propagation network. The environmental parameter acquisition unit specifically includes: a temperature sensor with a measuring range of -30°C to +80°C and an accuracy of ±0.5°C; a humidity sensor with a measuring range of 5-95%RH and an accuracy of ±3%RH; a barometer with a measuring range of 300-1100hPa and an accuracy of ±1hPa; a rain gauge with a measuring range of 0-200mm / h and an accuracy of ±0.5mm; an ultraviolet intensity detector with a measuring range of 0-1200μW / cm² and an accuracy of ±10μW / cm²; a wind speed sensor with a measuring range of 0-50m / s and an accuracy of ±0.5m / s; and a sulfur dioxide concentration sensor with a measuring range of 0-10ppm and an accuracy of ±0.05ppm.

4. The method for remote monitoring of concrete strength in complex environments according to claim 1, characterized in that: Step 2 includes the following steps: Principal component analysis was used to reduce the dimension of environmental data and extract the main variables, and wavelet transform was used to perform time-frequency analysis to construct the characteristic vector of environmental status. Combining concrete mix parameters, curing conditions, and measured strength grades, this multi-scale material model quantitatively describes the microstructural characteristics of concrete, including pore distribution, hydration product ratio, and interfacial transition zone characteristics. By constructing a comprehensive material state descriptor, this method enables accurate digital representation of concrete material properties. An exponentially weighted attenuation factor is introduced to process historical data and construct a recursive time series structure to capture the evolution of concrete strength. Simultaneously, the strength development curves at different stages are simulated to achieve an accurate mathematical expression of the time-varying characteristics of concrete strength. A mapping relationship between microscopic physicochemical processes and macroscopic mechanical properties is established through a scale conversion function to eliminate differences in monitoring data at different structural scales and achieve reliable inference from local monitoring points to overall structural performance. Introducing performance degradation rate and limit state function, building a comprehensive evaluation system based on performance indicators, and using reliability theory to set the weight of each indicator to form a multi-objective performance evaluation criterion; Construct a five-dimensional tensor structure T(E,M,τ,S,P), where each dimension corresponds to environmental factors, material properties, time evolution, structural scale and performance indicators; T(E, M, τ, S, P) is decomposed into the product of a core tensor and five factor matrices. The main characteristic modes of each dimension are extracted, and the coupling strength coefficients between the dimensions are calculated to quantitatively characterize the influence of environmental factors and material properties on concrete performance at different times and scales.

5. The method for remote monitoring of concrete strength in complex environments according to claim 1, characterized in that: Step 3 includes the following steps: A piecewise nonlinear model of concrete strength development is established, dividing the entire age period into a rapid growth period, a transition period, and a stable period. Each stage is connected by a piecewise continuous function to achieve an accurate mathematical expression of the strength evolution over the entire age period. A recursive prediction model based on a long short-term memory network is constructed. This model takes historical monitoring data and a five-dimensional tensor feature expression as input to learn the inherent laws of concrete strength development, enabling intelligent prediction and real-time correction of the strength development trajectory. By integrating and accumulating environmental impact factors, the actual calendar age is converted into an equivalent age that can accurately reflect the actual hydration degree, thereby achieving a unified expression of the strength development curve under different environmental conditions; An exponentially weighted moving average method is used to enhance the importance of recent data, and a forgetting factor is set to gradually reduce the impact of long-term data. At the same time, mandatory calibration of model parameters is performed at key age nodes to ensure that the forecast results are highly consistent with actual intensity development. By calculating the deviation between the measured strength and the theoretical curve, multi-level warning thresholds are set. When the deviation exceeds the allowable range, the abnormal analysis process is triggered, and the Bayesian inference method is introduced to identify the influencing factors to distinguish normal strength fluctuations from potential quality problems. Real-time monitoring data are aggregated and analyzed at different time scales, and wavelet transform is used to extract characteristic patterns at different time scales. At the same time, a cross-scale time correlation network is established to achieve full-spectrum monitoring and prediction from short-term intensity fluctuations to long-term performance evolution.

6. The method for remote monitoring of concrete strength in complex environments according to claim 1, characterized in that: Step 4 includes the following steps: By calculating the environmental-material sensitivity matrix, the sensitivity of different types of concrete to environmental factors is quantified; A segmented temperature compensation function is established, dividing the temperature range into low temperature, normal temperature, and high temperature zones. Different mathematical models are used in each zone to describe the effect of temperature on the hydration reaction rate, enabling accurate prediction of concrete strength development across the entire temperature range. Combining the size effect of concrete components, a moisture gradient-strength distribution mapping relationship is constructed, thereby achieving reliable inference from surface moisture monitoring to internal strength distribution; The environmental comprehensive effect index is used to quantitatively characterize environmental conditions and serve as a standardized input parameter; The dynamic relationship between the strength development rate index and the environmental comprehensive effect index is described through differential equations, and an SDR-ECI response surface is constructed to quantify the acceleration or deceleration of strength development under different environmental conditions. Furthermore, a material sensitivity factor is introduced to adjust the response surface morphology to adapt to the characteristics of different concrete mixes, enabling accurate prediction of strength development patterns in changing environments. The relationship between equivalent age and actual strength development is established, the strength contribution rate in different time periods is calculated, and a strength history evolution archive is established to record the long-term impact of key environmental events on the strength development of concrete.

