Cross-domain big data fusion analysis and intelligent prediction system

Abstract

The present application relates to the technical field of predictive regulation, and specifically relates to a cross-domain big data fusion analysis and intelligent prediction system, comprising the following modules: a data acquisition module for collecting spatial and temporal data from multiple surveying and mapping domains; a data preprocessing module for cleaning, standardizing and formatting the collected data; a data integration module for fusing and unifying data from different surveying and mapping domains and providing a unified data access interface; an intelligent prediction module for performing trend analysis and prediction on the integrated data using a machine learning algorithm; a monitoring and sensing module for collecting environmental condition data in real time through an environmental sensor network and combining the data with the surveying and mapping data; and a regulation module for automatically adjusting data acquisition strategies and analysis parameters and optimizing data processing procedures according to the intelligent prediction and monitoring results. The present application more accurately reflects current and future environmental conditions, and this comprehensive prediction method can timely capture environmental changes, thereby providing more accurate support for decisions related to geographic information.

Description

Technical Field

[0001] The present invention relates to the field of prediction and regulation technology, and in particular to a cross-domain big data fusion analysis and intelligent prediction system. Background Art

[0002] In today's data-driven era, big data technologies have become a core pillar of many industries, particularly in surveying and mapping, where geographic information systems (GIS), satellite remote sensing, drone surveying, and ground-based surveying are widely used. These technologies can provide a wealth of data on geography, climate, and environmental change, but the challenge is how to effectively integrate and analyze surveying and mapping data from diverse sources and formats.

[0003] Traditional data processing systems often suffer from inefficient data integration, insufficient forecasting accuracy, and slow response to environmental changes. This is especially true when responding to rapid environmental changes, such as natural disasters or rapidly developing urban areas. Existing data processing methods often fail to provide timely and accurate information to support decision-making. Furthermore, rapidly changing environmental conditions, such as climate fluctuations and land surface movements, also require forecasting systems to be more adaptable and flexible.

[0004] Furthermore, while there are numerous attempts to employ machine learning and artificial intelligence algorithms for data analysis and prediction, these efforts often lack effective mechanisms to dynamically adjust data collection and analysis strategies to adapt to environmental changes. This limits the effectiveness of these systems in practical applications, particularly when processing large-scale, cross-domain mapping data.

[0005] Therefore, there is an urgent need to develop a system that can effectively integrate cross-domain mapping data, achieve high-precision intelligent prediction, and dynamically adjust its data processing strategy according to environmental changes. Summary of the Invention

[0006] Based on the above objectives, the present invention provides a cross-domain big data fusion analysis and intelligent prediction system.

[0007] The cross-domain big data fusion analysis and intelligent prediction system includes the following modules:

[0008] Data acquisition module for collecting spatial and temporal data from multiple mapping domains;

[0009] Data preprocessing module, used to clean, standardize and format the collected data;

[0010] Data integration module, used to integrate and unify data from different surveying and mapping domains and provide a unified data access interface;

[0011] Intelligent prediction module, which uses machine learning algorithms to perform trend analysis and prediction on integrated data;

[0012] The monitoring and perception module collects environmental condition data (climate change, surface change) in real time through an environmental sensor network and combines it with surveying and mapping data to enhance the environmental adaptability and accuracy of predictions.

[0013] The adjustment module automatically adjusts data collection strategies and analysis parameters based on intelligent prediction and monitoring results, and optimizes the data processing process.

[0014] Furthermore, the multiple surveying and mapping domains include geographic information system (GIS), satellite remote sensing, drone measurement, and ground stations. The data acquisition module specifically includes:

[0015] Interface integration mechanism: It has a data interface compatible with multiple surveying and mapping domains and can simultaneously receive multiple data formats, including vector data, raster data, and real-time streaming data;

[0016] Data filtering and preliminary classification: During the data collection process, automated data quality assessment and filtering are implemented to eliminate erroneous or incomplete data input, and preliminary classification is performed according to data source and type.

[0017] Furthermore, the data integration module includes automatic alignment of spatial data from different sources and at different scales, using geocoding and coordinate conversion techniques to ensure that all data are represented in the same spatial reference, and using time synchronization technology to ensure that data from different surveying and mapping domains have consistent time tags to obtain time series data.

