A concrete grouting temperature shrinkage synergic control method and system

Abstract

This invention relates to the field of concrete grouting technology, and discloses a method and system for coordinated control of temperature shrinkage in concrete grouting. The method involves receiving temperature control trigger signals from temperature monitoring nodes, extracting parameters and calibrating boundaries from pre-acquired global temperature control parameters of the grouting structure to construct a temperature control constraint benchmark library, extracting data and performing spatiotemporal correlation processing on pre-acquired zoned temperature control monitoring data to obtain candidate temperature control parameters and source location information, matching parameters and verifying deviations based on the source location information corresponding to the target area and the temperature control constraint benchmark library, obtaining deviation verification results, quantifying the deviation verification results to obtain a temperature control coordination index, and executing a temperature shrinkage coordinated control task based on the temperature control constraint benchmark library, the target area, candidate temperature control parameters, and deviation verification results to achieve precise triggering and adaptive regulation of the temperature shrinkage coordinated control task.

Description

Technical Field

[0001] This invention relates to the field of concrete grouting technology, and more specifically, to a method and system for synergistic control of temperature shrinkage in concrete grouting. Background Technology

[0002] Temperature shrinkage synergistic control of concrete grouting refers to a technical means that, during the grouting process of large-volume concrete, by monitoring changes in the temperature field and stress field and adjusting cooling and insulation measures, shrinkage deformation and stress release caused by temperature gradient are kept in synergy in time and space, thereby suppressing structural cracks. It is widely used in projects such as dams, nuclear power plant foundations and large bridge abutments.

[0003] Existing temperature control technologies mostly employ single-point temperature threshold alarms or fixed-zone empirical control schemes. Temperature data is collected by embedded thermometers and compared with preset empirical thresholds to trigger the on / off of cooling water or the application of insulation. However, existing technologies fail to establish a structured constraint benchmark library for temperature control parameters across the entire grouting structure, resulting in a lack of unified calibration and dynamic correlation of constraint conditions across different zones. Furthermore, zone monitoring data lacks spatiotemporal correlation processing, node data is isolated, and it is difficult to identify cross-zone heat conduction and gradient accumulation effects. Simultaneously, deviation verification and control execution are disconnected; multi-dimensional collaborative quantification of temperature, temperature difference, temperature change rate, and temperature stress coupling is not performed, causing the control strategy to fail to adaptively adjust according to the structural temperature control stage. This easily leads to localized overcooling or insufficient insulation, affecting the crack resistance and structural durability of large-volume concrete. Summary of the Invention

[0004] To address the technical problem of mismatched control strategies caused by the lack of a global constraint benchmark and multi-dimensional collaborative quantification in existing concrete grouting temperature control, this application aims to provide a method and system for collaborative control of concrete grouting temperature shrinkage. By constructing a temperature control constraint benchmark library and regional spatiotemporal correlation monitoring, combined with multi-dimensional parameter constraint matching and temperature control collaborative quantification, the method achieves precise triggering and adaptive control of temperature shrinkage collaborative control tasks.

[0005] To achieve the above objectives, the present invention provides a method for synergistic control of temperature shrinkage in concrete grouting, comprising: In response to receiving a temperature control trigger signal from a temperature control monitoring node, the pre-acquired temperature control parameters of the entire grouting structure are extracted and the boundary calibration is performed to construct a temperature control constraint benchmark library. Based on the preset target areas, the pre-acquired zone temperature control monitoring data is extracted and spatiotemporally correlated to obtain each candidate temperature control parameter and each source location information; For each target region, based on the source location information corresponding to the target region and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target region to obtain the deviation verification result; The temperature control synergy index is obtained by quantifying the various deviation verification results. In response to determining that the temperature control coordination index is greater than the preset coordination threshold, the temperature control adjustment unit is controlled to perform a temperature contraction coordination control task based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results.

[0006] Furthermore, in response to receiving a temperature control trigger signal from the temperature control monitoring node, parameter extraction and boundary calibration are performed on the pre-acquired global temperature control parameters of the grouting structure to construct a temperature control constraint benchmark library, including: The temperature control trigger signal sent by the temperature control monitoring node is analyzed, and the trigger type identifier is identified. The trigger type identifier includes at least single-point temperature over-limit trigger, adjacent measuring point temperature difference over-limit trigger, and abnormal temperature change rate trigger. Extract structural geometric parameters, which include at least the location of dam segment joints, the geometric dimensions of the dam surface, and the sequence of cross-sectional thicknesses. Extract thermodynamic physical property parameters, which include at least the thermal conductivity, specific heat capacity, cumulative heat release curve of hydration, and coefficient of linear expansion of concrete. Extract environmental boundary parameters, which include at least the time-series values ​​of ambient temperature, solar radiation intensity, and cooling water inlet temperature.

[0007] Furthermore, it also includes: The structural geometric parameters are partitioned and the boundaries are calibrated. The spatial coordinate envelope range of each temperature control sub-zone is determined based on the temperature control compartment diagram. The thermodynamic physical properties are calibrated for time-varying boundaries, and the effective range of parameters under different age windows is determined based on the mapping relationship between grouting age and hydration heat release stage. The environmental boundary parameters are dynamically calibrated, and the floating range of environmental constraints is determined based on seasonal operating conditions and extreme weather forecasts. The calibrated structural geometric parameters, thermodynamic physical property parameters, and environmental boundary parameters are structured and indexed according to the temperature control sub-region identifier and age window identifier to generate the temperature control constraint benchmark library. The temperature control constraint benchmark library includes at least the upper temperature threshold set, the lower temperature threshold set, the allowable temperature difference gradient set, and the allowable temperature change rate set.

