A method and system for predicting ground subsidence hazards

Abstract

The present invention relates to the technical field of land subsidence prediction, and discloses a method and system for predicting land subsidence hazards. The method includes: dividing the ground to be detected into several regions to be detected, using the Eclat algorithm to obtain the correlation result data of the target underground structure data and precipitation intensity data, obtaining the precipitation prediction settlement value by using the SARIMA model based on the correlation result data, drawing a time series graph of precipitation intensity and land subsidence according to the precipitation intensity data of several historical years and all historical settlement data, determining the settlement similar data set through a clustering algorithm, optimizing the precipitation prediction settlement value based on the settlement optimization factor, using a random forest model to obtain the disaster settlement trend between historical years and settlement amounts, and determining the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend. The present invention comprehensively predicts land subsidence by integrating precipitation data and disaster data, and has good prediction accuracy and comprehensiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of land subsidence prediction, and more particularly, to a method and system for predicting land subsidence hazards. Background Art

[0002] Land subsidence refers to the phenomenon of the ground sinking or subsiding due to the settlement of underground soil, rock formations, or buildings, etc. Land subsidence not only affects the structural safety of buildings, but may also have a significant impact on infrastructure such as transportation, water supply, and power supply systems, and even trigger a series of geological disasters such as collapses and cracks. Therefore, accurately predicting the occurrence trend and degree of land subsidence is of great significance for disaster prevention and control and engineering construction.

[0003] Precipitation and disasters are two main influencing factors of land subsidence. Changes in precipitation directly affect the humidity and compaction degree of the soil, thereby causing land subsidence. When there is precipitation, the soil absorbs water and swells, causing local settlement. In addition, natural disasters will also exacerbate land subsidence. In arid environments, the soil will shrink, leading to land subsidence, while floods and dust storms will also cause land subsidence to varying degrees. However, due to the contingency and uncertainty of the occurrence of these disasters, current prediction technologies cannot effectively predict land subsidence using disaster data and precipitation data, and cannot comprehensively consider the impact on land subsidence.

[0004] Therefore, it is necessary to design a method and system for predicting land subsidence hazards to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a method and system for predicting land subsidence hazards, aiming to solve the problem of being unable to comprehensively predict land subsidence using precipitation data and disaster data.

[0006] On the one hand, the present invention proposes a method for predicting land subsidence hazards, including:

[0007] Dividing the ground to be detected into several areas to be detected, obtaining precipitation intensity data of several historical years for each area to be detected based on big data, acquiring initial underground structure data of each area to be detected using a ground penetrating radar, preprocessing the initial underground structure data, and obtaining target underground structure data based on the preprocessing result;

[0008] Using the Eclat algorithm to obtain correlation result data of the target underground structure data and the precipitation intensity data, and obtaining precipitation prediction settlement values using the SARIMA model based on the correlation result data;

[0009] Based on big data, obtain all historical settlement data for each area to be detected. According to the precipitation intensity data for several historical years and all historical settlement data, draw a time series graph of precipitation intensity and ground settlement. Determine datasets with similar settlements through a clustering algorithm. Extract settlement optimization factors based on the datasets with similar settlements. Optimize the precipitation prediction settlement value based on the settlement optimization factors to obtain the target settlement value;

[0010] Obtain multiple historical disaster data and corresponding historical years in the local area. Calculate the settlement amount caused by disasters to the ground according to the historical disaster data. Use a random forest model to obtain the disaster settlement trend between the historical years and the settlement amount;

[0011] Determine the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend.

[0012] Further, when using ground penetrating radar to obtain the initial underground structure data for each area to be detected and preprocessing the initial underground structure data, it includes:

[0013] Set the ground penetrating radar frequency in each area to be detected;

[0014] Move along a preset route in each area to be detected and collect the initial underground structure data for each area to be detected at different frequencies;

[0015] The initial underground structure data includes soil data and underground crack data;

[0016] Preprocess the initial underground structure data, and the preprocessing includes data cleaning and standardization processing.

[0017] Further, when using the Eclat algorithm to obtain the correlation result data between the target underground structure data and the precipitation intensity data, it includes:

[0018] Use the Eclat algorithm to set the target underground structure data as the first transaction and the precipitation intensity data as the second transaction. Find the intersection of the first transaction and the second transaction through intersection operation to obtain the initial frequent item set, and recursively perform intersection operation on the initial frequent item set to obtain the frequent item set of the target underground structure data and the precipitation intensity data. Determine the correlation result data between the target underground structure data and the precipitation intensity data according to the frequent item set.

[0019] Further, when using the SARIMA model to obtain the precipitation prediction settlement value based on the correlation result data, it includes:

[0020] Divide the correlation result data into a training set and a test set;

[0021] Using cross - validation and combined with grid search, find the optimal parameters of the SARIMA model to be established, and use the training set to fit the SARIMA model to be established to obtain the SARIMA model;

[0022] Substitute the test set into the SARIMA model and calculate the accuracy rate of the precipitation settlement value;

[0023] When the accuracy rate reaches the preset accuracy threshold, obtain the precipitation predicted settlement value according to the correlation result data.