[0018] Furthermore, the machine learning algorithm in the intelligent prediction module adopts the ARIMA model (autoregressive integrated moving average model), which specifically includes:

[0019] Apply differencing to the time series data after integration by the data integration module to achieve stationarity requirements, especially for data that show high autocorrelation and seasonal fluctuations. For seasonally varying data, such as satellite data affected by cloud cover in a particular season, seasonal adjustment methods are applied to eliminate cyclical effects;

[0020] Model parameter identification and optimization: Based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the integrated data, identify the ARIMA model parameters p, d, q and seasonal parameters P, D, Q, S. Use historical data sets to train the ARIMA model and select the optimal model using the Akaike Information Criterion (AIC).

[0021] The optimized ARIMA model is used to predict future trends in geographic information changes, including ground deformation and vegetation cover changes. The prediction results are input into the adjustment module to automatically adjust the data collection strategy and analysis parameters and optimize the data processing process.

[0022] Furthermore, the difference processing is used to make the non-stationary time series data stationary, as follows:

[0023] First-order difference: Removes the linear trend of the data and is calculated as: in, is a time series at a point in time The observed value of is the value after the first-order difference. If the data is still not stable after the first-order difference, the second-order difference is performed:

[0024] ;

[0025] The seasonal adjustment method is based on seasonal differences and is used to process time series data that show seasonal changes, removing changes within a fixed seasonal cycle and making the series stable on the seasonal cycle. Let the seasonal cycle be (For annual periodic data, Take 12), the seasonal difference is expressed as: ,in, is the value after seasonal difference.

[0026] Furthermore, the autocorrelation function (ACF) shows the correlation between the time series and itself at different lags, which is used to identify the order q of the MA model; the partial autocorrelation function (PACF) shows the correlation between the time series and itself under the influence of given other lag values, which is used to identify the order p of the AR model;

[0027] The non-seasonal parameters p, d, q are identified as follows:

[0028] p: Observe the PACF plot and find the lag number with obvious truncation (suddenly dropping to 0 and close to 0), which represents the order of the AR part;

[0029] q: Observe the ACF plot and find the number of lags that are clearly truncated, which indicates the order of the MA part;

[0030] d: The difference order d is determined by ADF test until the time series becomes stationary;

[0031] The identification of seasonal parameters P, D, Q, and S is as follows:

[0032] S: seasonal period, determined according to the periodicity of the dataset, such as annually, quarterly, or monthly;

[0033] P, Q: Same as non-seasonal parameters, determined from seasonal PACF and ACF plots respectively;

[0034] D: seasonal difference order, determined by seasonal stationarity test;

[0035] The ARIMA model is trained using a historical data set according to determined model parameters to construct the ARIMA model, including dividing the data into a training set and a test set, applying differencing and seasonal differencing processing, and ensuring the stationarity of the model input data.

[0036] Furthermore, the Akaike Information Criterion AIC is expressed as: ,in, the number of model parameters, It is the maximum likelihood estimate of the model. During the model training process, AIC values ​​are calculated for different parameter combinations, and the model with the lowest AIC value is selected as the optimal model, because a lower AIC value indicates a better balance between model fitting quality and complexity.

[0037] Furthermore, the ARIMA model is expressed as ARIMA(p, d, q), where p is the order of the autoregressive term, which indicates the number of past values ​​used in the model to predict the current value; d is the number of differences, which indicates the number of non-seasonal differences required to make the time series stationary; and q is the order of the moving average term, which indicates the number of past forecast errors used to predict the current value. The model is expressed as: ,in, It's time The original sequence, is the parameter of the autoregressive term, is the parameter of the moving average term, is the lag operator , is the white noise error term;

[0038] The seasonal ARIMA is expressed as SARIMA. When the time series data show obvious seasonal fluctuations, SARIMA is used, which is expressed as , where P, D, Q represent the orders of seasonal autoregressive term, seasonal difference and seasonal moving average term respectively, and S represents the seasonal length of the time series. SARIMA is expressed as: ,in, and are the parameters of the seasonal autoregressive and seasonal moving average terms, is the seasonal lag operator, , for or .

