Concrete quality full-process control system and method

Abstract

The invention relates to a pouring and curing temperature control system and method for improving the quality of concrete. The system comprises a sensing unit used for collecting physical parameters of concrete in the pouring and curing process; the infrared detection unit is used for collecting infrared image data of the concrete product; the analysis unit is used for predicting the concrete state according to the collected physical parameters and image data; and the control unit is used for correcting physical parameters of the concrete according to the predicted concrete state. The sensing unit, the infrared detection unit, the analysis unit and the control unit are in signal connection with one another, and the analysis unit can combine infrared image data and temperature data to evaluate all parts in the concrete, an average temperature difference value between the interior and the exterior and a possible thermal abnormal area; the sensing unit and the infrared detection unit can dynamically adjust the concrete temperature distribution and the collection frequency and range of the curing process according to an instruction of the analysis unit, and the control unit adjusts the temperature parameter of the curing process of the concrete member according to an evaluation result of the analysis unit.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete, and particularly to a full-process control system and method for concrete quality. Background Art

[0002] In recent years, the construction of urban rail transit in China has developed rapidly. By the end of 2023, there were 50 subway cities across the country, 289 operating subway lines, with an operating mileage of 10,447.71 km, and 170 under-construction lines.

[0003] The construction control of concrete is the key to ensuring project quality. Among them, cast-in-place concrete has the characteristics of high anti-seepage requirements, long pumping distance, multiple block pourings, and high curing requirements. However, at present, the concrete construction technology and quality control are almost still the same as in the pre-information era, continuing the extensive production mode with low mechanization level, insufficient informatization level, and backward technical system. The phenomenon of unqualified quality still occurs from time to time, seriously affecting the quality of concrete finished products.

[0004] The rapid development of AI model technology has provided new research ideas for concrete construction and management. Machine learning algorithms can directly read the internal relationships between data through the analysis and learning of a large amount of experimental data without any assumptions. Through machine learning modeling, high-performance computing, and intelligent decision-making, the precise control and execution of intelligent sensors driven by digital technology are realized, and the lean construction of concrete structures is completed, which has become an inevitable trend for the high safety, high quality, and high efficiency development of urban rail transit, and also a new opportunity and challenge faced by the innovative development of construction technology.

[0005] CN111077184A discloses a method for identifying the void defect of a concrete-filled steel tubular member based on infrared thermal imaging. This method heats the area to be measured of the concrete-filled steel tubular member through a heating device, takes the surface image of the specimen, and then processes and analyzes it through infrared digital image processing software. By comparing and analyzing the temperature distribution laws of the heated area and the non-heated area, the temperature distribution gradient on the surface of the concrete-filled steel tube is calculated; further, the range of the void area of the concrete-filled steel tube is judged according to the temperature distribution gradient, and the boundary of the void area of the concrete-filled steel tube is identified.

[0006] This patent document uses the method of temperature change rate to identify the boundaries of void defects in concrete-filled steel tubular members. The test results are not affected by human factors and the test cost is relatively low. However, the method of this patent document is only limited to the identification of void defects and does not cover other types of defects in concrete members, such as cracks. This limits its application in comprehensively evaluating the quality of concrete members. In addition, although the method of this patent document can identify void defects, it does not mention automated responses or corrective measures after detecting defects. This means that in practical applications, manual intervention may be required to decide how to handle the detected defects, which not only increases the workload but also may lead to delays in response time.

[0007] In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, although the applicant studied a large number of documents and patents when making this invention, due to space limitations, not all details and content were listed in detail. However, this does not mean that this invention does not possess the features of these prior arts. On the contrary, this invention already possesses all the features of the prior arts, and the applicant reserves the right to add relevant prior arts in the background art. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the present invention provides a full-process control system and method for concrete quality to solve at least some of the above technical problems.

[0009] The present invention relates to a full-process control system for concrete quality, which includes: a sensing unit for collecting physical parameters of concrete in each production process; an infrared detection unit for collecting infrared image data of concrete products; an analysis unit for predicting the state of concrete based on the collected physical parameters and image data; a control unit for correcting the physical parameters of concrete according to the predicted state of concrete; the sensing unit, the infrared detection unit, the analysis unit, and the control unit are signal-connected to each other. Among them, the analysis unit can combine the infrared image data with temperature data to evaluate the average temperature difference between internal and external parts of concrete and possible thermal anomaly regions. The sensing unit and the infrared detection unit can dynamically adjust the acquisition frequency and range of the concrete temperature distribution and curing process according to the instructions of the analysis unit. The control unit adjusts the temperature parameters required for the curing process of concrete members according to the evaluation results of the analysis unit.

[0010] The sensing unit of the present invention can collect the physical parameters (such as temperature, humidity and stress) of concrete in various production processes in real time, ensuring the real-time monitoring of key parameters during the production and curing of concrete. The sensing unit is mainly used for the normal monitoring of concrete. Through continuously updated data information, it can timely detect potential problems and ensure that the performance of concrete meets the design requirements. The introduction of the infrared detection unit enables the system to collect infrared image data of concrete products, which provides a visual basis for the evaluation of the concrete state. By analyzing the infrared images, the system can identify the average temperature difference between internal and external parts of the concrete and possible thermal anomaly areas, and then judge the curing state of the concrete and the health of the structure. This visual analysis effectively improves the monitoring ability of concrete quality. The analysis unit combines the infrared image data with the temperature data to conduct in-depth analysis and predict the state of the concrete. This comprehensive analysis not only improves the evaluation accuracy of the current concrete state, but also provides an important basis for subsequent control and adjustment. By accurately predicting the state of the concrete, the system can take preventive measures before problems occur, reducing quality problems caused by environmental factors. In addition, by dynamically adjusting the temperature distribution of the concrete and the acquisition frequency and range of the curing process, the system can ensure that the concrete cures under the best conditions, thus significantly improving the strength and durability of the concrete. This intelligent feedback mechanism realizes the automatic adjustment of the production process and reduces the errors that may be brought by human intervention.

[0011] According to a preferred embodiment, the analysis unit combines the concrete temperature data collected by the sensing unit and the concrete infrared image data collected by the infrared detection unit to evaluate the size of the thermal anomaly regions at various internal parts and the internal and external parts of the concrete member, as well as the average temperature difference between the thermal anomaly regions and the surrounding thermally normal regions. Among them, the analysis unit can send optimization instructions for dynamically adjusting the acquisition frequency and range of the temperature distribution and curing process of the concrete member to the sensing unit and the infrared detection unit according to the evaluation results; the analysis unit can identify the evolution trend of the thermal anomaly regions based on the comparison results of the infrared image data at different time points, and send regulation instructions for adjusting the temperature parameters required for the curing process of the concrete member to the control unit. By combining the temperature data and the infrared image data, the analysis unit can more comprehensively understand the temperature distribution inside and outside the concrete, including the size of the thermal anomaly regions and the average temperature difference. This multi-parameter analysis method improves the accuracy of the evaluation of the concrete condition. The analysis unit can dynamically adjust the acquisition frequency and range of the sensing unit and the infrared detection unit according to the real-time evaluation results. This means that the system can optimize the data acquisition strategy according to the current curing process and temperature distribution, thereby improving the monitoring efficiency and reducing unnecessary data acquisition, saving resources. In addition, by analyzing the infrared image data at different time points, the system can identify the evolution trend of the thermal anomaly regions. This helps to predict possible quality problems, such as cracks and voids, so as to take preventive measures in advance.

