Flood season pollution traceability evidence obtaining monitoring system and method based on multi-source evidence chain fusion

Abstract

The invention discloses a flood season pollution source-tracing evidence-obtaining monitoring system and method based on multi-source evidence chain fusion, and relates to the cross technical field of environment monitoring and judicial evidence-obtaining, and the system comprises an intelligent cooperative monitoring network unit, a full evidence chain collection evidence-storage module, a multi-modal source-tracing analysis engine unit and a block chain evidence-fixing platform module. Multi-source heterogeneous information such as water quality spectrums, drain outlet thermodynamic diagrams, enterprise production logs and Beidou hydrological data is integrated through a millisecond-level timestamp synchronization technology, a unique'pollution event fingerprint 'is generated, the problems that a traditional monitoring system is single in data dimension and lacks relevance are solved, a complete evidence chain of time-space relevance is formed, and the system has a good application prospect. And meanwhile, a block chain double-channel architecture is innovatively adopted, and an electronic evidence packet which conforms to judicial expertise specifications and is attached with an electronic signature is automatically generated, so that the legal credibility and the credibility of data are remarkably enhanced, and the core defects that traditional electronic data are easy to tamper and have no legal endorsement are overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring and judicial evidence, in particular to a flood season pollution traceability evidence monitoring system and method based on multi-source evidence chain fusion. BACKGROUND

[0002] At present, in the field of water quality monitoring and management, there are various technical means, but these technologies have obvious limitations. The traditional monitoring system only focuses on water quality parameter early warning, without integrating multi-dimensional data such as working conditions of polluting enterprises, meteorological and hydrological data; while the existing technology of river and lake partition monitoring improves the accuracy of regional positioning to a certain extent, but lacks the ability to analyze the spatio-temporal correlation of pollution events and surrounding industrial activities. The conventional spectral monitoring in the prior art does not incorporate a blockchain storage mechanism, the dynamic early warning system does not solidify the logic chain of "pollution event-evidence set-responsible subject", and the pollution intensity calculation relies on an algorithm black box, which lacks auditability in the intermediate process.

[0003] On the one hand, the prior art limits the target to a single monitoring or early warning function from the beginning of design, lacks consideration of multi-dimensional data correlation, and does not construct a monitoring system from a holistic and systematic perspective, resulting in ineffective integration and correlation analysis of data. On the other hand, in the process of technology research and development, the safety, reliability and judicial evidence requirements of data are not considered, advanced technologies and mechanisms such as blockchain storage are not introduced in a timely manner, and the standardization and auditability requirements of the technical process are also ignored.

[0004] In addition, the data silo problem makes it difficult to comprehensively and accurately trace the pollution source when a pollution event occurs, lacks sufficient evidence support, affects the rapid response and effective handling of pollution events, and may lead to the expansion of the pollution range and the intensification of the pollution degree. The lack of evidence effectiveness makes it difficult for monitoring data to be accepted in judicial proceedings, and it is difficult to hold the legal responsibility of the responsible subject, which is difficult to effectively deter the recurrence of pollution behavior. The lack of standardization leads to doubts about the credibility of the pollution intensity calculation results, which does not meet the requirements of relevant technical specifications, affects the scientificity and fairness of environmental damage judicial identification, and is not conducive to the orderly development of environmental management and protection work.

[0005] Therefore, the flood season pollution traceability evidence monitoring system and method based on multi-source evidence chain fusion are proposed to solve the above problems. SUMMARY

[0006] Therefore, the present application aims to solve the technical problems mentioned in the background art by providing a flood season pollution traceability evidence monitoring system and method based on multi-source evidence chain fusion.

