Environmental protection management system based on big data service integrated management

Abstract

The invention discloses an environmental protection management system based on big data service integrated management, and the system comprises a data collection layer which is used for collecting multi-source environmental data; the data management layer is used for storing, cleaning and standardizing the collected data and encrypting the data transmission and storage process; the data analysis layer is used for carrying out deep mining and intelligent analysis on the processed data; the application service layer is used for visually displaying the analysis result and realizing query, management and sharing of environment information through a user interface; and the block chain traceability layer is used for recording a source, a timestamp and a processing process of the data, and realizing tampering prevention and traceability of the data through a block chain technology. By integrating various advanced technologies, efficient acquisition, processing, analysis and management of environment data are realized, and comprehensive and scientific technical support is provided for environment management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental management, and particularly relates to an environmental management system based on big data service integration management. BACKGROUND

[0002] With the rapid development of industrialization and urbanization, environmental management is facing problems such as scattered data sources, information silos, and low data processing efficiency. Traditional environmental management systems are mostly independent business platforms with single data types, lacking unified data standards and sharing mechanisms, which makes it difficult to integrate and analyze environmental data. In addition, existing systems have deficiencies in data security and credibility, making it difficult to meet the requirements of modern environmental management for data integrity and accuracy.

[0003] Although the existing environmental management system has achieved the collection and analysis of environmental data to some extent, there are still many deficiencies. For example, the data collection method is relatively single, mainly relying on fixed monitoring sites, which is difficult to cover remote areas and complex terrain. In the data processing process, there is a lack of efficient cleaning and standardization methods, resulting in uneven data quality. In addition, the existing system lacks depth in data analysis, lacking intelligent mining of environmental data and real-time decision support functions.

[0004] In environmental management, data security and credibility are crucial. However, existing systems lack effective encryption measures in data transmission and storage processes, and data is vulnerable to tampering or loss. In addition, existing systems lack a traceability mechanism for data sources and processing processes, making it difficult to ensure the authenticity and reliability of data. These problems seriously limit the effectiveness and credibility of environmental management systems.

[0005] In order to overcome the deficiencies of existing environmental management systems, it is necessary to develop an environmental management system based on big data service integration management that can integrate multi-source data and achieve efficient data processing and intelligent analysis. SUMMARY

[0006] The present application provides an environmental management system based on big data service integration management to solve the technical problems of existing environmental data dispersion, low processing efficiency, and insufficient security and credibility.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] The present application provides an environmental management system based on big data service integration management, comprising:

[0009] a data collection layer for collecting multi-source environmental data, the multi-source environmental data including environmental monitoring device data, enterprise information system data, government department data, public reporting data, satellite remote sensing data, and unmanned aerial vehicle monitoring data;

[0010] a data management layer for storing, cleaning and standardizing the collected data, and encrypting the data transmission and storage process;

[0011] a data analysis layer for deep mining and intelligent analysis of the processed data, realizing environmental quality trend prediction, pollution source identification and tracing, and risk assessment and early warning through machine learning models and data analysis algorithms;

[0012] an application service layer for visualizing the analysis results and realizing environmental information query, management and sharing through a user interface;

[0013] a blockchain tracing layer for recording the source, timestamp and processing process of the data, realizing data non-tamperability and tracing through blockchain technology.

[0014] The technical solution provided by the present application has at least the following beneficial effects:

[0015] The present application integrates multiple data sources through the data collection layer, realizes comprehensive integration of environmental data, realizes non-tamperability of environmental data through the distributed ledger technology of the blockchain tracing layer, solves the problems of scattered data sources, lack of unified management and data security mechanism in the prior art, and improves the authenticity and credibility of environmental data.

[0016] The present application realizes deep mining, efficient storage and access of environmental data through efficient data management and intelligent analysis modules, solves the problem of low data processing efficiency in the prior art, provides scientific and timely decision-making basis for environmental managers, and ensures the scientificity and timeliness of environmental management.

[0017] The present application realizes visual display and convenient management of environmental information through geographic information system technology and user interface design, ensures intuitive presentation and user-friendly nature of environmental data, and improves the usability and user experience of the system.

