A method and system for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures

Abstract

This invention discloses a method and system for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures. The method includes: constructing a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings and structures - surrounding medium" based on underground engineering geological conditions, building and structure distribution, and surrounding environment. The monitoring devices conform to metrological standards, and the frequency is dynamically adapted according to construction conditions and risk levels. Real-time transmission or encrypted acquisition is performed in the core area. The monitoring data from different sources is standardized and preprocessed to extract characteristic parameters related to excavation face instability and building and structure deformation, constructing a dynamically updated characteristic parameter database, and clarifying the temporal variation law and statistical characteristics of each parameter. Combining multi-source parameters, a composite modeling approach using engineering mechanics and data-driven methods is adopted, introducing geological, structural, and environmental parameters as correction factors to construct a mutual feedback evolution model, quantifying the interaction characteristics of both, inputting real-time parameters to deduce the two-way mechanism, and predicting the collaborative evolution trend. This improves construction safety and controllability.

Description

Technical Field

[0001] This invention belongs to the field of underground engineering construction safety monitoring and risk management technology, and more specifically, relates to a method and system for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures. Background Technology

[0002] During underground engineering construction, excavation directly disturbs the stress field of the surrounding strata, leading to deformation of nearby buildings and structures. This deformation, in turn, alters the stress distribution of the strata, exacerbating the risk of instability at the excavation face. This feedback effect is one of the core challenges in engineering safety management. Current engineering practice often employs a single-dimensional, independent monitoring model for both the excavation face and buildings and structures, focusing only on the support stress at the excavation face or the settlement data of the buildings and structures. This makes it difficult to simultaneously capture the dynamic correlation between the two, resulting in a fragmented data coupling and an inability to fully reflect the evolution of the feedback effect.

[0003] Meanwhile, existing analytical methods mostly rely on single engineering mechanics calculations or statistical data fitting. The former is difficult to cover nonlinear changes under complex geological and environmental conditions, while the latter lacks physical mechanism support and is prone to fitting biases that deviate from engineering realities. This makes it impossible for engineers to accurately predict the coordinated evolution trend of excavation face instability and building deformation. They often can only take passive measures after risks have emerged, which not only increases construction safety hazards but may also lead to resource waste due to over-reinforcement or structural damage and project delays due to untimely handling.

[0004] As underground engineering projects become deeper, larger, and more complex, the distribution of existing buildings and structures in the surrounding area is becoming increasingly dense, and the interaction between excavation and these structures is becoming more significant, highlighting the limitations of traditional monitoring and analysis methods. Developing technical methods that can accurately characterize the feedback relationship between these two processes and enable early prediction of evolutionary trends is of great practical significance for improving the safety, economy, and controllability of underground engineering construction. It is also one of the key technical problems that urgently need to be solved in the field of underground engineering. Summary of the Invention

[0005] This invention aims to solve the technical problem of accurately characterizing the feedback effect between excavation face instability and deformation of adjacent buildings in underground engineering construction. By constructing a multi-dimensional monitoring system, integrating heterogeneous characteristic parameters, and establishing a composite feedback evolution model, the invention dynamically deduces the two-way interaction mechanism and synergistic evolution trend of the two, providing a scientific basis for engineering construction safety management and control, and improving construction safety and controllability.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures, comprising:

[0007] S1. Based on the geological conditions, building and structure distribution patterns and surrounding environmental characteristics of the underground engineering construction scenario, construct a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings and structures - surrounding media"; among which, the monitoring device must meet relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode.

[0008] S2. Standardize and preprocess the heterogeneous data collected by the multi-dimensional monitoring system; extract the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building from the preprocessed dataset, construct a dynamically updated characteristic parameter database, and clarify the temporal change law and statistical characteristics of each parameter.

[0009] S3. Combining multi-source correlation parameters, a composite modeling method combining engineering mechanics analysis and data-driven modeling is adopted, and geological, structural and environmental related parameters are introduced as correction factors to construct a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures.

[0010] S4. Based on the time-series variation data of characteristic parameters, quantify the correlation characteristics and critical state threshold of the interaction between the two; input the real-time updated parameter data into the model to dynamically deduce the bidirectional interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two.

[0011] Furthermore, the method for setting up the multi-dimensional monitoring system in S1 is as follows:

[0012] Deploy parameter monitoring devices that reflect the stability of the excavation area to collect mechanical parameters of the excavation face and construction and tunneling parameters; install deformation monitoring devices at key locations of adjacent buildings and structures to collect settlement, tilt and related derivative parameters of the buildings and structures; and deploy environmental parameter monitoring devices, including those for ground displacement and seepage, in the surrounding medium between the excavation area and the buildings and structures.

[0013] Furthermore, the process of dynamically adapting the monitoring frequency in S1 according to the construction conditions and risk level is as follows:

[0014] The ground disturbance intensity corresponding to the construction conditions is Its value is derived from the excavation face advance speed, the completion degree of support operation, and the physical and mechanical parameters of the strata, that is... ,in To accelerate the excavation process, The strata are heavily soiled. The bearing capacity coefficient of the support structure, The percentage of time required to complete the support work;

[0015] The safety warning threshold value corresponding to the risk level is Its value is determined comprehensively by the allowable deformation of the building structure, the stability threshold of the surrounding medium, and the engineering safety control standards, i.e. ,in The allowable cumulative deformation of the building or structure The allowable displacement threshold for the surrounding medium;

[0016] Monitoring frequency and , satisfy ,in As the baseline safety warning threshold, As the reference stratum disturbance intensity, The baseline monitoring frequency;

[0017] Based on the temporal change rate of the characteristic parameters, a secondary dynamic correction is performed on the monitoring frequency. Let the temporal change rate of the characteristic parameters associated with excavation face instability be... The temporal rate of change of the characteristic parameters associated with the deformation of the building is Define the coefficient of coordination for parameter changes ,in , These represent the maximum permissible rates of change for the two types of parameters;

[0018] Corrected monitoring frequency When the parameters change, the coefficient of coordination When the value approaches 1, it indicates that the feedback effect between the excavation face condition and the deformation of the structure is becoming more intense, and the monitoring frequency is simultaneously increased to twice the reference correction frequency; when When the value approaches 0, it indicates that both are in a stable state, and the monitoring frequency remains unchanged at the reference correction frequency.

