A method and system for concrete curing optimization

Abstract

The application discloses a kind of concrete maintenance optimization method and system, comprising: obtaining elevation and floor environment data, the internal temperature and humidity data of concrete core and surface are corrected after obtaining environmental evaporation eigenvalue by support vector machine model;Obtain initial strength state data and corrected temperature and humidity data, and obtain strength development prediction value using random forest algorithm;If less than preset strength threshold, calculate heating power value;If the humidity value in corrected temperature and humidity data is lower than preset humidity threshold, determine the spray water amount value, update maintenance duration;According to maintenance duration, spray water amount value and heating power value, generate layered maintenance control instruction, address coding is carried out to layered maintenance control instruction, and is issued to field execution equipment through Internet of Things terminal.The application establishes the differentiated control system of multiple equipment cooperation, significantly improves the cooperative operation efficiency of field execution equipment and the accuracy and timeliness of maintenance control.

Description

Technical Field

[0001] This invention belongs to the field of concrete curing optimization, and particularly relates to a concrete curing optimization method and system. Background Technology

[0002] Concrete engineering is the cornerstone of modern architecture, and the quality of its post-construction curing directly determines the final strength and service life of the building structure. Traditional concrete curing methods mostly rely on manual experience, employing uniform watering, covering, or steam curing strategies. This "one-size-fits-all" approach reveals significant limitations in complex construction environments. As buildings develop towards higher altitudes and super high-rises, the geographical elevation of the construction site and the differences between building floors lead to significantly different microclimate environments, such as low air pressure and strong sunlight at high altitudes, and strong winds at the top and dampness at the bottom of high-rise buildings. Existing curing systems typically only collect temperature and humidity data at a single location and perform simple on / off control based on fixed thresholds. They cannot perceive the differences in environmental evaporation caused by spatial variations, nor can they take into account the dynamic balance between the heat of hydration in the concrete core and heat dissipation from the surface.

[0003] However, under current technological conditions, several technical problems still urgently need to be solved in the concrete curing process. First, changes in altitude and building environment directly alter the rate of moisture evaporation. Traditional sensors struggle to accurately quantify evaporation characteristics, leading to a significant lag in water replenishment control compared to the actual water loss process. Second, the temperature gradient between the core and surface of large-volume concrete lacks a continuous, closed-loop correction mechanism. Excessive internal and external temperature differences can easily trigger through-cracks, and existing temperature control equipment typically starts and stops based on a single temperature point, failing to dynamically calculate heating power based on temperature differences. Third, concrete strength development depends on real-time temperature and humidity conditions, but the lack of on-site strength prediction methods based on multi-source data (such as ultrasonic rebound and environmental parameters) results in curing time settings that are often too conservative or insufficient, unable to be flexibly adjusted according to the actual hydration process. Finally, construction sites typically deploy multiple spraying and heating devices, lacking a unified addressing and layered control command system, leading to low efficiency in multi-device collaborative operations and hindering precise and timely differentiated curing. These problems severely restrict further improvements in concrete curing quality and resource utilization efficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for optimizing concrete curing.

[0005] One method for optimizing concrete curing includes: Acquire the elevation data and floor environment data of the construction site, and process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation characteristic values. Based on the environmental evaporation characteristic values, the internal temperature data and internal humidity data of the concrete core and surface are corrected to obtain corrected temperature and humidity data; Based on the corrected temperature and humidity data, and the initial strength state data of concrete obtained by the ultrasonic rebound device, the random forest algorithm is used to extract features and obtain the strength development prediction value. If the predicted intensity development value is less than the preset intensity threshold, the heating power value of the temperature control device is calculated based on the temperature difference in the corrected temperature and humidity data. If the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold, the spray water volume value is determined based on the environmental evaporation characteristic value and the intensity development prediction value. Based on the spray water volume value and the heating power value, the currently executed initial curing time is updated to obtain the target curing time; Based on the target curing time, the spray water volume, and the heating power, a stratified curing control command is generated. The device identifier of the field execution device is obtained, the layered maintenance control command is addressed and encoded to obtain the target control command, and the target control command is sent to the field execution device through the Internet of Things terminal.

[0006] Preferably, the process of obtaining environmental evaporation characteristic values ​​includes: Obtain the first altitude and first floor number of the construction site, and read the corresponding first ambient temperature and first humidity values ​​through the data acquisition terminal; The sliding window tool is used to capture the first ambient temperature and the first humidity value to obtain the second ambient temperature and the second humidity value; If the second ambient temperature is greater than a preset temperature threshold and the second humidity value is less than a preset humidity threshold, then the second ambient temperature and the second humidity value constitute a first dry and hot environment vector. The first dry and hot environment vector is classified using a support vector machine model to obtain the first environment evaporation feature value. If the first environmental evaporation characteristic value is greater than the preset evaporation threshold, then the first environmental evaporation characteristic value is determined to correspond to the first high evaporation state identifier; Based on the first high evaporation state identifier, the corresponding first water replenishment amount is matched by a lookup table method; If the first water replenishment exceeds the preset replenishment limit, then the first abnormal alarm signal is triggered.

[0007] Preferably, the process of obtaining corrected temperature and humidity data includes: Calculate the temperature gradient value by measuring the difference between the core temperature and the surface temperature of the concrete. Based on the temperature gradient value and the environmental evaporation characteristic value, a random forest algorithm is used to perform regression prediction to obtain the temperature and humidity deviation. If the temperature and humidity deviation is greater than the preset deviation threshold, then the corresponding humidity compensation item is extracted based on the temperature and humidity deviation. Based on the humidity compensation term and the environmental evaporation characteristic value, calculate the dynamic correction amount of the internal humidity; Based on the dynamic correction amount, the core temperature, the surface temperature, and the internal humidity are superimposed to obtain an initial correction set; The target corrected sequence set is obtained by performing Kalman filtering on the initial corrected set.

