Big data water quality dynamic monitoring and early warning method and system

Abstract

The invention belongs to the technical field of water quality monitoring, and discloses a big data water quality dynamic monitoring and early warning method and system, a'water environment-social economy-ecosystem 'ternary data system is constructed through a cross-domain data symbiosis module, social and economic data such as enterprise pollution discharge, agricultural irrigation and the like and ecological microscopic data such as algae gene expression and the like are fused, and the water quality dynamic monitoring and early warning system is established. Implicit factor dominance is realized in combination with a dynamic weight algorithm; meanwhile, the quantum dot marking technology breaks through chemical analysis blind areas, accurately locates multi-source pollution, greatly improves the novel combined pollution identification capability, solves the problem that the traditional technology is lagged in response to complex pollution, and provides omnibearing data support for pollution traceability; in extreme weather, a parameter mutation adaptation mechanism is started, sub-models are rapidly switched, and an enhanced feature set generated by a cross-domain feature distillation step is combined, so that the model can accurately simulate the coupling effect of pollutants and a biologic chain, the long-term prediction stability is remarkably improved, and a reliable trend basis is provided for early warning decision making.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water quality monitoring, and specifically relates to a big data water quality dynamic monitoring and early warning method and system. BACKGROUND

[0002] Water quality monitoring is a core technical support in the fields of drinking water safety guarantee, river basin ecological management, industrial pollution supervision, etc., and its effectiveness is directly related to public health and ecological balance. The current water quality monitoring technology still has the following technical problems, which seriously restrict the effectiveness of ecological safety prevention and control: First, the data collection is limited in dimension and falls into the data island dilemma. The existing technology only focuses on the monitoring of physical and chemical parameters and cannot extend to the microscopic response level of the water ecosystem, such as the inability to capture key signals such as changes in algal community gene expression and changes in the structure of aquatic microbial groups, resulting in weak recognition ability for new composite pollution (such as microplastic and antibiotic synergistic pollution). Second, the prediction model has mechanism defects and presents model solidification problems. Traditional models are based on static mathematical formulas and are difficult to simulate the dynamic coupling effect of pollutants in water and the biological chain, and cannot adapt to complex and variable hydrological environments, resulting in insufficient prediction accuracy of pollution diffusion trends.

[0003] Third, the emergency response has process lag and is bound by artificial decision-making chains. From pollution identification to intervention execution, the whole process relies on artificial judgment and cross-department coordination, making it difficult to achieve spatial and temporal interruption of pollution diffusion, so that the early warning lead time of sudden pollution can only be maintained for a short time, which cannot meet the rapid response demand. SUMMARY

[0004] The purpose of the present application is to provide a big data water quality dynamic monitoring and early warning method and system to solve the problems raised in the background art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a big data water quality dynamic monitoring and early warning system, which comprises: Cross-domain data symbiotic module: break through the traditional data boundary, build a ternary data symbiotic mechanism, collect multiple types of data and interface with social and economic data, adjust the data influence weight through a dynamic weight algorithm, realize the explicit correlation of implicit pollution factors, and identify risk areas; Quantum dot marking and tracing module: based on the risk areas identified by the cross-domain data symbiotic module, a marking device is set at the key pollution discharge outlet, and coded quantum dots are released in abnormal conditions to track the trajectory and reverse the source, solving the problem of multi-source pollution definition and providing initial parameters for the digital twin driven prediction module; The key pollution discharge outlet is classified and managed according to the "Implementation Opinions on Strengthening the Supervision and Management of Discharge Outlets into Rivers and Seas"; It is stipulated that when the concentration of specific pollutants (such as chemical oxygen demand (COD), ammonia nitrogen, etc.) exceeds a certain percentage (such as more than 50%) of the national or local emission standards, or when the concentration of pollutants rises or falls by more than a preset range (such as an increase of 10 mg / L) within a certain period of time (such as 1 hour), it is judged as abnormal and the coded quantum dots are automatically released at this time; Digital twin-driven prediction module: This module builds a twin based on data from the cross-domain data symbiosis module and the quantum dot labeling traceability module. The module is embedded in a simulator, trained to learn migration rules, and parameters are modified through a virtual-real interaction mechanism. The prediction results are then passed to the immune algorithm early warning module. Immune algorithm early warning module: Receives the prediction results of the digital twin driven prediction module, builds a rule base based on the biological immune principle, generates early warning strategies, uses the immune memory mechanism to adjust the threshold, realizes the autonomous evolution of the system, and triggers the blockchain linkage response module with early warnings; Blockchain linkage response module: Based on the early warning of the immune algorithm early warning module and the prediction of the digital twin driven prediction module, a cross-departmental network is established. When a serious early warning occurs, smart contract execution instructions are generated. The response efficiency mining mechanism is used to incentivize execution, and the effect data is fed back to the cross-domain data symbiosis module. For example, when the pollution spread exceeds the area of ​​a specific region (such as affecting the surrounding 5 square kilometers of water area), or the concentration of major pollutants reaches extremely high levels (such as COD concentration exceeds 100 mg / L and continues to rise), or it is assessed that it may pose a serious threat to drinking water sources, ecologically sensitive areas, etc., it will be judged as a serious warning. At this time, the system will automatically generate smart contract execution instructions.