[0018] The present application realizes safe storage and efficient access of data through distributed storage and encryption technology, solves the problems of insufficient data storage security and low access efficiency in the prior art, ensures the security and availability of data, and provides reliable data support for environmental management. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a system structure schematic diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0022] EMBODIMENT

[0023] The embodiment provides an environmental management system based on big data service integration management, and has the characteristics that the system comprises:

[0024] A data acquisition layer is configured to acquire multi-source environmental data, and the multi-source environmental data comprises environmental monitoring device data, enterprise information system data, government department data, public reporting data, satellite remote sensing data and unmanned aerial vehicle monitoring data.

[0025] A data management layer is configured to store, clean and standardize the acquired data, and encrypt the data transmission and storage process.

[0026] A data analysis layer is configured to deeply mine and intelligently analyze the processed data, and realize environmental quality trend prediction, pollution source identification and tracing, and risk assessment and early warning through a machine learning model and a data analysis algorithm.

[0027] An application service layer is configured to visually display the analysis results, and realize environmental information query, management and sharing through a user interface.

[0028] A blockchain tracing layer is configured to record the source, timestamp and processing process of the data, and realize data non-tamperability and tracing through a blockchain technology.

[0029] Please refer to Figure 1 is a system structure schematic diagram provided by the embodiment of the present application.

[0030] I. Data acquisition layer:

[0031] The data acquisition layer comprises an Internet of Things monitoring module, an enterprise information system interface module, a government department data interface module, a satellite remote sensing module, an unmanned aerial vehicle monitoring module and a public participation module.

[0032] The Internet of Things monitoring module acquires environmental data in real time through a sensor network deployed at an environmental monitoring site, and the sensors comprise air quality sensors, water quality sensors, soil sensors and noise sensors.

[0033] The enterprise information system interface module acquires environmental monitoring data and pollutant emission data of an enterprise by accessing an environmental management information system in the enterprise.

[0034] Government data interface module for accessing environmental policies, regulations, and environmental quality reports published by government agencies;

[0035] Satellite remote sensing module for obtaining large-scale environmental data through satellite remote sensing technology, including land use, vegetation coverage, and air pollution;

[0036] Unmanned aerial vehicle monitoring module for collecting environmental data in remote areas, complex terrains, and specific regions through unmanned aerial vehicle monitoring technology, including water quality, air quality, and ecological conditions;

[0037] Public participation module for collecting environmental data through online platforms for public reporting and feedback.

[0038] It should be noted that the sensor network is a distributed network composed of multiple sensor nodes, which can realize real-time and continuous monitoring of environmental data, support remote management and data transmission, and ensure the timeliness and accuracy of data. The sensor nodes transmit the collected data to the data management layer through wireless communication technology.

[0039] Air quality sensor: used in urban air quality monitoring stations, industrial areas, and major transportation routes to monitor air quality changes in real time. The collected data includes PM2.5, PM10, SO2, NO2, CO, O3 pollutant concentrations, and temperature, humidity, and barometric pressure meteorological parameters.

[0040] Water quality sensor: used for monitoring the water quality of rivers, lakes, reservoirs, and sewage treatment plants to assess water pollution in real time. The collected data includes pH value, dissolved oxygen (DO), chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) water quality indicators.

[0041] Soil sensor: used for monitoring soil quality in farmland, forest land, and ecological protection areas to assess soil health. The collected data includes soil moisture, soil temperature, soil pH, soil electrical conductivity, and soil nutrients.

[0042] Noise sensor: used for monitoring urban traffic noise, industrial noise, and residential area noise to assess noise pollution levels. The collected data includes noise intensity (decibel value) and noise frequency distribution.

[0043] The environmental management information system within an enterprise is an integrated software platform that helps the enterprise manage and monitor its environmental activities, resource usage, and environmental compliance. It can record and manage data collected by environmental monitoring devices within the enterprise.

[0044] Environmental monitoring data refers to the data collected by enterprises through various monitoring equipment and technical means to reflect the environmental quality, including water quality data, soil quality data, noise data, and ecological data.

[0045] Pollutant emission data refers to the amount and types of pollutants emitted by enterprises during production, including waste gas emission data: emission amount and concentration; waste water emission data: emission amount and concentration; solid waste emission data: types, quantity, and treatment method of industrial solid waste; hazardous waste emission data: types, quantity, and disposal situation.

