Hydrogeological environment intelligent monitoring method

Abstract

The invention provides a hydrogeological environment intelligent monitoring method, which comprises the following steps of: in a cloud server, storing mass monitoring data by adopting a distributed storage architecture, establishing an index according to a data type and a time dimension, and realizing rapid retrieval and access of the data; aiming at the stored monitoring data, a time sequence analysis method is adopted to extract the change trend of indexes such as water level and water quality, and if abnormal fluctuation is detected, an early warning mechanism is triggered to generate early warning information; a machine learning algorithm is adopted to train historical monitoring data, a hydrogeological environment prediction model is established, and if real-time monitoring data is input, an environment change prediction result in a period of time in the future is output; and according to the prediction result and the early warning information, generating a decision suggestion report, and if the prediction result exceeds a preset threshold value, automatically adjusting the acquisition frequency of the monitoring equipment to improve the data acquisition density.

Description

Technical Field

[0001] The present invention relates to the field of hydrogeology, and particularly to an intelligent monitoring method for hydrogeological environment. Background Art

[0002] In hydrogeological environment monitoring, real-time data collection and transmission are key technical problems. Due to the complexity and variability of the hydrogeological environment, monitoring points are often distributed in remote and harsh environments. How to ensure the stable operation of monitoring equipment and the real-time backhaul of data is an urgent problem to be solved. At the same time, there are many types of hydrogeological environment monitoring data, including multiple indicators such as water level, water quality, soil humidity, and groundwater level. There are significant differences in the collection frequency, accuracy requirements, data formats, etc. for different types of data. How to achieve efficient and reliable data transmission under limited transmission bandwidth is also a technical challenge. In addition, hydrogeological environment monitoring often needs to be carried out continuously for a long time. How to store, manage, and analyze a large amount of monitoring data, how to extract valuable information from the vast data, and how to achieve visual display and intelligent early warning of data are all urgent technical problems to be solved. The solution of these technical problems requires the comprehensive application of new-generation information technologies such as the Internet of Things, big data, and cloud computing, and deep integration with the professional knowledge in the field of hydrogeology, so as to realize the full-process optimization of hydrogeological environment monitoring data collection, transmission, storage, management, and analysis, and provide solid technical support for hydrogeological environment monitoring. Summary of the Invention

[0003] The present invention provides an intelligent monitoring method for hydrogeological environment, mainly including:

[0004] According to the preset collection frequency, obtain multi-type data such as water level, water quality, soil humidity, and groundwater level from monitoring equipment distributed in remote environments, and adopt an adaptive sampling algorithm to adjust the collection parameters according to the accuracy requirements of different data types;

[0005] If equipment anomalies occur during data collection, perform fault diagnosis through the Internet of Things edge computing module to determine the type of anomaly. If it is a hardware fault, start the standby equipment; if it is a software fault, execute the remote repair program;

[0006] For the format differences of different types of data, adopt a unified data conversion protocol to convert the original data into a standard format, and select a compression algorithm to compress the data according to the data volume and transmission bandwidth;

[0007] Transmit the compressed data through low-power wide-area network technology. If data loss occurs during transmission, perform data retransmission according to the preset retransmission mechanism until the data completely reaches the cloud server;

[0008] In the cloud server, a distributed storage architecture is adopted to store a large amount of monitoring data, and indexes are established according to data types and time dimensions to achieve fast retrieval and access to the data;

[0009] For the stored monitoring data, a time series analysis method is adopted to extract the change trends of indicators such as water level and water quality. If abnormal fluctuations are detected, an early warning mechanism is triggered to generate early warning information;

[0010] According to the analysis results, data visualization technology is adopted to convert the monitoring data into a chart form. If a user needs to view the data for a specific time period, the corresponding data is extracted from the database and a visualization report is generated;

[0011] Machine learning algorithms are used to train historical monitoring data to establish a hydrogeological environment prediction model. If real-time monitoring data is input, the prediction results of the environmental changes for a future period are output;

[0012] According to the prediction results and early warning information, a decision-making suggestion report is generated. If the prediction results exceed the preset threshold, the acquisition frequency of the monitoring equipment is automatically adjusted to increase the data acquisition density.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] The present invention discloses an intelligent monitoring method for hydrogeological environment. Multi-type data such as water level, water quality, and soil humidity are collected by monitoring equipment distributed in remote environments, and an adaptive sampling algorithm is used to optimize the acquisition parameters. When the equipment malfunctions, the edge computing module conducts fault diagnosis and takes corresponding measures. The collected data is transmitted to the cloud server through a low-power wide area network after being converted and compressed in a unified format. The server adopts a distributed storage architecture to store a large amount of data and establishes indexes for fast retrieval. The system uses a time series analysis method to extract the change trends of environmental indicators, detects abnormal fluctuations and triggers early warnings. At the same time, machine learning algorithms are applied to establish a prediction model to predict future environmental changes based on real-time data. Based on the analysis results, the system generates visualization reports and decision-making suggestions, and automatically adjusts the acquisition frequency of the monitoring equipment to achieve intelligent monitoring and early warning of the hydrogeological environment, providing a scientific basis for environmental protection and resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of an intelligent monitoring method for hydrogeological environment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] As Figure 1As shown in the figure, a method for intelligent monitoring of hydrogeological environment in this embodiment may specifically include:

[0018] S101. According to a preset acquisition frequency, obtain multi-type data such as water level, water quality, soil humidity, and groundwater level from monitoring devices distributed in remote environments. For the accuracy requirements of different data types, an adaptive sampling algorithm is used to adjust the acquisition parameters.

