132 results about "Source tracing" patented technology

The invention relates to the technical field of pollutant tracing, and discloses a method and a system for tracing new pollutants in various environmental media based on machine learning. The method comprises the following steps: collecting multivariate environment data to construct a pollutant fingerprint database; performing space-time correlation analysis on the fingerprint database and the environment data, and reconstructing a transmission path; converting the data, the fingerprint database and the path diagram into source node fusion to construct an analytical model, and forming a contribution rate matrix; and positioning the pollution source through the three-dimensional features based on the matrix. According to the method and the device, an intelligent decision-making system based on machine learning can be constructed by integrating multi-source heterogeneous data under a complex environment background, automatic, standardized and precise traceability of new pollutants is realized, and the traceability efficiency and accuracy are improved.

The invention discloses a vertical large language model training method and system in the carbon neutralization field, and the method comprises the following steps: collecting data of the carbon neutralization field, carrying out the data preprocessing, constructing a carbon neutralization field knowledge base, and updating the carbon neutralization field knowledge base through a dynamic updating mechanism; performing dynamic semantic partitioning and vectorization coding on the text of the carbon neutralization domain knowledge base, and storing the text into a vector database; performing staged fine tuning on the pre-trained large language model based on a low-rank adaptation technology, wherein the fine tuning comprises general instruction fine tuning and carbon neutralization field professional fine tuning; a retrieval enhancement generation mechanism is adopted, knowledge fragments related to user query are retrieved through a vector database, and a large language model is input to generate answers. Compared with the prior art, the method has the advantages that the answer reliability is improved through conflict detection and source tracing, so that the large language model can more accurately adapt to knowledge requirements in the carbon neutralization field.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention discloses an ecological environment pollution source tracing method based on data analysis, and relates to the field of ecological environment, and the method comprises the steps: carrying out the type selection of equipment, obtaining original data, carrying out the arrangement of the equipment after the type selection of the equipment, carrying out the multi-source data access after the arrangement of the equipment, and carrying out the data format standardization based on the obtained original data. And performing data anomaly detection based on the original data after data format standardization, and performing vacancy value filling based on the original data after data anomaly detection. According to the method, through multi-source data fusion, data cleaning, pollution diffusion modeling, pollution source identification and contribution rate calculation and visual early warning, the precision and reliability of pollution tracing are improved, firstly, format standardization, time synchronization and anomaly detection are carried out on pollution monitoring data of different sources, and data consistency and integrity are ensured; and then combining a Gaussian plume model, a fluid dynamic model and a Fick diffusion model.

The invention provides an underground water pollution traceability method and system based on deep learning and multi-source data fusion, and the method employs multi-source data fusion to accurately describe pollution source features, builds a pollution source multi-source data set, builds a pollution source recognition model based on a CNN convolutional neural network and an LSTM long-short-term memory network, and achieves the traceability of underground water pollution. The multi-source data set is used for training, and the features and potential relationships of the multi-source data are fully mined, so that the traceability model can accurately identify the features of the main pollution source and contributions thereof, the limitation of single-dimensional data traceability and the difficulty of multi-source data fusion are overcome, and the accuracy and efficiency of groundwater pollution traceability are improved.

The invention belongs to the technical field of river and lake water quality monitoring, and discloses a river and lake water quality intelligent traceability monitoring system and method.Dissolved organic matter fluorescent fingerprints are analyzed in real time through a three-dimensional fluorescent monitoring unit, pollution markers are detected, and parameters such as COD, ammonia nitrogen and total phosphorus are synchronously detected through a full-spectrum monitoring unit; the video monitoring unit is used for collecting section images / videos of rivers and lakes, other sensors are used for collecting real-time values of chlorophyll, pH, conductivity, turbidity and dissolved oxygen in a water body, then the data processing module is used for carrying out normalization processing on multi-source data, and the analysis and early warning module is used for analyzing water quality early warning scores, spectrum early warning scores and video early warning scores. All scores are synthesized to obtain a comprehensive early warning score, traceability is started when comprehensive early warning is larger than or equal to the grade II, pollution sources are recognized based on three-dimensional fluorescence spectrum comparison and upstream patrol, and a river and lake water body monitoring-early warning-traceability-verification closed loop is achieved.