[0008] Preferably, the process of obtaining the intensity development prediction value includes: The initial state data is obtained by performing a weighted summation operation on the ultrasonic data and rebound value data collected by the ultrasonic rebound device. If the initial state data has missing values, the mean imputation method is used to complete the initial state data to obtain complete initial state data; The random forest algorithm is used to extract features from the complete initial state data, the externally input corrected value data, and the corrected temperature and humidity data to obtain multidimensional feature set data; Decision tree nodes are constructed based on the multidimensional feature set data. If the splitting information gain of the decision tree node is greater than a preset gain threshold, the decision tree node is branched to obtain a random tree set. Regression calculations are performed on the multidimensional feature set data using the random tree set to obtain independent strength value data corresponding to each random tree; The independent intensity value data are aggregated using the arithmetic mean method to determine the final intensity development prediction value.

[0009] Preferably, the process of calculating the heating power value of the temperature control equipment includes: If the predicted intensity development value is less than the preset intensity threshold, a command to obtain corrected temperature and humidity data is triggered. According to the acquisition instruction, initial temperature and humidity data are collected, and the initial temperature and humidity data are denoised using a Kalman filter algorithm to obtain the corrected temperature and humidity data. Extract the current temperature value and the target temperature value from the corrected temperature and humidity data, calculate the difference between the target temperature value and the current temperature value, and obtain the temperature difference value; Obtain the first heating power coefficient corresponding to the temperature difference, and use a linear regression algorithm to process the temperature difference and the first heating power coefficient to determine the second heating power coefficient. The initial heating power value is obtained by multiplying the second heating power coefficient with the preset base power value. If the initial heating power value is greater than the preset power safety threshold, the initial heating power value is limited to determine the final heating power value of the temperature control device.

[0010] Preferably, the process of determining the spray water volume includes: Initial humidity data is obtained, and the initial humidity data is denoised using a Kalman filter algorithm to obtain the humidity value in the corrected temperature and humidity data; If the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold, then the initial evaporation data and initial intensity data of the current environment are obtained. Based on the initial evaporation data and the initial intensity data, the environmental evaporation characteristic value and the intensity development prediction value are extracted using the principal component analysis algorithm. The initial spray water volume is calculated based on the environmental evaporation characteristic value and the intensity development prediction value. Obtain the current wind speed data, and calculate the compensation for the initial spray water volume value based on the current wind speed data to determine the target spray water volume value; A spray control command is generated based on the target spray water volume value, and the spray control command is output to the spray execution device.

[0011] Preferably, the process of obtaining the target maintenance duration includes: Obtain the initial curing duration that has been executed, and obtain the spray water volume and heating power values; The random forest regression algorithm is used to process the spray water volume value and the heating power value to obtain the duration compensation amount; Obtain a preset baseline compensation amount. If the duration compensation amount is greater than the baseline compensation amount, subtract the duration compensation amount from the baseline compensation amount to obtain the duration deviation value. Obtain a preset duration weight value, and determine the duration correction amount by multiplying the duration deviation value by the duration weight value; The first maintenance duration is obtained by adding the duration correction amount to the initial maintenance duration; Obtain a preset duration determination value; if the first maintenance duration is less than the duration determination value, then use the first maintenance duration as the second maintenance duration. Obtain the preset system delay duration, and determine the target maintenance duration by adding the second maintenance duration to the system delay duration.

[0012] Preferably, the process of generating stratified maintenance control instructions includes: The initial set of curing parameters is obtained by splicing the target curing time, spray water volume and heating power. The initial maintenance parameter set is classified using a decision tree algorithm to determine the corresponding target hierarchical level; Extract the layered maintenance parameters corresponding to the target layer level, and determine whether the layered maintenance parameters are greater than a preset parameter threshold; If the layered maintenance parameter is greater than the preset parameter threshold, the layered maintenance parameter is analyzed to obtain the spray valve opening value and the heater setting value. An initial control command is generated based on the spray valve opening value and the heater setting value; By encoding the initial control command, the corresponding status code is extracted, and the tiered maintenance control command is obtained based on the status code.

[0013] Preferably, the process of obtaining and issuing target control commands includes: Obtain the device identifier at the field end, extract the corresponding network physical address, and obtain the first addressing code; Obtain the character sequence from the first addressing code, and use a hash algorithm to encrypt and convert the character sequence to obtain the second addressing code; The layered maintenance control command is addressed and encoded according to the second addressing code to obtain the initial control command; Extract the instruction length value of the initial control instruction. If the instruction length value is greater than a preset threshold, then the initial control instruction is processed into packets to obtain the target control instruction. A communication connection channel for transmitting the target control commands is established through an IoT terminal, thereby obtaining the target communication channel; The target control command is sent to the field terminal through the target communication channel.