[0019] Obtain multi-type data of water level, water quality, soil humidity, and groundwater level collected by monitoring devices distributed in remote environments, and the data is collected according to a preset acquisition frequency; for the accuracy requirements of different data types in the multi-type data, an adaptive sampling algorithm is used to dynamically adjust the data acquisition parameters; if the current acquisition frequency is lower than a preset threshold, increase the acquisition frequency, otherwise decrease the acquisition frequency to reduce data redundancy; perform trend analysis on the water level data to obtain the predicted water level change in a future period of time. If the predicted water level exceeds the preset threshold, trigger an early warning mechanism; use a machine learning algorithm to classify the water quality data to obtain water quality abnormal data, and judge the pollution source of water quality abnormality according to the characteristics of the water quality abnormal data; use a support vector machine algorithm to perform regression analysis on the soil humidity data to obtain the change law of soil humidity; based on the groundwater level data, use a time series analysis method to establish a groundwater level prediction model.

[0020] Specifically, deploying monitoring devices in remote environments to collect multi-type data is an important means of environmental monitoring. Taking a water resource monitoring project in a certain mountainous area as an example, by deploying sensors such as water level, water quality, soil humidity, and groundwater level, the hydrogeological conditions of this area can be comprehensively grasped.

[0021] In the initial stage, the system collects data according to a preset acquisition frequency (such as once an hour). As data accumulates, the system uses an adaptive sampling algorithm to dynamically adjust the acquisition parameters. For example, if the accuracy requirement for water level data is high, the sampling interval can be shortened to 15 minutes; while the change of soil humidity is relatively slow, the sampling interval can be appropriately extended to 4 hours. This differential strategy not only ensures the accuracy of key data but also avoids data redundancy.

[0022] In terms of water level monitoring, the system conducts trend analysis on historical data to predict the water level changes in the next week. If the predicted water level exceeds the preset warning line (such as exceeding the historical average water level by 1.5 meters), the system will automatically trigger the warning mechanism to notify relevant departments to take flood control measures. For water quality monitoring, machine learning algorithms are used for classification. Through comprehensive analysis of indicators such as pH value, dissolved oxygen, and conductivity, the system can identify abnormal data. For example, if a certain sampling shows that the pH value suddenly drops to 5.5, far lower than the normal range (6.5 - 8.5), the system will mark it as abnormal. Further analysis reveals that this abnormality is highly correlated with the sewage discharge behavior of a chemical plant upstream, providing an important basis for determining the pollution source.

[0023] For soil moisture data, the system uses the support vector machine algorithm for regression analysis. By establishing a relationship model between soil moisture and factors such as rainfall and temperature, the changing trend of future soil moisture can be predicted. This has important guiding significance for agricultural production planning and water resource management.

[0024] The processing of groundwater level data adopts the time series analysis method. The system establishes an ARIMA model that includes seasonal factors, long-term trends, and random fluctuations, and can accurately predict the groundwater level changes in the next month. This provides a scientific basis for the sustainable utilization of groundwater resources. Through this intelligent data acquisition and analysis system, the management department can timely grasp the water resource situation, effectively prevent floods and droughts, and achieve scientific management and sustainable utilization of water resources. At the same time, the adaptive characteristics and diversified analysis methods of the system provide a technical solution that can be borrowed for environmental monitoring in other fields.

[0025] S102. If equipment abnormalities occur during the data acquisition process, fault diagnosis is carried out through the Internet of Things edge computing module to determine the type of abnormality. If it is a hardware fault, the standby equipment is started; if it is a software fault, the remote repair program is executed.

[0026] Obtain the performance parameters and exception information of the data acquisition device, which are obtained by the Internet of Things edge computing module through real-time monitoring; according to the preset fault diagnosis rules and exception detection algorithms, determine whether the data acquisition device has an exception; if the data acquisition device has an exception, trigger the fault diagnosis process; use the fault diagnosis decision tree algorithm to comprehensively analyze the various parameters of the data acquisition device to determine the specific exception type; if the exception type is a hardware fault, send a start command to the standby device and switch the data acquisition task of the data acquisition device to the standby device; if the exception type is a software fault, match the corresponding repair plan from the remote repair program library, send the repair plan to the data acquisition device, and perform an automatic repair operation; during the fault repair process, continuously monitor the running status of the data acquisition device, and determine whether the repair is successful through the exception detection algorithm; if the repair fails, perform the fault diagnosis again until the repair is successful; when the data acquisition device resumes normal operation, report the fault diagnosis and repair results to the Internet of Things management platform, update the device status, and record the fault handling log.

[0027] Specifically, when the Internet of Things edge computing module monitors the data acquisition device in real time, it will obtain performance parameters such as CPU usage rate, memory occupancy, network latency, etc., as well as exception information such as too high device temperature and unstable power supply. These information provide important basis for subsequent fault diagnosis.

[0028] The preset fault diagnosis rules may include: when the CPU usage rate continuously exceeds 90%, the memory occupancy exceeds 95%, or the network latency exceeds 200ms, it is determined that the device may have an exception. The exception detection algorithm may adopt a statistics-based method, such as calculating the mean and standard deviation of the performance parameters. When a certain parameter deviates from the normal range by more than 3 standard deviations, an exception alarm is triggered.

[0029] The fault diagnosis decision tree algorithm locates the specific exception type by analyzing various parameters layer by layer. For example, first determine whether it is a hardware or software fault, and then further subdivide. If the CPU usage rate is abnormally high and the memory occupancy is normal, it may be that a certain process has a dead loop; if the memory occupancy is abnormally high and the CPU usage rate is normal, it may be a memory leak. For hardware faults, such as abnormal data caused by sensor damage, the system will start the standby device. This redundant design ensures the continuity and reliability of data acquisition.