[0008] Furthermore, based on the preset target areas, the pre-acquired zone temperature control monitoring data is extracted and subjected to spatiotemporal correlation processing, including: Based on the compartmentalization diagram of the grouting structure and the temperature control sub-zone division scheme, each target area is preset, and each target area corresponds to a temperature control sub-zone identifier and spatial coordinate range; From the temperature control monitoring data of the partitions, extract the monitoring node data that intersects with the spatial coordinate range of each target area; The monitoring node data corresponding to each target area are correlated in time dimension, and the collection timestamps of each monitoring node are aligned with a unified time synchronization benchmark to establish a time-series aligned data frame of multi-source monitoring nodes at the same time. Spatial dimension correlation is performed on the monitoring node data corresponding to each target area. The spatial adjacency matrix is ​​calculated based on the layout coordinates of each monitoring node to identify directly adjacent nodes and cross-regional boundary nodes within the same target area.

[0009] Furthermore, the candidate temperature control parameters and the location information of each source are obtained, including: Extract the candidate temperature control parameters from the time-aligned monitoring node data; The location information from each source is extracted from the equipment ledger of each monitoring node.

[0010] Furthermore, based on the source location information corresponding to the target area and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target area to obtain deviation verification results, including: Based on the target area identifier and sensor type label in the source positioning information corresponding to the target area, retrieve the matching target area constraint entry from the temperature control constraint benchmark library; Match the candidate temperature control parameters corresponding to the target area with the target area constraint entries item by item; For candidate temperature control parameters that exceed the corresponding threshold after matching, deviation verification processing is performed, and the absolute value of temperature deviation, absolute value of temperature gradient deviation, and absolute value of temperature change rate deviation are calculated. Based on the ratio of the absolute value of the temperature deviation to the corresponding temperature threshold and the ratio of the absolute value of the temperature gradient deviation to the corresponding temperature gradient threshold, a deviation level identifier is determined. The deviation level identifier includes at least a normal level, a warning level, and an over-limit level. The deviation level identifier, the corresponding absolute value of the deviation, the deviation direction identifier, and the associated target area identifier are combined to form the deviation verification result.

[0011] Furthermore, the obtained deviation verification results are subjected to temperature control synergy quantification to obtain the temperature control synergy index, including: The deviation level identifier and comprehensive deviation characterization are extracted from each deviation verification result, and the spatial distribution statistics are performed according to the target area. The deviation level difference between each target area is calculated. The deviation level difference is obtained by comparing the dispersion of the deviation level identifiers of adjacent target areas. Based on the comprehensive deviation characterization of each target region, the coefficient of variation of the global deviation distribution is calculated, and the reciprocal of the coefficient of variation is mapped to the spatial equilibrium cooperative component. The deviation level of each target area is analyzed from the time-series dimension. When the deviation level shows a convergence trend in adjacent monitoring periods, a time-series convergence co-co-component is generated. When it shows a divergence trend, a time-series divergence suppression component is generated. Based on the temperature stress coupling constraint entries in the temperature control constraint benchmark library, the temperature stress coupling index of each target region is extracted, and the closeness between the temperature stress coupling index and the corresponding constraint threshold is mapped to the parameter coupling cooperative component. The spatial equilibrium collaborative component, the temporal convergence collaborative component or the temporal divergence suppression component, and the parameter coupling collaborative component are adaptively weighted and fused according to the current temperature control stage of the grouting structure to generate the temperature control collaborative index.

[0012] Further, in response to determining that the temperature control coordination index is greater than a preset coordination threshold, the following steps are taken: In response to determining that the temperature control coordination index is greater than the preset coordination threshold, a temperature control adjustment command generation process is triggered. The preset coordination threshold is dynamically set according to the grouting structure section thickness, ambient temperature level, and temperature control stage.

[0013] Furthermore, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results, the temperature control adjustment unit is controlled to perform a temperature contraction coordinated control task, including: Based on the deviation level and deviation direction identifiers in the various deviation verification results, the corresponding control strategy template is retrieved from the temperature control constraint benchmark library; Based on the spatial location of each target area and the gradient anomaly direction in the deviation verification result, the spatial action object sequence of the temperature control unit is determined, and the control is preferentially applied to the target area with the deviation level exceeding the limit and located in the center of the hot zone. Based on the node temperature value and node temperature change rate in each of the candidate temperature control parameters, calculate the quantitative value of the control intensity of the temperature control unit; Send a collaborative control command containing the spatial action object sequence, the control strategy template, and the control intensity quantification value to the temperature control unit to control the temperature control unit to execute the temperature contraction collaborative control task.

