A method for dynamic environmental settlement observation

Abstract

The present invention relates to a method for dynamic environmental settlement observation, belonging to the technical field of data analysis. High-precision equipment is used to collect settlement data and environmental data in the settlement area, and the time stamp, monitoring point number, temperature, and humidity information of the data are recorded. The settlement data and environmental data of the collected settlement area are preprocessed, and the processed data is stored in a database. The newly stored settlement data and environmental data are extracted from the database, and the settlement data and environmental data are merged to form a new data set. For each monitoring point, the settlement rate of each time period is calculated to form a settlement rate time series, and trend analysis is carried out. According to the settlement data and analysis results, a settlement prediction model is constructed, and a dynamic settlement warning threshold is set based on the prediction results. When the monitoring data exceeds the warning threshold, an alarm is triggered, and it is checked whether the settlement height of each monitoring point exceeds the warning threshold. The monitoring points that trigger the warning are marked as settlement abnormal areas, and a monitoring report is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and more specifically, to a dynamic environment settlement observation method. Background Art

[0002] With the acceleration of the global urbanization process, the continuous expansion of urban infrastructure and the rapid change of land use patterns, the problem of land subsidence has become increasingly prominent. Land subsidence refers to the gradual subsidence of the earth's surface or ground due to natural or human activities, and the reasons behind it are diverse. Especially under the combined action of factors such as overexploitation of groundwater resources, drastic changes in soil moisture, and construction site construction, the risk of subsidence is constantly increasing. This not only poses challenges to urban resource management and sustainable development, but also directly affects public safety, and may cause damage to infrastructure such as roads, bridges, and buildings, resulting in economic losses and social problems.

[0003] Traditional static measurement methods often cannot capture and reflect settlement changes in a dynamic environment in a timely manner due to their time lag and insufficient accuracy. Therefore, it is necessary to develop a new type of dynamic environment settlement observation method that can realize real-time monitoring and data analysis of settlement conditions, provide timely and accurate information support for relevant departments, and enable quick response and necessary countermeasures. Summary of the Invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a dynamic environment settlement observation method to solve the problems raised in the above background art.

[0005] The technical solution for the present invention to solve the above technical problems is as follows: A dynamic environment settlement observation method specifically includes the following steps:

[0006] Step 101: Collect settlement data and environmental data of the settlement area, perform preprocessing, and store the processed data in a database;

[0007] Step 102: Analyze the settlement data and environmental data stored in the database, and calculate the settlement rate and trend;

[0008] Step 103: Based on the settlement data and analysis results, construct a settlement prediction model, set a settlement warning threshold based on the prediction results, and trigger an alarm when the monitored data exceeds the warning threshold;

[0009] Step 104: According to the settlement warning threshold calculation method in Step 103, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring points that trigger the warning as settlement abnormal areas, and generate a monitoring report.

[0010] In a preferred embodiment, in step 101, the settlement data and environmental data of the settlement area are collected and preprocessed, and the processed data are stored in a database. The specific steps are as follows:

[0011] Step A1, data collection: using high-precision equipment including GPS equipment, laser rangefinder, environmental sensor to collect settlement data and environmental data in the settlement area, and record the timestamp, monitoring point number, temperature, humidity information of the data, further including the following steps:

[0012] Step A101: determine the land subsidence area to be monitored, construct an electronic map of the area, mark key infrastructure, terrain features and monitoring point locations, and divide the monitoring area into multiple sub-areas on the electronic map;

[0013] Step A102: deploying settlement monitoring points in each sub-area and installing settlement monitoring equipment at the monitoring points to monitor the change of ground height in real time;

[0014] Step A103, setting the frequency and time interval of data collection to collect settlement data and environmental data once every hour, including settlement height, timestamp, monitoring point number, temperature and humidity information;

[0015] Step A2, preprocessing: perform a preliminary check on the collected raw data, identify and remove outliers and erroneous data, use interpolation to complete the missing data to maintain data continuity, create a settlement data table and an environmental data table, and store the processed settlement data and environmental data in a database. The settlement data table records the monitoring point ID, collection time, and settlement height information; the environmental data table records the collection time, temperature, and humidity information.

