Gradient temperature and humidity control curing system for factory prefabricated column

Abstract

The present application relates to the technical field of temperature gradient monitoring, and specifically discloses a gradient temperature and humidity control maintenance system for a factory building prefabricated column, comprising: dividing a monitoring area for the prefabricated column, collecting temperature data in real time, calculating the temperature gradient change amount between adjacent areas, screening out a temperature deviation area, combining the temperature adjustment data of the historical temperature deviation area in the historical temperature adjustment database, analyzing whether there is a heat conduction phenomenon in the temperature adjustment process of the temperature deviation area, which causes a collateral effect on the adjacent area, if there is, extracting the influence coefficient of the temperature change of the adjacent area when the temperature deviation area is adjusted in the temperature adjustment process, integrating to obtain a temperature influence coefficient matrix, combining the temperature change in the temperature deviation area, determining a temperature control area in the temperature deviation area, adjusting the temperature control parameters according to the temperature deviation degree of the adjacent area of the temperature control area, which is conducive to improving the uniformity of the temperature field in the column maintenance temperature adjustment stage.

Description

Technical Field

[0001] This invention relates to the field of temperature gradient monitoring technology, specifically to a gradient temperature and humidity control and maintenance system for prefabricated columns in factory buildings. Background Technology

[0002] In the curing process of precast columns in concrete building material factories, the uniformity of the temperature field during the heating stage is one of the key factors in ensuring the performance of the column materials and avoiding structural defects. Temperature monitoring of precast columns often employs a fixed-specification area division method, and temperature sampling is mostly done at discrete points. This makes it difficult to accurately capture abnormal temperature gradients between adjacent areas during the heating process, easily creating temperature monitoring blind spots in some areas. This makes it impossible to identify areas with lagging or abnormal heating in a timely manner, resulting in a lack of precise target basis for subsequent temperature adjustments, directly affecting the stability of curing quality.

[0003] In existing technologies, it is difficult to accurately capture abnormal temperature gradients in adjacent areas during the heating process. In the adjustment of areas with abnormal heating, single parameter adjustments are often made for independent areas without referencing historical adjustment data from similar column maintenance scenarios. The temperature changes in adjacent areas caused by heat conduction during the adjustment process are not analyzed, and there is a lack of quantitative reference for the degree of temperature influence between areas. This results in a mismatch between the setting of adjustment parameters and actual needs: if the adjustment is insufficient, the problem in areas with abnormal heating cannot be effectively improved; if the adjustment is excessive, it will cause temperature fluctuations in adjacent areas, further disrupting the uniformity of the overall temperature field. This makes it difficult to meet the requirements of precise control of the curing temperature for the thermal stability of precast column materials, which may ultimately lead to defects such as shrinkage cracks in the columns, reducing their structural strength and service life.

[0004] Therefore, the present invention provides a gradient temperature and humidity control curing system for prefabricated columns in factory buildings. Summary of the Invention

[0005] The purpose of this invention is to provide a gradient temperature and humidity control curing system for prefabricated columns in factory buildings, in order to solve the aforementioned background problems.

[0006] The objective of this invention can be achieved through the following technical solutions: A gradient temperature and humidity control curing system for precast columns in factory buildings includes: Division and Screening Module: Divide the precast columns into monitoring areas, collect temperature data in real time according to the divided areas, calculate the temperature gradient change between adjacent areas, and screen out areas with temperature rise deviation. Analysis and correlation module: Based on the identified temperature rise deviation area, combined with the temperature regulation data of the historical temperature rise deviation area in the historical temperature regulation database, it analyzes whether there is heat conduction during the temperature regulation process of the temperature rise deviation area, which may lead to a cascading effect on adjacent areas. Statistical integration module: If there are cascading effects, extract the influence coefficients of the temperature change of adjacent areas when adjusting the heating rate in the heating deviation area, count all the influence coefficients with cascading effects in the heating deviation area, and integrate them to obtain the heating influence coefficient matrix. Extraction and Adjustment Module: Within the heating influence coefficient matrix, obtain the temperature changes in each heating deviation region, determine the heating control region within the heating deviation region, and adjust the heating control parameters according to the degree of temperature deviation of the regions adjacent to the heating control region.

