Deep foundation phase change execution active compensation supporting system and method based on internet of things perception

Abstract

The application discloses a deep foundation pit phase change execution active compensation supporting system and method based on Internet of Things sensing, relates to the technical field of intelligent supporting of deep foundation pits, and comprises the following steps: determining a target cell and a target supporting force change amount that need to be adjusted according to active compensation supporting force requirements, determining a phase change regulation action of the target cell according to a phase change execution strategy, and combining pipe network blockage discrimination information to dredge and dispose the circulating pipe network to form an executable control scheme; configuring Internet of Things sensors and controlled execution units according to the executable control scheme, and controlling the phase change medium to switch between liquid and solid states in the target cell to obtain supporting force regulation execution data; collecting supporting execution feedback data according to the supporting force regulation execution data to generate a supporting safety regulation report. The application achieves the effects of improving the accuracy, foresight and decision-making pertinence of deep foundation pit supporting risk assessment.

Description

Technical Field

[0001] This invention relates to the field of intelligent support technology for deep foundation pits, and in particular to an active compensation support system and method for phase change execution in deep foundation pits based on Internet of Things (IoT) sensing. Background Technology

[0002] With the increasing intensity of urban underground space development, deep foundation pit projects are gradually moving towards greater depths, complex geological formations, proximity to existing buildings, and dense pipeline environments. The safety and stability of the foundation pit support structure has become a crucial control factor in underground construction. Deep foundation pit construction typically relies on retaining structures, support systems, anchor cable systems, dewatering measures, and construction monitoring methods to ensure safety. Data on foundation pit deformation, support stress, and the surrounding environment are collected using displacement gauges, settlement gauges, axial force gauges, water level gauges, and environmental monitoring equipment. With the development of IoT sensing, edge computing, and intelligent analytics technologies, deep foundation pit safety management is gradually moving towards real-time sensing and dynamic assessment.

[0003] However, existing intelligent monitoring technologies for deep foundation pits mostly remain at the "data acquisition-threshold judgment-alarm prompt" stage, lacking a direct linkage between monitoring data and active control of the support structure. When the retaining structure experiences increased displacement or abnormal settlement, it relies on manual experience to determine the location and method of support reinforcement, making it difficult to promptly generate active compensation support force requirements for specific support units. Furthermore, existing support adjustment methods often employ overall reinforcement or temporary supports, making it difficult to achieve precise control by combining cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation; when the pipeline is blocked or the temperature response is delayed, it can easily lead to insufficient freezing reinforcement, untimely unloading, or localized overpressure. Summary of the Invention

[0004] Purpose of the invention: This invention provides a method for active compensation support based on IoT sensing in deep foundation pits to solve the problem of disconnect between monitoring and early warning and support control in existing intelligent monitoring technologies for deep foundation pits, and the difficulty in achieving refined active compensation support at the cell level. This invention also provides a system for active compensation support based on IoT sensing in deep foundation pits.

[0005] Technical Solution: The present invention provides a method for active compensation support based on IoT sensing in deep foundation pits using phase change operation. This method includes: collecting historical and real-time data from the deep foundation pit construction site, performing correction, standardization, and risk labeling to obtain a phase change support risk sample set; constructing a target risk assessment model using the phase change support risk sample set, and deploying the target risk assessment model in an edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies; determining the target cell to be adjusted and the target support force change amount based on the active compensation support force requirements, determining the phase change control action of the target cell based on the phase change execution strategy, and dredging the circulating pipeline network in conjunction with the pipeline blockage identification information to form an executable control scheme; configuring IoT sensors and controlled execution units according to the executable control scheme, and controlling the phase change medium to switch between liquid and solid states in the target cell to obtain support force control execution data; collecting support execution feedback data based on the support force control execution data, and generating a support safety control report.

[0006] Furthermore, the real-time data includes cell pressure data, phase change medium temperature data, circulating pipeline flow data, enclosure structure displacement data, settlement data, and environmental parameter data.

[0007] Furthermore, the specific steps for obtaining the phase change support risk sample set are as follows:

[0008] Historical and real-time data from the deep foundation pit construction site were collected, and the historical and real-time data were synchronously corrected and standardized to obtain effective phase change support status data.

[0009] Risk labeling is performed on the changes in support status and the changes in foundation pit deformation in the effective phase change support status data to obtain a risk sample set for phase change support.

[0010] Furthermore, the specific steps for constructing the target risk assessment model are as follows:

[0011] Based on the mapping of the correspondence between monitoring points of the cell, pipeline and enclosure structure, the phase change support risk sample set is organized and mapped into a phase change causal diagram sample;

[0012] The phase transition causal diagram samples are input into the continuous-time state encoding layer to extract the continuous evolution characteristics of cell pressure, phase transition medium temperature, circulating pipeline flow rate and enclosure structure deformation, thus obtaining the phase transition state characterization.

[0013] The phase change state characterization is input into the hypergraph interaction propagation layer and the causal attention gating layer to calculate the risk intensity of the high-order interaction relationship between cell pressure, phase change medium temperature, circulating pipeline flow rate and retaining structure deformation, so as to obtain the phase change support risk intensity corresponding to each target cell.

[0014] Based on the risk intensity of phase change support and the risk labeling in the phase change support risk sample set, the node influence weight and risk discrimination threshold are trained and corrected to obtain the target risk assessment model.

[0015] Furthermore, the specific steps for generating the active compensation support force requirement, phase change execution strategy, pipeline blockage identification information, and linkage control strategy are as follows:

[0016] The target risk assessment model is deployed in the edge computing device for deep foundation pit construction safety monitoring, and real-time data streams are received through the target risk assessment model to obtain the real-time phase change support status of each target cell;

[0017] Based on the real-time phase change support status, identify the insufficient support status, excessive support status, and abnormal pipeline distribution status of each target cell to obtain phase change compensation and control information.

[0018] Based on the phase change compensation control information, the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information are generated, and a linkage control strategy is generated based on the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information.