[0039] Furthermore, the monitoring and perception module specifically includes:

[0040] Environmental data integration: Real-time environmental condition data collected from environmental sensor networks is synchronized in time and space with survey and mapping data to ensure that all data items are aligned before analysis. Features are extracted from environmental condition data, including climate factors (temperature, humidity, rainfall) and surface characteristics (vegetation cover, terrain changes), and converted into a format that can be used for model input.

[0041] The environmental characteristic data are fused with the integrated surveying and mapping data to create an extended dataset that contains not only time series data but also environmental variables to provide comprehensive predictive factors. Difference and transformation processing are applied to the extended dataset to ensure that the data meets the stationarity requirements of the ARIMA model.

[0042] ARIMA model enhancement: Adjust the ARIMA model parameters (p, d, q) based on the expanded dataset, consider the impact of environmental factors on model dynamics, re-evaluate seasonal parameters (P, D, Q, S), and retrain the ARIMA model using the expanded dataset to ensure that the model can learn the effects of climate change and surface changes;

[0043] The enhanced ARIMA model was cross-validated to check its prediction accuracy, especially its performance in reflecting changes in complex environmental conditions. The Akaike Information Criterion (AIC) was used again to evaluate the goodness of fit of the model and select the optimal model parameter configuration.

[0044] Based on the prediction results and real-time environmental monitoring data, the surveying and mapping data collection strategy and analysis parameters are dynamically adjusted to respond to environmental changes.

[0045] Furthermore, the adjustment module specifically includes:

[0046] Adjust the frequency of data collection: Adjust the frequency of data collection based on the severity or speed of the predicted results and environmental changes.

[0047] Adjustment of collection scope: Adjust the spatial scope of surveying and mapping based on regional environmental changes shown by environmental monitoring data.

[0048] Data resolution: In areas where detailed monitoring is required, the resolution of data collection can be increased to capture changes more accurately.

[0049] Analytical algorithm adjustment: Adjust the parameters used in data analysis, including changing the parameters of the statistical model, or adjusting the feature selection and weights of the model to cope with the changing data characteristics and environmental conditions.

[0050] Beneficial effects of the present invention:

[0051] The present invention, a cross-domain big data fusion analysis and intelligent prediction system, significantly improves the environmental adaptability and accuracy of the prediction model by integrating multi-source mapping data from geographic information systems (GIS), satellite remote sensing, drone measurement and ground measurement stations, and combining it with real-time environmental monitoring data. By collecting climate change and surface change data in real time, the prediction model can be dynamically adjusted to more accurately reflect current and future environmental conditions. This comprehensive prediction method can capture environmental changes in a timely manner, thereby providing more accurate support for decision-making related to geographic information.

[0052] The present invention implements dynamic adjustments to the frequency, scope, and resolution of surveying and mapping data acquisition. These adjustments are based on the prediction results provided by the ARIMA model and real-time environmental monitoring data. This flexible data acquisition strategy not only ensures the efficiency and economy of the data acquisition process, but also improves data quality and relevance. Especially in rapidly changing environmental conditions, by automatically adjusting acquisition parameters, the system can more effectively respond to sudden geographical events and seasonal changes, ensuring the timeliness and accuracy of the collected data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of system function modules according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of the ARIMA model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0057] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0058] like Figure 1-Figure 2 As shown in the figure, the cross-domain big data fusion analysis and intelligent prediction system includes the following modules:

[0059] Data acquisition module for collecting spatial and temporal data from multiple mapping domains;

[0060] Data preprocessing modules clean, standardize, and format collected data. They convert data from different sources to consistent units (e.g., converting inches to centimeters) to eliminate the effects of measurement discrepancies. Data is normalized to a specific range or distribution (e.g., 0-1 or a standard normal distribution) to facilitate subsequent data processing and analysis. Data structures are adjusted to meet the input requirements of subsequent processing modules, such as converting flat data into structured or semi-structured formats.

[0061] Data integration module, used to integrate and unify data from different surveying and mapping domains and provide a unified data access interface;

[0062] Intelligent prediction module, which uses machine learning algorithms to perform trend analysis and prediction on integrated data;

[0063] The monitoring and perception module collects environmental condition data (climate change, surface change) in real time through an environmental sensor network and combines it with surveying and mapping data to enhance the environmental adaptability and accuracy of predictions.

[0064] The adjustment module automatically adjusts data collection strategies and analysis parameters based on intelligent prediction and monitoring results, and optimizes the data processing process.

