Intelligent concrete curing control method based on Internet of Things

Abstract

The invention discloses an intelligent concrete curing control method based on the Internet of Things. The method comprises the steps that temperature and humidity data of a concrete curing environment are collected in real time through a temperature and humidity sensor; transmitting the acquired data to a cloud server for storage and processing by using an Internet of Things gateway; the controller carries out threshold value judgment on the real-time data based on preset maintenance parameters and generates start-stop instructions of maintenance devices such as a steam generator and a heating plate; the maintenance device automatically adjusts the temperature and humidity to a target range after receiving the instruction, and semi-automatic control over the maintenance process is achieved. The system has the beneficial effects that the maintenance device is automatically controlled to start and stop through the Internet of Things platform by utilizing data of the temperature and humidity sensor and the intelligent equipment, and semi-automatic control over the maintenance process is achieved; through a unique threshold triggering mechanism, automatic starting and stopping of the maintenance device are achieved, the frequency and workload of manual intervention are greatly reduced, and a user can conduct manual adjustment at any time according to the actual situation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete construction maintenance, and particularly relates to a concrete intelligent maintenance control method based on Internet of Things. BACKGROUND

[0002] In building construction, the maintenance of concrete is crucial to ensure its strength and durability. The traditional concrete maintenance method mainly relies on manual monitoring of temperature and humidity. According to the relevant standard, such as GB / T50081-2002 "Standard for Test Methods of Mechanical Properties of Ordinary Concrete", the temperature of standard concrete maintenance should be kept at 20±2℃, and the humidity should be greater than 95%. However, manual monitoring has many drawbacks, such as insufficient real-time performance, inability to capture instantaneous changes in temperature and humidity, high labor costs, and the need to arrange dedicated personnel for continuous monitoring on site. The control method is relatively extensive, and the temperature and humidity fluctuate greatly in the temporary maintenance room, making it difficult to accurately maintain within the standard range.

[0003] In a large building construction site, due to the negligence of manual maintenance, the maintenance measures were not adjusted in time according to the weather changes, resulting in insufficient strength of some concrete test blocks, which could not meet the design requirements, and ultimately had to be demolished and rebuilt, causing waste of materials, delaying the construction period, and increasing the cost. In the temporary maintenance room, due to the lack of effective automatic control means, the temperature and humidity fluctuate in the range of ±5℃ and ±10%, respectively, which makes the internal structure of the concrete imperfect and prone to cracking and other quality problems, seriously affecting the service life and safety of the building.

[0004] With the rapid development of the construction industry, the requirements for concrete maintenance quality are increasing, and the traditional manual maintenance method has been difficult to meet the needs.

[0005] Therefore, it is necessary to provide a concrete intelligent maintenance control method based on Internet of Things for the above problems. SUMMARY

[0006] In view of the deficiencies in the prior art, the present application aims to provide a concrete intelligent maintenance control method based on Internet of Things to solve the above problems.

[0007] A concrete intelligent maintenance control method based on Internet of Things, the method steps are:

[0008] S1, collecting the temperature and humidity data of the concrete maintenance environment in real time through a temperature and humidity sensor;

[0009] S2, transmitting the collected data to a cloud server for storage and processing using an Internet of Things gateway;

[0010] S3, the controller performs threshold judgment on the real-time data based on preset curing parameters to generate start-stop instructions of curing devices such as steam generators and heating plates;

[0011] S4, the curing device automatically adjusts the temperature and humidity to the target range after receiving the instructions, realizing semi-automatic control of the curing process.

[0012] The preset curing parameter threshold includes a temperature range of 20±2℃ and a relative humidity of not less than 95%, and the threshold judgment period of the controller is not more than 2 minutes.

[0013] The Internet of Things gateway supports at least one of LoRa, NB-IoT or 4G / 5G wireless transmission protocols, and the total delay time from sensor data collection to uploading to the cloud server is ≤5 seconds.

[0014] The plurality of temperature and humidity sensors are evenly distributed in the curing area, and at least one sensor probe head is directly in contact with or close to the concrete surface to obtain the temperature data of the concrete body, and the remaining sensors are used to obtain the air temperature and humidity data of the curing environment; the temperature and humidity sensor is a digital sensor, the temperature measurement accuracy is not less than ±0.5℃, the humidity measurement accuracy is not less than ±2%RH, and the sensor node adopts low-power design, and the battery endurance is not less than 6 months.