[0019] Furthermore, the specific steps for determining the target cell and target support force change based on the active compensation support force requirement are as follows:

[0020] Based on the active compensation support force requirement, the displacement and settlement data of the retaining structure are compared with the corresponding control limits to obtain the deformation compensation requirement of each retaining structure monitoring point.

[0021] Based on the deformation compensation requirements, combined with the spatial correspondence between each cell and the monitoring points of the enclosure structure, the cell pressure margin, the temperature state of the phase change medium and the flow state of the circulating pipe network, the priority of support adjustment is determined.

[0022] The target cells that need to be adjusted are screened by the support adjustment priority, and the active compensation support force demand is allocated according to the support adjustment priority to obtain the target support force change corresponding to each target cell.

[0023] Furthermore, the specific steps for forming an executable control scheme are as follows:

[0024] Based on the change in target support force and phase change execution strategy, determine the pressure regulation requirements of the target cell, and determine the corresponding phase change control action of the target cell based on the pressure regulation requirements;

[0025] The feasibility of the circulating pipe network corresponding to the target cell is judged by using phase change control action and pipe network blockage discrimination information, and the circulating pipe network with blockage risk is cleared to obtain the clearing status.

[0026] Based on the phase change control actions and the dredging and disposal status, the execution sequence, feedback acquisition requirements, and control command content of the target cells are determined to form an executable control scheme.

[0027] Furthermore, the specific steps for obtaining the support force control execution data are as follows:

[0028] Based on the executable control scheme and linkage control strategy, the IoT sensor and the controlled execution unit corresponding to the target cell are bound together to form a sensing and execution relationship, and liquid-solid switching control command is sent to the controlled execution unit to control the phase change medium to perform phase change regulation in the target cell.

[0029] Data on cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation during the phase change control process are collected to obtain support force control execution data.

[0030] Furthermore, the specific steps for generating the support safety control report are as follows:

[0031] Establish support execution verification records corresponding to active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies based on support force regulation execution data.

[0032] The support execution verification record is used to collect the support execution feedback data corresponding to the target cell. The support execution feedback data is then compared and analyzed with the active compensation support force requirement, phase change execution strategy, pipeline blockage identification information and linkage control strategy to generate a support safety control report.

[0033] This invention also provides a deep foundation pit phase change active compensation support system based on Internet of Things (IoT) sensing, comprising: a data acquisition module for collecting historical and real-time data from the deep foundation pit construction site, and performing correction, standardization, and risk labeling to obtain a phase change support risk sample set; a risk assessment module for constructing a target risk assessment model using the phase change support risk sample set, and deploying the target risk assessment model in an edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies; and an execution control module for... Based on the active compensation support force requirements, the target cell and target support force change amount are determined. The phase change control action of the target cell is determined according to the phase change execution strategy. Combined with the pipeline blockage judgment information, the circulating pipeline is dredged and treated to form an executable control scheme. The phase change execution module is used to configure IoT sensors and controlled execution units according to the executable control scheme, and control the phase change medium to switch between liquid and solid states in the target cell to obtain support force control execution data. The report generation module is used to collect support execution feedback data based on the support force control execution data and generate a support safety control report.

[0034] Beneficial effects: Compared with the prior art, the significant advantages of this invention are that by constructing a target risk assessment model through a phase change support risk sample set, it achieves joint analysis and risk intensity identification of cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation state, thereby improving the accuracy, foresight, and decision-making pertinence of deep foundation pit support risk assessment; and by forming an executable control scheme based on the active compensation support force requirement, phase change execution strategy, and pipeline blockage discrimination information, it achieves the coordinated determination of target cells, phase change control actions, pipeline dredging and disposal, and execution sequence, thereby improving the control accuracy, execution reliability, and on-site response efficiency of active compensation support. Attached Figure Description

[0035] Figure 1 A flowchart illustrating an active compensation support method for phase change in deep foundation pits based on IoT sensing.

[0036] Figure 2 A flowchart for generating a risk sample set for phase change support;

[0037] Figure 3 A flowchart for generating an active compensation support strategy;

[0038] Figure 4 A flowchart for generating a support safety control report. Detailed Implementation

[0039] The technical solution of the present invention will be further described in detail below with reference to the embodiments and accompanying drawings.

[0040] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for active compensation support of deep foundation pit phase change based on Internet of Things (IoT) sensing, including the following steps:

[0041] S1. Collect historical and real-time data from the deep foundation pit construction site, and perform correction, standardization, and risk labeling to obtain a risk sample set for phase change support.

[0042] Historical and real-time data from the deep foundation pit construction site were collected, and the historical and real-time data were synchronously corrected and standardized to obtain effective phase change support status data.

[0043] The specific process includes acquiring cell pressure data through pressure sensors, acquiring phase change medium temperature data through temperature sensors, acquiring circulating pipe network flow data through flow meters, acquiring enclosure structure displacement data through displacement meters, acquiring settlement data through settlement meters, and acquiring environmental parameter data through environmental monitoring equipment.

[0044] The historical and real-time data, after being summarized and organized, are synchronously corrected according to the acquisition sequence to eliminate time deviations between different acquisition sources, and outliers are identified and removed. After completing the synchronous correction, the cell pressure data, phase change medium temperature data, circulating pipeline flow data, retaining structure displacement data, settlement data, and environmental parameter data are converted into dimensionless values ​​with unified dimensions using a normalization method. The data are then organized according to the same acquisition time period and the same spatial location to obtain effective phase change support status data.

[0045] It should be noted that historical data includes cell pressure data, phase change medium temperature data, circulating pipe network flow data, retaining structure displacement data, settlement data, and environmental parameter data from previous construction phases; effective phase change support status data includes cell pressure data, phase change medium temperature data, circulating pipe network flow data, retaining structure displacement data, settlement data, and environmental parameter data after synchronous correction and standardization; support risk labels refer to the marking information used to characterize the risk level of effective phase change support status data, including low-risk labels, medium-risk labels, and high-risk labels.