[0006] Preferably, the cross-domain data symbiosis module includes: (1) Data collection and association mechanism: Break through the boundaries of traditional monitoring data and design a three-dimensional data symbiosis mechanism of "water environment-socio-ecosystem". Through the Internet of Things, water quality sensor data, including pH, COD, etc., and satellite remote sensing ecological data, including vegetation coverage and wetland area, are collected. At the same time, social and economic data such as enterprise production ERP system, irrigation records of agricultural and rural bureaus, and e-commerce logistics data are connected to build a multi-dimensional data network. (2) Dynamic weighting and data support: Design a dynamic data weighting algorithm to automatically adjust the impact weight of each dimension of data according to the pollution type. For example, in the case of industrial pollution, the weight of enterprise pollution data is prioritized to achieve explicit association of implicit pollution factors, providing comprehensive and accurate multi-dimensional data support for subsequent tracing and prediction; Dynamic data weight allocation formula: ; Indicates the In the pollution scenario (e.g. industrial pollution i=1, agricultural pollution i=2), The weight of the data type (range 0-1); for example, in industrial pollution, the weight of enterprise pollution data (j=1) will be higher than that of meteorological data (j=2); No. The correlation factor between the pollution scenario and the kth type of data (value range 0-1), which is used to quantify the intrinsic correlation between a specific pollution type and a certain type of data; The confidence level of the kth category of data (value range 0-1) comprehensively reflects the reliability of the data and is determined by the data integrity and collection accuracy; Traverse the index, representing the k-th category of data (traverse k = 1 to n when summing, and calculate the sum of α·C for all data); Indicates the Type 2 pollution and The correlation factor of the data type (range 0-1) is obtained through historical data training; for example, the correlation factor of industrial pollution and enterprise pollution data The value is close to 1, which is consistent with the vegetation data. The value is close to 0; Indicates the The confidence level of the data (range 0-1) is determined by the completeness of the data (e.g. missing rate < 5%). ) and sensor accuracy (if the error is <196 ) jointly decide; Indicates the total dimension of the data (in this solution , covering 3 major categories of water quality, ecology, and social economy, with a total of 12 subcategories of data); Preferably, the quantum dot labeling traceability module includes: (1) Marking device and quantum dot release: Based on the potential pollution risk areas identified by the cross-domain data symbiosis module, intelligent marking devices are installed at key sewage outlets, and nano-scale quantum dot marking technology is introduced; when abnormal pollutant concentrations are monitored, quantum dot particles with unique spectral codes are automatically released, and each type of pollution source corresponds to a unique code to facilitate identification and tracing; (2) Trajectory tracking and parameter provision: Tracking the quantum dot diffusion trajectory through an underwater distributed spectral sensor array, and inverting the coordinates of the pollution source in combination with a fluid mechanics simulation model; breaking through the blind spot of traditional water quality chemical analysis, solving the problem of responsibility definition in scenarios where multiple pollution sources overlap, and its tracing results provide accurate initial pollution parameters for the digital twin drive prediction module; Quantum dot diffusion trajectory inversion formula: ; Where: represents the three-dimensional coordinates of the pollution source obtained by inversion (unit: meters), in the format of , where is the water depth; represents the coordinates of the candidate pollution source (to be solved variable), which is consistent with ; represents ; represents the quantum dot concentration (unit: pieces per liter) at the monitoring point at the moment, measured by the underwater optical spectrum sensor array; represents the theoretical concentration at the moment , calculated by the fluid mechanics model; represents the number of time samples (in this scheme , that is, sampling once every 1 minute for 1 hour).

[0007] Preferably, the digital twin driven prediction module comprises: (1) Twin construction and simulator embedding: based on the data output by the cross-domain data symbiotic module and the pollution source information determined by the quantum dot labeling traceability module, a 1:1 digital twin of the water area is constructed, which integrates three-dimensional terrain data and water dynamics model to form a dynamically mapped virtual water area mirror; embed a "pollution diffusion simulator" based on reinforcement learning in the twin, which is trained by virtual pollution events and learns the pollution diffusion law autonomously; (2) Virtual-real interaction and result output: adopt a "virtual-real interaction" prediction mechanism, input real-time monitoring data into the twin to drive virtual evolution, and at the same time, correct the virtual model parameters through the actual pollution cloud image taken by the unmanned aerial vehicle, improve the prediction accuracy, and the prediction result is synchronized to the immune algorithm warning module; Digital twin virtual-real deviation correction formula: ; In the formula: represents the corrected twin parameters at the moment, such as diffusion coefficient, flow rate coefficient, etc. represents the original parameters at the moment (initialized by historical data); represents the correction coefficient (range 0.1-0.3), which is dynamically adjusted according to water quality stability, such as in the rapid flow field , and in the static flow field ; represents ​​Actual pollution cloud boundary parameters (such as area and diffusion radius) taken by drone at any given moment; express Pollution cloud boundary parameters for moment-to-moment twin simulation.

[0008] Preferably, the immune algorithm early warning module includes: (1) Rule base construction and strategy generation: Receive the pollution diffusion prediction result data output by the digital twin driven prediction module, draw on the antigen recognition principle of the biological immune system, and build an adaptive early warning rule base; regard abnormal fluctuations in water quality parameters as "antigens" and the disposal plans of historical pollution events as "antibodies", and generate early warning strategies for new pollution events through the immune clone selection algorithm; Immune algorithm warning threshold adjustment formula: ; Where: Indicates the The warning threshold of the same pollution event (such as COD threshold, unit: ); Indicates the The original threshold of the event (initialized by the national water quality standard); represents the adjustment coefficient (fixed at 0.2), which controls the threshold contraction / expansion amplitude; Indicates the The error signal of the secondary event (positive error Indicates missed reports and requires shrinking the threshold; negative error Indicates a false positive, requiring the threshold to be expanded); (2) Immune memory and early warning triggering: Design an "immune memory" mechanism for the early warning threshold. When the same type of pollution incident occurs again, the threshold automatically shrinks to improve sensitivity. In the event of a false alarm, the threshold is expanded to avoid redundant warnings and achieve autonomous evolution of the system. Its early warning level and strategy directly trigger the operation of the blockchain linkage response module.

[0009] Preferably, the blockchain linkage response module includes: (1) Collaborative network and smart contracts: Based on the early warning signals issued by the immune algorithm early warning module and the pollution spread range predicted by the digital twin, a cross-departmental response collaborative network is constructed, and environmental protection, water affairs, emergency and other departments are included in the distributed nodes; when a serious early warning is triggered, the system automatically generates a smart contract containing the hash value of the pollution data, pushes it to the relevant nodes, and enforces the preset response instructions; (2) Incentive mechanism and closed-loop optimization: Introduce a "response efficiency mining" mechanism to grant blockchain credit points to nodes that quickly execute response tasks. The credit points can be exchanged for priority use of monitoring resources. At the same time, the response effect data is fed back to the cross-domain data symbiosis module to form a closed-loop optimization and improve the overall efficiency of the system. Blockchain credit score calculation formula: ; Where: Indicates the Nodes (such as the Environmental Protection Bureau node )’s current credit score; Represents the historical integral of the node (initial value is 100); Indicates the integral coefficient (fixed at 5), which amplifies the effect of response efficiency; Indicates the rate of decrease of pollutant concentration after response (unit: ), the faster the rate, the more the integral increases; Indicates the node response time coefficient (response time < 10 minutes , more than 30 minutes ).