[0019] Introducing influencing factors such as the duration of construction conditions and the duration of risk levels, and improving the long-term adaptation mechanism of monitoring frequency, the duration of construction conditions is set as follows: The risk level duration is Define the timeliness impact factor ,in The standard lifespan for operating conditions or risk levels;

[0020] Final Adaptation Monitoring Frequency When the duration of the working condition or the duration of the risk level is shorter than the standard period, the timeliness impact factor... The frequency of monitoring is further increased to cope with short-term high-intensity disturbances or sudden risks; when the duration is longer than the standard period and the parameter changes tend to stabilize, The value approaches 1, and the monitoring frequency remains stable.

[0021] Furthermore, the construction process of the dynamically updated feature parameter database in S2 is as follows:

[0022] Let the standardized sequence of instability-related characteristic parameters of the excavation face be... ,in , The timestamp is used for data collection; the sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: ,in The associated correction parameter sequence is as follows: ,in ;

[0023] First, validate the data validity formula. Filter valid data. It is a "single parameter data" to be verified. The time series mean of the parameter. Standard deviation For the data sample size, when The data is then determined to be valid and included in the corresponding parameter subset of the database.

[0024] Construct a parameter dynamic update triggering model, determine the update timing based on the parameter change rate and the strength of the feedback correlation, and define the parameter change rate at the excavation face. Rate of change of building parameters , The sampling time interval corresponding to the monitoring frequency;

[0025] Through the mutual feedback correlation formula Calculate the correlation strength between the two, when ,or ,or A real-time update mechanism is triggered, instantly entering new data into the database and synchronously updating the parameter time-series change curves and statistical characteristic values. The maximum allowable rate of change of the parameter. The critical correlation degree;

[0026] Establish an iterative optimization formula for the database to achieve dynamic adaptation of parameter storage and mutual feedback evolution model; assume the total amount of historical valid data in the database is... The latest amount of newly added valid data is Define data freshness Through data adaptation formula Assign priority to data adaptation for different time periods. For the current time, The initial database setup time; combined with the parameter calling requirements of the mutual feedback evolution model, through... The parameter storage logic is dynamically optimized to ensure that the model prioritizes the retrieval of core data with high freshness and high relevance when it is invoked, while preserving the complete time-series data chain. The "adaptation priority comprehensive value" represents the stored parameters. The higher the value, the higher the priority that the data will be called by the model.

[0027] Furthermore, the mutual feedback evolution model in S3 is specifically as follows:

[0028] Let the sequence of characteristic parameters associated with the instability of the excavation face be... The sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: The set of variables was corrected to These correspond to the unit weight of the stratum, void ratio, elastic modulus, foundation dimensions of buildings and structures, foundation depth of buildings and structures, groundwater level, and surrounding loads, respectively.

[0029] Based on the theory of strata-structure interaction, the relationship between excavation face instability and the deformation of buildings and structures is derived: This formula uses integral calculations to characterize the cumulative effect of excavation face parameters over time, and combines correction variables to reflect the comprehensive impact of geological, structural and environmental factors on the deformation of buildings and structures, and is directly related to the multi-source data collected by the monitoring system.

[0030] The reaction relationship between the deformation of structures and the stability of the excavation face is constructed. Based on the stress redistribution mechanism of the strata induced by the deformation of structures, the following is derived: This formula calculates the stress adjustment of the soil around the excavation face by coupling the deformation parameters of the building structure with the correction variable.

[0031] Simultaneously, a data-driven error compensation term is introduced, utilizing historical time-series data from the feature parameter database to define the error compensation function. ,in These are actual monitored values. The values ​​are calculated for the mechanistic model. Given the sample size, this function dynamically captures nonlinear correlations not covered by the mechanistic model, ultimately forming a complete mutual feedback evolution model:

[0032] ,

[0033] in , These are the error compensation terms for the two types of relationships, respectively.

[0034] Furthermore, the deduction process of the co-evolution trend of the two in S4 is as follows:

[0035] Let the predicted sequence of excavation face instability related characteristic parameters output by the mutual feedback evolution model be... The predicted sequence of characteristic parameters related to the deformation of buildings and structures is as follows: ,in To predict the step size, first use... , Calculate the predicted changes in parameters over future time periods, and then... , The predicted rate of change is obtained, and the intensity and speed of future changes in both are quantified.

[0036] Introducing co-evolutionary correlation factors The synergistic characteristics of their interaction are characterized by a definition based on the principle of correlation analysis:

[0037] ,

[0038] in , The maximum rate of change of the parameter in historical monitoring data;

[0039] when This indicates that the instability of the excavation face and the deformation of the building structure evolve in the same direction and in a coordinated manner; that is, when the rate of change of one increases, the rate of change of the other increases synchronously.

[0040] when At that time, the two exhibit reverse co-evolution, that is, when the rate of change of one increases, the rate of change of the other decreases accordingly. This factor can accurately capture the direction and intensity of the interaction between the two.

[0041] Based on the monotonicity of functions and the determination of the stability of evolutionary trends using limit theory, a function for predicting the changing trend of parameter-predicted sequences is defined. , ;

[0042] when and and When this occurs, it indicates that the rate of parameter change tends to slow down, the synergistic effect tends to level off, and the evolutionary trend stabilizes;

[0043] when or or When this occurs, it indicates that the rate of parameter change is amplified or the synergistic effect is intensified, and the evolutionary trend is developing in an unstable direction.

[0044] Furthermore, step S5 is included after step S4, as follows:

[0045] Based on engineering safety control standards, relevant specifications, and risk assessment results, multi-level early warning thresholds are set. When monitoring data and model simulation results trigger the corresponding early warning threshold, a graded response mechanism is activated. Early warning information and targeted handling suggestions are generated simultaneously and pushed to relevant responsible parties until the risk is reduced to a safe range and normal construction procedures are resumed.

[0046] Furthermore, the graded response mechanism in S5 includes, but is not limited to, adjusting the monitoring frequency, optimizing construction parameters, taking structural protection measures, suspending construction, and activating emergency response plans.

[0047] As a second aspect of the present invention, a feedback evolution system for excavation face instability and deformation of adjacent buildings and structures is also provided, comprising:

[0048] The monitoring system construction unit is used to build a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings - surrounding media" based on the geological conditions, building and structure distribution pattern and surrounding environmental characteristics of the underground engineering construction scenario. Among them, the monitoring device must meet the relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode.

[0049] The data processing and database building unit is used to standardize and preprocess heterogeneous data collected by the multi-dimensional monitoring system; from the preprocessed dataset, it extracts the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building structure, constructs a dynamically updated characteristic parameter database, and clarifies the temporal change law and statistical characteristics of each parameter.

[0050] The mutual feedback model building unit is used to combine multi-source correlation parameters and adopt a composite modeling method that combines engineering mechanics analysis and data-driven modeling. It introduces geological, structural and environmental related parameters as correction factors to build a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures.