[0014] To achieve the above objectives, the present invention also provides a concrete grouting temperature shrinkage coordinated control system, comprising: The parameter extraction module is used to extract parameters and perform boundary calibration processing on the pre-acquired temperature control parameters of the entire grouting structure in response to the temperature control trigger signal sent by the temperature control monitoring node, so as to build a temperature control constraint benchmark library. The parameter processing module is used to extract and perform spatiotemporal correlation processing on the pre-acquired zone temperature control monitoring data based on each preset target area to obtain each candidate temperature control parameter and each source location information; The deviation verification module is used to perform parameter constraint matching and deviation verification processing on the candidate temperature control parameters corresponding to each of the target regions, based on the source positioning information corresponding to the target region and the temperature control constraint benchmark library, to obtain the deviation verification result. The index determination module is used to perform temperature control coordination quantification on the obtained deviation verification results to obtain the temperature control coordination index. The temperature control module is used to control the temperature control adjustment unit to perform a temperature contraction coordinated control task in response to determining that the temperature control coordination index is greater than a preset coordination threshold, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a method and system for coordinated control of temperature shrinkage in concrete grouting. The method receives temperature control trigger signals from temperature monitoring nodes, extracts and calibrates pre-acquired temperature control parameters across the entire grouting structure, and constructs a temperature control constraint benchmark library. It then extracts and performs spatiotemporal correlation processing on pre-acquired zoned temperature control monitoring data to obtain candidate temperature control parameters and source location information. Based on the source location information corresponding to the target area and the temperature control constraint benchmark library, it performs parameter constraint matching and deviation verification on the candidate temperature control parameters to obtain deviation verification results. Finally, it performs temperature control coordination quantification on the deviation verification results to obtain a temperature control coordination index. Based on the temperature control constraint benchmark library, the target area, candidate temperature control parameters, and deviation verification results, it executes a temperature shrinkage coordinated control task, achieving precise triggering and adaptive regulation of the temperature shrinkage coordinated control task. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for coordinated control of temperature shrinkage in concrete grouting according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of a concrete grouting temperature shrinkage synergistic control system according to an embodiment of the present invention is shown. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a method for synergistic control of temperature shrinkage in concrete grouting, comprising: S110: In response to receiving the temperature control trigger signal sent by the temperature control monitoring node, extract parameters and perform boundary calibration processing on the pre-acquired temperature control parameters of the entire grouting structure to construct a temperature control constraint benchmark library; In some embodiments of this application, in response to receiving a temperature control trigger signal sent by a temperature control monitoring node, parameter extraction and boundary calibration processing are performed on pre-acquired global temperature control parameters of the grouting structure to construct a temperature control constraint benchmark library, including: The temperature control trigger signal sent by the temperature control monitoring node is analyzed, and the trigger type identifier is identified. The trigger type identifier includes at least single-point temperature over-limit trigger, adjacent measuring point temperature difference over-limit trigger, and abnormal temperature change rate trigger. Extract structural geometric parameters, which include at least the location of dam segment joints, the geometric dimensions of the dam surface, and the sequence of cross-sectional thicknesses. Extract thermodynamic physical property parameters, which include at least the thermal conductivity, specific heat capacity, cumulative heat release curve of hydration, and coefficient of linear expansion of concrete. Extract environmental boundary parameters, which include at least the time-series values ​​of ambient temperature, solar radiation intensity, and cooling water inlet temperature.

[0023] In some embodiments of this application, it also includes: The structural geometric parameters are partitioned and the boundaries are calibrated. The spatial coordinate envelope range of each temperature control sub-zone is determined based on the temperature control compartment diagram. The thermodynamic physical properties are calibrated for time-varying boundaries, and the effective range of parameters under different age windows is determined based on the mapping relationship between grouting age and hydration heat release stage. The environmental boundary parameters are dynamically calibrated, and the floating range of environmental constraints is determined based on seasonal operating conditions and extreme weather forecasts. The calibrated structural geometric parameters, thermodynamic physical property parameters, and environmental boundary parameters are structured and indexed according to the temperature control sub-region identifier and age window identifier to generate the temperature control constraint benchmark library. The temperature control constraint benchmark library includes at least the upper temperature threshold set, the lower temperature threshold set, the allowable temperature difference gradient set, and the allowable temperature change rate set.

[0024] Specifically, the temperature control monitoring nodes are platinum resistance temperature sensors embedded inside the concrete. A temperature control trigger signal is generated when the temperature at any node exceeds a single-point temperature threshold (this threshold is set based on the concrete's design age strength and crack resistance safety factor, typically the initial temperature plus 25 degrees Celsius; for example, if the initial temperature is 15 degrees Celsius, the single-point threshold is 40 degrees Celsius), the temperature difference between two adjacent measuring points exceeds 8 degrees Celsius, or the node's temperature change rate exceeds 2 degrees Celsius per hour. The temperature control trigger signal is analyzed to identify the trigger type: 01 indicates a single-point temperature exceeding the limit trigger, 02 indicates an adjacent measuring point temperature difference exceeding the limit trigger, and 03 indicates an abnormal temperature change rate trigger. Structural geometric parameters are extracted from the grouting structure design documents: the location of the dam section's compartment joints (coordinate accuracy 0.01 meters), the geometric dimensions of the compartment surface (length × width, in meters), and the cross-sectional thickness sequence (thickness values ​​every 0.5 meters along the height direction, in meters). Thermodynamic properties of concrete were extracted from the materials testing database: thermal conductivity of 2.33 W / m Kelvin, specific heat capacity of 0.96 kJ / kg Celsius, cumulative heat release curve of hydration (approximately 250 kJ / kg cement over 7 days and approximately 300 kJ / kg cement over 28 days), and coefficient of linear expansion of 1.0 × 10⁻⁶. -5 Per degree Celsius. Environmental boundary parameters are extracted from the environmental monitoring interface (weather station and cooling water station PLC): ambient temperature time series value (once per hour, unit: degree Celsius), solar radiation intensity (range 0 to 1200 W / m², accuracy 5%), and cooling water inlet temperature (unit: degree Celsius, accuracy 0.5 degrees Celsius). Structural geometric parameters are zonal boundary calibration: the grouting structure is divided into several temperature-controlled sub-zones according to the temperature control compartment diagram, each sub-zone corresponding to a spatial coordinate envelope range (minimum bounding rectangle of three-dimensional coordinates, coordinate unit: meter). Thermodynamic property parameters are time-varying boundary calibration: based on the mapping relationship between grouting age and hydration heat release stage, the 0 to 72 hour age window is set as the peak temperature rise period (accumulated hydration heat release accounts for approximately 60%), and the 72 hour to 28 day cooling stabilization period (accumulated hydration heat release accounts for approximately 40%). Thermal conductivity and specific heat capacity are dynamically determined according to the experimental fitting curve under different age windows. Dynamic boundary calibration of environmental boundary parameters: Environmental constraint fluctuation ranges are set based on seasonal operating conditions (summer / winter) and extreme weather forecasts (maximum temperature and sunshine hours in the next 24 hours). For example, the upper limit of summer environmental temperature is the historical average for the same period plus 3 degrees Celsius, and the lower limit in winter is the historical average for the same period minus 3 degrees Celsius. The calibrated structural geometric parameters, thermodynamic properties, and environmental boundary parameters are structured and indexed according to temperature control sub-region identifiers and age window identifiers to generate a temperature control constraint benchmark library. This library includes at least a set of upper temperature thresholds (a two-dimensional matrix of sub-region and age, in degrees Celsius), a set of lower temperature thresholds (same as above), a set of allowable temperature gradients (8 degrees Celsius horizontally and 5 degrees Celsius vertically), and a set of allowable temperature change rates (2 degrees Celsius per hour during peak temperature rise and 1 degree Celsius per hour during stable temperature drop, set based on temperature control experience).

