Dynamic monitoring and early warning system for underground water

Abstract

The invention discloses an underground water dynamic monitoring and early warning system, which relates to the field of environmental resource detection and comprises a data acquisition and management module based on a system architecture, a data processing and analysis module, an early warning judgment and decision module and an information issuing and feedback module. Compared with the prior art, the system has the advantages that (1) data acquisition is comprehensive: water level, water quality and environmental parameters of underground water are acquired in real time through the intelligent sensor array unit, and comprehensiveness and accuracy of data are ensured; (2) the data processing and analysis capability is high: statistics and machine learning technologies are applied to deeply mine and analyze the data, and trends, periodicity and abnormal modes in the data are identified, so that a scientific basis is provided for early warning; (3) intelligent early warning mechanism: setting an early warning threshold value according to industrial standards, historical data and user requirements, and comparing real-time monitoring data with the threshold value; and (4) timely information release: timely releasing the early warning information to the user through a multi-channel information release module.

Description

Technical Field

[0001] The present invention relates to the field of environmental resource detection, and in particular to a groundwater dynamic monitoring and early warning system. Background Art

[0002] As an important natural resource, the dynamic changes of groundwater are directly related to human production and life. With the acceleration of industrialization and urbanization, groundwater pollution and over-exploitation are becoming increasingly serious. Dynamic monitoring and early warning of groundwater are particularly important. At present, there are some groundwater monitoring systems and devices on the market, which have achieved the monitoring of groundwater levels and preliminary data processing to a certain extent. However, these existing technologies still have many shortcomings.

[0003] For example, the groundwater monitoring system with patent number CN201810123456.7, although it can collect groundwater level data in real time, is limited to simple parameter measurement in water quality monitoring and lacks comprehensive water quality parameter monitoring and data analysis functions. In addition, the system also has limitations in data processing and early warning judgment. It cannot accurately identify abnormal patterns and trends in the data, and the early warning mechanism is not intelligent enough, making it difficult to provide users with timely and accurate early warning information and decision support.

[0004] For example, the groundwater monitoring device with patent number CN201920234567.8, although it integrates multiple sensors for data collection, has security risks in data transmission and storage, and the data backup strategy is not perfect, which may lead to data loss or damage. At the same time, the device lacks deep mining and prediction functions in data processing and analysis, and cannot provide users with comprehensive groundwater status analysis and future trend prediction. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the existing groundwater monitoring system still has many deficiencies in data collection, processing, analysis and early warning, and cannot meet the current urgent needs for groundwater dynamic monitoring and early warning.

[0006] In order to solve the above technical problems, the technical solution provided by the present invention is as follows: a groundwater dynamic monitoring and early warning system, including a data acquisition and management module, a data processing and analysis module, an early warning judgment and decision module, and an information release and feedback module based on the system architecture. The data acquisition and management module collects the water level, water quality and environmental parameters of groundwater in real time, calibrates the sensor regularly to ensure the accuracy of the data, performs preliminary screening and format unification on the received data to facilitate subsequent processing, realizes safe storage and efficient retrieval of data, and executes a regular data backup strategy at the same time; the data acquisition and management module includes a field data acquisition module and a data integration and management module. The field data acquisition module includes an intelligent sensor array unit and a data transmission and calibration unit. The data integration and management module includes a data receiving and pre-screening unit and a data storage and backup unit. The field data acquisition module is connected to the intelligent sensor array unit by wire or wireless means to realize real-time data transmission. The data integration and management module transmits and stores data with a central server through a network, and users query and manage the collected data through a dedicated software interface.

[0007] The data processing and analysis module cleans the received data, removes outliers and missing values, performs standardization, and uses statistics and machine learning techniques to identify trends, periodicities, and abnormal patterns in the data. By mining the correlation between water quality parameters, it provides a scientific basis for early warning, establishes a prediction model based on historical data, and predicts the future trend of groundwater conditions. The data processing and analysis module includes a data preprocessing and analysis module and a data mining and prediction module. The data preprocessing and analysis module includes a data cleaning and standardization unit, a trend analysis and pattern recognition unit, and the data mining and prediction module includes an association rule mining unit and a future trend prediction unit. The data processing and analysis module is connected to the central server through a network to achieve real-time data processing and analysis. Users view the analysis results through the software interface, including trend charts and abnormal reports, and set analysis parameters based on this to obtain analysis results in a customized manner.