[0065] Multi-surveying and mapping domains include geographic information systems (GIS), satellite remote sensing, drone surveying, and ground stations. The data acquisition modules specifically include:

[0066] Interface integration mechanism: It has a data interface compatible with multiple surveying and mapping domains and can simultaneously receive multiple data formats, including vector data, raster data, and real-time streaming data;

[0067] Data filtering and preliminary classification: During the data collection process, automated data quality assessment and filtering are implemented to eliminate erroneous or incomplete data input, and preliminary classification is performed according to data source and type.

[0068] The data integration module includes automatic alignment of spatial data from different sources and at different scales, using geocoding and coordinate conversion techniques to ensure that all data are represented in the same spatial reference, and using time synchronization technology to ensure that data from different surveying and mapping domains have consistent time tags to obtain time series data.

[0069] The machine learning algorithm in the intelligent prediction module uses the ARIMA model (autoregressive integrated moving average model), which includes:

[0070] Apply differencing to the time series data after integration by the data integration module to achieve stationarity requirements, especially for data that show high autocorrelation and seasonal fluctuations. For seasonally varying data, such as satellite data affected by cloud cover in a particular season, seasonal adjustment methods are applied to eliminate cyclical effects;

[0071] Model parameter identification and optimization: Based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the integrated data, identify the ARIMA model parameters p, d, q and seasonal parameters P, D, Q, S. Use historical data sets to train the ARIMA model and select the optimal model using the Akaike Information Criterion (AIC).

[0072] The optimized ARIMA model is used to predict future trends in geographic information changes, including ground deformation and vegetation cover changes. The prediction results are input into the adjustment module to automatically adjust the data collection strategy and analysis parameters and optimize the data processing process.

[0073] By comparing the actual surveying and mapping data with the predicted data, the prediction accuracy is evaluated. According to the deviation between the predicted results and the actual data, the parameters of the ARIMA model are adjusted to improve the accuracy and reliability of the prediction.

[0074] Difference processing is used to make non-stationary time series data stationary, as follows:

[0075] First-order difference: removes the linear trend of the data and is calculated as: in, is a time series at a point in time The observed value of is the value after the first-order difference. If the data is still not stable after the first-order difference, the second-order difference is performed:

[0076] ;

[0077] The seasonal adjustment method is based on seasonal differences and is used to process time series data that show seasonal changes, remove the changes within a fixed seasonal cycle, and make the series stable on the seasonal cycle. Let the seasonal cycle be (For annual periodic data, Take 12), the seasonal difference is expressed as: ,in, is the value after seasonal difference.

[0078] In the present invention, the steps of applying differencing and seasonal differencing are as follows:

[0079] Time series evaluation after data integration: First, evaluate the integrated time series data to determine whether the data shows obvious trends and / or seasonal fluctuations.

[0080] Apply differencing: Apply first-order and, if necessary, second-order differencing to the data until the time series exhibits stationary statistical properties (e.g., confirmed by an ADF test).

[0081] Apply seasonal differencing: If the time series shows seasonal fluctuations, apply seasonal differencing, especially when the seasonal effect is very obvious in the original data.

[0082] Model fitting: After necessary differencing, the time series data should be suitable for fitting and forecasting the ARIMA model.

[0083] The autocorrelation function (ACF) shows the correlation between the time series and itself at different lags, which is used to identify the order q of the MA model. The partial autocorrelation function (PACF) shows the correlation between the time series and itself under the influence of other lag values, which is used to identify the order p of the AR model.

[0084] The identification of non-seasonal parameters p, d, q is as follows:

[0085] p: Observe the PACF plot and find the lag number with obvious truncation (suddenly dropping to 0 and close to 0), which represents the order of the AR part;

[0086] q: Observe the ACF plot and find the number of lags that are clearly truncated, which indicates the order of the MA part;

[0087] d: The difference order d is determined by ADF test until the time series becomes stationary;

[0088] The identification of seasonal parameters P, D, Q, and S is as follows:

[0089] S: seasonal period, determined according to the periodicity of the dataset, such as annually, quarterly, or monthly;

[0090] P, Q: Same as non-seasonal parameters, determined from seasonal PACF and ACF plots respectively;

[0091] D: seasonal difference order, determined by seasonal stationarity test;

[0092] The ARIMA model uses historical data sets and is trained based on determined model parameters to construct the ARIMA model, including dividing the data into training and test sets, applying differencing and seasonal differencing processing, and ensuring the stationarity of the model input data.