[0024] Furthermore, when obtaining all historical settlement data of each area to be detected based on big data, and drawing a time - series graph of precipitation intensity and land subsidence according to the precipitation intensity data of several historical years and all historical settlement data, it includes:

[0025] Obtain the precipitation intensity data of several historical years and all historical settlement data, with the selected time periods corresponding one by one. The x - axis of the time - series graph represents the year, and the y - axis of the time - series graph represents numbers;

[0026] The time - series graph includes a precipitation intensity sequence graph and a land subsidence sequence graph. The precipitation intensity sequence graph is obtained based on the precipitation intensity data of several historical years, and the land subsidence sequence graph is obtained based on all historical settlement data. The precipitation intensity sequence graph is a bar graph, and the land subsidence sequence graph is a line graph.

[0027] Furthermore, when determining the settlement - similar data set through a clustering algorithm, extracting a settlement optimization factor according to the settlement - similar data set, and optimizing the precipitation predicted settlement value based on the settlement optimization factor to obtain the target settlement value, it includes:

[0028] Take the precipitation intensity in the precipitation intensity sequence graph and the land subsidence in the land subsidence sequence graph as the data set to be aggregated. Determine that the expected number of clusters k is 3, and initialize the parameters of the Gaussian distribution. Calculate the probability that each data in the data set to be aggregated belongs to each Gaussian distribution and obtain the responsibility value. Obtain the data set corresponding to the precipitation intensity and land subsidence according to the responsibility value, and use the data set as the settlement - similar data set;

[0029] Take the average value of all data in the settlement - similar data set to obtain the settlement optimization factor, and the target settlement value is the product value of the settlement optimization factor and the precipitation predicted settlement value;

[0030] Preset a first settlement threshold and a second settlement threshold;

[0031] When the target settlement value is greater than or equal to the first settlement threshold, then judge that the target settlement value is severe settlement;

[0032] When the target settlement value is less than the first settlement threshold and greater than or equal to the second settlement threshold, it is determined that the target settlement value is medium settlement;

[0033] When the target settlement value is less than the second settlement threshold, it is determined that the target settlement value is slight settlement.

[0034] Further, when obtaining a plurality of historical disaster data and corresponding historical years of a local area and calculating the settlement amount generated by the disaster on the ground according to the historical disaster data, it includes:

[0035] The historical disaster data includes flood data, drought data, and dust storm data;

[0036] The settlement amount is obtained by the following formula:

[0037] ;

[0038] Wherein, represents the settlement amount, represents the water flow velocity of the flood, represents the sediment concentration in the flood, represents the flood duration, represents the average diameter of sediment particles deposited on the ground, represents the drought duration, represents at the duration the groundwater extraction volume, represents the groundwater extraction reference value, represents the soil type of the ground, represents the dust storm wind speed, represents the dust storm duration.

[0039] Further, when obtaining the disaster settlement trend between the historical year and the settlement amount by using a random forest model, it includes:

[0040] Arrange the historical years in chronological order into a disaster sequence, extract the characteristics of the disaster sequence and substitute them into the random forest model, and predict the disaster settlement trend according to the settlement amount. The disaster settlement trend includes an upward trend, a downward trend, and a stable trend.

[0041] Further, when determining the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend, it includes:

[0042] The settlement levels include a first predicted settlement level, a second predicted settlement level, and a third predicted settlement level;

[0043] When it is determined that the target settlement value is severe settlement and the disaster settlement shows an upward trend or a stable trend, it is determined as the first predicted settlement level;

[0044] When it is determined that the target settlement value is the severe settlement and the disaster settlement trend is the downward trend, it is determined as the second predicted settlement level. Or, when it is determined that the target settlement value is the medium settlement and the disaster settlement trend is the upward trend or the stable trend, it is determined as the second predicted settlement level. Or, when it is determined that the target settlement value is the slight settlement and the disaster settlement trend is the upward trend, it is determined as the second predicted settlement level;

[0045] When it is determined that the target settlement value is the medium settlement and the disaster settlement trend is the downward trend, it is determined as the third predicted settlement level. Or, when it is determined that the target settlement value is the slight settlement and the disaster settlement trend is the downward trend or the stable trend, it is determined as the third predicted settlement level.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: By dividing the area to be detected into multiple sub-areas, obtaining historical precipitation intensity and settlement data based on big data, and combining with the underground structure data of ground penetrating radar, the settlement risk of each area can be accurately analyzed. By combining the application of the Eclat algorithm and the SARIMA model, the impact of precipitation on ground settlement can be accurately predicted, thereby improving the reliability and accuracy of the prediction results. Using the time series graph drawn from historical precipitation intensity and settlement data, and extracting settlement optimization factors through the clustering algorithm, the settlement optimization factors are used to optimize the precipitation prediction settlement value, so as to obtain an accurate target settlement value, avoiding the error caused by a single prediction model and improving the stability and accuracy of the prediction. By introducing historical disaster data and using the random forest model to analyze the impact of disasters on ground settlement, the potential threat of natural disasters and other factors to ground settlement can be considered. The prediction of the disaster settlement trend provides comprehensive data for evaluating the severity of ground settlement. According to the target settlement value and the disaster settlement trend, the accurate classification of the settlement level can be realized, clearly understanding the settlement danger of the ground to be detected, and improving the accuracy of the prediction.