[0030] The repair of software faults is more flexible. The remote repair library may include solutions such as restarting specific services, clearing caches, and updating firmware. The system will select the appropriate repair solution according to the fault type and execute it automatically. During the repair process, the anomaly detection algorithm will continuously monitor the device status. For example, for memory leak problems, the algorithm will pay attention to whether the memory occupancy gradually decreases and stabilizes within the normal range. If the problem is not solved, the system will re-execute the fault diagnosis and may try more aggressive repair solutions, such as resetting the device to the factory settings. This automated fault diagnosis and repair mechanism greatly improves the reliability and maintenance efficiency of the system.

[0031] By reporting the fault handling results to the Internet of Things management platform, managers can timely understand the device status, analyze the fault patterns, and optimize the maintenance strategies. At the same time, the detailed fault handling logs provide valuable data support for subsequent system optimization and fault prevention.

[0032] S103. For the format differences of different types of data, adopt a unified data conversion protocol to convert the original data into a standard format. According to the data volume size and transmission bandwidth, select a compression algorithm to compress the data.

[0033] Obtain the data volume size of the standard format data, and judge the data volume level according to a preset threshold; if the data volume is greater than the threshold, obtain the current transmission bandwidth; according to the data volume level and the transmission bandwidth, select the optimal compression algorithm from the preset compression algorithm library, and obtain the parameter configuration of the compression algorithm; input the standard format data into the compression algorithm, and compress the standard format data according to the parameter configuration to obtain the compressed data; for different types of original data, establish a preset data format template library, automatically match the data format of the original data through a data format recognition algorithm, and dynamically call the corresponding conversion protocol to complete the data format conversion of the original data to obtain the standard format data.

[0034] Specifically, the judgment of the data volume size is a key step in data processing. The preset threshold can be determined according to factors such as the system processing capacity and network bandwidth. For example, 1GB can be set as the threshold. When the data volume exceeds 1GB, the system will automatically obtain the current network bandwidth information. Assuming the current bandwidth is 100Mbps, the system will select the optimal compression algorithm according to the data volume and bandwidth.

[0035] The compression algorithm library may contain multiple algorithms, such as LZ77, Huffman coding, etc. The system will select the most suitable algorithm based on data characteristics and compression efficiency. For example, for text data, Huffman coding may be more effective; while for image data, JPEG compression may be more appropriate. After selecting the algorithm, the system will set corresponding parameter configurations, such as compression level, window size, etc., to balance the compression ratio and processing speed. During the compression process, the system inputs standard format data into the selected compression algorithm. Taking the LZ77 algorithm as an example, it reduces data redundancy by searching for repeated strings. Suppose there is a text data with a high degree of repetition, LZ77 may compress "abcabcabc" into "abc(0,3,6)", significantly reducing the amount of data.

[0036] For the format conversion of the original data, the system has established a preset data format template library. This library contains common data formats, such as CSV, JSON, XML, etc. Through the data format recognition algorithm, the system can automatically identify the format of the input data. For example, if it is recognized that the data is separated by commas and each line has the same number of fields, the system may determine it as the CSV format. After identifying the format, the system will call the corresponding conversion protocol. Suppose the original data is in CSV format and the target is to convert it to JSON format. The system will parse each line of the CSV file and convert it into a JSON object. This standardization process makes subsequent data processing more unified and efficient.

[0037] The design of this entire process aims to optimize data transmission and processing efficiency. By dynamically selecting compression algorithms and automatically performing format conversion, the system can adapt to different types and sizes of data, maximizing the transmission efficiency while ensuring data integrity. This is particularly important for scenarios such as big data analysis and Internet of Things applications that need to process a large amount of heterogeneous data, and can significantly improve the overall performance and scalability of the system.

[0038] S104. Transmit the compressed data through low-power wide-area network technology. If data loss occurs during the transmission process, data retransmission will be performed according to the preset retransmission mechanism until the data arrives at the cloud server completely.

[0039] Obtain the original data to be transmitted, compress the original data using a preset data compression algorithm to obtain compressed data; divide the compressed data into several data packets, and add a sequence number and check information to each data packet; transmit the data packets to the cloud server in sequence through low-power wide-area network technology; after receiving the data packets, the cloud server determines whether there is data loss according to the sequence number and check information of the data packets; if the cloud server determines that there is data loss, the cloud server sends a retransmission request to the data sender, requesting to retransmit the lost data packets; after receiving the retransmission request, the data sender retransmits the corresponding data packets according to the sequence number specified in the retransmission request; the cloud server assembles all the received data packets according to the sequence number to obtain the complete compressed data, and uses a decompression algorithm corresponding to the data compression algorithm to decompress the complete compressed data to obtain the complete original data.

[0040] Specifically, during the data transmission process, compression processing is a key step to improve transmission efficiency. The preset data compression algorithm can be selected according to the data characteristics. For example, Huffman coding can be used for text data, and JPEG compression can be used for image data. After compression, the data volume is significantly reduced. For example, a 100MB text file may be compressed to about 20MB. Data packet division is the basis of network transmission. Divide the compressed data into data packets of a fixed size, such as 1KB per packet, and add a unique sequence number and checksum to each packet. The sequence number ensures that the receiving end can correctly assemble the data, and the checksum is used to detect transmission errors. For example, the first data packet may contain the sequence number "1" and the checksum "0x1234". Low-power wide-area network technologies such as LoRaWAN or NB-IoT are suitable for long-distance and low-power scenarios. These technologies can achieve data transmission within a few kilometers even when powered by batteries for several months or even years. For example, LoRaWAN can reach a transmission distance of 15 kilometers and a data rate of 5.5 kbps under ideal conditions.