[0007] To achieve the above object, the application provides the following technical scheme: a flood season pollution traceability evidence monitoring system and method based on multi-source evidence chain fusion, comprising an intelligent collaborative monitoring network unit, a full evidence chain collection and storage module, a multi-modal traceability analysis engine unit and a blockchain evidence fixation platform module;

[0008] The intelligent collaborative monitoring network unit comprises:

[0009] This module is the field perception layer of the system, responsible for stable deployment of monitoring nodes (such as drainage pipe network monitors) in complex environments such as flood seasons; it uses NB-IoT+LoRa dual-mode communication to ensure connection, has self-organizing network capability (automatic registration, optimized routing) and edge storage capability (key data sharding storage, 72 hours of offline storage), ensures reliable data collection and temporary storage, and outputs the data to the full evidence chain collection and storage module;

[0010] The full evidence chain collection and storage module comprises:

[0011] This module is the data integration and trusted evidence layer of the system, which receives monitoring data from the intelligent collaborative monitoring network unit, accurately collects multi-source heterogeneous data through millisecond-level timestamp synchronization technology, generates a unique "pollution event fingerprint" by algorithm fusion of pollutant spectrum, sewage outlet heat map and enterprise production log, uses a blockchain dual-channel architecture for fixed evidence storage, and automatically triggers unmanned aerial vehicle aerial evidence collection based on smart contracts when the pollutant exceeds the standard, builds a tamper-proof and spatiotemporal correlation full-chain evidence, provides a solid data foundation for subsequent traceability, and sends the evidence data to the traceability engine.

[0012] As a preferred embodiment, the multi-modal traceability analysis engine unit comprises:

[0013] This module is the core of pollution source analysis and responsibility identification of the system, which receives data from the evidence storage module and uses a three-level traceability model for in-depth analysis: first, it identifies the characteristics of pollutant components through chemical fingerprint matching, second, it simulates the migration path and source location of the pollutant by using hydrological diffusion inversion, and finally, it calculates the responsibility weight of enterprises to quantify the responsibility proportion of each potential pollution source; the core function is to realize accurate traceability and responsibility attribution of pollution events, and the analysis results (including responsibility information) are output to the blockchain evidence fixation platform;

[0014] The blockchain evidence fixation platform module comprises:

[0015] This module is the judicial evidence transformation and output layer of the system, which receives the responsibility results and related evidence data from the traceability engine, and the core function is to automatically generate an electronic evidence package that meets the requirements of judicial authentication specifications; the evidence package integrates the full-chain information of monitoring, evidence storage and traceability, ensures its legal effect, and finally pushes it to the environmental law enforcement platform, providing direct, legal and traceable punishment basis for law enforcement departments, and completing the closed loop from pollution monitoring to law enforcement landing.

[0016] As a preferred option, the intelligent collaborative monitoring network unit includes:

[0017] Dynamic networking communication module: adopts NB-IoT+LoRa dual-mode communication, adapting to the field environment during the flood season; newly added nodes are automatically registered and routes are optimized, with latency <200ms;

[0018] Edge disaster recovery design module: Key data collected by the terminal is stored in segments on edge nodes (such as drainage network monitoring instruments). Data can be saved for 72 hours when the network is down, and uploaded to the full evidence chain collection and storage module after recovery.

[0019] Preferably, the full evidence chain acquisition and storage module includes a multi-source sensor matrix module, a pollution event fingerprint generation algorithm module, and a blockchain dual-channel storage module.

[0020] Multi-source sensor matrix module:

[0021] It integrates four-dimensional data sources, including water quality spectral sensors (detecting the characteristic spectra of pollutants, such as absorbance in the 250-600nm range), drone sewage outlet images (real-time capture of pollution emission heat maps), enterprise operating current probes (monitoring the start-up and shutdown status of production equipment and energy consumption), and Beidou hydrological terminals (acquiring hydrological data such as water level and flow velocity), and synchronizes pollution event-related data through millisecond-level timestamps (±1ms).

[0022] Pollution event fingerprint generation algorithm module: integrates pollutant spectral characteristics, sewage outlet heat map, and enterprise production logs to generate a unique event identifier code.

[0023] Preferably, the blockchain dual-channel evidence storage module specifically includes:

[0024] Private Chain: The raw data collected by the multi-source sensor matrix module is stored in the private chain (judicial institution node) to ensure the security of the raw data;

[0025] Public blockchain: Stores feature hash values ​​and supports smart contracts to automatically trigger evidence collection (such as activating drone aerial photography when pollutants exceed the standard).

[0026] Preferably, the three-level source tracing model in the multimodal source tracing analysis engine unit includes:

[0027] Chemical fingerprint matching: Comparing the spectral characteristics of pollutants with the standard spectra of the company's raw material library (similarity calculation);

[0028] Hydrological diffusion inversion: Pollution pathway inversion based on LSTM-Transformer hybrid model (accuracy 92%);

[0029] Enterprise responsibility weight calculation: combined with production process (IC card data), pollution control equipment state, output responsibility weight matrix.