[0047] On the other hand, the present application also provides a ground settlement hazard prediction system for applying the above-mentioned ground settlement hazard prediction method, including:

[0048] A data acquisition module, a settlement prediction module, a settlement correction module, a disaster settlement module and a settlement level module;

[0049] The data acquisition module is configured to divide the ground to be detected into several areas to be detected, obtain precipitation intensity data of several historical years for each area to be detected based on big data, acquire initial underground structure data of each area to be detected by using a ground penetrating radar, preprocess the initial underground structure data, and obtain target underground structure data based on the preprocessing result;

[0050] The settlement prediction module is configured to obtain correlation result data between the target underground structure data and the precipitation intensity data by using the Eclat algorithm, and obtain precipitation prediction settlement values by using the SARIMA model based on the correlation result data;

[0051] The settlement correction module is configured to obtain all historical settlement data of each area to be detected based on big data, draw a time series graph of precipitation intensity and ground settlement according to the precipitation intensity data of several historical years and all historical settlement data, determine settlement similar data sets through a clustering algorithm, extract settlement optimization factors according to the settlement similar data sets, and optimize the precipitation prediction settlement values based on the settlement optimization factors to obtain target settlement values;

[0052] The disaster settlement module is configured to obtain multiple historical disaster data and corresponding historical years of the local area, calculate the settlement amount generated by the disaster on the ground according to the historical disaster data, and obtain the disaster settlement trend between the historical years and the settlement amount by using a random forest model;

[0053] The settlement level module is configured to determine the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend.

[0054] It can be understood that the above-mentioned method and system for predicting ground settlement hazards have the same beneficial effects, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0056] Figure 1 is a flowchart of a method for predicting ground settlement hazards provided by an embodiment of the present invention;

[0057] Figure 2 is a functional block diagram of a system for predicting ground settlement hazards provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0059] In some embodiments of the present application, referring to Figure 1 as shown, a method for predicting the risk of land subsidence includes:

[0060] S100: Divide the ground to be detected into several areas to be detected, obtain the precipitation intensity data of each area to be detected for several historical years based on big data, obtain the initial underground structure data of each area to be detected by using a ground penetrating radar, preprocess the initial underground structure data, and obtain the target underground structure data based on the preprocessing result;

[0061] S200: Use the Eclat algorithm to obtain the correlation result data of the target underground structure data and the precipitation intensity data, and obtain the precipitation prediction settlement value by using the SARIMA model based on the correlation result data;

[0062] S300: Obtain all the historical settlement data of each area to be detected based on big data, draw a time series graph of precipitation intensity and land subsidence according to the precipitation intensity data of several historical years and all the historical settlement data, determine the settlement similar data set by using a clustering algorithm, extract the settlement optimization factor according to the settlement similar data set, and optimize the precipitation prediction settlement value based on the settlement optimization factor to obtain the target settlement value;

[0063] S400: Obtain a plurality of historical disaster data and the corresponding historical years in the local area, calculate the settlement amount generated by the disaster on the ground according to the historical disaster data, and obtain the disaster settlement trend between the historical years and the settlement amount by using a random forest model;

[0064] S500: Determine the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend.

[0065] Specifically, the ground to be detected is divided into a number of detection areas. The number of detection areas is preferably 20. Based on big data technology, precipitation intensity data for several historical years of each detection area are obtained. The several historical years are preferably the past 5 years of the current year, including the 5th year in the past. Through the big data platform, precipitation intensity data for the past 5 years are collected to ensure that the time span of the obtained data is long enough to facilitate subsequent capture of the potential relationship between precipitation and settlement. The ground penetrating radar can provide comprehensive soil data and underground crack data below the ground, laying a data foundation for subsequent processing. The Eclat algorithm is a frequent item set mining algorithm. By analyzing the frequent item sets in the data, it can reveal the potential relationship between precipitation intensity data and target underground structure data. Once the correlation result data between precipitation intensity data and target underground structure data are obtained, the next step is to use the SARIMA model based on these correlation data to obtain the precipitation prediction settlement value. The SARIMA model is a prediction model for time series analysis. By combining the SARIMA model and the correlation data, the precipitation prediction settlement value can be obtained, providing a prediction result based on precipitation intensity data for the prediction of ground settlement. Again, using big data technology, all historical settlement data of each detection area are obtained. The historical settlement data include the actual settlement amounts in the past 5 years, including the 5th year. By combining the precipitation intensity data of historical years and the historical settlement amount data, a time series graph between precipitation intensity and ground settlement is drawn. This sequence graph can show the correlation between the fluctuations of precipitation intensity and the changes in settlement amount, providing an intuitive basis for the subsequent optimization of precipitation prediction settlement values. Based on the time series data, a clustering algorithm is used to group the historical settlement data, grouping the data with similar settlement trends into one category, determining the settlement similar data sets and extracting settlement optimization factors. By deeply analyzing the historical data, the prediction result is adjusted, improving the accuracy of the target settlement value.