[0014] The present invention also provides a concrete curing optimization system, comprising: The feature acquisition module is used to acquire the elevation data and floor environment data of the construction site, and process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation feature values. The temperature and humidity correction module is used to correct the internal temperature data and internal humidity data of the concrete core and surface based on the environmental evaporation characteristic value, so as to obtain corrected temperature and humidity data. The strength development prediction module is used to extract features based on the corrected temperature and humidity data and the initial strength state data of concrete obtained by the ultrasonic rebound device, and to obtain the strength development prediction value. The heating power calculation module is used to calculate the heating power value of the temperature control device based on the temperature difference in the corrected temperature and humidity data if the intensity development prediction value is less than the preset intensity threshold. The spray water volume determination module is used to determine the spray water volume value based on the environmental evaporation characteristic value and the intensity development prediction value if the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold. The maintenance duration update module is used to update the currently executed initial maintenance duration based on the spray water volume value and the heating power value to obtain the target maintenance duration; The instruction generation module is used to generate layered curing control instructions based on the target curing time, the spray water volume value, and the heating power value. The instruction issuing module is used to obtain the device identifier of the field execution device, perform addressing encoding on the layered maintenance control instruction to obtain the target control instruction, and issue the target control instruction to the field execution device through the Internet of Things terminal.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention achieves precise quantification of evaporation rate in the construction space by acquiring altitude and floor environment data and extracting environmental evaporation feature values ​​using a support vector machine model, effectively solving the problem of lag in evaporation control. Based on the environmental evaporation feature values, the temperature and humidity data of the concrete core and surface are corrected, and a random forest algorithm is used to predict strength development in conjunction with ultrasonic rebound data. When the predicted strength value is lower than a threshold, the heating power is calculated based on the temperature difference; when the humidity is lower than a threshold, the spraying water volume is determined by combining evaporation features and the predicted strength value. This achieves dynamic compensation for internal and external temperature differences and on-demand water replenishment, avoiding the risk of cracks caused by temperature gradients. Furthermore, the initial curing time is dynamically updated based on the spraying water volume and heating power, matching the curing cycle with the actual hydration reaction process, overcoming the shortcomings of fixed and inflexible curing times. Finally, by addressing and encoding layered curing control commands through equipment identification and distributing them via IoT terminals, a differentiated control system for multi-device collaboration is established, significantly improving the collaborative operation efficiency of on-site execution equipment and the accuracy and timeliness of curing control. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0019] Example 1 like Figure 1 As shown, this embodiment provides a method for optimizing concrete curing, including: Acquire elevation data and floor environment data of the construction site, and process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation characteristic values; Based on the environmental evaporation characteristics, the internal temperature and humidity data of the concrete core and surface are corrected to obtain corrected temperature and humidity data; Based on the corrected temperature and humidity data, and the initial strength state data of concrete obtained through ultrasonic rebound equipment, the random forest algorithm is used to extract features and obtain the strength development prediction value. If the predicted intensity development value is less than the preset intensity threshold, the heating power value of the temperature control equipment is calculated based on the temperature difference in the corrected temperature and humidity data. If the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold, the spray water volume value is determined based on the environmental evaporation characteristic value and the intensity development prediction value. Based on the spray water volume and heating power values, the currently executed initial curing time is updated to obtain the target curing time; Based on the target curing time, spray water volume, and heating power, generate stratified curing control instructions; The system acquires the device identifier of the field execution equipment, encodes the layered maintenance control instructions for addressing, obtains the target control instructions, and sends the target control instructions to the field execution equipment through the Internet of Things terminal.

[0020] Furthermore, the process of obtaining environmental evaporation characteristic values ​​includes: Obtain the first altitude and first floor number of the construction site, and read the corresponding first ambient temperature and first humidity values ​​through the data acquisition terminal; The sliding window tool is used to capture the first ambient temperature and the first humidity value to obtain the second ambient temperature and the second humidity value; If the second ambient temperature is greater than the preset temperature threshold and the second humidity value is less than the preset humidity threshold, then the second ambient temperature and the second humidity value constitute the first dry and hot environment vector. The first dry and hot environment vector is classified using a support vector machine model to obtain the evaporation feature value of the first environment; If the first environmental evaporation characteristic value is greater than the preset evaporation threshold, then the first environmental evaporation characteristic value is determined to correspond to the first high evaporation state identifier; Based on the first highest evaporation state indicator, the corresponding first water replenishment amount is matched by looking up a table. If the initial water replenishment exceeds the preset replenishment limit, the first abnormal alarm signal will be triggered.

[0021] Furthermore, in one possible implementation, the data acquisition terminal at the construction site obtains the first altitude of the current working surface as 500 meters and the first floor number as 15, and reads the corresponding first ambient temperature as 35.5 degrees Celsius and first humidity value as 30%. To eliminate instantaneous fluctuations caused by dust interference to the sensors, this embodiment uses a sliding window tool to capture the continuous data stream. The sliding window length is set to 5 minutes, and by calculating the moving average of the data within the window, a smoothing process is performed to obtain the second ambient temperature as 35.2 degrees Celsius and the second humidity value as 32%. This effectively filters environmental noise, ensuring that the data for subsequent environmental assessments are more stable and reliable.

[0022] Specifically, in this embodiment, after obtaining the smoothed data, it is compared with the baseline conditions. A preset temperature threshold of 33.0 degrees Celsius and a preset humidity threshold of 40% are set. Since 35.2 degrees Celsius is greater than 33.0 degrees Celsius and 32% humidity is less than 40%, the current conditions are determined to be dry and hot. The two values ​​are combined to form a first dry and hot environment vector. For this vector, a support vector machine model is introduced to perform classification processing. Using the first dry and hot environment vector as input, the radial basis function is used to map the low-dimensional data to a high-dimensional space to find the optimal hyperplane, outputting a first environmental evaporation feature value between 0 and 1, assumed to be 0.85. This method can accurately capture the rate of moisture loss under temperature and humidity interaction, improving the scientific rigor of the assessment.

[0023] For example, after obtaining the first environmental evaporation characteristic value of 0.85, it is compared with the preset evaporation threshold of 0.75. Since 0.85 is greater than 0.75, it is determined that the current state is at the first high evaporation level. Based on this threshold, the corresponding first water replenishment amount is matched to 2.5 liters per hour using a lookup table of environmental and moisture requirements, which is used to guide concrete curing. This replenishment amount is then obtained and verified against the preset upper limit of 2.0 liters per hour specified in the standard. Since 2.5 liters exceeds the upper limit, it is determined that this replenishment amount triggers the first abnormal alarm signal. This can promptly remind managers to adjust the work plan and avoid construction quality defects caused by extreme hot and dry environments.

[0024] Furthermore, the process of obtaining corrected temperature and humidity data includes: Calculate the temperature gradient value by measuring the difference between the core temperature and the surface temperature of the concrete. Based on the temperature gradient value and the environmental evaporation characteristics, a random forest algorithm is used for regression prediction to obtain the temperature and humidity deviation. If the temperature and humidity deviation is greater than the preset deviation threshold, then the corresponding humidity compensation item is extracted based on the temperature and humidity deviation. The dynamic correction amount of internal humidity is calculated based on the humidity compensation term and the environmental evaporation characteristic value. The core temperature, surface temperature, and internal humidity are superimposed based on the dynamic correction amount to obtain an initial correction set; The target corrected sequence set is obtained by performing Kalman filtering on the initial corrected set.