[0015] Preferably, the algorithm steps of the temperature and humidity sensor are:

[0016] S11, sensor data collection, the temperature and humidity sensor converts physical quantities (temperature / humidity) into electrical signals through internal sensing elements, and then converts them into digital quantities through an ADC (analog-to-digital converter);

[0017] S12, data calibration and compensation algorithm;

[0018] S13, temperature conversion, the original temperature value output by the sensor needs to be converted into the actual temperature, and the formula is as follows:

[0019] Where H raw is the 16-bit humidity original value;

[0020] S14, temperature compensation, the measurement result of the humidity sensor is greatly affected by temperature, and needs to be temperature compensated, and the formula is as follows:

[0021] RH compensated = RH raw +(25-T(℃)×0.01

[0022] Where RG raw is the uncompensated humidity value, and T(℃) is the current measured temperature value;

[0023] S15, data processing and output; data filtering uses a sliding average filtering algorithm: X1~X n is the temperature and humidity data collected continuously n times; is the final data after filtering, used for subsequent threshold judgment.

[0024] The data calibration step of step S12 is:

[0025] S21, initialize the CRC register to 0xFF;

[0026] S22, for each byte of data (high byte first, low byte last), XOR it with the CRC register;

[0027] S23, for each bit (from the highest to the lowest), if the highest bit of the CRC register is 1, shift left by 1 and XOR with the polynomial, otherwise, only shift left by 1;

[0028] S24, after processing all bytes, the value of the CRC register should be consistent with the check bit returned by the sensor, otherwise the data is invalid and needs to be re-collected.

[0029] Preferably, the controller is built-in with an adaptive adjustment algorithm, which can dynamically adjust the temperature and humidity control strategy and parameter threshold according to the needs of different concrete grades and different curing stages, as well as the analysis results of historical curing data, to optimize the curing effect.

[0030] The adaptive adjustment algorithm step is:

[0031] S31, curing stage division, according to the hydration reaction law of concrete, the curing process is divided into 3 stages, the demand for temperature and humidity is significantly different in different stages:

[0032] Initial setting period: t∈[0, 24)h, high humidity, stable temperature is required;

[0033] Final setting period: t∈[24, 72)h, humidity needs to be maintained, temperature needs to be moderately increased;

[0034] Hardening period: t≥72h, humidity can be gradually reduced, temperature approaches the environment;

[0035] The stage coefficient S(t) is defined as:

[0036] S32, concrete grade correction, the hydration heat and strength demand of different grade concrete are different, introduce grade coefficient K C ,

[0037] K C =1+0.02×(C-30);

[0038] S33. Dynamic temperature threshold calculation, using standard temperature threshold.

[0039] T std_min =18℃, T std_max =22℃ as the baseline, adjusted for stage, label, and environmental interference:

[0040] T min (t)=T std_min +K C ×S(t)×(25-T out )×0.05

[0041] T max (t)=T std_max +K C ×S(t)×(T out -25)×0.03

[0042] T out This refers to the outdoor ambient temperature, used to compensate for the impact of outdoor temperature on the curing room.

[0043] S34. Dynamic humidity threshold calculation: The humidity baseline value is dynamically adjusted according to the stage and temperature. RH target (t)=95%×S(t)+0.5%×(T now -20);

[0044] S35. Response sensitivity adjustment: The device response coefficient is dynamically adjusted according to the temperature and humidity deviation.

[0045]

[0046] Where α is the temperature regulation sensitivity, β is the humidity regulation sensitivity, and T avg =(T min (t)+T max (t)) / 2 is the average target temperature for the current stage;

[0047] S36. Historical data feedback iteration: daily closed-loop optimization based on maintenance data from the previous 24 hours. new (t)=S old (t)×[1+0.1×(R-0.95)+0.05×(F / F target -1)];

[0048] Where R is the compliance rate, F is the strength test value, S(t) is the correction stage coefficient, and T is the strength test value. now RH now Current maintenance environment temperature and humidity; T con C represents the surface temperature of the concrete; C represents the concrete grade; T represents the surface temperature of the concrete. min (t) represents the lower limit of the dynamic temperature threshold; T max(t) represents the upper limit of the dynamic temperature threshold; RH target (t) represents the dynamic humidity threshold.

[0049] The maintenance execution device includes a steam generator or spray system for regulating humidity, and a heating plate or air conditioning system for regulating temperature; the controller independently or collaboratively controls the start and stop of these devices based on the comparison results of temperature and humidity data with preset thresholds.

[0050] It also includes remote monitoring and interaction steps: the cloud server pushes the processed temperature and humidity data and the operating status information of the maintenance execution device to the user terminal in real time. The user can view real-time data and historical curves through the user terminal, and can send manual intervention commands to the controller through the user terminal when necessary to realize manual intervention in semi-automatic mode.

[0051] The system also includes anomaly alarm procedures: when the controller determines that the collected temperature and humidity data exceed the preset threshold range multiple times (e.g., 3 times), or when the maintenance execution device malfunctions and cannot respond to control commands normally, the system automatically triggers an alarm mechanism to notify relevant management personnel via SMS, APP push notifications, or sound alarms. The cloud server also has data storage, analysis, and report generation functions, capable of storing at least 30 days of historical monitoring data and equipment operation logs, and can generate maintenance process reports according to user needs, including key indicators such as temperature and humidity compliance rate and equipment runtime.