[0046] Environmental policy refers to the guiding documents established by the state or local government on environmental protection and sustainable development, including environmental protection goals, policy direction, and action plans.

[0047] Regulations refer to legally binding environmental protection-related laws, regulations, and rules, including the Environmental Protection Law and the Air Pollution Prevention and Control Law, which specify specific requirements and penalties for violations of environmental protection.

[0048] Environmental quality report refers to the monitoring and assessment report on environmental quality published by government departments regularly, including air quality, water quality, and soil quality statistics and analysis results.

[0049] Satellite remote sensing technology is a technology that uses sensors carried by satellites to obtain information from the Earth's surface in orbit, which can capture electromagnetic radiation at different wavelengths to generate high-resolution images and data.

[0050] Land use refers to the use and distribution of land, including urban land, agricultural land, forest land, and water areas. Satellite remote sensing technology can obtain land use distribution maps and analyze the trends of land use changes.

[0051] Vegetation coverage refers to the degree of vegetation coverage on the ground, usually evaluated by the vegetation index (NDVI). Satellite remote sensing technology can generate vegetation coverage maps to monitor vegetation growth and ecological changes.

[0052] Air pollution refers to the concentration and distribution of pollutants in the atmosphere, including sulfur dioxide, nitrogen oxides, and particulate matter. Satellite remote sensing technology can monitor the distribution of pollutants in the atmosphere to assess the scope and extent of air pollution.

[0053] Unmanned aerial vehicle monitoring technology is a technology that uses unmanned aerial vehicles equipped with various sensors (optical cameras, gas sensors, thermal imagers) for environmental monitoring. Unmanned aerial vehicles can fly according to pre-set routes or manual operation to collect environmental data in real time.

[0054] Remote areas: refers to areas with poor transportation and few people, including mountains, deserts, and polar regions, which are difficult to monitor through traditional monitoring methods.

[0055] Complex terrain: refers to areas with large topographic relief and complex geological conditions, including mountainous areas, valleys, and river basins, which require special techniques and equipment for environmental monitoring.

[0056] Specific areas: refers to areas with special environmental significance or that require special attention, including nature reserves, ecologically fragile areas, and areas surrounding pollution sources.

[0057] Water quality: refers to the physical, chemical, and biological properties of water bodies, including water temperature, pH value, dissolved oxygen, chemical oxygen demand (COD), and ammonia nitrogen indicators, used to assess the degree of water pollution and ecological health.

[0058] Air quality: refers to the physical, chemical, and biological properties of the atmosphere, including particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3) concentrations, air quality monitoring is used to assess the impact of air pollution on human health and the ecological environment.

[0059] Ecological status: refers to the state of biological and environmental interactions in an ecosystem, including biodiversity, ecosystem function, and ecological health, ecological status monitoring can be assessed through vegetation cover, species distribution, and ecological indices.

[0060] Online platform: refers to an interactive platform provided through the Internet, including mobile applications and websites, users can report environmental problems and feedback through these platforms.

[0061] Public reporting: refers to environmental problems reported by the public through online platforms, including pollution incidents and ecological damage.

[0062] Feedback: refers to public opinions and suggestions on environmental management work, submitted through online platforms.

[0063] II. Data management layer:

[0064] The data management layer includes data storage modules, data cleaning modules, data standardization modules, and data encryption modules.

[0065] Data storage module: used to store collected multi-source data in a distributed storage system, through a distributed file system and a distributed database to realize data storage and access.

[0066] Data cleaning module: used to clean the collected data, through data filtering algorithms to remove invalid data, duplicate data, and abnormal data.

[0067] A data standardization module for standardizing data and converting data of different formats and structures into a unified format.

[0068] A data encryption module for encrypting data transmission and storage processes using encryption algorithms, including AES and RSA.

[0069] It should be noted that a distributed storage system is a system architecture that stores data in multiple independent storage devices, including physical servers, virtual machines or cloud storage nodes, which are connected through a network to provide storage services together.

[0070] A distributed file system is a file system that stores files in multiple storage nodes. It divides files into multiple blocks and stores them in different nodes through a network.