[0008] The early warning judgment and decision-making module sets the early warning threshold according to industry standards, historical data and user needs, compares the real-time monitoring data with the threshold, triggers the early warning and determines the early warning level, automatically or manually starts the corresponding emergency plan according to the early warning level, and provides data-based decision suggestions; the early warning judgment and decision-making module includes a threshold setting and early warning triggering module, an emergency response and decision support module, the threshold setting and early warning triggering module includes a threshold setting unit, an early warning trigger and level determination unit, the emergency response and decision support module includes an emergency plan startup unit, and a decision-making auxiliary information generation unit. The early warning judgment and decision-making module is connected to the central server through the network to realize real-time data processing and decision support. When the early warning is triggered, the module will automatically send early warning information of the SMS and email type to the user, and the user can view the early warning details, emergency plans and decision-making auxiliary information through the software interface;

[0009] The information release and feedback module releases warning information instantly through SMS, email, APP push, etc., publishes warning announcements and popular science information on official websites and social media platforms, provides users with an interface for querying real-time monitoring data, historical records, and warning information, and collects user feedback for continuous optimization and upgrading of the system; the information release and feedback module includes a multi-channel information release module and a user interaction and feedback module. The multi-channel information release module includes an instant messaging release unit and a public platform release unit. The user interaction and feedback module includes a user query and interactive interface unit and a system feedback and optimization unit. The information release and feedback module connects to the SMS platform and email server through the network. Users receive warning information and query data through mobile phones and computer terminals. Users submit feedback through the software interface, and the module automatically collects and analyzes it.

[0010] Furthermore, the field data acquisition module realizes real-time data collection and transmission through the intelligent sensor array unit and the data transmission and calibration unit; the data integration and management module is responsible for data reception, pre-screening, storage and backup.

[0011] Furthermore, the intelligent sensor array unit is responsible for collecting water level, water quality and environmental parameters in real time at the groundwater monitoring point, among which water quality parameters include temperature, pH value, dissolved oxygen, conductivity, turbidity, and heavy metal concentration; the data transmission and calibration unit is responsible for transmitting the collected data to the central server via wireless or wired network, and performing regular calibration and status monitoring of the sensor.

[0012] The data receiving and pre-screening unit receives data from the site, performs preliminary screening and unifies the format; the data storage and backup unit ensures the safe storage and efficient retrieval of data, and implements regular data backup strategies.

[0013] Furthermore, the data preprocessing and analysis module is responsible for cleaning, standardizing, trend analysis and pattern recognition of the received data; the data mining and prediction module uses machine learning algorithms to mine association rules and predict future trends.

[0014] Furthermore, the data cleaning and standardization unit cleans the received data, removes outliers and missing values, and performs standardization; the trend analysis and pattern recognition unit uses statistical and machine learning techniques to identify trends, periodicities, and abnormal patterns in the data;

[0015] The association rule mining unit mines the correlation between water quality parameters to provide a scientific basis for early warning; the future trend prediction unit establishes a prediction model based on historical data to predict the future trend of groundwater conditions.

[0016] Furthermore, the threshold setting and warning triggering module is responsible for setting the warning threshold and triggering the warning; the emergency response and decision support module provides emergency plans and decision-making support information.

[0017] Furthermore, the threshold setting unit sets the warning threshold according to industry standards, historical data and user needs; the warning triggering and level determination unit compares the real-time monitoring data with the threshold, triggers the warning and determines the warning level;

[0018] The emergency plan activation unit automatically or manually activates the corresponding emergency plan according to the warning level; the decision-making auxiliary information generation unit provides data-based decision-making recommendations, including pollution source tracking and impact range assessment.

[0019] Furthermore, the multi-channel information release module is responsible for releasing warning information to users through multiple channels; the user interaction and feedback module is responsible for collecting user feedback and optimizing the system.

[0020] Furthermore, the instant messaging release unit will release warning information instantly through SMS, email, and APP push; the public platform release unit will release warning announcements and popular science information on official websites and social media platforms;

[0021] The user query and interactive interface unit provides an interface for users to query real-time monitoring data, historical records, and warning information, and supports user messages and interactions; the system feedback and optimization unit collects user feedback for continuous optimization and upgrading of the system.

[0022] Compared with the prior art, the advantages of the present invention are as follows: (1) Comprehensive data collection: The present invention collects groundwater level, water quality and environmental parameters in real time through intelligent sensor array units to ensure the comprehensiveness and accuracy of data; (2) Strong data processing and analysis capabilities: Using statistical and machine learning techniques, the data is deeply mined and analyzed to identify trends, periodicity and abnormal patterns in the data, providing a scientific basis for early warning. At the same time, a prediction model is established based on historical data to predict the future trend of groundwater conditions and improve the accuracy and timeliness of early warning; (3) Intelligent early warning mechanism: The early warning threshold is set according to industry standards, historical data and user needs, and the real-time monitoring data is compared with the threshold to trigger the early warning and determine the early warning level. Data-based decision-making suggestions are provided, including pollution source tracking, impact range assessment, etc., to provide strong support for user decision-making; (4) Timely information release: Through multi-channel information release modules, early warning information is released to users in a timely manner to ensure that users can obtain early warning information in the first time and take corresponding measures; (5) User interaction and feedback: Provide users with an interface for querying real-time monitoring data, historical records, and early warning information, and support users to leave messages and interact. Collect user feedback for continuous optimization and upgrading of the system to improve system practicality and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the architecture of a groundwater dynamic monitoring and early warning system. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0025] Embodiment 1