[0012] According to a preferred embodiment, the analysis unit sets two quantitative threshold criteria, namely a first preset value and a second preset value, to evaluate the thermal anomaly regions according to the factors that affect the thermal conductivity of the concrete and the temperature distribution during the curing process of the concrete member. Among them, the first preset value refers to the threshold of the average temperature difference between the temperature of the thermal anomaly region of the concrete member and the average temperature of the surrounding thermally normal region; the second preset value refers to the area threshold of the thermal anomaly regions at various parts inside the concrete member. Through these two preset values, potential thermal anomaly regions can be discovered and identified in a timely manner during the concrete curing process, so as to take preventive measures and reduce the occurrence of defects. In addition, through the effective control and management of the thermal anomaly regions, the quality of the concrete member can be improved, its service life can be extended, and the costs of maintenance and repair can be reduced.

[0013] According to a preferred embodiment, the analysis unit is configured to: when it determines, based on the data collected by the sensing unit and the infrared detection unit, that the average temperature difference in the thermally abnormal area inside the concrete is lower than a first preset value and the size of the thermally abnormal area inside the concrete is lower than a second preset value, send a first optimization instruction to the control unit, where the first optimization instruction includes reducing the original acquisition frequency of the sensing unit and the infrared detection unit to a first frequency and reducing their original acquisition range to a first range. When both the average temperature difference and the size of the thermally abnormal area inside the concrete are lower than the preset safety threshold, it indicates that the current curing process is in a controllable state and high-frequency data acquisition is not required. At this time, reducing the acquisition frequency and range of the sensing unit and the infrared detection unit can reduce unnecessary data volume, thereby improving the efficiency of data processing. In this way, the system can concentrate resources on processing key data and improve the response speed to the state of concrete components.

[0014] According to a preferred embodiment, the analysis unit is configured to: when it determines, based on the data collected by the sensing unit and the infrared detection unit, that the average temperature difference in the thermally abnormal area inside the concrete exceeds the first preset value and the size of the thermally abnormal area inside the concrete exceeds the second preset value, send a second optimization instruction to the control unit, where the second optimization instruction includes increasing the original acquisition frequency of the sensing unit and the infrared detection unit to a second frequency and increasing their original acquisition range to a second range. Expanding the acquisition range of the infrared detection unit can ensure the monitoring of a larger area, which helps to comprehensively understand the temperature distribution inside the concrete, especially when the area of the thermally abnormal area is large. By increasing the acquisition frequency and range, the system can identify and respond to thermal anomalies faster, which is crucial for preventing quality problems of concrete components caused by too high or too low temperatures. Higher-frequency and wider-range data acquisition helps to more accurately identify the boundaries and distribution of thermally abnormal areas, thereby improving the accuracy of defect detection.

[0015] According to a preferred embodiment, the analysis unit is configured to: when it determines, based on the data collected by the sensing unit and the infrared detection unit, that the average temperature difference in the thermally abnormal area inside the concrete exceeds the first preset value and the size of the thermally abnormal area inside the concrete is lower than the second preset value, send a third optimization instruction to the control unit, where the third optimization instruction includes increasing the original acquisition frequency of the sensing unit and the infrared detection unit to a second frequency and reducing their original acquisition range to a first range. Increasing the acquisition frequency when the average temperature difference exceeds the preset value ensures sensitivity to temperature changes; at the same time, by reducing the acquisition range, the system can avoid resource waste caused by over-monitoring while maintaining monitoring accuracy.

[0016] According to a preferred embodiment, the analysis unit is configured to: when it determines that the average temperature difference of the thermal anomaly area inside the concrete is lower than the first preset value based on the data collected by the sensing unit and the infrared detection unit, and the size of the thermal anomaly area inside the concrete exceeds the second preset value, send a fourth optimization instruction to the control unit, wherein the fourth optimization instruction includes reducing the original acquisition frequency of the sensing unit and the infrared detection unit to the first frequency, and increasing the original acquisition range of the two to the second range. Reducing the acquisition frequency can reduce the consumption of resources, especially when the average temperature difference is low, indicating that the temperature inside the concrete changes slowly and does not require high-frequency monitoring. At the same time, increasing the acquisition range can ensure that the thermal anomaly areas in a larger area are monitored. Even if the average temperature difference of these areas is not high, it may be necessary to pay attention to their overall temperature distribution. Reducing the acquisition frequency when the average temperature difference is low can reduce the wear and energy consumption of the sensing unit and the detection unit. At the same time, by expanding the acquisition range, resources can be used more effectively to monitor a wider area.

[0017] According to a preferred embodiment, the analysis unit is configured to: when it determines the type of the thermal abnormal area inside the concrete and the size relationship between the average temperature difference between the area and the surrounding normal thermal area and the first preset value according to the concrete temperature data collected by the sensing unit and the concrete infrared image data collected by the infrared detection unit, send a control instruction to the control unit, the control instruction is one of the first control instruction, the second control instruction and the third control instruction, wherein the first control instruction is: start the cooling device to reduce the temperature of the concrete component; the second control instruction is: start the heating device to increase the temperature of the concrete component; the third control instruction is: start the cooling device or the heating device to maintain the current temperature of the concrete component; the type of thermal abnormal area includes hot spots and cold spots. The analysis unit can determine the thermal abnormal area (such as hot spots and cold spots) inside the concrete, and analyze the average temperature difference between the area and the surrounding normal area. This precise recognition capability enables the system to take effective measures for specific thermal anomalies to ensure that the concrete is in the best temperature state during the curing process. This accuracy is of great significance for reducing cracks and strength reduction caused by uneven temperature.

[0018] According to a preferred embodiment, the analysis unit realizes the prediction of potential structural defects of concrete components by integrating real-time monitoring data during the pouring process and crack detection results after form removal. The analysis unit integrates real-time monitoring data during the pouring process with crack detection results after form removal, accumulates empirical data to optimize the model, constructs an initial prediction framework, and improves prediction accuracy through a continuous update and feedback loop mechanism. Among them, when the coincidence degree between the crack prediction result and the actual detection result reaches the preset standard, the analysis unit judges potential structural defects, automatically retrieves the rectification plan, and feeds it back to the control unit. During the pouring process, machine learning algorithms are used to predict possible crack risks in advance, enabling the construction unit to identify potential risk factors before cracks form. This helps to implement an early warning mechanism, take rectification measures in a timely manner, reduce or even eliminate factors that may cause cracks, and thus achieve preventive maintenance management, reducing repair costs and time. Specific rectification measures include controlling the temperature at the construction site to avoid the influence of extreme environments on concrete curing, using professional crack repair materials (such as epoxy resin and polyurethane) to fill cracks and restore structural integrity, and optimizing the construction process to reduce internal defects. This method effectively prevents and repairs crack problems, ensuring the long-term stability and safety of concrete structures.