[0052] Compared with existing technologies, the present invention offers the following advantages: It utilizes temperature and humidity sensors and data from smart devices to automatically control the start and stop of the maintenance device via an IoT platform, achieving semi-automatic control of the maintenance process. Through a unique threshold triggering mechanism, the invention enables automatic start and stop of the maintenance device, significantly reducing the frequency and workload of manual intervention. Simultaneously, to meet operational needs in special circumstances, a remote manual control interface is retained, allowing users to make manual adjustments at any time according to actual conditions. This semi-automatic control mode combines the efficiency of automation with the flexibility of manual intervention.

[0053] This invention achieves automatic start-up and shutdown of the maintenance device through a unique threshold triggering mechanism, greatly reducing the frequency and workload of manual intervention. Meanwhile, to meet operational needs in special circumstances, a remote manual control interface is retained, allowing users to make manual adjustments at any time according to actual conditions. This semi-automatic control mode combines the efficiency of automation with the flexibility of manual intervention. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a flowchart of the algorithm for the temperature and humidity sensor of the present invention. Detailed Implementation

[0056] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0057] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0058] like Figure 1 and combined Figure 2 As shown, a method for intelligent curing control of concrete based on the Internet of Things (IoT) includes the following steps:

[0059] S1. Real-time temperature and humidity data of the concrete curing environment are collected using temperature and humidity sensors;

[0060] S2. Use an IoT gateway to transmit the collected data to a cloud server for storage and processing;

[0061] S3. The controller performs threshold judgment on real-time data based on preset maintenance parameters and generates start and stop commands for maintenance devices such as steam generators and heating plates.

[0062] S4. After receiving the instruction, the maintenance device automatically adjusts the temperature and humidity to the target range, realizing semi-automatic control of the maintenance process.

[0063] The preset maintenance parameter thresholds include a temperature range of 20±2℃ and a relative humidity of not less than 95%, and the threshold judgment cycle of the controller does not exceed 2 minutes.

[0064] The IoT gateway supports at least one of LoRa, NB-IoT or 4G / 5G wireless transmission protocols, and the total latency from data collection by the sensor to uploading to the cloud server is ≤5 seconds.

[0065] Multiple temperature and humidity sensors are evenly distributed within the curing area, with at least one sensor's probe directly contacting or close to the concrete surface to obtain temperature data of the concrete itself. The remaining sensors are used to obtain air temperature and humidity data of the curing environment. The temperature and humidity sensors are digital sensors with a temperature measurement accuracy of no less than ±0.5℃ and a humidity measurement accuracy of no less than ±2%RH. The sensor nodes adopt a low-power design with a battery life of no less than 6 months.

[0066] Preferably, the algorithm steps of the temperature and humidity sensor are as follows:

[0067] S11. Sensor data acquisition: The temperature and humidity sensor converts physical quantities (temperature / humidity) into electrical signals through internal sensing elements, and then converts them into digital quantities through ADC (analog-to-digital converter).

[0068] S12, Data calibration and compensation algorithm;

[0069] S13. Temperature Conversion: The raw temperature value output by the sensor needs to be converted into the actual temperature. The formula is as follows:

[0070] Where H raw This is the raw 16-bit humidity value;

[0071] S14. Temperature Compensation: The measurement results of the humidity sensor are greatly affected by temperature, so temperature compensation is required. The formula is as follows:

[0072] RH compensated =RH raw +(25-T(℃))×0.01

[0073] RH raw The value represents the uncompensated humidity, and T (°C) represents the currently measured temperature.

[0074] S15, Data Processing and Output; Data filtering employs a moving average filtering algorithm: X1~X n The data represents temperature and humidity collected consecutively from n data points. This is the final filtered data, used for subsequent threshold determination.

[0075] The data calibration step in step S12 is as follows:

[0076] S21. Initialize the CRC register to 0xFF;

[0077] S22. For each byte of data (high byte first, low byte last), XOR it with the CRC register;

[0078] S23. For each bit (from the most significant bit to the least significant bit), if the most significant bit of the CRC register is 1, shift it left by 1 bit and XOR it with the polynomial; otherwise, shift it left by 1 bit only.

[0079] S24. After processing all bytes, the value of the CRC register should be consistent with the check bit returned by the sensor; otherwise, the data is invalid and needs to be collected again.

[0080] Preferably, the controller has a built-in adaptive adjustment algorithm that can dynamically adjust the temperature and humidity control strategy and parameter thresholds according to the needs of different concrete grades, different curing stages, and the analysis results of historical curing data, so as to optimize the curing effect.