[0093] Akaike Information Criterion AIC is expressed as: ,in, the number of model parameters, It is the maximum likelihood estimate of the model. During the model training process, AIC values ​​are calculated for different parameter combinations, and the model with the lowest AIC value is selected as the optimal model, because a lower AIC value indicates a better balance between model fitting quality and complexity.

[0094] The ARIMA model, or Autoregressive Integrated Moving Average model, is a statistical model used for time series data analysis and forecasting. It combines the properties of autoregression (AR), differencing (I), and moving average (MA) to adapt to the characteristics of various time series data, especially non-seasonal data. In addition, when data exhibit seasonal variations, the extended seasonal ARIMA (SARIMA) is also commonly used.

[0095] The ARIMA model is expressed as ARIMA(p, d, q), where p is the order of the autoregressive term, which represents the number of past values ​​used in the model to predict the current value, d is the number of differences, which represents the number of non-seasonal differences required to make the time series stationary, and q is the order of the moving average term, which represents the number of past forecast errors used to predict the current value. The model is expressed as: ,in, It's time The original sequence, is the parameter of the autoregressive term, is the parameter of the moving average term, is the lag operator , is the white noise error term;

[0096] The seasonal ARIMA is expressed as SARIMA. When the time series data show obvious seasonal fluctuations, SARIMA is used, which is expressed as , where P, D, and Q represent the orders of the seasonal autoregressive term, seasonal difference, and seasonal moving average term, respectively, and S represents the seasonal length of the time series. SARIMA is expressed as: ,in, and are the parameters of the seasonal autoregressive and seasonal moving average terms, is the seasonal lag operator, , for or .

[0097] The monitoring and perception module specifically includes:

[0098] Environmental data integration: Real-time environmental condition data collected from environmental sensor networks is synchronized in time and space with survey and mapping data to ensure that all data items are aligned before analysis. Features are extracted from environmental condition data, including climate factors (temperature, humidity, rainfall) and surface characteristics (vegetation cover, terrain changes), and converted into a format that can be used for model input.

[0099] The environmental characteristic data are fused with the integrated surveying and mapping data to create an extended dataset that contains not only time series data but also environmental variables to provide comprehensive predictive factors. Difference and transformation processing are applied to the extended dataset to ensure that the data meets the stationarity requirements of the ARIMA model.

[0100] ARIMA model enhancement: Adjust the ARIMA model parameters (p, d, q) based on the expanded dataset, consider the impact of environmental factors on model dynamics, re-evaluate seasonal parameters (P, D, Q, S), and retrain the ARIMA model using the expanded dataset to ensure that the model can learn the effects of climate change and surface changes;

[0101] The enhanced ARIMA model was cross-validated to check its prediction accuracy, especially its performance in reflecting changes in complex environmental conditions. The Akaike Information Criterion (AIC) was used again to evaluate the goodness of fit of the model and select the optimal model parameter configuration.

[0102] Based on the prediction results and real-time environmental monitoring data, the surveying and mapping data collection strategy and analysis parameters are dynamically adjusted to respond to environmental changes.

[0103] The adjustment module specifically includes:

[0104] Prediction and Real-Time Data Integration: Combine the prediction results of ARIMA models with real-time environmental monitoring data to create a comprehensive data view for decision support. By comparing the model's prediction results with the latest environmental monitoring data collected, assess the accuracy and reliability of the prediction model.

[0105] Adjusting Collection Frequency: Adjust the frequency of data collection based on the severity or speed of environmental changes predicted. For example, if the prediction model predicts that a certain area will experience rapid surface changes, increase the frequency of data collection in that area to capture details and changes.

[0106] Adjusting Collection Range: Adjust the spatial range of mapping based on the environmental changes in the area shown by the environmental monitoring data. For example, expand coverage in areas severely affected by climate or focus on key areas of change.

[0107] Data Resolution: Increase the resolution of data collection in areas where detailed monitoring is needed to capture changes more accurately.

[0108] Adjusting Analysis Algorithms: Adjust parameters used in data analysis, including changing statistical model parameters or adjusting feature selection and weights of the model to adapt to changing data characteristics and environmental conditions.