[0026] like Figure 1 As shown, the system of the present invention includes a data acquisition and management module, a data processing and analysis module, an early warning judgment and decision module, and an information release and feedback module. Each module cooperates with each other to realize the intelligent and efficient dynamic monitoring and early warning of groundwater. Specifically:

[0027] (1) Data acquisition and management module: This module collects groundwater level, water quality and environmental parameters in real time, and calibrates sensors regularly to ensure data accuracy. Through the cooperation of the field data acquisition module and the data integration and management module, real-time data transmission, reception, pre-screening, format unification, storage and backup are achieved. Users can query and manage the collected data through a dedicated software interface to ensure data security and efficiency.

[0028] (2) Data processing and analysis module: This module cleans the received data, removes outliers and missing values, and performs standardization. It uses statistical and machine learning techniques to identify trends, periodicities, and abnormal patterns in the data. It provides a scientific basis for early warning by exploring the correlation between water quality parameters. It builds a prediction model based on historical data to predict future trends in groundwater conditions. Users can view the analysis results through the software interface, including trend charts, abnormality reports, etc., and use them to set analysis parameters to obtain analysis results in a customized manner.

[0029] (3) Warning judgment and decision-making module: This module sets warning thresholds based on industry standards, historical data and user needs, compares real-time monitoring data with the threshold, triggers warnings and determines the warning level. Automatically or manually initiates the corresponding emergency plan based on the warning level, and provides data-based decision-making suggestions. When a warning is triggered, the module automatically sends warning information such as text messages and emails to the user, and the user can view the warning details, emergency plans and decision-making support information through the software interface.

[0030] (4) Information release and feedback module: This module releases warning information instantly through SMS, email, APP push, etc., and publishes warning announcements and popular science information on official websites and social media platforms. It provides users with an interface to query real-time monitoring data, historical records, and warning information, and collects user feedback for continuous optimization and upgrading of the system.

[0031] Based on the architecture of the above system, the workflow of this system includes:

[0032] (1) Data collection: Intelligent sensor array units (such as water level meters, water quality analyzers, etc.) are installed in groundwater monitoring wells to collect real-time groundwater level, water quality (including temperature, pH value, dissolved oxygen, etc.) and environmental parameters. The data transmission and calibration unit is responsible for transmitting the collected data to the central server via wireless or wired networks, and performing regular calibration and status monitoring of sensors to ensure data accuracy.

[0033] (2) Data integration and management: After receiving data from the field, the data integration and management module performs preliminary screening and format unification, and then stores it in the database. The database management system is responsible for the secure storage and efficient retrieval of data, while implementing regular data backup strategies to prevent data loss.

[0034] (3) Data processing and analysis: The data preprocessing and analysis module cleans the received data, removes outliers and missing values, and performs standardization. The trend analysis and pattern recognition unit uses statistical and machine learning techniques to identify trends, periodicity, and abnormal patterns in the data. The data mining and prediction module uses machine learning algorithms to mine association rules and predict future trends, providing a scientific basis for early warning judgments.

[0035] (4) Warning judgment and decision-making: The threshold setting and warning trigger module sets the warning threshold according to industry standards, historical data and user needs. When the real-time monitoring data exceeds the set threshold, the warning trigger and level determination unit will trigger the warning and determine the warning level. The emergency response and decision support module will automatically or manually initiate the corresponding emergency plan according to the warning level and provide data-based decision-making recommendations.

[0036] (5) Information release and feedback: The multi-channel information release module instantly releases warning information to user terminals through SMS, email, APP push, etc. The user interaction and feedback module provides users with an interface for querying real-time monitoring data, historical records, and warning information, and collects user feedback for continuous optimization and upgrading of the system.

[0037] Embodiment 2

[0038] Based on the first embodiment, Figure 1 As shown, the working principle of this system includes:

[0039] (1) Sensor working principle: Various sensors in the intelligent sensor array unit (such as water level meter, water quality analyzer, etc.) measure groundwater level, water quality and other parameters through physical or chemical methods. The sensor converts the measured data into electrical signals or digital signals, and then sends them to the central server through the data transmission module.

[0040] (2) Data transmission and processing: The data transmission module is responsible for transmitting the data collected by the sensor to the central server through wireless or wired networks. After the central server receives the data, it performs preliminary processing and storage through the data integration and management module. The data processing and analysis module further processes and analyzes the stored data, extracts useful information and generates reports.

[0041] (3) Warning and decision-making: The warning judgment and decision-making module determines whether a warning needs to be triggered based on the preset threshold and data analysis results. When a warning is triggered, the module automatically generates warning information and sends it to the user terminal through the multi-channel information release module. Users can take corresponding emergency measures or decision-making actions based on the warning information.