[0066] It can be understood that regional disasters can also cause ground settlement. According to historical disaster data, the settlement amount caused by disaster events each year is calculated. By analyzing the settlement amounts before and after the disasters, a random forest model can be used to quantitatively evaluate the disaster settlement trend of disaster events on ground settlement each year. Based on the target settlement value and disaster settlement trend obtained from big data and the model, and combining to obtain the settlement level, the settlement risk of the ground to be detected can be comprehensively predicted, improving the accuracy and scientific nature of hazard prediction.

[0067] In some embodiments of the present application, when obtaining the initial underground structure data of each area to be detected by using a ground penetrating radar and preprocessing the initial underground structure data, it includes: setting the ground penetrating radar frequency in each area to be detected, moving along a preset route in each area to be detected, collecting the initial underground structure data of each area to be detected at different frequencies, where the initial underground structure data includes soil data and underground fracture data, and preprocessing the initial underground structure data, and the preprocessing includes data cleaning and normalization processing.

[0068] It can be understood that two frequencies are set according to the geological conditions of the area to be detected. The high-frequency radar wave can detect the deeper underground structure below the area to be detected to obtain the underground fracture data, and the low-frequency radar wave can detect the shallower underground structure below the area to be detected to obtain the soil data. Moving from the center of the area to be detected to the edge of the area to be detected can effectively obtain the underground fracture data of the area to be detected and improve the accuracy of subsequent prediction. Through preprocessing, the accuracy and quality of the target underground structure data can be ensured. Data cleaning removes the incorrect or incomplete data in the initial underground structure data, and then performs data normalization processing on it to ensure the consistency of the format of the target underground structure data, which is convenient for subsequent processing and analysis.

[0069] In some embodiments of the present application, when obtaining the correlation result data between the target underground structure data and the precipitation intensity data by using the Eclat algorithm, it includes: setting the target underground structure data as the first transaction and the precipitation intensity data as the second transaction by using the Eclat algorithm, finding the intersection of the first transaction and the second transaction through intersection operation to obtain the initial frequent item set, and recursively performing intersection operation on the initial frequent item set to obtain the frequent item set of the target underground structure data and the precipitation intensity data, and determining the correlation result data between the target underground structure data and the precipitation intensity data according to the frequent item set.

[0070] It can be understood that the Eclat algorithm is an algorithm for frequent itemset mining. It finds frequent itemsets in data transactions through operations based on set intersections. To deeply analyze the relationship between the target underground structure data and precipitation intensity data, the Eclat algorithm sets the target underground structure data and precipitation intensity data as the first transaction and the second transaction respectively, and finds the initial frequent itemsets between the two through intersection operations. First, the target underground structure data is used as the first transaction, and the precipitation intensity data is used as the second transaction. The intersection of the first transaction and the second transaction is obtained through intersection. For example, when the precipitation is 400 mm, the situation where there is a 0.5 mm extension of the underground crack with sandy soil type frequently appears. Thus, the initial frequent itemsets are obtained. After determining the preliminary frequent itemsets, the Eclat algorithm will further perform intersection operations on the obtained initial frequent itemsets through a recursive method. By continuously performing recursive operations, higher-order frequent itemsets can be screened out. Compared with traditional algorithms, the Eclat algorithm improves the mining efficiency of frequent itemsets through intersection operations, can quickly extract effective information from a large amount of data and reveal the potential association between the target underground structure data and precipitation intensity data, obtain correlation result data, and provide a scientific basis for subsequent further analysis and prediction.

[0071] In some embodiments of the present application, when obtaining the precipitation prediction settlement value using the SARIMA model based on the correlation result data, it includes: dividing the correlation result data into a training set and a test set, using cross-validation and combining with grid search to find the optimal parameters of the SARIMA model to be established, using the training set to fit the SARIMA model to be established, obtaining the SARIMA model, substituting the test set into the SARIMA model and calculating the accuracy rate of the precipitation settlement value. When the accuracy rate reaches the preset accuracy rate threshold, the precipitation prediction settlement value is obtained according to the correlation result data.

[0072] It can be understood that after obtaining the correlation result data, the correlation result data is divided into a training set and a test set. 50% to 70% of the correlation result data is used as the training set, and the remaining data is used as the test set. Ensure that both the training set and the test set contain various types of data to improve the generalization ability of the SARIMA model. At the same time, cross-validation and grid search are used to find the optimal parameters of the SARIMA model. Cross-validation divides the data into several parts and trains the SARIMA model multiple times to ensure the stability and reliability of the SARIMA model. In the absence of appropriate hyperparameters, the SARIMA model may suffer from overfitting or underfitting. Overfitting means that the model performs well on the training set but has poor performance on the test set, while underfitting means that the model fails to capture the complex patterns in the data. Finding appropriate hyperparameters through grid search can help improve the generalization ability of the model and avoid excessive performance differences between the training set and the test set. After the SARIMA model reaches the preset accuracy threshold, it can predict the correlation result data in real time to obtain the precipitation prediction settlement value, thereby providing an efficient and accurate prediction of the impact of precipitation on land subsidence. The accuracy of the land subsidence hazard prediction is improved.