[0071] A distributed database is a database system that stores data in multiple database nodes. It distributes data to different nodes through sharding technology and supports high concurrency access.

[0072] A data filtering algorithm is an algorithm used to identify and remove data records in a dataset that do not meet certain conditions or standards. These non-compliant data include invalid data, duplicate data and abnormal data. The purpose of the data filtering algorithm is to improve the quality and consistency of the data, providing a reliable data foundation for subsequent data processing and analysis.

[0073] Invalid data: data that does not meet the preset format or business rules, including data type errors, data missing and data exceeding reasonable range.

[0074] Duplicate data: repeated data records in the dataset, including the same sensor repeatedly uploading the same data within a short period of time.

[0075] Abnormal data: data that deviates significantly from the normal range, including abnormal high or low values caused by sensor failure.

[0076] Standardization is a process of converting data into a unified format and standard to ensure data consistency and comparability. Methods include data format unification: converting data of different formats into a unified format; data structure unification: converting data into a unified structure through ETL (Extract, Transform, Load) tools; data normalization: normalizing data to unify numerical units to international units.

[0077] AES (Advanced Encryption Standard): a symmetric encryption algorithm that uses the same key for encryption and decryption. It supports multiple key lengths (128-bit, 192-bit, 256-bit) and has high efficiency and security.

[0078] RSA (asymmetric encryption algorithm): An asymmetric encryption algorithm that uses a pair of keys (public key and private key) for encryption and decryption. The public key is used for encryption, and the private key is used for decryption.

[0079] III. Data analysis layer:

[0080] The data analysis layer includes an environmental quality trend prediction module, a pollution source identification and tracing module, and a risk assessment and early warning module.

[0081] The environmental quality trend prediction module predicts the change trend of environmental quality through time series analysis, uses the comparison analysis of historical data and real-time data, and combines LSTM neural network to realize the prediction of environmental quality.

[0082] The pollution source identification and tracing module locates the pollution source by combining geographic information and monitoring data, and realizes the positioning of pollution source through geographic information system and data analysis algorithm, including K-means clustering algorithm and DBSCAN clustering algorithm.

[0083] The risk assessment and early warning module is used to evaluate environmental risk in real time and generate early warning information, which evaluates environmental risk based on rule-based risk model and real-time data, generates early warning information through threshold triggering mechanism, and early warning information includes risk description, risk location, risk time, affected range, and early warning level.

[0084] It should be noted that time series analysis is a statistical technique used to analyze data points arranged in chronological order to reveal trends, periodicity, and other patterns in the data.

[0085] LSTM (Long Short-Term Memory Network) is a special recurrent neural network (RNN) that can handle and predict long-term dependencies in time series data. By training the LSTM model, it uses the comparison analysis of historical data and real-time data to achieve high-precision prediction of environmental quality.

[0086] The code for environmental quality trend prediction based on time series analysis and LSTM neural network is as follows:

[0087] import pandas as pd

[0088] import numpy as np

[0089] from sklearn.preprocessing import MinMaxScaler

[0090] from keras.models import Sequential

[0091] from keras.layers import LSTM, Dense

[0092] # Data Preprocessing

[0093] def preprocess_data(data):

[0094] scaler = MinMaxScaler(feature_range=(0, 1))

[0095] scaled_data = scaler.fit_transform(data)

[0096] return scaled_data, scaler

[0097] # Building LSTM Model

[0098] def build_lstm_model(input_shape):

[0099] model = Sequential()

[0100] model.add(LSTM(units=50, return_sequences=True, input_shape=input_shape))

[0101] model.add(LSTM(units=50))

[0102] model.add(Dense(units=1))

[0103] model.compile(optimizer='adam', loss='mean_squared_error')

[0104] return model

[0105] # Training Model

[0106] def train_model(model, X_train, y_train, epochs=50, batch_size=32):

[0107] model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)

[0108] # Predict environmental quality

[0109] def predict_environmental_quality(model, scaler, data):

[0110] scaled_data, _ = preprocess_data(data)

[0111] X_test = np.reshape(scaled_data, (scaled_data.shape[0], scaled_data.shape[1], 1))

[0112] predictions = model.predict(X_test)

[0113] predictions = scaler.inverse_transform(predictions)

[0114] return predictions

[0115] # Main function

[0116] def main():