[0042] (4) User interaction and feedback: Users can query real-time monitoring data, historical records, and warning information through the user interaction and feedback module. Users can also submit feedback or suggestions for continuous optimization and upgrading of the system.

[0043] Embodiment 3

[0044] On the basis of the first and second embodiments, Figure 1 As shown, the smart sensor array unit consists of multiple sensors, each of which is responsible for measuring different environmental parameters (such as temperature, humidity, pressure, etc.). These sensors usually have analog outputs that need to be converted into digital signals through an analog-to-digital converter (ADC). The sensor output can be expressed as:

[0045] Y i =f i (X i )+N i

[0046] Among them, Y i is the digital output of the sensor, f i is the conversion function of the sensor (converting physical quantity into digital signal), X i is the physical quantity being measured, N i is noise. The execution code is given below:

[0047] for each sensor in sensor_array:

[0048] read_analog_signal(sensor) / / Read the analog signal of the sensor

[0049] digital_output = ADC_convert(analog_signal) / / Convert analog signal to digital signal

[0050] send_to_data_transmission_unit(digital_output) / / Send digital signal to data transmission unit

[0051] Embodiment 4

[0052] On the basis of the first and second embodiments, Figure 1 As shown, the data transmission and calibration unit is responsible for transmitting the digital output of the sensor to the data integration and management module via wired or wireless means, and performs preliminary calibration on the data before transmission. The calibration process can be expressed as:

[0053] Y ealibrated =g(Y raw )

[0054] Among them, Y calibrated is the calibrated data, g is the calibration function (may be linear, polynomial or other form), Y raw is the raw digital output of the sensor. The execution code is given below:

[0055] while receiving_data_from_sensor_array:

[0056] raw_data = receive_data() / / Receive data from the sensor array

[0057] calibrated_data = apply_calibration_function(raw_data) / / Apply calibration function

[0058] send_to_data_integration_module(calibrated_data) / / Send calibrated data to the data integration and management module

[0059] Embodiment 5

[0060] On the basis of the first and second embodiments, Figure 1 As shown, the module algorithm of the data integration and management module is as follows:

[0061] (1) Data reception: receiving data from the field data acquisition module through the network. The execution code is given below:

[0062] while True:

[0063] received_data = receive_data_from_data_transmission_unit() / / Receive data from the data transmission unit

[0064] process_received_data(received_data) / / Process received data

[0065] (2) Data pre-screening is used to remove outliers and duplicate data. Outlier detection uses the Z-score statistical method, and the formula is:

[0066]

[0067] Where Z is the Z-score, Y is the data point, μ is the mean of the data, and σ is the standard deviation of the data. Usually, data points with |Z|>3 are considered outliers. The execution code is given below:

[0068]

[0069] (3) Data storage and backup: store the filtered data in the database and back up the database regularly. The execution code is given below:

[0070]

[0071] while True:

[0072] ifcurrent_time==backup_schedule_time: / / If the current time is equal to the backup schedule time

[0073] backup_database() / / Execute database backup

[0074] The overall algorithm code is given below:

[0075] importtime

[0076] importrandom

[0077] import sqlite3

[0078] import shutil#for file copying (backup)

[0079] #Hypothetical smart sensor and data transfer functions (in a real application, these functions would interface with the hardware)

[0080] defread_sensor_data():

[0081] #Simulate reading sensor data (in a real application this would be the raw data read from the ADC)

[0082] return random.uniform(20.0,30.0)#Assumed temperature data

[0083] defcalibrate_data(raw_data):

[0084] #Simple linear calibration function (in real applications, this may be a more complex function)

[0085] return raw_data*0.95+5.0#Assumed calibration parameters

[0086] #Functions of data integration and management modules

[0087] defstore_data_in_database(data):

[0088] #Use SQLite database to store data (in actual applications, more complex database systems may be used)

[0089] conn=sqlite3.connect('data.db')

[0090] cursor = conn.cursor()

[0091] cursor.execute('CREATE TABLE IF NOT EXISTS sensor_data(id INTEGERPRIMARYKEY AUTOINCREMENT,timestamp TEXT,value REAL)')

[0092] timestamp=time.strftime('%Y-%m-%d%H:%M:%S')

[0093] cursor.execute('INSERT INTO sensor_data(timestamp,value)VALUES(?,?)',(timestamp,data))

[0094] conn.commit()

[0095] conn.close()

[0096] defbackup_database():

[0097]

[0098] Embodiment 6

[0099] On the basis of the first and second embodiments, Figure 1 As shown, the algorithm steps of the data preprocessing and analysis module include:

[0100] (1) Data cleaning: For missing values, use the mean method to fill them. For outliers, use the Z-score method to detect and process them. First, traverse the data set, identify missing values ​​and outliers, and use the mean to fill in missing values. The Z-score detection method identifies and processes outliers (such as deleting, replacing with the mean, etc.). Among them, for numerical data, if there are not many missing values, the mean of the variable can be used to fill them. The mean is the sum of all observations divided by the number of observations. The formula is:

[0101]

[0102] Among them, x i is the observation and n is the number of observations.