[0025] This application's solution uses trigger signal-driven global parameter extraction and three-dimensional boundary calibration to uniformly encode structural geometry, material thermodynamics, and environmental meteorology into a structured constraint benchmark library. This solves the problem that traditional temperature control relies on empirical thresholds and lacks correlation with structural zoning, material time-varying properties, and dynamic environmental boundaries, thus providing a traceable and dynamically adjustable quantitative basis for subsequent deviation verification.

[0026] S120: Based on each preset target area, perform data extraction and spatiotemporal correlation processing on the pre-acquired zone temperature control monitoring data to obtain each candidate temperature control parameter and each source location information; In some embodiments of this application, based on preset target areas, data extraction and spatiotemporal correlation processing are performed on pre-acquired zone temperature control monitoring data, including: Based on the compartmentalization diagram of the grouting structure and the temperature control sub-zone division scheme, each target area is preset, and each target area corresponds to a temperature control sub-zone identifier and spatial coordinate range; From the temperature control monitoring data of the partitions, extract the monitoring node data that intersects with the spatial coordinate range of each target area; The monitoring node data corresponding to each target area are correlated in time dimension, and the collection timestamps of each monitoring node are aligned with a unified time synchronization benchmark to establish a time-series aligned data frame of multi-source monitoring nodes at the same time. Spatial dimension correlation is performed on the monitoring node data corresponding to each target area. The spatial adjacency matrix is ​​calculated based on the layout coordinates of each monitoring node to identify directly adjacent nodes and cross-regional boundary nodes within the same target area.

[0027] In some embodiments of this application, various candidate temperature control parameters and various source location information are obtained, including: Extract the candidate temperature control parameters from the time-aligned monitoring node data; The location information from each source is extracted from the equipment ledger of each monitoring node.

[0028] Specifically, the monitoring node data includes at least temperature measurements, stress measurements, cooling water flow measurements, and acquisition timestamps; the candidate temperature control parameters include at least node temperature values, horizontal temperature differences between nodes, vertical temperature differences between nodes, node temperature change rate, and node temperature-stress coupling index; the source location information includes at least the sensor's unique code, its three-dimensional coordinates, the target area identifier, and the sensor type label.

[0029] Specifically, based on the compartmentalization diagram of the grouting structure and the temperature control sub-zone division scheme, each target area is pre-defined. Each target area corresponds to a temperature control sub-zone identifier (e.g., TZ-A-01 represents sub-zone 1 of dam section A) and a spatial coordinate range (the boundary of a cuboid formed by the minimum and maximum values ​​of the three-dimensional coordinates, in meters). From the zoned temperature control monitoring data, monitoring node data that intersects with the spatial coordinate range of each target area are extracted according to the sensor deployment log. This data includes at least temperature measurements, stress measurements, cooling water flow measurements, and acquisition timestamps. The monitoring node data corresponding to each target area is correlated in the time dimension: using GPS time synchronization as a unified time reference, the acquisition timestamps of each monitoring node are aligned, allowing a maximum deviation of 5 seconds. Data exceeding this deviation is marked as out of step and linear interpolation is used to complete the alignment. A time-series aligned data frame of multiple source monitoring nodes at the same time is established, with a frame period of 10 minutes. Spatial dimensional correlation is performed on the monitoring node data corresponding to each target area: A spatial adjacency matrix is ​​calculated based on the deployment coordinates (X, Y, Z, in meters, with an accuracy of 0.01 meters) of each monitoring node. Nodes with an Euclidean distance of less than or equal to 3 meters are marked as directly adjacent nodes (strong correlation within the same sub-region), while nodes with a distance greater than 3 meters but less than or equal to 6 meters are marked as cross-region boundary nodes (weak correlation between sub-regions). Directly adjacent nodes and cross-region boundary nodes within the same target area are identified. Candidate temperature control parameters are extracted from the time-aligned monitoring node data: node temperature value, horizontal temperature difference between nodes, vertical temperature difference between nodes, node temperature change rate, and node temperature-stress coupling index. Location information from each monitoring node's equipment log is extracted: unique sensor code, 3D deployment coordinates, target area identifier, and sensor type label (T-temperature, S-stress, F-flow rate).

[0030] This application's solution uses spatiotemporal dual correlation processing to transform discrete monitoring node data into structured data frames under the same spatiotemporal reference, and establishes spatial adjacency relationships to identify cross-regional heat conduction paths. This solves the problems of heat zone diffusion and gradient accumulation misjudgment caused by isolated data and inconsistent time scales in traditional zonal monitoring, and provides a spatiotemporally consistent data foundation for subsequent parameter constraint matching.

[0031] S130: For each target region in the target regions, based on the source positioning information corresponding to the target region and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target region to obtain the deviation verification result; In some embodiments of this application, based on the source location information corresponding to the target area and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target area to obtain deviation verification results, including: Based on the target area identifier and sensor type label in the source positioning information corresponding to the target area, retrieve the matching target area constraint entry from the temperature control constraint benchmark library; Match the candidate temperature control parameters corresponding to the target area with the target area constraint entries item by item; For candidate temperature control parameters that exceed the corresponding threshold after matching, deviation verification processing is performed, and the absolute value of temperature deviation, absolute value of temperature gradient deviation, and absolute value of temperature change rate deviation are calculated. Based on the ratio of the absolute value of the temperature deviation to the corresponding temperature threshold and the ratio of the absolute value of the temperature gradient deviation to the corresponding temperature gradient threshold, a deviation level identifier is determined. The deviation level identifier includes at least a normal level, a warning level, and an over-limit level. The deviation level identifier, the corresponding absolute value of the deviation, the deviation direction identifier, and the associated target area identifier are combined to form the deviation verification result.