[0019] The present invention also relates to a method for controlling the entire process of concrete quality, which includes the following steps:

[0020] Combining infrared image data and temperature data to evaluate the average temperature difference between the inside and outside of the concrete and possible thermal anomaly areas;

[0021] Dynamically adjusting the acquisition frequency and range of the concrete temperature distribution and curing process;

[0022] Adjusting the temperature parameters required for the curing process of concrete components;

[0023] Using infrared image data to detect surface cracks in the concrete, predicting potential structural defects through crack feature analysis, and retrieving the rectification plan when anomalies are detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is an exemplary block diagram of the entire process control system for concrete quality provided by the present invention;

[0025] Figure 2 is another exemplary block diagram of the entire process control system for concrete quality provided by the present invention;

[0026] Figure 3 is a schematic flow diagram of the entire process control system for concrete quality provided by the present invention;

[0027] Figure 4It is a schematic flowchart of the optimization instruction sent by the analysis unit of the control system provided by the present invention to the control unit;

[0028] Figure 5 It is a schematic flowchart of the regulation instruction sent by the analysis unit of the control system provided by the present invention to the control unit;

[0029] Figure 6 It is a schematic flowchart of the analysis unit of the control system provided by the present invention for obtaining the area feature of the thermal anomaly area;

[0030] Figure 7 It is a schematic flowchart of the analysis unit of the control system provided by the present invention for obtaining the shape feature of the thermal anomaly area;

[0031] Figure 8 It is a schematic flowchart of the analysis unit of the control system provided by the present invention for obtaining the position feature of the thermal anomaly area.

[0032] List of reference numerals

[0033] 100: Sensing unit; 200: Infrared detection unit; 300: Analysis unit; 400: Control unit. Detailed implementation manners

[0034] The following is a detailed description with reference to the accompanying drawings.

[0035] Embodiment 1

[0036] This embodiment provides a full-process control system for concrete quality, as Figure 1 shown, which includes a sensing unit 100, an infrared detection unit 200, an analysis unit 300, and a control unit 400. The sensing unit 100 can be a temperature sensor (such as a thermocouple), and the sensors are arranged at different positions in the concrete mixing and pouring areas to obtain data in different areas of the environment and concrete materials. The sensing unit 100 is used to collect the physical parameters of concrete in each production process, especially parameters such as temperature that affect the quality of concrete products. The sensing unit 100 is mainly used for the normal monitoring of concrete. After the monitored parameters are collected, they can be preliminarily processed and then transmitted to the subsequent analysis unit 300.

[0037] Preferably, as Figure 1As shown in the figure, the infrared detection unit 200 is responsible for collecting infrared image data of concrete products, monitoring the temperature distribution and curing process of the concrete. This unit is equipped with a high-resolution infrared thermal imager, which can be installed above the concrete pouring and curing area to ensure that the entire pouring surface can be overlooked, so as to comprehensively monitor the temperature distribution of the concrete. More preferably, the infrared detection unit 200 can be set on a movable bracket to facilitate adjusting the angle and position as needed. The infrared detection unit 200 can capture the temperature changes on the concrete surface in real time, identify overheated points and overcooled points. Through image processing algorithms, this unit can analyze the thermal characteristics of the concrete, provide the average temperature difference and distribution. These infrared image data will be integrated into the analysis unit 300 for further state prediction and monitoring decision-making.

[0038] Preferably, as Figure 1 shown, the analysis unit 300 predicts the state of the concrete based on the collected physical parameters and infrared image data, using data analysis and machine learning algorithms. The analysis unit 300 can be a high-performance computer, equipped with corresponding data analysis software, such as a dedicated machine learning platform, which is set in a control room or monitoring center near the concrete pouring area to analyze data in real time and make decisions. This unit can calculate the average temperature difference, identify overheated points and overcooled points, monitor temperature fluctuations and their durations, and analyze abnormal heat flow directions and color differences. At the same time, it can also judge the position information and area expansion of the concrete. These analysis results will provide a basis for regulation for the control unit 400, improving the quality and stability of the concrete.

[0039] Preferably, as Figure 1 shown, the control unit 400 automatically adjusts the physical parameters in the subsequent concrete product manufacturing process according to the concrete state prediction provided by the analysis unit 300 to ensure the stability and quality of the concrete components. This unit can optimize the curing environment of the concrete by controlling the settings of cooling and heating equipment in real time. By being closely connected to the sensing unit 100 and the infrared detection unit 200, the control unit 400 can quickly respond to on-site changes and adjust the operating state of the equipment. The control unit 400 can adopt a programmable logic controller (PLC), which is installed in the control room or the equipment control panel to facilitate self-response or manual intervention by the operator.

[0040] Preferably, as Figure 1 shown, the analysis unit 300 is respectively signal-connected to the sensing unit 100, the infrared detection unit 200 and the control unit 400. Specifically, the sensing unit 100, the infrared detection unit 200 and the control unit 400 exchange data with the analysis unit 300 through a wireless communication module. This wireless communication module supports at least one of the following communication protocols: GPRS, 3G, 4G, 5G, WIFI or ZIGBEE.

[0041] Preferably, as Figure 2 , Figure 3 shown, the analysis unit 300 can be configured to: combine the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, and evaluate the size of the thermal anomaly area inside the concrete member and the average temperature difference between it and the surrounding thermally normal area. On the one hand, the analysis unit 300 can send optimization instructions for dynamically adjusting the acquisition frequency and range of the temperature distribution and curing process of the concrete member to the sensing unit 100 and the infrared detection unit 200 according to the evaluation results; on the other hand, based on the comparison results of the infrared image data at different time points, it can identify the evolution trend of the thermal anomaly area and send control instructions for adjusting the temperature parameters required for the curing process of the concrete member to the control unit 400.

[0042] Preferably, the analysis unit 300 can be configured to: set two quantitative threshold criteria for evaluating the thermal anomaly area according to factors such as the concrete material properties and construction techniques of the concrete member, which affect the heat conductivity of the concrete and the temperature distribution during the curing process, thereby affecting the average temperature difference and the formation of the thermal anomaly area.

[0043] During the curing process, if the temperature of a local area (thermal anomaly area) of the concrete member deviates significantly from the surrounding area, this indicates that there may be a thermal anomaly. This abnormal change in the average temperature difference is a quantitative index for evaluating the curing quality. In addition, the area size of the thermal anomaly area is also an important quantitative index for evaluating the uniformity of the temperature distribution during the curing process. Abnormal high or low temperatures in local areas may lead to uneven curing and affect the overall performance of the concrete member.