[0016] In a preferred embodiment, in step 102, the sedimentation data and environmental data stored in the database are analyzed to calculate the sedimentation rate and trend. The specific steps are as follows:

[0017] Step B1, merging data sets: extracting newly stored settlement data and environmental data from the database, merging the settlement data with the environmental data, and connecting them using the acquisition time as the key field to form a new data set D = {ID, T, H, Te, Hμ}, where ID represents the monitoring point ID, T is the acquisition time, which exists in both the settlement data and the environmental data, H is the settlement height information, Te is the temperature, and Hμ is the humidity;

[0018] Step B2, calculating the sedimentation rate: for each monitoring point i, calculate the sedimentation rate in each time period to form a sedimentation rate time series. The sedimentation rate refers to the rate of change of the sedimentation height per unit time. The specific calculation formula is as follows:

[0019]

[0020] Among them, represents the settlement height at time T1, is the settlement height at time T2, and Δh i is the settlement rate of monitoring point i;

[0021] The settlement rate time series is Among them, represents the settlement rate corresponding to monitoring point i at time T m ;

[0022] Step B2. Analyze the settlement trend: Use a linear regression model to analyze the settlement trend. For the relationship between the settlement height H and time t, establish a linear regression model as: H i (t) = α + βt + ε. Obtain the model parameters α and β by least squares fitting. Among them, β is the settlement change rate, and ε is the error term.

[0023] In a preferred embodiment, in step 103, according to the settlement data and analysis results, construct a settlement prediction model, and set a settlement warning threshold based on the prediction results. When the monitoring data exceeds the warning threshold, trigger an alarm. The specific steps are as follows:

[0024] Step C1. Introduce the temperature and humidity in the environmental data into the settlement trend analysis, and establish a prediction model for the settlement data and environmental data: H i (T) = β0 + β1T + β2Te + β3Hμ + ε1, where H i (T) represents the predicted settlement height of monitoring point i at time T, β0 is the intercept of the model, (β1, β2, β3) are the regression coefficients of time T, temperature Te, and humidity Hμ respectively, and ε1 is the error term;

[0025] Step C2. Model training: Divide the data set D = {ID, T, H, Te, Hμ} into a training set and a validation set. Use the training set data to fit the established prediction model, and use the least squares method to minimize the sum of the squared errors between the predicted value and the actual value. The specific calculation formula is as follows:

[0026]

[0027] Among them, S(λ) represents the loss function, represents the actual settlement height of monitoring point i at time T, n represents the total number of samples of all monitoring points in the training set, λ0 is the intercept term of the model, (λ1, λ2, λ3) are the regression coefficients related to time, temperature, and humidity respectively, Te i is the temperature of monitoring point i at time T, and Hμ iis the humidity of monitoring point i at time T;

[0028] Step C3, Model Evaluation: Use the coefficient of determination and root mean square error to evaluate the fitting effect of the model. According to the evaluation results, adjust the model parameters (λ0, λ1, λ2, λ3). The specific calculation formulas are as follows:

[0029]

[0030] where, R 2 is the coefficient of determination, RMSE is the root mean square error, represents the actual settlement height of monitoring point i at time T, is the mean value of the actual settlement height, and n represents the total number of samples of all monitoring points in the training set;

[0031] Step C4, Set the dynamic settlement warning threshold: Use the predicted settlement height output by the model and environmental data, combine historical settlement data and error distribution, combine the changes in temperature and humidity with the prediction results of settlement data through a weighting factor, and set a comprehensive dynamic warning threshold as where, H i (T) represents the predicted settlement height of monitoring point i at time T, Te is the temperature, and Hμ is the humidity, is the weighting coefficient, indicating the influence degree of temperature, humidity and predicted settlement value on the threshold. When the monitored settlement height exceeds the threshold, an alarm is triggered.

[0032] In a preferred embodiment, in step 104, according to the settlement warning threshold calculation method of step 103, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring points where the warning is triggered as settlement abnormal areas, and generate a monitoring report. The specific steps are as follows:

[0033] Step D1, Abnormality Identification and Location: Compare the current settlement height of each monitoring point with the warning threshold of this point to determine whether it exceeds the set threshold. When the settlement height exceeds the warning threshold, mark this monitoring point as a settlement abnormal area, and calculate the percentage of the actual settlement exceeding the threshold as an indicator of the severity of the abnormality; when the settlement height does not exceed the warning threshold, continue to monitor and do not mark it as abnormal;

[0034] The specific calculation formula for the severity of the abnormality is as follows:

[0035]

[0036] where, Y(i,T) is the severity of the abnormality, is the actual settlement height of monitoring point i at time T, T threshold (i,T) is the warning threshold of monitoring point i at time T;