[0007] Further, collect temperature data: The column is divided into several temperature monitoring areas of equal area along its length, and the temperature monitoring areas are numbered and sorted sequentially from bottom to top of the column. The temperature monitoring area is divided into equidistant grids using a grid sampling method. Sampling points are deployed at each grid node, and temperature data of each sampling point is collected within the same timestamp. The data are then integrated to obtain the temperature data sequence of each temperature monitoring area.

[0008] Furthermore, the temperature gradient change between adjacent regions is calculated: The average temperature value of each temperature monitoring area is obtained by summing and averaging the temperature data within the temperature data sequence. The temperature gradient change between adjacent temperature monitoring areas is then calculated based on the average temperature value of each area. The average temperature values ​​between adjacent temperature monitoring areas are processed to obtain the temperature difference value, and the temperature gradient change per unit length is calculated by combining the distance between the temperature monitoring areas.

[0009] Furthermore, the temperature rise deviation area was identified: Compare the temperature gradient change with a temperature gradient change threshold; If the temperature gradient change is greater than or equal to the temperature gradient change threshold, then there is an abnormal temperature rise trend between adjacent temperature monitoring areas, which is determined to be a temperature rise deviation area.

[0010] Furthermore, the analysis examines whether heat conduction occurs during temperature adjustment of the temperature rise deviation region. Retrieve the temperature deviation area adjustment records from the historical temperature adjustment database that are consistent with the current precast column geometry, material properties, and curing environment. Extract the adjustment parameters of the temperature deviation area, the temperature change sequence of the temperature deviation area during the adjustment process, the temperature change sequence of the adjacent area at the same time, and the temperature change sequence of the adjacent area without temperature adjustment. These are respectively denoted as the temperature change sequence of the deviation area, the temperature change sequence of the adjacent area, and the temperature change sequence of the adjacent area without adjustment. The temperature data difference between the temperature change sequence of the adjacent zone with the temperature change sequence of the adjacent zone without regulation is processed to obtain the temperature change difference sequence of the adjacent zone. Correlation analysis is performed between the temperature change sequence of the deviation zone and the temperature difference sequence of the adjacent zone to determine whether there is a heat conduction phenomenon that has a cascading effect on the adjacent areas.

[0011] Further, determine whether heat conduction occurs: The correlation coefficient between the temperature variation sequence of the deviation zone and the temperature variation difference sequence of the adjacent zone was calculated using the Pearson correlation coefficient method. If the correlation coefficient is greater than or equal to the correlation coefficient threshold, then the temperature change sequence of the deviation area is correlated with the temperature difference sequence of the adjacent area. During the temperature regulation process, there is a heat conduction phenomenon, which has a cascading effect on the adjacent areas.

[0012] Furthermore, the influence coefficient of temperature change in adjacent regions when adjusting the heating rate in the temperature deviation region is extracted: Based on the existence of cascading effects, a spatial distribution analysis of the temperature rise deviation area is conducted to determine whether the temperature rise deviation areas are adjacent. If the temperature rise deviation area is an isolated temperature rise deviation area, obtain the temperature variation sequence of adjacent areas, sum and average the temperature data in the temperature variation sequence of adjacent areas, calculate the single-area influence coefficient of the corresponding temperature rise deviation area on each of its adjacent areas, and average the single-area influence coefficients of each of the adjacent areas to obtain the influence coefficient. If the temperature rise deviation area is a cluster of adjacent temperature rise deviation areas, when adjusting any temperature rise deviation area within the cluster, obtain the single-area influence coefficient of the adjacent area corresponding to the temperature rise deviation area. Then, average the single-area influence coefficients of each adjacent area to obtain the influence coefficient. Similarly, obtain the influence coefficient of each temperature rise deviation area within the cluster on the adjacent area.

[0013] Furthermore, the integration yields the temperature rise influence coefficient matrix: The influence coefficients corresponding to each temperature deviation region are integrated. The integration method is to match the temperature deviation region location number with the influence coefficient corresponding to the temperature deviation region location one by one, so as to obtain the temperature influence coefficient matrix.