[0081] The adaptive adjustment algorithm steps are as follows:

[0082] S31. Curing Stage Division: Based on the hydration reaction law of concrete, the curing process is divided into 3 stages, and the temperature and humidity requirements of different stages differ significantly:

[0083] Initial condensation period: t∈(0,24)h, requires high humidity and stable temperature;

[0084] Final condensation period: t∈[24, 72)h, requires humidity maintenance and moderate temperature increase;

[0085] Hardening period: t > 72h, humidity can be gradually reduced and temperature can approach ambient temperature;

[0086] The stage coefficient S(t) is defined as:

[0087] S32. Concrete Grade Correction: Different grades of concrete have different heat of hydration and strength requirements, so a grade coefficient K is introduced. C ,

[0088] K C =1 + 0.02 × (C - 30);

[0089] S33. Dynamic temperature threshold calculation, using standard temperature threshold.

[0090] T std_min =18℃,T std_max =22℃ as the baseline, adjusted for stage, label, and environmental interference:

[0091] T min (t)=T std_min +K C ×S(t)×(25-T out )×0.05

[0092] T max (t)=T std_max +K C ×S(t)×(T out -25)×0.03

[0093] T out This refers to the outdoor ambient temperature, used to compensate for the impact of outdoor temperature on the curing room.

[0094] S34. Dynamic humidity threshold calculation: The humidity baseline value is dynamically adjusted according to the stage and temperature. RH target (t)=95%×S(t)+0.5%×(T now -20);

[0095] S35. Response sensitivity adjustment: The device response coefficient is dynamically adjusted according to the temperature and humidity deviation.

[0096]

[0097] Where α is the temperature regulation sensitivity, β is the humidity regulation sensitivity, and T avg=(T min (t)+T max (t)) / 2 is the average target temperature for the current stage;

[0098] S36. Historical data feedback iteration: daily closed-loop optimization based on maintenance data from the previous 24 hours. new (t)=S old (t)×[1+0.1×(R-0.95)+0.05×(F / F targett -1)];

[0099] Where R is the compliance rate, F is the strength test value, S(t) is the correction stage coefficient, and T is the strength test value. now RH now Current maintenance environment temperature and humidity; T com C represents the surface temperature of the concrete; C represents the concrete grade; T represents the surface temperature of the concrete. min (t) represents the lower limit of the dynamic temperature threshold; T max (t) represents the upper limit of the dynamic temperature threshold; RH target (t) represents the dynamic humidity threshold.

[0100] The maintenance execution device includes a steam generator or spray system for regulating humidity, and a heating plate or air conditioning system for regulating temperature; the controller independently or collaboratively controls the start and stop of these devices based on the comparison results of temperature and humidity data with preset thresholds.

[0101] It also includes remote monitoring and interaction steps: the cloud server pushes the processed temperature and humidity data and the operating status information of the maintenance execution device to the user terminal in real time. The user can view real-time data and historical curves through the user terminal, and can send manual intervention commands to the controller through the user terminal when necessary to realize manual intervention in semi-automatic mode.

[0102] The system also includes anomaly alarm procedures: when the controller determines that the collected temperature and humidity data exceed the preset threshold range multiple times (e.g., 3 times), or when the maintenance execution device malfunctions and cannot respond to control commands normally, the system automatically triggers an alarm mechanism to notify relevant management personnel via SMS, APP push notifications, or sound alarms. The cloud server also has data storage, analysis, and report generation functions, capable of storing at least 30 days of historical monitoring data and equipment operation logs, and can generate maintenance process reports according to user needs, including key indicators such as temperature and humidity compliance rate and equipment runtime.

[0103] Compared with existing technologies, the present invention offers the following advantages: It utilizes temperature and humidity sensors and data from smart devices to automatically control the start and stop of the maintenance device via an IoT platform, achieving semi-automatic control of the maintenance process. Through a unique threshold triggering mechanism, the invention enables automatic start and stop of the maintenance device, significantly reducing the frequency and workload of manual intervention. Simultaneously, to meet operational needs in special circumstances, a remote manual control interface is retained, allowing users to make manual adjustments at any time according to actual conditions. This semi-automatic control mode combines the efficiency of automation with the flexibility of manual intervention.

[0104] This invention achieves automatic start-up and shutdown of the maintenance device through a unique threshold triggering mechanism, greatly reducing the frequency and workload of manual intervention. Meanwhile, to meet operational needs in special circumstances, a remote manual control interface is retained, allowing users to make manual adjustments at any time according to actual conditions. This semi-automatic control mode combines the efficiency of automation with the flexibility of manual intervention.

