Intelligent environment monitoring and early warning system based on multi-sensor fusion

Abstract

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent environmental monitoring and early warning, and in particular to an intelligent environmental monitoring and early warning system based on multi-sensor fusion. Background Art

[0002] Environmental monitoring refers to the systematic, continuous or periodic observation, measurement, analysis and evaluation of pollutants or ecological indicators in the natural environment (such as air, water quality, soil, noise, radiation, etc.) and the man-made environment (such as industrial emissions, urban climate, etc.) through scientific means. Its core goal is to understand the environmental quality status, identify pollution sources, warn of environmental risks, and provide data support for environmental management, pollution control and ecological protection.

[0003] At present, with the increasing attention to ecological environment quality and the growing demand for refined management, traditional monitoring methods have gradually exposed their drawbacks. Early single-sensor monitoring was difficult to fully and accurately reflect the complex environmental status due to its limited monitoring dimensions. For example, relying solely on a single air quality sensor, it was impossible to accurately obtain comprehensive information such as the concentration of multiple pollutants and meteorological conditions at the same time, which easily led to one-sided data. In addition, the data storage and processing links had problems such as unstable network transmission, weak edge-side data processing capabilities, extensive data storage management, and single intelligent analysis algorithms. These problems restricted the real-time nature, accuracy, and effectiveness of early warning of environmental monitoring, making it difficult to provide reliable support for environmental management decisions, pollution prevention and control, etc. in a timely manner, and could not meet the current needs for accurate monitoring and efficient early warning of dynamic and complex environments. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an intelligent environmental monitoring and early warning system based on multi-sensor fusion, which has the advantages of multi-dimensional precise perception, edge intelligent processing, efficient and stable transmission, deep intelligent analysis and multiple convenient applications. It solves the problems of traditional systems such as limited monitoring dimensions of single sensors, inefficient data processing and transmission, insufficient analysis and early warning capabilities, and difficulty in adapting to complex environmental monitoring needs.

[0006] (2) Technical solution

[0007] To achieve the above-mentioned multi-dimensional precise perception, edge intelligent processing, efficient and stable transmission, deep intelligent analysis, and diversified and convenient applications, the present invention provides the following technical solutions: an intelligent environment monitoring and early warning system based on multi-sensor fusion, including an intelligent environment monitoring and early warning system, the intelligent environment monitoring and early warning system including an intelligent perception layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer, and a system support layer;

[0008] The intelligent perception layer is used to collect environmental data with the help of various environmental sensors (such as air sensors, water quality sensors, meteorological sensors, etc.), such as temperature, humidity, pollutant concentration, meteorological parameters and other basic information. It is the "sense" for the system to "acquire environmental intelligence" and provide raw data for subsequent analysis and early warning;

[0009] The edge computing layer is used to perform preliminary processing on the edge side (close to the data collection end) of the data collected by the intelligent perception layer, such as data screening, outlier removal, and simple feature extraction. This reduces the amount of data transmission and can also achieve some real-time responses (such as local preliminary warnings), making data processing more efficient and timely. It is equivalent to the system's "frontier processing station";

[0010] The network transport layer plays the role of a data "porter". It transmits data processed by the intelligent perception layer and edge computing layer to the data storage layer and intelligent analysis layer through wired / wireless communication networks (such as 5G, WiFi, and private networks), ensuring smooth data flow and serving as the "channel" for system data interaction.

[0011] The data storage layer is responsible for storing various environmental data, including original collected data, edge-processed data, analysis result data, etc., building storage systems such as databases and data lakes to achieve long-term data retention and classified management, and "retaining memory" for the system for use in scenarios such as intelligent analysis and historical backtracking;

[0012] Intelligent analysis uses artificial intelligence algorithms (machine learning, deep learning models) and big data analysis technologies to deeply mine storage layer data, such as environmental trend prediction (future pollution trends), anomaly diagnosis (identifying environmental mutations), and correlation analysis (finding correlations between pollution factors). It is the "brain" of the system, outputting intelligent analysis results to assist in early warning decision-making;

[0013] The application service layer is used to provide functional services to users (environmental protection departments, enterprises, and the public), converting intelligent analysis results into visual reports, early warning information, decision-making suggestions, query interfaces, etc. It is the "window" for interaction between the system and users, making the value of environmental monitoring and early warning a reality and meeting the environmental information and management needs of different users;

[0014] The system support layer is used to provide basic guarantees for the operation of the entire system, and provides support for other layers such as hardware (servers, sensor equipment, etc.), software (operating system, basic service framework), network (communication protocol, bandwidth resources), and security (protection system, data encryption).

[0015] Preferably, the intelligent perception layer deploys multiple types of monitoring equipment, such as fixed monitoring stations, mobile monitoring vehicles / drones, wearable devices, and satellite remote sensing. It uses multi-sensor fusion technology to synchronously collect multi-dimensional environmental data (such as pollutant concentrations, meteorological parameters, ecological indicators, etc.), breaking through the limitations of single sensor monitoring and achieving comprehensive perception.

[0016] Preferably, the edge computing layer includes data preprocessing and multi-sensor feature fusion on the data collected by the intelligent perception layer, conducts local anomaly detection, and optimizes the transmission strategy, completes preliminary data processing on the edge side, reduces the pressure of transmission and back-end analysis, and improves response timeliness.

[0017] Preferably, the network transmission layer relies on the hybrid networking mode of 5G+LoRa+satellite, cooperates with the dynamic routing selection algorithm and blockchain verification channel to ensure the stability, efficiency and security of data transmission, and ensures that environmental data is transmitted reliably and in real time to the back-end system.

[0018] Preferably, the data storage layer constructs a real-time database and a historical data lake, and stores the original monitoring data and edge-processed data in a classified manner, providing comprehensive and structured data support for subsequent analysis and backtracking.