7. The method for remote monitoring of concrete strength in complex environments according to claim 1, characterized in that: Step 5 includes the following steps: The first-order derivative and second-order derivative of the intensity development rate are calculated in real time. The first-order derivative reflects the direction of rate change, and the second-order derivative represents the magnitude of acceleration. When an inflection point or increased fluctuation in the intensity development rate is detected, the optimal allocation of monitoring resources is achieved through intelligent control of the sampling density. Integrating temperature change rate, humidity gradient, carbon dioxide concentration fluctuation and chloride ion penetration rate parameters, and using a multivariate weight function to calculate a comprehensive sensitivity score, it achieves accurate quantification and hierarchical monitoring of changes in environmental conditions; Establish normal fluctuation ranges for environmental parameters, taking into account seasonal variations and geographic location, dynamically update threshold settings, and identify environmental change patterns to distinguish normal seasonal fluctuations from abnormal climate events; Build a multi-level trigger mechanism that divides environmental parameter fluctuations into three levels: attention, warning, and emergency. Simultaneously, consider the combined trigger conditions of multiple parameters to achieve intelligent response to complex environmental changes. Lightweight computing modules are deployed at on-site monitoring nodes to perform real-time data preprocessing and preliminary analysis. When abnormal environmental conditions are detected, local sampling strategies are adjusted immediately. Data caching mechanisms are activated to ensure critical data can be preserved even in the event of communication interruptions. Clear division of labor and data synchronization mechanisms are established with the central data processing center. When a specific environmental event is detected, it automatically enters high-frequency sampling mode and activates additional environmental monitoring units to comprehensively record data from the entire environmental event process. After the event, a special assessment of concrete strength is performed and a causal relationship database of environmental events and strength changes is constructed.

8. The method for remote monitoring of concrete strength in complex environments according to claim 1, characterized in that: Step 6 includes the following steps: Construct a multi-source heterogeneous data fusion framework to extract abnormal characteristic patterns from various sensor data and establish a concrete strength evolution feature library; Achieve rolling forecasts of intensity values ​​for the next 7 days, 30 days, and 90 days; Establish regional and graded strength safety threshold systems based on the structural importance level and service environment characteristics. Combined with seasonal environmental variation characteristics, automatically calibrate warning threshold parameters and adjust safety margins in a timely manner based on the structural aging rate. Different levels of early warning responses are triggered based on the degree of intensity decline: when the intensity is lower than 90% of the design value, a level 1 warning is triggered, alerting attention to potential risks; when the intensity is lower than 85%, a level 2 intervention warning is triggered, recommending preventive measures; when the intensity is lower than 80% or the intensity decline rate exceeds 0.5 MPa / day, a level 3 emergency warning is triggered, requiring immediate intervention measures to prevent further deterioration; Based on the finite element meshing principle, a three-dimensional distribution cloud map of structural strength is generated. At the same time, a structural health assessment report including strength numerical analysis, environmental impact assessment, safety risk classification and maintenance recommendations is automatically generated. Combining strength development trends and environmental sensitivity analysis, maintenance decision recommendations are automatically generated, including adjustments to inspection frequency, strengthening of protective measures, and necessary reinforcement plans, to ensure the long-term stability and safety of concrete structures in complex environments.

9. The method for remote monitoring of concrete strength in complex environments according to claim 8, characterized in that: The method for generating the three-dimensional distribution cloud map is: Based on tetrahedral mesh generation technology, 5000-20000 cells are adaptively generated according to the complexity of the structure; Each grid cell is associated with the data of 2-3 nearby sensor nodes; The intensity values ​​within the grid cells are calculated using linear interpolation; Use the Laplace smoothing algorithm to take into account spatial correlation and smooth the transition between adjacent units; A three-color coloring scheme is used, with red representing dangerous areas where the intensity is less than 80% of the design value, yellow representing warning areas where the intensity is 80%-90% of the design value, and green representing safe areas where the intensity is greater than 90% of the design value; The visualization effect is achieved through a simplified 3D rendering algorithm, ensuring smooth display on ordinary mobile terminals.

10. A remote monitoring system for concrete strength in complex environments, used to implement a remote monitoring method for concrete strength in complex environments as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Multi-level intelligent sensing monitoring module, which realizes all-round monitoring of the internal chemical environment, acoustic properties and strain distribution of concrete through sensor networks at the micro, meso and macro levels; Environmental parameter acquisition and communication module, responsible for acquiring environmental parameters around the concrete structure and transmitting data to the processing center through low-power Internet of Things technology; Data processing module, used to achieve a unified mathematical expression of the coupling relationship between environmental factors, material properties, time evolution, structural scale and performance indicators; The time-series strength evolution prediction module realizes the full spectrum prediction of concrete strength from short-term fluctuations to long-term evolution under different environmental conditions, accurately capturing nonlinear time-varying characteristics; Strength development rate assessment module, used to quantify the impact of different environmental conditions on the strength development rate of concrete, and achieve accurate assessment of age effects; Adaptive monitoring and control module, based on intensity development rate derivative analysis and environmental sensitivity scores, intelligently adjusts sampling frequency and tracks environmental events, achieving optimal allocation of monitoring resources and accurate capture of key environmental changes; The structural safety status assessment module integrates multi-source heterogeneous data to perform rolling strength prediction, establishes a hierarchical safety threshold system and a multi-level early warning response mechanism, and generates a three-dimensional strength distribution cloud map and health assessment report.

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