[0014] Furthermore, obtain the temperature changes within each temperature rise deviation region: Based on the temperature rise influence coefficient matrix, the influence coefficient of each temperature rise deviation area on other areas is extracted. Combined with the current actual temperature data of each area, the expected temperature value of other areas affected by the associated heat conduction is calculated when the temperature rise deviation area is adjusted.

[0015] Furthermore, the temperature control zone is determined within the temperature rise deviation zone: Compare the calculated expected temperature values ​​for other regions with the current allowable temperature range; If the expected temperature value is greater than the current allowable temperature range, it means that the associated effects will cause abnormal temperatures in other areas, causing the temperatures in other areas to exceed the current allowable temperature range. The corresponding temperature rise deviation area will be marked as the temperature rise control area.

[0016] The beneficial effects of this invention are as follows: 1. This invention divides temperature monitoring areas by combining the geometric dimensions, material properties, and curing environment of prefabricated columns. It uses an orthogonal grid sampling method to collect temperature data from each area, calculates the temperature gradient change between adjacent areas, and filters out areas with temperature deviations. Then, it correlates historical temperature regulation data matching the current scene, extracting the temperature change sequences of the deviation areas, the temperature change sequences of adjacent areas, and the temperature change sequences of adjacent areas without regulation. After calculating the temperature difference sequence between adjacent areas, it combines the Pearson correlation coefficient method to analyze the heat conduction-related effects during temperature regulation. This achieves refined coverage of temperature monitoring and accurate identification of areas with temperature deviations, avoiding analytical biases caused by scene differences, and providing crucial data support for subsequent quantification of temperature influence between areas.

[0017] 2. This invention classifies the spatial distribution of temperature rise deviation areas, extracting temperature influence coefficients on adjacent areas in two cases: isolated areas and adjacent clusters. These coefficients are then integrated to obtain a temperature rise influence coefficient matrix. This matrix, combined with actual temperature data for each area, analyzes the cascading temperature effects of temperature rise regulation on other areas, identifying temperature control areas prone to overheating. Temperature control parameters are dynamically adjusted based on the degree of temperature deviation, transforming the dispersed temperature influence between areas into a systematic quantitative framework. This improves the accuracy and synergy of temperature rise regulation, providing a reliable basis for preventing the spread of abnormal temperatures and ensuring the uniformity and stability of the overall temperature field during the precast column curing temperature rise stage. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of the gradient temperature and humidity control and curing system for prefabricated columns in factory buildings, as described in an embodiment of the present invention. Figure 2 This is a logic diagram of the gradient temperature and humidity control and maintenance system for prefabricated columns in factory buildings, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example