[0032] Specifically, the target region constraint items include at least the upper temperature threshold, lower temperature threshold, allowable temperature gradient threshold, and allowable temperature change rate threshold for the target region; the parameter constraint matching includes at least the interval matching between node temperature values ​​and the upper and lower temperature thresholds, the magnitude matching between the temperature difference between nodes and the allowable temperature gradient threshold, and the rate matching between the node temperature change rate and the allowable temperature change rate threshold.

[0033] Specifically, based on the target area identifier (e.g., TZ-A-01) and sensor type mark (T / S / F) in the source location information corresponding to the target area, the matching target area constraint entries are retrieved from the temperature control constraint benchmark library: query the upper limit threshold (e.g., 40 degrees Celsius), lower limit threshold (e.g., 5 degrees Celsius), allowable temperature difference gradient threshold (8 degrees Celsius horizontally, 5 degrees Celsius vertically), and allowable temperature change rate threshold (2 degrees Celsius per hour) of the sub-region under the current age window (e.g., the peak temperature rise period within 72 hours). The candidate temperature control parameters corresponding to the target area are matched with the target area constraint entries item by item: Node temperature values ​​are matched with upper and lower temperature thresholds (determining whether the temperature value is between the lower and upper limits); the horizontal temperature difference between nodes is matched with the allowable horizontal temperature difference gradient threshold (determining whether the absolute value is less than or equal to 8 degrees Celsius); the vertical temperature difference between nodes is matched with the allowable vertical temperature difference gradient threshold (determining whether the absolute value is less than or equal to 5 degrees Celsius); and the node temperature change rate is matched with the allowable temperature change rate threshold (determining whether the absolute value is less than or equal to 2 degrees Celsius per hour). For candidate temperature control parameters that exceed the corresponding thresholds after matching, deviation verification processing is performed: the absolute value of temperature deviation (node ​​temperature value minus the absolute value of the corresponding upper threshold), the absolute value of temperature difference gradient deviation (inter-node temperature difference minus the absolute value of the corresponding allowable temperature difference), and the absolute value of temperature change rate deviation (node ​​temperature change rate minus the absolute value of the allowable temperature change rate) are calculated. Based on the ratio of the absolute value of temperature deviation to the corresponding temperature threshold (a ratio greater than 0 and less than or equal to 0.2 indicates a warning level, and greater than 0.2 indicates an over-limit level), the ratio of the absolute value of temperature gradient deviation to the corresponding temperature gradient threshold (same as above), and the ratio of the absolute value of temperature change rate deviation to the corresponding temperature change rate threshold (same as above), deviation level identifiers are determined: 00 indicates a normal level (no deviation or a ratio less than or equal to 0), 01 indicates a warning level, and 02 indicates an over-limit level. The deviation level identifiers, the corresponding absolute values ​​of deviation, the deviation direction identifiers (positive indicates above the upper limit or a positive temperature difference, negative indicates below the lower limit or a negative temperature difference), and the associated target area identifiers are combined to form the deviation verification result.

[0034] This application's solution uses a constraint benchmark library-driven parameter matching and proportional deviation level classification to uniformly map three heterogeneous parameters—temperature, temperature difference, and temperature change rate—to comparable deviation levels. This solves the problem that traditional single temperature threshold alarms cannot distinguish the severity and dimensionality of deviations, enabling subsequent collaborative quantification to have multi-dimensional, graded, and structured inputs.

[0035] S140: Perform temperature control synergy quantification on the obtained deviation verification results to obtain the temperature control synergy index; In some embodiments of this application, the obtained deviation verification results are subjected to temperature control synergy quantification processing to obtain a temperature control synergy index, including: The deviation level identifier and comprehensive deviation characterization are extracted from each deviation verification result, and the spatial distribution statistics are performed according to the target area. The deviation level difference between each target area is calculated. The deviation level difference is obtained by comparing the dispersion of the deviation level identifiers of adjacent target areas. Based on the comprehensive deviation characterization of each target region, the coefficient of variation of the global deviation distribution is calculated, and the reciprocal of the coefficient of variation is mapped to the spatial equilibrium cooperative component. The deviation level of each target area is analyzed from the time-series dimension. When the deviation level shows a convergence trend in adjacent monitoring periods, a time-series convergence co-co-component is generated. When it shows a divergence trend, a time-series divergence suppression component is generated. Based on the temperature stress coupling constraint entries in the temperature control constraint benchmark library, the temperature stress coupling index of each target region is extracted, and the closeness between the temperature stress coupling index and the corresponding constraint threshold is mapped to the parameter coupling cooperative component. The spatial equilibrium collaborative component, the temporal convergence collaborative component or the temporal divergence suppression component, and the parameter coupling collaborative component are adaptively weighted and fused according to the current temperature control stage of the grouting structure to generate the temperature control collaborative index.