[0041] After receiving the data packets, the cloud server first checks the continuity of the sequence numbers and the correctness of the checksums. If it is found that the data packet with the sequence number "5" is lost, the server will request the sender to retransmit the packet. This mechanism ensures the integrity and reliability of the data. The retransmission mechanism is an effective method for dealing with network instability. When the sender receives the retransmission request, it will give priority to processing and quickly retransmit the lost data packets. This method is more efficient than retransmitting the entire file, especially in the case of large file transmission. Data packet assembly is a key step in restoring the original data.

[0042] The cloud server assembles all data packets in sequence according to the serial numbers to form complete compressed data. For example, 100 data packets of 1 KB will be assembled into a compressed file of 100 KB. Finally, the decompression process restores the compressed data to its original form. The decompression algorithm corresponds one-to-one with the compression algorithm. For example, Huffman decoding is used to restore the text data compressed by Huffman. After this process, the cloud obtains the original data that is exactly the same as that at the sending end, realizing reliable data transmission and storage. This data transmission scheme comprehensively considers efficiency, reliability, and resource consumption, and is particularly suitable for scenarios such as the Internet of Things. By compression, the amount of transmitted data is reduced. By packetization and retransmission, the transmission reliability is ensured. By low-power technologies, the service life of devices is extended. Finally, efficient and reliable data transmission is achieved.

[0043] S105. In the cloud server, a distributed storage architecture is adopted to store the massive monitoring data, and indexes are established according to the data type and time dimension to achieve fast retrieval and access of the data.

[0044] According to the data type and time dimension of the massive monitoring data, a distributed storage architecture is designed and deployed in the cloud server. Among them, the distributed storage architecture is used to disperse the storage of the massive monitoring data to multiple cloud server nodes to achieve load balancing and high availability. For different data types and time dimensions, appropriate data storage formats and data compression algorithms are adopted to store the massive monitoring data to improve the storage efficiency of the massive monitoring data. According to the data type and time dimension, an efficient data index structure is established. The data index structure includes inverted index and B+ tree index to accelerate the retrieval and access speed of the massive monitoring data. The distributed cache technology is adopted to cache the frequently accessed monitoring data. The distributed cache technology includes Redis cluster to reduce the database query pressure and improve the data access performance. The big data processing framework is used to perform parallel computing and analysis on the massive monitoring data. The big data processing framework includes Hadoop and Spark to mine the value of the massive monitoring data and support business decisions. Based on machine learning algorithms, intelligent analysis is performed on the massive monitoring data. The machine learning algorithms include time series prediction and anomaly detection to achieve fault warning and anomaly diagnosis and improve the system operation and maintenance efficiency.

[0045] Specifically, the distributed storage architecture is the key to the management of massive monitoring data. This architecture not only achieves load balancing but also improves the availability of the system. When a certain node fails, other nodes can continue to provide services to ensure the continuous availability of the data.

[0046] For different data types, corresponding storage formats and compression algorithms can be adopted. For low-frequency data such as device status, row-based storage such as HBase can be used to facilitate the quick retrieval of the complete information of a single device. An efficient data index structure is crucial for quick retrieval. A B+ tree index can be established for device IDs to support range queries; an inverted index can be established for fault types to facilitate the quick positioning of fault records of specific types. In this way, when querying overvoltage faults in a specific substation within a certain time period, the relevant data can be quickly located.

[0047] Distributed cache technologies such as Redis clusters can significantly improve the access speed of hot data. Big data processing frameworks such as Hadoop and Spark can make full use of distributed computing resources. Hadoop is suitable for processing massive historical data, such as analyzing fault records in the past five years to mine fault patterns and rules. Machine learning algorithms play an important role in intelligent analysis. Anomaly detection algorithms can monitor device operation parameters in real time and give an alarm in time when an abnormal trend is detected, preventing problems before they occur. These intelligent analyses not only improve the operation and maintenance efficiency of the system but also provide strong support for management decision-making.

[0048] S106. For the stored monitoring data, a time series analysis method is adopted to extract the change trends of indicators such as water level and water quality. If an abnormal fluctuation is detected, the early warning mechanism is triggered to generate early warning information.

[0049] Obtain the water level and water quality monitoring data within a preset time interval to construct a time series data set; use the moving average method to smooth the time series data set to obtain a smoothed time series data set; based on the smoothed time series data set, use the autoregressive integrated moving average model to predict the future change trends of the water level and water quality indicators to obtain predicted time series data; calculate the residuals between the predicted time series data and the actual monitoring data, compare the residuals with a preset threshold. If the residuals exceed the preset threshold, it is determined that there is an abnormal fluctuation; if an abnormal fluctuation in the water level or water quality indicator is detected, determine the early warning level and early warning information according to the preset early warning rules; send the early warning information to a preset early warning information receiving end; store the monitoring data of the abnormal fluctuation, the early warning information and the relevant analysis results in the early warning information database.

[0050] Specifically, water level and water quality monitoring are important links in water resource management. By constructing a time series data set, historical trends can be effectively analyzed and future changes can be predicted. Taking a river monitoring station as an example, the water level and dissolved oxygen data per hour in the past 30 days can be obtained to form a time series of 720 data points. Smoothing the original data can eliminate short-term fluctuations and highlight long-term trends.

[0051] The moving average method is a commonly used smoothing technique that replaces the original value by calculating the average of several data points before and after. For example, using a 5-point moving average can effectively remove data fluctuations caused by measurement errors or short-term disturbances. The autoregressive integrated moving average (ARIMA) model is a statistical model widely used in time series prediction. By analyzing the smoothed data, an ARIMA(1,1,1) model can be established to predict the water level and dissolved oxygen changes in the next 24 hours. This model takes into account the autocorrelation, trend, and seasonality of the data and can accurately capture the changing patterns of hydrological parameters.