[0073] In some embodiments of the present application, when obtaining all historical settlement data of each area to be detected based on big data and drawing a time series graph of precipitation intensity and land subsidence according to the precipitation intensity data of several historical years and all historical settlement data, it includes: obtaining the precipitation intensity data of several historical years and all historical settlement data, with the selected time periods corresponding one by one. The x-axis of the time series graph represents the year, and the y-axis of the time series graph represents numbers. The time series graph includes a precipitation intensity sequence graph and a land subsidence sequence graph. The precipitation intensity sequence graph is obtained based on the precipitation intensity data of several historical years, and the land subsidence sequence graph is obtained based on all historical settlement data. The precipitation intensity sequence graph is a bar graph, and the land subsidence sequence graph is a line graph.

[0074] It can be understood that, once again, all historical settlement data of each area to be detected is obtained according to the big data platform. The all historical settlement data is also the data of the past 5 years, including the 5th year. The historical settlement data corresponds one by one to the selected past time period to ensure that the precipitation intensity data and the historical settlement data are at the same time point. The precipitation intensity data and the land subsidence data are presented in the form of a time series graph, which provides a visual image for subsequent prediction and analysis of land subsidence. The x-axis of the time series graph is arranged by year, with each year as 1 unit. For example, 2021 and 2022, intuitively showing the past 5 years, while the y-axis is arranged by numbers, with 100mm and 1mm as units respectively, representing the actual values of precipitation intensity and land subsidence. The precipitation intensity sequence graph is in the form of a bar chart, which is convenient for showing the precipitation fluctuations in different years. The land subsidence sequence graph uses a line chart to show the trend of settlement changes. Through the combination of the bar chart and the line chart, the change trends of precipitation intensity and land subsidence can be clearly shown, providing data support for subsequent cluster analysis.

[0075] In some embodiments of the present application, when determining the settlement-similar data set through the clustering algorithm, extracting the settlement optimization factor according to the settlement-similar data set, and optimizing the precipitation prediction settlement value based on the settlement optimization factor to obtain the target settlement value, it includes: taking the precipitation intensity in the precipitation intensity sequence graph and the land subsidence in the land subsidence sequence graph as the data set to be aggregated, determining that the expected number of clusters k is 3, and initializing the parameters of the Gaussian distribution, calculating the probability that each data in the data set to be aggregated belongs to each Gaussian distribution and obtaining the responsibility value, obtaining the data set corresponding to the precipitation intensity and the land subsidence according to the responsibility value, taking the data set as the settlement-similar data set, taking the average value of all the data in the settlement-similar data set to obtain the settlement optimization factor, and the target settlement value is the product value of the settlement optimization factor and the precipitation prediction settlement value. The first settlement threshold and the second settlement threshold are preset. When the target settlement value is greater than or equal to the first settlement threshold, it is determined that the target settlement value is severe settlement. When the target settlement value is less than the first settlement threshold and greater than or equal to the second settlement threshold, it is determined that the target settlement value is moderate settlement. When the target settlement value is less than the second settlement threshold, it is determined that the target settlement value is slight settlement.

[0076] It can be understood that by analyzing precipitation intensity data and historical settlement data through a clustering algorithm, a settlement similar dataset is found, and the average value of all data in the settlement similar dataset is taken to obtain a settlement optimization factor. The obtained settlement optimization factor is derived from various data, which improves the compatibility and accuracy of the settlement optimization factor. Moreover, the optimization mechanism of the settlement optimization factor for precipitation prediction settlement value improves the accuracy of the target settlement value, reduces the errors caused by manual data acquisition and model prediction, and enhances the reliability of land subsidence prediction. The first settlement threshold is greater than the second settlement threshold. By comparing with the target settlement value in real time, the situation of land subsidence can be preliminarily predicted, laying a theoretical basis for subsequent processing.

[0077] In some embodiments of the present application, when obtaining multiple historical disaster data and corresponding historical years of a local area and calculating the settlement amount caused by the disaster to the ground according to the historical disaster data, it includes: The historical disaster data includes flood data, drought data, and dust storm data, and the settlement amount is obtained by the following formula:

[0078] ;

[0079] Wherein, represents the settlement amount, represents the water flow velocity of the flood, represents the sediment concentration in the flood, represents the flood duration, represents the average diameter of sediment particles deposited on the ground, represents the drought duration, represents during the duration the extraction amount of groundwater, represents the reference value of groundwater extraction, represents the soil type of the ground, represents the dust storm wind speed, represents the dust storm duration.

[0080] It can be understood that obtaining multiple historical years of a local area reflects the comprehensiveness of disaster data for land subsidence prediction. The historical years are preferably the past 10 years, including the 10th year. The soil type is represented as sandy soil, clay soil, and loam. The value of sandy soil is preferably 1.5, the value of clay soil is preferably 1, and the value of loam is preferably 0.8. The disaster data is the data of floods, droughts, and dust storms occurring in the local area. By comprehensively analyzing the influence of different disaster types on the settlement amount of the ground, the reliability and comprehensiveness of land subsidence prediction are improved.