[0044] Based on this, the analysis unit 300 sets a first preset value for the average temperature difference inside the concrete member, which is used to quantitatively judge whether the temperature difference between the thermal anomaly area and the normal area in the concrete member is within a reasonable range. This preset value takes into account the heat conduction characteristics of the concrete material and the construction process to ensure that the temperature distribution during the curing process meets the expectations. The analysis unit 300 also sets a second preset value for the sum of the areas of the thermal anomaly areas inside the concrete member, which is used to quantitatively evaluate the size of the thermal anomaly area. This standard is based on the temperature change pattern of the concrete member during normal curing, where the temperature should be uniform or change according to a predetermined law.

[0045] Specifically, the operator responsible for concrete quality control can collect temperature data under different concrete materials and construction processes through experiments, and analyze these data to determine the average temperature difference and the formation of thermal anomaly regions. These experimental data provide an actual reference basis for determining the first preset value and the second preset value. In addition, the operator can use numerical simulation software to simulate the heat conduction behavior during the concrete curing process. This simulation can evaluate the temperature changes under different environmental and construction conditions, thereby providing theoretical support for setting the first preset value (the threshold of the average temperature difference) and the second preset value (the threshold of the area of the thermal anomaly region).

[0046] Preferably, as Figure 4 shown, the analysis unit 300 may be configured to: when it determines, based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, that the temperature gradient between the internal thermal anomaly region of the concrete and the surrounding thermally normal region is lower than the first preset value, and the size of the internal thermal anomaly region of the concrete is lower than the second preset value, send a first optimization instruction to the control unit 400, where the first optimization instruction includes reducing the original acquisition frequency of each of the sensing unit 100 and the infrared detection unit 200 to a first frequency, and reducing the original acquisition range (which can be represented by the available radius) of each of them to a first range.

[0047] In this case, both the average temperature difference of the concrete and the thermal anomaly region are within the normal range. This indicates that the curing process is operating well and the internal temperature distribution of the concrete is relatively uniform. Therefore, the acquisition frequency and range of the sensing unit 100 and the infrared detection unit 200 can be reasonably reduced to reduce the burden of data acquisition and energy consumption, and maintain the efficient operation of the system. Reducing the acquisition frequency also means energy savings, while reducing the acquisition range ensures that the system only focuses on a smaller monitoring area and avoids redundant data collection.

[0048] Preferably, as Figure 4 shown, the analysis unit 300 may be configured to: when it determines, based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, that the average temperature difference between the internal thermal anomaly region of the concrete and the surrounding thermally normal region exceeds the first preset value, and the size of the internal thermal anomaly region of the concrete exceeds the second preset value, send a second optimization instruction to the control unit 400, where the second optimization instruction includes increasing the original acquisition frequency of each of the sensing unit 100 and the infrared detection unit 200 to a second frequency, and increasing the original acquisition range of each of them to a second range.

[0049] When the average temperature difference exceeds the first preset value and the thermally abnormal area also exceeds the second preset value, it indicates that there are relatively serious problems in the curing process. To monitor these problems in a timely manner, it is necessary to increase the acquisition frequency and range of the infrared detection unit 200. Increasing the acquisition frequency can obtain data more frequently, facilitating the rapid capture of changes; while expanding the acquisition range helps to comprehensively analyze the temperature conditions of the surrounding area to find possible abnormal sources and chain reactions. This monitoring strategy ensures that a rapid response can be made when problems occur, avoiding potential damage to the concrete structure.

[0050] Preferably, as Figure 4 shown, the analysis unit 300 may be configured to: when it determines, based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, that the average temperature difference between the thermally abnormal area inside the concrete and the thermally normal area around it exceeds the first preset value, and the size of the thermally abnormal area inside the concrete is lower than the second preset value, send a third optimization instruction to the control unit 400, where the third optimization instruction includes increasing the original acquisition frequency of each of the sensing unit 100 and the infrared detection unit 200 to the second frequency, and reducing the original acquisition range of each of them to the first range.

[0051] In this case, the average temperature difference exceeds the first preset value, indicating the presence of a thermal anomaly, but the size of the thermally abnormal area is still within a reasonable range. In this case, increasing the acquisition frequency is to more finely monitor the temperature changes in the abnormal area to ensure that potential problems can be captured in a timely manner. And reducing the acquisition range is to focus on the details of the thermally abnormal area and avoid interference from irrelevant areas, thereby improving the monitoring accuracy. This strategy can effectively balance the monitoring of anomalies and the utilization of system resources.

[0052] Preferably, as Figure 4 shown, the analysis unit 300 may be configured to: when it determines, based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, that the average temperature difference between the thermally abnormal area inside the concrete and the thermally normal area around it is lower than the first preset value, and the size of the thermally abnormal area inside the concrete exceeds the second preset value, send a fourth optimization instruction to the control unit 400, where the fourth optimization instruction includes reducing the original acquisition frequency of each of the sensing unit 100 and the infrared detection unit 200 to the first frequency, and increasing the original acquisition range of each of them to the second range.

[0053] In this case, although the average temperature difference is lower than the first preset value, indicating no obvious thermal anomaly, the size of the thermal anomaly area exceeds the second preset value, meaning there may be a potential risk of uneven curing. Since the temperature is still within the normal range, reducing the acquisition frequency can reduce the burden on the system. However, at the same time, the strategy of expanding the acquisition range aims to monitor a wider area to capture possible uneven curing phenomena. In this way, potential problems can be detected in a timely manner on the premise of ensuring resource utilization efficiency, preventing the decline of curing quality.

[0054] Preferably, the first frequency of the sensing unit 100 and the first frequency of the infrared detection unit 200 do not necessarily have to be the same. Similarly, the second frequency, the first range, and the second range of the two do not necessarily have to be the same.

[0055] The first frequency refers to a lower data acquisition frequency, which is used when the temperature change during the concrete curing process is within the expected range. Considering the issues of efficiency and energy consumption, the first frequency can be set to once per hour or lower, such as once every 2 - 4 hours. Such a frequency can not only meet the monitoring requirements but also avoid excessive energy consumption. The second frequency refers to a higher data acquisition frequency, which is used to obtain data more frequently when an abnormal situation is detected. To ensure that the trend of temperature change can be captured in a timely manner, the second frequency can be set to once every 5 minutes to once per hour. This can provide enough data points to analyze the problem when a thermal anomaly occurs.