[0103] After Z-score normalization, the data will conform to the standard normal distribution. Outliers are usually defined as observations whose absolute Z-score value is greater than a certain threshold (such as 3). The execution code is given below:

[0104] import pandas as pd

[0105] import numpy as np

[0106] #Assume that df is the loaded dataset

[0107] #Handling missing values ​​(taking mean filling as an example)

[0108] df.fillna(df.mean(),inplace=True)

[0109] #Handling outliers (taking Z-score>3 as an example)

[0110] from scipy import stats

[0111] z_scores=np.abs(stats.zscore(df.select_dtypes(include=[np.number])))

[0112] df=df[(z_scores<3).all(axis=1)]

[0113] (2) Standardization: Calculate the mean and standard deviation of each feature and use the formula to standardize each feature, aiming to scale the data to the same scale for subsequent analysis. Z-score standardization formula:

[0114]

[0115] Among them, X is the original data, μ is the mean, and σ is the standard deviation.

[0116] The execution code is given below:

[0117] from sklearn.preprocessing import StandardScaler

[0118] #Assume X is the feature dataset

[0119] scaler = StandardScaler()

[0120] X_scaled=scaler.fit_transform(X)

[0121] (3) Trend analysis and pattern recognition aim to identify long-term trends in data, while pattern recognition aims to discover recurring patterns in data.

[0122] The execution code is given below:

[0123] importmatplotlib.pyplot as plt

[0124] #Assume time_series is time series data

[0125] plt.plot(time_series)

[0126] plt.xlabel('Time')

[0127] plt.ylabel('Value')

[0128] plt.title('Time Series TrendAnalysis')

[0129] plt.show()

[0130] #Moving average (taking 3-point moving average as an example)

[0131] moving_average=time_series.rolling(window=3).mean()

[0132] plt.plot(moving_average)

[0133] plt.xlabel('Time')

[0134] plt.ylabel('MovingAverage')

[0135] plt.title('MovingAverage TrendAnalysis')

[0136] plt.show()

[0137] Embodiment 7

[0138] On the basis of the first and second embodiments, Figure 1As shown in the figure, the association rule mining of the data mining and prediction module aims to discover interesting relationships between data items. First, frequent item sets are generated using algorithms such as Apriori or FP-Growth, then association rules are generated based on frequent item sets, and finally the association rules are evaluated based on support, confidence, and lift. The execution code is given below:

[0139] from mlxtend.preprocessing import TransactionEncoder

[0140] from mlxtend.frequent_patterns import apriori,association_rules

[0141] #Assume that transactions is a transaction data set (a list of lists)

[0142] te = TransactionEncoder()

[0143] te_ary=te.fit(transactions).transform(transactions)

[0144] df=pd.DataFrame(te_ary,columns=te.columns_)

[0145] #Generate frequent itemsets

[0146] frequent_itemsets=apriori(df,min_support=0.05,use_colnames=True)

[0147] #Generate association rules

[0148] rules=association_rules(frequent_itemsets,metric="confidence",min_threshold=0.7)

[0149] Embodiment 8

[0150] On the basis of the first and second embodiments, Figure 1As shown, the threshold setting and warning trigger module sets the warning threshold based on historical data, industry standards, expert experience or risk tolerance. Setting a reasonable threshold is crucial to avoiding false positives and false negatives. The algorithm steps are: (1) Collect and analyze historical data to understand the distribution and fluctuation range of the data; (2) Set warning thresholds (such as upper limits, lower limits or intervals) based on business needs, risk tolerance and industry standards; (3) Store the set thresholds in the configuration file for use by the warning trigger module. The execution code is given below:

[0151] #Assume there is a DataFrame df containing historical data, and a column named 'metric' representing the indicator to be monitored

[0152] import pandas as pd

[0153] import numpy as np

[0154] #Calculate the mean and standard deviation of historical data

[0155] mean_value=df['metric'].mean()

[0156] std_dev = df['metric'].std()

[0157] #Set the warning threshold (for example, the mean plus or minus two standard deviations)

[0158] upper_threshold=mean_value+2*std_dev

[0159] lower_threshold=mean_value-2*std_dev

[0160] #Store the threshold in the configuration (here taking a dictionary as an example)

[0161] thresholds={'upper':upper_threshold,'lower':lower_threshold}

[0162] print(thresholds)

[0163] When an early warning is triggered, the early warning trigger module is responsible for real-time monitoring of data and comparing it with the set threshold. When the data exceeds or falls below the threshold, an early warning is triggered. Algorithm steps: (1) Obtain monitoring data in real time; (2) Compare monitoring data with the set threshold; (3) If the data exceeds or falls below the threshold, trigger an early warning (such as sending an email, text message, triggering an alarm, etc.). The execution code is given below:

[0164] #Assume there is a function get_real_time_data() used to obtain real-time data

[0165] #And a function send_alert(message) is used to send warning information

[0166] defcheck_and_trigger_alert(real_time_data,thresholds):

[0167] ifreal_time_data>thresholds['upper']orreal_time_data <thresholds['lower']:

[0168] alert_message=f"Warning:Real-time data{real_time_data}exceedsthresholds(upper:

[0169] {thresholds['upper']},lower:{thresholds['lower']})! "

[0170] send_alert(alert_message)

[0171] #Example: Get real-time data and check

[0172] real_time_data = get_real_time_data() # Assume that this function returns the currently monitored data value

[0173] check_and_trigger_alert(real_time_data,thresholds)

[0174] Embodiment 9

[0175] On the basis of the first and second embodiments, Figure 1 As shown in the figure, the emergency plan provided by the emergency response and decision support module is a response measure and action plan formulated based on potential risks or events that have occurred. Algorithm steps: (1) Identify potential risks or events that have occurred; (2) Select an appropriate emergency plan based on the type, severity and scope of impact of the risk or event; (3) Provide detailed information on the emergency plan, including response measures, responsible departments, time nodes, etc. The execution code is given below:

[0176] #Suppose there is a dictionary storing different types of emergency plans

[0177]

[0178] When providing decision-making support information, the decision-making support information is generated based on real-time data, historical data and expert knowledge, and is used to support decision makers in making reasonable decisions. The execution code is given below:

[0179]

[0180] impact=8#The impact of the event (assumed value)

[0181] risk_score=calculate_risk(probability,impact)

[0182] print(f"Risk Score:{risk_score}")

[0183] # Provide decision suggestions based on risk scores (here we take a simple threshold judgment as an example)

[0184] defprovide_decision_support(risk_score):

[0185] ifrisk_score>10:

[0186] return "High risk! Immediate action required."

[0187] elifrisk_score>5:

[0188] return "Medium risk.Monitor closely and prepare for actionifnecessary."

[0189] else:

[0190] return "Low risk.No immediate action required."

[0191] #Example: Providing decision suggestions

[0192] decision_support=provide_decision_support(risk_score)

[0193] print(decision_support)

[0194] Embodiment 10

[0195] On the basis of the first and second embodiments, Figure 1 As shown in the figure, the main task of the multi-channel information release module is to convey the warning information to users in a timely and accurate manner through different communication channels (such as SMS, email, social media, APP push, etc.). Algorithm steps: (1) Information formatting: Format the warning information into a format suitable for different channels. For example, SMS may need to be brief and clear, while email can contain more detailed information and attachments; (2) Channel selection: Select the appropriate release channel based on the user's preferences and the effectiveness of the channel. This may require considering factors such as the user's subscription settings, the coverage and reliability of the channel; (3) Information release: Release the warning information through the selected channel. This may involve calling a third-party API (such as an SMS gateway, email server, etc.) or an internal system (such as an APP push service); (4) Release confirmation: Confirm that the information has been successfully released to the target user. This may involve receiving a confirmation message from the channel or querying the release status; (5) Logging: Record information such as the time, channel, user ID and status of the information release for subsequent analysis and auditing. The execution code is given below:

[0196] import smtplib

[0197] import tweepy

[0198] from twilio.rest import Client

[0199] #Example Configuration

[0200] sms_client=Client(account='your_twilio_account_sid',token='your_twilio_auth_token')email_server='smtp.example.com'

[0201] email_user = 'your_email@example.com'

[0202] email_pass='your_email_password'

[0203] twitter_api_key='your_twitter_api_key'

[0204] twitter_api_secret='your_twitter_api_secret'

[0205] twitter_access_token = 'your_twitter_access_token'

[0206] twitter_access_token_secret = 'your_twitter_access_token_secret'

[0207] # Example warning message

[0208] alert_message = "EmergencyAlert: High levels of pollution detected in the area."

[0209] # Send SMS

[0210] def send_sms(user_phone, message):

[0211] message = sms_client.messages.create(

[0212] body = message,

[0213] from_ = '+1234567890', # Twilio phone number

[0214] to = user_phone )

[0216] print(f"Sent SMS to {user_phone}")

[0217] # Send email

[0218] def send_email(user_email, subject, message):

[0219] server = smtplib.SMTP(email_server)

[0220] server.login(email_user, email_pass)

[0221] server.sendmail(

[0222] email_user,

[0223] user_email,

[0224] f"Subject:{subject}\n\n{message}" )

[0226] server.quit()

[0227] print(f"Sent email to{user_email}")

[0228] # Send Twitter message

[0229] def send_twitter_message(message):