[0081] In some embodiments of the present application, when obtaining the disaster settlement trend between historical years and settlement amounts using a random forest model, it includes: arranging the historical years in chronological order into a disaster sequence, extracting the features of the disaster sequence and substituting them into the random forest model, and predicting the disaster settlement trend based on the settlement amount. The disaster settlement trend includes an upward trend, a downward trend, and a stable trend.

[0082] In some embodiments of the present application, when determining the settlement level of the ground to be detected based on the target settlement value and the disaster settlement trend, it includes: The settlement level includes a first predicted settlement level, a second predicted settlement level, and a third predicted settlement level. When it is determined that the target settlement value is severe settlement and the disaster settlement is in an upward trend or a stable trend, it is determined as the first predicted settlement level. When it is determined that the target settlement value is severe settlement and the disaster settlement trend is a downward trend, it is determined as the second predicted settlement level. Or, when it is determined that the target settlement value is moderate settlement and the disaster settlement trend is an upward trend or a stable trend, it is determined as the second predicted settlement level. Or, when it is determined that the target settlement value is slight settlement and the disaster settlement trend is an upward trend, it is determined as the second predicted settlement level. When it is determined that the target settlement value is moderate settlement and the disaster settlement trend is a downward trend, it is determined as the third predicted settlement level. Or, when it is determined that the target settlement value is slight settlement and the disaster settlement trend is a downward trend or a stable trend, it is determined as the third predicted settlement level.

[0083] It can be understood that the same soil type as the ground to be detected is selected, and the historical years are sorted into a disaster sequence in chronological order. This sequence takes the years as the time series input, extracts the time features from the disaster sequence, uses the extracted features as input, combines the settlement amount corresponding to each year as input data, substitutes them into the random forest model to generate multiple decision trees and combines the output results, and trains and predicts the disaster settlement trend for the output results. The risk levels of the first predicted settlement level, the second predicted settlement level, and the third predicted settlement level decrease accordingly. By combining the target settlement value and the disaster settlement trend, the potential ground settlement risk can be accurately predicted. For example: Even if the target settlement value is judged to be slight settlement, if the disaster settlement trend is an upward trend, it can still be predicted as the second predicted settlement level, avoiding the influence of single - data prediction on the overall prediction level.

[0084] In summary, the beneficial effects of the present invention are as follows: By dividing the area to be detected into multiple sub - regions, obtaining historical precipitation intensity and settlement data based on big data, and combining with the underground structure data of ground penetrating radar, the settlement risk of each region can be accurately analyzed. By combining the application of the Eclat algorithm and the SARIMA model, the impact of precipitation on land subsidence can be accurately predicted, thereby improving the reliability and accuracy of the prediction results. Using the time - series graph drawn from historical precipitation intensity and settlement data, and extracting settlement optimization factors through the clustering algorithm, the settlement optimization factors are used to optimize the precipitation - predicted settlement value, so as to obtain an accurate target settlement value, avoiding the errors brought by a single prediction model and enhancing the stability and accuracy of the prediction. By introducing historical disaster data and using the random forest model to analyze the impact of disasters on land subsidence, the potential threats of natural disasters and other factors to land subsidence can be taken into account. The prediction of the disaster settlement trend provides comprehensive data for evaluating the severity of land subsidence. According to the target settlement value and the disaster settlement trend, the accurate classification of the settlement level can be realized, clearly understanding the settlement risk of the ground to be detected and enhancing the accuracy of the prediction.

[0085] In another preferred embodiment based on the above - mentioned embodiment, referring to Figure 2 as shown, this embodiment provides a land subsidence hazard prediction system for applying the above - mentioned land subsidence hazard prediction method, including:

[0086] a data acquisition module, a settlement prediction module, a settlement correction module, a disaster settlement module, and a settlement level module;

[0087] The data acquisition module is configured to divide the ground to be detected into several areas to be detected, obtain precipitation intensity data of several historical years for each area to be detected based on big data, use ground penetrating radar to obtain the initial underground structure data of each area to be detected, pre - process the initial underground structure data, and obtain the target underground structure data based on the pre - processing result;

[0088] The settlement prediction module is configured to use the Eclat algorithm to obtain the correlation result data of the target underground structure data and the precipitation intensity data, and obtain the precipitation - predicted settlement value based on the correlation result data using the SARIMA model;

[0089] The settlement correction module is configured to obtain all historical settlement data of each area to be detected based on big data, draw a time - series graph of precipitation intensity and land subsidence according to the precipitation intensity data of several historical years and all historical settlement data, determine the settlement - similar data set through the clustering algorithm, extract the settlement optimization factor according to the settlement - similar data set, and optimize the precipitation - predicted settlement value based on the settlement optimization factor to obtain the target settlement value;

[0090] The disaster settlement module is configured to obtain multiple historical disaster data and corresponding historical years in the local area, calculate the settlement amount caused by the disaster on the ground based on the historical disaster data, and use the random forest model to obtain the disaster settlement trend between the historical years and the settlement amount;

[0091] The settlement level module is configured to determine the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend.