[0105] Temperature and humidity sensors were installed on the top of the curing chamber, with three sets evenly distributed at a distance of 50cm from the concrete surface. This ensures that the sensors can comprehensively and accurately detect changes in temperature and humidity around the concrete. The sensor installation locations were carefully designed to avoid interference from curing equipment, ventilation openings, and other factors, guaranteeing the authenticity and reliability of the collected data.

[0106] The steam generator is connected to a spray system (0.5mm orifice, 30cm spacing) on ​​the ceiling of the curing chamber via a DN25 steam pipe, ensuring even steam distribution within the chamber and providing a stable humidity environment for the concrete. The steam pipe installation follows safe and efficient principles to avoid leaks and blockages. Heating plates, with a power of 500W / ㎡, are fixed to the side walls of the curing chamber. The number and installation positions of the heating plates are determined based on the size of the curing chamber and the curing requirements of the concrete to achieve rapid and uniform heating.

[0107] Temperature and humidity sensors were installed on the top of the curing chamber, with three sets evenly distributed at a distance of 50cm from the concrete surface. This ensures that the sensors can comprehensively and accurately detect changes in temperature and humidity around the concrete. The sensor installation locations were carefully designed to avoid interference from curing equipment, ventilation openings, and other factors, guaranteeing the authenticity and reliability of the collected data.

[0108] The steam generator is connected to a spray system (0.5mm orifice, 30cm spacing) on ​​the ceiling of the curing chamber via a DN25 steam pipe, ensuring even steam distribution within the chamber and providing a stable humidity environment for the concrete. The steam pipe installation follows safe and efficient principles to avoid leaks and blockages. Heating plates, with a power of 500W / ㎡, are fixed to the side walls of the curing chamber. The number and installation positions of the heating plates are determined based on the size of the curing chamber and the curing requirements of the concrete to achieve rapid and uniform heating.

[0109] Temperature and humidity sensors were installed on the top of the curing chamber, with three sets evenly distributed at a distance of 50cm from the concrete surface. This ensures that the sensors can comprehensively and accurately detect changes in temperature and humidity around the concrete. The sensor installation locations were carefully designed to avoid interference from curing equipment, ventilation openings, and other factors, guaranteeing the authenticity and reliability of the collected data.

[0110] The steam generator is connected to a spray system (0.5mm orifice, 30cm spacing) on ​​the ceiling of the curing chamber via a DN25 steam pipe, ensuring even steam distribution within the chamber and providing a stable humidity environment for the concrete. The steam pipe installation follows safe and efficient principles to avoid leaks and blockages. Heating plates, with a power of 500W / ㎡, are fixed to the side walls of the curing chamber. The number and installation positions of the heating plates are determined based on the size of the curing chamber and the curing requirements of the concrete to achieve rapid and uniform heating.

[0111] Example 1: Standard Curing Example of C30 Concrete in the Laboratory

[0112] I. Implementation Scenarios

[0113] This embodiment is applied to the standard curing room of a building materials testing laboratory. It is used to perform 28 days of standard curing on C30 ordinary silicate concrete specimens (150mm×150mm×150mm). The curing environment requires precise and controllable temperature and humidity, and complete curing data must be recorded for traceability of test reports. At the same time, it verifies the optimization effect of the adaptive adjustment algorithm on the standard curing scenario.

[0114] II. System Deployment Parameters

[0115] 1. Basic parameters of the curing room

[0116] The curing room measures 4m×3m×2.5m (length×width×height), is well-sealed, and is equipped with an insulation layer (50mm thick) to prevent environmental interference; the outdoor ambient temperature fluctuates between 15℃ and 28℃ (with auxiliary control by laboratory air conditioning, fluctuation range ≤3℃).

[0117] 2. Deployment of the perception layer

[0118] Temperature and humidity sensors: Four sets of SHT35 digital sensors are used. Three sets are evenly distributed in an equilateral triangle (1.5m spacing) on ​​the top of the curing chamber, 50cm above the concrete specimen surface, to collect ambient air temperature and humidity data. The other set of sensor probes is attached to the concrete specimen surface (fixed with high-temperature resistant adhesive) to collect the concrete's internal temperature. The sensor's temperature measurement accuracy is ±0.3℃, and humidity accuracy is ±1.5%RH. It supports LoRa wireless transmission and has a battery life of 8 months, meeting the long-term stable monitoring needs of the laboratory.

[0119] Auxiliary sensors: An additional set of outdoor temperature and humidity sensors (model DHT22) is deployed to collect outdoor ambient temperature (T_{out}) to provide interference compensation data for the adaptive algorithm.

[0120] 3. Transport Layer and Processing Layer Configuration

[0121] IoT Gateway: Utilizes a LoRa gateway (model RAK7249), deployed within 10m of the outside of the maintenance room, with data transmission latency ≤3 seconds, supporting real-time communication with cloud servers (Alibaba Cloud ECS), and encrypted data transmission to prevent leakage.

