Solar energy based intelligent maintenance control method for thermal imaging precast beam

Abstract

The application discloses a kind of based on solar energy utilization's thermal imaging precast beam intelligent maintenance control method, belong to precast beam intelligent maintenance field, this method includes obtaining the maintenance data of precast beam historical piece of same material;Desired maintenance length of time is preset, based on the maintenance data of precast beam historical piece of same material, temperature and humidity influence model on the performance of precast beam is constructed;Pre-cast beam is placed into the intelligent maintenance room configured with thermal imaging sensor, humidity sensor, humidifying device and solar heat collector;According to the influence model of temperature and humidity on the performance of precast beam, based on thermal imaging sensor and humidity sensor, the operation of humidifying device and solar heat collector is controlled in real time, and the intelligent maintenance of precast beam is realized.The application solves the problem of precast beam maintenance that existing scheme does not consider actual rush demand, limits total maintenance time to ensure quality.

Description

Technical Field

[0001] This invention belongs to the field of intelligent maintenance of precast beams, and particularly relates to an intelligent maintenance control method for precast beams based on solar energy utilization and thermal imaging. Background Technology

[0002] In the actual curing process of precast beams, temperature and humidity are key factors affecting concrete performance, and the two interact to jointly influence the hydration process, strength development, and durability of concrete. Related background technologies mainly focus on how to accurately control the temperature and humidity environment: Intelligent temperature and humidity monitoring technology: This technology uses temperature and humidity sensors to collect real-time temperature and humidity data in the precast beam curing environment. These sensors can be deployed at different locations on the precast beam (such as the beam surface or the internal center) to comprehensively acquire temperature and humidity information about the surrounding area and interior of the beam. The data is transmitted to a monitoring system in real-time via wireless or wired transmission, enabling real-time monitoring of the curing environment. For example, some large precast beam yards utilize distributed temperature and humidity sensor networks, combined with IoT technology, allowing staff to remotely view real-time temperature and humidity changes in the curing area.

[0003] Temperature and humidity control technologies: To create a suitable temperature and humidity environment, various control technologies have been developed. In terms of temperature control, in addition to traditional methods such as steam heating and electric heating, heat pump heating technology has emerged. This technology absorbs heat from a low-temperature heat source and transfers it to the curing environment by consuming a small amount of electricity, exhibiting high efficiency and energy saving. For humidity control, in addition to common methods such as sprinkling and spraying, ultrasonic atomization humidification technology has been introduced. This produces finer water mist particles, resulting in a more uniform humidity distribution in the curing environment and improving the moisture retention effect on the concrete surface. Furthermore, some intelligent control systems can automatically adjust the operating status of heating, humidification, or ventilation equipment based on real-time monitored temperature and humidity data to maintain the temperature and humidity within the set range.

[0004] Research on the Relationship between Temperature and Humidity and Concrete Performance: Through extensive experiments and theoretical analysis, researchers have conducted in-depth studies on the influence of temperature and humidity on concrete performance. For example, a kinetic model of concrete hydration reaction based on the coupling effect of temperature and humidity was established. This model considers the accelerating effect of temperature on the hydration reaction rate and the influence of humidity on the degree of hydration reaction, and can accurately predict the development process of concrete strength under different temperature and humidity conditions. In addition, the effects of temperature and humidity changes on the volumetric deformation characteristics of concrete, such as shrinkage and creep, were studied, providing a theoretical basis for the formulation of curing processes for precast beams.

[0005] Advantages of existing technology Intelligent temperature and humidity monitoring technology: High real-time performance: It can acquire temperature and humidity data in real time and promptly detect abnormal changes in the curing environment, such as sudden rises or drops in temperature or insufficient humidity, so that staff can take measures to adjust quickly and effectively avoid adverse effects on the quality of precast beams caused by abnormal temperature and humidity.

[0006] Comprehensive and accurate data: By deploying sensors at different locations, the temperature and humidity distribution of the precast beam curing environment can be fully monitored, providing reliable data support for precise control. Furthermore, modern sensors possess high precision and stability, ensuring data accuracy.