[0230] auth = tweepy.OAuthHandler(twitter_api_key, twitter_api_secret)

[0231] auth.set_access_token(twitter_access_token, twitter_access_token_secret)

[0232] api = tweepy.API(auth)

[0233] api.update_status(status = message)

[0234] print("Sent Twitter message")

[0235] # Example user list and channel preferences

[0236] users =

[0237] {'phone': '+0987654321', 'email': 'user1@example.com', 'prefers_sms': true, 'prefers_email': false},

[0238] {'phone': null, 'email': 'user2@example.com', 'prefers_sms': false, 'prefers_email': true},

[0239] {'phone': '+1122334455', 'email': null, 'prefers_sms': true, 'prefers_email': false}, It should be noted that in the original text, there are some potential issues such as the incorrect capitalization of "true" and "false" in the Python code which should be "True" and "False" for boolean values in Python. Also, "null" should be "None" in Python. These have been left as in the original for the purpose of strict translation following the rules.

[0240] #...other users ]

[0242] #Publish warning information

[0243] for user in users:

[0244] ifuser['prefers_sms']anduser['phone']:

[0245] send_sms(user['phone'],alert_message)

[0246] ifuser['prefers_email']anduser['email']:

[0247] send_email(user['email'],'EmergencyAlert',alert_message)

[0248] #Note: For simplicity, here the Twitter message is not sent to a specific user, but a message is sent directly.

[0249] #In real applications, you may need to send messages based on the user's Twitter ID or other identifiers.

[0250] send_twitter_message(alert_message) #Assume this is a public message to all followers

[0251] Embodiment 11

[0252] On the basis of the first and second embodiments, Figure 1As shown in the figure, the main task of the user interaction and feedback module is to collect user feedback, analyze the feedback to identify system problems and improvement points, and optimize the system accordingly. Algorithm steps: (1) Collect feedback: Collect user feedback through questionnaires, online forms, user comments, customer service feedback and other channels; (2) Preprocess feedback: Clean and organize the collected feedback data to remove duplicate, invalid or irrelevant information; (3) Sentiment analysis: Use natural language processing technology to perform sentiment analysis on feedback to identify users' positive, negative or neutral emotions; (4) Topic extraction: Use text mining technology to extract key topics or problem points in the feedback; (5) Prioritization: Prioritize the extracted problems based on factors such as the severity of the problem, the scope of impact and the frequency of occurrence; (6) Formulate optimization plans: Formulate specific optimization plans or improvement measures for high-priority problems; (7) Implementation and optimization: Execute the optimization plan and continuously monitor the system's performance and user feedback to evaluate the optimization effect and make necessary adjustments.

[0253] The execution code is given below:

[0254] fromtextblob import TextBlob

[0255] from collections import Counter

[0256] #Example user feedback list

[0257] feedback_list=[

[0258] "The app is great,but the notifications are too frequent.",

[0259] "I love the new features,but the app crashes sometimes.",

[0260] "The interface is user-friendly,but the loading times are too long.",

[0261] #...Other user feedback ]

[0263] #Preprocess the feedback (here just simply remove spaces and line breaks)

[0264] cleaned_feedback=[fb.strip()for fb in feedback_list]

[0265] #Sentiment Analysis

[0266] defanalyze_sentiment(feedback):

[0267] analysis = TextBlob(feedback)

[0268] return analysis.sentiment.polarity#Polarity value (range from -1 to 1)

[0269] sentiments=[analyze_sentiment(fb)for fb in cleaned_feedback]

[0270] #Topic extraction (here we use simple keyword counting as an example)

[0271] defextract_topics(feedback_list):

[0272] all_words=".join(feedback_list).lower()

[0273] words = all_words.split()

[0274]

[0275] #Note: In actual applications, the formulation and implementation of optimization plans will involve more details and complexity.

[0276] #You may need to collaborate with team members, stakeholders, or outside experts to develop and implement these plans.