[0010] The present invention also provides a big data water quality dynamic monitoring and early warning method. Based on the above system, the specific steps of the method are as follows: S1. Cross-domain feature distillation stage: Based on the cross-domain data symbiosis module, ternary data are integrated, and features are extracted using federated learning. An enhanced feature set is generated through feature distillation to analyze the relationship between pollution and water quality, providing model training data for the twin prediction stage; S2, Twin Adaptive Prediction Phase: Initialize the digital twin based on the feature set from the cross-domain feature distillation phase. Real-time data drives the virtual evolution of the twin. When extreme weather conditions such as heavy rain and strong winds occur, the adaptation mechanism is activated and the virtual model parameters are corrected. The prediction results provide a decision-making basis for the blockchain's proactive intervention phase. S3, blockchain active intervention stage: Based on the prediction results and early warning signals, an intervention plan is generated and written into the smart contract. After the node is executed, the effect data is collected and fed back to the cross-domain data symbiosis module and the immune algorithm early warning module respectively to optimize the system performance.

[0011] Preferably, the specific steps of the cross-domain feature distillation stage in step S1 are as follows: S11. Data Fusion and Feature Extraction: This module uses a cross-domain data symbiosis module to perform heterogeneous fusion of ternary data. A federated learning framework is used to extract features without leaving the domain. This effectively addresses enterprise data privacy concerns and lays the foundation for subsequent data processing. Cross-domain feature fusion formula: ; In the formula: represents the enhanced feature set after fusion (128-dimensional vector); Attention represents the attention mechanism function, which calculates the correlation weight of water quality features and social and economic features; represents the water quality data feature vector, such as pH, COD, etc., 64-dimensional; represents the social and economic data feature vector, such as fertilizer use, pollution discharge, etc., 64-dimensional; represents the i-th Attention weight of the feature (range 0-1, sum is 1), the feature pair with high correlation (such as pollution discharge and COD) has a larger value; represents the i-th Splicing operation of features, such as splicing pollution discharge and COD features; Total number of feature pairs, q=64, i.e. 64 pairs of features are fused; S12, feature distillation and correlation analysis: through the feature distillation technology, the high-dimensional social and economic data is compressed into a "pollution impact factor vector", which is fused with the water quality data features through the attention mechanism, generating an enhanced feature set containing implicit effects; Design a causal reasoning model to quantitatively analyze the dynamic correlation between pollution behavior and water quality changes, and its output provides model training and real-time deduction data for the twin adaptive prediction stage.

[0012] Preferably, the specific steps of the twin adaptive prediction stage in step S2 are as follows: S21, twin initialization and evolution: based on the enhanced feature set generated in the cross-domain feature distillation stage, initialize the digital twin and input the historical data to complete the pre-training, and drive the twin to evolve dynamically through real-time monitoring data, ensuring that the model can reflect the water area conditions in real time; S22, adaptive mechanism and parameter correction: when extreme weather is detected, automatically start the "parameter mutation adaptation mechanism", freeze the regular water dynamic parameters, call the extreme scenario sub-model trained by reinforcement learning, realize the quick switching of pollution diffusion prediction; Design a "virtual-real deviation correction algorithm", regularly compare the spectral scanning data of the unmanned aerial vehicle with the prediction results of the twin, and dynamically adjust the diffusion coefficient, which provides decision basis for the blockchain active intervention stage; Extreme scenario model switching judgment formula: ; In the formula: Switch flag (1 means switch to extreme scenario submodel, 0 means keep normal model); Switch flag (1 means switch to extreme scenario submodel, 0 means keep normal model); Meteorological parameters at time (such as wind speed, rainfall, normalized to range 0-1); Normal meteorological parameter threshold (such as wind speed threshold 0.6, corresponding to 10 m / s); Deviation threshold (fixed at 0.2), trigger switch when actual meteorological parameter deviation exceeds 20% of normal threshold.

[0013] Preferably, the specific steps of the blockchain active intervention stage in the step S3 are as follows: S31, scheme generation and contract execution: receiving the pollution diffusion range output by the twin adaptive prediction stage and the warning level triggered by the immune algorithm warning module, the system generates a "spatiotemporal intervention scheme", and writes the scheme into the smart contract, and the alliance chain node verifies the feasibility of the scheme and automatically executes it, to ensure that the intervention measures are in place in time; S32, effect feedback and system optimization: collecting intervention effect data through Internet of Things devices, on the one hand, feeding back to the cross-domain data symbiosis module to update the feature library, and on the other hand, transmitting to the immune algorithm warning module to optimize the warning rule library; this step realizes the whole process of "prediction-decision-execution-feedback" blockchain storage, ensures the response timeliness, and traces the intervention effect to continuously optimize the system operation efficiency.

[0014] The beneficial effects of the present application are as follows: 1. The present application constructs a ternary data system of "water environment-social economy-ecological system" through a cross-domain data symbiosis module, integrates social and economic data such as enterprise pollution and agricultural irrigation, and ecological micro data such as algal gene expression, and realizes the explicitness of implicit factors by combining a dynamic weight algorithm; at the same time, quantum dot labeling technology breaks through the blind area of chemical analysis, accurately locates multiple sources of pollution, and this fusion mechanism greatly improves the identification ability of new complex pollution, solving the problem of lag response of traditional technology to complex pollution, and providing comprehensive data support for pollution tracing.

[0015] 2. The present application constructs a 1:1 virtual water mirror through a digital twin driven prediction module, embeds a reinforcement learning simulator, dynamically corrects parameters through a "virtual-real interaction" mechanism, starts a parameter mutation adaptation mechanism under extreme weather, quickly switches submodels, and combines the enhanced feature set generated by the cross-domain feature distillation step, so that the model can accurately simulate the coupling effect of pollutants and biological chains, significantly improve the long-term prediction stability compared with traditional models, and provide reliable trend basis for early warning decision-making.