[0021] Please see Figure 1 - Figure 2As shown in the embodiment of the present invention, the gradient temperature and humidity control curing system for precast columns in factory buildings addresses the problem of uneven temperature field in precast columns during curing. This is due to the difficulty in accurately identifying temperature rise deviation areas in temperature monitoring caused by differences in column geometry, material properties, and curing environment. Furthermore, adjusting these deviation areas often overlooks the associated effects of heat conduction, and the adjustment parameters lack synergistic quantitative basis. The system achieves precise control of the curing temperature of precast columns through a process of refined temperature monitoring by region, analysis of the associated effects of heat conduction, quantification of the temperature rise influence coefficient, and synergistic adjustment of temperature rise parameters. Specifically, it includes the following modules: Division and Screening Module: Divide the precast columns into monitoring areas, collect temperature data in real time according to the divided areas, calculate the temperature gradient change between adjacent areas, and screen out areas with temperature rise deviation. Specifically, based on the geometric dimensions (length, cross-sectional shape and size) and material properties of the precast column, as well as the curing environment (close to heating sources such as heaters, steam vents, etc.), the column is divided into several temperature monitoring areas of equal area along its length. The temperature monitoring areas are numbered and sorted sequentially from bottom to top according to the pillars; Based on the obtained temperature monitoring areas, temperature data for each monitoring area is collected in real time using temperature sensors, as follows: The temperature monitoring area is divided into equidistant grids using a grid sampling method. Sampling points are deployed at each grid node. Temperature data of each sampling point is collected within the same timestamp and integrated to obtain the temperature data sequence of each temperature monitoring area. It should be noted that the grid sampling method uses an orthogonal grid division method, and the grid spacing is set according to the column cross-sectional size and the temperature field change gradient to ensure that the sampling density is sufficient to reflect the spatial distribution characteristics of the temperature field. The average temperature value of each temperature monitoring area is obtained by summing and averaging the temperature data within the temperature data sequence. The temperature gradient change between adjacent temperature monitoring areas is then calculated based on the average temperature value of each area. The average temperature between adjacent temperature monitoring areas is processed to obtain the temperature difference, and the temperature gradient change per unit length is calculated by combining the temperature monitoring area spacing. It should be noted that the temperature monitoring zone spacing refers to the straight-line distance between the center points of two adjacent temperature monitoring zones; Compare the temperature gradient change with a temperature gradient change threshold; The temperature gradient change threshold is set comprehensively based on key factors such as the geometric dimensions, material properties, and curing environment of the precast columns. It is used to distinguish between normal temperature gradient changes and abnormal temperature gradient changes that may cause temperature rise deviations. If the temperature gradient change is greater than or equal to the temperature gradient change threshold, it indicates that there is an abnormal temperature rise trend between adjacent temperature monitoring areas, and it is identified as a temperature rise deviation area. If the temperature gradient change is less than the temperature gradient change threshold, it indicates that there is no abnormal heating trend between adjacent temperature monitoring areas, and the area is judged to be a temperature uniform area. It should be noted that this embodiment analyzes the temperature rise deviation when the temperature rise does not reach the target temperature of the area during temperature gradient change. For example, if the target temperature of the temperature monitoring area is 60°C and the current temperature is 45°C, it is in the temperature rise stage. If the temperature of the current temperature monitoring area is lower than that of the adjacent temperature monitoring area and the temperature gradient change is greater than or equal to the temperature gradient change threshold, then the area is determined to be a temperature rise deviation area. Understandably, the purpose of this module is to achieve refined regional coverage of columns with different structural features and under different maintenance conditions by dividing temperature monitoring areas of equal size according to the column geometry, material properties, and curing environment. This avoids temperature monitoring blind spots caused by factors such as differences in column length or local proximity to heating sources. By deploying sampling points using an orthogonal grid sampling method that adapts to the cross-sectional size and temperature field gradient, the sampling density can accurately reflect the spatial distribution characteristics of the temperature field in each area. The temperature gradient change is calculated based on the distance between the center points of adjacent areas, and areas with abnormal heating trends are identified for the stage where the temperature rise has not reached the target temperature, providing a data foundation for subsequent analysis of the associated effects of heat conduction. Analysis and correlation module: Based on the identified temperature rise deviation area, combined with the temperature regulation data of the historical temperature rise deviation area in the historical temperature regulation database, it analyzes whether there is heat conduction during the temperature regulation process of the temperature rise deviation area, which may lead to a cascading effect on adjacent areas. Specifically, the adjustment records of the temperature deviation area that are consistent with the current geometric dimensions, material properties and curing environment of the precast column are obtained from the historical temperature adjustment database. The adjustment parameters of the temperature deviation area are extracted, the temperature change sequence of the temperature deviation area during the adjustment process, the temperature change sequence of the adjacent area at the same time, and the temperature change sequence of the adjacent area without temperature adjustment are obtained. These are respectively recorded as the temperature change sequence of the deviation area, the temperature change sequence of the adjacent area, and the temperature change sequence of the adjacent area without adjustment. It should be noted that the historical temperature regulation database was established through long-term temperature regulation experiments and actual curing operations on precast columns with different geometric dimensions, material properties, and curing environments. The database records detailed parameters (such as regulation method, regulation time, regulation intensity, etc.), temperature change data of the temperature rise deviation area and adjacent areas for each regulation. The adjacent area unregulated temperature change sequence refers to the temperature change data sequence of adjacent areas within the same time period when no temperature regulation was performed on the temperature rise deviation area. It is used to compare and analyze with the adjacent area regulated temperature change sequence to accurately determine whether heat conduction occurs during the temperature regulation process. It should also be noted that the methods for obtaining the temperature change sequence of the deviation zone, the temperature change sequence of the adjacent zone, and the temperature change sequence of the adjacent zone without adjustment are as follows: It is necessary to extract the historical temperature monitoring records of precast columns of the same specifications and under the same curing environment in the same temperature deviation zone from the historical temperature adjustment data, as well as the temperature monitoring data of the same area adjacent to the temperature deviation zone that is not affected by the temperature deviation adjustment. The temperature data difference between the temperature change sequence of the adjacent zone with the temperature change sequence of the adjacent zone without regulation is processed to obtain the temperature change difference sequence of the adjacent zone. Correlation analysis is performed between the temperature change sequence in the deviation zone and the temperature difference sequence in the adjacent zone to determine whether heat conduction occurs and has a cascading effect on adjacent areas. The process is as follows: The correlation coefficient between the temperature variation sequence of the deviation zone and the temperature variation difference sequence of the adjacent zone was calculated using the Pearson correlation coefficient method. Compare the correlation coefficient with the correlation coefficient threshold; Among them, the correlation coefficient threshold can be obtained by classifying, statistically analyzing, and calibrating the heat conduction verification data of different prefabricated column geometric dimensions, material properties and curing environments in the historical temperature regulation database. This ensures that the existence of heat conduction-related effects can be distinguished and the correlation determination needs of temperature regulation and temperature changes in adjacent areas under different working conditions can be matched. If the correlation coefficient is greater than or equal to the correlation coefficient threshold, it indicates that there is a correlation between the temperature change sequence of the deviation area and the temperature difference sequence of the adjacent area, and it is determined that there is a heat conduction phenomenon in the temperature regulation process, which has a cascading effect on the adjacent areas. If the correlation coefficient is less than the correlation coefficient threshold, it indicates that the correlation between the temperature change sequence of the deviation area and the temperature difference sequence of the adjacent area is weak, and it is determined that there is no heat conduction phenomenon that has a cascading effect on the adjacent areas during the temperature regulation process. Understandably, the purpose of this module is to establish a correspondence between the adjustment scenario and historical data by associating historical temperature adjustment records that are consistent with the current precast column geometry, material properties, and curing environment, thus avoiding analytical biases caused by scenario differences. By using the temperature difference sequence between adjacent areas under adjustment and without adjustment, combined with the Pearson correlation coefficient method, quantitative correlation analysis is performed to determine whether the heat conduction-related effects exist during the adjustment process. This avoids blind adjustment actions due to neglecting heat conduction, which could lead to new temperature unevenness problems. It also provides key judgment results and quantitative data support for subsequent modules to construct the influence coefficient matrix of temperature adjustment. Example