[0056] The first range involves a smaller acquisition area and is applicable to the case when the thermal anomaly area is small. The first range can be defined as only covering the identified thermal anomaly area and its immediate surrounding areas, such as within a range of 1 to 2 meters in radius centered on the abnormal hot spot. This can concentrate resources on the areas that need the most attention and reduce redundant data. The second range is a larger acquisition area used to expand the monitoring of the potential impact area. If there is a large - area thermal anomaly or a wider impact needs to be evaluated, the second range can be set to a larger radius, such as 3 to 5 meters or even larger, depending on the scale and complexity of the project.

[0057] This embodiment lists the adjustment schemes for the acquisition frequency and range of the infrared detection unit 200 as shown in the following table.

[0058]

[0059] By sending different optimization instructions to the control unit 400, the analysis unit 300 enables the control unit 400 to adjust the acquisition frequency and range of the infrared detection unit 200 under different concrete curing conditions. Such a control method enables the system of this embodiment to flexibly cope with various situations during the concrete curing process, saving resources and improving the overall efficiency of the system while ensuring the curing quality.

[0060] Preferably, the analysis unit 300 can be configured to analyze the geometric features of each thermal anomaly region inside the concrete component according to the concrete infrared image data collected by the infrared detection unit 200. The geometric features include area features, shape features, and position features. The analysis unit 300 can calculate the characteristic value of the thermal anomaly region based on the characteristic coefficient corresponding to the geometric features of the obtained thermal anomaly region and the average temperature difference of the thermal anomaly region, so as to optimize the monitoring scheme of the infrared detection unit 200 by means of it.

[0061] Specifically, the area, shape, and position of the thermal anomaly region all have an impact on the stability and quality of the concrete component. First of all, the larger the area of the thermal anomaly region, the greater its impact, because a larger thermal anomaly region means that more concrete materials are affected by uneven temperature changes. This non-uniformity may lead to the concentration of internal stresses, which in turn may cause cracks or other structural damages. In addition, a large-area thermal anomaly region will also increase the conduction and diffusion of heat inside the concrete, which may cause abnormal temperatures in the surrounding areas and affect the curing process of the entire component, resulting in insufficient strength.

[0062] Secondly, the more irregular the shape of the thermal anomaly region, the greater the impact. An irregular thermal anomaly shape will lead to uneven heat distribution and exacerbate the complexity of internal stresses. Compared with regular shapes, the concentrated or sparse distribution of heat in irregular shapes is more difficult to predict and control, which may cause local weaknesses. In addition, an irregularly shaped thermal anomaly region may cause stress concentration at certain specific positions, making these positions more prone to plastic deformation or rupture.

[0063] In addition, the position of the thermal anomaly region also affects the stability of the concrete component. If the thermal anomaly region is close to the edge of the concrete component, especially at the position where it contacts other structures or bears external loads, it will have a more significant impact on the overall structural stability. Thermal anomalies in the edge region may cause deformation of the entire component, which will affect the strength and stability of the connection part. In addition, thermal anomalies at the edge position are more likely to cause local instability and even lead to the failure of the overall structure, because the edge region usually bears greater external loads and the connection points with other components are more sensitive.

[0064] Regarding the area feature, such as Figure 6As shown in the figure, the analysis unit 300 evaluates the area of the thermal anomaly region to determine the degree of its impact on the quality and stability of the concrete component. Specifically, the analysis unit 300 presets multiple area intervals, and each interval corresponds to an area characteristic coefficient to reflect the impact of this region. For example, the preset area intervals include A1 - A2, A2 - A3, and A3 - A4, where A1, A2, A3, and A4 are preset area values, and A4 > A3 > A2 > A1. The setting of these intervals enables the analysis unit to conduct a more detailed evaluation of the area of the thermal anomaly region.

[0065] In practical applications, the analysis unit 300 uses the concrete infrared image data collected by the infrared detection unit 200 to obtain the area value A0 of a certain thermal anomaly region. By comparing with the preset area values A1, A2, A3, and A4, the analysis unit can determine which area interval A0 falls into. Thus, the analysis unit 300 can select the corresponding area characteristic coefficient, such as α1, α2, or α3, to evaluate the impact of the thermal anomaly region. According to the preset rules, α3 represents the largest area interval with the most significant impact, while α1 represents the smallest area interval with a smaller impact. By quantifying the area of each thermal anomaly region and associating it with the corresponding area characteristic coefficient, the analysis unit 300 determines the area characteristic coefficient of this thermal anomaly region.

[0066] Preferably, for shape characteristics, such as Figure 7 As shown in the figure, the analysis unit 300 evaluates the impact of the shape characteristics of the thermal anomaly region on the quality and stability of the concrete component. Specifically, the analysis unit 300 presets multiple shape intervals, and each interval corresponds to a shape characteristic coefficient to quantify the degree of deviation of the shape of the thermal anomaly region. For example, the preset shape intervals include B1 - B2, B2 - B3, and B3 - B4, where the values of B1, B2, B3, and B4 are represented by the Hausdorff distance.

[0067] The analysis unit 300 first defines a standard shape (such as a circle or a rectangle) as the reference shape. Then, for each thermal anomaly region, the analysis unit 300 calculates the Hausdorff distance between the actual shape of this region and the standard shape to reflect its degree of shape deviation. Assuming that the point set of the actual shape is X and the point set of the standard shape is Y, the calculation formula for the Hausdorff distance H(X, Y) is:

[0068]

[0069] where d(x, y) represents the distance between point x and point y.

[0070] Through the above formula, the analysis unit 300 can determine specific values of B1, B2, B3, and B4. For example, B1 can be set to 0, B2 to a small value, B3 to a medium value, and B4 to a large value. The specific intervals are divided as follows: B1 - B2 indicates that the shape is close to the standard shape; B2 - B3 indicates that the shape moderately deviates; B3 - B4 indicates that the shape significantly deviates. The analysis unit 300 assigns corresponding shape characteristic coefficients (such as β1, β2, β3) to each interval to quantify its impact on the concrete component. According to the set rules, β3 represents the interval with the largest degree of shape deviation and the most significant impact, corresponding to the interval B3 - B4; while β1 corresponds to the interval B1 - B2 with the smallest degree of deviation and relatively less impact.

[0071] Finally, the analysis unit 300 uses the concrete infrared image data collected by the infrared detection unit 200 to calculate the Hausdorff distance B0 of the thermal anomaly region, and determines the shape characteristic parameters of the thermal anomaly region by judging the interval into which B0 falls.