[0016] 3. The immune algorithm early warning module of the present invention realizes threshold self-optimization through the immune memory mechanism, and the blockchain linkage response module builds a cross-departmental smart contract network, combined with the "response efficiency mining" mechanism to incentivize rapid execution; a closed loop is formed from pollution identification to intervention feedback, and the efficiency of response instruction issuance and execution is greatly improved, which can achieve temporal and spatial blocking of pollution spread. At the same time, the response effect data feeds back to the data module, continuously optimizes the system, completely changes the traditional passive disposal mode, and improves the initiative and accuracy of ecological safety prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Fig. 1 This is a flow chart of the big data water quality dynamic monitoring and early warning system of the present invention; Fig. 2 This is a flow chart of the big data water quality dynamic monitoring and early warning method of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] like Figs. 1-2 As shown, an embodiment of the present invention provides a big data water quality dynamic monitoring and early warning system, which includes: Cross-domain data symbiosis module: Breaking through traditional data boundaries, building a ternary data symbiosis mechanism, collecting multiple types of data and connecting them to socioeconomic data, adjusting data impact weights through a dynamic weighting algorithm, making implicit pollution factors explicit and identifying risk areas; Quantum dot tagging and traceability module: Based on the risk areas identified by the cross-domain data symbiosis module, marking devices are installed at key sewage outlets. When an anomaly occurs, coded quantum dots are released to track the trajectory and invert the source, solving the problem of multi-source pollution identification and providing initial parameters for the digital twin drive prediction module; Digital twin-driven prediction module: This module builds a twin based on data from the cross-domain data symbiosis module and the quantum dot labeling traceability module. The module is embedded in a simulator, trained to learn migration rules, and parameters are modified through a virtual-real interaction mechanism. The prediction results are then passed to the immune algorithm early warning module. Immune algorithm early warning module: Receives the prediction results of the digital twin driven prediction module, builds a rule base based on the biological immune principle, generates early warning strategies, uses the immune memory mechanism to adjust the threshold, realizes the autonomous evolution of the system, and triggers the blockchain linkage response module with early warnings; Blockchain linkage response module: based on the early warning of the immune algorithm early warning module and the prediction of the digital twin driven prediction module, a cross-department network is established, and intelligent contract execution instructions are generated in serious early warning. The response efficiency mining mechanism is used to encourage execution, and the effect data is fed back to the cross-domain data symbiosis module.

[0020] Embodiment one: taking a certain chemical industrial park surrounding watershed as the monitoring object, the watershed contains 3 chemical enterprise sewage outlets, 1 urban sewage treatment plant sewage outlet and agricultural irrigation tail water discharge area. The history has appeared COD exceeding standard situation for many times, the present application develops monitoring and early warning by using the system.

[0021] Firstly, the cross-domain data symbiosis module starts running. Through the Internet of Things sensor, the water environment parameters such as pH (6.5-8.5) and COD (0-100 mg / L) are collected in real time, and the sampling frequency is 5 minutes / time; at the same time, the daily sewage data of the ERP system of 3 chemical enterprises, the operation data of 10,000 tons of daily treatment of urban sewage treatment plant and the information of chemical raw material e-commerce logistics are connected, combined with the 60%-80% vegetation coverage data of the watershed obtained by satellite remote sensing. Because the watershed is mainly industrial pollution, the dynamic weight algorithm automatically increases the weight of enterprise sewage data to 0.6 and reduces the weight of agricultural data to 0.1, and finally identifies the high-risk area around the chemical enterprise sewage outlet.

[0022] Based on this result, the quantum dot marking traceability module installs intelligent marking devices at the 3 chemical enterprise sewage outlets (key sewage outlets, determined according to large sewage discharge and complex pollutant composition). The InP / ZnS core-shell structure quantum dots with 650 nm red light coding (industrial pollution exclusive coding) are built-in. When the COD of a certain enterprise sewage outlet suddenly rises to 120 mg / L (more than 100 mg / L of national standard, judged as abnormal), the device automatically releases quantum dots; underwater optical spectrum sensor array tracks the trajectory, combined with fluid mechanics model inversion, and locks the pollution source as the No. 2 sewage outlet of the enterprise (coordinates X=320 m, Y=150 m, water depth 5 m).

[0023] The digital twin driven prediction module immediately constructs a 1:1 digital twin based on 0.5 m precision three-dimensional terrain data of the watershed and the traceability result, and fuses the water power model of 0.8 m / s flow rate. After inputting real-time data, the simulation shows that the pollution cloud will spread to the downstream 1 km within 1 hour, and the COD concentration is 80-100 mg / L; if 3 east winds are encountered, the drinking water intake of the town may be affected in 2 hours. The unmanned aerial vehicle takes pictures of the actual pollution cloud every 10 minutes, adjusts the parameters through the deviation correction formula with β=0.2, so that the prediction error is controlled within 5%.

[0024] The immune algorithm early warning module receives the prediction result, considers "COD exceeding 20% and possibly spreading to the drinking water source" as a high-priority antigen, matches the 2022 similar event disposal scheme (antibody), and because the pollution may threaten the safety of drinking water (judged as a serious early warning), triggers the early warning threshold contraction (from 100 mg / L to 80 mg / L).

[0025] The blockchain linkage response module then generates a smart contract on the environmental protection, water affairs, and emergency department alliance chain, instructs the environmental protection department to dispatch 2 mobile monitoring vehicles within 15 minutes, the water affairs department to close the upstream gate of the drinking water intake, and the emergency department to allocate 3 tons of activated carbon. The environmental protection department completes the dispatch in 8 minutes (delta = 1), reduces the COD from 120 mg / L to 70 mg / L within 1 hour (delta C / delta t = 0.83 mg / L / min), and obtains 104.15 credit points; the response effect data is fed back to the cross-domain data symbiotic module to update the feature library.