[0025] Furthermore, exemplarily, in the scenario of pouring concrete for the foundation slab of a large building, the core temperature, surface temperature, and internal humidity of the concrete are obtained through pre-embedded temperature sensors and humidity probes. The core temperature mainly reflects the accumulation of hydration heat inside the concrete, while the surface temperature is greatly affected by the external atmospheric environment. When the core temperature is obtained to be 65 degrees and the surface temperature to be 40 degrees, the temperature gradient value is obtained by calculating the difference between the two, and the internal humidity is read as 80%.

[0026] Specifically, in this embodiment, the calculated temperature gradient value of 25 degrees and the previously monitored environmental evaporation value of 5 are used as input feature vectors and fed into a pre-trained random forest algorithm model for regression prediction. This random forest algorithm contains 100 decision trees. By constructing multiple independent decision trees and combining their prediction results, the temperature and humidity deviation under the current state is output. Assuming that the temperature and humidity deviation obtained after model regression calculation is 15,...

[0027] It should be noted that a preset deviation threshold of 10 was set. Since the predicted temperature and humidity deviation of 15 is greater than this preset threshold, it indicates that the current imbalance of temperature and humidity inside the concrete has triggered the compensation mechanism. At this time, based on the temperature and humidity deviation of 15, the corresponding humidity compensation item 8 is extracted through the preset mapping table. Subsequently, the extracted humidity compensation item 8 is weighted and calculated with the environmental evaporation value 5 to obtain the dynamic correction amount of internal humidity as 13.

[0028] For example, based on the calculated dynamic correction amount 13, the original core temperature of 65 degrees, surface temperature of 40 degrees, and internal humidity of 80 are numerically superimposed to generate an initial correction set containing data from multiple time points. Using this initial correction set as the observation input, the state transition matrix and observation matrix of the Kalman filter algorithm are recursively calculated to perform prediction and update steps, ultimately outputting the target correction sequence set.

[0029] Furthermore, the process of obtaining the intensity development forecast includes: The initial state data is obtained by performing a weighted summation operation on the ultrasonic data and rebound value data collected by the ultrasonic rebound device. If the initial state data has missing values, the mean imputation method is used to complete the initial state data to obtain complete initial state data; The random forest algorithm is used to extract features from the complete initial state data, the corrected external input data, and the corrected temperature and humidity data to obtain multidimensional feature set data; Decision tree nodes are constructed based on multidimensional feature set data. If the splitting information gain of the decision tree node is greater than the preset gain threshold, the decision tree node is branched to obtain a random tree set. By performing regression calculations on multidimensional feature set data using a set of random trees, the independent strength value data corresponding to each random tree is obtained; The arithmetic mean method was used to aggregate the independent intensity values ​​to determine the final intensity development forecast.

[0030] Furthermore, specifically, this embodiment obtains the acoustic time value and rebound value of the concrete component using the ultrasonic rebound combined method. The weight of the ultrasonic data is set to 0.4, and the weight of the rebound value data is set to 0.6. The collected sound velocity of 4200 meters per second is weighted and summed with the rebound value of 35 to generate initial state data.

[0031] For example, packet loss may occur during data transmission, resulting in gaps in the initial state data. In this embodiment, carbonization depth correction value data and ambient temperature and humidity data are obtained from external input. If the initial state data of a certain time series is detected to be empty, the initial state data of 5 time nodes before and after the missing point are extracted, the arithmetic mean of these 10 data points is calculated, and the average value is filled into the missing position to obtain complete initial state data.

[0032] In one possible implementation, this embodiment employs a random forest algorithm to process the aforementioned complete initial state data, carbonization depth correction values, and temperature and humidity data. These data are used as input features, and a multi-dimensional feature set is extracted through feature importance evaluation. When constructing decision tree nodes, the splitting information gain of each feature is calculated. A preset gain threshold is set to 0.05. If the information gain of a feature, such as ambient humidity, reaches 0.08, which is greater than this threshold, then branching is performed using ambient humidity as the node. This process is repeated continuously to construct a random tree set containing 100 decision trees.

[0033] Specifically, in this embodiment, multidimensional feature set data is input into a pre-constructed set of random trees for regression calculation. Each decision tree independently outputs a prediction result based on its own node partitioning logic; for example, the first tree outputs an independent strength value of 32.5 MPa, and the second tree outputs 33.1 MPa. All independent strength values ​​from these 100 trees are obtained, and the arithmetic mean is used to sum these values ​​and divide by 100 to complete the aggregation process. Finally, the predicted strength development value for concrete in the target area is determined to be 32.8 MPa.

[0034] Furthermore, the process of calculating the heating power value of the temperature control equipment includes: If the predicted intensity development value is less than the preset intensity threshold, a command to obtain corrected temperature and humidity data will be triggered. The initial temperature and humidity data are collected according to the acquisition command, and the initial temperature and humidity data are denoised by Kalman filtering algorithm to obtain corrected temperature and humidity data. Extract the current temperature value and the target temperature value from the corrected temperature and humidity data, calculate the difference between the target temperature value and the current temperature value, and obtain the temperature difference value; The first heating power coefficient corresponding to the temperature difference is obtained, and the temperature difference and the first heating power coefficient are processed by a linear regression algorithm to determine the second heating power coefficient. The initial heating power value is obtained by multiplying the second heating power coefficient with the preset base power value. If the initial heating power value is greater than the preset power safety threshold, the initial heating power value is limited to determine the final heating power value of the temperature control device.

[0035] Furthermore, specifically, the system acquires the previously obtained predicted concrete strength value, for example, a predicted value of 20 MPa, with a preset strength threshold of 25 MPa. If the predicted value fails to meet the threshold, a command to acquire corrected temperature and humidity data is triggered. This process ensures that during the early curing stage of concrete, when strength development lags behind, a temperature control intervention mechanism can be activated promptly, effectively avoiding the problem of slow strength growth caused by excessively low ambient temperatures.

[0036] For example, initial temperature and humidity data are collected using environmental sensors embedded in and on the surface of the concrete, according to the acquisition instructions. Due to the presence of large-scale mechanical vibrations and electromagnetic interference at the construction site, the collected initial temperature and humidity data often contains high-frequency noise. Using this noisy initial temperature and humidity data as input, a Kalman filter algorithm is employed for iterative estimation and covariance updating, outputting smoothed, corrected temperature and humidity data. For instance, if the collected initial temperature fluctuates drastically between 18 and 22 degrees Celsius, after processing with the Kalman filter algorithm, a stable corrected temperature of 20 degrees Celsius is output. This noise reduction significantly improves the reliability of subsequent temperature control calculations.