The invention discloses an intelligent construction and tracing method and device for an attack graph, and relates to the technical field of network security. The method comprises the steps of performing semantic analysis and entity relationship extraction on a multi-source heterogeneous security log according to a predefined structured security data model, and generating a standardized security entity relationship triple set; based on the set, taking an entity in an initial alarm as a starting point, and adopting an iterative closed loop driven by a large language model to dynamically construct an attack graph; and carrying out attack technique and tactics mapping and threat attribution based on the final map, and generating a response strategy of priority ranking. According to the method, automatic and high-precision source tracing and response of the attack chain are realized, and the problems that the prior art depends on static rules and semantic segmentation and lacks dynamic reasoning capability are effectively solved.

An attack detection and source tracing method includes acquiring entities in a target network environment and interaction event information between the entities and constructing a network event graph with the entities and the interaction event information; determining a graph embedding vector of each interaction event information in the network event graph as feature information based on a preset attack feature recognition model and determining an attack event in the network event graph according to the feature information; determining dependencies between the attack event and remaining interaction event information in the network event graph and searching for a corresponding interaction event information as source tracing information of the attack event according to the dependencies.

The invention discloses a network threat evidence fixation technology for solving source tracing evidence chain deficiency, and belongs to the field of network security. Aiming at the problems of distributed log time sequence chaos, easy tampering of virtual environment metadata, difficult association of cross-platform attack traces and the like existing in the traditional evidence obtaining technology, the invention constructs a technical system of dynamic data capture and space-time fusion modeling, evidence chain reconstruction and distributed verification, anti-quantum storage and security solidification. A self-adaptive sensor network is constructed through an improved BFGS algorithm, a space-time fusion evidence model is provided, and an improved MPT structure and a lattice-based cryptographic algorithm are designed in combination with a time sequence Petr i network and a Byzantine fault-tolerant protocol. Tests show that the attack feature capture rate reaches 91%, the cross-platform attack association accuracy rate reaches 93%, the quantum signature resistance speed is 1200 times per second, and the problem of source tracing evidence chain deficiency is effectively solved.

The invention provides a rapid source tracing method and system for a pollution source based on dual unmanned aerial vehicle cooperative gradient detection, and belongs to the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: initializing system parameters; deploying the first unmanned aerial vehicle to hover at an initial position, and measuring an atmospheric pollutant concentration value at the initial position and recording the atmospheric pollutant concentration value as a first concentration value; the second unmanned aerial vehicle is controlled to fly around, and the pollutant concentration value is measured and recorded as a second concentration value; calculating a pollutant concentration gradient value; based on the concentration gradient value, the direction with the maximum atmospheric pollutant concentration gradient in the whole-cycle range of 0-360 degrees is determined and recorded as the maximum gradient direction; the second unmanned aerial vehicle is controlled to move to the intersection point position of the maximum gradient direction and the fly-around circumference for hovering; the first unmanned aerial vehicle is controlled to fly around, and the concentration gradient measurement and maximum gradient direction determination process is repeated; and alternately executing the operation. Through the working mode that the two unmanned aerial vehicles alternately fly around and hover, rapid and efficient pollution source positioning is achieved.

The invention discloses a question and answer method based on medical institution internal knowledge base retrieval enhancement generation, electronic equipment and a storage medium. The method comprises the following steps: S100, constructing a medical institution internal knowledge base; s110, collecting an original document, preprocessing the original document, dividing the original document into text blocks, and storing the text blocks into a database; s120, establishing vectorization and indexing based on abstracts for the text blocks; s200, based on the questions of the user, performing retrieval to generate answers; s210, rewriting and expanding the original query to obtain an expanded query; s220, performing multi-path mixed retrieval, namely performing preliminary retrieval on the original query and the extended query to obtain N candidate text blocks; s230, rearrangement and careful selection are carried out, and M most relevant text blocks are selected by adopting a rearrangement model; and S240, answer generation and source tracing: integrating the original query, the M text blocks and the instruction large language model to generate a section of natural language question and answer to obtain an answer result. Accurate, efficient and traceable natural language question and answer service is provided for medical care and management personnel, and standardization, accuracy and efficiency of medical work are improved.