[0051] The evolution trend prediction unit is used to quantify the correlation characteristics and critical state thresholds of the interaction between the two based on the time-series change data of characteristic parameters; it inputs the real-time updated parameter data into the model to dynamically deduce the bidirectional interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two.

[0052] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor as described in any one of the inventions, a method for the mutual feedback evolution of excavation face instability and deformation of adjacent structures.

[0053] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0054] 1. The present invention provides a feedback evolution method for excavation face instability and deformation of adjacent buildings and structures. By combining the geological conditions, building and structure distribution patterns, and surrounding environmental characteristics of the underground engineering construction scenario, it constructs a multi-dimensional monitoring system covering the "excavation operation area - adjacent buildings and structures - surrounding media." The monitoring devices meet relevant metrological standards, and the monitoring frequency is dynamically adapted according to the construction conditions and risk level. The core monitoring area employs real-time data transmission or encrypted acquisition modes. This technical feature ensures the comprehensiveness, accuracy, and timeliness of the monitoring data, enabling the simultaneous capture of multi-source heterogeneous data from excavation operations, building and structure status, and surrounding media. It avoids analytical biases caused by missing monitoring dimensions or data lag, providing high-quality, highly reliable underlying data support for subsequent data processing and model construction, thus providing a solid data foundation for subsequent technical steps.

[0055] 2. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation of the present invention, through standardized preprocessing of heterogeneous data collected by a multi-dimensional monitoring system, extracts characteristic parameters related to excavation face instability and building deformation, constructs a dynamically updated characteristic parameter database, and clarifies the temporal variation law and statistical characteristics of each parameter. This technical feature achieves unified integration and effective screening of heterogeneous data, eliminating invalid interference data, focusing on core related parameters, and simultaneously, through the dynamic updating characteristics of the database, incorporating new monitoring data in real time, ensuring the timeliness and completeness of characteristic parameters. This provides accurate and dynamic input data for the mutual feedback evolution model, enabling the model to conduct analysis based on the latest data and improving the adaptability of the model input.

[0056] 3. The present invention provides a method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures. This method combines multi-source correlation parameters with a composite modeling approach integrating engineering mechanics analysis and data-driven modeling. It introduces geological, structural, and environmental parameters as correction factors to construct a mutual feedback evolution model. Then, based on the temporal variation data of characteristic parameters, it quantifies the correlation characteristics of the interaction between the two, inputs real-time updated parameters to dynamically deduce the two-way interaction mechanism, and predicts the co-evolution trend. This technical feature achieves a deep integration of mechanical mechanisms and data patterns, ensuring the physical rationality of the model while improving its adaptability to complex working conditions through correction factors. Furthermore, through real-time parameter updates and dynamic deduction, it accurately characterizes the two-way feedback relationship between excavation face instability and deformation of adjacent buildings and structures, enabling early prediction of evolution trends and providing a scientific basis for the safety management of engineering construction. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the mutual feedback evolution method between excavation face instability and adjacent building deformation according to an embodiment of the present invention.

[0058] Figure 2This is a calculation diagram for inclinometer observation analysis in an embodiment of the present invention; where L represents the segment length of the inclinometer monitoring.

[0059] Figure 3 This is a graph showing the measured lateral displacement-depth data of an embodiment of the present invention.

[0060] Figure 4 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0062] Example 1

[0063] Please refer to Figure 1 This embodiment 1 provides a method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures, including:

[0064] S1. Based on the geological conditions, building and structure distribution patterns and surrounding environmental characteristics of the underground engineering construction scenario, construct a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings and structures - surrounding media"; among which, the monitoring device must meet relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode.

[0065] S2. Standardize and preprocess the heterogeneous data collected by the multi-dimensional monitoring system; extract the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building from the preprocessed dataset, construct a dynamically updated characteristic parameter database, and clarify the temporal change law and statistical characteristics of each parameter.

[0066] S3. Combining multi-source correlation parameters, a composite modeling method combining engineering mechanics analysis and data-driven modeling is adopted, and geological, structural and environmental related parameters are introduced as correction factors to construct a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures.

[0067] S4. Based on the time-series variation data of characteristic parameters, quantify the correlation characteristics and critical state threshold of the interaction between the two; input the real-time updated parameter data into the model to dynamically deduce the bidirectional interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two.

[0068] This embodiment 1 further elaborates on the above steps.

[0069] (1) Construction of monitoring system

[0070] In underground engineering construction, the monitoring system needs to fully cover the three core areas of excavation operations, adjacent buildings and structures, and surrounding media to ensure that the status changes and mutual influences of these three areas can be systematically captured, providing complete data support for subsequent feedback relationship analysis.

[0071] Specifically, in the excavation operation area, targeted parameter monitoring devices need to be deployed to collect mechanical parameters that can directly reflect the stability of the excavation face and key operational parameters during the construction and tunneling process. These parameters are the core basis for judging whether there is a risk of instability in the excavation face.

[0072] At key locations adjacent to buildings and structures, deformation monitoring devices are deployed to accurately collect settlement and tilt data of buildings and structures, as well as related parameters derived from these basic data, so as to intuitively grasp the deformation state of buildings and structures under excavation disturbance.

[0073] In the surrounding medium between the excavation area and the building structure, a monitoring device covering environmental parameters such as ground displacement and seepage is specially installed. This is because the surrounding medium, as the carrier of the interaction between the two, directly affects the mutual feedback strength and transmission path of the instability of the excavation face and the deformation of the building structure.

[0074] For example, such as Figure 2 As shown: its inclination observation analysis and calculation diagram corresponds to the monitoring data acquisition logic of the building and surrounding medium area. By dividing the monitoring object into units according to the segment length and collecting the inclination angle of each unit, it can not only achieve accurate acquisition of the inclination parameters of the building, but also decompose the displacement changes of the surrounding medium and fully capture the state information of both affected by excavation disturbance.

[0075] Similarly, for example, such as Figure 3 As shown, the measured lateral displacement-depth data curve is the result of processing the stratum displacement parameters collected by the surrounding medium monitoring device. The curve clearly reflects the medium displacement distribution at different depths and intuitively demonstrates the state change of the mutual feedback transmission carrier between the excavation and the structure, providing data support for subsequent analysis of the mutual feedback strength between the two.