[0026] Among them, the cross-domain data symbiotic module includes: (1) Data collection and association mechanism: Break through the traditional monitoring data boundary, design a "water environment-socioeconomic-ecosystem" ternary data symbiotic mechanism, collect water quality sensor data including pH, COD, etc. through the Internet of Things, satellite remote sensing ecological data including vegetation coverage, wetland area, and at the same time, interface with enterprise production ERP system, agricultural irrigation account, e-commerce logistics data, etc. Social and economic data, build a multi-dimensional data network; use underwater gene sequencing sensors (model example: Illumina MiniSeq) to collect algal RNA sequences, sample once a week; after quality control (remove low-quality reads, retain Q30 above sequences), use Kraken2 software for species annotation to generate microbiome structure feature vectors; Enterprise ERP uses RESTful API (data format JSON, fields include "production batch, pollutant type, and pollutant type"); agricultural account uses database direct connection (MySQL protocol, table name "irrigation_record", key field "irrigation area, fertilizer use amount"); enterprise data is synchronized in real time (delay <5 minutes), and agricultural data is updated in batches every morning; (2) Dynamic weight and data support: Design a dynamic data weight distribution algorithm, automatically adjust the influence weight of each dimension data according to the pollution type, such as increasing the weight of enterprise pollution data when industrial pollution occurs, to realize the explicit correlation of implicit pollution factors, and provide comprehensive and accurate multi-dimensional data support for subsequent tracing and prediction; Dynamic data weight distribution formula: ; represents the ​Class pollution scenarios (such as industrial pollution , agricultural pollution ) of the first data weight (range 0-1). For example, industrial pollution, the weight of enterprise pollution data ) will be higher than the weather data ); The first pollution scenarios and the first data associated factor (value range 0-1), used to quantify the inherent relevance of a particular pollution type and a class of data; The first data confidence (value range 0-1), reflecting the reliability of data, determined by data integrity and collection accuracy; Traversal index, representing the first data (sum when traversing to , calculating the sum of all data ); The first pollution and the first data associated factor (range 0-1), derived from historical data training; such as industrial pollution and enterprise pollution data value close to 1, and the value of vegetation data close to 0; Training method: using random forest algorithm to train 1000+ pollution events in the past 5 years, input is pollution type and data dimension, output is associated factor , the training termination condition is that the validation set error is less than 5%; The first data confidence (range 0-1), determined by data integrity (such as missing rate <5% then ) and sensor accuracy (such as error <196 then ); Data confidence calculation: data integrity weight accounts for 60% (missing rate <5% counts 1, >20% counts 0), sensor accuracy weight accounts for 40% (error <1% counts 1, >5% counts 0), integrity score + 0.4 x accuracy score; The total dimension of data (in this scheme , covering water quality, ecology, social economy, 3 categories, a total of 12 small categories of data); Among them, the quantum dot labeling traceability module includes: (1) Marking device and quantum dot release: Based on the potential pollution risk areas identified by the cross-domain data symbiosis module, intelligent marking devices are installed at key sewage outlets, and nano-scale quantum dot marking technology is introduced; when abnormal pollutant concentrations are monitored, quantum dot particles with unique spectral codes are automatically released, and each type of pollution source corresponds to a unique code to facilitate identification and tracing; Using cadmium-free quantum dots, such as InP / ZnS core-shell structure, after acute toxicity test (96h-LC50>10mg / L, in line with GB18420.2-2008 standard); the degradation rate in natural water within 30 days is>90%, and the degradation products are harmless ions, such as ; Quantum dot coding rules: Industrial wastewater is coded with 650nm red light, and agricultural non-point source pollution is coded with 520nm green light. The coding is achieved by the ratio of doping elements in the quantum dots (for example, industrial coding contains 3% cadmium and agricultural coding contains 2% selenium). Code analysis method: The emission spectrum of quantum dots (wavelength range 400-800nm) is collected using a spectrometer. The characteristic peak positions (e.g., 650nm for industrial sources and 520nm for agricultural sources) and peak intensity ratios are analyzed (if the peak ratio is >1.5, it is determined to be a single pollution source; otherwise, it is mixed pollution). A wavelet transform denoising algorithm is used to filter out spectral drift caused by suspended particles in the water (the error is controlled within ±2nm). (2) Trajectory tracking and parameter provision: Tracking the quantum dot diffusion trajectory through an underwater distributed spectral sensor array, and inverting the coordinates of the pollution source in combination with a fluid mechanics simulation model; breaking through the blind spot of traditional water quality chemical analysis, solving the problem of responsibility definition in scenarios where multiple pollution sources overlap, and its tracing results provide accurate initial pollution parameters for the digital twin drive prediction module; Sensor array layout: One spectral sensor is deployed for every 100 square meters of water area, with a sampling frequency of 1 Hz. The signal is transmitted to the data center via optical fiber, and the analysis software uses the LabVIEW spectral analysis module; Quantum dot diffusion trajectory inversion formula: ; Where: Indicates the three-dimensional coordinates of the pollution source obtained by inversion (unit: meter), the format is ( ),in For water depth; represents the coordinates of candidate pollution sources (variables to be solved), and consistent format; express Constant monitoring points The quantum dot concentration (unit: pieces / liter) at the place is measured by the underwater optical spectrum sensor array; represents the theoretical concentration at the place when the pollution source is , the moment , calculated by the fluid mechanics model; represents the number of time sampling (in this scheme , that is, sampling once every 1 minute for 1 hour).

[0027] The digital twin driven prediction module includes: (1) Twin construction and simulator embedding: based on the data output by the cross-domain data symbiotic module and the pollution source information determined by the quantum dot marking traceability module, a 1:1 water area digital twin is constructed, and a dynamic mapping virtual water area mirror is formed by fusing three-dimensional terrain data and a water dynamics model; a "pollution diffusion simulator" based on reinforcement learning is embedded in the twin, which is trained by virtual pollution events and learns the pollution diffusion law autonomously; Three-dimensional terrain data: generated by splicing airborne LiDAR scanning (point cloud density ≥ 10 points / ㎡, height error <0.5m) combined with underwater sonar data (resolution 0.1m×0.1m); Water dynamics model parameters: time step 1 minute, spatial grid size 5m×5m, turbulence model using RNG k-ε model (standard wall function in near-wall region); Reinforcement learning training: using DDPG algorithm, training data set containing 500+ virtual pollution events (covering industrial, agricultural, and domestic pollution), state space for water quality parameters (pH, COD, etc.) + hydrological parameters (flow rate, water depth), action space for diffusion coefficient adjustment, reward function: R=1- (predicted concentration-actual concentration) / actual concentration, trained to converge when the average reward of the last 100 rounds is greater than 0.9; (2) Virtual-real interaction and result output: using a "virtual-real interaction" prediction mechanism, real-time monitoring data is input into the twin to drive virtual evolution, and at the same time, the virtual model parameters are corrected by the actual pollution cloud image taken by the unmanned aerial vehicle, to improve the prediction accuracy, and the prediction result is synchronized to the immune algorithm warning module; Unmanned aerial vehicle inspection frequency: once every 2 hours under normal working conditions, once every 10 minutes under warning state; Correction trigger threshold: when the boundary deviation between the virtual pollution cloud and the actual aerial photograph is greater than 10% (such as a virtual radius of 50m and an actual radius of 60m), parameter correction is immediately started; Digital twin virtual-real deviation correction formula: ; In the formula, represents twin parameters corrected at time t, such as diffusion coefficient, flow velocity coefficient, etc. denotes original parameters at time t (initialized by historical data); denotes correction factor (range 0.1-0.3), dynamically adjusted according to water quality stability, such as rapid flow scenario , static flow scenario ; denotes actual pollution cloud boundary parameters (such as area, diffusion radius) of UAV aerial photography at time t; denotes pollution cloud boundary parameters simulated by twin at time t.