The present application provides an attack source tracing method and apparatus, a device, and a medium. The method comprises: acquiring data related to an attack event, using the data related to the attack event to update a pre-constructed Bayesian attack graph, and determining an attack path with the maximum reachable probability in the updated Bayesian attack graph as a target attack path, wherein the pre-constructed Bayesian attack graph is a Bayesian attack graph constructed on the basis of data related to a historical attack event; and performing feature extraction from the data related to the attack event and the updated Bayesian attack graph, matching the extracted features with pre-formed feature clusters, and determining an attacker corresponding to the successfully matched feature cluster as a target attacker, wherein the pre-formed feature clusters are feature clusters which are formed by clustering the data related to the historical attack event and features extracted from the pre-constructed Bayesian attack graph and represent different attackers. According to the present application, the Bayesian attack graph and the feature clustering are utilized, effectively realizing rapid source tracing of the attack path and the attacker.

The invention belongs to the technical field of multi-source fusion, and particularly relates to a decision-making-level multi-source fusion gas leakage tracing method and a decision-making-level multi-source fusion gas leakage tracing device. The method comprises the following specific processes: data acquisition and space-time alignment: realizing decision-level multi-source acquisition on gas leakage data, and performing space-time alignment on the acquired data; preliminary leakage source positioning based on the evidence theory: processing the multi-source data by using the evidence theory, removing conflicting evidences, performing weak evidence weighting enhancement, and fusing the corrected evidences to obtain a region where leakage may occur; in the processing process, recording and obtaining a Jousseme distance; and accurate positioning of the leakage source: determining a dynamic radius based on combined adjustment of a detection error, time decay and a Jousseme distance, and performing weighted positioning by using a least square method to realize accurate source tracing of the leakage source.

The invention relates to the technical field of block chains, and discloses an artwork chaining evidence storage and source tracing method, which comprises the following steps: an artwork chaining stage: a seller signs an artwork needing to be chained, and broadcasts a signature and a perceptual hash value of the artwork to a block chain; the committee verifies whether the artwork is repeatedly linked, if not, the artwork is formally linked, and the artwork is sold on a transaction platform specified by a seller; in the artwork transaction stage, the purchaser places an order to purchase artworks; the transaction platform issues the transaction information to the block chain; the seller delivers goods; the purchaser verifies the received artwork, and if verification succeeds, the transaction is completed; in the artwork traceability stage, an inquirer inquires historical records of artworks through a transaction platform or a block chain, and the historical records comprise initial chaining time and previous transaction information. According to the invention, right confirmation, source tracing and trust of artwork transaction are realized based on the block chain.

The invention discloses an individualized precise brain regulation and control method and device based on dynamic brain network traceability, and relates to the technical field of electroencephalogram regulation and control. The method comprises the steps that resting state electroencephalogram signals of each lead of two resting state tasks are obtained; performing traceability analysis on the resting-state electroencephalogram signals of each lead of the two resting-state tasks, and calculating weighted phase lag indexes of lead pairs according to the traced resting-state data of the two leads in the pairwise lead pairs; constructing dynamic brain networks according to the weighted phase lag indexes of all lead pairs, performing modeling according to the first dynamic brain network and the second dynamic brain network, calculating an abnormal index of each brain region node, performing target stimulation based on the abnormal index of each brain region node by using a multi-channel stimulation controller, and performing target stimulation based on the abnormal index of each brain region node. According to the invention, the problems of inaccurate target spot positioning and single target spot in the existing cranial nerve regulation and control technology are solved.

The invention relates to the technical field of gantry crane dynamic operation safety testing, in particular to a crane dynamic operation safety testing method which comprises the following steps: step 1, multi-dimensional static calibration; step 2, fault coupling simulation; step 3, structured data processing; step 4, coupling judgment; step 5, source tracing; and step 6, carrying out targeted rectification. A multi-dimensional static calibration, fault coupling simulation and targeted rectification scheme is designed according to the structure and working conditions of cart one-way walking, main beam-supporting leg rigid welding, long track bearing, bilateral braking and open-air operation of the gantry crane. The limitation of insufficient adaptability to the integrated structure of the gantry crane in the prior art is broken through, and the problem of test distortion caused by neglecting equipment attributes is solved. Aiming at the characteristics that the box type girder of the gantry crane is large in torsional rigidity, small in torsional deformation and mostly caused by internal defects or uneven external stress, progressive traceability of internal defect detection, external load checking and connection state checking is adopted.