[0046] Risk labeling is performed on the changes in support status and the changes in foundation pit deformation in the effective phase change support status data to obtain a risk sample set for phase change support.

[0047] The specific process includes: organizing the effective phase change support status data according to the collection time sequence; identifying changes in cell pressure data, phase change medium temperature data, and circulating pipeline flow data from the effective phase change support status data to form support status changes; identifying changes in retaining structure displacement data and settlement data from the effective phase change support status data to form foundation pit deformation changes; performing corresponding analysis on support status changes and foundation pit deformation changes in conjunction with environmental parameter data, and determining whether there is a risk correlation based on deep foundation pit construction safety standards; marking the effective phase change support status data of the corresponding collection period with support risk labels based on the coupling relationship between retaining structure displacement data, settlement data, and phase change medium operating status; and collecting and organizing the effective phase change support status data with support risk labels to obtain a phase change support risk sample set.

[0048] S2. Construct a target risk assessment model using a phase change support risk sample set, and deploy the target risk assessment model in the edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies.

[0049] Based on the mapping of the correspondence between monitoring points in the cell, pipeline, and retaining structure, the phase change support risk sample set is organized and mapped into a phase change causal diagram sample.

[0050] The specific process includes: establishing physical connections between monitoring points of cell chambers, circulating pipe networks, and retaining structures based on the topology of the deep foundation pit construction site, forming a correspondence between these monitoring points; traversing the phase change support risk sample set, mapping the cell pressure data, phase change medium temperature data, and circulating pipe network flow data in each valid phase change support state data with a support risk label to support state nodes in the phase change causal graph sample; mapping the retaining structure displacement data and settlement data in each valid phase change support state data with a support risk label to foundation pit deformation nodes in the phase change causal graph sample; and establishing directed connection edges between support state nodes and foundation pit deformation nodes based on the correspondence between monitoring points of cell chambers, pipe networks, and retaining structures, forming a phase change causal graph sample representing the physical coupling relationship.

[0051] It should be noted that the causal direction in the phase transition causality diagram sample is determined based on the physical connection relationship between the monitoring points of the cell, pipeline network and retaining structure, as well as the sequential effect of the change in support status on the deformation of the foundation pit.

[0052] The phase transition causal diagram samples are input into the continuous-time state encoding layer to extract the continuous evolution characteristics of cell pressure, phase transition medium temperature, circulating pipeline flow rate, and enclosure structure deformation, thus obtaining the phase transition state characterization.

[0053] The specific process includes arranging the support state nodes and foundation pit deformation nodes in the phase change causality diagram sample according to the time series to form time series data containing cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure displacement data. The time series data is encoded using a continuous time state encoding layer to capture the dependencies and trends of cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure displacement data at different time steps. The evolution law of cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure displacement data changing continuously over time is extracted to form a phase change state characterization.

[0054] It should be noted that phase change state characterization refers to the comprehensive state expression data formed by encoding the continuous changes of cell pressure, phase change medium temperature, circulating pipeline flow rate, and enclosure structure deformation over time; the continuous time state encoding layer is a processing layer used to perform time decay weighted encoding on the state data and extract continuous evolution features based on the time interval between adjacent sampling times.

[0055] The phase change state characterization is input into the hypergraph interaction propagation layer and the causal attention gating layer to calculate the risk intensity of the high-order interaction relationship between cell pressure, phase change medium temperature, circulating pipeline flow rate and retaining structure deformation, so as to obtain the phase change support risk intensity corresponding to each target cell.

[0056] The specific process includes: writing the phase change state characterization into the hypergraph interaction propagation layer according to the correspondence between monitoring points of the cell, pipeline network, and retaining structure; and propagating and aggregating the correlation information between support state nodes and foundation pit deformation nodes based on the directed connection edges already established in the phase change causal graph sample, so that the cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure displacement data form a correlation expression oriented towards each target cell; writing the propagated and aggregated phase change state characterization into the causal attention gating layer; and distinguishing the strength of the effect under different correlation paths according to the influence direction between monitoring points of the cell, pipeline network, and retaining structure, increasing the attention to correlation information with a high degree of correlation with the target cell support state change and foundation pit deformation change, and decreasing the attention to correlation information with a low degree of correlation; and based on the high-order interaction relationship between cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation after the differentiation of the strength of the effect.

[0057] Based on the high-order interaction relationship, the correlation strength between cell pressure, phase change medium temperature, circulating pipeline flow rate and retaining structure deformation is comprehensively calculated to form a comprehensive risk characterization value. The comprehensive risk characterization value is normalized and mapped to obtain the phase change support risk intensity of the corresponding target cell. The higher the phase change support risk intensity, the higher the support risk, and the lower the phase change support risk intensity, the more stable the support state.

[0058] It should be noted that the phase change support risk intensity reflects the degree of support risk that occurs in the target cell under the combined effects of cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation.

[0059] The expression for calculating the phase change support risk intensity corresponding to each target cell is as follows:

[0060]

[0061]

[0062] in, Indicates the first The phase change support risk intensity corresponding to the target cell Indicates the index of the target cell. This represents the natural exponential function. This represents the first risk mapping coefficient. Indicates the first Each target cell corresponds to the first feature component in the higher-order interaction feature vector. This represents the second risk mapping coefficient. Indicates the first Each target cell corresponds to the second feature component in the higher-order interaction feature vector. This represents the third risk mapping coefficient. Indicates the first Each target cell corresponds to the third feature component in the higher-order interaction feature vector. This represents the fourth risk mapping coefficient. Indicates the first Each target cell corresponds to the fourth feature component in the higher-order interaction feature vector. This represents the fifth risk mapping coefficient. This represents the sixth risk mapping coefficient. Indicates the first The high-order interaction feature vectors corresponding to each target cell Represents the linear rectified activation function. This represents the total number of super-edges. Indicates the index of the superedge. This indicates the association marker between the i-th target cell and the e-th hyperedge. This represents the number of nodes in the e-th superedge. Let represent the feature mapping matrix in the hypergraph interaction propagation layer, b represent the identifier of the hypergraph interaction propagation, N represent the total number of nodes, and j represent the node index. This indicates the association marker between the j-th node and the e-th hyperedge. This represents the input state feature vector corresponding to the j-th node. Let represent the feature mapping matrix in the attention propagation layer, and 'a' represent the identifier for attention propagation. This represents the attention weight of the j-th node to the i-th target cell.