[0072] Preferably, for the position characteristics, such as Figure 8 As shown, the closer the thermal anomaly region is to the edge of the concrete component, the more significant its impact on the quality and stability of the component. Based on this principle, the analysis unit 300 presets multiple position intervals, and each position interval corresponds to a position characteristic parameter. For example, the analysis unit 300 sets position intervals such as C1 - C2, C2 - C3, and C3 - C4. Among them, C1, C2, C3, and C4 represent the deviation distances from the edge of the concrete component, and C1 > C2 > C3 > C4, indicating that the thermal anomaly region corresponding to C1 is closer to the center of the component. Further, the analysis unit 300 can assign corresponding position characteristic coefficients to each position interval. C1 - C2 indicates that the thermal anomaly region is very close to the edge of the concrete component and has a relatively large potential impact, and the position characteristic coefficient γ1 corresponding to this interval is relatively high; C2 - C3: indicates that the thermal anomaly region is moderately distant from the edge and has a relatively weak impact, and the position characteristic coefficient γ2 corresponding to this interval is between γ1 and γ3; C3 - C4 indicates that the thermal anomaly region is far from the edge and has a relatively small potential impact, and the position characteristic coefficient γ3 corresponding to this interval is the lowest. When the analysis unit 300 obtains the distance value C0 of a certain thermal anomaly region using the concrete infrared image data collected by the infrared detection unit 200, it can judge which position interval the thermal anomaly region falls into according to the relationship between C0 and C1, C2, C3, and C4. Thus, the analysis unit 300 can determine the position characteristic coefficient of the thermal anomaly region according to the corresponding relationship between the position interval and the position characteristic coefficient.

[0073] Preferably, the analysis unit 300 can calculate an eigenvalue V that reflects the degree of influence of the thermal anomaly region on the stability and quality of the concrete member by using the aforementioned area characteristic coefficient, shape characteristic coefficient, and position characteristic coefficient, and it can be obtained through the following formula:

[0074]

[0075] In the formula,

[0076] α represents the area characteristic coefficient;

[0077] β represents the shape characteristic coefficient;

[0078] γ represents the position characteristic coefficient;

[0079] Δt represents the average temperature difference;

[0080] k a 、k s 、k p are the weighting coefficients of area, shape, and position respectively. The area weighting coefficient can be determined according to the proportional relationship between the actual area of the thermal anomaly region and the preset area. For example, the importance of the area characteristic can be determined through experimental data or historical data analysis, so as to assign a weight value k a . The shape weighting coefficient can be determined according to the complexity of the shape of the thermal anomaly region. Geometric features (such as circles, rectangles, etc.) can be used to classify the shape, and a weight value K s is assigned to each shape. The position weighting coefficient can evaluate its importance according to the distance of the thermal anomaly region from the edge of the concrete member. The closer the distance, the higher the weight value k p , and it can be determined based on the distribution of the position characteristic coefficient.

[0081] Preferably, the analysis unit 300 assigns corresponding monitoring frequencies to the thermal anomaly regions according to the level of the eigenvalue. Specifically, high eigenvalue regions (eigenvalues exceeding the threshold) will be assigned high-frequency monitoring to timely capture potential thermal anomalies by increasing the number of monitoring times per unit time. Medium eigenvalue regions (eigenvalues in the medium range) will be set to medium-frequency monitoring to ensure effective tracking of abnormal changes under normal circumstances. Low eigenvalue regions (eigenvalues below the threshold) will adopt low-frequency monitoring, mainly focusing on trend changes. To achieve this differential monitoring frequency setting, the infrared detection unit 200 will be installed on a movable bracket. This design allows flexible adjustment of the angle and position of the infrared detection unit 200 according to needs, so as to optimize the monitoring effect and ensure that each thermal anomaly region can be effectively tracked and analyzed.

[0082] During the concrete curing process, the non-uniformity of temperature distribution may lead to the formation of thermally abnormal regions. The temperatures in these regions show significant differences, usually manifested as the phenomena of "hot spots" and "cold spots". The temperature of the thermally abnormal region is significantly higher than that of the surrounding thermally normal regions, forming a "hot spot". This phenomenon may be caused by various factors, such as the too-fast reaction rate of certain components in the concrete (such as overly intense hydration reaction), or the influence of external environmental factors (such as direct sunlight, climatic conditions, etc.). Such high-temperature regions may lead to insufficient strength and long-term durability problems of the concrete. The temperature of the thermally abnormal region is significantly lower than that of the surrounding thermally normal regions, forming a "cold spot". This may be due to the insufficient reaction in certain parts of the concrete (such as incomplete hydration of cement), or the external environment (such as shade, cold air, cold water pouring, etc.). The existence of cold spots may affect the uniformity and strength of the concrete, increasing the risk of later cracks and damage.

[0083] Preferably, as Figure 5 shown, the analysis unit 300 can be configured to: when it determines that the average temperature difference between the thermally abnormal region showing a hot spot inside the concrete and its surrounding thermally normal regions exceeds a first preset value based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, send a first regulation instruction to the control unit 400. The first regulation instruction includes: starting a cooling device (such as a spray, a fan, or a cooling water system) to reduce the temperature of the concrete member.

[0084] Preferably, as Figure 5 shown, the analysis unit 300 can be configured to: when it determines that the average temperature difference between the thermally abnormal region showing a cold spot inside the concrete and its surrounding thermally normal regions exceeds a first preset value based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, send a second regulation instruction to the control unit 400. The second regulation instruction includes: starting a heating device (such as an electric blanket or a heating pipe) to increase the temperature of the concrete member.

[0085] Preferably, as Figure 5 shown, the analysis unit 300 can be configured to: when it determines that the average temperature difference between the thermally abnormal region inside the concrete and its surrounding thermally normal regions does not exceed a first preset value based on the concrete temperature data collected by the sensing unit 100 and the concrete infrared image data collected by the infrared detection unit 200, send a third regulation instruction to the control unit 400. The third regulation instruction includes: maintaining the current temperature of the concrete member to avoid unnecessary temperature fluctuations.

[0086] Preferably, the control unit 400 can also receive the data transmitted back by the sensing unit 100 and / or the infrared detection unit 200 in real time under the regulation instructions of the analysis unit 300. The control unit 400 can dynamically evaluate the temperature change trend based on this data, so that the temperature in the thermal anomaly area can be quickly adjusted to or maintained within a preset safe range.

[0087] Preferably, during the concrete pouring and curing process, the analysis unit 300 monitors the temperature distribution and thermal anomalies inside and outside the concrete by integrating the infrared image data provided by the infrared detection unit 200. Based on these dynamic monitoring data, the analysis unit 300 can use a preset machine learning algorithm to predict in advance the possible crack positions and their characteristics (length, width, potential depth). This prediction is carried out before the formwork is removed, aiming to identify the risk factors that may lead to cracks in the later stage by monitoring the key indicators during the pouring process and taking preventive measures.

[0088] After the formwork is removed, a high-definition camera is used to conduct a detailed crack inspection on the concrete surface. Through image recognition algorithms, the specific size information (length and width) of the cracks is accurately extracted from the visible light images, and combined with other visual features (such as crack shape, distribution pattern, etc.) to comprehensively evaluate the appearance quality of the concrete components. In addition, other types of surface defect inspections such as bubble detection can also be carried out simultaneously to ensure the safety and durability of the overall structure.