[0277] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A groundwater dynamic monitoring and early warning system, including a data acquisition and management module, a data processing and analysis module, an early warning judgment and decision module, and an information release and feedback module based on a system architecture, characterized in that: The data acquisition and management module collects the water level, water quality and environmental parameters of groundwater in real time, calibrates the sensors regularly to ensure the accuracy of the data, performs preliminary screening and format unification of the received data to facilitate subsequent processing, realizes safe storage and efficient retrieval of data, and implements a regular data backup strategy at the same time; the data acquisition and management module includes a field data acquisition module and a data integration and management module. The field data acquisition module includes an intelligent sensor array unit and a data transmission and calibration unit. The data integration and management module includes a data receiving and pre-screening unit and a data storage and backup unit. The field data acquisition module is connected to the intelligent sensor array unit by wire or wireless means to realize real-time data transmission. The data integration and management module transmits and stores data with the central server through the network. Users query and manage the collected data through a dedicated software interface; The data processing and analysis module cleans the received data, removes abnormal values ​​and missing values, performs standardization, and uses statistical and machine learning techniques to identify trends, periodicities and abnormal patterns in the data. By mining the correlation between water quality parameters, a scientific basis is provided for early warning, and a prediction model is established based on historical data to predict future trends of groundwater conditions. The data processing and analysis module includes a data preprocessing and analysis module and a data mining and prediction module. The data preprocessing and analysis module includes a data cleaning and standardization unit and a trend analysis and pattern recognition unit. The data mining and prediction module includes an association rule mining unit and a future trend prediction unit. The data processing and analysis module is connected to the central server through a network to realize real-time processing and analysis of data. Users view the analysis results, including trend graphs and abnormality reports, through the software interface, and set analysis parameters based on them to obtain analysis results in a customized manner. The warning judgment and decision-making module sets the warning threshold according to industry standards, historical data and user needs, compares the real-time monitoring data with the threshold, triggers the warning and determines the warning level, automatically or manually starts the corresponding emergency plan according to the warning level, and provides data-based decision suggestions; the warning judgment and decision-making module includes a threshold setting and warning triggering module, an emergency response and decision support module, the threshold setting and warning triggering module includes a threshold setting unit, a warning trigger and level determination unit, the emergency response and decision support module includes an emergency plan starting unit, a decision-making auxiliary information generation unit, the warning judgment and decision-making module is connected to the central server through the network to realize real-time data processing and decision support, when the warning is triggered, the module will automatically send SMS, email type warning information to the user, the user through the software interface to view the warning details, emergency plan and decision-making auxiliary information; The information release and feedback module releases warning information instantly through SMS, email, APP push, etc., publishes warning announcements and popular science information on official websites and social media platforms, provides users with an interface for querying real-time monitoring data, historical records, and warning information, and collects user feedback for continuous optimization and upgrading of the system; the information release and feedback module includes a multi-channel information release module and a user interaction and feedback module. The multi-channel information release module includes an instant messaging release unit and a public platform release unit. The user interaction and feedback module includes a user query and interactive interface unit and a system feedback and optimization unit. The information release and feedback module is connected to the SMS platform and the email server through the network. Users receive warning information and query data through mobile phones and computer terminals. Users submit feedback through the software interface, and the module automatically collects and analyzes it.

2. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The field data acquisition module realizes real-time data acquisition and transmission through the intelligent sensor array unit and the data transmission and calibration unit; the data integration and management module is responsible for data reception, pre-screening, storage and backup.

3. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The intelligent sensor array unit is responsible for collecting water level, water quality and environmental parameters in real time at the groundwater monitoring point, where water quality parameters include temperature, pH value, dissolved oxygen, conductivity, turbidity, and heavy metal concentration; the data transmission and calibration unit is responsible for transmitting the collected data to the central server via a wireless or wired network, and performing regular calibration and status monitoring of the sensor. The data receiving and pre-screening unit receives data from the site, performs preliminary screening and format unification; the data storage and backup unit ensures safe storage and efficient retrieval of data, and implements a regular data backup strategy.

4. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The data preprocessing and analysis module is responsible for cleaning, standardizing, trend analysis and pattern recognition of the received data; the data mining and prediction module uses machine learning algorithms to mine association rules and predict future trends.

5. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The data cleaning and standardization unit cleans the received data, removes abnormal values ​​and missing values, and performs standardization processing; The trend analysis and pattern recognition unit uses statistical and machine learning techniques to identify trends, periodicities and abnormal patterns in the data; The association rule mining unit mines the association relationship between water quality parameters to provide a scientific basis for early warning; the future trend prediction unit establishes a prediction model based on historical data to predict the future trend of groundwater conditions.

6. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The threshold setting and warning triggering module is responsible for setting the warning threshold and triggering the warning; the emergency response and decision support module provides emergency plans and decision-making support information.

7. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The threshold setting unit sets the warning threshold according to industry standards, historical data and user needs; the warning trigger and level determination unit compares the real-time monitoring data with the threshold, triggers the warning and determines the warning level; The emergency plan activation unit automatically or manually activates the corresponding emergency plan according to the warning level; the decision-making auxiliary information generation unit provides data-based decision-making suggestions, including pollution source tracking and impact range assessment.

8. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The multi-channel information release module is responsible for releasing the warning information to users through multiple channels; the user interaction and feedback module is responsible for collecting user feedback and performing system optimization.

9. A groundwater dynamic monitoring and early warning system according to claim 1, characterized in that: The instant messaging publishing unit instantly publishes warning information via SMS, email, or APP push; the public platform publishing unit publishes warning announcements and popular science information on official websites and social media platforms; The user query and interaction interface unit provides an interface for users to query real-time monitoring data, historical records, and warning information, and supports user messages and interactions; the system feedback and optimization unit collects user feedback for continuous optimization and upgrading of the system.

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