[0092] It can be understood that each module in a ground subsidence hazard prediction system works together to achieve accurate prediction and effective correction of ground subsidence. The data acquisition module first divides the ground to be detected into multiple areas, and collects the historical precipitation intensity data of each area through big data technology. The initial underground structure data of each area to be detected is obtained using geological radar technology, and these data are preprocessed to obtain the target underground structure data, which lays a data foundation for subsequent analysis. The settlement prediction module uses the Eclat algorithm to calculate the correlation between the target underground structure data and the precipitation intensity data to obtain the correlation result data. Based on this data, the SARIMA model is used to calculate the precipitation predicted settlement value, which provides a prediction basis for subsequent settlement analysis. After obtaining the precipitation predicted settlement value, the settlement correction module draws a time series diagram of precipitation intensity and ground subsidence by combining historical settlement data and precipitation intensity data, clusters the data using a clustering algorithm to avoid extreme data, and extracts settlement optimization factors to optimize the precipitation predicted settlement value. Based on the extraction of multiple data, the historical settlement data and precipitation intensity data are comprehensively analyzed to improve the accuracy of the target settlement value. The disaster settlement module obtains historical disaster data of the local area, and combines it with the corresponding years, calculates the settlement caused by the disaster, and uses the random forest model to analyze the disaster settlement trend between historical years and settlement, reflecting the impact of disasters on ground settlement and improving the comprehensiveness and accuracy of ground settlement risk prediction. The settlement level module determines the settlement level of the ground to be tested based on the target settlement value and the disaster settlement trend, avoiding the impact of single data on the accuracy of the prediction and improving the accuracy of the prediction.

[0093] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0094] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor 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 implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for predicting land subsidence risk, characterized in that: include: The ground to be inspected is divided into several areas to be inspected, and the precipitation intensity data of several historical years of each area to be inspected are obtained based on big data. The initial underground structure data of each area to be inspected is obtained by using geological radar, and the initial underground structure data is preprocessed, and the target underground structure data is obtained based on the preprocessing results; The Eclat algorithm is used to obtain correlation result data between the target underground structure data and the precipitation intensity data, and the SARIMA model is used to obtain the precipitation predicted settlement value based on the correlation result data; Based on big data, all historical settlement data of each area to be detected are obtained. According to the precipitation intensity data of several historical years and all historical settlement data, a time series diagram of precipitation intensity and ground settlement is drawn. A settlement-similar data set is determined through a clustering algorithm. According to the settlement-similar data set, a settlement optimization factor is extracted. Based on the settlement optimization factor, the precipitation predicted settlement value is optimized to obtain a target settlement value. Obtain multiple historical disaster data and corresponding historical years in the local area, calculate the amount of ground settlement caused by the disaster based on the historical disaster data, and use a random forest model to derive the disaster settlement trend between the historical years and the settlement; The settlement level of the ground to be detected is determined according to the target settlement value and the disaster settlement trend.

2. The method for predicting land subsidence danger according to claim 1, characterized in that: When the initial underground structure data of each area to be detected is obtained by using geological radar, the initial underground structure data is preprocessed, including: Set the ground-penetrating radar frequency in each area to be inspected; Move along a preset route in each area to be inspected, and collect initial underground structure data of each area to be inspected at different frequencies; The initial underground structure data includes soil data and underground crack data; The initial underground structure data is preprocessed, and the preprocessing includes data cleaning and standardization.

3. The method for predicting land subsidence danger according to claim 2, characterized in that: When the Eclat algorithm is used to obtain the correlation result data of the target underground structure data and the precipitation intensity data, it includes: The Eclat algorithm is used to set the target underground structure data as the first transaction, and the precipitation intensity data as the second transaction. The intersection of the first transaction and the second transaction is found through an intersection operation to obtain an initial frequent item set, and the initial frequent item set is recursively intersected to obtain the frequent item sets of the target underground structure data and the precipitation intensity data. The correlation result data of the target underground structure data and the precipitation intensity data is determined according to the frequent item sets.

4. The method for predicting land subsidence danger according to claim 3, characterized in that: The SARIMA model is used to obtain the precipitation prediction settlement value based on the correlation result data, including: Dividing the correlation result data into a training set and a test set; Using cross validation combined with grid search, finding the optimal parameters of the SARIMA model to be established, using the training set to fit the SARIMA model to be established, and obtaining the SARIMA model; Substituting the test set into the SARIMA model and calculating the accuracy of precipitation deposition values; When the accuracy reaches a preset accuracy threshold, the precipitation prediction settlement value is obtained according to the correlation result data.

5. The method for predicting land subsidence danger according to claim 4, characterized in that: When all historical settlement data of each area to be detected are obtained based on big data, and a time series diagram of precipitation intensity and ground settlement is drawn based on precipitation intensity data of several historical years and all historical settlement data, it includes: Obtaining precipitation intensity data and all historical settlement data for several historical years, the selected time periods correspond one to one, the x-axis of the time series graph represents the year, and the y-axis of the time series graph represents the number; The time series graph includes a precipitation intensity sequence graph and a ground subsidence sequence graph. The precipitation intensity sequence graph is obtained based on precipitation intensity data of several historical years, and the ground subsidence sequence graph is obtained based on all historical subsidence data. The precipitation intensity sequence graph is a bar graph, and the ground subsidence sequence graph is a line graph.