[0028] The immune algorithm early warning module comprises: (1) Rule base construction and strategy generation: receiving the pollution diffusion prediction result data output by the digital twin driven prediction module, borrowing the antigen recognition principle of the biological immune system, constructing an adaptive early warning rule base; regarding abnormal fluctuations of water quality parameters as "antigens", and regarding disposal schemes of historical pollution events as "antibodies", generating early warning strategies for new pollution events through immune clone selection algorithm; Similarity calculation: cosine similarity algorithm is adopted, when the similarity of pollution parameter vector (such as pH, COD, quantum dot code) and historical antibody is ≥0.85, the historical disposal scheme is called; Threshold dynamic adjustment: for new pollution (no matching history), the similarity threshold is reduced to 0.6, triggering the artificial intervention channel; Rule base example: rule 1: if the COD concentration rises by >20 mg / L within 1 hour and the quantum dot code matches the industrial source, trigger level 2 early warning; rule 2: if the algae gene expression is abnormal (specific gene transcription volume rises by 3 times) and the agricultural irrigation data is abnormal, trigger level 1 early warning; Immune algorithm early warning threshold adjustment formula: ; In the formula: denotes the early warning threshold of the th similar pollution event (such as COD threshold, unit: ) denotes the original threshold of the th event (initialized by national water quality standard); denotes adjustment factor (fixed at 0.2), controls the contraction / expansion amplitude of threshold; Error signal (positive error indicates the first event, need to shrink the threshold; negative error indicates false alarm, need to expand the threshold); (2) Immune memory and early warning trigger: design an "immune memory" mechanism for the early warning threshold. When the same type of pollution event occurs again, the threshold is automatically contracted to improve sensitivity. False alarm events expand the threshold to avoid redundant early warning, and the system evolves autonomously. Its early warning level and strategy directly trigger the operation of the blockchain linkage response module. Historical event storage format: structured fields include "pollution type, occurrence time, early warning threshold, disposal effect, quantum dot code", stored in a time series database (such as InfluxDB), retaining the last 5 years of data; Update frequency: after 1 pollution event occurs, memory library update is completed within 24 hours, covering the oldest record of the same type of event; Immune memory mechanism: the same type of pollution is defined as 'pollution source type + same type of pollutant' (such as benzene and amine substances emitted by chemical enterprises), and the threshold contraction amplitude is 20% (such as the original threshold 100 mg / L, contracted to 80 mg / L).

[0029] Among them, the blockchain linkage response module includes: (1) Collaborative network and smart contract: based on the early warning signal issued by the immune algorithm early warning module and the pollution diffusion range predicted by the digital twin, a cross-department response collaborative network is constructed, including environmental protection, water affairs, and emergency departments as distributed nodes; When a serious early warning is triggered, the system automatically generates a smart contract containing the hash value of the pollution data, pushes it to the relevant nodes and forces the execution of the preset response instructions; Smart contract instructions: when a serious early warning is triggered, the environmental protection department node needs to start emergency monitoring within 15 minutes (instruction: dispatch 3 mobile monitoring vehicles to the predicted pollution area); the water affairs department node needs to close the upstream gate of the pollution area (instruction: send PLC control signal to the gate controller); Verification method of smart contract execution: Automatic verification: real-time data is returned through Internet of Things sensors, such as gate closing status confirmed by travel sensor and mobile monitoring vehicle location confirmed by GPS positioning, and data hash value written into blockchain; Manual supplement: for operations that cannot be automatically verified, such as on-site dredging, time-stamped on-site photos (verified by blockchain node consensus) need to be uploaded as execution basis; (2) Incentive mechanism and closed-loop optimization: Introduce the "response efficiency mining" mechanism, give blockchain credit points to nodes that quickly execute response tasks, and the points can be exchanged for priority use of monitoring resources; At the same time, the response effect data is fed back to the cross-domain data symbiosis module to form a closed-loop optimization, and the overall efficiency of the system is improved; Decay and exchange rules of credit points: Point decay: At the end of each year, the node points are converted by 80% (i.e. remaining 20%), to avoid long-term unused points occupying resources; Exchange priority: Nodes with high points (such as >=5000 points) enjoy priority in sensor resource scheduling, the specific process is: node submits application->blockchain smart contract audit->resource management module allocation (response within 24 hours); Credit point exchange: 1000 points can be used to preferentially use satellite remote sensing data (once a month), and 5000 points can apply for new sensor deployment quota (once a year); Blockchain credit point calculation formula: ; In the formula: represents the current credit points of the nth node (such as the environmental protection bureau node ); represents the historical points of the node (the initial value is 100); represents the integral coefficient (fixed as 5), which amplifies the influence of response efficiency; represents the decline rate of the pollutant concentration after response (unit: ), the faster the rate, the more points increase; represents the response time coefficient of the node (if the response time is less than 10 minutes , and more than 30 minutes ).

[0030] The embodiment of the application also provides a big data water quality dynamic monitoring and early warning method, based on the above system, the specific steps of the method are as follows: S1, cross-domain feature distillation stage: based on the cross-domain data symbiosis module, fuse ternary data, extract features by federated learning, generate enhanced feature set through feature distillation, analyze pollution and water quality association, and provide model training data for twin prediction stage; S2, twin adaptive prediction stage: initialize the digital twin based on the feature set of the cross-domain feature distillation stage, drive the twin virtual evolution in real time, start the adaptation mechanism in extreme weather, correct the virtual model parameters, and provide decision basis for the blockchain active intervention stage.​ S3, blockchain active intervention stage: based on the prediction results and early warning signals, an intervention scheme is generated and written into a smart contract, and the effect data is collected after the node executes, and is fed back to the cross-domain data symbiosis module and the immune algorithm early warning module, respectively, to optimize the system performance.