[0019] Preferably, the intelligent analysis layer uses a multimodal fusion engine, combined with knowledge graph reasoning, digital twin system and blockchain evidence storage technology, to deeply mine and analyze stored data, and achieve accurate diagnosis of environmental status, trend prediction and anomaly tracing.

[0020] Preferably, the application service layer creates a multi-dimensional visualization platform and an intelligent decision-making middle platform, sets up linkage control interfaces, external system APIs and user interaction interfaces, and provides monitoring data display, early warning release, decision-making assistance, interactive query and other services to different users such as environmental protection management departments and the public, thus opening up the last mile of data application.

[0021] Preferably, the system support layer ensures stable operation of the system through an adaptive operation and maintenance unit, uses a security protection system to protect data security and system reliability, and provides basic support such as hardware, software, operation and maintenance, and security for each layer.

[0022] (3) Beneficial effects

[0023] Compared with the existing technology, the present invention provides an intelligent environmental monitoring and early warning system based on multi-sensor fusion, which has the following beneficial effects:

[0024] 1. The intelligent environment monitoring and early warning system based on multi-sensor fusion, through the integration of fixed monitoring stations, mobile monitoring equipment, wearable terminals and satellite remote sensing and other multi-source monitoring means in the intelligent perception layer, combined with multi-sensor fusion technology, it breaks through the dimensional limitation of traditional single monitoring method, can capture environmental parameters (pollutant concentration, meteorological factors, ecological indicators, etc.) in all directions, realize multi-dimensional, high-precision data acquisition in complex environment, lay a solid data foundation for subsequent accurate monitoring and early warning, and make environmental status monitoring more comprehensive and accurate.

[0025] 2. The intelligent environment monitoring and early warning system based on multi-sensor fusion, through the edge computing layer front-end data preprocessing, feature fusion and local anomaly detection, combined with network transmission layer hybrid networking (5G+LoRa+satellite) and dynamic routing, block chain verification design, then linkage intelligent analysis layer multi-modal fusion engine (knowledge graph, digital twin, block chain storage and cooperation), build a whole process system of "edge light processing-transmission strong guarantee-back end deep analysis", not only reduce the data transmission pressure, guarantee the transmission safety and stability, but also can dig the data value through the depth algorithm, realize the early discovery of environmental anomalies, accurate prediction of trends, decision-making with basis, greatly improve the timeliness and intelligence of monitoring and early warning, help the environmental management from "passive response" to "active prevention and control" change. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The system schematic diagram of the present application is shown in the figure;

[0027] Figure 2 The running flow chart of the present application is shown in the figure. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] Please refer to Figure 1 and Figure 2 , an intelligent environment monitoring and early warning system based on multi-sensor fusion, including intelligent environment monitoring and early warning system, intelligent environment monitoring and early warning system including intelligent perception layer, edge computing layer, network transmission layer, data storage layer, intelligent analysis layer, application service layer and system support layer;

[0030] The intelligent perception layer is used to collect environmental data such as temperature, humidity, pollutant concentration, and meteorological parameters by various environmental sensors (such as air sensors, water quality sensors, and meteorological sensors), and is the "senses" of the system for obtaining environmental information, providing raw data for subsequent analysis and early warning.

[0031] The edge computing layer is used to preliminarily process the data collected by the intelligent perception layer on the edge side (close to the data collection end), such as data filtering, outlier removal, and simple feature extraction, to reduce data transmission volume and achieve partial real-time response (such as local preliminary warning), making data processing more efficient and timely, and equivalent to the "front processing station" of the system.

[0032] The network transmission layer plays the role of "mover" and stably transmits the data processed by the intelligent perception layer and the edge computing layer to the data storage layer and the intelligent analysis layer through wired / wireless communication networks (such as 5G, WiFi, and private network), ensuring smooth data flow and serving as the "channel" for data interaction of the system.

[0033] The data storage layer is responsible for storing various environmental data, including raw collected data, edge-processed data, and analysis result data, and constructs a storage system such as a database and a data lake to realize long-term storage and classified management of data, serving as the "memory" of the system for intelligent analysis and historical retrieval.

[0034] The intelligent analysis layer uses artificial intelligence algorithms (machine learning and deep learning models) and big data analysis techniques to deeply mine the data in the storage layer, such as environmental trend prediction (future pollution trend), anomaly diagnosis (identification of environmental mutations), and correlation analysis (identification of pollution factor correlations), and is the "brain" of the system that outputs intelligent analysis results to assist in early warning decision-making.

[0035] The application service layer is used to provide functional services to users (environmental protection departments, enterprises, and the public) by converting intelligent analysis results into visual reports, early warning information, decision-making suggestions, and query interfaces, and is the "window" for interaction between the system and users, making the value of environmental monitoring and early warning come true and meeting the needs of different users for environmental information and management.

[0036] The system support layer is used to provide basic support for the operation of the entire system, including hardware (servers, sensor devices, etc.), software (operating systems, basic service frameworks), networks (communication protocols, bandwidth resources), and security (protection systems, data encryption).

[0037] In case implementation, fixed monitoring stations, mobile monitoring vehicles / drones, wearable devices, and satellite remote sensing are deployed in the intelligent perception layer. Multi-sensor fusion technology is used to synchronously collect multi-dimensional environmental data (such as pollutant concentration, meteorological parameters, and ecological indicators). This breaks through the limitations of single sensor monitoring and achieves comprehensive perception.

[0038] The operation steps of the intelligent perception layer are as follows:

[0039] I. Preparation stage

[0040] 1. Device deployment

[0041] 1.1 Fixed monitoring station: Install various environmental sensors (such as air pollutant sensors, water quality sensors, and meteorological sensors) at selected environmental monitoring points (such as urban air quality monitoring points and water quality monitoring sections). Complete device debugging, calibration, and ensure stable data collection and system network adaptation.