[0030] As preferred, the electronic evidence required by the judicial authentication specification in the blockchain fixed platform includes original data (water quality spectrum, image), analysis report (traceability path diagram, responsibility weight matrix) and blockchain storage certificate (timestamp, hash value, judicial electronic seal).

[0031] The flood season pollution traceability evidence monitoring method based on multi-source evidence chain fusion comprises:

[0032] S1, pollution event triggering: the concentration of characteristic pollutants is monitored in real time through a river spectrum sensor, and once the continuous over-standard time is greater than or equal to 5 minutes, the intelligent contract is triggered to start the traceability program, realizing automatic identification and response initialization of the pollution event;

[0033] S2, automatic solidification of multi-source evidence: the system automatically schedules a drone to take a thermal map of a pollution outlet, and synchronously captures real-time working condition data of enterprises within 3 kilometers; multi-source heterogeneous data is integrated by using millisecond-level timestamp synchronization technology to generate a unique "pollution event fingerprint", and double storage is completed through a blockchain double-channel architecture (private chain stores original data + public chain anchors hash) to ensure that the evidence is tamper-proof;

[0034] S3, multi-modal traceability analysis: accurate accountability based on a three-level traceability model: first, identify the source of pollutants through chemical fingerprint matching (such as a spectrum similarity of 98.7%); second, simulate the pollution trajectory using a hydrological diffusion inversion model (such as a path overlap of 91%); finally, generate a pollution contribution matrix by combining enterprise production data to quantify the responsibility weight of each enterprise, and complete scientific accountability

[0035] S4, judicial evidence generation and output: automatically encapsulate a PDF evidence package containing all-chain data, analysis report and blockchain storage certificate, with additional judicial electronic seal and timestamp; generate legally effective evidence within 2 hours and push it to the law enforcement platform to realize the closed-loop disposal of monitoring-traceability-enforcement.

[0036] As preferred, the three-level traceability model analysis in S3 comprises:

[0037] S3.1, chemical fingerprint matching: compare the similarity of pollutant spectrum and enterprise raw material standard spectrum (for example: 98.7%)

[0038] S3.2, hydrological inversion path: LSTM-Transformer model simulates diffusion trajectory and calculates the overlap with enterprise drainage path (for example: 91%);

[0039] S3.3, responsibility weight calculation: generate pollution contribution matrix to quantify enterprise responsibility weight (example: enterprise A responsibility ratio 85%) by integrating production process and pollution control equipment status;

[0040] S3.4, output: traceability analysis report + responsibility attribution result, transmitted to the blockchain fixed evidence platform module.

[0041] Compared with the prior art, the flood season pollution traceability monitoring system and method based on multi-source evidence chain fusion provided by the application have the following beneficial effects:

[0042] 1. Breakthrough data island, build complete evidence chain: through millisecond level timestamp synchronization technology, integrate multi-source heterogeneous information such as water quality spectrum, pollution outlet heat map, enterprise production log, Beidou hydrological data, etc., generate a unique "pollution event fingerprint", solve the problem of single data dimension and lack of correlation in traditional monitoring system, and form a complete evidence chain with time and space correlation.

[0043] 2. Improve the effectiveness of judicial evidence and realize closed-loop evidence storage: innovative use of blockchain dual-channel architecture (private chain stores original data to ensure safety, public chain stores hash value and supports smart contract automatic triggering of evidence), and automatically generate electronic evidence package conforming to judicial authentication specifications with electronic seal, significantly enhance the legal credibility and adoptability of data, and solve the core defects of traditional electronic data that are easy to tamper with and lack legal endorsement.

[0044] 3. Strengthen the adaptability of extreme environment and ensure data reliability: use NB-IoT+LoRa dual-mode communication (time delay <200ms) to realize stable ad hoc network in flood season field environment, combined with edge disaster recovery design (key data sharding storage, can save for 72 hours in case of network interruption), effectively overcome the fatal shortcoming of traditional system that network interruption leads to data loss and cannot be traced in harsh conditions.