[0031] In step S1, the specific steps of the cross-domain feature distillation stage are as follows: S11, data fusion and feature extraction: based on the cross-domain data symbiosis module, the ternary data is fused, the feature extraction is completed under the premise that the data does not leave the domain by using the federated learning framework, the enterprise data privacy problem is effectively solved, and the foundation is laid for subsequent data processing; Participating nodes of federated learning: including 3 types of subjects (10 enterprise nodes, 5 environmental protection department nodes, and 3 scientific research institution nodes), using federated average (FedAvg) algorithm, aggregating 10 local models per round; Privacy protection: using differential privacy technology (noise strength ε=1.0) to prevent data leakage; Cross-domain feature fusion formula: ; In the formula: represents the enhanced feature set after fusion (128-dimensional vector); Attention represents the attention mechanism function, which calculates the correlation weight of water quality features and social and economic features; represents the water quality data feature vector, such as pH, COD, etc., 64-dimensional; represents the social and economic data feature vector, such as fertilizer use amount, pollution discharge amount, etc., 64-dimensional; represents the i-th attention weight of the feature (range 0-1, sum 1), the feature pair with high correlation (such as pollution discharge amount and COD) has a larger value; represents the i-th splicing operation of the feature, such as splicing the pollution discharge amount and the COD feature; Total number of feature pairs, q=64, i.e. 64 pairs of features are fused; S12, feature distillation and correlation analysis: through feature distillation technology, high-dimensional social and economic data is compressed into a "pollution impact factor vector", which is fused with water quality data features through attention mechanism, generating an enhanced feature set containing implicit effects; a causal reasoning model is designed to quantitatively analyze the dynamic correlation between pollution behavior and water quality changes, and its output provides model training and real-time deduction data for the twin adaptive prediction stage.

[0032] The specific steps of the twin adaptive prediction stage in step S2 are as follows: S21, twin initialization and evolution: based on the enhanced feature set generated in the cross-domain feature distillation stage, the digital twin is initialized and pre-trained by inputting historical data, and the twin is dynamically evolved by real-time monitoring data to ensure that the model can reflect the water area conditions in real time; S22, adaptive mechanism and parameter correction: when extreme weather is detected, the "parameter mutation adaptation mechanism" is automatically started, the regular hydrodynamic parameters are frozen, the extreme scenario sub-model trained by reinforcement learning is called to realize the quick switching of pollution diffusion prediction; The "virtual-real deviation correction algorithm" is designed, and the diffusion coefficient is dynamically adjusted by comparing the spectral scanning data of the unmanned aerial vehicle with the prediction results of the twin, and the output provides decision basis for the active intervention stage of the blockchain; Extreme weather detection logic: interface with the real-time API of the meteorological bureau (data update frequency 15 minutes / time), when "daily rainfall>50mm" or "wind speed>10m / s" is detected, the extreme scenario sub-model is automatically triggered; Sub-model switching time: the response time from the normal model to the extreme model is less than 30 seconds (by preloading sub-model parameters); Extreme scenario model switching judgment formula: ; In the formula: represents the switching flag (1 represents switching to the extreme scenario sub-model, and 0 represents keeping the normal model); Extreme scenario sub-model: two types of sub-models, namely heavy rain (daily rainfall>50mm) and strong wind (wind speed>10m / s), are pre-trained by historical pollution diffusion data under extreme weather (2018-2023 measured data), and the pre-trained parameters are directly called when switching; represents the meteorological parameter (such as wind speed, rainfall, normalized to the range 0-1) at the moment; represents the threshold value of the regular meteorological parameter (such as the wind speed threshold value 0.6, corresponding to 10m / s); represents the deviation threshold value (fixed at 0.2), which triggers the switching when the deviation of the actual meteorological parameter from the regular threshold value exceeds 20%.

[0033] The specific steps of the active intervention stage of the blockchain in step S3 are as follows: S31, scheme generation and contract execution: receiving the pollution diffusion range output by the twin adaptive prediction stage and the warning level triggered by the immune algorithm warning module, the system generates a "spatiotemporal intervention scheme", and writes the scheme into the smart contract. The alliance chain node verifies the feasibility of the scheme and automatically executes it after verification, ensuring that the intervention measures are in place in a timely manner; S32, effect feedback and system optimization: collect intervention effect data through Internet of Things devices, on the one hand, feedback to the cross-domain data symbiosis module to update the feature library, and on the other hand, transmit to the immune algorithm warning module to optimize the warning rule library; This step realizes the whole process of "prediction-decision-execution-feedback" block chain storage, ensures the timeliness of the response, and traces the intervention effect to continuously optimize the system operation efficiency.

[0034] Feedback field: contains intervention measure type, execution time, pollutant concentration change rate, coverage area; Feedback frequency: real-time feedback (1 times per second) for severe warning, and 5-minute summary feedback for general warning.

[0035] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0036] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A big data water quality dynamic monitoring and early warning system, characterized by: The system includes: Cross-domain data symbiosis module: Build a three-dimensional data symbiosis mechanism, collect data and connect it to socioeconomic data, adjust the data impact weight through a dynamic weighting algorithm, realize the explicit association of hidden pollution factors, and identify risk areas; Quantum dot tagging and traceability module: Based on the risk areas identified by the cross-domain data symbiosis module, marking devices are installed at key sewage outlets. When an abnormality occurs, coded quantum dots are released to track the trajectory and invert the source; Digital twin-driven prediction module: This module builds a twin based on the data from the cross-domain data symbiosis module and the quantum dot labeling traceability module. The module is embedded in a simulator, trained to learn the migration rules, and uses the virtual-reality interaction mechanism to modify parameters and output prediction results. Immune algorithm early warning module: After receiving the prediction results, it builds a rule base based on the biological immune principle, generates early warning strategies, and uses the immune memory mechanism to adjust the threshold. The early warning will trigger the blockchain linkage response module; Blockchain linkage response module: Based on the early warning of the immune algorithm early warning module and the prediction of the digital twin driven prediction module, a cross-departmental network is established. When a serious early warning is issued, smart contract execution instructions are generated, and the response efficiency mining mechanism is used to incentivize execution. The effect data is fed back to the cross-domain data symbiosis module.

2. A big data water quality dynamic monitoring and early warning system according to claim 1, characterized in that: The cross-domain data symbiosis module includes: (1) Data collection and association mechanism: Design a water environment-socio-economic-ecological system triadic data symbiosis mechanism, collect water quality sensor data and satellite remote sensing ecological data through the Internet of Things, and simultaneously connect with social and economic data including enterprise production ERP systems, irrigation records of agricultural and rural bureaus, and e-commerce logistics data; (2) Dynamic weight and data support: Design a dynamic data weight allocation algorithm to automatically adjust the impact weight of each dimension of data according to the pollution type, and realize the explicit association of implicit pollution factors.