[0007] Convenient remote monitoring: With the help of IoT technology, remote monitoring is enabled. Staff can check temperature and humidity data anytime and anywhere without being on-site, which improves management efficiency. It is especially suitable for large precast beam yards or situations where multiple maintenance areas are operating at the same time.

[0008] Temperature and humidity control technology: High precision control: The intelligent control system can accurately adjust the operating parameters of temperature and humidity equipment based on real-time monitoring data, keeping the temperature and humidity within the set range to meet the strict requirements of temperature and humidity at different curing stages of concrete, which helps to improve the curing quality of precast beams.

[0009] Energy efficient: New heating methods such as heat pump heating are more energy efficient than traditional steam heating, reducing energy consumption and operating costs while ensuring maintenance effects. Ultrasonic atomization humidification technology can achieve more efficient humidity control and uniform humidity distribution, improving water resource utilization efficiency.

[0010] High degree of automation: The intelligent control system can operate automatically without frequent human intervention, reducing the impact of human factors on temperature and humidity control and ensuring the stability and consistency of the maintenance process.

[0011] Study on the relationship between temperature and humidity and concrete performance: Theory guides practice: The established temperature and humidity-concrete performance relationship model provides a scientific theoretical basis for the formulation of precast beam curing processes. Construction personnel can use the model to predict the development trend of concrete performance under different temperature and humidity conditions, rationally arrange curing time, and adjust temperature and humidity parameters to ensure that precast beams meet design performance requirements.

[0012] Optimize curing process: By studying in depth the influence of temperature and humidity on concrete performance, we can continuously optimize the curing process, develop more scientific and reasonable curing solutions, improve the quality and durability of precast beams, and extend their service life.

[0013] Disadvantages of existing technology Intelligent temperature and humidity monitoring technology: Sensor reliability issues: In complex maintenance environments, such as high temperature, high humidity, and steam environments, sensors may be affected by corrosion, moisture, etc., leading to performance degradation or inaccurate data. Regular maintenance and calibration are required, which increases management costs and workload.

[0014] Insufficient data transmission stability: Wireless data transmission may be affected by signal interference, obstructions, and other factors, leading to data transmission interruptions or delays, affecting real-time monitoring results. Wired transmission, on the other hand, suffers from complex cabling, high construction difficulty, and inconvenient line adjustments when the maintenance area changes.

[0015] Temperature and humidity control technology: High equipment costs: Intelligent temperature and humidity control equipment, as well as new heating and humidification equipment, such as heat pump units and ultrasonic atomizers, have high initial purchase costs, which may be difficult for some small precast beam production enterprises or projects with limited funds to afford.

[0016] System maintenance is complex: The intelligent control system contains various devices and complex control software, requiring professional technicians for maintenance and management. If the system malfunctions, such as control software crashes or hardware damage, repairs are difficult and may affect the normal curing of precast beams, causing project delays.

[0017] Energy dependence: Although some control technologies have energy-saving advantages, overall, the operation of temperature and humidity control equipment still requires a large amount of energy, such as electricity and heat. Unstable energy supply or rising energy prices will increase the maintenance costs of precast beams.

[0018] Study on the relationship between temperature and humidity and concrete performance: Model limitations: Most of the temperature and humidity-concrete performance relationship models currently established are based on laboratory conditions or specific engineering cases. The actual curing environment is often more complex and variable, with multiple factors interacting, which may cause the model's prediction results to deviate from the actual situation. Further improvement and verification are needed.

[0019] In actual working conditions, when the demand for precast beams is urgent, the curing time for precast beams is limited. How to control the temperature and humidity within the limited time to obtain precast beams of the best quality is an urgent problem to be solved. Summary of the Invention

[0020] To address the aforementioned shortcomings in existing technologies, this invention provides an intelligent maintenance control method for precast beams based on solar energy utilization and thermal imaging. This method solves the problem of ensuring quality in precast beam maintenance under the constraint of limiting the total maintenance time, which is not taken into account in existing solutions that do not consider actual expedited construction needs.