[0042] 1.2 Mobile monitoring vehicle / drones: Equip mobile monitoring vehicles with on-board environmental monitoring equipment and drones with portable sensors. Complete device integration and testing, and plan mobile monitoring routes and task areas (such as urban pollution inspection routes and emergency monitoring areas for sudden environmental incidents).

[0043] 1.3 Wearable devices: Distribute to specific personnel (such as environmental inspectors and researchers). Complete device pairing and function settings (such as setting monitoring parameter thresholds and data upload frequency), and ensure that the device can be attached to the human body and work stably.

[0044] 1.4 Satellite remote sensing: Interface with satellite data reception systems, determine satellite transit time and monitoring areas (such as large-scale ecological protection areas and watershed ecological monitoring), debug remote sensing data analysis algorithms, and adapt to environmental monitoring needs.

[0045] II. Data collection stage

[0046] 1. Fixed monitoring station: Collect environmental data continuously according to the preset sampling frequency (such as collecting air pollutant concentration every 5 minutes). Cover multiple dimensions such as air quality (PM2.5, PM10, SO2, etc.), water quality (pH, dissolved oxygen, pollutant content), and meteorological parameters (temperature, humidity, wind speed). Real-time record and store data.

[0047] 2. Mobile monitoring vehicle / drones:

[0048] 2.1 Mobile monitoring vehicle: Travels along the planned route, triggers the on-board equipment to collect data at designated monitoring points or during dynamic inspections, can flexibly adjust the monitoring position, supplement the blind spots covered by fixed monitoring stations (such as mobile dust monitoring on urban roads and inspections of unorganized emissions in industrial parks), and the collected data is synchronously stored in the on-board terminal.

[0049] 2.2 UAV: ​​Takes off according to the mission plan, and collects environmental data (such as high-altitude atmospheric composition and large-scale water area monitoring) in designated airspace (such as high-altitude overlooking of regional pollution distribution and water surface pollution monitoring) through onboard sensors. The data is transmitted back to the ground control terminal in real time or temporarily stored in the UAV storage module.

[0050] 3. Wearable devices: follow the activities of the wearer (such as environmental personnel's foot inspections and scientific researchers' field surveys), continuously collect surrounding environmental data (such as the micro-environment air quality of the personnel activity area and the meteorological parameters of the specific site), record at a set frequency (such as once a minute), and can be temporarily stored locally or uploaded in real time.

[0051] 4. Satellite remote sensing: When a satellite passes by, remote sensing technology is used to collect data on large-area targets (such as ecological monitoring of forest-covered areas and monitoring of marine pollution ranges) to obtain macro-environmental information such as the surface, water bodies, and atmosphere. The data is transmitted via satellite to a ground receiving station for storage.

[0052] 3. Initial Data Processing and Transmission Stage

[0053] 1. Fixed monitoring station: Perform preliminary verification of the collected raw data (such as eliminating obvious outliers and determining the working status of sensors), and transmit the data to the edge computing layer or data storage layer of the intelligent environmental monitoring and early warning system through wired / wireless communication networks (such as private networks and 5G) at a set period (such as every 10 minutes) to enter the system's subsequent processing flow.

[0054] 2. Mobile monitoring vehicle / UAV:

[0055] 2.1 Mobile monitoring vehicle: The on-board terminal performs simple processing on the collected data (such as data format conversion and preliminary quality control), and transmits the data to the system through the on-board network (such as 4G / 5G) or through the wired network after returning to the fixed site to supplement the regional monitoring data.

[0056] 2.2 UAV: ​​After receiving the data sent back by the UAV, the ground control terminal performs preliminary analysis and verification, and then uploads it to the system through the network to supplement the environmental data of special areas (such as mountains and hazardous chemical areas).

[0057] 3. Wearable devices: Temporarily store data locally or connect in real time via Bluetooth or mobile network (such as 4G) to transmit the collected environmental data to related terminals (such as patrol personnel’s mobile phones, system data receiving terminals), and then import them into the system after simple sorting for refined environmental monitoring of personnel activity areas.

[0058] 4. Satellite remote sensing: After receiving satellite data, the ground receiving station uses pre-processing algorithms (such as radiation correction and geometric correction) to parse and process the remote sensing data, extract the information required for environmental monitoring (such as vegetation coverage and water pollution range), and then transmit it to the system through the network to provide data support for macro-environmental analysis.

[0059] Through the above steps, various devices in the intelligent perception layer work together to build a multi-dimensional environmental perception network of "fixed + mobile + wearable + remote sensing", collect and preliminarily process environmental data, and provide comprehensive and rich original information for the intelligent environmental monitoring and early warning system.

[0060] In the case implementation, the edge computing layer includes data preprocessing and multi-sensor feature fusion for the data collected by the intelligent perception layer, conducting local anomaly detection, and optimizing transmission strategies. It completes preliminary data processing on the edge side, reducing the pressure on transmission and back-end analysis and improving response timeliness.

[0061] The operation steps of the edge computing layer are carried out in sequence according to the data processing flow:

[0062] 1. Data Access and Preparation

[0063] Multi-source environmental data (such as pollutant concentrations and meteorological parameters) collected by the intelligent perception layer (fixed monitoring stations, mobile monitoring vehicles / drones, etc.) are connected to the edge computing layer through network transmission (such as 5G, wired private networks) to build a queue of data to be processed and prepare for subsequent operations.

[0064] 2. Data Preprocessing

[0065] 1. Data cleaning: For the newly received raw data, screen and identify abnormal values ​​(such as jump data caused by sensor failure and extreme values ​​beyond physical common sense), repair the data through interpolation methods (such as linear interpolation) or replacement with historical similar data to ensure data quality.