6. The method for predicting land subsidence danger according to claim 5, characterized in that: When a similar settlement data set is determined by a clustering algorithm, a settlement optimization factor is extracted according to the similar settlement data set, and the precipitation predicted settlement value is optimized based on the settlement optimization factor to obtain a target settlement value, the method includes: The precipitation intensity in the precipitation intensity sequence diagram and the ground settlement in the ground settlement sequence diagram are used as the data sets to be aggregated, the expected number of clusters k is determined to be 3, and the parameters of the Gaussian distribution are initialized, the probability that each data in the data sets to be aggregated belongs to each Gaussian distribution is calculated and the responsibility value is obtained, and the data sets corresponding to the precipitation intensity and the ground settlement are obtained according to the responsibility value, and the data sets are used as the settlement-similar data sets; The settlement optimization factor is obtained by taking the average of all the data in the settlement similar data set, and the target settlement value is the product of the settlement optimization factor and the precipitation predicted settlement value; Presetting a first sedimentation threshold and a second sedimentation threshold; When the target settlement value is greater than or equal to the first settlement threshold, the target settlement value is judged to be severe settlement; When the target sedimentation value is less than the first sedimentation threshold and greater than or equal to the second sedimentation threshold, the target sedimentation value is judged to be medium sedimentation; When the target settlement value is less than the second settlement threshold, the target settlement value is determined to be slight settlement.

7. The method for predicting land subsidence danger according to claim 6, characterized in that: When obtaining a plurality of historical disaster data and corresponding historical years of a local area and calculating the amount of ground settlement caused by the disaster according to the historical disaster data, it includes: The historical disaster data include flood data, drought data and dust storm data; The settlement is given by the following formula: ； in, Indicates the amount of sedimentation, The water velocity of the flood, represents the sediment concentration in the flood, Indicates the duration of the flood, represents the average diameter of sediment particles deposited on the ground, represents the duration of drought, Indicates duration The amount of groundwater extracted from represents the baseline value for groundwater extraction, Indicates the soil type of the ground. Indicates the dust storm wind speed, Indicates the duration of the dust storm.

8. The method for predicting land subsidence danger according to claim 7, characterized in that: When the random forest model is used to derive the disaster settlement trend between the historical years and the settlement amount, it includes: The historical years are arranged in chronological order into a disaster series, the features of the disaster series are extracted and substituted into the random forest model, and the disaster settlement trend is predicted based on the settlement amount, and the disaster settlement trend includes an upward trend, a downward trend and a stable trend.

9. The method for predicting land subsidence danger according to claim 8, characterized in that: When determining the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend, it includes: The settlement levels include a first predicted settlement level, a second predicted settlement level, and a third predicted settlement level; When it is determined that the target settlement value is severe settlement and the disaster settlement trend is an upward trend or a stable trend, it is determined as the first predicted settlement level; When it is judged that the target settlement value is the severe settlement and the disaster settlement trend is the downward trend, it is determined to be the second predicted settlement level; or, when it is judged that the target settlement value is the moderate settlement and the disaster settlement trend is the upward trend or the stable trend, it is determined to be the second predicted settlement level; or, when it is judged that the target settlement value is the slight settlement and the disaster settlement trend is the upward trend, it is determined to be the second predicted settlement level; When it is judged that the target settlement value is the medium settlement and the disaster settlement trend is the downward trend, it is determined to be the third predicted settlement level; or, when it is judged that the target settlement value is the slight settlement and the disaster settlement trend is the downward trend or the stable trend, it is determined to be the third predicted settlement level.

10. A land subsidence risk prediction system, used for applying the land subsidence risk prediction method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, settlement prediction module, settlement correction module, disaster settlement module and settlement level module; The data acquisition module is configured to divide the ground to be detected into a number of areas to be detected, obtain precipitation intensity data of a number of historical years for each area to be detected based on big data, use geological radar to obtain initial underground structure data for each area to be detected, pre-process the initial underground structure data, and obtain target underground structure data based on the pre-processing results; The settlement prediction module is configured to use an Eclat algorithm to obtain correlation result data between the target underground structure data and the precipitation intensity data, and use a SARIMA model based on the correlation result data to obtain a precipitation predicted settlement value; The settlement correction module is configured to obtain all historical settlement data of each area to be detected based on big data, draw a time series diagram of precipitation intensity and ground settlement according to precipitation intensity data of several historical years and all historical settlement data, determine a settlement similar data set through a clustering algorithm, extract a settlement optimization factor according to the settlement similar data set, optimize the precipitation predicted settlement value based on the settlement optimization factor, and obtain a target settlement value; The disaster settlement module is configured to obtain a plurality of historical disaster data and corresponding historical years of the local area, calculate the settlement amount caused by the disaster on the ground according to the historical disaster data, and use a random forest model to obtain the disaster settlement trend between the historical years and the settlement amount; The settlement level module is configured to determine the settlement level of the ground to be detected according to the target settlement value and the disaster settlement trend.

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