[0063] It should be noted that before calculating the risk intensity of phase change support, the cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation were respectively subtracted from their respective reference values ​​and normalized by combining the corresponding value ranges or variation ranges, thereby unifying each variable into dimensionless phase change state characterization data.

[0064] All of these were determined gradually through training and correction based on the correspondence between the risk intensity of phase change support and the risk label of support during the process of constructing the target risk assessment model using the risk sample set of phase change support. In the causal attention gating layer, attention is calculated based on the association characteristics and influence direction between the node and the target cell.

[0065] Based on the risk intensity of phase change support and the risk labeling in the phase change support risk sample set, the node influence weight and risk discrimination threshold are trained and corrected to obtain the target risk assessment model.

[0066] The specific process includes: comparing the phase change support risk intensity corresponding to each target cell with the support risk label in the phase change support risk sample set to form a risk intensity deviation value; adjusting the feature mapping matrix in the hypergraph interaction propagation layer and the feature mapping matrix in the causal attention gating layer based on the risk intensity deviation value, and updating the node influence weights; recalculating the phase change support risk intensity corresponding to each target cell based on the updated node influence weights until the risk intensity deviation value is less than the convergence judgment threshold, thus obtaining the trained target risk assessment model; matching the risk intensity in the trained target risk assessment model with the risk discrimination threshold to determine the risk discrimination threshold range corresponding to different support risk labels, thus completing the training correction of the risk discrimination threshold.

[0067] Furthermore, the target risk assessment model includes a continuous-time state encoding layer, a hypergraph interaction propagation layer, a causal attention gating layer, and a risk output layer. Trainable parameters include temporal encoding parameters, feature mapping matrix parameters, attention weight parameters, risk mapping coefficient parameters, and node influence weight parameters. Training employs a step-by-step approach. The first training iteration uses temporal sample data from the phase change support risk sample set to train the continuous-time state encoding layer, learning the continuous evolution patterns of cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure deformation data. The second training iteration, based on the training results of the continuous-time state encoding layer, uses phase change causal graph samples and support risk labels to train the hypergraph interaction propagation layer. The training process involves several steps: first, training the causal attention gating layer and node influence weights to learn the high-order correlations between monitoring points in the cell, pipeline network, and retaining structure; second, training the continuous-time state encoding layer, hypergraph interaction propagation layer, causal attention gating layer, and risk output layer, and simultaneously correcting parameters based on the deviation between the phase change support risk intensity and the support risk label; and third, training the risk discrimination threshold based on the correspondence between the phase change support risk intensity distribution and the support risk label distribution to obtain the target risk assessment model. This step-by-step training approach allows for the gradual completion of temporal feature learning, correlation learning, and overall parameter optimization, thereby improving training stability, convergence efficiency, and risk identification accuracy.

[0068] For example: when the risk intensity of phase change support is in the range of 0 to 0.3, it corresponds to the "low risk" label; when the risk intensity of phase change support is in the range of 0.3 to 0.7, it corresponds to the "medium risk" label; when the risk intensity of phase change support is in the range of 0.7 to 1, it corresponds to the "high risk" label.

[0069] It should be noted that the convergence threshold is an error limit set in advance before model training to determine whether training should stop, based on the requirements of deep foundation pit construction safety monitoring for model training accuracy. The exemplary value range is usually between 0.01 and 0.1. The risk discrimination threshold is based on the allowable limits for displacement and settlement of the retaining structure in the deep foundation pit construction safety standard, and is corrected according to the risk label distribution during model training. The exemplary value range is usually set to 0.3 and 0.7, which serve as the dividing line between low risk and medium risk, and medium risk and high risk, respectively.

[0070] The target risk assessment model is deployed in the edge computing device for deep foundation pit construction safety monitoring, and real-time data streams are received through the target risk assessment model to obtain the real-time phase change support status of each target cell.

[0071] The specific process includes deploying the target risk assessment model in the edge computing device for deep foundation pit construction safety monitoring. This allows data continuously collected from the deep foundation pit construction site, including cell pressure data, phase change medium temperature data, circulating pipe network flow data, retaining structure displacement data, settlement data, and environmental parameter data, to be fed into the edge computing device according to the acquisition sequence. The data is then processed using the synchronous correction and standardization methods employed when obtaining the phase change support risk sample set. Based on the established correspondence between monitoring points in the cells, pipe network, and retaining structure, real-time data from the same acquisition period are aggregated accordingly. Finally, by combining the node influence weights and risk discrimination thresholds already trained and corrected in the target risk assessment model, the status changes of each target cell during the current acquisition period are correlated and identified to obtain the real-time phase change support status of each target cell.

[0072] Based on the real-time phase change support status, identify the insufficient support status, excessive support status, and abnormal pipeline distribution status of each target cell to obtain phase change compensation and control information.

[0073] The specific process includes: identifying the real-time phase change support status of each target cell; extracting the cell pressure status, phase change medium temperature status, circulating pipeline flow status, and retaining structure deformation status from the real-time phase change support status of each target cell; comparing the cell pressure status and retaining structure deformation status with the risk discrimination thresholds that have been trained and corrected in the target risk assessment model; identifying the corresponding target cell as under-supported when the change in retaining structure deformation indicates insufficient support and the cell pressure status does not meet the corresponding support requirements; jointly judging the cell pressure status, phase change medium temperature status, and retaining structure deformation status; identifying the corresponding target cell as over-supported when the cell pressure status is high and the change in retaining structure deformation does not continue to increase; analyzing the relationship between the circulating pipeline flow status and the changes in phase change medium temperature status and cell pressure status; identifying the corresponding target cell as having an abnormal pipeline distribution status when the circulating pipeline flow status shows abnormal changes; and collecting and organizing the identification results corresponding to each target cell to obtain phase change compensation and control information.