[0066] 2. Format conversion and normalization: Unify the data formats of different devices and different types of sensors (for example, convert the custom formats output by various sensors into the system-wide JSON and CSV formats); normalize the data (for example, map different dimensional data such as pollutant concentrations and meteorological values ​​to the [0, 1] interval) to facilitate subsequent fusion and analysis.

[0067] 3. Multi-sensor feature fusion

[0068] 1. Feature extraction: For pre-processed data, extract features of each sensor data (such as air quality sensor to extract pollution concentration trend features, weather sensor to extract temperature and humidity fluctuation features), which can be obtained through sliding window, statistical analysis (mean, variance) and other methods.

[0069] 2. Fusion strategy execution: Use weighted fusion, decision-level fusion and other algorithms to integrate multi-sensor features, such as when fusing air quality and weather sensor data, assign different weights to features according to different scenarios (such as pollution tracing, weather influence analysis), generate more comprehensive and accurate fusion feature set, and improve environmental state representation ability.

[0070] Four, local anomaly detection

[0071] 1. Model loading and initialization: Load pre-trained anomaly detection model (such as Isolation Forest, LSTM-anomaly detection model), which is trained based on historical environmental data and can identify normal environmental patterns.

[0072] 2. Anomaly identification: Input the fused feature data into the model and compare it with the normal pattern. If the data deviates from the normal pattern (such as sudden increase in pollutant concentration exceeding historical fluctuation range), it is determined as an anomaly, and the type of anomaly (such as sudden pollution, sensor anomaly) and location (associated with intelligent sensing layer device deployment point) are marked, triggering local warning (such as sound and light prompt of edge computing device, pushing anomaly signal to operation and maintenance end).

[0073] Five, transmission strategy optimization

[0074] 1. Data classification and priority determination: Classify and label the data after preprocessing, fusion and anomaly detection (such as abnormal data, regular monitoring data), and set transmission priority (abnormal data first, key environmental indicator data first).

[0075] 2. Dynamic transmission scheduling: Combine edge side network status (bandwidth, delay) and use dynamic scheduling algorithm, for example, when the network is idle, batch transmission of regular historical data; when abnormal data is detected or network congestion occurs, prioritize transmission of abnormal data and compress non-critical data transmission volume to ensure important environmental information is uploaded to the upper layer of the system (such as data storage layer, intelligent analysis layer) in a timely manner, while reducing transmission cost.

[0076] Through the above steps, the edge computing layer is close to the data collection end, completing the closed-loop processing of "data preprocessing → feature fusion → anomaly detection → transmission optimization", which not only reduces the data processing pressure of the upper layer of the system, but also realizes the local rapid response of environmental anomalies, improving the real-time and efficiency of the intelligent environmental monitoring and early warning system.

[0077] In the case implementation, the network transmission layer relies on the mixed networking mode of 5G+LoRa+satellite, cooperates with the dynamic routing selection algorithm and the blockchain verification channel, guarantees the stability, efficiency and security of data transmission, and ensures that the environmental data is reliably and real-timely transmitted to the backend system.

[0078] The operation steps of the network transmission layer are as follows:

[0079] I. Network initialization and access preparation

[0080] 1. Mixed networking construction: Start the hardware facilities such as 5G base station, LoRa gateway and satellite ground receiving station, complete the basic networking of 5G network (responsible for high-speed and low-delay data transmission), LoRa network (adapted to low-power and wide-coverage scenarios such as remote area environmental monitoring equipment data backhaul), and satellite network (for wide-area and special areas without ground network coverage such as ocean and desert monitoring), build a multi-level and multi-scenario adaptive transmission network architecture, and access the corresponding network nodes for each intelligent sensing layer and edge computing layer device (such as monitoring station and mobile terminal).

[0081] 2. Algorithm and channel loading: In the server side of the network transmission layer, load the dynamic routing selection algorithm (used for intelligent planning of data transmission path) and deploy the blockchain verification channel (build a trusted environment for data transmission), complete the initialization of algorithm parameters (such as setting the weight factors of bandwidth, delay and cost for routing selection), and configure the blockchain nodes (determine the nodes participating in data verification and consensus mechanism).

[0082] II. Data transmission initiation and routing selection

[0083] 1. Data encapsulation and initiation: The data processed by the edge computing layer (such as preprocessed environmental data and abnormal warning information) is encapsulated into data packets according to the network transmission layer protocol (such as TCP / IP and MQTT), carries the data type (regular monitoring and abnormal alarm), priority (abnormal data high priority), source address (intelligent sensing layer device ID), destination address (data storage layer / server of intelligent analysis layer) and other identifiers, and initiates the transmission request.

[0084] 2. Dynamic routing planning: The dynamic routing selection algorithm receives the transmission request, collects the current state of each link in the mixed networking (real-time bandwidth and delay of 5G link, remaining capacity of LoRa link, and available time slot of satellite link), combines data priority, transmission cost (such as traffic fee and energy consumption), and other factors, uses intelligent algorithms (such as optimized version of Dijkstra algorithm and reinforcement learning routing strategy) to plan the optimal transmission path for the data packet, for example, high-priority abnormal data is preferentially selected for 5G link to ensure low-delay transmission; low-power and non-urgent regular monitoring data is adapted to LoRa link; data in areas without ground network coverage is automatically switched to satellite link.

[0085] 3. Data Transmission and Blockchain Verification

[0086] 1. Data forwarding and transmission: Network nodes (such as 5G base stations, LoRa gateways, and satellite ground stations) relay data packets according to the planned routing paths. During the forwarding process, the link status is monitored in real time. If link congestion (such as sudden high traffic at the 5G base station) or failure (such as power failure at the LoRa gateway) occurs, the dynamic routing algorithm triggers rerouting and replans the transmission path to ensure continuous data transmission.