[0122] Controller: It adopts the STM32F407 core controller, with built-in adaptive adjustment algorithm. It communicates with sensors and actuators through ModbusRTU protocol. The threshold judgment period is set to 1 minute to ensure real-time control.

[0123] 4. Execution layer deployment

[0124] Humidity control device: One 3kW electric heating steam generator is connected to the spray device on the top of the curing room (0.5mm orifice, 30cm spacing, 12 spray nozzles in total) through a DN25 stainless steel steam pipe. The steam output can be adjusted by the controller (0~100% power).

[0125] Temperature control device: 4 carbon fiber heating plates of 500W / ㎡, fixed on the two walls of the curing room (2 on each side, installation height 1.2m), with a total power of 6kW, supporting segmented start and stop control.

[0126] 5. Remote monitoring configuration

[0127] User terminals include a laboratory PC (with dedicated monitoring software installed) and a mobile app for management personnel (supporting iOS / Android systems). The cloud server stores historical data for 90 days and supports exporting maintenance reports in Excel format.

[0128] Example 2: Temporary curing method for C50 high-strength concrete on construction site

[0129] I. Implementation Scenarios

[0130] This embodiment is applied to the temporary curing of C50 high-strength concrete cap beams at a bridge construction site. The cap beams are 8m×1.2m×1.5m (length×width×height). After pouring, they need to be kept moist and warm for 7 days. The site environment is complex (outdoor temperature fluctuates from 5℃ to 32℃, and there is dust and mechanical vibration interference). The system is required to have anti-interference capabilities and remote emergency intervention functions.

[0131] II. System Deployment Parameters

[0132] 1. Basic parameters of temporary care room

[0133] A movable canvas curing chamber (8mm thick with built-in flame-retardant insulation layer) is used to cover the entire cover beam. The curing space is 9m×2m×2.5m in size and is fixed by a bracket to prevent it from shaking in the wind. The outdoor ambient temperature fluctuation range is ≤8℃, and a sunshade is provided to reduce the impact of direct sunlight.

[0134] 2. Deployment of the perception layer

[0135] Temperature and humidity sensors: Employs SHT30 digital sensors, with a total of 5 sets. Four sets are evenly distributed on the top and sides of the curing room (50cm from the surface of the cap beam) to collect ambient temperature and humidity data; one set of sensors is attached to the side of the cap beam (concrete surface) via a magnetic mounting bracket to collect the sensor's body temperature. The sensor's temperature measurement accuracy is ±0.5℃, and humidity accuracy is ±2%RH. It supports NB-IoT wireless transmission (suitable for construction sites without LoRa signals), has a battery life of 7 months, and features a dustproof and waterproof design (IP65 protection rating).

[0136] Auxiliary sensors: Deploy one set of wind speed sensor (model FC-2A) and one set of outdoor temperature and humidity sensor to monitor environmental interference factors at the construction site (the airtightness of the curing room needs to be enhanced when the wind speed is >3m / s).

[0137] 3. Transport Layer and Processing Layer Configuration

[0138] IoT Gateway: Adopts NB-IoT+4G dual-mode gateway (model Huawei AirEngine5760-10), supports automatic switching to 4G transmission when there is no NB-IoT signal, and the data upload latency is ≤5 seconds, meeting the needs of complex communication environments on construction sites.

[0139] Controller: Adopts industrial-grade ARM Cortex-A9 controller (model NXPi.MX6ULL), with IP54 protection rating, adaptable to construction site dust and vibration environments, threshold judgment cycle set to 2 minutes, and built-in power failure memory function.

[0140] 4. Execution layer deployment

[0141] Humidity control device: 2 five-watt fuel-fired steam generators (suitable for construction sites without stable power supply, supporting diesel power supply), connected to the spray device in the curing room (0.5mm orifice diameter, 30cm spacing, 20 spray nozzles arranged along the length of the cover beam) via DN25 high-temperature resistant rubber pipes, supporting dual-machine coordinated operation.

[0142] Temperature control device: 8 silicone rubber heating plates of 500W / ㎡, fixed on both sides and top of the curing room (fitted to the inside of the canvas), with a total power of 10kW, supporting zone heating (the power can be increased separately for the lower temperature areas at both ends of the cover beam).

[0143] 5. Remote monitoring configuration

[0144] User terminals include the project department's PC monitoring console and the project manager's and supervisor's mobile APP. The cloud server stores historical data with a period of 60 days, supports querying maintenance records by cap beam number, and has an abnormal alarm response time of ≤10 seconds.

[0145] III. Implementation Process

[0146] 1. Initialization Configuration

[0147] Static parameter input: Concrete grade C=50, initial curing time t=0 (curing chamber covered 1 hour after pouring), standard temperature threshold T std_min =18℃, T std_max =22℃, standard humidity threshold 95%RH.