[0074] It should be noted that the support requirements refer to the level of support that the target cell should achieve, as determined by the deep foundation pit construction safety standards, the deformation control limits of the retaining structure, and the identification results of the target risk assessment model; the phase change compensation control information is the control judgment information formed by collecting the current support adjustment needs of each target cell, including the support insufficiency state, support over-support state, and pipeline distribution abnormal state corresponding to each target cell.

[0075] Based on the phase change compensation control information, the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information are generated, and a linkage control strategy is generated based on the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information.

[0076] The specific process includes: organizing the insufficient support, excessive support, and abnormal pipeline distribution states in the phase change compensation and control information, and classifying them one by one according to the target cells; determining the level of additional support required for the target cells based on the support gap corresponding to the insufficient support state, forming an active compensation support force demand; determining the level of support required to be reduced for the target cells based on the excess support state, and determining the handling direction of freezing boosting action, heating thawing unloading action, liquid injection action, and liquid drainage action in combination with the cell pressure state, phase change medium temperature state, and circulating pipeline flow state, forming a phase change execution strategy; and judging the anomalies of the corresponding pipelines based on the correspondence between abnormal circulating pipeline flow state, abnormal phase change medium temperature state, and abnormal cell pressure state in the abnormal pipeline distribution state, forming pipeline blockage judgment information.

[0077] It should be noted that the linkage control strategy is used to uniformly arrange the support effect change requirements, phase change control actions and unblocking treatment contents corresponding to each target cell, so as to realize the coordinated execution of the phase change control process and improve the accuracy and reliability of support adjustment; the pipeline blockage discrimination information is discrimination information formed after judging the abnormal operation of the circulating pipeline network, including whether there is a risk of blockage in the corresponding circulating pipeline network and the location of the abnormal pipeline network.

[0078] The active compensation support requirements of each target cell are collected, and the direction and degree of support change required for each target cell are clarified. The phase change execution strategy corresponding to each target cell is matched with the active compensation support requirements to determine the phase change control actions that each target cell should perform. The pipeline blockage identification information is checked against the phase change control actions. When there is a risk of blockage in the circulating pipeline, the unblocking treatment is added to the treatment process corresponding to the phase change control actions. The support change requirements, phase change control actions, and unblocking treatment for each target cell are arranged in a unified manner according to their sequence to form a linkage control strategy.

[0079] It should be noted that the linkage control strategy includes liquid filling commands for specific compartments, freezing boost commands, heating and melting unloading commands, and liquid drainage and recovery commands.

[0080] S3. Based on the active compensation support force requirement, determine the target cell and the target support force change amount to be adjusted. Based on the phase change execution strategy, determine the phase change control action of the target cell. Combine the pipeline blockage judgment information to dredge the circulating pipeline and form an executable control scheme.

[0081] Based on the active compensation support force requirements, the displacement and settlement data of the retaining structure are compared with the corresponding control limits to obtain the deformation compensation requirements of each retaining structure monitoring point.

[0082] The specific process includes: organizing the monitoring points of the retaining structure point by point according to the active compensation support force requirement; extracting the retaining structure displacement data and settlement data corresponding to each monitoring point; comparing the retaining structure displacement data with the retaining structure displacement control limit and the settlement data with the settlement control limit to identify the difference between the current deformation degree and the allowable range of each retaining structure monitoring point; combining the compensation direction and degree of the support action reflected in the active compensation support force requirement to classify the state of the retaining structure displacement data and settlement data as exceeding the control limit, approaching the control limit, or falling below the control limit; and converting the classification results into the degree of support compensation requirement of each retaining structure monitoring point; and collecting and organizing the data according to the retaining structure monitoring points to obtain the deformation compensation requirement of each retaining structure monitoring point.

[0083] Based on the deformation compensation requirements, combined with the spatial correspondence between each cell and the monitoring points of the enclosure structure, the cell pressure margin, the temperature state of the phase change medium, and the flow state of the circulating pipeline, the priority of support adjustment is determined.

[0084] The specific process includes: based on the deformation compensation demand, matching the relevant cells corresponding to each monitoring point of the retaining structure one by one according to the spatial correspondence between each cell and the monitoring points of the retaining structure; focusing on the cells corresponding to the monitoring points of the retaining structure with large deformation compensation demands; and judging whether each relevant cell has the conditions to continue providing support compensation and the strength of its support compensation capacity by combining the current cell pressure margin, phase change medium temperature state, and circulation network flow state of each relevant cell. A comprehensive judgment is made based on the proximity reflected by the spatial correspondence, the urgency of compensation reflected by the deformation compensation demand, and the adjustment capacity reflected by the cell pressure margin, phase change medium temperature state, and circulation network flow state. Based on the comprehensive judgment results, each relevant cell is ranked to determine the priority of support adjustment.

[0085] It should be noted that the cell pressure margin is the difference between the upper limit of the cell's allowable pressure and the current cell pressure, used to characterize the available space for further pressure increase.

[0086] The target cells that need to be adjusted are screened by the support adjustment priority, and the active compensation support force demand is allocated according to the support adjustment priority to obtain the target support force change corresponding to each target cell.

[0087] The specific process includes ranking the relevant cells from high to low according to the priority of support adjustment, and taking into account the distribution of the retaining structure monitoring points corresponding to the deformation compensation demand, prioritizing the retention of cells that are spatially close to the retaining structure monitoring points with large deformation compensation demand, have sufficient cell pressure margin, have a phase change medium temperature state suitable for phase change control, and have a circulation network flow state that can meet the medium transportation and distribution requirements; reducing the adjustment priority of cells that do not meet the above conditions, and the retained cells are determined as the target cells that need to be adjusted.