[0036] Specifically, deviation level identifiers (00 / 01 / 02) and comprehensive deviation characterization quantities (0 to 100 dimensionless values) are extracted from each deviation verification result. Spatial distribution statistics are then performed according to the target region: the deviation level difference between each target region is calculated, which is equal to the absolute value of the difference between the deviation level identifiers of two adjacent target regions (0, 1, or 2). A difference of 0 indicates good coordination, while 2 indicates severe imbalance. Based on the comprehensive deviation characterization quantity of each target region, the coefficient of variation (CV) of the global deviation distribution is calculated, which is equal to the standard deviation divided by the mean (dimensionless). The reciprocal of the coefficient of variation, 1 / CV, is mapped to the spatial equilibrium coordination component (the smaller the CV, the more uniform the global deviation distribution and the better the spatial balance; the larger the 1 / CV value, ranging from 0 to 1). The temporal dimension is analyzed to determine the trend of deviation level changes in each target area within a continuous monitoring period (the current frame and the previous 5 frames, totaling 1 hour): when the deviation level shows a convergence trend in adjacent monitoring periods (e.g., from 02 to 01 or from 01 to 00), a temporal convergence co-component is generated (equal to the number of convergences divided by the total number of periods, ranging from 0 to 1); when it shows a divergence trend (e.g., from 00 to 01 or from 01 to 02), a temporal divergence suppression component is generated (equal to 1 minus the number of divergences divided by the total number of periods, ranging from 0 to 1). Based on the temperature stress coupling constraint entries in the temperature control constraint benchmark library, the temperature stress coupling index of each target region is extracted. The closeness of the temperature stress coupling index to the corresponding constraint threshold (set to 1.2) is mapped to the parameter coupling coordination component: when the coupling index is less than or equal to 1.0, the component is 1.0 (fully coordinated); when the coupling index is greater than 1.0 and less than or equal to 1.2, the component is (1.2-coupling index) / 0.2 (linearly decreasing); when it is greater than 1.2, the component is 0 (discoordinated). The spatial equilibrium coordination component, the temporal convergence coordination component (or the temporal divergence suppression component, whichever is currently applicable), and the parameter coupling coordination component are adaptively weighted and fused according to the current temperature control stage of the grouting structure: the weights for peak temperature rise are 0.3, 0.4, and 0.3 (temporal convergence takes priority), and the weights for cooling stabilization are 0.4, 0.3, and 0.3 (spatial equilibrium takes priority), generating a dimensionless temperature control coordination index with a value ranging from 0 to 1. The closer to 1 (the upper limit), the better the temperature control coordination situation; the closer to 0, the worse the coordination situation. The preset coordination threshold is set to 0.6 (this value is based on historical crack-free working conditions; the cracking rate is less than 5% for working conditions where the coordination index is consistently greater than 0.6).

[0037] This application proposes a solution that transforms dispersed deviation verification results into a single dimensionless collaborative index by using spatial equilibrium, temporal convergence, and parameter coupling in a three-dimensional collaborative component and its stage-adaptive weighting. This solves the problem that traditional temperature control multi-index independent alarms cannot comprehensively assess the overall collaborative situation, and enables control decisions to have a quantitative basis for global coordination and stage-adaptive control.

[0038] S150: In response to determining that the temperature control coordination index is greater than the preset coordination threshold, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters and the deviation verification results, the temperature control adjustment unit is controlled to perform the temperature contraction coordination control task.

[0039] In some embodiments of this application, in response to determining that the temperature control coordination index is greater than a preset coordination threshold, the following steps are taken: In response to determining that the temperature control coordination index is greater than the preset coordination threshold, a temperature control adjustment command generation process is triggered. The preset coordination threshold is dynamically set according to the grouting structure section thickness, ambient temperature level, and temperature control stage.

[0040] In some embodiments of this application, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results, the temperature control adjustment unit is controlled to perform a temperature contraction coordinated control task, including: Based on the deviation level and deviation direction identifiers in the various deviation verification results, the corresponding control strategy template is retrieved from the temperature control constraint benchmark library; Based on the spatial location of each target area and the gradient anomaly direction in the deviation verification result, the spatial action object sequence of the temperature control unit is determined, and the control is preferentially applied to the target area with the deviation level exceeding the limit and located in the center of the hot zone. Based on the node temperature value and node temperature change rate in each of the candidate temperature control parameters, calculate the quantitative value of the control intensity of the temperature control unit; Send a collaborative control command containing the spatial action object sequence, the control strategy template, and the control intensity quantification value to the temperature control unit to control the temperature control unit to execute the temperature contraction collaborative control task.

[0041] Specifically, the control strategy template includes at least a cooling intensity adjustment template, an insulation compensation template, and a water flow duration adjustment template; the temperature contraction coordinated control task includes at least a zoned cooling water flow rate adjustment, a zoned surface insulation layer coverage thickness adjustment, and a zoned maintenance duration adjustment.

[0042] Specifically, in response to the determination that the temperature control coordination index is greater than the preset coordination threshold of 0.6, the temperature control adjustment command generation process is triggered. Based on the deviation level identifier (prioritizing the 02 over-limit level) and deviation direction identifier (positive deviation requires enhanced cooling, negative deviation requires enhanced insulation) in the deviation verification results, the corresponding control strategy templates are retrieved from the temperature control constraint benchmark library: cooling intensity adjustment template (including the percentage increase or decrease in cooling water flow rate and the adjustment amount of water flow duration), insulation compensation template (including the increase or decrease in insulation layer coverage thickness and the coordinates of coverage range expansion), and water flow duration adjustment template (including the upper limit of continuous water flow duration and the intermittent water flow cycle). According to the spatial location of each target area and the gradient anomaly direction in the deviation verification results, the spatial action object sequence of the temperature control adjustment unit is determined: priority is given to the target area with deviation level 02 over-limit and located in the center of the hot zone (temperature higher than the sub-zone average plus 2 degrees Celsius) for control, followed by the 01 warning level area, and the normal level area remains unchanged. Based on the node temperature values ​​and node temperature change rates in each candidate temperature control parameter, the quantitative value of the control intensity of the temperature control unit is calculated: the quantitative value of the control intensity equals the absolute value of the temperature deviation multiplied by the temperature rise weighting coefficient (1.5 during the peak temperature rise period and 1.0 during the cooling stabilization period) divided by the absolute value of the current temperature change rate plus 0.1 (to prevent division by zero, unit: degrees Celsius per hour). This formula reflects a positive correlation with the temperature deviation (the larger the deviation, the stronger the control needs to be) and a negative correlation with the current temperature change rate (when the current temperature change rate is already large, the control needs to be weakened to avoid temperature shock). A coordinated control command containing the spatial action object sequence (sub-area identifier list), the control strategy template (cooling / insulation / water circulation template code), and the quantitative value of the control intensity (0 to 100 dimensionless) is sent to the temperature control unit (cooling water station PLC and insulation cover robotic arm controller). The temperature control unit performs temperature contraction coordination control tasks: zoned cooling water flow rate adjustment (e.g., adjustment intensity 50 corresponds to increasing the design flow rate from 100% to 120%, and intensity 100 corresponds to increasing it to 150%), zoned surface insulation layer coverage thickness adjustment (e.g., adjustment intensity 50 corresponds to increasing the thickness from 5 cm to 7 cm, and intensity 100 corresponds to increasing it to 10 cm), and zoned curing time adjustment (extending the water spraying curing time). The control objective is to ensure that the temperature field contraction trend and stress field release process in each target area maintain spatiotemporal coordination, that is, to match the temperature drop rate with the stress relaxation rate, avoiding tensile stress concentration caused by a sudden drop in temperature.