[0022] like Figure 1 - Figure 2 As shown in the embodiment of the present invention, the gradient temperature and humidity control curing system for precast columns in factory buildings includes the following modules: Statistical integration module: If there are cascading effects, extract the influence coefficients of the temperature change of adjacent areas when adjusting the heating rate in the heating deviation area, count all the influence coefficients with cascading effects in the heating deviation area, and integrate them to obtain the heating influence coefficient matrix. Specifically, based on the existence of cascading effects, a spatial distribution analysis is conducted on the temperature rise deviation areas to determine whether the temperature rise deviation areas are adjacent. It should be noted that the adjacency relationship of the temperature rise deviation regions refers to two or more temperature rise deviation regions being directly connected in physical space, wherein the connection method is a vertical connection. Based on the spatial distribution analysis results, the temperature rise deviation areas are divided into two categories: isolated temperature rise deviation areas and clusters of adjacent temperature rise deviation areas. If the temperature rise deviation area is an isolated temperature rise deviation area, obtain the temperature variation sequence of adjacent areas, sum and average the temperature data in the temperature variation sequence of adjacent areas, calculate the single-area influence coefficient of the corresponding temperature rise deviation area on each of its adjacent areas, and average the single-area influence coefficients of each of the adjacent areas to obtain the influence coefficient. If the temperature deviation area is a cluster of adjacent temperature deviation areas, when adjusting any temperature deviation area in the cluster, the single-area influence coefficient of the adjacent area corresponding to the temperature deviation area is obtained. The single-area influence coefficients of each adjacent area are averaged to obtain the influence coefficient. Similarly, the influence coefficient of each temperature deviation area in the cluster on the adjacent area is obtained. It should be noted that the influence coefficient refers to the quantitative value of the degree of influence of the temperature change in the temperature deviation area on the temperature change of the adjacent area when the temperature rise rate is adjusted. It reflects the intensity of the disturbance of the temperature of the adjacent area by the energy transfer during the heat conduction process. The influence coefficients corresponding to each temperature deviation region are integrated. The integration method is to match the temperature deviation region location number with the influence coefficient corresponding to the temperature deviation region location one by one to obtain the temperature influence coefficient matrix. It should be noted that the function of this module is to analyze the spatial distribution of the temperature rise deviation area and distinguish between isolated areas and adjacent clusters, obtain the influence coefficient extraction scenarios under different spatial forms, extract and optimize the influence coefficients for different types of deviation areas, and integrate them into a temperature rise influence coefficient matrix. This transforms the scattered regional influence relationships into a systematic quantitative data system, presents the influence intensity of each deviation area on the surrounding area, and provides a quantitative basis for the subsequent formulation of coordinated adjustment strategies. Extraction and adjustment module: Within the heating influence coefficient matrix, obtain the temperature change in each heating deviation region, determine the heating control region within the heating deviation region, and adjust the heating control parameters according to the degree of temperature deviation in the regions adjacent to the heating control region. Specifically, based on the temperature rise influence coefficient matrix, the influence coefficient of each temperature rise deviation area on other areas (including adjacent normal temperature areas and other temperature rise deviation areas) is extracted. Combined with the current actual temperature data of each area, the expected temperature value of other areas affected by the associated heat conduction is calculated when the temperature rise deviation area is adjusted. It should be noted that the calculation of the expected temperature value is based on the influence coefficient. The larger the influence coefficient, the more significant the impact of the temperature rise adjustment in the temperature rise deviation area on the associated temperature of the corresponding area. The expected temperature value is obtained by adding the actual current temperature of other areas. Based on the material thermal stability requirements of the precast columns and the temperature difference control standards during the curing stage (such as the maximum allowable temperature difference between the inside and outside of the column and between areas during the heating stage), the allowable temperature range for each area at the current stage is set. Compare the calculated expected temperature values ​​for other regions with the current allowable temperature range; If the expected temperature value is less than or equal to the current allowable temperature range, it means that when adjusting the temperature in the corresponding temperature deviation area, the associated effects will not cause the temperature difference in other areas to exceed the standard. If the expected temperature value is greater than the current allowable temperature range, it means that the ripple effect will cause abnormal temperatures in other areas, causing the temperatures in other areas to exceed the current allowable temperature range. The corresponding temperature rise deviation area will be marked as the temperature rise control area. The temperature deviation of the heating control area is analyzed, and the difference between the expected temperature value and the current target temperature is processed to obtain the temperature deviation value of the corresponding area. Adjust the temperature control parameters of the temperature rise control zone according to the degree of temperature deviation: Based on the temperature deviation value and the heating influence coefficient matrix, the adjustment amount of the heating control parameters is calculated; Among them, the heating control parameters include core indicators such as heating power and heating rate. The adjustment amount is positively correlated with the temperature deviation value. That is, the greater the deviation, the greater the adjustment range of the corresponding parameter. At the same time, the adjustment amount needs to be dynamically corrected in combination with the influence coefficient of each heating deviation area on the adjacent area. For example, the adjustment amount of the temperature control parameter is calculated by dividing the temperature deviation value by the influence coefficient to obtain the deviation ratio value, and then multiplying the deviation ratio value by the current adjustment parameter to obtain the adjustment amount of the temperature control parameter. Adjust the parameters of the heating control area according to the adjustment amount of the heating control parameters to ensure that the cascading effect of the heating control area on the adjacent areas is within a controllable range during the subsequent heating adjustment process. Understandably, the function of this module is to predict the cascading temperature effects of temperature regulation on other areas by combining the temperature rise influence coefficient matrix with the actual temperature data of each area, identify the temperature rise control areas that may cause overheating in the surrounding areas, and dynamically adjust the temperature rise control parameters based on the degree of temperature deviation. This achieves refined and coordinated control of the temperature rise regulation process, avoids abnormal temperature diffusion caused by blind adjustment, and ensures the uniformity and stability of the overall temperature field of the precast columns.