[0089] Preferably, the analysis unit 300 realizes the effective prediction and evaluation of potential structural defects of concrete components by integrating the real-time monitoring data during the pouring process and the crack detection results after the formwork is removed. During this process, the analysis unit 300 continuously accumulates and applies empirical data to improve the system's crack prediction performance. First, in the data collection and preprocessing stage, the analysis unit 300 receives the dynamic monitoring data reflecting the behavior characteristics of the concrete, such as the temperature change curve, humidity level, and pressure fluctuation provided by the sensing unit 100, as well as the infrared image data provided by the infrared detection unit 200. These information runs through the entire pouring and curing cycle. At the same time, after the formwork is removed, a high-definition camera is used to capture high-resolution images of the concrete surface. These images are analyzed by specialized image processing software to accurately extract the specific size (length and width) of the cracks and other forms of surface defect features. All physical parameters during the pouring period and the visual records after the formwork is removed are cleaned and labeled to remove noise interference and ensure the consistency and quality of the data, providing a reliable data set for the subsequent machine learning model training.

[0090] The analysis unit 300 can construct an initial model library based on the above dataset. This model library combines experimental data obtained under laboratory conditions and the experience of past engineering cases, covering the crack formation mechanisms and their warning thresholds under various common construction environments, thus forming a preliminary crack prediction framework. The so-called "crack prediction" refers to using the monitoring data during the pouring process and through data analysis and machine learning algorithms to identify potential crack risks in advance before form removal. This not only helps the construction team take preventive measures in a timely manner but also enables the early detection and resolution of potential problems, avoiding more serious structural defects in the later stage.

[0091] As the system is continuously used, new monitoring data and the crack detection results after form removal continuously flow into the system and become the empirical materials for system optimization. The analysis unit 300 will regularly incorporate these new data into the existing model for retraining to adjust the model parameters, making it more in line with the actual construction environment and improving the prediction accuracy.

[0092] To further improve the prediction model, the analysis unit 300 has established a feedback loop mechanism to evaluate the difference between each prediction result and the actual detection. When there is a deviation between the prediction result and the actual situation, such as frequent false alarms or missed detections under specific conditions, the system will specifically increase the sample quantity under this condition and recalibrate the relevant algorithms to achieve more accurate judgment. "Crack detection" is a detailed surface inspection method achieved by a high-definition camera after form removal, aiming to confirm whether there are cracks and measure their specific characteristics. This detection method provides direct and accurate verification data and is an indispensable part of optimizing the crack prediction model.

[0093] In summary, by effectively combining the monitoring data during the pouring process and the crack detection conclusions after form removal, the analysis unit 300 can not only construct an efficient and reliable prediction system but also gradually improve the prediction accuracy and service ability of the system through continuous self-learning and iteration.

[0094] Preferably, after the coincidence degree between the crack prediction result during the concrete member pouring by the analysis unit 300 and the crack detection result after form removal reaches a certain level, the analysis unit 300 can judge potential structural defects. The "coincidence degree" refers to the degree of consistency between the two in terms of crack location, characteristic parameters (such as length, width, and depth), etc., which reflects the matching situation between the crack information estimated by the prediction model and the actually detected crack information. Specifically, the coincidence degree not only involves whether the predicted crack location is the same as or close to the actually detected crack location, but also covers the similarity of the crack geometry and main dimension characteristics (length, width, depth), as well as the coincidence situation between the predicted number of cracks and the actually detected number. To evaluate this consistency more comprehensively, the analysis unit 300 can give a comprehensive score by combining the above-mentioned multiple factors, assign weights to each sub-item, and give corresponding scores according to their respective performances, and finally sum them up after weighting to reflect the matching situation in each dimension and visually display the overall coincidence level. When the analysis unit 300 finds that the coincidence degree between the crack prediction result and the crack detection result after form removal reaches the preset standard, it means that the prediction model has high accuracy and can more reliably guide the quality control during the construction process.

[0095] For the crack characteristics (such as length, width, and depth) shown by different crack prediction results, the analysis unit 300 will automatically retrieve the corresponding rectification scheme and feedback the information to the control unit 400 to ensure that measures are taken in a timely manner.

[0096] Preferably, the generation of the rectification scheme is based on the database in the analysis unit 300, which stores the standard repair schemes for different crack characteristics. The analysis unit 300 has carried out systematic scheme induction and standardization at the beginning of its design, forming a series of preset responses for the crack characteristic parameters. In this way, when an abnormal situation is detected, the analysis unit 300 can quickly retrieve the rectification scheme suitable for the current crack characteristics to ensure a rapid and effective response.

[0097] Preferably, the selection of the rectification scheme can follow the following principles: for cracks that are long and wide, high-strength repair materials such as epoxy resin are usually required to ensure strong filling and support of the cracks; while for cracks that are short and shallow, elastic materials such as polyurethane can be selected because they are more suitable for meeting the needs of small-amplitude deformation. In addition, if the depth of the crack exceeds a certain threshold, more complex repair processes such as injection repair may be required to ensure the overall stability of the structure.

[0098] Preferably, the control unit 400 takes corresponding actions after receiving the instructions from the analysis unit 300. First, it adjusts the environmental conditions at the construction site according to the real-time temperature data provided by the sensing unit 100 to avoid the impact of extreme temperature changes on the concrete curing process. When implementing a specific deviation correction plan that requires manual participation, the control unit 400 is responsible for communicating the specific steps and precautions to the operators according to the guidance of the analysis unit 300. Specifically, the analysis unit 300 sends the instruction information to the control unit 400 through a data interface or communication protocol. After receiving these instructions, the control unit 400 presents the relevant information to the operators using a visual interface (such as a tablet computer or touch screen). This information includes detailed instructions for repairing cracks, the specific materials used, filling methods, and required tools, etc. To ensure that the operators can accurately understand and execute these instructions, the control unit 400 is also equipped with an audio prompt or alarm system to give immediate feedback and reminders when necessary.

[0099] In addition, when the operators are working on-site, they can maintain real-time communication with the control unit 400 through wireless communication devices (such as walkie-talkies or mobile terminals) to ensure that they can obtain updated information or instructions at any time during the execution process. The control unit 400 is responsible for coordinating the on-site operations to ensure that the operators carry out the repairs according to the guidance of the analysis unit 300, thereby improving the construction efficiency and repair quality. At the same time, the control unit 400 will also optimize the construction process, reduce the vibration and vibration time, ensure the uniform distribution of the concrete, and further prevent the generation of future cracks.

[0100] Embodiment 2

[0101] This embodiment is a further improvement of Embodiment 1, and the repeated content will not be elaborated.

[0102] This embodiment provides a full-process control method for concrete quality, and this method includes one or more of the following steps:

[0103] S1: Combining infrared image data with temperature data to evaluate the average temperature difference between the inside and outside of the concrete and possible thermal anomaly areas;

[0104] S2: Dynamically adjusting the acquisition frequency and range of the concrete temperature distribution and curing process;

[0105] S3: Adjusting the temperature parameters required for the curing process of concrete components;

[0106] S4: Using infrared image data to detect surface cracks in the concrete, predicting potential structural defects through crack feature analysis, and retrieving a deviation correction plan when an anomaly is detected.