[0052] The difference between the prediction result and the actual monitoring data is called the residual, which can be used to judge abnormal situations. Assuming that the residual threshold is set to 3 times the historical standard deviation, when the residual at a certain moment exceeds this threshold, the system will determine it as an abnormal fluctuation. For example, if the predicted water level is 2.5 meters and the actual monitoring value suddenly rises to 3.2 meters, exceeding the preset threshold, an abnormal alarm will be triggered.

[0053] The formulation of early warning rules needs to consider multiple factors, such as the water level change speed, duration, etc. A three-level early warning mechanism can be set: a yellow warning indicates a minor abnormality, an orange warning indicates a moderate abnormality, and a red warning indicates a serious abnormality. When it is detected that the water level rises abnormally and lasts for more than 2 hours, the system may issue an orange warning to remind relevant departments to pay close attention to the situation. The timely transmission of early warning information is crucial for flood control and drought relief. The early warning information can be pushed to relevant units such as the water conservancy department and the emergency management department via text messages, emails, or a dedicated App. At the same time, storing the abnormal data, early warning information, and analysis results in the early warning information database is helpful for subsequent statistical analysis and decision-making support.

[0054] The implementation of this monitoring and early warning system can significantly improve the efficiency and accuracy of water resources management. Through the analysis of historical data and the prediction of future trends, the management department can take measures in advance, such as adjusting the reservoir water volume or strengthening pollution source control, so as to better respond to possible flood or water quality pollution events and ensure the sustainable utilization of water resources and the safety of the ecological environment.

[0055] S107. According to the analysis results, using data visualization technology, convert the monitoring data into a chart form. If the user needs to view the data for a specific time period, extract the corresponding data from the database and generate a visualization report.

[0056] The monitoring data is obtained, and at least one target chart form is determined according to the type and scope of the monitoring data, wherein the target chart forms include line charts, bar charts, and pie charts; a time series analysis algorithm is used to perform trend analysis and prediction on the monitoring data to obtain data change patterns; during visual display, a data clustering algorithm is used to identify abnormal points or mutations in the monitoring data, and if abnormal points or mutations are identified, they are marked in a preset eye-catching manner.

[0057] Specifically, the visualization of monitoring data is crucial for water resource management. First, determine the target chart format based on the data type and scope. For example, for the trend of water level changes, a line chart is the most intuitive; the proportion of water quality indicators can be represented by a pie chart; and a bar chart is suitable for comparing water consumption in different periods.

[0058] Time series analysis algorithms can reveal the patterns of data changes. Taking water level monitoring as an example, the autoregressive integrated moving average model (ARIMA) can be used to predict future water level trends. This model takes into account the autocorrelation, trend and seasonality of historical data and can accurately capture the water level change pattern. By analyzing the model parameters, the periodicity and influencing factors of water level changes can be understood, providing a basis for water resource scheduling.

[0059] Data clustering algorithms play an important role in identifying anomalies and mutations. Taking K-means clustering as an example, water quality monitoring data can be divided into three categories: normal, slightly polluted, and severely polluted. If the data of a certain monitoring point is too far away from the center of its category, it may be an outlier. This method can quickly locate potential water quality problems and improve regulatory efficiency.

[0060] In visual display, it is particularly important to mark abnormal points or sudden changes. You can use eye-catching methods such as color contrast and shape change. For example, in the water quality index line chart, mark the points that exceed the standard with red triangles and explain them in the legend. This can intuitively reflect water quality abnormalities and help managers quickly identify and deal with problems.

[0061] By comprehensively applying these technologies, a comprehensive water resource monitoring visualization system can be constructed. Taking a certain basin as an example, the main interface of the system displays real-time data and changing trends of key indicators such as water level, water quality, and water consumption. The recent water level changes are presented through line charts, and the ARIMA model is used to predict the trends for the next week. The water quality status is shown using pie charts, intuitively reflecting the proportion of each indicator. The water consumption data is compared using bar charts for different periods and purposes. The system should also have an abnormal warning function. When abnormal data is detected, the abnormal points in the corresponding charts will be prominently marked, and an alarm will be triggered simultaneously. For example, the total phosphorus content at a certain monitoring station suddenly increases, exceeding three times the historical average. The system immediately marks this data point in red on the water quality index chart and pops up a warning message, prompting the management staff to investigate the possible pollution sources in a timely manner. Such a comprehensive visualization solution can effectively improve the efficiency and accuracy of water resource monitoring. Through intuitive chart displays and intelligent abnormal identification, the management staff can quickly grasp the water resource status, make timely and accurate decisions, and thus better protect and utilize the precious water resources.

[0062] S108. Use machine learning algorithms to train historical monitoring data to establish a hydrogeological environment prediction model. If real-time monitoring data is input, the prediction results of environmental changes for a future period will be output.

[0063] Continuously obtain real-time monitoring data of the hydrogeological environment at a preset time interval and input it into the prediction model; determine whether the obtained real-time monitoring data meets the preset data quality threshold. If it meets the threshold, include it in the training data set, otherwise exclude this batch of data; adopt an incremental learning method to regularly retrain the prediction model using newly added high-quality monitoring data, continuously optimizing the performance of the prediction model; through feature engineering methods, extract the key features with the strongest correlation to hydrogeological environment changes from the original monitoring data, and use these key features as the input of the prediction model; during the training process of the prediction model, use the cross-validation method to divide the training data set into training subsets and validation subsets to avoid overfitting of the prediction model; when new real-time monitoring data is input, the prediction model automatically determines the hydrogeological environment category it belongs to and gives the prediction results of the change trends of this category for a future period; according to the prediction results, automatically generate a hydrogeological environment prediction report and present the prediction results in the form of visual charts, facilitating the management staff to timely grasp the environmental dynamics and formulate countermeasures.