[0148] Algorithm initial parameters: label coefficient K C =1+0.02×(50-30)=1.4, and the initial value of the stage coefficient S(t) is set to 0.8 according to the initial setting period.

[0149] 2. Data Acquisition and Processing

[0150] The sensor collects data at a frequency of 2 minutes per time. The raw data is uploaded after being checked by CRC. When the NB-IoT signal is weak, it automatically switches to 4G transmission to ensure that no data is lost.

[0151] The temperature and humidity conversion and compensation algorithm is the same as in Example 1. The data filtering adopts the moving average algorithm (n=8) to enhance the anti-interference ability (to cope with the fluctuation of sensor values ​​caused by construction site vibration).

[0152] 3. Adaptive algorithm execution

[0153] Curing stages are divided as follows: t∈[0,24)h is the initial setting stage, t∈[24,72)h is the final setting stage, and t≥72h is the hardening stage.

[0154] Dynamic threshold calculation:

[0155] When the final setting time t = 36 hours, T out =12℃, then

[0156] T min (t)=18+1.4×1.0×(25-12)×0.05=18+0.91=18.91℃ (rounded to 19℃), T max (t)=22+1.4×1.0×(12-25)×0.03=22-0.546=21.45℃ (rounded to 21℃);

[0157] Humidity threshold RH target (t) = 95% × 1.0 + 0.5% × (19-20) = 94.5% RH (constraint ≥ 90%);

[0158] Response sensitivity adjustment: When T now =18℃ (2℃ deviation from the target average of 20℃), α=0.7, the heating plates at both ends of the cap beam operate at full power, and the heating plate in the middle operates at 70% power; when RH now =89% (5.5% deviation from the target), β=1.0, both steam generators start at full power simultaneously.

[0159] Historical data iteration: Daily calculation of temperature and humidity compliance rate R; on day 5, R = 96.5%, intensity test value F = 42.3 MPa, target F target =40MPa, then S nem (t)=1.0×[1+0.1×(0.965-0.95)+0.05×(42.3 / 40-1)=1.02, optimize the subsequent control strategy.

[0160] 4. Remote monitoring and alarm

[0161] The project team monitors the maintenance status of the cap beams in real time via PC, while the supervisors view the data via APP to ensure compliance with the maintenance process.

[0162] On the third day, due to a power outage at the construction site, the steam generator stopped working, and the humidity dropped to 85% within 10 minutes. The system triggered SMS, APP push and on-site sound alarm in the curing room. The management personnel remotely started the backup generator through the APP, and power supply and curing were restored within 30 minutes, without affecting the quality of the concrete.

[0163] IV. Implementation Results

[0164] Temperature and humidity control accuracy: During the curing period, the temperature fluctuation range is 18.9℃~21.3℃, the humidity fluctuation range is 91.2%~95.8%, the compliance rate is 97.8%, and it has a strong ability to resist interference from the construction site environment.

[0165] Curing quality: The concrete compressive strength reaches 45.7MPa after 7 days, meeting the early strength requirements of C50 high-strength concrete. There are no shrinkage cracks on the surface of the cap beam, and the cracking rate is reduced by 25% compared with traditional manual curing. The system is adaptable to the environment of temporary power supply and unstable communication on the construction site. The remote intervention function can effectively deal with sudden failures. No special personnel are required to be on-site during the curing process, reducing labor costs by 75%.