[0088] After identifying the target cells requiring adjustment, the active compensation support force requirements are distributed one by one according to the target cells. The active compensation support force requirements are then allocated hierarchically based on the support adjustment priority of each target cell, the deformation compensation requirement corresponding to each target cell, the cell pressure margin, the phase change medium temperature state, and the circulating pipeline flow state. For target cells with high support adjustment priority and good support compensation conditions, larger support action change requirements are allocated; for target cells with relatively low support adjustment priority or relatively limited support compensation conditions, smaller support action change requirements are allocated, thus obtaining the target support force change amount corresponding to each target cell.

[0089] Based on the change in target support force and the phase change execution strategy, the pressure regulation requirements of the target cell are determined, and the corresponding phase change control actions of the target cell are determined based on the pressure regulation requirements.

[0090] The specific process includes: sorting out the target support force changes one by one according to the target cells, and distinguishing whether the target support force changes are used to improve or reduce the support effect in combination with the treatment direction already determined in the phase change execution strategy; comparing the target support force changes with the current cell pressure state of the target cells to determine the degree of pressure change that the target cells need to increase or decrease in order to achieve the target support force changes, and determining the pressure adjustment requirements of the target cells.

[0091] The pressure regulation demand is matched with the phase change execution strategy. When the pressure regulation demand indicates an increase in support, the freezing boosting action or the liquid injection action is determined by combining the phase change medium temperature state and the circulation network flow state. When the pressure regulation demand indicates a decrease in support, the heating melting unloading action or the liquid drainage action is determined by combining the cell pressure state, the phase change medium temperature state and the circulation network flow state. The pressure regulation demand corresponding to each target cell is matched with the phase change control action one by one to determine the phase change control action corresponding to the target cell.

[0092] The feasibility of the circulating pipe network corresponding to the target cell is judged by using phase change control actions and pipe network blockage discrimination information, and the circulating pipe network with blockage risk is cleared to obtain the clearing status.

[0093] The specific process includes: extracting phase change control actions one by one according to the target cells, and organizing the corresponding medium distribution direction, medium flow requirements, and duration requirements of the phase change control actions; matching the pipeline blockage identification information with the corresponding circulation pipeline of the target cells one by one to determine whether the circulation pipeline currently has the medium distribution conditions required to complete the phase change control actions; when the pipeline blockage identification information indicates that the circulation pipeline has no blockage risk and can meet the medium distribution requirements corresponding to the phase change control actions, the corresponding circulation pipeline is determined to be in an executable state; when the pipeline blockage identification information indicates that the circulation pipeline has a blockage risk or cannot meet the medium distribution requirements corresponding to the phase change control actions, the corresponding circulation pipeline is cleared, and the flow status, cell pressure status, and phase change medium temperature status of the circulation pipeline after clearing are re-checked; based on the re-checked situation, the circulation pipeline corresponding to each target cell is determined to be in an executable state or a state that needs to be cleared further, thus obtaining the clearing status.

[0094] Based on the phase change control actions and the dredging and disposal status, the execution sequence, feedback acquisition requirements, and control command content of the target cells are determined to form an executable control scheme.

[0095] The specific process includes, based on the phase change control action and the unblocking treatment status, sorting out the freezing boosting action, heating melting unloading action, liquid filling action and liquid drainage action corresponding to the phase change control action one by one according to the target cell, and matching the unblocking treatment status with the circulation pipe network corresponding to the target cell; prioritizing the target cells corresponding to the circulation pipe network in the executable state, and delaying the target cells corresponding to the circulation pipe network in the state that needs to continue unblocking, thereby determining the execution order.

[0096] Based on the execution sequence, and considering the medium flow requirements, cell pressure status, phase change medium temperature status, circulating pipe network flow status, and retaining structure deformation status corresponding to the phase change control actions, the cell pressure data, phase change medium temperature data, circulating pipe network flow data, retaining structure displacement data, and settlement data that need to be continuously collected during the execution process are identified, forming feedback collection requirements. Based on the execution sequence and feedback collection requirements, the execution objects, execution order, and execution conditions of the injection command, freezing and boosting command, heating and melting unloading command, drainage and recovery command, and dredging and disposal command are identified for each target cell, forming control command content. The execution sequence, feedback collection requirements, and control command content are collected and organized according to the target cells to form an executable control scheme.

[0097] S4. Configure IoT sensors and controlled execution units according to the executable control scheme, and control the phase change medium to switch between liquid and solid states in the target cell to obtain support force regulation execution data.

[0098] Based on the executable control scheme and linkage control strategy, the IoT sensors and controlled execution units corresponding to the target cell are bound together to form a sensing and execution relationship. Liquid-solid switching control commands are then sent to the controlled execution units to control the phase change medium to perform phase change regulation actions in the target cell.

[0099] The specific process includes, according to the executable control scheme and linkage control strategy, organizing the execution sequence, feedback collection requirements and control instructions for each target cell, matching the pressure sensors, temperature sensors, flow meters, displacement gauges, settlement gauges and environmental monitoring equipment corresponding to the target cells according to the established correspondence between the target cells and the circulating pipe network and the monitoring points of the retaining structure, and binding the controlled execution units corresponding to the target cells according to the action requirements of liquid filling, freezing and boosting, heating and melting unloading, liquid drainage and recycling and dredging disposal, thus forming a one-to-one sensing and execution relationship between the target cells, IoT sensors and controlled execution units.

[0100] Based on the sensor execution relationship, and in accordance with the execution sequence and control instructions in the executable control scheme, a liquid-solid switching control instruction is issued to the controlled execution unit corresponding to the target cell. Combined with the liquid filling instruction, freezing boosting instruction, heating melting unloading instruction, liquid drainage and recovery instruction, and unblocking and disposal instruction specified in the linkage control strategy, the flow direction, phase transition direction, and duration of action of the phase change medium in the target cell are controlled, so that the phase change medium in the target cell completes the corresponding switching between liquid and solid states, thereby completing the phase change regulation action corresponding to the target cell.