[0076] Meanwhile, the dynamic adaptation of monitoring frequency must fully consider the intensity of ground disturbance and the engineering risk level under the construction conditions, while also responding to real-time changes in parameters, ensuring that monitoring data not only meets the needs of risk warning but also avoids resource waste. Specifically, the process of dynamically adapting the monitoring frequency according to the construction conditions and risk level is as follows:

[0077] The ground disturbance intensity corresponding to the construction conditions is Its value is derived from the excavation face advance speed, the completion degree of support operation, and the physical and mechanical parameters of the strata, that is... ,in To accelerate the excavation process, The strata are heavily soiled. The bearing capacity coefficient of the support structure, The percentage of time required to complete the support work;

[0078] The safety warning threshold value corresponding to the risk level is Its value is determined comprehensively by the allowable deformation of the building structure, the stability threshold of the surrounding medium, and the engineering safety control standards, i.e. ,in The allowable cumulative deformation of the building or structure The allowable displacement threshold for the surrounding medium;

[0079] Monitoring frequency and , satisfy ,in As the baseline safety warning threshold, As the reference stratum disturbance intensity, The baseline monitoring frequency;

[0080] Based on the temporal change rate of the characteristic parameters, a secondary dynamic correction is performed on the monitoring frequency. Let the temporal change rate of the characteristic parameters associated with excavation face instability be... The temporal rate of change of the characteristic parameters associated with the deformation of the building is Define the coefficient of coordination for parameter changes ,in , These represent the maximum permissible rates of change for the two types of parameters;

[0081] Corrected monitoring frequency When the parameters change, the coefficient of coordination When the value approaches 1, it indicates that the feedback effect between the excavation face condition and the deformation of the structure is becoming more intense, and the monitoring frequency is simultaneously increased to twice the reference correction frequency; when When the value approaches 0, it indicates that both are in a stable state, and the monitoring frequency remains unchanged at the reference correction frequency.

[0082] Introducing influencing factors such as the duration of construction conditions and the duration of risk levels, and improving the long-term adaptation mechanism of monitoring frequency, the duration of construction conditions is set as follows: The risk level duration is Define the timeliness impact factor ,in The standard lifespan for operating conditions or risk levels;

[0083] Final Adaptation Monitoring Frequency When the duration of the working condition or the duration of the risk level is shorter than the standard period, the timeliness impact factor... The frequency of monitoring is further increased to cope with short-term high-intensity disturbances or sudden risks; when the duration is longer than the standard period and the parameter changes tend to stabilize, The value approaches 1, and the monitoring frequency remains stable.

[0084] Meanwhile, the selection and deployment of monitoring devices must strictly adhere to relevant national and industry metrological standards to ensure the accuracy, reliability, and comparability of monitoring data. The selected monitoring devices must be calibrated and certified by a legally recognized metrological verification institution. Their measurement range, accuracy level, resolution, and other technical specifications must match the actual variation range of the monitoring parameters and the analytical requirements. They must be able to operate stably under the complex geological environment, humidity, temperature, and vibration conditions of underground engineering projects, effectively resisting external influences such as electromagnetic interference and media erosion, and ensuring the continuity and effectiveness of data acquisition.

[0085] The core monitoring area, as a critical and sensitive region where excavation face instability and building deformation interact, requires a hybrid approach combining real-time data transmission and encrypted data acquisition. Real-time data transmission utilizes wireless communication and fiber optic transmission technologies to upload monitoring data to the data processing center in real time, ensuring that engineers can immediately grasp the dynamic changes in parameters within the core area and respond quickly to sudden anomalies. Encrypted data acquisition, based on the risk level and parameter change characteristics of the core area, appropriately shortens the data acquisition interval and increases the data sampling frequency, accurately capturing subtle changes and abrupt changes in parameters. This provides high-density, high-precision raw data support for analyzing the early evolution of excavation face instability and building deformation, and identifying the critical triggering conditions of the interaction. The flexible application of these two modes ensures both the timeliness of monitoring data and enhances the ability to capture key information, laying a solid foundation for subsequent data analysis and model calculations.

[0086] (2) Data processing and database construction

[0087] In underground engineering construction monitoring, the data collected by the multi-dimensional monitoring system comes from a wide range of sources and is of various types. Different monitoring devices have different measurement principles and units of measurement. If the data is used directly for analysis, it will lead to data conflicts or accuracy deviations. Therefore, it is necessary to standardize and preprocess the heterogeneous data first. By unifying the units of measurement and correcting systematic errors, the various types of data are transformed into standardized datasets that can be analyzed collaboratively, laying the foundation for subsequent parameter extraction and database construction.

[0088] Let the standardized sequence of instability-related characteristic parameters of the excavation face be... ,in , The timestamp is used for data collection; the sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: ,in The associated correction parameter sequence is as follows: ,in ;

[0089] First, validate the data validity formula. Filter valid data. It is a "single parameter data" to be verified. The time series mean of the parameter. Standard deviation For the data sample size, when The data is then determined to be valid and included in the corresponding parameter subset of the database.

[0090] Dynamic updates to the database must be based on a scientific triggering mechanism to ensure the timely inclusion of new data reflecting changes in operating conditions. The specific process involves: determining the update timing based on the parameter change rate and the strength of the feedback correlation, and defining the change rate of the excavation face parameters. Rate of change of building parameters , The sampling time interval corresponding to the monitoring frequency;

[0091] Through the mutual feedback correlation formula Calculate the correlation strength between the two, when ,or ,or A real-time update mechanism is triggered, instantly entering new data into the database and synchronously updating the parameter time-series change curves and statistical characteristic values. The maximum allowable rate of change of the parameter. The critical correlation degree;

[0092] Establish an iterative optimization formula for the database to achieve dynamic adaptation of parameter storage and mutual feedback evolution model; assume the total amount of historical valid data in the database is... The latest amount of newly added valid data is Define data freshness Through data adaptation formula Assign priority to data adaptation for different time periods. For the current time, The initial database setup time; combined with the parameter calling requirements of the mutual feedback evolution model, through... The parameter storage logic is dynamically optimized to ensure that the model prioritizes the retrieval of core data with high freshness and high relevance when it is invoked, while preserving the complete time-series data chain. The "adaptation priority comprehensive value" represents the stored parameters. The higher the value, the higher the priority that the data will be called by the model.

[0093] Meanwhile, in the process of constructing the feature parameter database, clarifying the temporal variation patterns and statistical characteristics of each parameter is a key link between data preprocessing and model application. Through systematic analysis, the evolution trajectory and inherent distribution characteristics of parameters with the construction process can be accurately explored, providing data pattern support for subsequent mutual feedback relationship modeling.