[0037] Specifically, the current temperature value of 20 degrees Celsius is extracted from the corrected temperature and humidity data, and the target concrete temperature value of 30 degrees Celsius required by the curing specifications is obtained. The difference between the two is 10 degrees Celsius. According to a pre-established mapping table of temperature difference and power coefficient, the first heating power coefficient corresponding to a temperature difference of 10 degrees Celsius is found to be 3. To adapt to the heat dissipation characteristics of concrete of different volumes, the temperature difference and the first heating power coefficient are used as input features, and a linear regression algorithm is used for fitting and prediction, outputting an adaptive second heating power coefficient of 4. This step dynamically adjusts the power coefficient through the linear regression algorithm, enhancing the adaptability of the temperature control strategy to complex curing environments.

[0038] For example, the second heating power coefficient 4 is multiplied by the preset base power value of 500 watts to obtain an initial heating power value of 2000 watts. Considering the hardware safety of the curing heating equipment and the requirements for preventing cracking of the concrete surface, a preset power safety threshold is set to 1800 watts. Since the initial heating power value is greater than this safety threshold, a limiting process is performed, forcibly setting the final heating power value of the temperature control equipment to 1800 watts. Through the limiting process, the safe and stable operation of the heating equipment is ensured, and concrete temperature cracks caused by excessive local temperature gradients are prevented, thus guaranteeing the construction quality of the overall structure.

[0039] Furthermore, the process of determining the spray water volume includes: The initial humidity data is obtained, and the initial humidity data is denoised using the Kalman filter algorithm to obtain the humidity value in the corrected temperature and humidity data; If the humidity value in the temperature and humidity data is lower than the preset humidity threshold, then the initial evaporation data and initial intensity data of the current environment are obtained. Based on the initial evaporation data and initial intensity data, principal component analysis algorithm is used to extract environmental evaporation characteristic values ​​and intensity development prediction values; The initial spray water volume was calculated based on the environmental evaporation characteristics and intensity development prediction values. Obtain the current wind speed data, calculate the compensation for the initial spray water volume based on the current wind speed data, and determine the target spray water volume. Spray control commands are generated based on the target spray water volume value and then output to the spray execution equipment.

[0040] Furthermore, exemplarily, this embodiment arranges humidity sensors within the curing space of the concrete component to acquire initial humidity data collected by the sensors. Due to the presence of water vapor condensation or airflow disturbances on site, the initial humidity data is often accompanied by high-frequency noise. The continuously acquired initial humidity data is used as input, and a Kalman filter algorithm is applied for state estimation and covariance update to denoise the data, outputting a corrected humidity value. Assuming that the initial humidity data acquired at a certain moment fluctuates drastically between 65 and 72, after processing by the Kalman filter algorithm, abnormal peaks are filtered out, resulting in a stable corrected humidity value of 68.

[0041] In one possible implementation, this embodiment sets a preset humidity threshold of 60. When the corrected humidity value drops to 55, which is lower than the preset humidity threshold, the initial evaporation data of the current environment and the initial intensity data of the components are simultaneously acquired. The initial evaporation data, which includes the water loss rate, and the initial intensity data, which includes the degree of hydration reaction, are constructed into a multidimensional data matrix and input into the principal component analysis algorithm model. By calculating the covariance matrix and eigenvalue decomposition, principal components with a cumulative contribution rate of over 90% are extracted, thereby outputting the dimensionality-reduced environmental evaporation characteristic values ​​and intensity development prediction values.

[0042] Specifically, this embodiment establishes a water volume mapping model, inputting the extracted environmental evaporation characteristic values ​​and intensity development prediction values ​​into the model to calculate an initial spray water volume of 150 liters. Considering that airflow in the curing environment will accelerate the evaporation of moisture from the component surface, the current wind speed data is further obtained. If the current wind speed is 4 meters per second, the initial spray water volume is positively compensated based on the wind speed compensation coefficient, determining the target spray water volume to be 180 liters. Finally, a spray control command, including the opening duration and valve opening degree, is generated based on the target spray water volume, and the spray control command is output to the spray execution equipment to complete the water replenishment operation.

[0043] Furthermore, the process of obtaining the target maintenance duration includes: Obtain the initial curing duration that has been executed, and obtain the spray water volume and heating power values; The random forest regression algorithm was used to process the spray water volume and heating power values ​​to obtain the duration compensation amount; Obtain the preset baseline compensation amount. If the duration compensation amount is greater than the baseline compensation amount, subtract the duration compensation amount from the baseline compensation amount to obtain the duration deviation value. Obtain the preset duration weight value, and determine the duration correction amount by multiplying the duration deviation value by the duration weight value; The first maintenance duration is obtained by adding the duration correction amount to the initial maintenance duration; Obtain a preset duration judgment value. If the first maintenance duration is less than the duration judgment value, then the first maintenance duration is used as the second maintenance duration. Obtain the preset system delay duration, and determine the target maintenance duration by adding the second maintenance duration to the system delay duration.

[0044] Furthermore, in one possible implementation, this embodiment obtains the initial curing duration, for example, initially set to a curing cycle of 48 hours. Regarding the spray water volume and heating power values, since water evaporation and heat transfer jointly affect the hydration reaction process of the material, they need to be jointly analyzed. This embodiment uses the currently collected spray water volume and heating power values ​​as input features, inputting them into a pre-trained random forest regression algorithm model. This algorithm performs ensemble learning by constructing multiple decision trees, outputting a corresponding duration compensation amount, for example, an output compensation amount of 3 hours, thereby quantifying the dynamic impact of hydrothermal conditions on the curing cycle.