[0117] # Load historical data

[0118] historical_data = pd.read_csv('historical_environmental_data.csv')

[0119] real_time_data = pd.read_csv('real_time_environmental_data.csv')

[0120] # Data preprocessing

[0121] combined_data = pd.concat([historical_data, real_time_data])

[0122] scaled_data, scaler = preprocess_data(combined_data)

[0123] # Build training data

[0124] X_train, y_train = create_dataset(scaled_data)

[0125] # Building an LSTM model

[0126] model = build_lstm_model((X_train.shape[1], 1))

[0127] # Training the model

[0128] train_model(model, X_train, y_train)

[0129] # Predicting environmental quality

[0130] predictions = predict_environmental_quality(model, scaler, real_time_data)

[0131] print("Predicted Environmental Quality:", predictions)

[0132] if __name__ == "__main__":

[0133] main()

[0134] Geographic Information System (GIS): A computer system used to collect, store, manage, analyze and display geospatial data. GIS technology is used to manage and analyze geographic information, and to combine monitoring data with geographic information to achieve the visual location of pollution sources.

[0135] K-means clustering algorithm: A distance-based clustering algorithm that identifies the distribution area of ​​pollution sources by dividing data points into K clusters.

[0136] DBSCAN clustering algorithm: A density-based clustering algorithm that can identify clusters of arbitrary shapes and can handle noisy data, making it suitable for pollution source identification in complex environments.

[0137] Pollution source location: By using data analysis algorithms, combined with monitoring data and geographic information, the specific location and distribution range of pollution sources are determined.

[0138] The code for pollution source identification based on K-means and DBSCAN clustering algorithms is as follows:

[0139] import pandas as pd

[0140] from sklearn.cluster import KMeans, DBSCAN

[0141] from sklearn.preprocessing import StandardScaler

[0142] # Data preprocessing

[0143] def preprocess_data(data):

[0144] scaler = StandardScaler()

[0145] scaled_data = scaler.fit_transform(data)

[0146] return scaled_data, scaler

[0147] # K-means clustering

[0148] def kmeans_clustering(data, n_clusters=3):

[0149] kmeans = KMeans(n_clusters=n_clusters)

[0150] clusters = kmeans.fit_predict(data)

[0151] return clusters

[0152] # DBSCAN clustering

[0153] def dbscan_clustering(data, eps=0.5, min_samples=5):

[0154] dbscan = DBSCAN(eps=eps, min_samples=min_samples)

[0155] clusters = dbscan.fit_predict(data)

[0156] return clusters

[0157] # Main function

[0158] def main():

[0159] # Load monitoring data

[0160] monitoring_data = pd.read_csv('monitoring_data.csv')

[0161] # Data preprocessing

[0162] scaled_data, _ = preprocess_data(monitoring_data)

[0163] # K-means clustering

[0164] kmeans_clusters = kmeans_clustering(scaled_data)

[0165] print("K-means Clusters:", kmeans_clusters)

[0166] # DBSCAN clustering

[0167] dbscan_clusters = dbscan_clustering(scaled_data)

[0168] print("DBSCAN Clusters:", dbscan_clusters)

[0169] if __name__ == "__main__":

[0170] main()

[0171] A rule-based risk model is a method of assessing risk through pre-set rules and thresholds. Based on environmental quality standards and empirical knowledge, a series of rules are defined to evaluate real-time monitoring data and determine whether they meet the pre-set rules. When the data exceeds the pre-set threshold, the corresponding risk level is triggered.

[0172] The code for rule-based risk assessment and early warning is as follows:

[0173] import pandas as pd

[0174] # Risk assessment rules

[0175] def assess_risk(data, threshold):

[0176] risk_level = []

[0177] for value in data:

[0178] if value > threshold:

[0179] risk_level.append('High')

[0180] else:

[0181] risk_level.append('Low')

[0182] return risk_level

[0183] # Generate warning messages

[0184] def generate_alerts(data, risk_levels):

[0185] alerts = []

[0186] for i, level in enumerate(risk_levels):

[0187] if level == 'High':