[0045] 4. Realize accurate traceability and scientific responsibility determination: rely on three-level traceability model (chemical fingerprint matching to quantify similarity, LSTM-Transformer hydrological diffusion inversion model with 92% accuracy, and responsibility weight matrix combined with production / pollution control data), convert fuzzy manual investigation into accurate accountability based on multi-modal analysis, significantly improve pollution source positioning accuracy (from ≤500m to ≤50m) and responsibility determination objectivity.

[0046] 5. Full-process automation speed-up, response efficiency leap: based on smart contract to realize automatic identification of pollution events (continuous over-standard triggers), automatic evidence collection by unmanned aerial vehicle (≤5 minutes), automatic solidification and analysis of full-chain evidence, and finally generate judicial evidence package and push to law enforcement platform within 2 hours, shorten the traditional evidence collection period of 15-30 days by 99.5%, realize "monitoring-traceability-enforcement" closed loop efficient disposal. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 Schematic diagram of the three-dimensional structure of the present application;

[0048] Fig. 2 Schematic diagram of the three-dimensional structure of the present application; DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely 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 fall within the scope of the present application.

[0050] The present application will be further described in detail below according to the accompanying drawings and embodiments.

[0051] Embodiment 1, please refer to Figs. 1-2 as shown:

[0052] To solve the problems mentioned in the technical solutions, the present application provides a flood season pollution trace evidence monitoring system and method based on multi-source evidence chain fusion,

[0053] Intelligent collaborative monitoring network unit:

[0054] This module is the field perception layer of the system, responsible for stable deployment of monitoring nodes (such as drainage pipe network monitoring instruments) in complex environments such as flood season; it uses NB-IoT+LoRa dual-mode communication to ensure connection, has self-organizing network capability (automatic registration, optimized routing) and edge storage capability (key data sharding storage, 72 hours of data can be saved in case of network interruption), ensures reliable data collection and temporary storage, and outputs them to the full evidence chain collection and storage module;

[0055] The intelligent collaborative monitoring network unit includes:

[0056] Dynamic networking communication module: uses NB-IoT+LoRa dual-mode communication to adapt to the field environment in flood season; newly added nodes automatically register and optimize routing, with a time delay of <200ms;

[0057] Edge disaster recovery design module: key data collected by the terminal is sharded and stored in the edge node (such as the drainage pipe network monitoring instrument), and 72 hours of data can be saved in case of network interruption, and uploaded to the full evidence chain collection and storage module after recovery.

[0058] The full evidence chain collection and storage module includes a multi-source sensing matrix module, a pollution event fingerprint generation algorithm module, and a blockchain double-channel storage module.

[0059] Full evidence chain collection and storage module:

[0060] This module is the data integration and trusted evidence layer of the system, which receives monitoring data from the intelligent collaborative monitoring network unit, accurately collects multi-source heterogeneous data through millisecond-level timestamp synchronization technology, and generates a unique "pollution event fingerprint" by algorithmically fusing pollutant spectra, pollution outlet heat maps, and enterprise production logs. It uses a blockchain dual-channel architecture for solidification and evidence, and automatically triggers unmanned aerial photography when pollutants exceed the standard based on smart contracts. It builds an unalterable and spatiotemporal evidence chain, providing a solid data foundation for subsequent tracing, and sends the evidence data to the tracing engine.

[0061] Multi-modal tracing analysis engine unit:

[0062] This module is the core of pollution source analysis and responsibility identification in the system, which receives data from the evidence storage module and uses a three-level tracing model for in-depth analysis. First, it identifies the characteristics of pollutant components through chemical fingerprint matching. Second, it simulates the migration path and source location of pollutants using hydrological diffusion inversion. Finally, it calculates the responsibility weight of enterprises to quantify the responsibility proportion of each potential pollution source. The core function is to achieve accurate pollution event tracing and responsibility attribution, and the analysis results (including liability information) are output to the blockchain evidence platform.

[0063] Among them, the three-level tracing model in the multi-modal tracing analysis engine unit includes:

[0064] Chemical fingerprint matching: comparing pollutant spectral characteristics with enterprise raw material library standard spectrum (similarity calculation);

[0065] Hydrological diffusion inversion: based on LSTM-Transformer hybrid model to invert pollution path (accuracy 92%);

[0066] Enterprise responsibility weight calculation: combining production process (IC card data) and pollution control equipment status to output responsibility weight matrix.