[0094] The analysis of temporal variation patterns should focus on the construction timeline, combined with key construction milestones (such as the excavation face advancing to a specific mileage, the completion of support structure installation, and the peak value of soil disturbance around buildings). Trend analysis should be conducted on standardized excavation face instability-related characteristic parameters, building deformation-related characteristic parameters, and related correction parameters. By plotting the temporal variation curves of each parameter, the fluctuation trend of the parameters over time can be visually presented—if the mechanical parameters of the excavation face increase in a stepwise manner as excavation progresses, it indicates that soil disturbance gradually accumulates with the construction process; if the settlement parameters of buildings experience a sudden change at a certain construction stage and then tend to stabilize, the key disturbance event at that stage can be identified. Simultaneously, by using sliding window analysis to capture the short-term fluctuation cycle and long-term variation trend of the parameters, it is possible to distinguish between regular changes caused by normal construction disturbances and sudden changes caused by abnormal risks. For example, the periodic fluctuation of the surrounding medium seepage parameters may be related to the construction drainage rhythm, while a sudden surge may indicate abnormal soil permeability stability.

[0095] The analysis of statistical characteristics should focus on the numerical distribution and dispersion of parameters, encompassing key indicators such as mean, standard deviation, extreme values, quantiles, and probability distribution types. The mean reflects the average level of a parameter within the monitoring period; for example, the average settlement of a building or structure can serve as a benchmark for judging the overall degree of deformation. The standard deviation and coefficient of variation quantify the dispersion of parameters; an excessively large standard deviation of the mechanical parameters at the excavation face indicates uneven stress distribution in the strata during excavation, requiring close attention to the risk of local instability. Extreme values ​​(maximum and minimum values) clarify the fluctuation boundaries of parameters, and combined with engineering safety standards, can preliminarily define risk warning intervals. Quantile analysis can accurately locate the concentrated distribution range of parameters; for example, the 95th percentile can be used to determine the common range of building or structure deformation, and values ​​exceeding this range should be included in anomaly investigation. Furthermore, fitting tests determine the probability distribution type of each parameter. If the stress parameters of the excavation face support conform to a normal distribution, the risk probability at different confidence levels can be calculated based on the distribution characteristics. If the tilt parameters of the building or structure exhibit a skewed distribution, the causes of the skewness need to be analyzed in conjunction with the construction conditions to provide statistical basis for subsequent model error compensation.

[0096] To ensure the accuracy of the analysis results, the temporal variation patterns and statistical characteristics need to be cross-validated. If the time-series curve shows that the parameter exhibits a continuous upward trend, the corresponding statistical mean should gradually increase with the extension of the monitoring period, and the standard deviation should be controlled within a reasonable range. If the time node of the statistical extreme value coincides with the key construction node, the rationality and correlation of the extreme value can be further verified. Through this systematic analysis process, we can clarify the temporal patterns of "when and how" each parameter changes, and also grasp the statistical characteristics of "how much it changes and how it is distributed," providing solid data pattern support for subsequent quantification of the mutual feedback correlation strength and setting model boundary conditions.

[0097] (3) Construction of mutual feedback model

[0098] In underground engineering, a complex two-way feedback relationship exists between excavation face instability and the deformation of adjacent structures. Relying solely on engineering mechanics analysis is insufficient to cover the nonlinear characteristics of the geological environment, while data-driven modeling alone lacks physical mechanism support. Therefore, a composite approach combining engineering mechanics analysis and data-driven modeling is necessary to construct an evolutionary model that accurately characterizes this feedback relationship. During the modeling process, in addition to the core characteristic parameters related to excavation face instability and structure deformation, geological, structural, and environmental parameters must be introduced as correction factors. These correction factors directly affect the intensity and transmission path of the feedback effect, and are crucial to ensuring the model accurately reflects engineering realities.

[0099] The model construction is first based on the theory of strata-structure interaction, clarifying the mechanism by which excavation face instability induces deformation of buildings and structures. The characteristic parameters associated with excavation face instability accumulate and act on the surrounding strata as construction progresses. This cumulative effect is transmitted through the strata to adjacent buildings and structures, triggering deformation. Specifically, the construction process of the mutual feedback evolution model is as follows:

[0100] Let the sequence of characteristic parameters associated with the instability of the excavation face be... The sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: The set of variables was corrected to These correspond to the unit weight of the stratum, void ratio, elastic modulus, foundation dimensions of buildings and structures, foundation depth of buildings and structures, groundwater level, and surrounding loads, respectively.

[0101] Based on the theory of strata-structure interaction, the relationship between excavation face instability and the deformation of buildings and structures is derived: This formula uses integral calculations to characterize the cumulative effect of excavation face parameters over time, and combines correction variables to reflect the comprehensive impact of geological, structural and environmental factors on the deformation of buildings and structures, and is directly related to the multi-source data collected by the monitoring system.

[0102] The reaction relationship between the deformation of structures and the stability of the excavation face is constructed. Based on the stress redistribution mechanism of the strata induced by the deformation of structures, the following is derived: This formula calculates the stress adjustment of the soil around the excavation face by coupling the deformation parameters of the building structure with the correction variable.

[0103] Meanwhile, considering the complexity of underground engineering geological conditions, a pure mechanistic model cannot fully cover all nonlinear correlations. Therefore, a data-driven error compensation term needs to be introduced to improve the model. Using historical time-series data accumulated in the feature parameter database, the calculated values ​​of the mechanistic model are compared with the actual monitored values. An error compensation function is constructed by analyzing the deviation patterns between the two.

[0104] ,

[0105] in These are actual monitored values. The values ​​are calculated for the mechanistic model. Given the sample size, this function dynamically captures nonlinear correlations not covered by the mechanistic model, ultimately forming a complete mutual feedback evolution model:

[0106] ,

[0107] in , These are the error compensation terms for the two types of relationships, respectively.

[0108] This function can dynamically capture nonlinear factors not covered by the mechanistic model, such as the effects of local geological heterogeneity and construction process fluctuations. It corrects errors in the two sets of relationships: the deformation of structures induced by the excavation face and the reaction of structures to the excavation face, ultimately forming a complete feedback evolution model. This combination of mechanism and data ensures the physical rationality of the model and improves its adaptability to complex working conditions through error compensation.

[0109] (4) Evolutionary trend prediction

[0110] In underground engineering construction, the co-evolution trend of excavation face instability and deformation of adjacent buildings and structures is directly related to project safety. It is necessary to accurately grasp the interaction and development direction of these two factors based on the temporal variation patterns of characteristic parameters through scientific quantitative analysis and dynamic simulation. This process is based on real-time updated characteristic parameter data, combined with the output results of the mutual feedback evolution model, forming a complete analytical chain from quantifying correlation characteristics and determining stability to predicting trends.