[0087] 2. Blockchain verification execution: When data is transmitted at key nodes (such as the aggregation node entering the core network and the verification node before reaching the destination server), it is connected to the blockchain verification channel. The node extracts the hash value, transmission identifier and other information of the data message, and compares it with the historical data characteristics and verification rules stored on the chain through the blockchain distributed ledger to verify the data integrity (whether it has been tampered with) and the authenticity of the source (whether it comes from a legitimate monitoring device). If the verification passes, the data continues to be transmitted; if tampering or forgery is found, an alarm is triggered, the transmission is interrupted and the source of the data is traced.

[0088] 4. Data Reception and Network Feedback

[0089] 1. Data reception and decapsulation: The data storage layer / intelligent analysis layer server receives the transmitted data packets, decapsulates them according to the protocol, restores the environmental data and warning information, and enters the subsequent storage and analysis process.

[0090] 2. Transmission status feedback: The server feeds back the data reception status (success / failure, data integrity) to the network transport layer. The network transport layer summarizes the transmission performance data of each link (bandwidth utilization, transmission delay, packet loss rate) to provide a basis for dynamic routing algorithm optimization. At the same time, the blockchain verification results are synchronized to the system operation and maintenance module for network security audits and device trust assessments, continuously improving the transmission efficiency and security of hybrid networks.

[0091] Through the steps of "hybrid networking to adapt to multiple scenarios → dynamic routing and intelligent route selection → transmission verification to ensure reliability → feedback optimization and continuous iteration", the network transmission layer realizes stable, efficient and secure transmission of environmental monitoring data, building a solid data flow "channel" for the intelligent environmental monitoring and early warning system.

[0092] In the case implementation, the data storage layer builds a real-time database and a historical data lake to categorize and store raw monitoring data and edge-processed data, providing comprehensive and structured data support for subsequent analysis and backtracking.

[0093] The operation steps of the data storage layer revolve around data inflow, classified storage, and basic management:

[0094] 1. Data Access Preparation

[0095] The network transmission layer of the intelligent environmental monitoring and early warning system pushes the transmitted and verified environmental data (such as the real-time monitoring values ​​collected by the intelligent perception layer and the fused data processed by the edge computing layer) to the access node of the data storage layer according to the preset data interface protocol (such as RESTful API, database connection protocol), and marks the data type (real-time monitoring, historical backtracking), source (such as fixed monitoring station ID, mobile monitoring vehicle number), timestamp and other metadata information.

[0096] 2. Real-time database processing flow

[0097] 1. Data writing: The data storage layer identifies the real-time nature of the accessed data (e.g., data that is updated frequently or requires immediate query) and directs it to the real-time database. The real-time database uses an efficient write engine (e.g., the fast write mechanism of the in-memory database Redis or the real-time tablespace strategy of the relational database Oracle) to quickly write data into corresponding data tables / data structures (e.g., storing air quality monitoring data by minute, associating monitoring station locations, and sensor type fields) based on dimensions such as time series and monitoring points, ensuring low-latency storage of real-time data.

[0098] 2. Data indexing and caching: Establish indexes for data in the real-time database (such as composite indexes based on timestamps and monitoring points) to accelerate subsequent queries and retrievals. At the same time, for frequently accessed data (such as the current regional environmental quality overview data), use caching mechanisms (such as Redis cache) to temporarily store it, reducing the pressure of direct database queries and supporting real-time data calls in the intelligent analysis layer and application service layer (such as real-time dashboard display on the application side and real-time anomaly warning calculation in the analysis layer).

[0099] 3. Historical Data Lake Processing Process

[0100] 1. Data screening and archiving: The data storage layer screens non-real-time data that needs to be retained for a long time (such as data that exceeds the storage period of the real-time database and historical accumulated data used for trend analysis), separates it from the real-time database, and directs it to the historical data lake. The historical data lake adopts a distributed storage architecture (such as Hadoop HDFS and cloud object storage) and performs layered and domain storage according to data topics (such as air quality history library and water quality history library) and time range (such as monthly archives and annual archives), supporting elastic expansion storage of massive environmental data.

[0101] 2. Data governance and structuring: During the storage process of the historical data lake, data governance operations are performed, including data cleaning (removing outliers and supplementing missing values), format standardization (unifying data formats for different periods and different devices), and metadata improvement (supplementing data collection methods and sensor calibration information). Through the data lake's schema-on-read (read-time mode) or schema-on-write (write-time mode) strategy, structured management of unstructured and semi-structured environmental data (such as satellite remote sensing raw images and sensor raw logs) is achieved, laying the foundation for in-depth data analysis (such as long-term environmental trend mining and cross-year pollution feature comparison).

[0102] 4. Data Interaction and Management

[0103] 1. Data query and call: When the intelligent analysis layer conducts real-time early warning (calling real-time database data) and historical trend analysis (calling historical data lake data), it obtains data on demand through the query interface of the data storage layer (such as SQL query statements and data lake query engine). The application service layer displays real-time environment dashboards (connected to real-time databases) and historical data reports (connected to historical data lakes) to realize the business application of data.

[0104] 2. Data backup and disaster recovery: Perform regular snapshot backups of real-time databases (such as full / incremental backups of relational databases) and synchronize the backup data to a historical data lake or off-site disaster recovery storage. For the historical data lake, adopt multi-copy storage and off-site redundancy strategies to ensure the security and reliability of environmental data, prevent data loss and damage, and support the long-term stable operation of the system.

[0105] Through the steps of "data classification access → real-time library rapid storage and response → historical lake archiving management and sedimentation → interactive management to ensure availability", the data storage layer realizes the "real-time-historical" full life cycle management of environmental monitoring data, providing a solid data asset foundation for the intelligent environmental monitoring and early warning system.