[0037] Step D2, generating an anomaly detection report: For each monitoring point marked as settlement anomaly, save the monitoring point number, actual settlement value, warning threshold, timestamp, and anomaly severity information, and generate a settlement anomaly detection report, including a list of anomaly monitoring points, the time window of the anomaly, settlement over-limit analysis, environmental data, and response suggestions; the list of anomaly monitoring points lists all the monitoring points that trigger alarms, including the number, actual settlement height, warning threshold, and anomaly severity of each monitoring point; the time window of the anomaly indicates the occurrence time and duration of the anomaly; the settlement over-limit analysis is to analyze the degree to which the settlement of each anomaly monitoring point exceeds the threshold, the environmental data provides the temperature and humidity at that time, and the response suggestions are that when the anomaly severity Y(i,T)≥50%, immediately stop work and perform structural reinforcement treatment, and when the anomaly severity Y(i,T)<50%, continue monitoring and update the data hourly.

[0038] The beneficial effects of the present invention are as follows: Using high-precision devices including GPS devices, laser rangefinders, and environmental sensors to collect settlement data and environmental data in the settlement area, and record the timestamp, monitoring point number, temperature, and humidity information of the data, preprocess the collected settlement data and environmental data of the settlement area, and store the processed data in a database, extract the newly stored settlement data and environmental data from the database, and merge the settlement data and environmental data to form a new data set. For each monitoring point, calculate the settlement rate for each time period to form a settlement rate time series, and perform trend analysis. Based on the settlement data and analysis results, construct a settlement prediction model, and set a settlement warning threshold based on the prediction results. When the monitoring data exceeds the warning threshold, trigger an alarm, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring points that trigger the warning as settlement anomaly areas, and generate a monitoring report. Through the dynamic environmental observation method, real-time monitoring of land subsidence can be achieved, and settlement changes can be effectively captured. Description of the Drawings

[0039] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0040] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0041] In the description of the present application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present application, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0042] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in the present application.

[0043] Embodiment 1

[0044] This embodiment provides a dynamic environment settlement observation method as shown in Figure 1 and specifically includes the following steps:

[0045] Step 101: Collect the settlement data and environmental data of the settlement area, perform preprocessing, and store the processed data in the database;

[0046] Step 102: Analyze the settlement data and environmental data stored in the database, and calculate the settlement rate and trend;

[0047] Step 103: Construct a settlement prediction model based on the settlement data and analysis results, and set a settlement warning threshold based on the prediction results. When the monitoring data exceeds the warning threshold, an alarm is triggered;

[0048] Step 104: According to the settlement warning threshold calculation method in Step 103, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring points that trigger the warning as settlement abnormal areas, and generate a monitoring report.

[0049] Preferably, in Step 101, the settlement data and environmental data of the settlement area are collected, preprocessed, and the processed data is stored in the database to achieve real-time monitoring of the settlement data, and through the database query interface, it is convenient to extract and analyze the data in a timely manner. The specific steps are as follows:

[0050] Step A1, Data Acquisition: Use high-precision devices including GPS devices, laser rangefinders, and environmental sensors to collect settlement data and environmental data within the settlement area, and record the timestamp, monitoring point number, temperature, and humidity information of the data. Further, it includes the following steps:

[0051] Step A101, Determine the ground settlement area to be monitored, construct an electronic map of the area, and mark key infrastructure, terrain features, and monitoring point locations. Divide the monitoring area into multiple sub-areas on the electronic map for easy management and data processing;

[0052] Step A102, Reasonably arrange settlement monitoring points within each sub-area to ensure coverage of key positions in each area, and install settlement monitoring devices at the monitoring points for real-time monitoring of ground height changes;

[0053] Step A103, Set the data acquisition frequency and time interval to collect settlement data and environmental data once per hour, including settlement height, timestamp, monitoring point number, temperature, and humidity information;

[0054] Step A2, Preprocessing: Conduct a preliminary inspection of the collected raw data, identify and remove obvious outliers and incorrect data. For missing data, use interpolation to complete it to maintain data continuity. Create a settlement data table and an environmental data table, and store the processed settlement data and environmental data in the database. The settlement data table records the monitoring point ID, collection time, and settlement height information; the environmental data table records the collection time, temperature, and humidity information.