[0111] The core of quantifying the correlation characteristics lies in capturing the direction and intensity of their interaction, which requires relying on the time-series changes in feature parameters. The deduction process of their co-evolutionary trend is as follows:

[0112] Let the predicted sequence of excavation face instability related characteristic parameters output by the mutual feedback evolution model be... The predicted sequence of characteristic parameters related to the deformation of buildings and structures is as follows: ,in To predict the step size, first use... , Calculate the predicted changes in parameters over future time periods, and then... , The predicted rate of change is obtained, and the intensity and speed of future changes in both are quantified.

[0113] Introducing co-evolutionary correlation factors The synergistic characteristics of their interaction are characterized by a definition based on the principle of correlation analysis:

[0114] ,

[0115] in , The maximum rate of change of the parameter in historical monitoring data;

[0116] when This indicates that the instability of the excavation face and the deformation of the building structure evolve in the same direction and in a coordinated manner; that is, when the rate of change of one increases, the rate of change of the other increases synchronously.

[0117] when At that time, the two exhibit reverse co-evolution, that is, when the rate of change of one increases, the rate of change of the other decreases accordingly. This factor can accurately capture the direction and intensity of the interaction between the two.

[0118] Based on the monotonicity of functions and the determination of the stability of evolutionary trends using limit theory, a function for predicting the changing trend of parameter-predicted sequences is defined. , ;

[0119] when and and When this occurs, it indicates that the rate of parameter change tends to slow down, the synergistic effect tends to level off, and the evolutionary trend stabilizes;

[0120] when or or When this occurs, it indicates that the rate of parameter change is amplified or the synergistic effect is intensified, and the evolutionary trend is developing in an unstable direction.

[0121] The entire simulation process requires continuous access to real-time updated data from the feature parameter database to dynamically correct model inputs and prediction results, ensuring the timeliness and accuracy of the analysis. By quantifying correlation characteristics and determining stability states, the system ultimately achieves a scientific prediction of the co-evolution trend of excavation face instability and building deformation, providing precise evidence for risk warning and safety management during engineering construction, and helping technical personnel to develop response strategies in advance to avoid potential safety hazards.

[0122] (5) Risk assessment and response

[0123] After predicting the co-evolution trend of excavation face instability and deformation of adjacent buildings and structures, a closed-loop risk management mechanism needs to be established. This mechanism translates model simulation results into specific construction safety management measures, achieving precise risk control through tiered early warning and response, and ensuring that construction remains within a safe and controllable range. This step is crucial for connecting trend prediction with on-site handling, effectively preventing a disconnect between risk prediction and actual management, and ensuring that early warning information is promptly translated into action.

[0124] The setting of tiered early warning thresholds must balance scientific rigor and practicality, taking engineering safety control standards and relevant industry regulations as the core basis, and refining the tiered standards based on the results of previous risk assessments. During the setting process, key parameters such as allowable deformation of structures, excavation face stability control indicators, and surrounding environmental safety requirements should be considered. Multiple early warning levels should be established according to the degree of risk, from low to high, with each level corresponding to a clearly defined threshold range. Low-level early warning thresholds can be set within the range where parameters are close to the upper limit of normal fluctuations but do not exceed safety standards, while high-level early warning thresholds correspond to the range where parameters are close to or reach the safety threshold. This ensures that different levels of early warning accurately reflect the severity of the risk, providing a clear basis for subsequent tiered responses.

[0125] When monitoring data shows that parameters reach a certain level of warning threshold, or when model simulations predict that the evolution trend will trigger a warning, the corresponding graded response mechanism should be activated immediately. The graded response mechanism needs to match differentiated handling measures according to the warning level, forming a gradient response system from light to heavy. If a low-level warning is triggered, priority should be given to adjusting the monitoring frequency, closely tracking parameter changes by increasing the density of data collection, and optimizing construction parameters based on the cause of the warning, such as slowing down the excavation speed and adjusting the support operation rhythm, controlling the risk development through fine-tuning the construction process. If a medium-level warning is triggered, structural protection measures should be taken based on adjusting monitoring and construction parameters, such as adding reinforcement structures to adjacent buildings and strengthening the support strength of the excavation face, proactively reducing the risk level. If a high-level warning is triggered, construction in the relevant area should be immediately suspended to prevent further risk expansion. At the same time, an emergency response plan should be activated, professional technicians should be organized to conduct on-site investigations, develop specific handling plans, and, if necessary, evacuate construction personnel to ensure the safety of personnel and structures.

[0126] Upon activating the tiered response mechanism, the system simultaneously generates standardized early warning information and targeted handling suggestions. The early warning information must clearly define the warning level, specific parameters of the trigger threshold, the scope of risk impact, and its development trend. The handling suggestions must, based on the cause of the warning and the on-site conditions, propose specific and actionable measures, such as the technical parameters of the reinforcement plan and specific numerical adjustments for construction. This information will be rapidly pushed to relevant responsible parties, including construction management personnel, technical supervisors, and safety supervisors, ensuring that they are aware of the risk situation immediately. During the handling process, it is necessary to continuously track and monitor data and model simulation results to evaluate the effectiveness of the handling measures. If the risk gradually decreases to a safe range, the warning can be gradually lifted according to the level, and normal construction procedures can be resumed. If the risk is not effectively controlled, the response mechanism will be escalated until the risk is effectively managed.

[0127] The application prospects of this embodiment are primarily reflected in its broad applicability in the field of underground engineering, especially in construction scenarios adjacent to existing buildings and structures, such as urban subways, integrated utility tunnels, and deep foundation pit excavation. In construction in core urban areas, existing buildings and structures are densely distributed, and ground disturbance caused by excavation operations can easily lead to deformation of these buildings and structures. Traditional methods struggle to accurately depict the interaction between these two factors. This embodiment, through multi-dimensional monitoring, composite modeling, and dynamic simulation, effectively solves this core problem, providing scientific support for pre-construction risk prediction and dynamic management during construction. It significantly reduces the risk of engineering accidents caused by excavation face instability or building deformation, while also reducing resource waste from excessive reinforcement, thus achieving both safety and economic benefits.

[0128] Furthermore, the tiered early warning and response mechanism of this embodiment can be deeply integrated with the existing engineering safety management system. Its modeling logic, which combines data-driven and mechanism analysis, can also be extended to different geological conditions and construction process scenarios, and the model can be flexibly adjusted by adapting correction factors. As underground engineering develops towards deeper and more complex directions, the requirements for the accuracy of construction safety management are increasing. The "monitoring-analysis-prediction-control" closed-loop technology system formed in this embodiment can serve as the core module of intelligent engineering safety management, promoting the transformation of underground engineering construction from "passive handling" to "proactive prevention and control," and providing important technical support for improving the industry's safety management level.