[0106] In the case implementation, the intelligent analysis layer uses a multimodal fusion engine, combined with knowledge graph reasoning, digital twin systems and blockchain evidence storage technology, to deeply mine and analyze stored data, achieving accurate diagnosis of environmental conditions, trend prediction and anomaly tracing;

[0107] The operation steps of the intelligent analysis layer are as follows:

[0108] 1. Data Input

[0109] Two types of data are obtained from the data storage layer: one is pre-processed real-time environmental monitoring data (such as pollutant concentrations and meteorological parameters), and the other is historically accumulated environmental data (including long-term trends and regional characteristics information), providing basic materials for intelligent analysis.

[0110] 2. Multimodal Fusion Engine Processing

[0111] 1. Data preprocessing and adaptation: Convert the input data format, extract features (such as extracting trend characteristics from time series monitoring data), unify the feature representation of multi-source data (from different monitoring devices and different spatiotemporal dimensions), and prepare for fusion analysis.

[0112] 2. Multimodal fusion computing: Integrate multimodal information such as numerical data from environmental monitoring (such as pollutant concentration values), image data (satellite remote sensing images), and text data (environmental event reports). Through deep learning models (such as multimodal Transformer), the association between different modal data (such as the correspondence between the pollution range in the image and the concentration monitoring value) is mined to output the fused comprehensive environmental characteristics.

[0113] 3. Knowledge Graph Reasoning

[0114] 1. Knowledge graph construction: Load the environmental knowledge graph (including knowledge on pollutant propagation patterns and correlations between environmental factors, such as "increased PM2.5 → reduced visibility" and "correlation between industrial emissions and concentrations of certain pollutants") and map the comprehensive features output by the fusion engine into entities (such as monitoring points and pollutant types) and relationships (such as concentration change relationships and impact relationships) in the knowledge graph.

[0115] 2. Reasoning Analysis: Using graph reasoning algorithms (such as path reasoning and rule reasoning), we can mine potential environmental laws based on knowledge graphs (such as inferring whether pollutant levels in a certain area exceed standards due to transmission from upstream pollution sources), predict environmental evolution trends (such as deducing future pollution diffusion paths based on the weather-pollution relationship), and output reasoning conclusions (such as pollution source tracing results and trend forecast reports).

[0116] 4. Application of Digital Twin Systems

[0117] 1. Environmental scene mapping: Based on the fused data and knowledge graph inference results, the digital twin model is driven to map the real environment (such as urban areas and watershed ecology) in the virtual space, restore the real-time status of environmental elements (topography, buildings, water bodies) and monitoring data (pollutant distribution, meteorological fields), and construct a dynamic twin scene.

[0118] 2. Simulation and decision verification: In the digital twin scenario, simulate environmental intervention measures (such as shutting down a pollution source, initiating emergency emission reduction), verify the effect of the measures on environmental improvement through model calculations (fluid mechanics simulation of pollution diffusion, ecological model deduction of recovery trends), and output optimal decision recommendations (such as precise emission reduction areas and ecological restoration plans).

[0119] 5. Blockchain Evidence Storage

[0120] 1. Data on-chain preparation: Extract key data in the intelligent analysis process (such as the original monitoring data summary, fusion analysis results, reasoning conclusions, and twin simulation parameters) and generate an unalterable hash value as a basis for evidence storage.

[0121] 2. Blockchain storage and ownership confirmation: The hash value and associated metadata (analysis time, analyst / algorithm identifier) ​​are uploaded to the blockchain network. Through a consensus mechanism (such as PoW, PoS), the data is stored on the chain, enabling credible traceability of environmental analysis data (such as tracing the original data source of pollution analysis conclusions and the compliance of the analysis process), providing credible data support for environmental management and responsibility determination.

[0122] 6. Result Output

[0123] Integrate multimodal fusion conclusions, knowledge graph reasoning results, and digital twin decision-making recommendations to form a standardized analysis report (including environmental status assessment, trend forecast, and decision-making plan), which is pushed to the application service layer to support scenarios such as environmental warning release, management decision-making, and public information services, completing the intelligent analysis closed loop.

[0124] In the case implementation, the application service layer created a multi-dimensional visualization platform and an intelligent decision-making platform, set up linkage control interfaces, external system APIs and user interaction interfaces, and provided monitoring data display, early warning release, decision support, interactive query and other services to different users such as environmental protection management departments and the public, thus opening up the last mile of data application;

[0125] The application service layer operation steps are as follows:

[0126] 1. Data Access and Initialization

[0127] Obtain processed environmental data (including monitoring results, analysis conclusions, decision recommendations, etc.) from the intelligent analysis layer, and at the same time connect to the system basic configuration (such as user permissions, functional module activation status), complete the application service layer function initialization, and prepare for the operation of each sub-module.

[0128] 2. Multi-dimensional Visualization Platform Operation

[0129] 1. Data mapping and rendering: Receive environmental data (real-time monitoring values, historical trends, spatial distribution, etc.), map it to visualization models (such as timeline curves, geographic information maps, and indicator dashboards) by dimension (time, space, and indicator type), and generate dynamic visualization interfaces (such as real-time updated pollution heat maps and multi-indicator trend comparison curves) through graphics rendering technology (WebGL and SVG).

[0130] 2. Interactive response: Respond to user operations (such as map zooming, timeline dragging, and indicator filtering), retrieve corresponding dimension data in real time, and update the visual display (such as zooming the map to load more detailed regional monitoring data and dragging the timeline to review historical environmental status), helping users to intuitively understand environmental information.

[0131] 3. Intelligent Decision-Making and Collaboration

[0132] 1. Decision model loading: Import the decision model output by the intelligent analysis layer (such as pollution prevention and control strategy model, ecological restoration plan model), combine it with environmental management goals (such as emission reduction indicators, water quality improvement requirements), and initialize the decision calculation parameters.