[0021] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a method for intelligent maintenance and control of precast beams based on solar energy utilization using thermal imaging, comprising: Obtain maintenance data for historical precast beams made of the same material; Based on the curing data of historical precast beams made of the same material, a model is constructed to assess the impact of temperature and humidity on the performance of precast beams by presetting the expected curing time. The precast beams are placed into an intelligent curing room equipped with thermal imaging sensors, humidity sensors, humidification devices, and solar thermal collectors. Based on the influence model of temperature and humidity on the performance of precast beams, the operation of humidification devices and solar thermal collectors is controlled in real time using thermal imaging sensors and humidity sensors to achieve intelligent maintenance of precast beams.

[0022] Furthermore, the maintenance data includes maintenance time-temperature correlation curves, maintenance time-humidity correlation curves, temperature-hydration reaction rate correlation curves, humidity-hydration reaction rate correlation curves, service life, and performance data.

[0023] Furthermore, the model for the influence of temperature and humidity on the performance of precast beams is constructed as follows: Based on the expected service life and expected performance, qualified samples are selected from the historical precast beams based on the service life and performance data to obtain a successful sample set. For the successful sample set, the temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship were constructed based on the curing time. A model of the influence of temperature and humidity on the performance of precast beams is constructed based on the temperature-humidity-reaction rate relationship and the temperature-humidity-mass relationship.

[0024] Furthermore, the temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship constructed based on curing time are specifically as follows: Quantify the quality score of each successful sample, and classify the quality level based on the quality score:

[0025] in, For the first The quality score of each successful sample; This is the normalization function; The importance factor is the service life factor. For the first Normalized value of the lifetime of a successful sample; For the first Importance coefficients of each performance parameter; For the first The first successful sample Normalized values ​​of each performance parameter; This represents the total number of performance parameters. For the successful sample set, the successful samples are clustered according to the maintenance time to obtain several successful sample clusters. ; For the first A successful sample cluster class; For the first The maintenance time of the cluster center sample of a successful sample cluster; For each successful sample cluster, the sub-cluster with the best quality level is selected based on the quality level. For each sub-cluster under different curing times, the relationships between temperature and humidity and hydration reaction rate and the relationship between temperature and humidity and mass were fitted respectively. The relationship between temperature and humidity and hydration reaction rate for each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the three-dimensional surface diagram of hydration reaction rate. The temperature, humidity and mass relationship of each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the mass three-dimensional surface diagram. Based on the expected curing time, the surface features of the three-dimensional surface plots of the reaction rate and the three-dimensional surface plots of the quality of each sub-cluster are integrated to obtain the temperature time history and humidity time history that optimize the quality, which are defined as the influence model of temperature and humidity on the performance of precast beams.

[0026] Furthermore, the relationships between temperature / humidity and hydration reaction rate, and between temperature / humidity and mass are as follows:

[0027]

[0028] in, The hydration reaction rate; Pre-exponential factors; It is the activation energy for the hydration reaction; It is the gas constant; The ambient temperature; Relative humidity; Humidity influence coefficient; This is a quantitative value for quality. and All are fitting coefficients; This represents the total number of performance parameters. For the first The fitting coefficients of each performance parameter; The ambient temperature is The relative humidity is At that time, the first The ratio of the baseline value to the predicted value of each performance parameter; The ambient temperature is The relative humidity is At that time, the ratio of the baseline value to the predicted value of the service life; This is the baseline value for service life; It is the reciprocal of the unit temperature; and All of these are coefficients to be fitted.

[0029] Furthermore, the expression for the model of the influence of temperature and humidity on the performance of precast beams is as follows:

[0030] in, for The optimal temperature at any given time; , and All are temperature time history fitting coefficients; for The optimal humidity at any given time; , and All are humidity time history fitting coefficients.