[0023] The technical solution of this invention is as follows: This invention divides temperature monitoring zones by combining the geometric dimensions, material properties, and curing environment of prefabricated columns. It uses an orthogonal grid sampling method to collect temperature data from each zone, calculates the temperature gradient change between adjacent zones, and identifies areas with temperature rise deviations. Then, it correlates this with historical temperature regulation data matching the current scenario, extracting the regulated temperature change sequence of the deviation zone, the regulated temperature change sequence of adjacent zones, and the unregulated temperature change sequence of adjacent zones. After calculating the temperature difference sequence between adjacent zones, it uses the Pearson correlation coefficient method to analyze the heat conduction-related effects during temperature regulation. This achieves refined temperature monitoring coverage and accurate identification of temperature rise deviation areas, avoiding analytical biases caused by scenario differences. It also provides a basis for subsequent quantification of inter-regional temperature variations. The temperature deviation analysis provides crucial data support. By spatially classifying the temperature deviation areas, the temperature influence coefficients on adjacent areas are extracted for isolated areas and adjacent clusters, and integrated to obtain a temperature influence coefficient matrix. Then, by combining this matrix with the actual temperature data of each area, the cascading temperature effects of temperature regulation on other areas are analyzed, and temperature control areas prone to overheating are screened out. The temperature control parameters are dynamically adjusted based on the degree of temperature deviation, transforming the dispersed temperature effects between areas into a systematic quantitative framework. This improves the accuracy and coordination of temperature regulation, provides a reliable basis for avoiding the spread of abnormal temperatures, and ensures the uniformity and stability of the overall temperature field during the precast column curing temperature rise stage.