[0064] Specifically, the hydrogeological environment monitoring system provides continuously updated inputs for the prediction model by regularly collecting data. For example, it automatically collects data such as groundwater level, water quality parameters, and soil moisture content every hour. The system sets data quality thresholds, such as the groundwater level change not exceeding 0.5 meters per hour and the pH value within the range of 6.5 - 8.5. Data that meet the conditions are included in the training set, while those that do not meet the standards are excluded to ensure the data quality for model training.

[0065] By adopting the incremental learning method, the model can adapt to the dynamic changes of the environment. For example, the model is retrained weekly with newly added high-quality data so that it can capture the impacts brought by seasonal changes or unexpected events. Feature engineering plays a key role in data processing, extracting the most relevant features from the original data. For example, the change rate of the groundwater level, the correlation coefficient of water quality parameters, etc. can be calculated as the input features of the model.

[0066] Cross-validation is an effective method to avoid model overfitting. The dataset can be divided into five parts, with four parts for training and one part for validation. The model performance is evaluated by rotating the validation set multiple times. This method can effectively detect whether the model overfits the training data, thus improving the generalization ability of the model.

[0067] Based on the input real-time data, the prediction model automatically determines the current hydrogeological environment category. For example, the environment may be classified into categories such as "normal", "lightly polluted", "moderately polluted", and "severely polluted". At the same time, the model can also predict the change trend of this category within the next week, such as "the pollution degree may increase" or "the water quality is expected to improve". Based on the prediction results, the system automatically generates prediction reports and visualization charts. The report may include an overview of the current environmental situation, future trend prediction, potential risk analysis, etc. Visualization charts such as line charts show the change trend of water quality parameters over time, and heat maps display the pollution degree distribution in different regions. These intuitive display methods help managers quickly understand the environmental dynamics and formulate response measures in a timely manner, such as increasing the monitoring of pollution sources and adjusting the water resource utilization plan.

[0068] Through this continuously optimized prediction system, the changing laws of the hydrogeological environment can be better grasped, and the scientific nature and foresight of environmental management can be improved. The prediction results of the system can not only be used for daily environmental supervision but also provide important basis for long-term water resource planning and ecological environment protection.

[0069] S109. Generate a decision-making suggestion report based on the prediction results and early warning information. If the prediction result exceeds the preset threshold, automatically adjust the collection frequency of the monitoring equipment to increase the data collection density.

[0070] Obtain the preset prediction result threshold and early warning information threshold for subsequent judgment and decision-making; obtain the real-time data collected by the monitoring device, preprocess the real-time data to obtain a standardized data set; input the standardized data set into a pre-trained machine learning model to obtain a prediction result; judge whether the prediction result exceeds the preset threshold, if it exceeds the threshold, trigger early warning information, and dynamically adjust the data collection frequency of the monitoring device according to the early warning level; if it does not exceed the threshold, keep the current data collection frequency unchanged; automatically generate a decision-making recommendation report according to the prediction result and early warning information, combined with the preset decision-making rules, and send it to relevant personnel; continuously monitor the changes in the prediction result and early warning information, and dynamically optimize the decision-making rules and machine learning model according to the feedback information to achieve closed-loop optimization.

[0071] Specifically, the setting of the preset threshold and early warning information threshold is crucial for the hydrogeological environment monitoring system. For example, for groundwater level monitoring, the prediction result threshold can be set at ±10% of the historical average water level, and the early warning information threshold can be set at ±15%. Such a setting can issue early warnings in a timely manner when the water level changes abnormally, while avoiding frequent triggering of early warnings due to small fluctuations.

[0072] Preprocessing of real-time data is a key step to ensure data quality. Taking water quality monitoring as an example, the original data may contain outliers or missing values. By methods such as median filling and moving average, a smoother and more continuous data set can be obtained.

[0073] Standardization processing can unify indicators with different dimensions (such as pH value, dissolved oxygen content, etc.) to the same scale, facilitating subsequent model processing.

[0074] The selection of the machine learning model needs to be determined according to the characteristics of specific problems. For hydrogeological environment prediction, time series models such as ARIMA or LSTM neural networks often perform well. These models can capture the time dependence of data and learn the future change rules from historical trends.

[0075] After the early warning is triggered, dynamically adjusting the data collection frequency is an efficient monitoring strategy. For example, under normal circumstances, data is collected once an hour. When potential risks are detected, the frequency can be increased to once every 15 minutes. This can not only capture abnormal changes in a timely manner but also save resources under normal circumstances.

[0076] The generation of the decision-making recommendation report needs to consider multiple factors. Taking geological disaster early warning as an example, the report may include: predicted disaster type, occurrence probability, possible affected range, recommended preventive measures, etc. This information can help decision-makers quickly understand the situation and formulate response strategies.

[0077] Closed-loop optimization is an effective means to improve system performance. By comparing the differences between the predicted results and the actual situation, the model parameters and decision rules can be continuously adjusted. For example, if it is found that some early warnings are often false alarms, the corresponding early warning thresholds can be appropriately increased; if some important events are not warned in time, it may be necessary to lower the thresholds or introduce new prediction indicators.

[0078] This intelligent hydrogeological environment monitoring system can greatly improve the monitoring efficiency and early warning accuracy. Through continuous learning and optimization, the system can adapt to the changing environmental conditions and provide strong support for fields such as water resource management and geological disaster prevention and control. However, in actual applications, it is still necessary to pay attention to human-machine cooperation to ensure that key decisions are reviewed and confirmed by professionals.

[0079] The above description is only the preferred embodiment of one or more embodiments of this specification, and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope protected by one or more embodiments of this specification.