[0188] alert = {

[0189] 'risk_description': f"High risk detected at {data.index[i]}",

[0190] 'risk_location': data.index[i],

[0191] 'risk_time': data.index[i],

[0192] 'affected_area': "Area X",

[0193] 'alert_level': level

[0194] }

[0195] alerts.append(alert)

[0196] return alerts

[0197] # Main function

[0198] def main():

[0199] # Load real-time data

[0200] real_time_data = pd.read_csv('real_time_environmental_data.csv',parse_dates=['timestamp'], index_col='timestamp')

[0201] # Risk assessment

[0202] threshold = 100 # Preset threshold

[0203] risk_levels = assess_risk(real_time_data['pollutant_concentration'], threshold)

[0204] # Generate alerts

[0205] alerts = generate_alerts(real_time_data, risk_levels)

[0206] for alert in alerts:

[0207] print(alert)

[0208] if __name__ == "__main__":

[0209] main()

[0210] Threshold trigger mechanism: When the monitoring data exceeds the preset threshold, the early warning mechanism is automatically triggered to generate early warning information.

[0211] Risk description: Specific description of the current environmental risk, including pollutant type, concentration, and over-standard situation.

[0212] Risk location: Determine the specific location of the risk occurrence through the geographic information system (GIS), including latitude and longitude coordinates and geographic area.

[0213] Risk time: Record the specific time of risk occurrence, including date and timestamp.

[0214] Affected Area: Assess the areas or population that may be affected, including surrounding residential areas, businesses.

[0215] Warning Level: Divide the warning level according to the risk level, help decision-makers quickly understand the severity of the risk.

[0216] Four, Application Service Layer:

[0217] The application service layer includes environmental information visualization module, mobile terminal and web application support module, user management module, event management module and data sharing and collaboration module.

[0218] Environmental information visualization module, through the geographic information system technology, the environmental data is displayed in the form of map and chart, through the GIS software to realize the visualization of data, GIS software includes ArcGIS and QGIS.

[0219] Mobile terminal and web application support module, used to realize the query and management of environmental information, through the mobile application development framework and web development framework to realize the query and management of environmental information.

[0220] User management module, used to manage user rights, including user registration, login, permission assignment and user behavior audit.

[0221] Event management module, used to record and manage environmental events, including event reporting, event tracking, event handling and event archiving.

[0222] Data sharing and collaboration module, used to support data sharing and collaborative management between different levels of environmental management departments, through data sharing protocol to realize cross-department data sharing.

[0223] It should be noted that geographic information system GIS technology: a computer system for collecting, storing, managing, analyzing and displaying geographic spatial data, combining geographic data (map, geographic coordinates) and attribute data (population, environmental quality indicators), supporting spatial analysis and visualization.

[0224] GIS software: a software tool specifically designed for processing and analyzing geographic spatial data, providing data collection, editing, analysis and visualization functions.

[0225] ArcGIS is a comprehensive GIS software platform developed by Esri company, widely used in enterprises and government agencies, including ArcGIS Pro: desktop application, used for 2D and 3D map making, spatial analysis and data management; ArcGIS Online: cloud-based GIS platform, supporting map making, data sharing and collaboration; ArcGIS Enterprise: enterprise-level GIS platform, supporting large-scale data management and analysis.

[0226] QGIS (Quantum GIS) is an open-source GIS software that supports multiple operating systems (Windows, Linux, Mac OS X) and features include data editing, spatial analysis, data visualization, and plugin support.

[0227] Mobile application development framework is a set of tools and libraries for developing cross-platform mobile applications, allowing developers to use a single codebase to build applications for iOS and Android.

[0228] Web development framework is a set of tools and libraries for developing dynamic and interactive web applications, providing support for both server-side and client-side development.

[0229] User registration is the process of creating an account, including filling out forms, verifying email and phone number, using form validation and encryption to protect user passwords, and supporting email or SMS verification.

[0230] User login is the process of verifying user identity using authentication mechanisms (JWT, OAuth), allowing users to verify their identity with a username and password, and supporting multi-factor authentication (SMS verification, email verification).

[0231] Permission assignment is the process of assigning different permissions based on user roles, ensuring that users can only access and operate authorized resources, using role-based access control (RBAC) and attribute-based access control (ABAC), and supporting fine-grained permission management.