[0031] The beneficial effects of this invention are as follows: This invention clusters and subdivides samples based on curing time and quality level, forming sub-clusters. Different sub-clusters correspond to specific curing conditions and quality performance, making subsequent relationship fitting more targeted and accurately characterizing the impact of temperature and humidity on precast beam performance under different curing scenarios. Temperature-humidity-hydration reaction rate and temperature-humidity-quality relationships are fitted separately for each sub-cluster, avoiding mixed interference from samples with different curing conditions and quality levels, and improving the model's adaptability to specific situations. Through "microscopic surface analysis → macroscopic model integration," a leap from sub-cluster data to the globally optimal solution is achieved, providing quantitative guidance for precast beam curing processes. Attached Figure Description

[0032] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0033] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0034] like Figure 1 As shown, in one embodiment of the present invention, a method for intelligent maintenance and control of precast beams based on solar energy utilization using thermal imaging includes: Obtain maintenance data for historical precast beams made of the same material; Based on the curing data of historical precast beams made of the same material, a model is constructed to assess the impact of temperature and humidity on the performance of precast beams by presetting the expected curing time. The precast beams are placed into an intelligent curing room equipped with thermal imaging sensors, humidity sensors, humidification devices, and solar thermal collectors. Based on the influence model of temperature and humidity on the performance of precast beams, the operation of humidification devices and solar thermal collectors is controlled in real time using thermal imaging sensors and humidity sensors to achieve intelligent maintenance of precast beams.

[0035] The maintenance data includes maintenance time-temperature correlation curves, maintenance time-humidity correlation curves, temperature-hydration reaction rate correlation curves, humidity-hydration reaction rate correlation curves, service life, and performance data.

[0036] Temperature and humidity during curing not only affect the quality of precast beams but also their curing time. When time is tight, effectively regulating temperature and humidity can shorten curing time, but the quality of the precast beams must remain a constant concern. Therefore, understanding the relationship between temperature, humidity, and curing time is crucial.

[0037] The specific steps for constructing a model of the effects of temperature and humidity on the performance of precast beams are as follows: Based on the expected service life and expected performance, qualified samples are selected from the historical precast beams based on the service life and performance data to obtain a successful sample set. For the successful sample set, the temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship were constructed based on the curing time. A model of the influence of temperature and humidity on the performance of precast beams is constructed based on the temperature-humidity-reaction rate relationship and the temperature-humidity-mass relationship.

[0038] In this embodiment, qualified samples that meet the quality requirements are first screened from the recorded historical precast beam curing data. That is, the curing quality is ensured first, and then the control of temperature and humidity is sought to shorten the curing time.

[0039] The temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship based on curing time are as follows: Quantify the quality score of each successful sample, and classify the quality level based on the quality score:

[0040] in, For the first The quality score of each successful sample; This is the normalization function; The importance factor is the service life factor. For the first Normalized value of the lifetime of a successful sample; For the first Importance coefficients of each performance parameter; For the first The first successful sample Normalized values ​​of each performance parameter; This represents the total number of performance parameters. For the successful sample set, the successful samples are clustered according to the maintenance time to obtain several successful sample clusters. ; For the first A successful sample cluster class; For the first The maintenance time of the cluster center sample of a successful sample cluster; For each successful sample cluster, the sub-cluster with the best quality level is selected based on the quality level. For each sub-cluster under different curing times, the relationships between temperature and humidity and hydration reaction rate and the relationship between temperature and humidity and mass were fitted respectively. The relationship between temperature and humidity and hydration reaction rate for each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the three-dimensional surface diagram of hydration reaction rate. The temperature, humidity and mass relationship of each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the mass three-dimensional surface diagram. Based on the expected curing time, the surface features of the three-dimensional surface plots of the reaction rate and the three-dimensional surface plots of the quality of each sub-cluster are integrated to obtain the temperature time history and humidity time history that optimize the quality, which are defined as the influence model of temperature and humidity on the performance of precast beams.

[0041] In this embodiment, the screening of the successful sample set has ensured the maintenance quality. On this basis, clustering based on maintenance time is to control the maintenance time of the samples in a single cluster used for analysis within a certain range, which is equivalent to a subset of samples with the same maintenance time. This allows the selection of the highest quality sample dataset (sub-cluster) from the sample set with the same maintenance time, thus initially linking maintenance time and maintenance quality.

[0042] The quality grading, similar to the clustering of maintenance time, aims to control the quality within a fluctuating range. This allows for the acquisition of more effective data and, to some extent, eliminates quality deviations caused by external interference.