[0045] For example, a preset baseline compensation amount is obtained, assuming this baseline value is 1 hour. When the calculated duration compensation amount is greater than the baseline compensation amount, a subtraction process is performed to obtain a duration deviation value of 2 hours. For this duration deviation value, a preset duration weight value needs to be obtained in conjunction with the seasonal characteristics of the current environment. Assuming the current environment is low temperature, the duration weight value is set to 2. By multiplying the duration deviation value by the duration weight value, the duration correction amount is determined to be 4 hours. This process, through weight adjustment, allows the compensation mechanism to adapt to the maintenance needs under different working conditions.

[0046] It should be noted that, based on the determined duration correction amount mentioned above, it is added to the initial curing duration to obtain a first curing duration of 52 hours. To prevent over-curing from degrading material performance, a preset duration judgment value is obtained, for example, setting the maximum curing limit to 60 hours. Since the first curing duration is less than the duration judgment value, it is directly used as the second curing duration. Considering the time difference between the control equipment command issuance and physical execution, a preset system delay duration is obtained, such as a delay duration of 1 hour. The second curing duration is added to the system delay duration, and the final target curing duration is determined to be 53 hours, thereby completing the precise closed-loop control of the curing cycle.

[0047] Furthermore, the process of generating tiered maintenance control instructions includes: The initial set of curing parameters is obtained by splicing the target curing time, spray water volume and heating power. The initial maintenance parameter set is classified using a decision tree algorithm to determine the corresponding target hierarchical level; Extract the layered maintenance parameters corresponding to the target layer level, and determine whether the layered maintenance parameters are greater than the preset parameter threshold. If the layered curing parameters are greater than the preset parameter threshold, the layered curing parameters are analyzed to obtain the spray valve opening value and the heater setting value. The initial control command is generated based on the spray valve opening value and the heater setting value; The initial control commands are encoded, the corresponding status codes are extracted, and the tiered maintenance control commands are obtained based on the status codes.

[0048] Furthermore, exemplarily, this embodiment obtains the target maintenance duration value as 120, the spray water volume value as 50, and the heating power value as 2000 from the acquisition end. The values ​​of these three dimensions are then concatenated according to a preset data frame format to form a one-dimensional feature vector, i.e., the initial maintenance parameter set. This concatenation method can integrate multi-source heterogeneous maintenance requirement data into a unified input format, providing a complete data foundation for subsequent model classification and effectively improving the standardization of data processing.

[0049] In one possible implementation, this embodiment uses a decision tree algorithm for classification processing of the aforementioned initial maintenance parameter set.

[0050] Specifically, feature vectors containing duration, water volume, and power are input into a pre-trained classification decision tree model. This model splits feature nodes by calculating the Gini index, assessing the impact of different parameter combinations on the maintenance level. After layer-by-layer judgment by the decision tree, the corresponding target level is output, for example, it is determined to be level 3. Through the classification of the decision tree algorithm, the complexity level of the current maintenance environment can be accurately identified, thereby enabling differentiated control strategies and avoiding the resource waste caused by single-mode control.

[0051] It should be noted that after determining the target stratum as stratum 3, the corresponding stratified maintenance parameters are extracted, such as a comprehensive load parameter value of 85. This stratified maintenance parameter is then compared with a preset parameter threshold of 80. Since 85 is greater than 80, a parsing mechanism is triggered. The stratified maintenance parameter is parsed using a lookup table mapping rule, yielding a spray valve opening value of 60 and a heater setting value of 3. This process ensures that high-load maintenance requirements are accurately translated into specific equipment action amplitudes, improving the accuracy of hardware response.

[0052] For example, based on the obtained spray valve opening value of 60 and heater setting value of 3, an initial control command is generated according to the communication protocol of the underlying hardware. This initial control command is then processed using baseband encoding to extract a status code that characterizes the equipment's operating status and action instructions, such as status code 1011. Finally, a complete layered maintenance control command is encapsulated based on this status code. Issuing this command directly drives the maintenance equipment to operate according to the predetermined opening and setting, ensuring the accurate execution and stable operation of the maintenance process.

[0053] Furthermore, the process of obtaining and issuing target control commands includes: Obtain the device identifier at the field end, extract the corresponding network physical address, and obtain the first addressing code; The character sequence is obtained from the first addressing code, and the character sequence is encrypted and converted using a hash algorithm to obtain the second addressing code; The layered maintenance control command is addressed and encoded according to the second addressing code to obtain the initial control command. Extract the instruction length value of the initial control instruction. If the instruction length value is greater than the preset threshold, the initial control instruction is processed into packets to obtain the target control instruction. A communication connection channel for transmitting target control commands is established through an IoT terminal to obtain the target communication channel; Target control commands are sent to the field terminal through the target communication channel.

[0054] Furthermore, in one possible implementation, the fixed equipment identifier of the concrete layer curing equipment at the site is obtained. The site typically deploys multiple spray heating integrated machines at different levels. By reading the identifier of the target level equipment, for example, equipment number 2048, the network physical address burned into its network card is extracted based on this equipment number, thus obtaining the first addressing code. This first addressing code consists of a unique character sequence representing the absolute physical location of the curing equipment on the network. After obtaining this character sequence, a secure hash algorithm is used to perform a one-way encryption transformation on the character sequence. The character sequence is input into the secure hash algorithm model, and after hashing, a fixed-length ciphertext sequence is output, which is used as the second addressing code. This encryption transformation process effectively hides the real physical address of the curing equipment, improves the security of data transmission, and prevents the curing control node from being illegally forged.

[0055] For example, in this embodiment, after obtaining the second addressing code, it is combined with the previously generated hierarchical maintenance control order. The hierarchical maintenance control order contains specific business data such as the spray valve opening value and the heater setting value. Using the second addressing code as the target address header information, the hierarchical maintenance control order is addressed and encoded, that is, the address header is concatenated and encapsulated with the business data to obtain the initial control order. Subsequently, the total instruction length value of the initial control order is extracted and compared with a preset length threshold. Assuming the preset threshold is 512 bytes, if the extracted instruction length value is 800 bytes, it is obviously greater than the preset threshold. At this time, the initial control order needs to be packetized. The original instruction is split into two consecutive data packets according to the maximum load of 512 bytes, and a sequence number is assigned to each data packet to finally obtain the target control order. Packetization ensures stable transmission of long instructions under limited bandwidth and avoids data loss during the issuance of maintenance parameters.