[0067] Blockchain evidence platform module:

[0068] This module is the judicial evidence transformation and output layer of the system, which receives liability results and related evidence data from the tracing engine. The core function is to automatically generate an electronic evidence package that meets the requirements of judicial authentication specifications. This evidence package integrates full-chain information from monitoring, evidence storage, and tracing, ensuring its legal effectiveness, and ultimately pushing it to the environmental law enforcement platform to provide direct, legal, and traceable punishment evidence for law enforcement departments, completing the closed loop from pollution monitoring to law enforcement landing.

[0069] Among them, the blockchain dual-channel evidence storage module specifically includes:

[0070] Private chain: store the raw data collected in the multi-source sensing matrix module to the private chain (judicial agency node) to ensure the security of the raw data;

[0071] Public chain: store the characteristic value hash value, support the automatic triggering of intelligent contract for evidence collection (such as when the pollutant exceeds the standard

[0072] Multi-source sensing matrix module:

[0073] Integrate four-dimensional data sources, including water quality spectrum sensor (detect the characteristic spectrum of pollutants such as 250-600nm absorbance), unmanned aerial vehicle sewage outlet image (real-time shooting of pollution emission thermal map), enterprise working condition current probe (monitor the start-stop state and energy consumption of production equipment), and Beidou hydrological terminal (obtain hydrological data such as water level and flow rate), and synchronize the pollution event related data through millisecond level timestamp (±1ms);

[0074] Pollution event fingerprint generation algorithm module: fuse the pollutant spectrum characteristics, sewage outlet thermal map, and enterprise production log to generate a unique event identification code.

[0075] The electronic evidence required by the judicial authentication specification in the block chain fixed evidence platform includes raw data (water quality spectrum, image), analysis report (traceability path diagram, responsibility weight matrix), and block chain storage certificate (timestamp, hash value, judicial agency electronic seal).

[0076] Example 2, dye pollution event in flood season in a textile industrial park;

[0077] Background: After heavy rain, an abnormal red water body appeared in a certain river section of a tributary of the Yangtze River. The traditional monitoring system only triggered the "chemical oxygen demand exceeds the standard" alarm, but could not lock the pollution source.

[0078] Implementation process of the scheme:

[0079] Event triggering: The river spectrum sensor detects the characteristic spectrum of azo dye (absorbance surge at 510nm), which continuously exceeds the standard for 8 minutes, and the intelligent contract automatically starts the traceability program.

[0080] Evidence solidification: The unmanned aerial vehicle immediately takes pictures and finds that a certain textile factory sewage outlet within 3 kilometers has a red wastewater thermal map (temperature anomaly +42℃).

[0081] Synchronously acquire the current data of the dyeing vat of the factory (the production log shows that the dyeing vat continuously runs during the pollution period), generate a unique "pollution event fingerprint" (fuse the spectrum characteristics, thermal map coordinates, and current fluctuation), and store through the block chain double channel (store the raw spectrum / image to the private chain, and store the hash value to the public chain).

[0082] Traceability analysis: Chemical fingerprint matching: pollutant spectrum similarity with textile factory dye standard library is 98.7%, hydrological diffusion inversion: LSTM-Transformer model simulation path shows 91% coincidence with the factory drain pipe direction. Responsibility weight calculation, combined with dyeing cylinder running time + pollution control equipment downtime record, output responsibility matrix (the factory accounts for 89%).

[0083] Judicial output: generate electronic evidence package (including spectrum raw data, heat map, responsibility matrix, blockchain certificate) within 2 hours, and push to environmental law enforcement platform. Law enforcement personnel seal the factory on the same day and issue a fine.

[0084] Example 3, heavy metal pollution after rainstorm in chemical gathering area;

[0085] Background: Lead exceeded downstream river in a certain chemical area, traditional monitoring network was interrupted due to heavy rain, data was lost, and it was impossible to trace back.

[0086] Implementation process of the scheme:

[0087] Event trigger: Beidou hydrological terminal detects sudden increase of flow rate, and spectrum sensor synchronously detects lead characteristic peak (283nm), which triggers intelligent contract for continuous exceeding for 6 minutes.