[0043] Specifically, when quantifying quality, performance parameters can be selected based on actual needs, and weights can be assigned to these parameters according to their importance. This allows for adaptive adjustments based on different application scenarios, ensuring the quality of the precast beams after subsequent curing.

[0044] The relationships between temperature / humidity and hydration reaction rate, and between temperature / humidity and mass are as follows:

[0045]

[0046] in, The hydration reaction rate; Pre-exponential factors; It is the activation energy for the hydration reaction; It is the gas constant; The ambient temperature; Relative humidity; Humidity influence coefficient; This is a quantitative value for quality. and All are fitting coefficients; This represents the total number of performance parameters. For the first The fitting coefficients of each performance parameter; The ambient temperature is The relative humidity is At that time, the first The ratio of the baseline value to the predicted value of each performance parameter; The ambient temperature is The relative humidity is At that time, the ratio of the baseline value to the predicted value of the service life; This is the baseline value for service life; It is the reciprocal of the unit temperature; and All of these are coefficients to be fitted.

[0047] In this embodiment, the predicted values ​​of performance parameters and service life can be obtained by fitting historical data using a regression model or neural network.

[0048] In this embodiment, temperature and humidity control affects the hydration reaction rate, which in turn affects curing time and quality. Therefore, for subclusters with equal curing times, converting the relationship between temperature / humidity and curing time into a relationship between temperature / humidity and hydration reaction rate indirectly reflects the relationship between hydration reaction rate and quality. In the subsequent integration of the three-dimensional surface plots of reaction rate and quality, temperature, humidity, hydration reaction rate, and quality can be closely correlated.

[0049] The expression for the model of the influence of temperature and humidity on the performance of precast beams is as follows:

[0050] in, for The optimal temperature at any given time; , and All are temperature time history fitting coefficients; for The optimal humidity at any given time; , and All are humidity time history fitting coefficients.

[0051] In this embodiment, each sub-cluster class obtains a set of three-dimensional surface plots of reaction rate and mass; this also means that the time-history changes of temperature and humidity-hydration reaction rate and temperature and humidity-mass under different curing times are obtained; based on the desired curing time, the time-history change data under the corresponding curing time are selected; through the three-dimensional surface plots of reaction rate and mass under the corresponding curing time, the relationship between hydration reaction rate and mass can be obtained; through the peak point of the three-dimensional surface plot of hydration reaction rate (the temperature and humidity corresponding to the maximum reaction rate), a function of total time and optimal temperature and humidity is fitted; and based on the relationship between hydration reaction rate and mass, the temperature and humidity are determined while ensuring that the mass is not lower than the mass threshold. That is, in the process of determining temperature and humidity, the mass threshold is an implicit constraint.

[0052] In this embodiment, for the temperature-humidity-hydration reaction rate relationship of each small cluster, temperature and humidity grid data are generated, the corresponding reaction rate is calculated, and a three-dimensional surface plot is drawn. For the X-axis, Y-axis (Using the Z-axis) to visually demonstrate the combined influence of temperature and humidity on the hydration reaction rate and to pinpoint the optimal temperature and humidity range.

[0053] Optimal temperature and humidity time history model fitting: Optimization objective: To maximize quality while meeting temperature and humidity engineering constraints within a fixed curing time.

[0054] Time history discretization and solution: The maintenance time is discretized into several time steps, and the temperature and humidity of each time step are used as decision variables. The optimal temperature and humidity combination is solved by using a genetic algorithm or gradient descent method.

[0055] Time history fitting: The discrete optimal temperature and humidity sequence is fitted into a continuous polynomial time history relationship to obtain the influence model of temperature and humidity on the performance of precast beams, which can be used to guide actual maintenance.