[0133] 2. Decision-making deduction and output: Input real-time environmental data (such as monitoring values ​​of sudden pollution incidents), drive decision-making model deduction (simulate the environmental improvement effects under different measures), output multi-scenario decision-making suggestions (such as optimal emission reduction path, emergency response process), and support comparative analysis of schemes (cost, effect, and timeliness dimensions) to provide decision-making basis for management departments.

[0134] 4. Linkage Control Interface Execution

[0135] 1. Command parsing: Receive control commands triggered by the intelligent decision-making platform or users (such as starting pollution purification equipment in a certain area, adjusting the sampling frequency of monitoring equipment), and parse the command content (device ID, operation type, parameter settings).

[0136] 2. Equipment linkage: Through the Internet of Things protocol (MQTT, Modbus), the parsed instructions are sent to related hardware devices (such as environmental management facilities, intelligent sensing terminals), real-time control of equipment actions (such as starting purification devices, modifying sensor collection cycles), and feedback of execution status (success / failure, current working conditions of the equipment) to achieve closed-loop control of environmental management.

[0137] 5. External System API Interaction

[0138] 1. Interface adaptation and calling: According to the requirements of external systems (such as environmental government platforms requiring environmental data, scientific research systems requiring historical monitoring values), call predefined API interfaces and encapsulate environmental data (real-time / historical, analysis results, etc.) in standard data formats (JSON, XML).

[0139] 2. Data interaction and feedback: Push the packaged data to the external system and receive feedback from the external system (such as data verification results and business instructions) to achieve cross-system data sharing and collaboration (such as synchronizing pollution warnings to the emergency command system and receiving data verification requests from the scientific research platform).

[0140] 6. User Interface Response

[0141] 1. Interface rendering and display: Integrate the output content of the multi-dimensional visualization platform and the intelligent decision-making platform, render the user interaction interface (PC / mobile), and display functional modules such as environmental monitoring dashboards, decision-making portals, and equipment control buttons.

[0142] 2. User operation processing: Receive user operations (such as querying environmental reports, submitting decision applications, and reporting issues), trigger corresponding business processes (such as retrieving historical reports to generate PDFs, forwarding decision applications to the approval module), and provide real-time feedback on operation results (success prompts, process progress) to ensure that users can conveniently use system functions and complete the application service closed loop.

[0143] During the case implementation, the system support layer ensures stable system operation through adaptive operation and maintenance units, and uses a security protection system to protect data security and system reliability, providing basic support for hardware, software, operation and maintenance, and security for each layer;

[0144] Among them, the system support layer operation steps are:

[0145] 1. Initialization startup

[0146] When the system starts, the system support layer is activated first, the adaptive operation and maintenance unit and the security protection system complete the initialization loading, and the readiness check of the hardware resources (servers, sensors, etc.) and the software environment (operating system, basic services) ensures that the underlying support capabilities are ready.

[0147] 2. Adaptive Operation and Maintenance Unit Operation

[0148] 1. Resource monitoring: Real-time collection of operating data from all layers of the system, including the device status (power level, sensor accuracy) of the intelligent perception layer, computing power usage (CPU / memory usage) of the edge computing layer, bandwidth flow of the network transmission layer, read and write performance of the data storage layer, algorithm operation time of the intelligent analysis layer, and user access volume of the application service layer, to build a "panoramic view" of system resources.

[0149] 2. Intelligent Diagnosis and Optimization: Based on monitoring data, machine learning algorithms (such as anomaly detection models and resource prediction models) are applied to identify system anomalies (such as sensor failure warnings and excessive server load). Resource allocation is dynamically adjusted, such as temporarily expanding computing power for the highly loaded intelligent analysis layer or optimizing the distribution of hot and cold data based on access patterns at the data storage layer to ensure efficient system operation.

[0150] 3. Automatic repair and backup: For diagnosed software failures (such as service process crashes), automatic restart and redundancy switching are triggered; for hardware risks (such as sub-healthy disks), data migration and spare parts warnings are initiated. At the same time, full / incremental system backups are performed according to the strategy, overwriting configuration files and key data to prevent the risk of data loss.

[0151] 3. Security Protection System Collaboration

[0152] 1. Identity authentication and permission control: Connect with user / device access requests at all levels of the system (such as sensor networking at the intelligent perception layer and user login at the application service layer), verify the legitimacy of the identity through multi-factor authentication (password, digital certificate, biometrics), and limit the scope of resource access based on preset permission policies (such as administrators can configure the system, ordinary users can only view data), thereby building a solid "access security line of defense."

[0153] 2. Data protection and encryption: Environmental data in transmission (network transmission layer) and storage (data storage layer) are encrypted using encryption algorithms (such as AES and RSA) to generate ciphertext for transmission / storage. For key data (such as decision models in the intelligent analysis layer and sensitive user information in the application service layer), blockchain evidence storage and hash verification are additionally added to prevent data tampering and leakage.

[0154] 3. Threat monitoring and response: Deploy intrusion detection systems (IDS) and malicious code protection modules to scan system network traffic and process behavior in real time, identify threats such as network attacks (DDoS, SQL injection) and virus infections, and automatically trigger response mechanisms once an anomaly is detected, such as isolating the attack source, intercepting malicious requests, and notifying operation and maintenance personnel for manual intervention to ensure system security and stability.

[0155] 4. Continuous Collaborative Guarantee

[0156] The adaptive operation and maintenance unit continuously interacts with the security protection system. Operation and maintenance optimization actions (such as resource scheduling) trigger security protection verification (ensuring compliance and the absence of security vulnerabilities in the scheduling process). Security incident handling (such as intrusion interception) is simultaneously fed back to the operation and maintenance unit to adjust resource strategies (such as strengthening resource protection for attacked modules). The two work together to provide the foundational support for "stable operation + security protection" at all levels of the intelligent environmental monitoring and early warning system, 24 / 7, throughout the system's entire lifecycle.