The invention provides an intelligent diagnosis and source tracing method for inflow and infiltration of a sewage pipe network and a related device. Comprising the steps of performing regional division and historical data analysis on a to-be-diagnosed sewage pipe network, and preliminarily determining problem partitions in the to-be-diagnosed sewage pipe network; obtaining a water body sample in the problem partition, carrying out two-dimensional index detection, and respectively constructing a two-dimensional index database, a fingerprint spectrum and a water body distribution spectrum; then fingerprint comparison and numerical diagnosis are carried out, the proportions of various types of water bodies in the problem subareas are determined, so that the risk levels corresponding to the problem subareas are determined, and a risk level distribution diagram of the sewage pipe network to be diagnosed is generated after rendering is carried out; and identifying the severe risk pipe network subarea, constructing a sewage-external water bidirectional seepage model of the severe risk pipe network subarea based on the rainstorm management model, and realizing traceability processing of the severe risk pipe network subarea so as to obtain a traceability result corresponding to the severe risk pipe network subarea. And the diagnosis accuracy of the water inflow and infiltration problem outside the sewage pipe network is improved.

The invention discloses an illegal unmanned aerial vehicle source tracing method and system based on visible light, thermal imaging and radio frequency fingerprint fusion. The method comprises the following steps: synchronously acquiring target visible light, a thermal infrared image and a radio frequency signal; extracting appearance, thermal radiation and radio frequency fingerprint features in parallel; multi-modal features are dynamically weighted and fused through an adaptive cross-modal attention fusion network (ACMAF-Net), and unmanned aerial vehicle model identification and individual association are completed; reconstructing a three-dimensional track of the unmanned aerial vehicle by using multi-sensor observation data; the possible positions of a take-off point and an operator are deduced by combining track features and geographic information. The system comprises a multi-mode sensing subsystem, a data processing and fusion subsystem, a traceability analysis subsystem, a display control storage subsystem and the like. According to the invention, through three-mode complementary fusion, the limitation of a single technology is overcome, all-weather and high-precision detection identification and effective traceability of the illegal unmanned aerial vehicle are realized, and the low-altitude security and protection capability is remarkably improved.

The invention relates to the technical field of data processing, and discloses a medical knowledge base question answering method and system, electronic equipment and a storage medium. Performing mixed retrieval on the medical problem based on a medical knowledge base to obtain a set number of text blocks as retrieval results, constructing cue words based on the medical problem and the retrieval results, and generating a preliminary answer containing a preliminary source identifier through a large language model; source verification and source tracing processing is carried out on the preliminary answer to generate a final answer, and the source verification and source tracing processing comprises the steps of analyzing the preliminary answer to obtain a plurality of independent claim points, carrying out sentence-level and block-level correlation judgment on text blocks pointed by the claim points and the preliminary source identifier based on a reordering model, and executing source judgment based on a threshold value; generating a corresponding source mark; according to the medical knowledge retrieval method and system, the accuracy and integrity of medical knowledge retrieval can be improved, and the fact accuracy of answer generation is ensured.

The invention discloses an RAG question and answer traceability positioning method and device based on a text coordinate index, relates to the technical field of RAG and traceability positioning, and solves the problems that in the prior art, coordinate positioning precision is poor, and traceability in an original text is difficult after deconstruction. The method comprises the following steps of: analyzing and storing a text and corresponding coordinates into a mixed retrieval mode of Elasticsearch, meanwhile, carrying out vectorization storage on text fragments, deconstructing an original text paragraph or keyword in advance, storing the original text paragraph or keyword into mongoDB, carrying out vector similarity matching with Elasticsearch to find the original text when in use, and carrying out uniform format conversion on an input file, so that the extraction precision of the original text coordinates is higher, and the extraction efficiency is improved. The problem of tracing difficulty caused by RAG model text reconstruction is avoided to the greatest extent by utilizing an Elasticsearch technology, so that the most accurate tracing result can be obtained.