[0055] Preferably, in step 102, analyze the settlement data and environmental data stored in the database, calculate the settlement rate and trend, and be able to quantify the impact of settlement on building structures and foundation stability. The specific steps are as follows:

[0056] Step B1, Merge data sets: Extract the newly stored settlement data and environmental data from the database, and merge the settlement data with the environmental data. Use the collection time as the key field for connection to form a new data set D = {ID, T, H, Te, Hμ}, where ID represents the monitoring point ID, T is the collection time, which exists in both the settlement data and the environmental data, H is the settlement height information, Te is the temperature, and Hμ is the humidity;

[0057] Step B2, Calculate the settlement rate: For each monitoring point i, calculate the settlement rate for each time period to form a settlement rate time series. The settlement rate refers to the rate of change of settlement height per unit time. The specific calculation formula is as follows:

[0058]

[0059] Among them, represents the settlement height at time T1, is the settlement height at time T2, Δh i is the settlement rate of monitoring point i, with the unit of height / time;

[0060] The settlement rate time series is Among them, represents the settlement rate corresponding to monitoring point i at time T m ;

[0061] Step B2, analyze the settlement trend: Use a linear regression model for settlement trend analysis. For the relationship between the settlement height H and time t, establish a linear regression model as: H i (t) = α + βt + ε. Obtain the model parameters α and β by least squares fitting. Among them, β is the settlement change rate, and ε is the error term.

[0062] Preferably, in step 103, according to the settlement data and analysis results, construct a settlement prediction model, and set a settlement warning threshold based on the prediction results. When the monitoring data exceeds the warning threshold, trigger an alarm to improve the pertinence and accuracy of monitoring. The specific steps are as follows:

[0063] Step C1, introduce the temperature and humidity in the environmental data into the settlement trend analysis, and establish a prediction model for the settlement data and environmental data: H i (T) = β0 + β1T + β2Te + β3Hμ + ε1, where H i (T) represents the predicted settlement height of monitoring point i at time T. β0 is the intercept of the model, (β1, β2, β3) are the regression coefficients of time T, temperature Te, and humidity Hμ respectively, and ε1 is the error term;

[0064] Step C2, model training: Divide the data set D = {ID, T, H, Te, Hμ} into a training set and a validation set. Use the training set data to fit the established prediction model, and use the least squares method to minimize the sum of the squares of the errors between the predicted value and the actual value. The specific calculation formula is as follows:

[0065]

[0066] Among them, S(λ) represents the loss function, represents the actual settlement height of monitoring point i at time T. n represents the total number of samples of all monitoring points in the training set. λ0 is the intercept term of the model, (λ1, λ2, λ3) are the regression coefficients related to time, temperature, and humidity respectively. Te i is the temperature of monitoring point i at time T, and Hμ i is the humidity of monitoring point i at time T;

[0067] Step C3, Model Evaluation: Use the coefficient of determination and root mean square error to evaluate the fitting effect of the model. According to the evaluation results, adjust the model parameters (λ0, λ1, λ2, λ3). The specific calculation formulas are as follows:

[0068]

[0069] where R 2 is the coefficient of determination, which measures the fitting degree of the model to the observed data. Its value range is between 0 and 1. The closer it is to 1, the better the fitting effect of the model. RMSE is the root mean square error, which is used to measure the deviation between the predicted value and the actual value. represents the actual settlement height of monitoring point i at time T.

[0070] is the mean value of the actual settlement height, and n represents the total number of samples of all monitoring points in the training set.

[0071] Step C4, Set the Dynamic Settlement Warning Threshold: Use the predicted settlement height value output by the model and environmental data, combine historical settlement data and error distribution, combine the changes in temperature and humidity with the prediction results of settlement data through a weighting factor, and set a comprehensive dynamic warning threshold as where H i (T) represents the predicted settlement height of monitoring point i at time T, Te is the temperature, and Hμ is the humidity. is the weighting coefficient, which represents the influence degree of temperature, humidity, and predicted settlement value on the threshold. When the monitored settlement height exceeds the threshold, an alarm is triggered.

[0072] Preferably, in step 104, according to the settlement warning threshold calculation method in step 103, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring points where the warning is triggered as settlement abnormal areas, and generate a monitoring report, which helps to reduce the probability of accidents and avoid building inclination caused by abnormal settlement. The specific steps are as follows:

[0073] Step D1, Abnormality Identification and Location: Compare the current settlement height of each monitoring point with the warning threshold of this point to determine whether it exceeds the set threshold. When the settlement height exceeds the warning threshold, mark this monitoring point as a settlement abnormal area, and calculate the percentage of the actual settlement exceeding the threshold as an indicator of the abnormality severity; when the settlement height does not exceed the warning threshold, continue to monitor and do not mark it as abnormal.