Claims

1. A hydrogeological environment intelligent monitoring method, characterized in that: The method comprises: According to the preset collection frequency, multiple types of data such as water level, water quality, soil moisture, groundwater level, etc. are obtained from monitoring equipment distributed in remote environments. Adaptive sampling algorithms are used to adjust collection parameters according to the accuracy requirements of different data types. If an equipment anomaly occurs during data collection, the IoT edge computing module will be used to diagnose the fault and determine the type of anomaly. If it is a hardware failure, the backup device will be started; if it is a software failure, the remote repair program will be executed; In view of the format differences of different types of data, a unified data conversion protocol is used to convert the original data into a standard format. According to the data volume and transmission bandwidth, a compression algorithm is selected to compress the data. The compressed data is transmitted through low-power wide area network technology. If data is lost during the transmission process, the data is retransmitted according to the preset retransmission mechanism until the data reaches the cloud server completely; In the cloud server, a distributed storage architecture is used to store massive monitoring data, and indexes are established according to data types and time dimensions to achieve fast retrieval and access of data; For the stored monitoring data, the time series analysis method is used to extract the changing trends of indicators such as water level and water quality. If abnormal fluctuations are detected, the early warning mechanism is triggered and early warning information is generated; According to the analysis results, data visualization technology is used to convert the monitoring data into charts. If the user needs to view the data for a specific time period, the corresponding data is extracted from the database and a visualization report is generated; Use machine learning algorithms to train historical monitoring data and establish a hydrogeological environment prediction model. If real-time monitoring data is input, the prediction results of environmental changes in the future will be output; A decision-making recommendation report is generated based on the prediction results and warning information. If the prediction results exceed the preset threshold, the collection frequency of the monitoring equipment is automatically adjusted to increase the data collection density.

2. A hydrogeological environment intelligent monitoring method according to claim 1, characterized in that: According to the preset acquisition frequency, multiple types of data such as water level, water quality, soil moisture, groundwater level, etc. are obtained from monitoring equipment distributed in remote environments. According to the accuracy requirements of different data types, an adaptive sampling algorithm is used to adjust the acquisition parameters, including: Acquire multiple types of data on water level, water quality, soil moisture and groundwater level collected by monitoring equipment distributed in remote environments, wherein the data is collected according to a preset collection frequency; According to the accuracy requirements of different data types in the multi-type data, an adaptive sampling algorithm is used to dynamically adjust the data acquisition parameters; If the current collection frequency is lower than the preset threshold, the collection frequency is increased, otherwise the collection frequency is reduced to reduce data redundancy; Perform trend analysis on the water level data to obtain predicted water level changes in the future. If the predicted water level exceeds a preset threshold, an early warning mechanism is triggered. Using a machine learning algorithm to classify the water quality data to obtain abnormal water quality data, and judging the pollution source of the abnormal water quality according to the characteristics of the abnormal water quality data; A support vector machine algorithm is used to perform regression analysis on the soil moisture data to obtain a variation pattern of soil moisture; Based on the groundwater level data, a groundwater level prediction model is established by using a time series analysis method.

3. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: If an equipment abnormality occurs during the data collection process, the IoT edge computing module is used to perform fault diagnosis and determine the abnormality type. If it is a hardware failure, the backup device is started; if it is a software failure, a remote repair program is executed, including: Obtaining performance parameters and abnormal information of data acquisition equipment, which are obtained by real-time monitoring of the IoT edge computing module; According to preset fault diagnosis rules and abnormality detection algorithms, determine whether the data acquisition device has an abnormality; If the data acquisition device is abnormal, a fault diagnosis process is triggered; Using a fault diagnosis decision tree algorithm, comprehensively analyzing various parameters of the data acquisition equipment to determine the specific abnormality type; If the abnormality type is a hardware failure, a startup instruction is sent to the backup device to switch the data acquisition task of the data acquisition device to the backup device; If the abnormality type is a software fault, a corresponding repair solution is matched from a remote repair program library, the repair solution is sent to the data acquisition device, and an automatic repair operation is performed; During the fault repair process, the operating status of the data acquisition device is continuously monitored, and the anomaly detection algorithm is used to determine whether the repair is successful; If the repair fails, perform the fault diagnosis again until the repair is successful; When the data acquisition device resumes normal operation, the fault diagnosis and repair results are reported to the Internet of Things management platform, the device status is updated, and the fault handling log is recorded.

4. A hydrogeological environment intelligent monitoring method according to claim 1, characterized in that: In view of the format differences of different types of data, a unified data conversion protocol is used to convert the original data into a standard format, and a compression algorithm is selected to compress the data according to the data volume and transmission bandwidth, including: Obtaining the data volume of the standard format data, and determining the data volume level according to a preset threshold; If the data volume is greater than the threshold, obtaining the current transmission bandwidth; According to the data volume level and the transmission bandwidth, an optimal compression algorithm is selected from a preset compression algorithm library, and a parameter configuration of the compression algorithm is obtained; Inputting the standard format data into the compression algorithm, and compressing the standard format data according to the parameter configuration to obtain compressed data; For different types of original data, a preset data format template library is established, the data format of the original data is automatically matched through a data format recognition algorithm, and the corresponding conversion protocol is dynamically called to complete the data format conversion of the original data to obtain the standard format data.

5. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: The compressed data is transmitted through the low-power wide area network technology. If data loss occurs during the transmission process, the data is retransmitted according to a preset retransmission mechanism until the data reaches the cloud server completely, including: Acquire original data to be transmitted, and compress the original data using a preset data compression algorithm to obtain compressed data; Dividing the compressed data into a plurality of data packets, and adding a sequence number and check information to each of the data packets; The data packets are sequentially transmitted to a cloud server via low power wide area network technology; After receiving the data packet, the cloud server determines whether there is data loss according to the sequence number and verification information of the data packet; If the cloud server determines that data is lost, the cloud server sends a retransmission request to the data sending end, requesting retransmission of the lost data packet; After receiving the retransmission request, the data transmitting end retransmits the corresponding data packet according to the data packet sequence number specified in the retransmission request; The cloud server assembles all received data packets according to the sequence numbers to obtain complete compressed data, and uses a decompression algorithm corresponding to the data compression algorithm to decompress the complete compressed data to obtain complete original data.

6. A hydrogeological environment intelligent monitoring method according to claim 1, characterized in that: In the cloud server, a distributed storage architecture is used to store massive monitoring data, and indexes are established according to data types and time dimensions to achieve fast retrieval and access of data, including: Design a distributed storage architecture based on the data type and time dimension of the massive monitoring data, and deploy and implement it in the cloud server; The distributed storage architecture is used to disperse and store the massive monitoring data in multiple cloud server nodes to achieve load balancing and high availability; According to different data types and time dimensions, appropriate data storage formats and data compression algorithms are used to store the massive monitoring data, thereby improving the storage efficiency of the massive monitoring data; According to the data type and time dimension, an efficient data index structure is established, wherein the data index structure includes an inverted index and a B+ tree index, so as to accelerate the retrieval and access speed of the massive monitoring data; Distributed caching technology is used to cache frequently accessed monitoring data. The distributed caching technology includes Redis cluster, which is used to reduce database query pressure and improve data access performance; Perform parallel computation and analysis on the massive monitoring data using a big data processing framework, wherein the big data processing framework includes Hadoop and Spark, and is used to mine the value of the massive monitoring data and support business decision-making; The massive monitoring data is intelligently analyzed based on a machine learning algorithm, wherein the machine learning algorithm includes time series prediction and anomaly detection, which is used to realize fault warning and anomaly diagnosis and improve system operation and maintenance efficiency.

7. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: The stored monitoring data is analyzed using a time series analysis method to extract the changing trends of indicators such as water level and water quality. If abnormal fluctuations are detected, an early warning mechanism is triggered to generate early warning information, including: Obtain water level and water quality monitoring data within a preset time interval and construct a time series data set; The time series data set is smoothed by using a moving average method to obtain a smoothed time series data set; Based on the smoothed time series data set, an autoregressive integrated moving average model is used to predict the future change trend of water level and water quality indicators to obtain predicted time series data; Calculating the residual between the predicted time series data and the actual monitoring data, comparing the residual with a preset threshold, and if the residual exceeds the preset threshold, determining that an abnormal fluctuation exists; If abnormal fluctuations in water level or water quality indicators are detected, the warning level and warning information will be determined according to the preset warning rules; Sending the warning information to a preset warning information receiving terminal; The abnormally fluctuating monitoring data and warning information as well as the related analysis results are stored in the warning information database.

8. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: According to the analysis results, data visualization technology is used to convert the monitoring data into a chart form. If the user needs to view the data for a specific time period, the corresponding data is extracted from the database and a visualization report is generated, including: Acquire the monitoring data, and determine at least one target chart form according to the type and range of the monitoring data, wherein the target chart form includes a line chart, a bar chart, and a pie chart; Using a time series analysis algorithm, the monitoring data is trend analyzed and predicted to obtain the law of data change; During the visual display, a data clustering algorithm is used to identify abnormal points or mutations in the monitoring data. If abnormal points or mutations are identified, they are marked in a preset eye-catching manner.

9. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: The machine learning algorithm is used to train historical monitoring data to establish a hydrogeological environment prediction model. If real-time monitoring data is input, the prediction results of environmental changes in the future period are output, including: Continuously obtain real-time monitoring data of the hydrogeological environment at pre-set time intervals and input it into the prediction model; Determine whether the acquired real-time monitoring data meets the preset data quality threshold. If so, include it in the training data set; otherwise, remove the batch of data; Adopting an incremental learning approach, regularly retraining the prediction model with newly added high-quality monitoring data to continuously optimize the performance of the prediction model; By using a feature engineering method, the key features with the strongest correlation with the hydrogeological environment change are extracted from the original monitoring data, and the key features are used as inputs of the prediction model; In the training process of the prediction model, a cross-validation method is used to divide the training data set into a training subset and a validation subset to avoid overfitting of the prediction model; When new real-time monitoring data is input, the prediction model automatically determines the hydrogeological environment category to which it belongs, and gives the prediction result of the change trend of the category in the future; Based on the prediction results, a hydrogeological environment prediction report is automatically generated, and the prediction results are presented in the form of visual charts, so that managers can grasp the environmental dynamics in a timely manner and formulate response measures.

10. The method for intelligent monitoring of hydrogeological environment according to claim 1, characterized in that: The decision-making recommendation report is generated based on the prediction results and warning information. If the prediction results exceed the preset threshold, the collection frequency of the monitoring equipment is automatically adjusted to increase the data collection density, including: Obtain preset prediction result thresholds and warning information thresholds for subsequent judgment and decision-making; Acquire real-time data collected by monitoring equipment, and pre-process the real-time data to obtain a standardized data set; Inputting the standardized data set into a pre-trained machine learning model to obtain a prediction result; Determine whether the prediction result exceeds the preset threshold value, and if so, trigger an early warning message, and dynamically adjust the data collection frequency of the monitoring device according to the early warning level; If the threshold is not exceeded, the current data collection frequency is maintained unchanged; According to the prediction results and warning information, combined with the preset decision rules, a decision recommendation report is automatically generated and sent to relevant personnel; Continuously monitor changes in the prediction results and warning information, and dynamically optimize the decision rules and machine learning models based on feedback information to achieve closed-loop optimization.

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