The invention discloses an industrial fault traceability analysis method based on neural causal discovery and causal feature screening, which introduces a neural Granger component-by-component long and short-term memory network with group sparse characteristics, and can effectively capture sparse causal relationships in a system. Meanwhile, in combination with an arrangement feature importance method, the extracted causal relationships are screened by using model characteristics, false causals are removed, a causal adjacency matrix is obtained, and the robustness and credibility of the result are enhanced. And constructing a reachable matrix through the causal adjacency matrix, and expressing and positioning a root node in a causal path in combination with position scoring, thereby realizing accurate tracing of the industrial fault. The method is based on a data driving method, does not need to depend on mechanism knowledge, can be effectively applied to an industrial system with a complex control loop, and aims to improve the effectiveness of causal analysis, better perform root cause analysis and determine a fault source, and improve the safety and reliability of an industrial production process.

The application discloses a security vulnerability tracing analysis method and system for application software, and relates to the technical field of network security.The application solves the technical problem that it is difficult to quickly and accurately locate the source of the vulnerability and assess the risk level, thereby leading to the difficulty in formulating an effective security protection strategy.The application determines the source and propagation path of the vulnerability attack by performing correlation analysis on abnormal traffic, performs reverse DNS query and authenticity verification on the source IP address, and further improves the accuracy of the tracing result, thereby providing strong support for quickly locating the attack source.In terms of vulnerability risk level calculation, basic index scores, time index scores and environment index scores are calculated respectively, and the vulnerability score index is calculated in combination with respective weights, so that the actual situation is fully considered, the evaluation result is more in line with the actual risk, more accurate risk early warning, more targeted vulnerability repair and protection strategies can be provided, and the application software security protection level is improved.

This disclosure provides a method for unknown malicious source tracing analysis based on cross-platform behavior graph fusion. The method includes: inducing the collection of collaborative behaviors of multiple processes across platforms through an adversarial sandbox; semantically reorganizing the collected raw behavior data into a set of collaborative behavior events; constructing a subject-attribution-enhanced heterogeneous behavior graph by adding collaborative attribution edges; performing subject attribution determination on the subject-attribution-enhanced heterogeneous behavior graph based on a graph neural network model, merging the dispersed collaborative processes into a unified attack subject subgraph set; identifying high-risk behavior subgraphs and their stage propagation chains from the unified attack subject subgraph set; performing reverse tracing along the time sequence, resource transfer sequence, and control transfer sequence; and combining cross-platform same-source comparison to shrink into a single-source attack chain, outputting a source tracing conclusion. This method can solve the technical problems of multiple false starting points and the inability to form a single attack source attribution in the source tracing results.

The present invention discloses a method and system for tracing and warning the source of infectious diseases based on privacy protection and anti-attack, which belongs to the field of information protection and attack detection. The method collects a user's contact flow data time series; determines a normal contact flow data time series based on the mutation of the contact flow data time series at a time switching node, injects noise into the normal contact flow data time series to form a pseudo-labeled flow time series, and trains a comparison model. The normal contact flow data time series is determined based on the trained comparison model; constructs a traceability graph based on the normal contact flow data time series, and adopts an iterative aggregation method to aggregate the multi-hop connections between the nodes in the traceability graph to obtain a contact knowledge graph of direct contact and indirect contact. According to the contact knowledge graph, all users' contact information is obtained as training data, and a prediction model is trained based on the training data. The trained model outputs the user's infection probability.

The invention provides a large language model text traceability method based on virtual cue word embedding, and belongs to the technical field of artificial intelligence natural language processing and the technical field of digital content security, the method comprises the following steps: (1) constructing a chat input instruction text with a role format; (2) obtaining embedded vector input; (3) obtaining system cue word embedding representation; (4) obtaining a generated text with a user identity information watermark; and (5) recovering user identity information to realize traceability of the generated text. According to the method, on the premise that main body parameters of the large language model are not modified and model text generation quality and response efficiency are not affected, instant embedding of traceable identity information in the large language model generation process is achieved, meanwhile, robustness of embedded watermark information in a text transformation scene is guaranteed, and the method is suitable for large language model generation. Verifiable and reproducible identity identification information can be stably extracted from the generated text, and reliable technical support is provided for source tracing of the generated text.