[0109] Implement an automated system that can automatically adjust collection strategies and analysis parameters based on real-time monitoring data and prediction results, including using controllers to determine when and where to increase mapping frequency or adjust analysis methods.

[0110] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary and is not intended to imply that the scope of the present application is limited to these examples; under the idea of the present application, the above embodiments or technical features in different embodiments can also be combined, steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above. For the sake of brevity, they are not provided in detail.

[0111] The present application is intended to cover all such alternatives, modifications and variations as fall within the broad scope of the claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. Cross-domain big data fusion analysis and intelligent prediction system, characterized by: Includes the following modules: Data acquisition module for collecting spatial and temporal data from multiple mapping domains; Data preprocessing module, used to clean, standardize and format the collected data; Data integration module, used to integrate and unify data from different surveying and mapping domains and provide a unified data access interface; Intelligent prediction module, which uses machine learning algorithms to perform trend analysis and prediction on integrated data; The monitoring and perception module collects environmental condition data in real time through an environmental sensor network and combines it with surveying and mapping data to enhance the environmental adaptability and accuracy of predictions. The adjustment module automatically adjusts data collection strategies and analysis parameters based on intelligent prediction and monitoring results to optimize the data processing process; The monitoring and perception module specifically includes: Environmental data integration: Real-time environmental condition data collected from environmental sensor networks are synchronized in time and space with survey and mapping data to ensure that all data items are aligned before analysis. Feature extraction is performed on the environmental condition data, including climate factors and surface characteristics, and converted into a format that can be used for model input. The environmental characteristic data are fused with the integrated surveying and mapping data to create an extended dataset that contains not only time series data but also environmental variables to provide comprehensive predictive factors. Difference and transformation processing are applied to the extended dataset to ensure that the data meets the stationarity requirements of the ARIMA model. ARIMA model enhancement: Adjust the ARIMA model parameters p, d, and q based on the expanded dataset, consider the impact of environmental factors on model dynamics, re-evaluate the seasonal parameters P, D, Q, and S, and retrain the ARIMA model using the expanded dataset to ensure that the model can learn the effects of climate change and surface changes; The enhanced ARIMA model was cross-validated to check its prediction accuracy, and the Akaike Information Criterion (AIC) was used again to evaluate the goodness of fit of the model and select the optimal model parameter configuration; Based on the prediction results and real-time environmental monitoring data, the surveying and mapping data collection strategy and analysis parameters are dynamically adjusted to respond to environmental changes.

2. The cross-domain big data fusion analysis and intelligent prediction system according to claim 1 is characterized in that: The multi-surveying and mapping domains include geographic information system (GIS), satellite remote sensing, drone surveying, and ground survey stations. The data acquisition module specifically includes: Interface integration mechanism: It has a data interface compatible with multiple surveying and mapping domains and can simultaneously receive multiple data formats, including vector data, raster data, and real-time streaming data; Data filtering and preliminary classification: During the data collection process, automated data quality assessment and filtering are implemented to eliminate erroneous or incomplete data input, and preliminary classification is performed according to data source and type.

3. The cross-domain big data fusion analysis and intelligent prediction system according to claim 1 is characterized in that: The data integration module includes automatically aligning spatial data from different sources and at different scales, using geocoding and coordinate conversion techniques to ensure that all data are represented in the same spatial reference, and using time synchronization technology to ensure that data from different surveying and mapping domains have consistent time tags to obtain time series data.

4. The cross-domain big data fusion analysis and intelligent prediction system according to claim 3 is characterized in that: The machine learning algorithm in the intelligent prediction module adopts the ARIMA model, which specifically includes: Apply differential processing to the time series data after integration by the data integration module to achieve stationarity requirements, and apply seasonal adjustment methods to seasonally changing data to eliminate cyclical effects; Model parameter identification and optimization: Based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the integrated data, identify the ARIMA model parameters p, d, q and seasonal parameters P, D, Q, S. Use historical data sets to train the ARIMA model and select the optimal model using the Akaike Information Criterion (AIC). The optimized ARIMA model is used to predict future trends in geographic information changes, including ground deformation and vegetation cover changes. The prediction results are input into the adjustment module to automatically adjust the data collection strategy and analysis parameters and optimize the data processing process.