[0129] Example 2

[0130] Please refer to Figure 4 This embodiment 2 provides a feedback evolution system for excavation face instability and deformation of adjacent buildings and structures, including:

[0131] The monitoring system construction unit is used to build a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings - surrounding media" based on the geological conditions, building and structure distribution pattern and surrounding environmental characteristics of the underground engineering construction scenario. Among them, the monitoring device must meet the relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode.

[0132] The data processing and database building unit is used to standardize and preprocess heterogeneous data collected by the multi-dimensional monitoring system; from the preprocessed dataset, it extracts the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building structure, constructs a dynamically updated characteristic parameter database, and clarifies the temporal change law and statistical characteristics of each parameter.

[0133] The mutual feedback model building unit is used to combine multi-source correlation parameters and adopt a composite modeling method that combines engineering mechanics analysis and data-driven modeling. It introduces geological, structural and environmental related parameters as correction factors to build a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures.

[0134] The evolution trend prediction unit is used to quantify the correlation characteristics and critical state thresholds of the interaction between the two based on the time-series change data of characteristic parameters; it inputs the real-time updated parameter data into the model to dynamically deduce the bidirectional interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two.

[0135] Example 3

[0136] This embodiment 3 also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement any step of a method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures.

[0137] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0139] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings and structures, characterized in that, include: S1. Based on the geological conditions, building and structure distribution patterns and surrounding environmental characteristics of the underground engineering construction scenario, construct a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings and structures - surrounding media"; among which, the monitoring device must meet relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode. S2. Standardize and preprocess the heterogeneous data collected by the multi-dimensional monitoring system; extract the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building from the preprocessed dataset, construct a dynamically updated characteristic parameter database, and clarify the temporal change law and statistical characteristics of each parameter. S3. Combining multi-source correlation parameters, a composite modeling method combining engineering mechanics analysis and data-driven modeling is adopted, and geological, structural and environmental related parameters are introduced as correction factors to construct a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures. S4. Based on the time-series variation data of characteristic parameters, quantify the correlation characteristics and critical state threshold of the interaction between the two; input the real-time updated parameter data into the model to dynamically deduce the bidirectional interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two. The specific mutual feedback evolution model in S3 is as follows: Let the sequence of characteristic parameters associated with the instability of the excavation face be... The sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: The set of variables was corrected to These correspond to the unit weight of the stratum, void ratio, elastic modulus, foundation dimensions of buildings and structures, foundation depth of buildings and structures, groundwater level, and surrounding loads, respectively. Based on the theory of strata-structure interaction, the relationship between excavation face instability and the deformation of buildings and structures is derived: This formula uses integral calculations to characterize the cumulative effect of excavation face parameters over time, and combines correction variables to reflect the comprehensive impact of geological, structural and environmental factors on the deformation of buildings and structures, and is directly related to the multi-source data collected by the monitoring system. The reaction relationship between the deformation of structures and the stability of the excavation face is constructed. Based on the stress redistribution mechanism of the strata induced by the deformation of structures, the following is derived: This formula calculates the stress adjustment of the soil around the excavation face by coupling the deformation parameters of the building structure with the correction variable. Simultaneously, a data-driven error compensation term is introduced, utilizing historical time-series data from the feature parameter database to define the error compensation function. ,in These are actual monitored values. The values ​​are calculated for the mechanistic model. Given the sample size, this function dynamically captures nonlinear correlations not covered by the mechanistic model, ultimately forming a complete mutual feedback evolution model: ， in , These are the error compensation terms for the two types of relationships, respectively.

2. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, The method for setting up the multi-dimensional monitoring system in S1 is as follows: Deploy parameter monitoring devices that reflect the stability of the excavation area to collect mechanical parameters of the excavation face and construction and tunneling parameters; install deformation monitoring devices at key locations of adjacent buildings and structures to collect settlement, tilt and related derivative parameters of the buildings and structures; and deploy environmental parameter monitoring devices, including those for ground displacement and seepage, in the surrounding medium between the excavation area and the buildings and structures.

3. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, The process of dynamically adapting the monitoring frequency in S1 according to the construction conditions and risk level is as follows: The ground disturbance intensity corresponding to the construction conditions is Its value is derived from the excavation face advance speed, the completion degree of support operation, and the physical and mechanical parameters of the strata, that is... ,in To accelerate the excavation process, The strata are heavily soiled. The bearing capacity coefficient of the support structure, The percentage of time required to complete the support work; The safety warning threshold value corresponding to the risk level is Its value is determined comprehensively by the allowable deformation of the building structure, the stability threshold of the surrounding medium, and the engineering safety control standards, i.e. ,in The allowable cumulative deformation of the building or structure The allowable displacement threshold for the surrounding medium; Monitoring frequency and , satisfy ,in As the baseline safety warning threshold, As the reference stratum disturbance intensity, The baseline monitoring frequency; Based on the temporal change rate of the characteristic parameters, a secondary dynamic correction is performed on the monitoring frequency. Let the temporal change rate of the characteristic parameters associated with excavation face instability be... The temporal rate of change of the characteristic parameters associated with the deformation of the building is Define the coefficient of coordination for parameter changes ,in , These represent the maximum permissible rates of change for the two types of parameters; Corrected monitoring frequency When the parameters change, the coefficient of coordination When the value approaches 1, it indicates that the feedback effect between the excavation face condition and the deformation of the structure is becoming more intense, and the monitoring frequency is simultaneously increased to twice the reference correction frequency; when When the value approaches 0, it indicates that both are in a stable state, and the monitoring frequency remains unchanged at the reference correction frequency. Introducing influencing factors such as the duration of construction conditions and the duration of risk levels, and improving the long-term adaptation mechanism of monitoring frequency, the duration of construction conditions is set as follows: The risk level duration is Define the timeliness impact factor ,in The standard lifespan for operating conditions or risk levels; Final Adaptation Monitoring Frequency When the duration of the working condition or the duration of the risk level is shorter than the standard period, the timeliness impact factor... The frequency of monitoring is further increased to cope with short-term high-intensity disturbances or sudden risks; when the duration is longer than the standard period and the parameter changes tend to stabilize, The value approaches 1, and the monitoring frequency remains stable.

4. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, The construction process of the dynamically updated feature parameter database in S2 is as follows: Let the standardized sequence of instability-related characteristic parameters of the excavation face be... ,in , The timestamp is used for data collection; the sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: ,in The associated correction parameter sequence is as follows: ,in ; First, validate the data validity formula. Filter valid data. It is a "single parameter data" to be verified. The time series mean of the parameter. Standard deviation For the data sample size, when The data is then determined to be valid and included in the corresponding parameter subset of the database. Construct a parameter dynamic update triggering model, determine the update timing based on the parameter change rate and the strength of the feedback correlation, and define the parameter change rate at the excavation face. Rate of change of building parameters , The sampling time interval corresponding to the monitoring frequency; Through the mutual feedback correlation formula Calculate the correlation strength between the two, when ,or ,or A real-time update mechanism is triggered, instantly entering new data into the database and synchronously updating the parameter time-series change curves and statistical characteristic values. The maximum allowable rate of change of the parameter. The critical correlation degree; Establish an iterative optimization formula for the database to achieve dynamic adaptation of parameter storage and mutual feedback evolution model; assume the total amount of historical valid data in the database is... The latest amount of newly added valid data is Define data freshness Through data adaptation formula Assign priority to data adaptation for different time periods. For the current time, The initial database setup time; combined with the parameter calling requirements of the mutual feedback evolution model, through... The parameter storage logic is dynamically optimized to ensure that the model prioritizes the retrieval of core data with high freshness and high relevance when it is invoked, while preserving the complete time-series data chain. The "adaptation priority comprehensive value" represents the stored parameters. The higher the value, the higher the priority that the data will be called by the model.

5. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, The deduction process of the co-evolution trend of the two in S4 is as follows: Let the predicted sequence of excavation face instability related characteristic parameters output by the mutual feedback evolution model be... The predicted sequence of characteristic parameters related to the deformation of buildings and structures is as follows: ,in To predict the step size, first use... , Calculate the predicted changes in parameters over future time periods, and then... , The predicted rate of change is obtained, and the intensity and speed of future changes in both are quantified. Introducing co-evolutionary correlation factors The synergistic characteristics of their interaction are characterized by a definition based on the principle of correlation analysis: ， in , The maximum rate of change of the parameter in historical monitoring data; when This indicates that the instability of the excavation face and the deformation of the building structure evolve in the same direction and in a coordinated manner; that is, when the rate of change of one increases, the rate of change of the other increases synchronously. when At that time, the two exhibit reverse co-evolution, that is, when the rate of change of one increases, the rate of change of the other decreases accordingly. This factor can accurately capture the direction and intensity of the interaction between the two. Based on the monotonicity of functions and the determination of the stability of evolutionary trends using limit theory, a function for predicting the changing trend of parameter-predicted sequences is defined. , ; when and and When this occurs, it indicates that the rate of parameter change tends to slow down, the synergistic effect tends to level off, and the evolutionary trend stabilizes; when or or When this occurs, it indicates that the rate of parameter change is amplified or the synergistic effect is intensified, and the evolutionary trend is developing in an unstable direction.

6. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, Step S5 is included after step S4, as follows: Based on engineering safety control standards, relevant specifications, and risk assessment results, multi-level early warning thresholds are set. When monitoring data and model simulation results trigger the corresponding early warning threshold, a graded response mechanism is activated. Early warning information and targeted handling suggestions are generated simultaneously and pushed to relevant responsible parties until the risk is reduced to a safe range and normal construction procedures are resumed.

7. The method for the mutual feedback evolution of excavation face instability and adjacent building deformation according to claim 1, characterized in that, The graded response mechanism in S5 includes, but is not limited to, adjusting monitoring frequency, optimizing construction parameters, taking structural protection measures, suspending construction, and activating emergency response plans.

8. A feedback evolution system for excavation face instability and deformation of adjacent buildings and structures, characterized in that, include: The monitoring system construction unit is used to build a multi-dimensional monitoring system covering "excavation operation area - adjacent buildings and structures - surrounding media" based on the geological conditions, building and structure distribution pattern and surrounding environmental characteristics of the underground engineering construction scenario. Among them, the monitoring device must meet the relevant metrological standards, the monitoring frequency is dynamically adapted according to the construction conditions and risk level, and the core monitoring area adopts real-time data transmission or encrypted acquisition mode. The data processing and database building unit is used to standardize and preprocess heterogeneous data collected by the multi-dimensional monitoring system; from the preprocessed dataset, it extracts the instability-related characteristic parameters of the excavation face and the deformation-related characteristic parameters of the building structure, constructs a dynamically updated characteristic parameter database, and clarifies the temporal change law and statistical characteristics of each parameter. The mutual feedback model building unit is used to combine multi-source correlation parameters and adopt a composite modeling method that combines engineering mechanics analysis and data-driven modeling. It introduces geological, structural and environmental related parameters as correction factors to build a mutual feedback evolution model of excavation face instability and deformation of adjacent buildings and structures. The evolution trend prediction unit is used to quantify the correlation characteristics and critical state thresholds of the interaction between the two based on the time-series change data of characteristic parameters; it inputs the real-time updated parameter data into the model to dynamically deduce the two-way interaction mechanism between excavation face instability and building deformation, and predict the co-evolution trend of the two. The mutual feedback evolution model in the mutual feedback model construction unit is specifically as follows: Let the sequence of characteristic parameters associated with the instability of the excavation face be... The sequence of characteristic parameters associated with the deformation of buildings and structures is as follows: The set of variables was corrected to These correspond to the unit weight of the stratum, void ratio, elastic modulus, foundation dimensions of buildings and structures, foundation depth of buildings and structures, groundwater level, and surrounding loads, respectively. Based on the theory of strata-structure interaction, the relationship between excavation face instability and the deformation of buildings and structures is derived: This formula uses integral calculations to characterize the cumulative effect of excavation face parameters over time, and combines correction variables to reflect the comprehensive impact of geological, structural and environmental factors on the deformation of buildings and structures, and is directly related to the multi-source data collected by the monitoring system. The reaction relationship between the deformation of structures and the stability of the excavation face is constructed. Based on the stress redistribution mechanism of the strata induced by the deformation of structures, the following is derived: This formula calculates the stress adjustment of the soil around the excavation face by coupling the deformation parameters of the building structure with the correction variable. Simultaneously, a data-driven error compensation term is introduced, utilizing historical time-series data from the feature parameter database to define the error compensation function. ,in These are actual monitored values. The values ​​are calculated for the mechanistic model. Given the sample size, this function dynamically captures nonlinear correlations not covered by the mechanistic model, ultimately forming a complete mutual feedback evolution model: ， in , These are the error compensation terms for the two types of relationships, respectively.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-7, a method for the mutual feedback evolution of excavation face instability and deformation of adjacent buildings.

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