[0157] To sum up, this intelligent environmental monitoring and early warning system based on multi-sensor fusion integrates multi-source monitoring methods such as fixed monitoring stations, mobile monitoring equipment, wearable terminals and satellite remote sensing through the intelligent perception layer, and cooperates with multi-sensor fusion technology to break through the dimensional limitations of traditional single monitoring methods. It can capture environmental parameters (pollutant concentrations, meteorological factors, ecological indicators, etc.) in all directions, and realize multi-dimensional and high-precision data collection in complex environments, laying a solid data foundation for subsequent precise monitoring and early warning, making environmental status monitoring more comprehensive and accurate.

[0158] Furthermore, through the front-end data preprocessing, feature fusion, and local anomaly detection at the edge computing layer, combined with hybrid networking (5G+LoRa+satellite) and dynamic routing and blockchain verification design at the network transmission layer, and then linked with the multimodal fusion engine of the intelligent analysis layer (knowledge graph, digital twin, blockchain evidence collaboration), a full-process system of "light edge processing - strong transmission guarantee - deep back-end analysis" is constructed. This not only reduces the pressure of data transmission and ensures secure and stable transmission, but also mines the value of data through deep algorithms, achieving early detection of environmental anomalies, accurate trend prediction, and informed decision-making, significantly improving the timeliness and intelligence of monitoring and early warning, and helping environmental management shift from "passive response" to "active prevention and control."

[0159] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0160] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent environmental monitoring and early warning system based on multi-sensor fusion, including an intelligent environmental monitoring and early warning system, characterized by: The intelligent environmental monitoring and early warning system includes an intelligent perception layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer; The intelligent perception layer is used to collect environmental data such as temperature, humidity, pollutant concentration, meteorological parameters and other basic information with the help of various environmental sensors (such as air sensors, water quality sensors, meteorological sensors, etc.). It is the "sense" for the system to "acquire environmental intelligence" and provides raw data for subsequent analysis and early warning; The edge computing layer is used to perform preliminary processing on the edge side (close to the data collection end) of the data collected by the intelligent perception layer, such as data screening, outlier removal, and simple feature extraction. This reduces the amount of data transmission and can also achieve some real-time responses (such as local preliminary warnings), making data processing more efficient and timely. It is equivalent to the system's "frontier processing station"; The network transport layer plays the role of a data "porter". It transmits data processed by the intelligent perception layer and edge computing layer to the data storage layer and intelligent analysis layer through wired / wireless communication networks (such as 5G, WiFi, and private networks), ensuring smooth data flow and serving as the "channel" for system data interaction. The data storage layer is responsible for storing various environmental data, including original collected data, edge-processed data, and analysis result data. It builds storage systems such as databases and data lakes to achieve long-term data retention and classified management, and "retains memory" for the system for use in scenarios such as intelligent analysis and historical backtracking. Intelligent analysis uses artificial intelligence algorithms (machine learning, deep learning models) and big data analysis technologies to deeply mine storage layer data. For example, it can predict environmental trends (future pollution trends), diagnose anomalies (identify environmental mutations), and perform correlation analysis (find correlations between pollution factors). It is the "brain" of the system, outputting intelligent analysis results to assist in early warning decision-making. The application service layer is used to provide functional services to users (environmental protection departments, enterprises, and the public), converting intelligent analysis results into visual reports, early warning information, decision-making suggestions, query interfaces, etc. It is the "window" for interaction between the system and users, making the value of environmental monitoring and early warning a reality and meeting the environmental information and management needs of different users; The system support layer is used to provide basic guarantees for the operation of the entire system, and provides support for other layers such as hardware (servers, sensor equipment, etc.), software (operating system, basic service framework), network (communication protocol, bandwidth resources), and security (protection system, data encryption).

2. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1, characterized in that: The intelligent perception layer deploys multiple types of monitoring equipment, including fixed monitoring stations, mobile monitoring vehicles / drones, wearable devices, and satellite remote sensing. It uses multi-sensor fusion technology to synchronously collect multi-dimensional environmental data (such as pollutant concentrations, meteorological parameters, ecological indicators, etc.), breaking through the limitations of single sensor monitoring and achieving comprehensive perception.

3. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1 is characterized by: The edge computing layer includes data preprocessing and multi-sensor feature fusion for the data collected by the intelligent perception layer, conducts local anomaly detection, and optimizes the transmission strategy, completing preliminary data processing on the edge side, reducing the pressure of transmission and back-end analysis, and improving response timeliness.

4. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1, characterized in that: The network transmission layer relies on the hybrid networking mode of 5G+LoRa+satellite, cooperates with the dynamic routing selection algorithm and blockchain verification channel to ensure the stability, efficiency and security of data transmission, and ensures that environmental data is transmitted reliably and in real time to the back-end system.

5. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1 is characterized by: The data storage layer builds a real-time database and a historical data lake, and categorizes and stores original monitoring data and edge-processed data to provide comprehensive and structured data support for subsequent analysis and backtracking.

6. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1, characterized in that: The intelligent analysis layer uses a multimodal fusion engine, combined with knowledge graph reasoning, digital twin system and blockchain evidence storage technology, to deeply mine and analyze stored data, and achieve accurate diagnosis of environmental status, trend prediction and anomaly tracing.

7. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1, characterized in that: The application service layer creates a multi-dimensional visualization platform and an intelligent decision-making middle platform, sets up linkage control interfaces, external system APIs and user interaction interfaces, and provides monitoring data display, early warning release, decision-making assistance, interactive query and other services to different users such as environmental protection management departments and the public, thus opening up the last mile of data application.

8. The intelligent environmental monitoring and early warning system based on multi-sensor fusion according to claim 1, characterized in that: The system support layer ensures stable operation of the system through adaptive operation and maintenance units, uses a security protection system to protect data security and system reliability, and provides basic support such as hardware, software, operation and maintenance, and security for each layer.

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