[0107] It should be noted that the above specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the description of the present invention and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents. The description of the present invention contains multiple inventive concepts. For example, "preferably" or "according to a preferred embodiment" indicates that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the features guided by "preferably" are only optional and should not be understood as must be provided. Therefore, the applicant reserves the right to waive or delete relevant preferred features at any time.

Claims

1. A full-process control system for concrete quality, comprising: A sensing unit (100) for collecting physical parameters of concrete in each production process; An infrared detection unit (200) for collecting infrared image data of concrete products; An analysis unit (300) for predicting the state of concrete based on the collected physical parameters and image data; A control unit (400) for correcting the physical parameters of concrete according to the predicted state of concrete; Characterized in that The sensing unit (100), the infrared detection unit (200), the analysis unit (300) and the control unit (400) are signal-connected to each other, wherein The analysis unit (300) can combine the infrared image data with the temperature data to evaluate the average temperature difference and possible thermal anomaly regions between various parts inside the concrete and between the inside and outside. The sensing unit (100) and the infrared detection unit (200) can dynamically adjust the acquisition frequency and range of the concrete temperature distribution and the curing process according to the instructions of the analysis unit (300). The control unit (400) adjusts the temperature parameters required for the curing process of the concrete component according to the evaluation result of the analysis unit (300).

2. The system according to claim 1, wherein The analysis unit (300) combines the concrete temperature data collected by the sensing unit (100) and the concrete infrared image data collected by the infrared detection unit (200) to evaluate the size of the thermal anomaly regions inside the concrete component and the average temperature difference between them and the surrounding thermally normal regions. Among them, The analysis unit (300) can send optimization instructions for dynamically adjusting the acquisition frequency and range of the temperature distribution and curing process of the concrete component to the sensing unit (100) and the infrared detection unit (200) according to the evaluation result; The analysis unit (300) can identify the evolution trend of the thermal anomaly region based on the comparison result of the infrared image data at different time points, and send a control instruction for adjusting the temperature parameters required for the curing process of the concrete component to the control unit (400).

3. The system according to claim 1 or 2, characterized in that, The analysis unit (300) sets two quantitative threshold criteria, namely a first preset value and a second preset value, to evaluate the thermal anomaly region according to the factors affecting the thermal conductivity of the concrete in the concrete component and the temperature distribution during the curing process. Among them, The first preset value refers to the threshold of the average temperature difference between the temperature of the thermal anomaly region of the concrete component and the average temperature of the surrounding thermally normal region; The second preset value refers to the area threshold of each thermal anomaly region inside the concrete component.

4. The system according to any one of claims 1 to 3, characterized in that The analysis unit (300) is configured to: When it determines according to the data collected by the sensing unit (100) and the infrared detection unit (200) that the average temperature difference of the thermal anomaly region inside the concrete is lower than the first preset value, and the size of the thermal anomaly region inside the concrete is lower than the second preset value, send a first optimization instruction to the control unit (400). Among them, the first optimization instruction includes reducing the original acquisition frequency of the sensing unit (100) and the infrared detection unit (200) to a first frequency, and reducing the original acquisition range of the two to a first range.

5. The system according to any one of claims 1 to 4, characterized in that The analysis unit (300) is configured to: When it determines, based on the data collected by the sensing unit (100) and the infrared detection unit (200), that the average temperature difference in the thermally abnormal area inside the concrete exceeds a first preset value and the size of the thermally abnormal area inside the concrete exceeds a second preset value, send a second optimization instruction to the control unit (400), where the second optimization instruction includes increasing the original collection frequency of the sensing unit (100) and the infrared detection unit (200) to a second frequency and increasing their original collection range to a second range.

6. The system according to any one of claims 1 to 5, characterized in that The analysis unit (300) is configured to: When it determines, based on the data collected by the sensing unit (100) and the infrared detection unit (200), that the average temperature difference in the thermally abnormal area inside the concrete exceeds a first preset value and the size of the thermally abnormal area inside the concrete is lower than the second preset value, send a third optimization instruction to the control unit (400), where the third optimization instruction includes increasing the original collection frequency of the sensing unit (100) and the infrared detection unit (200) to a second frequency and reducing their original collection range to a first range.

7. The system according to any one of claims 1 to 6, characterized in that, The analysis unit (300) is configured to: When it determines, based on the data collected by the sensing unit (100) and the infrared detection unit (200), that the average temperature difference in the thermally abnormal area inside the concrete is lower than the first preset value and the size of the thermally abnormal area inside the concrete exceeds the second preset value, send a fourth optimization instruction to the control unit (400), where the fourth optimization instruction includes reducing the original collection frequency of the sensing unit (100) and the infrared detection unit (200) to a first frequency and increasing their original collection range to a second range.

8. The system according to any one of claims 1 to 7, characterized in that The analysis unit (300) is configured to: When it determines, based on the concrete temperature data collected by the sensing unit (100) and the concrete infrared image data collected by the infrared detection unit (200), the type of the thermally abnormal area inside the concrete and the size relationship between the average temperature difference between it and the surrounding thermally normal area and the first preset value, send a regulation instruction to the control unit (400), and the regulation instruction is one of a first regulation instruction, a second regulation instruction, and a third regulation instruction, where The first regulation instruction is: start the cooling device to reduce the temperature of the concrete member; The second regulation instruction is: start the heating device to increase the temperature of the concrete member; The third regulation instruction is: start the cooling device or the heating device to maintain the current temperature of the concrete member; The types of the thermally abnormal area include overheating points and overcooling points.

9. The system according to any one of claims 1 to 8, characterized in that, The analysis unit (300) realizes the prediction of potential structural defects of the concrete member by integrating the real-time monitoring data during the pouring process and the crack detection results after form removal. The analysis unit (300) integrates the real-time monitoring data during the pouring process and the crack detection results after form removal, accumulates empirical data to optimize the model, constructs an initial prediction framework, and improves the prediction accuracy through a continuous update and feedback loop mechanism. Among them, when the coincidence degree between the crack prediction result and the actual detection result reaches the preset standard, the analysis unit (300) determines that there are potential structural defects, automatically retrieves the rectification plan, and feeds it back to the control unit (400).

10. A full-process control method for concrete quality, characterized in that The method includes the following steps: Combining infrared image data and temperature data to evaluate the average temperature difference between the inside and outside of the concrete and the possible thermal anomaly areas; Dynamically adjusting the acquisition frequency and range of the concrete temperature distribution and the curing process; Adjusting the temperature parameters required for the curing process of concrete components; Using infrared image data to detect surface cracks in the concrete, predicting potential structural defects through crack feature analysis, and retrieving the rectification plan when anomalies are detected.

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