3. A big data water quality dynamic monitoring and early warning system according to claim 2, characterized in that: The quantum dot marking traceability module includes: (1) Marking device and quantum dot release: Based on the potential pollution risk areas identified by the cross-domain data symbiosis module, intelligent marking devices are installed at key sewage outlets, and nano-scale quantum dot marking technology is introduced. When abnormal pollutant concentrations are detected, quantum dot particles with spectral coding are automatically released, and each type of pollution source corresponds to a unique code; (2) Trajectory tracking and parameter provision: The quantum dot diffusion trajectory is tracked by an underwater distributed spectral sensor array, and the coordinates of the pollution source are inverted by combining the fluid mechanics simulation model. The tracing results provide pollution source information for the digital twin drive prediction module.

4. A big data water quality dynamic monitoring and early warning system according to claim 3, characterized in that: The digital twin drive prediction module includes: (1) Twin construction and simulator embedding: Based on the data output by the cross-domain data symbiosis module and the pollution source information determined by the quantum dot labeling traceability module, a 1:1 water area digital twin is constructed, integrating three-dimensional terrain data and hydrodynamic models to form a dynamically mapped virtual water area mirror image; a pollution diffusion simulator based on reinforcement learning is embedded in the twin, and after training with virtual pollution events, the simulator autonomously learns the diffusion laws of pollutants; (2) Virtual-real interaction and result output: A virtual-real interaction prediction mechanism is used to input real-time monitoring data into the twin to drive virtual evolution. At the same time, the virtual model parameters are corrected through the actual pollution cloud images taken by drones to output the prediction results.

5. The big data water quality dynamic monitoring and early warning system according to claim 4 is characterized by: The immune algorithm early warning module includes: (1) Rule base construction and strategy generation: Receive the prediction result data output by the digital twin driven prediction module, draw on the antigen recognition principle of the biological immune system, and build an adaptive early warning rule base; regard abnormal fluctuations in water quality parameters as antigens, and the disposal plans of historical pollution events as antibodies, and generate early warning strategies for pollution events through the immune clone selection algorithm; (2) Immune memory and early warning triggering: Design an immune memory mechanism for the early warning threshold. When the same type of pollution incident occurs again, the threshold automatically shrinks; in the case of a false alarm, the threshold expands to avoid redundant early warnings, and outputs the early warning level and strategy.

6. The big data water quality dynamic monitoring and early warning system according to claim 5, characterized in that: The blockchain linkage response module includes: (1) Collaborative network and smart contracts: Based on the early warning signals issued by the immune algorithm early warning module and the pollution spread range predicted by the digital twin, a cross-departmental response collaborative network is constructed, incorporating departments including environmental protection, water affairs, and emergency response into distributed nodes; when a serious early warning is triggered, the system automatically generates a smart contract containing the hash value of the pollution data, pushes it to the relevant nodes, and executes the preset response instructions; (2) Incentive mechanism and closed-loop optimization: Introduce a response efficiency mining mechanism to grant blockchain credit points to nodes that perform response tasks, and at the same time feed back the response effect data to the cross-domain data symbiosis module.

7. A big data water quality dynamic monitoring and early warning method, based on the system of claim 6, characterized in that: The specific steps of this method are as follows: S1, Cross-domain feature distillation stage: Based on the cross-domain data symbiosis module, ternary data are integrated, and features are extracted using federated learning. After feature distillation, an enhanced feature set is generated to analyze the relationship between pollution and water quality, providing model training data for the twin adaptive prediction stage; S2, Twin Adaptive Prediction Phase: Initialize the digital twin based on the feature set from the cross-domain feature distillation phase. Real-time data drives the virtual evolution of the twin. When extreme weather conditions such as heavy rain and strong winds occur, the adaptation mechanism is activated and the virtual model parameters are corrected. The prediction results provide a decision-making basis for the blockchain intervention phase. S3, blockchain active intervention stage: Based on the prediction results and early warning signals, an intervention plan is generated and written into the smart contract. After the node is executed, the effect data is collected and fed back to the cross-domain data symbiosis module and the immune algorithm early warning module respectively.

8. The big data water quality dynamic monitoring and early warning method according to claim 7, characterized in that: The specific steps of the cross-domain feature distillation stage in step S1 are as follows: S11. Data fusion and feature extraction: Based on the cross-domain data symbiosis module, heterogeneous fusion of ternary data is performed, and the federated learning framework is used to complete feature extraction without leaving the domain. S12. Feature Distillation and Correlation Analysis: Through feature distillation technology, socioeconomic data is compressed into pollution impact factor vectors, which are then fused with water quality data features through an attention mechanism to generate an enhanced feature set that includes implicit impacts. A causal inference model is designed to quantitatively analyze the dynamic correlation between pollution behavior and water quality changes.

9. The big data water quality dynamic monitoring and early warning method according to claim 8, characterized in that: The specific steps of the twin adaptive prediction stage in step S2 are as follows: S21. Twin initialization and evolution: Based on the enhanced feature set generated in step S1, the digital twin is initialized and historical data is input to complete pre-training. The twin is driven to dynamically evolve through real-time monitoring data. S22. Adaptation mechanism and parameter correction: When extreme weather is detected, the parameter mutation adaptation mechanism is automatically activated, the conventional hydrodynamic parameters are frozen, and the extreme scenario sub-model trained by reinforcement learning is called to achieve rapid switching of pollution diffusion prediction; a virtual-real deviation correction algorithm is designed, and the UAV spectral scanning data is regularly compared with the twin prediction results to dynamically adjust the diffusion coefficient.

10. The big data water quality dynamic monitoring and early warning method according to claim 9, characterized in that: The specific steps of the blockchain active intervention phase in step S3 are as follows: S31, Plan Generation and Contract Execution: Based on the pollution spread range in step S2 and the warning level of the immune algorithm warning module, a spatiotemporal intervention plan is generated and written into the smart contract. The alliance chain node automatically executes the plan after verifying its feasibility; S32. Effect feedback and system optimization: Intervention effect data is collected through IoT devices. On the one hand, it is fed back to the cross-domain data symbiosis module to update the feature library, and on the other hand, it is transmitted to the immune algorithm early warning module to optimize the early warning rule library. This step realizes the blockchain notarization of the entire process of prediction-decision-execution-feedback.

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