[0056] It should be noted that after obtaining the subcontracted target control order, a network connection request is initiated through a narrowband IoT terminal deployed on-site. Based on the addressing information in the target control order, the IoT terminal establishes a dedicated data transmission communication channel—the target communication channel—between the cloud control center and the on-site curing equipment. Finally, the target control order is sequentially sent to the corresponding layered curing equipment on-site via this target communication channel, according to its sequence number. Upon receiving the order, the equipment can then execute the corresponding concrete curing actions based on the spraying and heating parameters specified in the command.

[0057] This embodiment acquires elevation data and floor environment data at the construction site, processes the data using a support vector machine model to obtain environmental evaporation characteristic values, and corrects the internal temperature and humidity data of the concrete core and surface accordingly to obtain corrected temperature and humidity data. Then, combining the initial strength state data collected by the ultrasonic rebound device, a random forest algorithm is used for feature extraction to obtain a predicted strength development value. When the predicted value is lower than a threshold, the heating power is calculated based on the temperature difference; when the humidity is lower than a threshold, the spraying water volume is determined by combining evaporation characteristics and the predicted strength value. The initial curing time is dynamically updated to obtain the target curing time. Finally, a layered curing control command is generated and executed via an IoT terminal through equipment identification addressing encoding. This invention achieves adaptive layered curing control based on environmental perception and strength prediction, significantly improving the accuracy, timeliness, and equipment coordination efficiency of concrete curing, and ensuring construction quality.

[0058] Example 2 like Figure 2 As shown, based on the same inventive concept, this embodiment also provides a concrete curing optimization system, including: The feature acquisition module is used to acquire the elevation data and floor environment data of the construction site, and to process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation feature values. The temperature and humidity correction module is used to correct the internal temperature and humidity data of the concrete core and surface based on the environmental evaporation characteristics, so as to obtain corrected temperature and humidity data. The strength development prediction module is used to extract features based on corrected temperature and humidity data and initial strength state data of concrete obtained through ultrasonic rebound equipment, and to obtain strength development prediction values. The heating power calculation module is used to calculate the heating power value of the temperature control equipment based on the temperature difference in the corrected temperature and humidity data if the predicted intensity development value is less than the preset intensity threshold. The spray water volume determination module is used to determine the spray water volume value based on the environmental evaporation characteristic value and the intensity development prediction value if the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold. The maintenance duration update module is used to update the currently executed initial maintenance duration based on the spray water volume value and heating power value to obtain the target maintenance duration; The instruction generation module is used to generate layered curing control instructions based on the target curing time, spray water volume, and heating power. The instruction issuing module is used to obtain the device identifier of the field execution equipment, address and encode the layered maintenance control instructions to obtain the target control instructions, and issue the target control instructions to the field execution equipment through the Internet of Things terminal.

[0059] The concrete curing optimization system provided in this embodiment has all the advantages of the concrete curing optimization method provided in Embodiment 1.

[0060] Example 3 This embodiment also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in Embodiment 1.

[0061] Example 4 This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0062] Example 5 This embodiment also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0063] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing concrete curing, characterized in that, include: Acquire the elevation data and floor environment data of the construction site, and process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation characteristic values. Based on the environmental evaporation characteristic values, the internal temperature data and internal humidity data of the concrete core and surface are corrected to obtain corrected temperature and humidity data; Based on the corrected temperature and humidity data, and the initial strength state data of concrete obtained by the ultrasonic rebound device, the random forest algorithm is used to extract features and obtain the strength development prediction value. If the predicted intensity development value is less than the preset intensity threshold, the heating power value of the temperature control device is calculated based on the temperature difference in the corrected temperature and humidity data. If the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold, the spray water volume value is determined based on the environmental evaporation characteristic value and the intensity development prediction value. Based on the spray water volume value and the heating power value, the currently executed initial curing time is updated to obtain the target curing time; Based on the target curing time, the spray water volume, and the heating power, a stratified curing control command is generated. The device identifier of the field execution device is obtained, the layered maintenance control command is addressed and encoded to obtain the target control command, and the target control command is sent to the field execution device through the Internet of Things terminal.

2. The method according to claim 1, characterized in that, The process of obtaining environmental evaporation characteristic values ​​includes: Obtain the first altitude and first floor number of the construction site, and read the corresponding first ambient temperature and first humidity values ​​through the data acquisition terminal; The sliding window tool is used to capture the first ambient temperature and the first humidity value to obtain the second ambient temperature and the second humidity value; If the second ambient temperature is greater than a preset temperature threshold and the second humidity value is less than a preset humidity threshold, then the second ambient temperature and the second humidity value constitute a first dry and hot environment vector. The first dry and hot environment vector is classified using a support vector machine model to obtain the first environment evaporation feature value. If the first environmental evaporation characteristic value is greater than the preset evaporation threshold, then the first environmental evaporation characteristic value is determined to correspond to the first high evaporation state identifier; Based on the first high evaporation state identifier, the corresponding first water replenishment amount is matched by a lookup table method; If the first water replenishment exceeds the preset replenishment limit, then the first abnormal alarm signal is triggered.

3. The method according to claim 1, characterized in that, The process of obtaining corrected temperature and humidity data includes: Calculate the temperature gradient value by measuring the difference between the core temperature and the surface temperature of the concrete. Based on the temperature gradient value and the environmental evaporation characteristic value, a random forest algorithm is used to perform regression prediction to obtain the temperature and humidity deviation. If the temperature and humidity deviation is greater than the preset deviation threshold, then the corresponding humidity compensation item is extracted based on the temperature and humidity deviation. Based on the humidity compensation term and the environmental evaporation characteristic value, calculate the dynamic correction amount of the internal humidity; Based on the dynamic correction amount, the core temperature, the surface temperature, and the internal humidity are superimposed to obtain an initial correction set; The target corrected sequence set is obtained by performing Kalman filtering on the initial corrected set.