[0166] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An Internet of Things-based concrete intelligent maintenance control method, characterized in that: The method steps are: S1, real-time collection of temperature and humidity data of the concrete curing environment by a temperature and humidity sensor; S2, transmission of the collected data to a cloud server for storage and processing by using an Internet of Things gateway; S3, threshold judgment of real-time data by a controller based on preset curing parameters to generate start-stop instructions of a steam generator and a heating plate curing device; S4, automatic adjustment of temperature and humidity to a target range by the curing device after receiving the instructions to realize semi-automatic control of the curing process. 2.The concrete intelligent maintenance control method based on the Internet of Things according to claim 1, characterized in that: The preset curing parameter threshold value includes a temperature range of 20±2℃ and a relative humidity of not less than 95%, and the threshold judgment period of the controller is not more than 2 minutes. 3.The method of claim 2, wherein the method further comprises, The Internet of Things gateway supports at least one of LoRa, NB-IoT or 4G / 5G wireless transmission protocols, and the total delay time of data from sensor collection to uploading to the cloud server is ≤5 seconds. 4.The method of claim 3, wherein the method further comprises: A plurality of temperature and humidity sensors are uniformly distributed in the curing area, and at least one sensor probe head is directly in contact with or close to the concrete surface to obtain the temperature data of the concrete body, and the remaining sensors are used to obtain the air temperature and humidity data of the curing environment; the temperature and humidity sensor is a digital sensor, the temperature measurement accuracy is not less than ±0.5℃, the humidity measurement accuracy is not less than ±2%RH, and the sensor node adopts a low-power design, and the battery endurance is not less than 6 months. 5.The method of claim 4, wherein the method further comprises: The algorithm steps of the temperature and humidity sensor are: S11, sensor data collection, the temperature and humidity sensor converts physical quantities into electrical signals through internal sensing elements, and then converts them into digital quantities through ADC; S12, data calibration and compensation algorithm; S13, temperature conversion, the original temperature value output by the sensor needs to be converted into the actual temperature, and the formula is as follows: where H raw is the 16-bit humidity raw value; S14, temperature compensation, the measurement result of the humidity sensor is greatly affected by temperature, and needs to be compensated, and the formula is as follows: RH compensated = RH raw + (25 - T(°C)) x 0.01 wherein RG raw is the uncompensated humidity value, T (°C) is the current measured temperature value; S15, data processing and output; The data filtering adopts a sliding average filtering algorithm: X1~X n are the temperature and humidity data collected continuously n times; are the final data after filtering, which are used for subsequent threshold judgment. 6.The method of claim 5, wherein the method further comprises: The data calibration step of step S12 is: S21, initialize the CRC register to θ×FF; S22, for each byte of data, XOR it with the CRC register; S23, for each bit, if the highest bit of the CRC register is 1, shift it left by 1 and XOR it with the polynomial, otherwise, only shift it left by 1; S24, after processing all bytes, the value of the CRC register should be consistent with the check bit returned by the sensor, otherwise the data is invalid and needs to be collected again. 7.The method of claim 6, wherein the method further comprises: The controller has a built-in adaptive adjustment algorithm, which can dynamically adjust the temperature and humidity control strategy and parameter threshold value according to the requirements of different concrete grades and different curing stages, as well as the analysis results of historical curing data, to optimize the curing effect. 8.The method of claim 7, wherein the method further comprises: The adaptive adjustment algorithm steps are: S31, curing stage division, according to the curing reaction law of concrete, the curing process is divided into three stages, and the requirements for temperature and humidity are significantly different in different stages: Initial setting period: t∈[0, 24)h, high humidity and stable temperature are required; Final setting period: t∈[24, 72)h, humidity needs to be maintained and temperature needs to be moderately increased; Hardening period: t≥72h, humidity can be gradually reduced and temperature approaches the environment; The phase coefficient S(t) is defined as: S32, concrete grade correction, the hydration heat of different grade concrete and the strength requirement are different, introduce grade coefficient K C , K C = 1 + 0.02 x (C - 30); S33, dynamic temperature threshold calculation, the standard temperature threshold, T std_min = 18°C, T std_max = 22°C, as a reference, corrected for the phase, the reference number and the environmental disturbances: T min (t) = T std_min + K C × S(t) × (25 - T out ) × 0.05 T max (t) = T std_max + K C × S(t) × (T out - 25) × 0.03 T out T is the outdoor ambient temperature, used to compensate for the effect of outdoor temperature on the curing room S34, Dynamic humidity threshold calculation, humidity reference value dynamically adjusted with stage and temperature, RH target (t) = 95% x S(t) + 0.5% x (T now - 20); S35, in response to the sensitivity adjustment, dynamically adjusting the device response coefficient according to the temperature and humidity deviation amplitude; where a is a temperature adjustment sensitivity, β is a humidity adjustment sensitivity, T avg = (T min (t) + T max (t)) / 2 is the target temperature mean value of the current stage; S36, history data feedback iteration, based on the maintenance data of the previous 24 hours, forming a closed loop optimization: S new (t) = S old (t) x [1 + 0.1 x (R - 0.95) + 0.05 x (F / F target - 1)]; wherein R is the pass rate, F is the strength test value, S(t) is the correction stage coefficient, T now ,RH now Current curing environment temperature and humidity; T con is the concrete surface temperature; C is the concrete grade; T min (t) is a dynamic temperature threshold lower limit; T max (t) is a dynamic temperature threshold upper limit; RH target (t) is a dynamic humidity threshold value. 9.The method of claim 8, wherein the method further comprises: The maintenance execution device includes a steam generator or a spraying system for adjusting humidity, and a heating plate or an air conditioning system for adjusting temperature; the controller independently or cooperatively controls the start and stop of these devices according to the comparison result of the temperature and humidity data with the preset threshold. 10.The method of claim 9, wherein the method further comprises: It also includes remote monitoring and interaction steps: the cloud server pushes the processed temperature and humidity data and the running state information of the maintenance execution device to the user terminal in real time, and the user can view the real-time data and historical curves through the user terminal, and can send manual intervention instructions to the controller through the user terminal when necessary.

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