[0043] This application's solution uses graded template control triggered by a synergistic index threshold and intensity quantification calculation to transform multi-dimensional deviation information into executable zoned cooling, insulation, and maintenance commands. This solves the problem of response lag and intensity mismatch caused by traditional temperature control relying on manual adjustment based on experience, and achieves spatiotemporal synergistic control of the contraction trend of the temperature field and stress field.

[0044] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0045] Correspondingly, such as Figure 2 As shown, this application also provides a concrete grouting temperature shrinkage coordinated control system, including: The parameter extraction module 210 is used to extract parameters and perform boundary calibration processing on the pre-acquired temperature control parameters of the grouting structure in response to the temperature control trigger signal sent by the temperature control monitoring node, so as to build a temperature control constraint benchmark library. The parameter processing module 220 is used to extract and perform spatiotemporal correlation processing on the pre-acquired partition temperature control monitoring data based on each preset target area to obtain each candidate temperature control parameter and each source location information; The deviation verification module 230 is used to perform parameter constraint matching and deviation verification processing on the candidate temperature control parameters corresponding to each of the target regions, based on the source positioning information corresponding to the target region and the temperature control constraint benchmark library, to obtain the deviation verification result. The index determination module 240 is used to perform temperature control coordination quantification on the obtained deviation verification results to obtain the temperature control coordination index. Temperature control module 250 is used to control temperature control adjustment unit to perform temperature contraction coordinated control task in response to determining that the temperature control coordination index is greater than a preset coordination threshold, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters and the deviation verification results.

[0046] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0047] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0048] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for temperature and shrinkage control of concrete grouting, characterized by, include: In response to receiving a temperature control trigger signal from a temperature control monitoring node, the pre-acquired temperature control parameters of the entire grouting structure are extracted and the boundary calibration is performed to construct a temperature control constraint benchmark library. Based on the preset target areas, the pre-acquired zone temperature control monitoring data is extracted and spatiotemporally correlated to obtain each candidate temperature control parameter and each source location information; For each target region, based on the source location information corresponding to the target region and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target region to obtain the deviation verification result; The temperature control synergy index is obtained by quantifying the various deviation verification results. In response to determining that the temperature control coordination index is greater than the preset coordination threshold, the temperature control adjustment unit is controlled to perform a temperature contraction coordination control task based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results.

2. The concrete grouting temperature shrinkage synergic control method according to claim 1, characterized in that, In response to receiving a temperature control trigger signal from a temperature control monitoring node, the pre-acquired global temperature control parameters of the grouting structure are extracted and boundary calibration is performed to construct a temperature control constraint benchmark library, including: The temperature control trigger signal sent by the temperature control monitoring node is analyzed, and the trigger type identifier is identified. The trigger type identifier includes at least single-point temperature over-limit trigger, adjacent measuring point temperature difference over-limit trigger, and abnormal temperature change rate trigger. Extract structural geometric parameters, which include at least the location of dam segment joints, the geometric dimensions of the dam surface, and the sequence of cross-sectional thicknesses. Extract thermodynamic physical property parameters, which include at least the thermal conductivity, specific heat capacity, cumulative heat release curve of hydration, and coefficient of linear expansion of concrete. Extract environmental boundary parameters, which include at least the time-series values ​​of ambient temperature, solar radiation intensity, and cooling water inlet temperature.

3. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 2, characterized in that, Also includes: The structural geometric parameters are partitioned and the boundaries are calibrated. The spatial coordinate envelope range of each temperature control sub-zone is determined based on the temperature control compartment diagram. The thermodynamic physical properties are calibrated for time-varying boundaries, and the effective range of parameters under different age windows is determined based on the mapping relationship between grouting age and hydration heat release stage. The environmental boundary parameters are dynamically calibrated, and the floating range of environmental constraints is determined based on seasonal operating conditions and extreme weather forecasts. The calibrated structural geometric parameters, thermodynamic physical property parameters, and environmental boundary parameters are structured and indexed according to the temperature control sub-region identifier and age window identifier to generate the temperature control constraint benchmark library. The temperature control constraint benchmark library includes at least the upper temperature threshold set, the lower temperature threshold set, the allowable temperature difference gradient set, and the allowable temperature change rate set.

4. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 1, characterized in that, Based on the preset target areas, data extraction and spatiotemporal correlation processing are performed on the pre-acquired zone temperature control monitoring data, including: Based on the compartmentalization diagram of the grouting structure and the temperature control sub-zone division scheme, each target area is preset, and each target area corresponds to a temperature control sub-zone identifier and spatial coordinate range; From the temperature control monitoring data of the partitions, extract the monitoring node data that intersects with the spatial coordinate range of each target area; The monitoring node data corresponding to each target area are correlated in time dimension, and the collection timestamps of each monitoring node are aligned with a unified time synchronization benchmark to establish a time-series aligned data frame of multi-source monitoring nodes at the same time. Spatial dimension correlation is performed on the monitoring node data corresponding to each target area. The spatial adjacency matrix is ​​calculated based on the layout coordinates of each monitoring node to identify directly adjacent nodes and cross-regional boundary nodes within the same target area.

5. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 4, characterized in that, Obtain the candidate temperature control parameters and the location information of each source, including: Extract the candidate temperature control parameters from the time-aligned monitoring node data; The location information from each source is extracted from the equipment ledger of each monitoring node.

6. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 1, characterized in that, Based on the source location information corresponding to the target area and the temperature control constraint benchmark library, parameter constraint matching and deviation verification processing are performed on the candidate temperature control parameters corresponding to the target area to obtain deviation verification results, including: Based on the target area identifier and sensor type label in the source positioning information corresponding to the target area, retrieve the matching target area constraint entry from the temperature control constraint benchmark library; Match the candidate temperature control parameters corresponding to the target area with the target area constraint entries item by item; For candidate temperature control parameters that exceed the corresponding threshold after matching, deviation verification processing is performed, and the absolute value of temperature deviation, absolute value of temperature gradient deviation, and absolute value of temperature change rate deviation are calculated. Based on the ratio of the absolute value of the temperature deviation to the corresponding temperature threshold and the ratio of the absolute value of the temperature gradient deviation to the corresponding temperature gradient threshold, a deviation level identifier is determined. The deviation level identifier includes at least a normal level, a warning level, and an over-limit level. The deviation level identifier, the corresponding absolute value of the deviation, the deviation direction identifier, and the associated target area identifier are combined to form the deviation verification result.

7. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 1, characterized in that, The obtained deviation verification results are subjected to temperature control synergy quantification to obtain the temperature control synergy index, including: The deviation level identifier and comprehensive deviation characterization are extracted from each deviation verification result, and the spatial distribution statistics are performed according to the target area. The deviation level difference between each target area is calculated. The deviation level difference is obtained by comparing the dispersion of the deviation level identifiers of adjacent target areas. Based on the comprehensive deviation characterization of each target region, the coefficient of variation of the global deviation distribution is calculated, and the reciprocal of the coefficient of variation is mapped to the spatial equilibrium cooperative component. The deviation level of each target area is analyzed from the time-series dimension. When the deviation level shows a convergence trend in adjacent monitoring periods, a time-series convergence co-co-component is generated. When it shows a divergence trend, a time-series divergence suppression component is generated. Based on the temperature stress coupling constraint entries in the temperature control constraint benchmark library, the temperature stress coupling index of each target region is extracted, and the closeness between the temperature stress coupling index and the corresponding constraint threshold is mapped to the parameter coupling cooperative component. The spatial equilibrium collaborative component, the temporal convergence collaborative component or the temporal divergence suppression component, and the parameter coupling collaborative component are adaptively weighted and fused according to the current temperature control stage of the grouting structure to generate the temperature control collaborative index.

8. The method for synergistic control of temperature shrinkage in concrete grouting according to claim 1, characterized in that, In response to determining that the temperature control coordination index is greater than a preset coordination threshold, the following steps are taken: In response to determining that the temperature control coordination index is greater than the preset coordination threshold, a temperature control adjustment command generation process is triggered. The preset coordination threshold is dynamically set according to the grouting structure section thickness, ambient temperature level, and temperature control stage.

9. The method for coordinated control of temperature shrinkage in concrete grouting according to claim 8, characterized in that, Based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results, the temperature control adjustment unit is controlled to perform a temperature contraction coordinated control task, including: Based on the deviation level and deviation direction identifiers in the various deviation verification results, the corresponding control strategy template is retrieved from the temperature control constraint benchmark library; Based on the spatial location of each target area and the gradient anomaly direction in the deviation verification result, the spatial action object sequence of the temperature control unit is determined, and the control is preferentially applied to the target area with the deviation level exceeding the limit and located in the center of the hot zone. Based on the node temperature value and node temperature change rate in each of the candidate temperature control parameters, calculate the quantitative value of the control intensity of the temperature control unit; Send a collaborative control command containing the spatial action object sequence, the control strategy template, and the control intensity quantification value to the temperature control unit to control the temperature control unit to execute the temperature contraction collaborative control task.

10. A concrete grouting temperature shrinkage coordinated control system, applied to the concrete grouting temperature shrinkage coordinated control method as described in any one of claims 1-9, characterized in that, include: The parameter extraction module is used to extract parameters and perform boundary calibration processing on the pre-acquired temperature control parameters of the entire grouting structure in response to the temperature control trigger signal sent by the temperature control monitoring node, so as to build a temperature control constraint benchmark library. The parameter processing module is used to extract and perform spatiotemporal correlation processing on the pre-acquired zone temperature control monitoring data based on each preset target area to obtain each candidate temperature control parameter and each source location information; The deviation verification module is used to perform parameter constraint matching and deviation verification processing on the candidate temperature control parameters corresponding to each of the target regions, based on the source positioning information corresponding to the target region and the temperature control constraint benchmark library, to obtain the deviation verification result. The index determination module is used to perform temperature control coordination quantification on the obtained deviation verification results to obtain the temperature control coordination index. The temperature control module is used to control the temperature control adjustment unit to perform a temperature contraction coordinated control task in response to determining that the temperature control coordination index is greater than a preset coordination threshold, based on the temperature control constraint benchmark library, the target regions, the candidate temperature control parameters, and the deviation verification results.

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