5. The cross-domain big data fusion analysis and intelligent prediction system according to claim 4 is characterized in that: The difference processing is used to make non-stationary time series data stationary, as follows: First-order difference: removes the linear trend of the data and is calculated as: Δy t =y t -y t-1 , where y t is the observed value of the time series at time point t, Δy t is the value after the first-order difference. If the data is still not stable after the first-order difference, the second-order difference is performed: Δ 2 y t =Δy t -Δy t-1 =(y t -y t-1 )-(y t-1 -y t-2 ); The seasonal adjustment method is based on seasonal differences and is used to process time series data that show seasonal changes, remove changes within a fixed seasonal cycle, and make the series stationary on the seasonal cycle. Let the seasonal cycle be S, and the seasonal difference is expressed as: Δ S y t =y t -y t-S , where Δ S y t is the value after seasonal difference.

6. The cross-domain big data fusion analysis and intelligent prediction system according to claim 4 is characterized in that: The autocorrelation function (ACF) shows the correlation between the time series and itself at different lags, and is used to identify the order q of the MA model. The partial autocorrelation function (PACF) shows the correlation between the time series and itself under the influence of other lag values, and is used to identify the order p of the AR model. The identification of non-seasonal parameters p, d, q is as follows: p: Observe the PACF plot and find the number of lags that are obviously truncated, which indicates the order of the AR part; q: Observe the ACF plot and find the number of lags that are clearly truncated, which indicates the order of the MA part; d: The difference order d is determined by ADF test until the time series becomes stationary; The identification of seasonal parameters P, D, Q, and S is as follows: S: seasonal period, determined according to the periodicity of the dataset; P, Q: Same as non-seasonal parameters, determined from seasonal PACF and ACF plots respectively; D: seasonal difference order, determined by seasonal stationarity test; The ARIMA model is trained using a historical data set according to determined model parameters to construct the ARIMA model, including dividing the data into a training set and a test set, applying differencing and seasonal differencing processing, and ensuring the stationarity of the model input data.

7. The cross-domain big data fusion analysis and intelligent prediction system according to claim 4 is characterized in that: The Akaike Information Criterion (AIC) is expressed as: AIC = 2k-2ln(L), where k is the number of model parameters and L is the maximum likelihood estimate of the model. During model training, AIC values ​​are calculated for different parameter combinations, and the model with the lowest AIC value is selected as the optimal model.

8. The cross-domain big data fusion analysis and intelligent prediction system according to claim 4 is characterized in that: The ARIMA model is expressed as ARIMA(p,d,q), where p is the order of the autoregressive term, which represents the number of past values ​​used in the model to predict the current value, d is the number of differences, which represents the number of non-seasonal differences required to make the time series stationary, and q is the order of the moving average term, which represents the number of past forecast errors used to predict the current value. The model is expressed as: Among them, Y t is the original sequence at time t, is the parameter of the autoregressive term, θ j is the parameter of the moving average term, L is the lag operator (L k Y t =Y t-k ),∈ t is the white noise error term; The seasonal ARIMA is expressed as SARIMA. When the time series data shows obvious seasonal fluctuations, SARIMA is used, which is expressed as SARIMA(p,d,q)(P,D,Q)[S], where P, D, Q represent the orders of the seasonal autoregressive term, seasonal difference, and seasonal moving average term, respectively, and S represents the seasonal length of the time series. SARIMA is expressed as: Among them, Φ i and Θ j are the parameters of seasonal autoregressive and seasonal moving average terms, L S is the seasonal lag operator, L kS Y t =Y t-kS , k is i or j.

9. The cross-domain big data fusion analysis and intelligent prediction system according to claim 8 is characterized in that: The adjustment module specifically includes: Adjust the frequency of data collection: Adjust the frequency of data collection based on the severity or speed of the forecast results and environmental changes; Adjustment of collection scope: Adjust the spatial scope of surveying and mapping based on regional environmental changes revealed by environmental monitoring data; Data resolution: In areas where detailed monitoring is required, the resolution of data collection is increased to capture changes more accurately; Analytical algorithm adjustment: Adjust the parameters used in data analysis, including changing the parameters of the statistical model, or adjusting the feature selection and weights of the model to cope with the changed data characteristics and environmental conditions.

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