4. The method according to claim 1, characterized in that, The process of obtaining intensity development forecasts includes: The initial state data is obtained by performing a weighted summation operation on the ultrasonic data and rebound value data collected by the ultrasonic rebound device. If the initial state data has missing values, the mean imputation method is used to complete the initial state data to obtain complete initial state data; The random forest algorithm is used to extract features from the complete initial state data, the externally input corrected value data, and the corrected temperature and humidity data to obtain multidimensional feature set data; Decision tree nodes are constructed based on the multidimensional feature set data. If the splitting information gain of the decision tree node is greater than a preset gain threshold, the decision tree node is branched to obtain a random tree set. Regression calculations are performed on the multidimensional feature set data using the random tree set to obtain independent strength value data corresponding to each random tree; The independent intensity value data are aggregated using the arithmetic mean method to determine the final intensity development prediction value.

5. The method according to claim 1, characterized in that, The process of calculating the heating power value of a temperature control device includes: If the predicted intensity development value is less than the preset intensity threshold, a command to obtain corrected temperature and humidity data is triggered. According to the acquisition instruction, initial temperature and humidity data are collected, and the initial temperature and humidity data are denoised using a Kalman filter algorithm to obtain the corrected temperature and humidity data. Extract the current temperature value and the target temperature value from the corrected temperature and humidity data, calculate the difference between the target temperature value and the current temperature value, and obtain the temperature difference value; Obtain the first heating power coefficient corresponding to the temperature difference, and use a linear regression algorithm to process the temperature difference and the first heating power coefficient to determine the second heating power coefficient. The initial heating power value is obtained by multiplying the second heating power coefficient with the preset base power value. If the initial heating power value is greater than the preset power safety threshold, the initial heating power value is limited to determine the final heating power value of the temperature control device.

6. The method according to claim 1, characterized in that, The process of determining the spray water volume includes: Initial humidity data is obtained, and the initial humidity data is denoised using a Kalman filter algorithm to obtain the humidity value in the corrected temperature and humidity data; If the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold, then the initial evaporation data and initial intensity data of the current environment are obtained. Based on the initial evaporation data and the initial intensity data, the environmental evaporation characteristic value and the intensity development prediction value are extracted using the principal component analysis algorithm. The initial spray water volume is calculated based on the environmental evaporation characteristic value and the intensity development prediction value. Obtain the current wind speed data, and calculate the compensation for the initial spray water volume value based on the current wind speed data to determine the target spray water volume value; A spray control command is generated based on the target spray water volume value, and the spray control command is output to the spray execution device.

7. The method according to claim 1, characterized in that, The process of obtaining the target maintenance duration includes: Obtain the initial curing duration that has been executed, and obtain the spray water volume and heating power values; The random forest regression algorithm is used to process the spray water volume value and the heating power value to obtain the duration compensation amount; Obtain a preset baseline compensation amount. If the duration compensation amount is greater than the baseline compensation amount, subtract the duration compensation amount from the baseline compensation amount to obtain the duration deviation value. Obtain a preset duration weight value, and determine the duration correction amount by multiplying the duration deviation value by the duration weight value; The first maintenance duration is obtained by adding the duration correction amount to the initial maintenance duration; Obtain a preset duration determination value; if the first maintenance duration is less than the duration determination value, then use the first maintenance duration as the second maintenance duration. Obtain the preset system delay duration, and determine the target maintenance duration by adding the second maintenance duration to the system delay duration.

8. The method according to claim 1, characterized in that, The process of generating stratified maintenance control instructions includes: The initial set of curing parameters is obtained by splicing the target curing time, spray water volume and heating power. The initial maintenance parameter set is classified using a decision tree algorithm to determine the corresponding target hierarchical level; Extract the layered maintenance parameters corresponding to the target layer level, and determine whether the layered maintenance parameters are greater than a preset parameter threshold; If the layered maintenance parameter is greater than the preset parameter threshold, the layered maintenance parameter is analyzed to obtain the spray valve opening value and the heater setting value. An initial control command is generated based on the spray valve opening value and the heater setting value; The initial control command is encoded, the corresponding status code is extracted, and the tiered maintenance control command is obtained based on the status code.

9. The method according to claim 1, characterized in that, The process of receiving and issuing target control commands includes: Obtain the device identifier at the field end, extract the corresponding network physical address, and obtain the first addressing code; Obtain the character sequence from the first addressing code, and use a hash algorithm to encrypt and convert the character sequence to obtain the second addressing code; The layered maintenance control command is addressed and encoded according to the second addressing code to obtain the initial control command; Extract the instruction length value of the initial control instruction. If the instruction length value is greater than a preset threshold, then the initial control instruction is processed into packets to obtain the target control instruction. A communication connection channel for transmitting the target control commands is established through an IoT terminal, thereby obtaining the target communication channel; The target control command is sent to the field terminal through the target communication channel.

10. A concrete curing optimization system, characterized in that, include: The feature acquisition module is used to acquire the elevation data and floor environment data of the construction site, and process the elevation data and floor environment data through a support vector machine model to obtain environmental evaporation feature values. The temperature and humidity correction module is used to correct the internal temperature data and internal humidity data of the concrete core and surface based on the environmental evaporation characteristic value, so as to obtain corrected temperature and humidity data. The strength development prediction module is used to extract features based on the corrected temperature and humidity data and the initial strength state data of concrete obtained by the ultrasonic rebound device, and to obtain the strength development prediction value. The heating power calculation module is used to calculate the heating power value of the temperature control device based on the temperature difference in the corrected temperature and humidity data if the intensity development prediction value is less than the preset intensity threshold. The spray water volume determination module is used to determine the spray water volume based on the environmental evaporation characteristic value and the intensity development prediction value if the humidity value in the corrected temperature and humidity data is lower than the preset humidity threshold. The maintenance time update module is used to update the currently executed initial maintenance time based on the spray water volume value and the heating power value to obtain the target maintenance time; The instruction generation module is used to generate layered curing control instructions based on the target curing time, the spray water volume value, and the heating power value. The instruction issuing module is used to obtain the device identifier of the field execution device, perform addressing encoding on the layered maintenance control instruction to obtain the target control instruction, and issue the target control instruction to the field execution device through the Internet of Things terminal.

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