[0232] User behavior audit is the process of recording user operation logs, used to monitor and trace user behavior, recording user login time, operation records, and access path information, and supporting log analysis and audit reports.

[0233] Event reporting supports users submitting environmental event reports through mobile terminals or web applications, including event description, location, and time information.

[0234] Event tracking is the process of recording event handling progress, supporting real-time tracking and updating, and providing visual display of event status.

[0235] Event handling provides event handling functions, supports task allocation and processing records, and supports custom event handling processes.

[0236] Event archiving archives completed events, supports historical event query and analysis, and provides statistical reports on event archiving.

[0237] Notification mechanism: timely inform relevant personnel of the latest status of the event through SMS, email or in-app notifications.

[0238] Data sharing protocol: cross-departmental data sharing through standardized data sharing protocols (RESTful API, GraphQL).

[0239] Data access control: control data access and use based on user permissions and department levels, support role-based access control (RBAC).

[0240] Five, blockchain traceability layer:

[0241] The blockchain traceability layer includes data traceability record module, data verification module and data traceability module;

[0242] Data traceability record module, using blockchain technology to record data source, timestamp and processing process, through the distributed ledger technology of blockchain, each data record is encrypted and stored on multiple nodes, preventing data tampering or loss;

[0243] Data verification module, through the smart contract technology to verify the collected environmental data, according to the automatic execution of the preset verification rules to filter the data, only allow the data that meet the verification rules to record in the blockchain;

[0244] Data traceability module, used to realize the traceability function of environmental data, through the chain structure of blockchain to locate the source and flow process of data, support the traceability and audit of data source and processing process.

[0245] It should be noted that blockchain technology: blockchain is a distributed ledger technology, through encryption algorithm and consensus mechanism to ensure the data tamper-proof.

[0246] Data encryption: each data record will be encrypted before storage, to ensure the confidentiality and integrity of data.

[0247] Distributed storage: data records are stored on multiple nodes, through the distributed ledger technology to ensure the high availability and fault tolerance of data.

[0248] Timestamp: each data record is attached with a timestamp, recording the specific time of data generation and processing.

[0249] Data structure: data is stored in the form of blocks, each block contains multiple data records, blocks are linked through hash value, forming an unalterable chain structure.

[0250] Smart contract: smart contract is an automated script on the blockchain, which can be automatically executed when the preset conditions are met.

[0251] Verification rules: The preset verification rules define the validity criteria of data, including data format, data range, and data source.

[0252] Automatic execution: The smart contract automatically triggers when data is submitted and verifies the data.

[0253] Data filtering: Data that does not meet the verification rules will be rejected, and only data that passes the verification will be recorded on the blockchain.

[0254] Traceability: The chain structure of the blockchain ensures that the source and flow of each data record can be traced.

[0255] Data query: Through the query function of the blockchain, the generation, processing, and storage process of the data can be traced.

[0256] Audit support: Provides detailed audit logs to support auditing of data sources and processing procedures.

[0257] In addition, it should be noted that the present application can be provided as a method, device, or computer program product. Therefore, the embodiments of the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code.

[0258] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, terminal device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device produce an article of manufacture including a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing the functions specified in the flowcharts and / or block diagrams Figure 1 The means for implementing the functions specified in the flowcharts and / or block diagrams

[0259] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing the functions specified in the flowcharts and / or block diagrams Figure 1The computer program instructions can also be loaded onto a computer or other programmable data processing terminal device to cause a series of operational steps to be performed on the computer or other programmable terminal device to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal device provide steps for implementing the flow Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing terminal device to cause a series of operational steps to be performed on the computer or other programmable terminal device to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal device provide steps for implementing the flow Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing terminal device to cause a series of operational steps to be performed on the computer or other programmable terminal device to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal device provide steps for implementing the flow

[0260] It is also noted that the aforementioned illustrative languages, such as first and second, are intended to merely distinguish one entity or action from another, without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0261] Finally, it is to be noted that the above described embodiments are merely exemplary of the application of the principles of the present application. It is to be understood that, within the scope of the principles disclosed herein, further modifications and adaptations can be made by those skilled in the art without departing from the spirit and scope of the application. Therefore, the appended claims are intended to cover all adaptations and modifications of the described embodiments.