[0024] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A gradient temperature and humidity control curing system for precast columns in factory buildings, characterized in that: Includes the following modules: Division and Screening Module: Divide the precast columns into monitoring areas, collect temperature data in real time according to the divided areas, calculate the temperature gradient change between adjacent areas, and screen out areas with temperature rise deviation. Analysis and correlation module: Based on the identified temperature rise deviation area, combined with the temperature regulation data of the historical temperature rise deviation area in the historical temperature regulation database, it analyzes whether there is heat conduction during the temperature regulation of the temperature rise deviation area, which may lead to a cascading effect on adjacent areas. Statistical integration module: If there are cascading effects, extract the influence coefficients of the temperature change of adjacent areas when adjusting the heating rate in the heating deviation area, count all the influence coefficients with cascading effects in the heating deviation area, and integrate them to obtain the heating influence coefficient matrix. Extraction and Adjustment Module: Within the heating influence coefficient matrix, obtain the temperature changes in each heating deviation region, determine the heating control region within the heating deviation region, and adjust the heating control parameters according to the degree of temperature deviation in the regions adjacent to the heating control region.

2. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Collect temperature data: The column is divided into several temperature monitoring areas of equal area along its length, and the temperature monitoring areas are numbered and sorted sequentially from bottom to top of the column. The temperature monitoring area is divided into equidistant grids using a grid sampling method. Sampling points are deployed at each grid node, and temperature data of each sampling point is collected within the same timestamp. The data are then integrated to obtain the temperature data sequence of each temperature monitoring area.

3. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Calculate the temperature gradient change between adjacent regions: The average temperature value of each temperature monitoring area is obtained by summing and averaging the temperature data within the temperature data sequence. The temperature gradient change between adjacent temperature monitoring areas is then calculated based on the average temperature value of each area. The average temperature values ​​between adjacent temperature monitoring areas are processed to obtain the temperature difference value, and the temperature gradient change per unit length is calculated by combining the distance between the temperature monitoring areas.

4. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Filter out the temperature rise deviation area: Compare the temperature gradient change with a temperature gradient change threshold; If the temperature gradient change is greater than or equal to the temperature gradient change threshold, then there is an abnormal temperature rise trend between adjacent temperature monitoring areas, which is determined to be a temperature rise deviation area.

5. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Analyze whether heat conduction occurs during temperature adjustment of the temperature rise deviation region: Retrieve the temperature deviation area adjustment records from the historical temperature adjustment database that are consistent with the current precast column geometry, material properties, and curing environment. Extract the adjustment parameters of the temperature deviation area, the temperature change sequence of the temperature deviation area during the adjustment process, the temperature change sequence of the adjacent area at the same time, and the temperature change sequence of the adjacent area without temperature adjustment. These are respectively denoted as the temperature change sequence of the deviation area, the temperature change sequence of the adjacent area, and the temperature change sequence of the adjacent area without adjustment. The temperature data difference between the temperature change sequence of the adjacent zone with the temperature change sequence of the adjacent zone without regulation is processed to obtain the temperature change difference sequence of the adjacent zone. Correlation analysis is performed between the temperature change sequence of the deviation zone and the temperature difference sequence of the adjacent zone to determine whether there is a heat conduction phenomenon that has a cascading effect on the adjacent areas.

6. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 5, characterized in that: Determine if heat conduction is present: The correlation coefficient between the temperature variation sequence of the deviation zone and the temperature variation difference sequence of the adjacent zone was calculated using the Pearson correlation coefficient method. If the correlation coefficient is greater than or equal to the correlation coefficient threshold, then the temperature change sequence of the deviation area is correlated with the temperature difference sequence of the adjacent area. During the temperature regulation process, there is a heat conduction phenomenon, which has a cascading effect on the adjacent areas.

7. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: The influence coefficient of temperature change in adjacent regions when adjusting the heating rate in the region of heating deviation is extracted: Based on the existence of cascading effects, a spatial distribution analysis of the temperature rise deviation area is conducted to determine whether the temperature rise deviation areas are adjacent. If the temperature rise deviation area is an isolated temperature rise deviation area, obtain the temperature variation sequence of adjacent areas, sum and average the temperature data in the temperature variation sequence of adjacent areas, calculate the single-area influence coefficient of the corresponding temperature rise deviation area on each of its adjacent areas, and average the single-area influence coefficients of each of the adjacent areas to obtain the influence coefficient. If the temperature rise deviation area is a cluster of adjacent temperature rise deviation areas, when adjusting any temperature rise deviation area within the cluster, obtain the single-area influence coefficient of the adjacent area corresponding to the temperature rise deviation area. Then, average the single-area influence coefficients of each adjacent area to obtain the influence coefficient. Similarly, obtain the influence coefficient of each temperature rise deviation area within the cluster on the adjacent area.

8. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: The resulting matrix of temperature rise influence coefficients is obtained through integration: The influence coefficients corresponding to each temperature deviation region are integrated. The integration method is to match the temperature deviation region location number with the influence coefficient corresponding to the temperature deviation region location one by one, so as to obtain the temperature influence coefficient matrix.

9. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Obtain the temperature changes within each temperature rise deviation range: Based on the temperature rise influence coefficient matrix, the influence coefficient of each temperature rise deviation area on other areas is extracted. Combined with the current actual temperature data of each area, the expected temperature value of other areas affected by the associated heat conduction is calculated when the temperature rise deviation area is adjusted.

10. The gradient temperature and humidity control curing system for precast columns in factory buildings according to claim 1, characterized in that: Determine the temperature control zone within the temperature rise deviation zone: Compare the calculated expected temperature values ​​for other regions with the current allowable temperature range; If the expected temperature value is greater than the current allowable temperature range, it means that the associated effects will cause abnormal temperatures in other areas, causing the temperatures in other areas to exceed the current allowable temperature range. The corresponding temperature rise deviation area will be marked as the temperature rise control area.

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