[0101] It should be noted that the controlled actuator includes at least one or more of the following: a circulating pump, a reversing valve, a refrigeration unit, a heating unit, a drain valve, and a drain control valve.

[0102] Data on cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation during the phase change control process are collected to obtain support force control execution data.

[0103] The specific process includes: during the execution of the phase change control action corresponding to the target cell, continuously collecting data from the pressure sensor, temperature sensor, flow meter, and displacement meter corresponding to the target cell according to the sensor execution relationship and feedback acquisition requirements; organizing the cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure deformation data according to the execution order and acquisition sequence; collecting and recording the cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure deformation data of the same target cell before, during, and after the phase change control action, and correspondingly identifying the data change process under each acquisition period in conjunction with the control command content; and uniformly organizing the cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure deformation data with target cell identifier, execution order identifier, and acquisition sequence identifier to obtain the support force control execution data.

[0104] S5. Collect support execution feedback data based on support force control execution data, and generate a support safety control report.

[0105] Based on the support force regulation execution data, establish support execution verification records corresponding to active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies.

[0106] The specific process includes: classifying and organizing the support force regulation execution data according to the target cell, execution sequence, and data collection time sequence; extracting cell pressure data, phase change medium temperature data, circulating pipe network flow data, and retaining structure deformation data corresponding to the phase change regulation action execution process from the support force regulation execution data; and correspondingly aggregating the active compensation support force demand, phase change execution strategy, pipe network blockage identification information, and linkage control strategy according to the target cell; and combining the support effect change requirements in the active compensation support force demand, the phase change regulation action in the phase change execution strategy, and the circulating pipe network blockage identification information. The execution sequence and control requirements in the ring network blockage risk and linkage control strategy are matched with the same target cell, the same execution sequence, and the same acquisition time sequence in the support force regulation execution data. The support action change requirements, phase change regulation actions, circulating network blockage risk, execution sequence, control requirements, as well as cell pressure data, phase change medium temperature data, circulating network flow data, and retaining structure deformation data corresponding to each target cell are uniformly registered, and support execution verification records corresponding to active compensation support force requirements, phase change execution strategies, network blockage identification information, and linkage control strategies are established.

[0107] It should be noted that the support force control execution data is the execution process data, while the support execution feedback data is the continuous monitoring data within the feedback period after the execution is completed.

[0108] The support execution verification record is used to collect the support execution feedback data corresponding to the target cell. The support execution feedback data is then compared and analyzed with the active compensation support force requirement, phase change execution strategy, pipeline blockage identification information and linkage control strategy to generate a support safety control report.

[0109] The specific process includes organizing the data according to the target cells, execution sequence, and data collection time sequence in the support execution verification record, extracting the cell pressure data, phase change medium temperature data, circulating pipeline flow data, and retaining structure deformation data corresponding to the phase change control action execution process, and continuously tracking and collecting data on the target cells after the phase change control action is executed, in conjunction with the phase change control action, execution sequence, and control requirements registered in the support execution verification record, and organizing the relevant data with the corresponding target cells and data collection time periods, and collecting and recording the continuous data collected on the same target cell after the phase change control action is executed to form the support execution feedback data corresponding to the target cell.

[0110] The data on support execution feedback from the target cells is compared with the active compensation support force requirements to determine whether the changes in support effect meet the required level of change for active compensation support force. The support execution feedback data is then checked against the phase change execution strategy, pipeline blockage identification information, and linkage control strategy to determine whether the changes in phase change medium, the changes in circulating pipeline flow, and the actual control process are consistent with the established plan. The comparison, verification, and abnormal situations corresponding to each target cell are collected and organized to generate a support safety control report.

[0111] It should be noted that the support execution feedback data includes support force feedback data, retaining structure displacement feedback data, circulating pipeline flow feedback data, and phase change medium temperature feedback data; the support safety control report includes records of the target cell, risk level, active compensation support force requirement, actual support force change, phase change control actions, pipeline dredging results, retaining structure displacement feedback, settlement feedback, and abnormal handling recommendations.

[0112] This embodiment also provides an IoT-based deep foundation pit phase change active compensation support system, including: a data acquisition module for collecting historical and real-time data from the deep foundation pit construction site, and performing correction, standardization, and risk labeling to obtain a phase change support risk sample set; a risk assessment module for constructing a target risk assessment model using the phase change support risk sample set, and deploying the target risk assessment model in the edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies; and an execution control module for... Based on the active compensation support force requirement, the target cell and target support force change amount are determined. The phase change control action of the target cell is determined according to the phase change execution strategy. Combined with the pipeline blockage judgment information, the circulating pipeline is dredged and treated to form an executable control scheme. The phase change execution module is used to configure IoT sensors and controlled execution units according to the executable control scheme, and control the phase change medium to switch between liquid and solid states in the target cell to obtain support force control execution data. The report generation module is used to collect support execution feedback data based on the support force control execution data and generate a support safety control report.

[0113] In summary, this invention constructs a target risk assessment model using a phase change support risk sample set, enabling joint analysis and risk intensity identification of cell pressure, phase change medium temperature, circulating pipeline flow, and retaining structure deformation state. This improves the accuracy, foresight, and decision-making relevance of deep foundation pit support risk assessment. Furthermore, it formulates an executable control scheme based on active compensation support force requirements, phase change execution strategies, and pipeline blockage identification information. This collaboratively determines the target cell, phase change control actions, pipeline dredging procedures, and execution sequence, thereby enhancing the control accuracy, execution reliability, and on-site response efficiency of active compensation support.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for active compensation support of deep foundation pit phase change based on Internet of Things (IoT) sensing, characterized in that, include: Historical and real-time data from the deep foundation pit construction site were collected, and then corrected, standardized, and risk-labeled to obtain a risk sample set for phase change support. A target risk assessment model was constructed using a phase change support risk sample set, and the target risk assessment model was deployed in the edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies. Based on the active compensation support force requirement, determine the target cell and the target support force change amount to be adjusted. Based on the phase change execution strategy, determine the phase change control action of the target cell. Combine the pipeline blockage judgment information to dredge the circulating pipeline and form an executable control scheme. Based on the executable control scheme, IoT sensors and controlled execution units are configured, and the phase change medium is controlled to switch between liquid and solid states in the target cell to obtain support force regulation execution data; Based on the support force control execution data, support execution feedback data is collected, and a support safety control report is generated.

2. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The real-time data includes cell pressure data, phase change medium temperature data, circulating pipeline flow data, enclosure structure displacement data, settlement data, and environmental parameter data.

3. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for obtaining the phase change support risk sample set are as follows: Historical and real-time data from the deep foundation pit construction site were collected, and the historical and real-time data were synchronously corrected and standardized to obtain effective phase change support status data. Risk labeling is performed on the changes in support status and the changes in foundation pit deformation in the effective phase change support status data to obtain a risk sample set for phase change support.

4. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for constructing the target risk assessment model are as follows: Based on the mapping of the correspondence between monitoring points of the cell, pipeline and enclosure structure, the phase change support risk sample set is organized and mapped into a phase change causal diagram sample; The phase transition causal diagram samples are input into the continuous-time state encoding layer to extract the continuous evolution characteristics of cell pressure, phase transition medium temperature, circulating pipeline flow rate and enclosure structure deformation, thus obtaining the phase transition state characterization. The phase change state characterization is input into the hypergraph interaction propagation layer and the causal attention gating layer to calculate the risk intensity of the high-order interaction relationship between cell pressure, phase change medium temperature, circulating pipeline flow rate and retaining structure deformation, so as to obtain the phase change support risk intensity corresponding to each target cell. Based on the risk intensity of phase change support and the risk labeling in the phase change support risk sample set, the node influence weight and risk discrimination threshold are trained and corrected to obtain the target risk assessment model.

5. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1 or 4, characterized in that, The specific steps for generating the active compensation support force requirement, phase change execution strategy, pipeline blockage identification information, and linkage control strategy are as follows: The target risk assessment model is deployed in the edge computing device for deep foundation pit construction safety monitoring, and real-time data streams are received through the target risk assessment model to obtain the real-time phase change support status of each target cell; Based on the real-time phase change support status, identify the insufficient support status, excessive support status, and abnormal pipeline distribution status of each target cell to obtain phase change compensation and control information. Based on the phase change compensation control information, the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information are generated, and a linkage control strategy is generated based on the active compensation support force requirement, phase change execution strategy, and pipeline blockage judgment information.

6. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for determining the target cell and target support force change based on the active compensation support force requirement are as follows: Based on the active compensation support force requirement, the displacement and settlement data of the retaining structure are compared with the corresponding control limits to obtain the deformation compensation requirement of each retaining structure monitoring point. Based on the deformation compensation requirements, combined with the spatial correspondence between each cell and the monitoring points of the enclosure structure, the cell pressure margin, the temperature state of the phase change medium and the flow state of the circulating pipe network, the priority of support adjustment is determined. The target cells that need to be adjusted are screened by the support adjustment priority, and the active compensation support force demand is allocated according to the support adjustment priority to obtain the target support force change corresponding to each target cell.

7. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for forming an executable control scheme are as follows: Based on the change in target support force and phase change execution strategy, determine the pressure regulation requirements of the target cell, and determine the corresponding phase change control action of the target cell based on the pressure regulation requirements; The feasibility of the circulating pipe network corresponding to the target cell is judged by using phase change control action and pipe network blockage discrimination information, and the circulating pipe network with blockage risk is cleared to obtain the clearing status. Based on the phase change control actions and the dredging and disposal status, the execution sequence, feedback acquisition requirements, and control command content of the target cells are determined to form an executable control scheme.

8. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for obtaining the support force control execution data are as follows: Based on the executable control scheme and linkage control strategy, the IoT sensor and the controlled execution unit corresponding to the target cell are bound together to form a sensing and execution relationship, and liquid-solid switching control command is sent to the controlled execution unit to control the phase change medium to perform phase change regulation in the target cell. Data on cell pressure, phase change medium temperature, circulating pipeline flow rate, and retaining structure deformation during the phase change control process are collected to obtain support force control execution data.

9. The method for active compensation support of deep foundation pit phase change based on IoT sensing according to claim 1, characterized in that, The specific steps for generating the support safety control report are as follows: Establish support execution verification records corresponding to active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies based on support force regulation execution data. The support execution verification record is used to collect the support execution feedback data corresponding to the target cell. The support execution feedback data is then compared and analyzed with the active compensation support force requirement, phase change execution strategy, pipeline blockage identification information and linkage control strategy to generate a support safety control report.

10. A deep foundation pit phase change active compensation support system based on Internet of Things (IoT) sensing, based on the deep foundation pit phase change active compensation support method based on IoT sensing as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect historical and real-time data from the deep foundation pit construction site, and to perform correction, standardization, and risk labeling to obtain a risk sample set for phase change support. The risk assessment module is used to construct a target risk assessment model using a phase change support risk sample set, and deploy the target risk assessment model in the edge computing device for deep foundation pit construction safety monitoring to generate active compensation support force requirements, phase change execution strategies, pipeline blockage identification information, and linkage control strategies. The execution control module is used to determine the target cell and the change in target support force based on the active compensation support force requirement, determine the phase change control action of the target cell based on the phase change execution strategy, and combine the pipeline blockage judgment information to dredge the circulating pipeline network and form an executable control scheme. The phase change execution module is used to configure IoT sensors and controlled execution units according to the executable control scheme, and control the phase change medium to switch between liquid and solid states in the target cell to obtain support force regulation execution data. The report generation module is used to collect support execution feedback data based on support force control execution data and generate support safety control reports.

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