Claims

1. A method for intelligent maintenance and control of precast beams based on solar energy utilization using thermal imaging, characterized in that, include: Obtain maintenance data for historical precast beams made of the same material; Based on the curing data of historical precast beams made of the same material, a model is constructed to assess the impact of temperature and humidity on the performance of precast beams by presetting the expected curing time. The precast beams are placed into an intelligent curing room equipped with thermal imaging sensors, humidity sensors, humidification devices, and solar thermal collectors. Based on the influence model of temperature and humidity on the performance of precast beams, the operation of humidification devices and solar thermal collectors is controlled in real time using thermal imaging sensors and humidity sensors to achieve intelligent maintenance of precast beams.

2. The intelligent maintenance and control method for precast beams based on solar energy utilization using thermal imaging, as described in claim 1, is characterized in that... The maintenance data includes maintenance time-temperature correlation curves, maintenance time-humidity correlation curves, temperature-hydration reaction rate correlation curves, humidity-hydration reaction rate correlation curves, service life, and performance data.

3. The intelligent maintenance and control method for precast beams based on solar energy utilization using thermal imaging, as described in claim 2, is characterized in that... The specific steps for constructing a model of the effects of temperature and humidity on the performance of precast beams are as follows: Based on the expected service life and expected performance, qualified samples are selected from the historical precast beams based on the service life and performance data to obtain a successful sample set. For the successful sample set, the temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship were constructed based on the curing time. A model of the influence of temperature and humidity on the performance of precast beams is constructed based on the temperature-humidity-reaction rate relationship and the temperature-humidity-mass relationship.

4. The intelligent maintenance and control method for precast beams based on solar energy utilization using thermal imaging, as described in claim 3, is characterized in that... The temperature-humidity-hydration reaction rate relationship and the temperature-humidity-mass relationship based on curing time are as follows: Quantify the quality score of each successful sample, and classify the quality level based on the quality score: in, For the first The quality score of each successful sample; This is the normalization function; The importance factor is the service life factor. For the first Normalized value of the lifetime of a successful sample; For the first Importance coefficients of each performance parameter; For the first The first successful sample Normalized values ​​of each performance parameter; This represents the total number of performance parameters. For the successful sample set, the successful samples are clustered according to the maintenance time to obtain several successful sample clusters. ; For the first A successful sample cluster class; For the first The maintenance time of the cluster center sample of a successful sample cluster; For each successful sample cluster, the sub-cluster with the best quality level is selected based on the quality level. For each sub-cluster under different curing times, the relationships between temperature and humidity and hydration reaction rate and the relationship between temperature and humidity and mass were fitted respectively. The relationship between temperature and humidity and hydration reaction rate for each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the three-dimensional surface diagram of hydration reaction rate. The temperature, humidity and mass relationship of each sub-cluster is plotted as a three-dimensional surface diagram, which is defined as the mass three-dimensional surface diagram. Based on the expected curing time, the surface features of the three-dimensional surface plots of the reaction rate and the three-dimensional surface plots of the quality of each sub-cluster are integrated to obtain the temperature time history and humidity time history that optimize the quality, which are defined as the influence model of temperature and humidity on the performance of precast beams.

5. The intelligent maintenance and control method for precast beams based on solar energy utilization using thermal imaging, as described in claim 4, is characterized in that... The relationships between temperature / humidity and hydration reaction rate, and between temperature / humidity and mass are as follows: in, The hydration reaction rate; Pre-exponential factors; It is the activation energy for the hydration reaction; It is the gas constant; Ambient temperature; Relative humidity; Humidity influence coefficient; This is a quantitative value for quality. and All are fitting coefficients; This represents the total number of performance parameters. For the first The fitting coefficients of each performance parameter; The ambient temperature is The relative humidity is At that time, the first The ratio of the baseline value to the predicted value of each performance parameter; The ambient temperature is The relative humidity is At that time, the ratio of the baseline value to the predicted value of the service life; This is the baseline value for service life; It is the reciprocal of the unit temperature; and All of these are coefficients to be fitted.

6. The intelligent maintenance and control method for precast beams based on solar energy utilization using thermal imaging, as described in claim 4, is characterized in that... The expression for the model of the influence of temperature and humidity on the performance of precast beams is as follows: in, for The optimal temperature at any given time; , and All are temperature time history fitting coefficients; for The optimal humidity at any given time; , and All are humidity time history fitting coefficients.

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