[0074] The specific calculation formula for the abnormality severity is as follows:

[0075]

[0076] Among them, Y(i,T) is the anomaly severity level, is the actual settlement height of monitoring point i at time T, and T threshold (i,T) is the warning threshold of monitoring point i at time T;

[0077] Step D2, generate an anomaly detection report: For each monitoring point marked as having settlement anomalies, save the monitoring point number, actual settlement value, warning threshold, timestamp, and anomaly severity level information, and generate a settlement anomaly detection report, including a list of anomaly monitoring points, the time window of the anomaly, settlement over - standard analysis, environmental data, and response suggestions; The list of anomaly monitoring points lists all monitoring points that trigger an alarm, including the number, actual settlement height, warning threshold, and anomaly severity level of each monitoring point; The time window of the anomaly indicates the occurrence time and duration of the anomaly; The settlement over - standard analysis is to analyze, for each anomaly monitoring point, the degree to which its settlement exceeds the threshold. The environmental data provides the temperature and humidity at that time. The response suggestions are that when the anomaly severity level Y(i,T) ≥ 50%, immediately stop work and perform structural reinforcement treatment, and when the anomaly severity level Y(i,T) < 50%, continue monitoring and update the data hourly.

[0078] It should be noted that in the above - mentioned embodiments, the descriptions of each embodiment have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memory, CD - ROM, optical memory, etc.) containing computer - usable program code.

[0080] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general - purpose computer, a special - purpose computer, an embedded computer, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data - processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0081] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the function specified in one or more of the procedures Figure 1 a procedure or procedures and / or Figure 1 a block or blocks.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the procedures Figure 1 a procedure or procedures and / or Figure 1 a block or blocks.

[0083] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0084] It is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A dynamic environmental settlement observation method, characterized in that: The specific steps include: Step 101: Collect settlement data and environmental data of the settlement area, perform preprocessing, and store the processed data in a database; Step 102, analyzing the settlement data and environmental data stored in the database to calculate the settlement rate and trend; Step 103: construct a settlement prediction model based on the settlement data and analysis results, and set a settlement warning threshold based on the prediction results. When the monitoring data exceeds the warning threshold, an alarm is triggered. The specific steps are as follows: Step C1: Introduce the temperature and humidity in the environmental data into the settlement trend analysis, and establish a prediction model for the settlement data and environmental data: i (T)=β0+β1T+β2Te+β3Hμ+ε1, where H i (T) represents the predicted settlement height of monitoring point i at time T, β0 is the intercept of the model, (β1, β2, β3) are the regression coefficients of time T, temperature Te and humidity Hμ respectively, and ε1 is the error term; Step C2, model training: divide the data set D = {ID, T, H, Te, Hμ} into a training set and a validation set, use the training set data to fit the established prediction model, and use the least squares method to minimize the sum of squared errors between the predicted value and the actual value. The specific calculation formula is as follows: Among them, S(λ) represents the loss function, represents the actual settlement height of monitoring point i at time T, n represents the total number of samples of all monitoring points in the training set, λ0 is the intercept term of the model, (λ1, λ2, λ3) are the regression coefficients related to time, temperature and humidity respectively, Te i is the temperature of monitoring point i at time T, Hμ i is the humidity at monitoring point i at time T; Step C3, model evaluation: Use the coefficient of determination and root mean square error to evaluate the fitting effect of the model. According to the evaluation results, adjust the model parameters (λ0, λ1, λ2, λ3). The specific calculation formula is as follows: Among them, R 2 is the coefficient of determination, RMSE is the root mean square error, represents the actual settlement height of monitoring point i at time T, is the mean of the actual settlement height, and n represents the total number of samples of all monitoring points in the training set; Step C4, setting dynamic settlement warning threshold: using the settlement height prediction value and environmental data output by the model, combined with historical settlement data and error distribution, the changes in temperature and humidity are combined with the predicted results of settlement data through weighting factors, and a comprehensive dynamic warning threshold is set as Among them, H i (T) represents the predicted settlement height of monitoring point i at time T, Te is the temperature, Hμ is the humidity, is the weighting coefficient. When the monitored settlement height exceeds the threshold, an alarm is triggered; Step 104: According to the settlement warning threshold calculation method of step 103, check whether the settlement height of each monitoring point exceeds the warning threshold, mark the monitoring point that triggers the warning as an abnormal settlement area, and generate a monitoring report. The specific steps are as follows: Step D1, abnormality identification and location: compare the current settlement height of each monitoring point with the warning threshold of the point to determine whether it exceeds the set threshold. When the settlement height exceeds the warning threshold, mark the monitoring point as a settlement abnormal area, and calculate the percentage of actual settlement exceeding the threshold as an indicator of the severity of the abnormality; when the settlement height does not exceed the warning threshold, continue monitoring and do not mark it as an abnormality; The specific calculation formula for the severity of the abnormality is as follows: Among them, Y(i,T) is the severity of the abnormality, is the actual settlement height of monitoring point i at time T, T threshold (i,T) is the warning threshold of monitoring point i at time T; Step D2, generate anomaly detection report: for each monitoring point marked as abnormal settlement, save the monitoring point number, actual settlement value, warning threshold, timestamp and abnormal severity information, and generate a settlement abnormality detection report, including a list of abnormal monitoring points, abnormal time window, settlement exceeding standard analysis, environmental data and response suggestions; the abnormal monitoring point list lists all monitoring points that trigger alarms, including the number of each monitoring point, actual settlement height, warning threshold, and abnormal severity; the abnormal time window indicates the occurrence time and duration of the abnormality; the settlement exceeding standard analysis is to analyze the degree of settlement exceeding the threshold for each abnormal monitoring point, the environmental data is to provide the temperature and humidity at that time, and the response suggestion is that when the abnormal severity Y(i,T)≥50%, immediately stop work and carry out structural reinforcement; when the abnormal severity Y(i,T)<50%, continue monitoring and update data every hour.