[0088] Evidence solidification: edge disaster recovery mechanism starts: key data is stored in pipe network monitor, and is uploaded after 72 hours of network interruption; unmanned aerial vehicle shoots heat map of dark pipe discharge of a certain enterprise (night infrared image shows high temperature wastewater), and millisecond level timestamp synchronizes enterprise IC card data (production log shows abnormal start and stop of lead-acid battery production line).

[0089] Traceability analysis, chemical fingerprint matching, pollutant spectrum similarity with enterprise raw material "lead ingot" standard spectrum is 97.5%. Hydrological inversion, model simulation pollution path accurately points to the enterprise dark pipe coordinates (accuracy 92%).

[0090] Responsibility weight, combined with pollution control equipment offline record, calculates that the responsibility of the enterprise accounts for 95%.

[0091] Judicial output evidence includes electronic signature of judicial organ, including dark pipe image, hydrological inversion path diagram, blockchain storage certificate (timestamp + hash value), and is directly used as criminal litigation evidence.

[0092] Table 1 is a statistical table of core advantages of the scheme and prior art;

[0093]

[0094] Table 2 represents a statistical table of technical effect description of the scheme;

[0095]

[0096] It has to be noted that, in the present document, the terms "first", "second", etc. merely serve to identify different entities or actions from each other, without necessarily requiring or implying any actual relationship or order between these entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0097] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous further modifications and changes can be apparent to one skilled in the art without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. A flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion, characterized in that, It includes an intelligent collaborative monitoring network unit, a full evidence chain collection and storage module, a multimodal traceability analysis engine unit, and a blockchain evidence consolidation platform module; Intelligent collaborative monitoring network unit: This module is the field perception layer of the system, responsible for the stable deployment of monitoring nodes in complex environments such as flood season. It adopts NB-IoT+LoRa dual-mode communication to ensure connection, has self-organizing network capability and edge storage capability, stores key data in segments, and can retain data for 72 hours when the network is disconnected, ensuring reliable data collection and temporary storage, and outputs it to the full evidence chain collection and storage module. Full evidence chain collection and storage module: This module serves as the system's data integration and trusted evidence storage layer. It receives monitoring data from the intelligent collaborative monitoring network unit, accurately collects multi-source heterogeneous data through millisecond-level timestamp synchronization technology, and uses algorithms to fuse pollutant spectra, discharge outlet heat maps, and enterprise production logs to generate a unique "pollution event fingerprint." It employs a blockchain dual-channel architecture for solidified evidence storage and automatically triggers drone aerial photography for evidence collection when pollutants exceed standards based on smart contracts. This constructs an immutable and spatiotemporally correlated full-chain of evidence, providing a solid data foundation for subsequent source tracing, and sends the stored evidence data to the source tracing engine.

2. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 1, characterized in that, Multimodal source tracing analysis engine unit: This module is the core of the system's pollution source analysis and liability determination. It receives data from the evidence storage module and uses the constructed three-level source tracing model for in-depth analysis: first, it identifies the characteristics of pollutant components through chemical fingerprint matching; second, it uses hydrological diffusion inversion to simulate the migration path and source location of pollutants; and finally, it calculates the corporate responsibility weight to quantify the responsibility ratio of each potential pollution source. Its core function is to achieve accurate source tracing and liability attribution of pollution incidents and output the analysis results to the blockchain evidence platform. Blockchain-based Evidence Platform Module: This module is the system's judicial evidence transformation and output layer. It receives the attribution results and related evidence data from the source tracing engine. Its core function is to automatically generate electronic evidence packages that meet the requirements of judicial appraisal standards. This evidence package integrates the entire chain of information from monitoring, evidence storage, and source tracing to ensure its legal validity. Finally, it is pushed to the environmental law enforcement platform to provide law enforcement agencies with direct, legal, and traceable basis for punishment, completing the closed loop from pollution monitoring to law enforcement implementation.

3. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 1, characterized in that, The intelligent collaborative monitoring network unit includes: Dynamic networking communication module: adopts NB-IoT+LoRa dual-mode communication, adapting to the field environment during the flood season; newly added nodes are automatically registered and routes are optimized, with latency <200ms; Edge disaster recovery design module: Key data collected by the terminal is stored in fragments on edge nodes. Data can be saved for 72 hours when the network is down, and uploaded to the full evidence chain collection and storage module after recovery.

4. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 1, characterized in that, The full evidence chain acquisition and storage module includes a multi-source sensor matrix module, a pollution event fingerprint generation algorithm module, and a blockchain dual-channel storage module. Multi-source sensor matrix module: It integrates four-dimensional data sources, including water quality spectral sensors, drone sewage outlet images, enterprise operating current probes, and Beidou hydrological terminals, and synchronizes pollution event-related data through ±1ms timestamps; Pollution event fingerprint generation algorithm module: integrates pollutant spectral characteristics, sewage outlet heat map, and enterprise production logs to generate a unique event identifier code.

5. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 4, characterized in that, The blockchain dual-channel evidence storage module specifically includes: Private chain: The raw data collected by the multi-source sensor matrix module is stored in the private chain to ensure the security of the raw data; Public blockchain: Stores feature hash values ​​and supports automatic evidence collection triggered by smart contracts.

6. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 1, characterized in that, The three-level source tracing model in the multimodal source tracing analysis engine unit includes: Chemical fingerprint matching: comparing the spectral characteristics of pollutants with the standard spectra of the company's raw material library; Hydrological diffusion inversion: Pollution pathway inversion based on LSTM-Transformer hybrid model; Corporate responsibility weight calculation: Based on the production process and the status of pollution control equipment, a responsibility weight matrix is ​​output.

7. The flood season pollution source tracing and evidence collection monitoring system based on multi-source evidence chain fusion according to claim 1, characterized in that, The electronic evidence required by the judicial appraisal standards in the blockchain-based evidence platform includes water quality spectra, images, analysis reports, and blockchain-based evidence certificates from the original data.

8. A method for monitoring and tracing pollution sources during the flood season based on multi-source evidence chain fusion, applicable to the monitoring and tracing system for monitoring pollution sources during the flood season based on multi-source evidence chain fusion as described in any one of claims 1-7, characterized in that, include: S1, Pollution event trigger: The concentration of characteristic pollutants is monitored in real time by river spectral sensors. Once the concentration exceeds the standard for ≥5 minutes, the smart contract is immediately triggered to start the source tracing procedure, realizing the automatic identification and response initialization of the pollution event. S2, Automatic Solidification of Multi-Source Evidence: The system automatically dispatches drones to capture heat maps of sewage outlets and simultaneously captures real-time operating data of enterprises within 3 kilometers; it integrates multi-source heterogeneous data using millisecond-level timestamp synchronization technology to generate a unique "pollution event fingerprint" and completes dual-proofing through a blockchain dual-channel architecture, with the private chain storing the original data and the public chain anchoring the hash to ensure that the evidence is tamper-proof. S3, Multimodal Source Tracing Analysis: Precise Attribution Based on a Three-Level Source Tracing Model: First, the source of pollutants is identified through chemical fingerprint matching; then, the pollution trajectory is simulated using a hydrological diffusion inversion model; finally, a pollution contribution matrix is ​​generated by combining enterprise production data to quantify the responsibility weight of each enterprise and complete the scientific determination of responsibility. S4, Judicial Evidence Generation and Output: Automatically package PDF evidence packages containing full-chain data, analysis reports, and blockchain storage certificates, and attach judicial electronic signatures and timestamps; generate legally valid evidence within 2 hours and push it to the law enforcement platform to achieve closed-loop processing of monitoring-tracing-law enforcement.

9. The method for monitoring and tracing pollution sources during the flood season based on multi-source evidence chain fusion according to claim 8, characterized in that, The three-level source tracing model analysis described in S3 includes: S3.1, Chemical fingerprint matching: Comparing the similarity between the spectra of pollutants and the standard spectra of the company's raw materials. S3.2, Hydrological Inversion Path: The LSTM-Transformer model simulates the diffusion trajectory and calculates the overlap with the enterprise's drainage path; S3.3, Responsibility Weight Calculation: A pollution contribution matrix is ​​generated based on the comprehensive production process and the status of pollution control equipment to quantify the enterprise's responsibility weight; S3.4 Output: Source tracing analysis report + responsibility attribution results, transmitted to the blockchain evidence platform module.

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