Claims

1. An environmental protection management system based on big data service integration management, characterized in that, The system comprises: a data collection layer for collecting multi-source environmental data, including environmental monitoring device data, enterprise information system data, government department data, public reporting data, satellite remote sensing data, and unmanned aerial vehicle monitoring data; a data management layer for storing, cleaning, and standardizing the collected data, and encrypting the data transmission and storage process; a data analysis layer for deep mining and intelligent analysis of the processed data, realizing environmental quality trend prediction, pollution source identification and tracing, and risk assessment and early warning through machine learning models and data analysis algorithms; an application service layer for visualizing the analysis results and realizing environmental information query, management, and sharing through a user interface; a blockchain tracing layer for recording the source, timestamp, and processing process of the data, realizing data non-tamperability and tracing through blockchain technology. 2.The environmental protection management system based on big data service integration management according to claim 1, wherein, The data collection layer comprises: an Internet of Things monitoring module for real-time collection of environmental data through a sensor network deployed at environmental monitoring sites, including air quality sensors, water quality sensors, soil sensors, and noise sensors; an enterprise information system interface module for accessing enterprise internal environmental management information systems to obtain enterprise environmental monitoring data and pollutant emission data; a government department data interface module for accessing environmental policies, regulations, and environmental quality reports published by government departments; a satellite remote sensing module for obtaining large-scale environmental data through satellite remote sensing technology, including land use, vegetation coverage, and air pollution; an unmanned aerial vehicle monitoring module for collecting environmental data in remote areas, complex terrains, and specific regions through unmanned aerial vehicle monitoring technology, including water quality, air quality, and ecological conditions; a public participation module for collecting public reporting and feedback environmental data through an online platform. 3.The environmental protection management system based on big data service integration management of claim 1, wherein, The data management layer comprises: a data storage module for storing the collected multi-source data in a distributed storage system, realizing data storage and access through a distributed file system and a distributed database; a data cleaning module for cleaning the collected data, removing invalid, duplicate, and abnormal data through data filtering algorithms; a data standardization module for standardizing the data, converting data of different formats and structures into a unified format; a data encryption module for encrypting the data transmission and storage process through encryption algorithms, including AES and RSA. 4.The environmental protection management system based on big data service integration management of claim 1, wherein, The data analysis layer comprises: an environmental quality trend prediction module for predicting the change trend of environmental quality through time series analysis, utilizing historical data and real-time data comparison analysis, and combining LSTM neural networks to realize environmental quality prediction; a pollution source identification and tracing module for locating pollution sources through geographic information and monitoring data, realizing pollution source positioning through a geographic information system and data analysis algorithms, including K-means clustering algorithm and DBSCAN clustering algorithm; A risk assessment and early warning module for real-time assessment of environmental risks and generation of early warning information, which assesses environmental risks based on rule-based risk models and real-time data, generates early warning information using threshold triggering mechanisms, and the early warning information includes risk description, risk location, risk time, affected range, and early warning level. 5.The environmental protection management system based on big data service integration management of claim 1, wherein, The application service layer includes: An environmental information visualization module that displays environmental data in the form of maps and charts through geographic information system technology, and realizes data visualization through GIS software, including ArcGIS and QGIS; A mobile terminal and Web application support module for querying and managing environmental information through mobile application development frameworks and Web development frameworks; A user management module for managing user permissions, including user registration, login, permission allocation, and user behavior auditing; An event management module for recording and managing environmental events, including event reporting, event tracking, event handling, and event archiving; A data sharing and collaboration module for supporting data sharing and collaborative management between different levels of environmental management departments through data sharing protocols. 6.The environmental protection management system based on big data service integration management of claim 1, wherein, The blockchain traceability layer includes: A data traceability record module that records the source, timestamp, and processing of data using blockchain technology, and through the distributed ledger technology of blockchain, each data record is encrypted and stored on multiple nodes, preventing data from being tampered with or lost; A data verification module that verifies collected environmental data through smart contract technology, filters data according to pre-set verification rules that are automatically executed, and only allows data that meets the verification rules to be recorded on the blockchain; A data traceability module for realizing the traceability function of environmental data, locating the source and flow process of data through the chain structure of blockchain, and supporting the traceability and audit of data sources and processing.

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