This invention discloses a method and system for identifying abnormal behavior in wind farms based on multi-model cascaded calls, belonging to the field of abnormal behavior identification technology. The method includes: acquiring real-time video streams and determining scene semantic information; constructing an anomaly identification task graph; adaptively filtering and arranging a wind farm model library; obtaining a model cascaded call chain; performing multi-level progressive detection on the real-time video stream according to the model cascaded call chain; obtaining the output sequences of each model; performing error assessment and source tracing; and establishing a wind farm identification interference tree; performing multi-dimensional redundancy verification on the output sequences of each model; and establishing a wind farm identification redundancy verification space; and performing credibility enhancement processing on the output sequences of each model based on the wind farm identification interference tree and the wind farm identification redundancy verification space; and establishing a wind farm abnormal behavior heatmap. This invention solves the technical problem of insufficient accuracy in wind farm abnormal behavior identification in existing technologies, achieving the technical effect of improving the accuracy of wind farm abnormal behavior identification.

The invention relates to the technical field of aero-engines, and discloses a cartridge receiver quality problem tracing method, which improves the tracing analysis efficiency and reduces the influence of human subjective factors. A data confidence coefficient formula is adopted to quantify data reliability, so that interference of low-quality data on a traceability result is avoided, and data support is more scientific; through a Bayesian network posterior probability formula, quantitative sorting of problem reasons is realized, manual subjective judgment errors are reduced, and traceability precision is improved; by integrating data of a casing design stage, a processing stage and a detection stage, a'data island 'is broken, a constructed knowledge graph realizes historical experience reuse, and a systematic knowledge accumulation and reuse mechanism is established, so that a historical solution can be quickly called when similar problems appear repeatedly, and the tracing efficiency is improved; through dual verification of an evidence chain and a relation chain, accuracy and interpretability of problem positioning are provided.

The invention relates to a traceability circuit, an equivalent apparent power value obtaining method and device and computer equipment. The method comprises the following steps: acquiring a measurement condition of a traceability circuit, operating the traceability circuit according to the measurement condition, acquiring a circuit parameter measurement value of the traceability circuit under the condition that the operation time length of the traceability circuit reaches a preset time length, acquiring a reference equivalent apparent power value according to the circuit parameter measurement value, and determining the traceability circuit according to the reference equivalent apparent power value. And performing uncertainty evaluation on the reference equivalent apparent power value according to the circuit parameter measurement value, and obtaining a target equivalent apparent power value according to an uncertainty evaluation result. By adopting the method, the traceability of the equivalent apparent power quantity value can be realized, so that the measurement accuracy of the equivalent apparent power is improved.

This invention discloses a method and system for real-time tracing of live stream hotlinking based on multi-source heterogeneous signaling fusion. The method includes multi-source heterogeneous signaling acquisition, multi-source heterogeneous signaling feature extraction, construction of a hotlinking adaptation loss function, hotlinking tracing model design, parameter dynamic optimization, and real-time hotlinking determination of live streams. This invention belongs to the field of data processing, specifically referring to a method and system for real-time tracing of live stream hotlinking based on multi-source heterogeneous signaling fusion. This solution constructs a hotlinking adaptation loss function based on the arc-tangent power-law fallback characteristic to adapt to multi-signaling modal noise fluctuations; embeds a risk probability site inversion penalty term to effectively improve the reliability of real-time hotlinking tracing of live streams; and constructs a full-process prevention and control system: designs a parameter dynamic optimization strategy driven by inversion rate to dynamically adapt to data fluctuations, and establishes a structure-gradient dual-dimensional inversion control mechanism to avoid inversion risks throughout the entire link from model structure design to parameter gradient optimization, significantly improving the hotlinking tracing effect.

This invention provides a method and system for collaborative environmental monitoring and pollution source tracing using UAV swarms in complex terrain, relating to the fields of UAV environmental monitoring and pollution source tracing. The method includes: a ground control center planning layered and altitude-based tasks for the target area and sending them to each group containing master and sub-UAVs; the master UAV decomposing and encoding the sub-tasks and transmitting the results back; the ground-based optimization of flight paths being distributed; the sub-UAVs collecting pollution source data, which is then preprocessed by the master UAV and uploaded; ground-based analysis results being fed back; and the master UAV calculating the contribution value C using a source tracing model. en Once the source tracing is complete, the results are uploaded to the server, and the drone returns to base. The system includes a ground control center and at least one drone swarm; each swarm consists of a master drone and sub-drones. This invention can plan flight paths based on the geographical environment of the monitored target area, avoiding detection blind spots and conflicts, and coordinate actions based on the flight path, thus improving the convenience and accuracy of environmental monitoring and pollution source tracing.