2. A dynamic environmental settlement observation method according to claim 1, characterized in that: In step 101, the settlement data and environmental data of the settlement area are collected and preprocessed, and the processed data are stored in a database. The specific steps are as follows: Step A1, data collection: Use high-precision equipment to collect settlement data and environmental data in the settlement area, and record the timestamp, monitoring point number, temperature, and humidity information of the data; Step A2, preprocessing: perform a preliminary check on the collected raw data, identify and remove outliers and erroneous data, use interpolation to complete the missing data to maintain data continuity, create a settlement data table and an environmental data table, and store the processed settlement data and environmental data in a database. The settlement data table records the monitoring point ID, collection time, and settlement height information; the environmental data table records the collection time, temperature, and humidity information.

3. A dynamic environmental settlement observation method according to claim 2, characterized in that: In the data collection of step A1, high-precision equipment including GPS equipment, laser rangefinder, and environmental sensor are used to collect settlement data and environmental data in the settlement area, and the timestamp, monitoring point number, temperature, and humidity information of the data are recorded, further comprising the following steps: Step A101: determine the land subsidence area to be monitored, construct an electronic map of the area, mark key infrastructure, terrain features and monitoring point locations, and divide the monitoring area into multiple sub-areas on the electronic map; Step A102: deploying settlement monitoring points in each sub-area and installing settlement monitoring equipment at the monitoring points to monitor the change of ground height in real time; Step A103, set the frequency and time interval of data collection to collect settlement data and environmental data once every hour, including settlement height, timestamp, monitoring point number, temperature and humidity information.

4. A dynamic environmental settlement observation method according to claim 1, characterized in that: In step 102, the settlement data and environmental data stored in the database are analyzed to calculate the settlement rate and trend. The specific steps are as follows: Step B1, merging data sets: extracting newly stored settlement data and environmental data from the database, merging the settlement data with the environmental data, and connecting them using the acquisition time as the key field to form a new data set D = {ID, T, H, Te, Hμ}, where ID represents the monitoring point ID, T is the acquisition time, which exists in both the settlement data and the environmental data, H is the settlement height information, Te is the temperature, and Hμ is the humidity; Step B2, calculate the sedimentation rate: for each monitoring point i, calculate the sedimentation rate in each time period to form a sedimentation rate time series. The specific calculation formula is as follows: in, represents the settlement height at time T1, is the settlement height at time T2, Δh i is the sedimentation rate of monitoring point i; Step B2, analysis of settlement trend: Use linear regression model to analyze settlement trend, and establish a linear regression model for the relationship between settlement height H and time t: H i (t) = α + βt + ε, and the model parameters α and β are obtained by least squares fitting, where β is the sedimentation change rate and ε is the error term.

5. A dynamic environmental settlement observation method according to claim 4, characterized in that: In step B2, the sedimentation rate time series is: in, Indicates the time T corresponding to the monitoring point i m sedimentation rate.

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